Video-based convolutional neural networks for activity recognition from robot-centric videos

NASA Astrophysics Data System (ADS)

Ryoo, M. S.; Matthies, Larry

2016-05-01

In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

Human body contour data based activity recognition.

PubMed

Myagmarbayar, Nergui; Yuki, Yoshida; Imamoglu, Nevrez; Gonzalez, Jose; Otake, Mihoko; Yu, Wenwei

2013-01-01

This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

A Novel Energy-Efficient Approach for Human Activity Recognition.

PubMed

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Peng, Ao; Tang, Biyu; Lu, Hai; Shi, Haibin; Zheng, Huiru

2017-09-08

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition.

PubMed

Ming, Yue; Wang, Guangchao; Fan, Chunxiao

2015-01-01

With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

A triboelectric motion sensor in wearable body sensor network for human activity recognition.

PubMed

Hui Huang; Xian Li; Ye Sun

2016-08-01

The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running. The k-Nearest Neighbor (kNN) clustering algorithm is adopted to recognize these activities and validate the feasibility of this new approach. The results show that our system can perform physical activity recognition with a successful rate over 80% for walking, sitting and standing. The triboelectric structure can also be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion.

Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

PubMed Central

Mala, S.; Latha, K.

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185

Feature selection in classification of eye movements using electrooculography for activity recognition.

PubMed

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

A Random Forest-based ensemble method for activity recognition.

PubMed

Feng, Zengtao; Mo, Lingfei; Li, Meng

2015-01-01

This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity recognition. To evaluate the algorithm, PA data collected from the PAMAP (Physical Activity Monitoring for Aging People), which is a standard, publicly available database, was utilized to train and test. The experimental results show that the algorithm is able to correctly recognize 19 PA types with an accuracy of 93.44%, while the training is faster than others. The ensemble classifier system based on the RF (Random Forest) algorithm can achieve high recognition accuracy and fast calculation.

A Novel Energy-Efficient Approach for Human Activity Recognition

PubMed Central

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Tang, Biyu; Lu, Hai; Shi, Haibin

2017-01-01

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper. PMID:28885560

Face Encoding and Recognition in the Human Brain

NASA Astrophysics Data System (ADS)

Haxby, James V.; Ungerleider, Leslie G.; Horwitz, Barry; Maisog, Jose Ma.; Rapoport, Stanley I.; Grady, Cheryl L.

1996-01-01

A dissociation between human neural systems that participate in the encoding and later recognition of new memories for faces was demonstrated by measuring memory task-related changes in regional cerebral blood flow with positron emission tomography. There was almost no overlap between the brain structures associated with these memory functions. A region in the right hippocampus and adjacent cortex was activated during memory encoding but not during recognition. The most striking finding in neocortex was the lateralization of prefrontal participation. Encoding activated left prefrontal cortex, whereas recognition activated right prefrontal cortex. These results indicate that the hippocampus and adjacent cortex participate in memory function primarily at the time of new memory encoding. Moreover, face recognition is not mediated simply by recapitulation of operations performed at the time of encoding but, rather, involves anatomically dissociable operations.

Robust Indoor Human Activity Recognition Using Wireless Signals.

PubMed

Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

2015-07-15

Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Human-assisted sound event recognition for home service robots.

PubMed

Do, Ha Manh; Sheng, Weihua; Liu, Meiqin

This paper proposes and implements an open framework of active auditory learning for a home service robot to serve the elderly living alone at home. The framework was developed to realize the various auditory perception capabilities while enabling a remote human operator to involve in the sound event recognition process for elderly care. The home service robot is able to estimate the sound source position and collaborate with the human operator in sound event recognition while protecting the privacy of the elderly. Our experimental results validated the proposed framework and evaluated auditory perception capabilities and human-robot collaboration in sound event recognition.

A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments.

PubMed

Jalal, Ahmad; Kamal, Shaharyar; Kim, Daijin

2014-07-02

Recent advancements in depth video sensors technologies have made human activity recognition (HAR) realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital.

A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments

PubMed Central

Jalal, Ahmad; Kamal, Shaharyar; Kim, Daijin

2014-01-01

Recent advancements in depth video sensors technologies have made human activity recognition (HAR) realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital. PMID:24991942

Transfer Learning for Improved Audio-Based Human Activity Recognition.

PubMed

Ntalampiras, Stavros; Potamitis, Ilyas

2018-06-25

Human activities are accompanied by characteristic sound events, the processing of which might provide valuable information for automated human activity recognition. This paper presents a novel approach addressing the case where one or more human activities are associated with limited audio data, resulting in a potentially highly imbalanced dataset. Data augmentation is based on transfer learning; more specifically, the proposed method: (a) identifies the classes which are statistically close to the ones associated with limited data; (b) learns a multiple input, multiple output transformation; and (c) transforms the data of the closest classes so that it can be used for modeling the ones associated with limited data. Furthermore, the proposed framework includes a feature set extracted out of signal representations of diverse domains, i.e., temporal, spectral, and wavelet. Extensive experiments demonstrate the relevance of the proposed data augmentation approach under a variety of generative recognition schemes.

A Human Activity Recognition System Using Skeleton Data from RGBD Sensors.

PubMed

Cippitelli, Enea; Gasparrini, Samuele; Gambi, Ennio; Spinsante, Susanna

2016-01-01

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

Hand gesture recognition in confined spaces with partial observability and occultation constraints

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

2016-05-01

Human activity detection and recognition capabilities have broad applications for military and homeland security. These tasks are very complicated, however, especially when multiple persons are performing concurrent activities in confined spaces that impose significant obstruction, occultation, and observability uncertainty. In this paper, our primary contribution is to present a dedicated taxonomy and kinematic ontology that are developed for in-vehicle group human activities (IVGA). Secondly, we describe a set of hand-observable patterns that represents certain IVGA examples. Thirdly, we propose two classifiers for hand gesture recognition and compare their performance individually and jointly. Finally, we present a variant of Hidden Markov Model for Bayesian tracking, recognition, and annotation of hand motions, which enables spatiotemporal inference to human group activity perception and understanding. To validate our approach, synthetic (graphical data from virtual environment) and real physical environment video imagery are employed to verify the performance of these hand gesture classifiers, while measuring their efficiency and effectiveness based on the proposed Hidden Markov Model for tracking and interpreting dynamic spatiotemporal IVGA scenarios.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

PubMed Central

2018-01-01

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

Human L-ficolin, a recognition molecule of the lectin activation pathway of complement, activates complement by binding to pneumolysin, the major toxin of Streptococcus pneumoniae.

PubMed

Ali, Youssif M; Kenawy, Hany I; Muhammad, Adnan; Sim, Robert B; Andrew, Peter W; Schwaeble, Wilhelm J

2013-01-01

The complement system is an essential component of the immune response, providing a critical line of defense against different pathogens including S. pneumoniae. Complement is activated via three distinct pathways: the classical (CP), the alternative (AP) and the lectin pathway (LP). The role of Pneumolysin (PLY), a bacterial toxin released by S. pneumoniae, in triggering complement activation has been studied in vitro. Our results demonstrate that in both human and mouse sera complement was activated via the CP, initiated by direct binding of even non-specific IgM and IgG3 to PLY. Absence of CP activity in C1q(-/-) mouse serum completely abolished any C3 deposition. However, C1q depleted human serum strongly opsonized PLY through abundant deposition of C3 activation products, indicating that the LP may have a vital role in activating the human complement system on PLY. We identified that human L-ficolin is the critical LP recognition molecule that drives LP activation on PLY, while all of the murine LP recognition components fail to bind and activate complement on PLY. This work elucidates the detailed interactions between PLY and complement and shows for the first time a specific role of the LP in PLY-mediated complement activation in human serum.

Human L-ficolin, a Recognition Molecule of the Lectin Activation Pathway of Complement, Activates Complement by Binding to Pneumolysin, the Major Toxin of Streptococcus pneumoniae

PubMed Central

Ali, Youssif M.; Kenawy, Hany I.; Muhammad, Adnan; Sim, Robert B.

2013-01-01

The complement system is an essential component of the immune response, providing a critical line of defense against different pathogens including S. pneumoniae. Complement is activated via three distinct pathways: the classical (CP), the alternative (AP) and the lectin pathway (LP). The role of Pneumolysin (PLY), a bacterial toxin released by S. pneumoniae, in triggering complement activation has been studied in vitro. Our results demonstrate that in both human and mouse sera complement was activated via the CP, initiated by direct binding of even non-specific IgM and IgG3 to PLY. Absence of CP activity in C1q−/− mouse serum completely abolished any C3 deposition. However, C1q depleted human serum strongly opsonized PLY through abundant deposition of C3 activation products, indicating that the LP may have a vital role in activating the human complement system on PLY. We identified that human L-ficolin is the critical LP recognition molecule that drives LP activation on PLY, while all of the murine LP recognition components fail to bind and activate complement on PLY. This work elucidates the detailed interactions between PLY and complement and shows for the first time a specific role of the LP in PLY-mediated complement activation in human serum. PMID:24349316

Learning and Recognition of a Non-conscious Sequence of Events in Human Primary Visual Cortex.

PubMed

Rosenthal, Clive R; Andrews, Samantha K; Antoniades, Chrystalina A; Kennard, Christopher; Soto, David

2016-03-21

Human primary visual cortex (V1) has long been associated with learning simple low-level visual discriminations [1] and is classically considered outside of neural systems that support high-level cognitive behavior in contexts that differ from the original conditions of learning, such as recognition memory [2, 3]. Here, we used a novel fMRI-based dichoptic masking protocol-designed to induce activity in V1, without modulation from visual awareness-to test whether human V1 is implicated in human observers rapidly learning and then later (15-20 min) recognizing a non-conscious and complex (second-order) visuospatial sequence. Learning was associated with a change in V1 activity, as part of a temporo-occipital and basal ganglia network, which is at variance with the cortico-cerebellar network identified in prior studies of "implicit" sequence learning that involved motor responses and visible stimuli (e.g., [4]). Recognition memory was associated with V1 activity, as part of a temporo-occipital network involving the hippocampus, under conditions that were not imputable to mechanisms associated with conscious retrieval. Notably, the V1 responses during learning and recognition separately predicted non-conscious recognition memory, and functional coupling between V1 and the hippocampus was enhanced for old retrieval cues. The results provide a basis for novel hypotheses about the signals that can drive recognition memory, because these data (1) identify human V1 with a memory network that can code complex associative serial visuospatial information and support later non-conscious recognition memory-guided behavior (cf. [5]) and (2) align with mouse models of experience-dependent V1 plasticity in learning and memory [6]. Copyright © 2016 Elsevier Ltd. All rights reserved.

Activity recognition from minimal distinguishing subsequence mining

NASA Astrophysics Data System (ADS)

Iqbal, Mohammad; Pao, Hsing-Kuo

2017-08-01

Human activity recognition is one of the most important research topics in the era of Internet of Things. To separate different activities given sensory data, we utilize a Minimal Distinguishing Subsequence (MDS) mining approach to efficiently find distinguishing patterns among different activities. We first transform the sensory data into a series of sensor triggering events and operate the MDS mining procedure afterwards. The gap constraints are also considered in the MDS mining. Given the multi-class nature of most activity recognition tasks, we modify the MDS mining approach from a binary case to a multi-class one to fit the need for multiple activity recognition. We also study how to select the best parameter set including the minimal and the maximal support thresholds in finding the MDSs for effective activity recognition. Overall, the prediction accuracy is 86.59% on the van Kasteren dataset which consists of four different activities for recognition.

Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition.

PubMed

Tao, Dapeng; Jin, Lianwen; Yuan, Yuan; Xue, Yang

2016-06-01

With the rapid development of mobile devices and pervasive computing technologies, acceleration-based human activity recognition, a difficult yet essential problem in mobile apps, has received intensive attention recently. Different acceleration signals for representing different activities or even a same activity have different attributes, which causes troubles in normalizing the signals. We thus cannot directly compare these signals with each other, because they are embedded in a nonmetric space. Therefore, we present a nonmetric scheme that retains discriminative and robust frequency domain information by developing a novel ensemble manifold rank preserving (EMRP) algorithm. EMRP simultaneously considers three aspects: 1) it encodes the local geometry using the ranking order information of intraclass samples distributed on local patches; 2) it keeps the discriminative information by maximizing the margin between samples of different classes; and 3) it finds the optimal linear combination of the alignment matrices to approximate the intrinsic manifold lied in the data. Experiments are conducted on the South China University of Technology naturalistic 3-D acceleration-based activity dataset and the naturalistic mobile-devices based human activity dataset to demonstrate the robustness and effectiveness of the new nonmetric scheme for acceleration-based human activity recognition.

Multilevel depth and image fusion for human activity detection.

PubMed

Ni, Bingbing; Pei, Yong; Moulin, Pierre; Yan, Shuicheng

2013-10-01

Recognizing complex human activities usually requires the detection and modeling of individual visual features and the interactions between them. Current methods only rely on the visual features extracted from 2-D images, and therefore often lead to unreliable salient visual feature detection and inaccurate modeling of the interaction context between individual features. In this paper, we show that these problems can be addressed by combining data from a conventional camera and a depth sensor (e.g., Microsoft Kinect). We propose a novel complex activity recognition and localization framework that effectively fuses information from both grayscale and depth image channels at multiple levels of the video processing pipeline. In the individual visual feature detection level, depth-based filters are applied to the detected human/object rectangles to remove false detections. In the next level of interaction modeling, 3-D spatial and temporal contexts among human subjects or objects are extracted by integrating information from both grayscale and depth images. Depth information is also utilized to distinguish different types of indoor scenes. Finally, a latent structural model is developed to integrate the information from multiple levels of video processing for an activity detection. Extensive experiments on two activity recognition benchmarks (one with depth information) and a challenging grayscale + depth human activity database that contains complex interactions between human-human, human-object, and human-surroundings demonstrate the effectiveness of the proposed multilevel grayscale + depth fusion scheme. Higher recognition and localization accuracies are obtained relative to the previous methods.

A Review on Human Activity Recognition Using Vision-Based Method.

PubMed

Zhang, Shugang; Wei, Zhiqiang; Nie, Jie; Huang, Lei; Wang, Shuang; Li, Zhen

2017-01-01

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.

A Review on Human Activity Recognition Using Vision-Based Method

PubMed Central

Nie, Jie

2017-01-01

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research. PMID:29065585

Learning dictionaries of sparse codes of 3D movements of body joints for real-time human activity understanding.

PubMed

Qi, Jin; Yang, Zhiyong

2014-01-01

Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D) videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.

Activity Recognition on Streaming Sensor Data.

PubMed

Krishnan, Narayanan C; Cook, Diane J

2014-02-01

Many real-world applications that focus on addressing needs of a human, require information about the activities being performed by the human in real-time. While advances in pervasive computing have lead to the development of wireless and non-intrusive sensors that can capture the necessary activity information, current activity recognition approaches have so far experimented on either a scripted or pre-segmented sequence of sensor events related to activities. In this paper we propose and evaluate a sliding window based approach to perform activity recognition in an on line or streaming fashion; recognizing activities as and when new sensor events are recorded. To account for the fact that different activities can be best characterized by different window lengths of sensor events, we incorporate the time decay and mutual information based weighting of sensor events within a window. Additional contextual information in the form of the previous activity and the activity of the previous window is also appended to the feature describing a sensor window. The experiments conducted to evaluate these techniques on real-world smart home datasets suggests that combining mutual information based weighting of sensor events and adding past contextual information into the feature leads to best performance for streaming activity recognition.

Foot-mounted inertial measurement unit for activity classification.

PubMed

Ghobadi, Mostafa; Esfahani, Ehsan T

2014-01-01

This paper proposes a classification technique for daily base activity recognition for human monitoring during physical therapy in home. The proposed method estimates the foot motion using single inertial measurement unit, then segments the motion into steps classify them by template-matching as walking, stairs up or stairs down steps. The results show a high accuracy of activity recognition. Unlike previous works which are limited to activity recognition, the proposed approach is more qualitative by providing similarity index of any activity to its desired template which can be used to assess subjects improvement.

Recognition of Propionibacterium acnes by human TLR2 heterodimers.

PubMed

Su, Qi; Grabowski, Maria; Weindl, Günther

2017-02-01

Propionibacterium acnes has been considered as a crucial contributor to the pathogenesis of acne vulgaris. The interaction between P. acnes and the host is mainly mediated by Toll like receptor (TLR) 2 recognition. TLR2 homodimers recognize P. acnes in mice, but here we describe the prerequisite of TLR2/1 and TLR2/6 heterodimers in human cells for P. acnes recognition. P. acnes-induced NF-κB and AP-1activation observed in HEK hTLR2-transfected but not control cells confirmed the specificity of TLR2 recognition. The activation was blocked by neutralizing antibodies against TLR2, TLR1 and TLR6, as well as the TLR2 antagonist CU-CPT22, which showed no selectivity towards human TLR2 heterodimers. The combination of anti-TLR1 and anti-TLR6 antibodies completely abrogated activation by P. acnes. In primary human keratinocytes, P. acnes-increased NF-κB phosphorylation was inhibited by anti-TLR6 and anti-TLR2 antibodies. Furthermore, P. acnes-induced inflammatory responses were impaired by anti-TLR2 neutralizing antibodies and fully blocked by CU-CPT22. Our study suggests species-specific recognition of P. acnes by TLR2 heterodimers which can be exploited therapeutically by small molecules targeting TLR2 for the control of inflammatory responses. Copyright © 2016 Elsevier GmbH. All rights reserved.

Influence of music with different volumes and styles on recognition activity in humans.

PubMed

Pavlygina, R A; Sakharov, D S; Davydov, V I; Avdonkin, A V

2010-10-01

The efficiency of the recognition of masked visual images (Arabic numerals) increased when accompanied by classical (62 dB) and rock music (25 dB). These changes were accompanied by increases in the coherence of potentials in the frontal areas seen on recognition without music. Changes in intercenter EEG relationships correlated with the formation a dominant at the behavioral level. When loud music (85 dB) and music of other styles was used, these changes in behavior and the EEG were not seen; however, the coherence of potentials in the temporal and motor cortex of the right hemisphere increased and the latent periods of motor reactions of the hands decreased. These results provide evidence that the "recognition" dominant is formed when there are particular ratios of the levels of excitation in the corresponding centers, which should be considered when there is a need to increase the efficiency of recognition activity in humans.

Influence of time and length size feature selections for human activity sequences recognition.

PubMed

Fang, Hongqing; Chen, Long; Srinivasan, Raghavendiran

2014-01-01

In this paper, Viterbi algorithm based on a hidden Markov model is applied to recognize activity sequences from observed sensors events. Alternative features selections of time feature values of sensors events and activity length size feature values are tested, respectively, and then the results of activity sequences recognition performances of Viterbi algorithm are evaluated. The results show that the selection of larger time feature values of sensor events and/or smaller activity length size feature values will generate relatively better results on the activity sequences recognition performances. © 2013 ISA Published by ISA All rights reserved.

Recognition of human activity characteristics based on state transitions modeling technique

NASA Astrophysics Data System (ADS)

Elangovan, Vinayak; Shirkhodaie, Amir

2012-06-01

Human Activity Discovery & Recognition (HADR) is a complex, diverse and challenging task but yet an active area of ongoing research in the Department of Defense. By detecting, tracking, and characterizing cohesive Human interactional activity patterns, potential threats can be identified which can significantly improve situation awareness, particularly, in Persistent Surveillance Systems (PSS). Understanding the nature of such dynamic activities, inevitably involves interpretation of a collection of spatiotemporally correlated activities with respect to a known context. In this paper, we present a State Transition model for recognizing the characteristics of human activities with a link to a prior contextbased ontology. Modeling the state transitions between successive evidential events determines the activities' temperament. The proposed state transition model poses six categories of state transitions including: Human state transitions of Object handling, Visibility, Entity-entity relation, Human Postures, Human Kinematics and Distance to Target. The proposed state transition model generates semantic annotations describing the human interactional activities via a technique called Casual Event State Inference (CESI). The proposed approach uses a low cost kinect depth camera for indoor and normal optical camera for outdoor monitoring activities. Experimental results are presented here to demonstrate the effectiveness and efficiency of the proposed technique.

Gender recognition from unconstrained and articulated human body.

PubMed

Wu, Qin; Guo, Guodong

2014-01-01

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.

Gender Recognition from Unconstrained and Articulated Human Body

PubMed Central

Wu, Qin; Guo, Guodong

2014-01-01

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition. PMID:24977203

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening †

PubMed Central

Yoon, Sang Min

2018-01-01

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches. PMID:29614767

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening.

PubMed

Cho, Heeryon; Yoon, Sang Min

2018-04-01

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.

Super Normal Vector for Human Activity Recognition with Depth Cameras.

PubMed

Yang, Xiaodong; Tian, YingLi

2017-05-01

The advent of cost-effectiveness and easy-operation depth cameras has facilitated a variety of visual recognition tasks including human activity recognition. This paper presents a novel framework for recognizing human activities from video sequences captured by depth cameras. We extend the surface normal to polynormal by assembling local neighboring hypersurface normals from a depth sequence to jointly characterize local motion and shape information. We then propose a general scheme of super normal vector (SNV) to aggregate the low-level polynormals into a discriminative representation, which can be viewed as a simplified version of the Fisher kernel representation. In order to globally capture the spatial layout and temporal order, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time cells. In the extensive experiments, the proposed approach achieves superior performance to the state-of-the-art methods on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D.

Deep Recurrent Neural Networks for Human Activity Recognition

PubMed Central

Murad, Abdulmajid

2017-01-01

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs. PMID:29113103

Deep Recurrent Neural Networks for Human Activity Recognition.

PubMed

Murad, Abdulmajid; Pyun, Jae-Young

2017-11-06

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

Learning a Taxonomy of Predefined and Discovered Activity Patterns

PubMed Central

Krishnan, Narayanan; Cook, Diane J.; Wemlinger, Zachary

2013-01-01

Many intelligent systems that focus on the needs of a human require information about the activities that are being performed by the human. At the core of this capability is activity recognition. Activity recognition techniques have become robust but rarely scale to handle more than a few activities. They also rarely learn from more than one smart home data set because of inherent differences between labeling techniques. In this paper we investigate a data-driven approach to creating an activity taxonomy from sensor data found in disparate smart home datasets. We investigate how the resulting taxonomy can help analyze the relationship between classes of activities. We also analyze how the taxonomy can be used to scale activity recognition to a large number of activity classes and training datasets. We describe our approach and evaluate it on 34 smart home datasets. The results of the evaluation indicate that the hierarchical modeling can reduce training time while maintaining accuracy of the learned model. PMID:25302084

Towards Smart Homes Using Low Level Sensory Data

PubMed Central

Khattak, Asad Masood; Truc, Phan Tran Ho; Hung, Le Xuan; Vinh, La The; Dang, Viet-Hung; Guan, Donghai; Pervez, Zeeshan; Han, Manhyung; Lee, Sungyoung; Lee, Young-Koo

2011-01-01

Ubiquitous Life Care (u-Life care) is receiving attention because it provides high quality and low cost care services. To provide spontaneous and robust healthcare services, knowledge of a patient’s real-time daily life activities is required. Context information with real-time daily life activities can help to provide better services and to improve healthcare delivery. The performance and accuracy of existing life care systems is not reliable, even with a limited number of services. This paper presents a Human Activity Recognition Engine (HARE) that monitors human health as well as activities using heterogeneous sensor technology and processes these activities intelligently on a Cloud platform for providing improved care at low cost. We focus on activity recognition using video-based, wearable sensor-based, and location-based activity recognition engines and then use intelligent processing to analyze the context of the activities performed. The experimental results of all the components showed good accuracy against existing techniques. The system is deployed on Cloud for Alzheimer’s disease patients (as a case study) with four activity recognition engines to identify low level activity from the raw data captured by sensors. These are then manipulated using ontology to infer higher level activities and make decisions about a patient’s activity using patient profile information and customized rules. PMID:22247682

Recognizing stationary and locomotion activities using combinational of spectral analysis with statistical descriptors features

NASA Astrophysics Data System (ADS)

Zainudin, M. N. Shah; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran

2017-10-01

Prior knowledge in pervasive computing recently garnered a lot of attention due to its high demand in various application domains. Human activity recognition (HAR) considered as the applications that are widely explored by the expertise that provides valuable information to the human. Accelerometer sensor-based approach is utilized as devices to undergo the research in HAR since their small in size and this sensor already build-in in the various type of smartphones. However, the existence of high inter-class similarities among the class tends to degrade the recognition performance. Hence, this work presents the method for activity recognition using our proposed features from combinational of spectral analysis with statistical descriptors that able to tackle the issue of differentiating stationary and locomotion activities. The noise signal is filtered using Fourier Transform before it will be extracted using two different groups of features, spectral frequency analysis, and statistical descriptors. Extracted signal later will be classified using random forest ensemble classifier models. The recognition results show the good accuracy performance for stationary and locomotion activities based on USC HAD datasets.

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition.

PubMed

Janidarmian, Majid; Roshan Fekr, Atena; Radecka, Katarzyna; Zilic, Zeljko

2017-03-07

Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

PubMed

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-18

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation.

21 CFR 310.533 - Drug products containing active ingredients offered over-the-counter (OTC) for human use as an...

Code of Federal Regulations, 2010 CFR

2010-04-01

...); however, there are inadequate data to establish general recognition of the effectiveness of this... milligram) but there are inadequate data to establish general recognition of the effectiveness of these... are inadequate safety and effectiveness data to establish general recognition of the safety and/or...

Patterns recognition of electric brain activity using artificial neural networks

NASA Astrophysics Data System (ADS)

Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

2017-04-01

An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic Sensing

DTIC Science & Technology

2013-09-01

recognition, a Gaussian Process Dynamic Model with Social Network Analysis (GPDM-SNA) for a small human group action recognition, an extended GPDM-SNA...44  3.2. Small Human Group Activity Modeling Based on Gaussian Process Dynamic Model and Social Network Analysis (SN-GPDM...51  Approved for public release; distribution unlimited. 3 3.2.3. Gaussian Process Dynamical Model and

[-25]A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks.

PubMed

García-Hernández, Alejandra; Galván-Tejada, Carlos E; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Velasco-Elizondo, Perla; Cárdenas-Vargas, Rogelio

2017-11-21

Human Activity Recognition (HAR) is one of the main subjects of study in the areas of computer vision and machine learning due to the great benefits that can be achieved. Examples of the study areas are: health prevention, security and surveillance, automotive research, and many others. The proposed approaches are carried out using machine learning techniques and present good results. However, it is difficult to observe how the descriptors of human activities are grouped. In order to obtain a better understanding of the the behavior of descriptors, it is important to improve the abilities to recognize the human activities. This paper proposes a novel approach for the HAR based on acoustic data and similarity networks. In this approach, we were able to characterize the sound of the activities and identify those activities looking for similarity in the sound pattern. We evaluated the similarity of the sounds considering mainly two features: the sound location and the materials that were used. As a result, the materials are a good reference classifying the human activities compared with the location.

A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks

PubMed Central

García-Hernández, Alejandra; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Velasco-Elizondo, Perla; Cárdenas-Vargas, Rogelio

2017-01-01

Human Activity Recognition (HAR) is one of the main subjects of study in the areas of computer vision and machine learning due to the great benefits that can be achieved. Examples of the study areas are: health prevention, security and surveillance, automotive research, and many others. The proposed approaches are carried out using machine learning techniques and present good results. However, it is difficult to observe how the descriptors of human activities are grouped. In order to obtain a better understanding of the the behavior of descriptors, it is important to improve the abilities to recognize the human activities. This paper proposes a novel approach for the HAR based on acoustic data and similarity networks. In this approach, we were able to characterize the sound of the activities and identify those activities looking for similarity in the sound pattern. We evaluated the similarity of the sounds considering mainly two features: the sound location and the materials that were used. As a result, the materials are a good reference classifying the human activities compared with the location. PMID:29160799

Oceans and human health: Emerging public health risks n the marine environment

PubMed Central

Fleming, L.E.; Broad, K.; Clement, A.; Dewailly, E.; Elmir, S.; Knap, A.; Pomponi, S.A.; Smith, S.; Gabriele, H. Solo; Walsh, P.

2008-01-01

There has been an increasing recognition of the inter-relationship between human health and the oceans. Traditionally, the focus of research and concern has been on the impact of human activities on the oceans, particularly through anthropogenic pollution and the exploitation of marine resources. More recently, there has been recognition of the potential direct impact of the oceans on human health, both detrimental and beneficial. Areas identified include: global change, harmful algal blooms (HABs), microbial and chemical contamination of marine waters and seafood, and marine models and natural products from the seas. It is hoped that through the recognition of the inter-dependence of the health of both humans and the oceans, efforts will be made to restore and preserve the oceans. PMID:16996542

CNN based approach for activity recognition using a wrist-worn accelerometer.

PubMed

Panwar, Madhuri; Dyuthi, S Ram; Chandra Prakash, K; Biswas, Dwaipayan; Acharyya, Amit; Maharatna, Koushik; Gautam, Arvind; Naik, Ganesh R

2017-07-01

In recent years, significant advancements have taken place in human activity recognition using various machine learning approaches. However, feature engineering have dominated conventional methods involving the difficult process of optimal feature selection. This problem has been mitigated by using a novel methodology based on deep learning framework which automatically extracts the useful features and reduces the computational cost. As a proof of concept, we have attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor. The validation of the proposed model is done with different pre-processing and noisy data condition which is evaluated using three possible methods. The results show that our proposed methodology achieves an average recognition rate of 99.8% as opposed to conventional methods based on K-means clustering, linear discriminant analysis and support vector machine.

Evaluation of a smartphone human activity recognition application with able-bodied and stroke participants.

PubMed

Capela, N A; Lemaire, E D; Baddour, N; Rudolf, M; Goljar, N; Burger, H

2016-01-20

Mobile health monitoring using wearable sensors is a growing area of interest. As the world's population ages and locomotor capabilities decrease, the ability to report on a person's mobility activities outside a hospital setting becomes a valuable tool for clinical decision-making and evaluating healthcare interventions. Smartphones are omnipresent in society and offer convenient and suitable sensors for mobility monitoring applications. To enhance our understanding of human activity recognition (HAR) system performance for able-bodied and populations with gait deviations, this research evaluated a custom smartphone-based HAR classifier on fifteen able-bodied participants and fifteen participants who suffered a stroke. Participants performed a consecutive series of mobility tasks and daily living activities while wearing a BlackBerry Z10 smartphone on their waist to collect accelerometer and gyroscope data. Five features were derived from the sensor data and used to classify participant activities (decision tree). Sensitivity, specificity and F-scores were calculated to evaluate HAR classifier performance. The classifier performed well for both populations when differentiating mobile from immobile states (F-score > 94 %). As activity recognition complexity increased, HAR system sensitivity and specificity decreased for the stroke population, particularly when using information derived from participant posture to make classification decisions. Human activity recognition using a smartphone based system can be accomplished for both able-bodied and stroke populations; however, an increase in activity classification complexity leads to a decrease in HAR performance with a stroke population. The study results can be used to guide smartphone HAR system development for populations with differing movement characteristics.

The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors.

PubMed

Park, Heesu; Dong, Suh-Yeon; Lee, Miran; Youn, Inchan

2017-07-24

Human-activity recognition (HAR) and energy-expenditure (EE) estimation are major functions in the mobile healthcare system. Both functions have been investigated for a long time; however, several challenges remain unsolved, such as the confusion between activities and the recognition of energy-consuming activities involving little or no movement. To solve these problems, we propose a novel approach using an accelerometer and electrocardiogram (ECG). First, we collected a database of six activities (sitting, standing, walking, ascending, resting and running) of 13 voluntary participants. We compared the HAR performances of three models with respect to the input data type (with none, all, or some of the heart-rate variability (HRV) parameters). The best recognition performance was 96.35%, which was obtained with some selected HRV parameters. EE was also estimated for different choices of the input data type (with or without HRV parameters) and the model type (single and activity-specific). The best estimation performance was found in the case of the activity-specific model with HRV parameters. Our findings indicate that the use of human physiological data, obtained by wearable sensors, has a significant impact on both HAR and EE estimation, which are crucial functions in the mobile healthcare system.

Design and test of a hybrid foot force sensing and GPS system for richer user mobility activity recognition.

PubMed

Zhang, Zelun; Poslad, Stefan

2013-11-01

Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

PubMed Central

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-01

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

An investigation into non-invasive physical activity recognition using smartphones.

PubMed

Kelly, Daniel; Caulfield, Brian

2012-01-01

Technology utilized to automatically monitor Activities of Daily Living (ADL) could be a key component in identifying deviations from normal functional profiles and providing feedback on interventions aimed at improving health. However, if activity recognition systems are to be implemented in real world scenarios such as health and wellness monitoring, the activity sensing modality must unobtrusively fit the human environment rather than forcing humans to adhere to sensor specific conditions. Modern smart phones represent a ubiquitous computing device which has already undergone mainstream adoption. In this paper, we investigate the feasibility of using a modern smartphone, with limited placement constraints, as the sensing modality for an activity recognition system. A dataset of 4 subjects performing 7 activities, using varying sensor placement conditions, is utilized to investigate this. Initial experiments show that a decision tree classifier performs activity classification with precision and recall scores of 0.75 and 0.73 respectively. More importantly, as part of this initial experiment, 3 main problems, and subsequently 3 solutions, relating to unconstrained sensor placement were identified. Using our proposed solutions, classification precision and recall scores were improved by +13% and +14.6% respectively.

Human target acquisition performance

NASA Astrophysics Data System (ADS)

Teaney, Brian P.; Du Bosq, Todd W.; Reynolds, Joseph P.; Thompson, Roger; Aghera, Sameer; Moyer, Steven K.; Flug, Eric; Espinola, Richard; Hixson, Jonathan

2012-06-01

The battlefield has shifted from armored vehicles to armed insurgents. Target acquisition (identification, recognition, and detection) range performance involving humans as targets is vital for modern warfare. The acquisition and neutralization of armed insurgents while at the same time minimizing fratricide and civilian casualties is a mounting concern. U.S. Army RDECOM CERDEC NVESD has conducted many experiments involving human targets for infrared and reflective band sensors. The target sets include human activities, hand-held objects, uniforms & armament, and other tactically relevant targets. This paper will define a set of standard task difficulty values for identification and recognition associated with human target acquisition performance.

Human Activity Recognition by Combining a Small Number of Classifiers.

PubMed

Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin

2016-09-01

We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

Sensitivity-Enhanced Wearable Active Voiceprint Sensor Based on Cellular Polypropylene Piezoelectret.

PubMed

Li, Wenbo; Zhao, Sheng; Wu, Nan; Zhong, Junwen; Wang, Bo; Lin, Shizhe; Chen, Shuwen; Yuan, Fang; Jiang, Hulin; Xiao, Yongjun; Hu, Bin; Zhou, Jun

2017-07-19

Wearable active sensors have extensive applications in mobile biosensing and human-machine interaction but require good flexibility, high sensitivity, excellent stability, and self-powered feature. In this work, cellular polypropylene (PP) piezoelectret was chosen as the core material of a sensitivity-enhanced wearable active voiceprint sensor (SWAVS) to realize voiceprint recognition. By virtue of the dipole orientation control method, the air layers in the piezoelectret were efficiently utilized, and the current sensitivity was enhanced (from 1.98 pA/Hz to 5.81 pA/Hz at 115 dB). The SWAVS exhibited the superiorities of high sensitivity, accurate frequency response, and excellent stability. The voiceprint recognition system could make correct reactions to human voices by judging both the password and speaker. This study presented a voiceprint sensor with potential applications in noncontact biometric recognition and safety guarantee systems, promoting the progress of wearable sensor networks.

Coding of visual object features and feature conjunctions in the human brain.

PubMed

Martinovic, Jasna; Gruber, Thomas; Müller, Matthias M

2008-01-01

Object recognition is achieved through neural mechanisms reliant on the activity of distributed coordinated neural assemblies. In the initial steps of this process, an object's features are thought to be coded very rapidly in distinct neural assemblies. These features play different functional roles in the recognition process--while colour facilitates recognition, additional contours and edges delay it. Here, we selectively varied the amount and role of object features in an entry-level categorization paradigm and related them to the electrical activity of the human brain. We found that early synchronizations (approx. 100 ms) increased quantitatively when more image features had to be coded, without reflecting their qualitative contribution to the recognition process. Later activity (approx. 200-400 ms) was modulated by the representational role of object features. These findings demonstrate that although early synchronizations may be sufficient for relatively crude discrimination of objects in visual scenes, they cannot support entry-level categorization. This was subserved by later processes of object model selection, which utilized the representational value of object features such as colour or edges to select the appropriate model and achieve identification.

Serum Amyloid P Is a Sialylated Glycoprotein Inhibitor of Influenza A Viruses

PubMed Central

Job, Emma R.; Bottazzi, Barbara; Gilbertson, Brad; Edenborough, Kathryn M.; Brown, Lorena E.; Mantovani, Alberto; Brooks, Andrew G.; Reading, Patrick C.

2013-01-01

Members of the pentraxin family, including PTX3 and serum amyloid P component (SAP), have been reported to play a role in innate host defence against a range of microbial pathogens, yet little is known regarding their antiviral activities. In this study, we demonstrate that human SAP binds to human influenza A virus (IAV) strains and mediates a range of antiviral activities, including inhibition of IAV-induced hemagglutination (HA), neutralization of virus infectivity and inhibition of the enzymatic activity of the viral neuraminidase (NA). Characterization of the anti-IAV activity of SAP after periodate or bacterial sialidase treatment demonstrated that α(2,6)-linked sialic acid residues on the glycosidic moiety of SAP are critical for recognition by the HA of susceptible IAV strains. Other proteins of the innate immune system, namely human surfactant protein A and porcine surfactant protein D, have been reported to express sialylated glycans which facilitate inhibition of particular IAV strains, yet the specific viral determinants for recognition of these inhibitors have not been defined. Herein, we have selected virus mutants in the presence of human SAP and identified specific residues in the receptor-binding pocket of the viral HA which are critical for recognition and therefore susceptibility to the antiviral activities of SAP. Given the widespread expression of α(2,6)-linked sialic acid in the human respiratory tract, we propose that SAP may act as an effective receptor mimic to limit IAV infection of airway epithelial cells. PMID:23544079

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

PubMed

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-12-02

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

PubMed Central

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-01-01

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works. PMID:27918414

Modulation of neonatal microbial recognition: TLR-mediated innate immune responses are specifically and differentially modulated by human milk.

PubMed

LeBouder, Emmanuel; Rey-Nores, Julia E; Raby, Anne-Catherine; Affolter, Michael; Vidal, Karine; Thornton, Catherine A; Labéta, Mario O

2006-03-15

The mechanisms controlling innate microbial recognition in the neonatal gut are still to be fully understood. We have sought specific regulatory mechanisms operating in human breast milk relating to TLR-mediated microbial recognition. In this study, we report a specific and differential modulatory effect of early samples (days 1-5) of breast milk on ligand-induced cell stimulation via TLRs. Although a negative modulation was exerted on TLR2 and TLR3-mediated responses, those via TLR4 and TLR5 were enhanced. This effect was observed in human adult and fetal intestinal epithelial cell lines, monocytes, dendritic cells, and PBMC as well as neonatal blood. In the latter case, milk compensated for the low capacity of neonatal plasma to support responses to LPS. Cell stimulation via the IL-1R or TNFR was not modulated by milk. This, together with the differential effect on TLR activation, suggested that the primary effect of milk is exerted upstream of signaling proximal to TLR ligand recognition. The analysis of TLR4-mediated gene expression, used as a model system, showed that milk modulated TLR-related genes differently, including those coding for signal intermediates and regulators. A proteinaceous milk component of > or =80 kDa was found to be responsible for the effect on TLR4. Notably, infant milk formulations did not reproduce the modulatory activity of breast milk. Together, these findings reveal an unrecognized function of human milk, namely, its capacity to influence neonatal microbial recognition by modulating TLR-mediated responses specifically and differentially. This in turn suggests the existence of novel mechanisms regulating TLR activation.

Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals.

PubMed

Vanrell, Sebastian Rodrigo; Milone, Diego Humberto; Rufiner, Hugo Leonardo

2017-07-03

Unobtrusive activity monitoring can provide valuable information for medical and sports applications. In recent years, human activity recognition has moved to wearable sensors to deal with unconstrained scenarios. Accelerometers are the preferred sensors due to their simplicity and availability. Previous studies have examined several \\azul{classic} techniques for extracting features from acceleration signals, including time-domain, time-frequency, frequency-domain, and other heuristic features. Spectral and temporal features are the preferred ones and they are generally computed from acceleration components, leaving the acceleration magnitude potential unexplored. In this study, based on homomorphic analysis, a new type of feature extraction stage is proposed in order to exploit discriminative activity information present in acceleration signals. Homomorphic analysis can isolate the information about whole body dynamics and translate it into a compact representation, called cepstral coefficients. Experiments have explored several configurations of the proposed features, including size of representation, signals to be used, and fusion with other features. Cepstral features computed from acceleration magnitude obtained one of the highest recognition rates. In addition, a beneficial contribution was found when time-domain and moving pace information was included in the feature vector. Overall, the proposed system achieved a recognition rate of 91.21% on the publicly available SCUT-NAA dataset. To the best of our knowledge, this is the highest recognition rate on this dataset.

Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors.

PubMed

Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J M

2016-03-24

The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2-30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available.

Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors.

PubMed

Yurtman, Aras; Barshan, Billur

2017-08-09

Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage.

Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition

PubMed Central

Zhang, Zelun; Poslad, Stefan

2013-01-01

Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals. PMID:24189333

Emotional Memory Persists Longer than Event Memory

ERIC Educational Resources Information Center

Kuriyama, Kenichi; Soshi, Takahiro; Fujii, Takeshi; Kim, Yoshiharu

2010-01-01

The interaction between amygdala-driven and hippocampus-driven activities is expected to explain why emotion enhances episodic memory recognition. However, overwhelming behavioral evidence regarding the emotion-induced enhancement of immediate and delayed episodic memory recognition has not been obtained in humans. We found that the recognition…

Designing a robust activity recognition framework for health and exergaming using wearable sensors.

PubMed

Alshurafa, Nabil; Xu, Wenyao; Liu, Jason J; Huang, Ming-Chun; Mortazavi, Bobak; Roberts, Christian K; Sarrafzadeh, Majid

2014-09-01

Detecting human activity independent of intensity is essential in many applications, primarily in calculating metabolic equivalent rates and extracting human context awareness. Many classifiers that train on an activity at a subset of intensity levels fail to recognize the same activity at other intensity levels. This demonstrates weakness in the underlying classification method. Training a classifier for an activity at every intensity level is also not practical. In this paper, we tackle a novel intensity-independent activity recognition problem where the class labels exhibit large variability, the data are of high dimensionality, and clustering algorithms are necessary. We propose a new robust stochastic approximation framework for enhanced classification of such data. Experiments are reported using two clustering techniques, K-Means and Gaussian Mixture Models. The stochastic approximation algorithm consistently outperforms other well-known classification schemes which validate the use of our proposed clustered data representation. We verify the motivation of our framework in two applications that benefit from intensity-independent activity recognition. The first application shows how our framework can be used to enhance energy expenditure calculations. The second application is a novel exergaming environment aimed at using games to reward physical activity performed throughout the day, to encourage a healthy lifestyle.

Human-Computer Interaction in Smart Environments

PubMed Central

Paravati, Gianluca; Gatteschi, Valentina

2015-01-01

Here, we provide an overview of the content of the Special Issue on “Human-computer interaction in smart environments”. The aim of this Special Issue is to highlight technologies and solutions encompassing the use of mass-market sensors in current and emerging applications for interacting with Smart Environments. Selected papers address this topic by analyzing different interaction modalities, including hand/body gestures, face recognition, gaze/eye tracking, biosignal analysis, speech and activity recognition, and related issues.

A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data.

PubMed

Manzi, Alessandro; Dario, Paolo; Cavallo, Filippo

2017-05-11

Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

The species recognition system: a new corollary for the human fetoembryonic defense system hypothesis.

PubMed

Clark, G F; Dell, A; Morris, H R; Patankar, M S; Easton, R L

2001-01-01

We have previously suggested that the human fetus is protected during human development by a system of both soluble and cell surface associated glycoconjugates that utilize their carbohydrate sequences as functional groups to enable them to evoke tolerance. The proposed model has been referred to as the human fetoembryonic defense system hypothesis (hu-FEDS). In this paradigm, it has previously been proposed that similar oligosaccharides are used to mediate crucial recognition events required during both human sperm-egg binding and immune-inflammatory cell interactions. This vertical integration suggested to us that the sperm-egg binding itself is related to universal recognition events that occur between immune and inflammatory cells, except that in this case recognition of 'species' rather than recognition of 'self' is being manifested. In this paper, we have designated this component of hu-FEDS as the species recognition system (SRS). We propose that the SRS is an integral component of the hu-FEDS used to enable sperm-egg recognition and protection of the gametes from potential immune responses. Recent structural data indicates that the glycan sequences implicated in mediating murine gamete recognition are also expressed on CD45 in activated murine T lymphocytes and cytotoxic T lymphocytes. This overlap supports our contention that there is an overlap between the immune and gamete recognition systems. Therefore the hu-FEDS paradigm may be a subset of a larger model that also applies to other placental mammals. We therefore propose that the hu-FEDS model for protection should in the future be referred to as the eutherian fetoembryonic defense system hypothesis (eu-FEDS) to account for this extension. The possibility exists that the SRS component of eu-FEDS could predate eutherians and extend to all sexually reproducing organisms. Future investigation of the interactions between the immune and gamete recognition system will be required to determine the degree of overlap. Copyright 2001 S. Karger AG, Basel

Recognition of human activities using depth images of Kinect for biofied building

NASA Astrophysics Data System (ADS)

Ogawa, Ami; Mita, Akira

2015-03-01

These days, various functions in the living spaces are needed because of an aging society, promotion of energy conservation, and diversification of lifestyles. To meet this requirement, we propose "Biofied Building". The "Biofied Building" is the system learnt from living beings. The various information is accumulated in a database using small sensor agent robots as a key function of this system to control the living spaces. Among the various kinds of information about the living spaces, especially human activities can be triggers for lighting or air conditioning control. By doing so, customized space is possible. Human activities are divided into two groups, the activities consisting of single behavior and the activities consisting of multiple behaviors. For example, "standing up" or "sitting down" consists of a single behavior. These activities are accompanied by large motions. On the other hand "eating" consists of several behaviors, holding the chopsticks, catching the food, putting them in the mouth, and so on. These are continuous motions. Considering the characteristics of two types of human activities, we individually, use two methods, R transformation and variance. In this paper, we focus on the two different types of human activities, and propose the two methods of human activity recognition methods for construction of the database of living space for "Biofied Building". Finally, we compare the results of both methods.

Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

NASA Astrophysics Data System (ADS)

Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

2016-10-01

Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

CD56 Is a Pathogen Recognition Receptor on Human Natural Killer Cells.

PubMed

Ziegler, Sabrina; Weiss, Esther; Schmitt, Anna-Lena; Schlegel, Jan; Burgert, Anne; Terpitz, Ulrich; Sauer, Markus; Moretta, Lorenzo; Sivori, Simona; Leonhardt, Ines; Kurzai, Oliver; Einsele, Hermann; Loeffler, Juergen

2017-07-21

Aspergillus (A.) fumigatus is an opportunistic fungal mold inducing invasive aspergillosis (IA) in immunocompromised patients. Although antifungal activity of human natural killer (NK) cells was shown in previous studies, the underlying cellular mechanisms and pathogen recognition receptors (PRRs) are still unknown. Using flow cytometry we were able to show that the fluorescence positivity of the surface receptor CD56 significantly decreased upon fungal contact. To visualize the interaction site of NK cells and A. fumigatus we used SEM, CLSM and dSTORM techniques, which clearly demonstrated that NK cells directly interact with A. fumigatus via CD56 and that CD56 is re-organized and accumulated at this interaction site time-dependently. The inhibition of the cytoskeleton showed that the receptor re-organization was an active process dependent on actin re-arrangements. Furthermore, we could show that CD56 plays a role in the fungus mediated NK cell activation, since blocking of CD56 surface receptor reduced fungal mediated NK cell activation and reduced cytokine secretion. These results confirmed the direct interaction of NK cells and A. fumigatus, leading to the conclusion that CD56 is a pathogen recognition receptor. These findings give new insights into the functional role of CD56 in the pathogen recognition during the innate immune response.

Artificial neural network detects human uncertainty

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

2018-03-01

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

Joint object and action recognition via fusion of partially observable surveillance imagery data

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Chan, Alex L.

2017-05-01

Partially observable group activities (POGA) occurring in confined spaces are epitomized by their limited observability of the objects and actions involved. In many POGA scenarios, different objects are being used by human operators for the conduct of various operations. In this paper, we describe the ontology of such as POGA in the context of In-Vehicle Group Activity (IVGA) recognition. Initially, we describe the virtue of ontology modeling in the context of IVGA and show how such an ontology and a priori knowledge about the classes of in-vehicle activities can be fused for inference of human actions that consequentially leads to understanding of human activity inside the confined space of a vehicle. In this paper, we treat the problem of "action-object" as a duality problem. We postulate a correlation between observed human actions and the object that is being utilized within those actions, and conversely, if an object being handled is recognized, we may be able to expect a number of actions that are likely to be performed on that object. In this study, we use partially observable human postural sequences to recognition actions. Inspired by convolutional neural networks (CNNs) learning capability, we present an architecture design using a new CNN model to learn "action-object" perception from surveillance videos. In this study, we apply a sequential Deep Hidden Markov Model (DHMM) as a post-processor to CNN to decode realized observations into recognized actions and activities. To generate the needed imagery data set for the training and testing of these new methods, we use the IRIS virtual simulation software to generate high-fidelity and dynamic animated scenarios that depict in-vehicle group activities under different operational contexts. The results of our comparative investigation are discussed and presented in detail.

Combining users' activity survey and simulators to evaluate human activity recognition systems.

PubMed

Azkune, Gorka; Almeida, Aitor; López-de-Ipiña, Diego; Chen, Liming

2015-04-08

Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant.

Infrared sensing of non-observable human biometrics

NASA Astrophysics Data System (ADS)

Willmore, Michael R.

2005-05-01

Interest and growth of biometric recognition technologies surged after 9/11. Once a technology mainly used for identity verification in law enforcement, biometrics are now being considered as a secure means of providing identity assurance in security related applications. Biometric recognition in law enforcement must, by necessity, use attributes of human uniqueness that are both observable and vulnerable to compromise. Privacy and protection of an individual's identity is not assured during criminal activity. However, a security system must rely on identity assurance for access control to physical or logical spaces while not being vulnerable to compromise and protecting the privacy of an individual. The solution resides in the use of non-observable attributes of human uniqueness to perform the biometric recognition process. This discussion will begin by presenting some key perspectives about biometric recognition and the characteristic differences between observable and non-observable biometric attributes. An introduction to the design, development, and testing of the Thermo-ID system will follow. The Thermo-ID system is an emerging biometric recognition technology that uses non-observable patterns of infrared energy naturally emanating from within the human body. As with all biometric systems, the infrared patterns recorded and compared within the Thermo-ID system are unique and individually distinguishable permitting a link to be confirmed between an individual and a claimed or previously established identity. The non-observable characteristics of infrared patterns of human uniqueness insure both the privacy and protection of an individual using this type of biometric recognition system.

Distinct parietal sites mediate the influences of mood, arousal, and their interaction on human recognition memory.

PubMed

Greene, Ciara M; Flannery, Oliver; Soto, David

2014-12-01

The two dimensions of emotion, mood valence and arousal, have independent effects on recognition memory. At present, however, it is not clear how those effects are reflected in the human brain. Previous research in this area has generally dealt with memory for emotionally valenced or arousing stimuli, but the manner in which interacting mood and arousal states modulate responses in memory substrates remains poorly understood. We investigated memory for emotionally neutral items while independently manipulating mood valence and arousal state by means of music exposure. Four emotional conditions were created: positive mood/high arousal, positive mood/low arousal, negative mood/high arousal, and negative mood/low arousal. We observed distinct effects of mood valence and arousal in parietal substrates of recognition memory. Positive mood increased activity in ventral posterior parietal cortex (PPC) and orbitofrontal cortex, whereas arousal condition modulated activity in dorsal PPC and the posterior cingulate. An interaction between valence and arousal was observed in left ventral PPC, notably in a parietal area distinct from the those identified for the main effects, with a stronger effect of mood on recognition memory responses here under conditions of relative high versus low arousal. We interpreted the PPC activations in terms of the attention-to-memory hypothesis: Increased arousal may lead to increased top-down control of memory, and hence dorsal PPC activation, whereas positive mood valence may result in increased activity in ventral PPC regions associated with bottom-up attention to memory. These findings indicate that distinct parietal sites mediate the influences of mood, arousal, and their interplay during recognition memory.

Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors

PubMed Central

Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J. M.

2016-01-01

The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2–30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available. PMID:27023543

Vasopressin modulates social recognition-related activity in the left temporoparietal junction in humans.

PubMed

Zink, C F; Kempf, L; Hakimi, S; Rainey, C A; Stein, J L; Meyer-Lindenberg, A

2011-04-04

The neuropeptide vasopressin is a key molecular mediator of social behavior in animals and humans, implicated in anxiety and autism. Social recognition, the ability to assess the familiarity of others, is essential for appropriate social interactions and enhanced by vasopressin; however, the neural mechanisms mediating this effect in humans are unknown. Using functional magnetic resonance imaging (fMRI) and an implicit social recognition matching task, we employed a double-blinded procedure in which 20 healthy male volunteers self-administered 40 UI of vasopressin or placebo intranasally, 45 min before performing the matching task in the scanner. In a random-effects fMRI analysis, we show that vasopressin induces a regionally specific alteration in a key node of the theory of mind network, the left temporoparietal junction, identifying a neurobiological mechanism for prosocial neuropeptide effects in humans that suggests novel treatment strategies.

Process dissociation between contextual retrieval and item recognition.

PubMed

Weis, Susanne; Specht, Karsten; Klaver, Peter; Tendolkar, Indira; Willmes, Klaus; Ruhlmann, Jürgen; Elger, Christian E; Fernández, Guillén

2004-12-22

We employed a source memory task in an event related fMRI study to dissociate MTL processes associated with either contextual retrieval or item recognition. To introduce context during study, stimuli (photographs of buildings and natural landscapes) were transformed into one of four single-color-scales: red, blue, yellow, or green. In the subsequent old/new recognition memory test, all stimuli were presented as gray scale photographs, and old-responses were followed by a four-alternative source judgment referring to the color in which the stimulus was presented during study. Our results suggest a clear-cut process dissociation within the human MTL. While an activity increase accompanies successful retrieval of contextual information, an activity decrease provides a familiarity signal that is sufficient for successful item recognition.

High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections.

PubMed

Zhu, Xiangbin; Qiu, Huiling

2016-01-01

Human activity recognition(HAR) from the temporal streams of sensory data has been applied to many fields, such as healthcare services, intelligent environments and cyber security. However, the classification accuracy of most existed methods is not enough in some applications, especially for healthcare services. In order to improving accuracy, it is necessary to develop a novel method which will take full account of the intrinsic sequential characteristics for time-series sensory data. Moreover, each human activity may has correlated feature relationship at different levels. Therefore, in this paper, we propose a three-stage continuous hidden Markov model (TSCHMM) approach to recognize human activities. The proposed method contains coarse, fine and accurate classification. The feature reduction is an important step in classification processing. In this paper, sparse locality preserving projections (SpLPP) is exploited to determine the optimal feature subsets for accurate classification of the stationary-activity data. It can extract more discriminative activities features from the sensor data compared with locality preserving projections. Furthermore, all of the gyro-based features are used for accurate classification of the moving data. Compared with other methods, our method uses significantly less number of features, and the over-all accuracy has been obviously improved.

High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections

PubMed Central

2016-01-01

Human activity recognition(HAR) from the temporal streams of sensory data has been applied to many fields, such as healthcare services, intelligent environments and cyber security. However, the classification accuracy of most existed methods is not enough in some applications, especially for healthcare services. In order to improving accuracy, it is necessary to develop a novel method which will take full account of the intrinsic sequential characteristics for time-series sensory data. Moreover, each human activity may has correlated feature relationship at different levels. Therefore, in this paper, we propose a three-stage continuous hidden Markov model (TSCHMM) approach to recognize human activities. The proposed method contains coarse, fine and accurate classification. The feature reduction is an important step in classification processing. In this paper, sparse locality preserving projections (SpLPP) is exploited to determine the optimal feature subsets for accurate classification of the stationary-activity data. It can extract more discriminative activities features from the sensor data compared with locality preserving projections. Furthermore, all of the gyro-based features are used for accurate classification of the moving data. Compared with other methods, our method uses significantly less number of features, and the over-all accuracy has been obviously improved. PMID:27893761

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

PubMed Central

Hong, Ha; Solomon, Ethan A.; DiCarlo, James J.

2015-01-01

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT (“face patches”) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. SIGNIFICANCE STATEMENT We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. PMID:26424887

Pose Invariant Face Recognition Based on Hybrid Dominant Frequency Features

NASA Astrophysics Data System (ADS)

Wijaya, I. Gede Pasek Suta; Uchimura, Keiichi; Hu, Zhencheng

Face recognition is one of the most active research areas in pattern recognition, not only because the face is a human biometric characteristics of human being but also because there are many potential applications of the face recognition which range from human-computer interactions to authentication, security, and surveillance. This paper presents an approach to pose invariant human face image recognition. The proposed scheme is based on the analysis of discrete cosine transforms (DCT) and discrete wavelet transforms (DWT) of face images. From both the DCT and DWT domain coefficients, which describe the facial information, we build compact and meaningful features vector, using simple statistical measures and quantization. This feature vector is called as the hybrid dominant frequency features. Then, we apply a combination of the L2 and Lq metric to classify the hybrid dominant frequency features to a person's class. The aim of the proposed system is to overcome the high memory space requirement, the high computational load, and the retraining problems of previous methods. The proposed system is tested using several face databases and the experimental results are compared to a well-known Eigenface method. The proposed method shows good performance, robustness, stability, and accuracy without requiring geometrical normalization. Furthermore, the purposed method has low computational cost, requires little memory space, and can overcome retraining problem.

Design of an efficient framework for fast prototyping of customized human-computer interfaces and virtual environments for rehabilitation.

PubMed

Avola, Danilo; Spezialetti, Matteo; Placidi, Giuseppe

2013-06-01

Rehabilitation is often required after stroke, surgery, or degenerative diseases. It has to be specific for each patient and can be easily calibrated if assisted by human-computer interfaces and virtual reality. Recognition and tracking of different human body landmarks represent the basic features for the design of the next generation of human-computer interfaces. The most advanced systems for capturing human gestures are focused on vision-based techniques which, on the one hand, may require compromises from real-time and spatial precision and, on the other hand, ensure natural interaction experience. The integration of vision-based interfaces with thematic virtual environments encourages the development of novel applications and services regarding rehabilitation activities. The algorithmic processes involved during gesture recognition activity, as well as the characteristics of the virtual environments, can be developed with different levels of accuracy. This paper describes the architectural aspects of a framework supporting real-time vision-based gesture recognition and virtual environments for fast prototyping of customized exercises for rehabilitation purposes. The goal is to provide the therapist with a tool for fast implementation and modification of specific rehabilitation exercises for specific patients, during functional recovery. Pilot examples of designed applications and preliminary system evaluation are reported and discussed. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Multi-modal low cost mobile indoor surveillance system on the Robust Artificial Intelligence-based Defense Electro Robot (RAIDER)

NASA Astrophysics Data System (ADS)

Nair, Binu M.; Diskin, Yakov; Asari, Vijayan K.

2012-10-01

We present an autonomous system capable of performing security check routines. The surveillance machine, the Clearpath Husky robotic platform, is equipped with three IP cameras with different orientations for the surveillance tasks of face recognition, human activity recognition, autonomous navigation and 3D reconstruction of its environment. Combining the computer vision algorithms onto a robotic machine has given birth to the Robust Artificial Intelligencebased Defense Electro-Robot (RAIDER). The end purpose of the RAIDER is to conduct a patrolling routine on a single floor of a building several times a day. As the RAIDER travels down the corridors off-line algorithms use two of the RAIDER's side mounted cameras to perform a 3D reconstruction from monocular vision technique that updates a 3D model to the most current state of the indoor environment. Using frames from the front mounted camera, positioned at the human eye level, the system performs face recognition with real time training of unknown subjects. Human activity recognition algorithm will also be implemented in which each detected person is assigned to a set of action classes picked to classify ordinary and harmful student activities in a hallway setting.The system is designed to detect changes and irregularities within an environment as well as familiarize with regular faces and actions to distinguish potentially dangerous behavior. In this paper, we present the various algorithms and their modifications which when implemented on the RAIDER serves the purpose of indoor surveillance.

Ongoing slow oscillatory phase modulates speech intelligibility in cooperation with motor cortical activity.

PubMed

Onojima, Takayuki; Kitajo, Keiichi; Mizuhara, Hiroaki

2017-01-01

Neural oscillation is attracting attention as an underlying mechanism for speech recognition. Speech intelligibility is enhanced by the synchronization of speech rhythms and slow neural oscillation, which is typically observed as human scalp electroencephalography (EEG). In addition to the effect of neural oscillation, it has been proposed that speech recognition is enhanced by the identification of a speaker's motor signals, which are used for speech production. To verify the relationship between the effect of neural oscillation and motor cortical activity, we measured scalp EEG, and simultaneous EEG and functional magnetic resonance imaging (fMRI) during a speech recognition task in which participants were required to recognize spoken words embedded in noise sound. We proposed an index to quantitatively evaluate the EEG phase effect on behavioral performance. The results showed that the delta and theta EEG phase before speech inputs modulated the participant's response time when conducting speech recognition tasks. The simultaneous EEG-fMRI experiment showed that slow EEG activity was correlated with motor cortical activity. These results suggested that the effect of the slow oscillatory phase was associated with the activity of the motor cortex during speech recognition.

Highly stretchable strain sensor based on SWCNTs/CB synergistic conductive network for wearable human-activity monitoring and recognition

NASA Astrophysics Data System (ADS)

Guo, Xiaohui; Huang, Ying; Zhao, Yunong; Mao, Leidong; Gao, Le; Pan, Weidong; Zhang, Yugang; Liu, Ping

2017-09-01

Flexible, stretchable, and wearable strain sensors have attracted significant attention for their potential applications in human movement detection and recognition. Here, we report a highly stretchable and flexible strain sensor based on a single-walled carbon nanotube (SWCNTs)/carbon black (CB) synergistic conductive network. The fabrication, synergistic conductive mechanism, and characterization of the sandwich-structured strain sensor were investigated. The experimental results show that the device exhibits high stretchability (120%), excellent flexibility, fast response (˜60 ms), temperature independence, and superior stability and reproducibility during ˜1100 stretching/releasing cycles. Furthermore, human activities such as the bending of a finger or elbow and gestures were monitored and recognized based on the strain sensor, indicating that the stretchable strain sensor based on the SWCNTs/CB synergistic conductive network could have promising applications in flexible and wearable devices for human motion monitoring.

Attentional biases and memory for emotional stimuli in men and male rhesus monkeys.

PubMed

Lacreuse, Agnès; Schatz, Kelly; Strazzullo, Sarah; King, Hanna M; Ready, Rebecca

2013-11-01

We examined attentional biases for social and non-social emotional stimuli in young adult men and compared the results to those of male rhesus monkeys (Macaca mulatta) previously tested in a similar dot-probe task (King et al. in Psychoneuroendocrinology 37(3):396-409, 2012). Recognition memory for these stimuli was also analyzed in each species, using a recognition memory task in humans and a delayed non-matching-to-sample task in monkeys. We found that both humans and monkeys displayed a similar pattern of attentional biases toward threatening facial expressions of conspecifics. The bias was significant in monkeys and of marginal significance in humans. In addition, humans, but not monkeys, exhibited an attentional bias away from negative non-social images. Attentional biases for social and non-social threat differed significantly, with both species showing a pattern of vigilance toward negative social images and avoidance of negative non-social images. Positive stimuli did not elicit significant attentional biases for either species. In humans, emotional content facilitated the recognition of non-social images, but no effect of emotion was found for the recognition of social images. Recognition accuracy was not affected by emotion in monkeys, but response times were faster for negative relative to positive images. Altogether, these results suggest shared mechanisms of social attention in humans and monkeys, with both species showing a pattern of selective attention toward threatening faces of conspecifics. These data are consistent with the view that selective vigilance to social threat is the result of evolutionary constraints. Yet, selective attention to threat was weaker in humans than in monkeys, suggesting that regulatory mechanisms enable non-anxious humans to reduce sensitivity to social threat in this paradigm, likely through enhanced prefrontal control and reduced amygdala activation. In addition, the findings emphasize important differences in attentional biases to social versus non-social threat in both species. Differences in the impact of emotional stimuli on recognition memory between monkeys and humans will require further study, as methodological differences in the recognition tasks may have affected the results.

Functional specialization and convergence in the occipito-temporal cortex supporting haptic and visual identification of human faces and body parts: an fMRI study.

PubMed

Kitada, Ryo; Johnsrude, Ingrid S; Kochiyama, Takanori; Lederman, Susan J

2009-10-01

Humans can recognize common objects by touch extremely well whenever vision is unavailable. Despite its importance to a thorough understanding of human object recognition, the neuroscientific study of this topic has been relatively neglected. To date, the few published studies have addressed the haptic recognition of nonbiological objects. We now focus on haptic recognition of the human body, a particularly salient object category for touch. Neuroimaging studies demonstrate that regions of the occipito-temporal cortex are specialized for visual perception of faces (fusiform face area, FFA) and other body parts (extrastriate body area, EBA). Are the same category-sensitive regions activated when these components of the body are recognized haptically? Here, we use fMRI to compare brain organization for haptic and visual recognition of human body parts. Sixteen subjects identified exemplars of faces, hands, feet, and nonbiological control objects using vision and haptics separately. We identified two discrete regions within the fusiform gyrus (FFA and the haptic face region) that were each sensitive to both haptically and visually presented faces; however, these two regions differed significantly in their response patterns. Similarly, two regions within the lateral occipito-temporal area (EBA and the haptic body region) were each sensitive to body parts in both modalities, although the response patterns differed. Thus, although the fusiform gyrus and the lateral occipito-temporal cortex appear to exhibit modality-independent, category-sensitive activity, our results also indicate a degree of functional specialization related to sensory modality within these structures.

An SRY mutation causing human sex reversal resolves a general mechanism of structure-specific DNA recognition: application to the four-way DNA junction.

PubMed

Peters, R; King, C Y; Ukiyama, E; Falsafi, S; Donahoe, P K; Weiss, M A

1995-04-11

SRY, a genetic "master switch" for male development in mammals, exhibits two biochemical activities: sequence-specific recognition of duplex DNA and sequence-independent binding to the sharp angles of four-way DNA junctions. Here, we distinguish between these activities by analysis of a mutant SRY associated with human sex reversal (46, XY female with pure gonadal dysgenesis). The substitution (168T in human SRY) alters a nonpolar side chain in the minor-groove DNA recognition alpha-helix of the HMG box [Haqq, C.M., King, C.-Y., Ukiyama, E., Haqq, T.N., Falsalfi, S., Donahoe, P.K., & Weiss, M.A. (1994) Science 266, 1494-1500]. The native (but not mutant) side chain inserts between specific base pairs in duplex DNA, interrupting base stacking at a site of induced DNA bending. Isotope-aided 1H-NMR spectroscopy demonstrates that analogous side-chain insertion occurs on binding of SRY to a four-way junction, establishing a shared mechanism of sequence- and structure-specific DNA binding. Although the mutant DNA-binding domain exhibits > 50-fold reduction in sequence-specific DNA recognition, near wild-type affinity for four-way junctions is retained. Our results (i) identify a shared SRY-DNA contact at a site of either induced or intrinsic DNA bending, (ii) demonstrate that this contact is not required to bind an intrinsically bent DNA target, and (iii) rationalize patterns of sequence conservation or diversity among HMG boxes. Clinical association of the I68T mutation with human sex reversal supports the hypothesis that specific DNA recognition by SRY is required for male sex determination.

Sensor-Based Human Activity Recognition in a Multi-user Scenario

NASA Astrophysics Data System (ADS)

Wang, Liang; Gu, Tao; Tao, Xianping; Lu, Jian

Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.

Multifractal analysis of real and imaginary movements: EEG study

NASA Astrophysics Data System (ADS)

Pavlov, Alexey N.; Maksimenko, Vladimir A.; Runnova, Anastasiya E.; Khramova, Marina V.; Pisarchik, Alexander N.

2018-04-01

We study abilities of the wavelet-based multifractal analysis in recognition specific dynamics of electrical brain activity associated with real and imaginary movements. Based on the singularity spectra we analyze electroencephalograms (EEGs) acquired in untrained humans (operators) during imagination of hands movements, and show a possibility to distinguish between the related EEG patterns and the recordings performed during real movements or the background electrical brain activity. We discuss how such recognition depends on the selected brain region.

Activity inference for Ambient Intelligence through handling artifacts in a healthcare environment.

PubMed

Martínez-Pérez, Francisco E; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C; Rodríguez, Marcela D

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user.

Activity Inference for Ambient Intelligence Through Handling Artifacts in a Healthcare Environment

PubMed Central

Martínez-Pérez, Francisco E.; González-Fraga, Jose Ángel; Cuevas-Tello, Juan C.; Rodríguez, Marcela D.

2012-01-01

Human activity inference is not a simple process due to distinct ways of performing it. Our proposal presents the SCAN framework for activity inference. SCAN is divided into three modules: (1) artifact recognition, (2) activity inference, and (3) activity representation, integrating three important elements of Ambient Intelligence (AmI) (artifact-behavior modeling, event interpretation and context extraction). The framework extends the roaming beat (RB) concept by obtaining the representation using three kinds of technologies for activity inference. The RB is based on both analysis and recognition from artifact behavior for activity inference. A practical case is shown in a nursing home where a system affording 91.35% effectiveness was implemented in situ. Three examples are shown using RB representation for activity representation. Framework description, RB description and CALog system overcome distinct problems such as the feasibility to implement AmI systems, and to show the feasibility for accomplishing the challenges related to activity recognition based on artifact recognition. We discuss how the use of RBs might positively impact the problems faced by designers and developers for recovering information in an easier manner and thus they can develop tools focused on the user. PMID:22368512

Neural correlates of auditory recognition memory in the primate dorsal temporal pole

PubMed Central

Ng, Chi-Wing; Plakke, Bethany

2013-01-01

Temporal pole (TP) cortex is associated with higher-order sensory perception and/or recognition memory, as human patients with damage in this region show impaired performance during some tasks requiring recognition memory (Olson et al. 2007). The underlying mechanisms of TP processing are largely based on examination of the visual nervous system in humans and monkeys, while little is known about neuronal activity patterns in the auditory portion of this region, dorsal TP (dTP; Poremba et al. 2003). The present study examines single-unit activity of dTP in rhesus monkeys performing a delayed matching-to-sample task utilizing auditory stimuli, wherein two sounds are determined to be the same or different. Neurons of dTP encode several task-relevant events during the delayed matching-to-sample task, and encoding of auditory cues in this region is associated with accurate recognition performance. Population activity in dTP shows a match suppression mechanism to identical, repeated sound stimuli similar to that observed in the visual object identification pathway located ventral to dTP (Desimone 1996; Nakamura and Kubota 1996). However, in contrast to sustained visual delay-related activity in nearby analogous regions, auditory delay-related activity in dTP is transient and limited. Neurons in dTP respond selectively to different sound stimuli and often change their sound response preferences between experimental contexts. Current findings suggest a significant role for dTP in auditory recognition memory similar in many respects to the visual nervous system, while delay memory firing patterns are not prominent, which may relate to monkeys' shorter forgetting thresholds for auditory vs. visual objects. PMID:24198324

Exploring 3D Human Action Recognition: from Offline to Online.

PubMed

Liu, Zhenyu; Li, Rui; Tan, Jianrong

2018-02-20

With the introduction of cost-effective depth sensors, a tremendous amount of research has been devoted to studying human action recognition using 3D motion data. However, most existing methods work in an offline fashion, i.e., they operate on a segmented sequence. There are a few methods specifically designed for online action recognition, which continually predicts action labels as a stream sequence proceeds. In view of this fact, we propose a question: can we draw inspirations and borrow techniques or descriptors from existing offline methods, and then apply these to online action recognition? Note that extending offline techniques or descriptors to online applications is not straightforward, since at least two problems-including real-time performance and sequence segmentation-are usually not considered in offline action recognition. In this paper, we give a positive answer to the question. To develop applicable online action recognition methods, we carefully explore feature extraction, sequence segmentation, computational costs, and classifier selection. The effectiveness of the developed methods is validated on the MSR 3D Online Action dataset and the MSR Daily Activity 3D dataset.

Exploring 3D Human Action Recognition: from Offline to Online

PubMed Central

Li, Rui; Liu, Zhenyu; Tan, Jianrong

2018-01-01

With the introduction of cost-effective depth sensors, a tremendous amount of research has been devoted to studying human action recognition using 3D motion data. However, most existing methods work in an offline fashion, i.e., they operate on a segmented sequence. There are a few methods specifically designed for online action recognition, which continually predicts action labels as a stream sequence proceeds. In view of this fact, we propose a question: can we draw inspirations and borrow techniques or descriptors from existing offline methods, and then apply these to online action recognition? Note that extending offline techniques or descriptors to online applications is not straightforward, since at least two problems—including real-time performance and sequence segmentation—are usually not considered in offline action recognition. In this paper, we give a positive answer to the question. To develop applicable online action recognition methods, we carefully explore feature extraction, sequence segmentation, computational costs, and classifier selection. The effectiveness of the developed methods is validated on the MSR 3D Online Action dataset and the MSR Daily Activity 3D dataset. PMID:29461502

Distinct spatio-temporal profiles of beta-oscillations within visual and sensorimotor areas during action recognition as revealed by MEG.

PubMed

Pavlidou, Anastasia; Schnitzler, Alfons; Lange, Joachim

2014-05-01

The neural correlates of action recognition have been widely studied in visual and sensorimotor areas of the human brain. However, the role of neuronal oscillations involved during the process of action recognition remains unclear. Here, we were interested in how the plausibility of an action modulates neuronal oscillations in visual and sensorimotor areas. Subjects viewed point-light displays (PLDs) of biomechanically plausible and implausible versions of the same actions. Using magnetoencephalography (MEG), we examined dynamic changes of oscillatory activity during these action recognition processes. While both actions elicited oscillatory activity in visual and sensorimotor areas in several frequency bands, a significant difference was confined to the beta-band (∼20 Hz). An increase of power for plausible actions was observed in left temporal, parieto-occipital and sensorimotor areas of the brain, in the beta-band in successive order between 1650 and 2650 msec. These distinct spatio-temporal beta-band profiles suggest that the action recognition process is modulated by the degree of biomechanical plausibility of the action, and that spectral power in the beta-band may provide a functional interaction between visual and sensorimotor areas in humans. Copyright © 2014 Elsevier Ltd. All rights reserved.

Humanized TLR4/MD-2 mice reveal LPS recognition differentially impacts susceptibility to Yersinia pestis and Salmonella enterica.

PubMed

Hajjar, Adeline M; Ernst, Robert K; Fortuno, Edgardo S; Brasfield, Alicia S; Yam, Cathy S; Newlon, Lindsay A; Kollmann, Tobias R; Miller, Samuel I; Wilson, Christopher B

2012-01-01

Although lipopolysaccharide (LPS) stimulation through the Toll-like receptor (TLR)-4/MD-2 receptor complex activates host defense against Gram-negative bacterial pathogens, how species-specific differences in LPS recognition impact host defense remains undefined. Herein, we establish how temperature dependent shifts in the lipid A of Yersinia pestis LPS that differentially impact recognition by mouse versus human TLR4/MD-2 dictate infection susceptibility. When grown at 37°C, Y. pestis LPS is hypo-acylated and less stimulatory to human compared with murine TLR4/MD-2. By contrast, when grown at reduced temperatures, Y. pestis LPS is more acylated, and stimulates cells equally via human and mouse TLR4/MD-2. To investigate how these temperature dependent shifts in LPS impact infection susceptibility, transgenic mice expressing human rather than mouse TLR4/MD-2 were generated. We found the increased susceptibility to Y. pestis for "humanized" TLR4/MD-2 mice directly paralleled blunted inflammatory cytokine production in response to stimulation with purified LPS. By contrast, for other Gram-negative pathogens with highly acylated lipid A including Salmonella enterica or Escherichia coli, infection susceptibility and the response after stimulation with LPS were indistinguishable between mice expressing human or mouse TLR4/MD-2. Thus, Y. pestis exploits temperature-dependent shifts in LPS acylation to selectively evade recognition by human TLR4/MD-2 uncovered with "humanized" TLR4/MD-2 transgenic mice.

Handling Real-World Context Awareness, Uncertainty and Vagueness in Real-Time Human Activity Tracking and Recognition with a Fuzzy Ontology-Based Hybrid Method

PubMed Central

Díaz-Rodríguez, Natalia; Cadahía, Olmo León; Cuéllar, Manuel Pegalajar; Lilius, Johan; Calvo-Flores, Miguel Delgado

2014-01-01

Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches. PMID:25268914

High-emulation mask recognition with high-resolution hyperspectral video capture system

NASA Astrophysics Data System (ADS)

Feng, Jiao; Fang, Xiaojing; Li, Shoufeng; Wang, Yongjin

2014-11-01

We present a method for distinguishing human face from high-emulation mask, which is increasingly used by criminals for activities such as stealing card numbers and passwords on ATM. Traditional facial recognition technique is difficult to detect such camouflaged criminals. In this paper, we use the high-resolution hyperspectral video capture system to detect high-emulation mask. A RGB camera is used for traditional facial recognition. A prism and a gray scale camera are used to capture spectral information of the observed face. Experiments show that mask made of silica gel has different spectral reflectance compared with the human skin. As multispectral image offers additional spectral information about physical characteristics, high-emulation mask can be easily recognized.

Structural coupling of SH2-kinase domains links Fes and Abl substrate recognition and kinase activation.

PubMed

Filippakopoulos, Panagis; Kofler, Michael; Hantschel, Oliver; Gish, Gerald D; Grebien, Florian; Salah, Eidarus; Neudecker, Philipp; Kay, Lewis E; Turk, Benjamin E; Superti-Furga, Giulio; Pawson, Tony; Knapp, Stefan

2008-09-05

The SH2 domain of cytoplasmic tyrosine kinases can enhance catalytic activity and substrate recognition, but the molecular mechanisms by which this is achieved are poorly understood. We have solved the structure of the prototypic SH2-kinase unit of the human Fes tyrosine kinase, which appears specialized for positive signaling. In its active conformation, the SH2 domain tightly interacts with the kinase N-terminal lobe and positions the kinase alphaC helix in an active configuration through essential packing and electrostatic interactions. This interaction is stabilized by ligand binding to the SH2 domain. Our data indicate that Fes kinase activation is closely coupled to substrate recognition through cooperative SH2-kinase-substrate interactions. Similarly, we find that the SH2 domain of the active Abl kinase stimulates catalytic activity and substrate phosphorylation through a distinct SH2-kinase interface. Thus, the SH2 and catalytic domains of active Fes and Abl pro-oncogenic kinases form integrated structures essential for effective tyrosine kinase signaling.

Timing, timing, timing: Fast decoding of object information from intracranial field potentials in human visual cortex

PubMed Central

Liu, Hesheng; Agam, Yigal; Madsen, Joseph R.; Kreiman, Gabriel

2010-01-01

Summary The difficulty of visual recognition stems from the need to achieve high selectivity while maintaining robustness to object transformations within hundreds of milliseconds. Theories of visual recognition differ in whether the neuronal circuits invoke recurrent feedback connections or not. The timing of neurophysiological responses in visual cortex plays a key role in distinguishing between bottom-up and top-down theories. Here we quantified at millisecond resolution the amount of visual information conveyed by intracranial field potentials from 912 electrodes in 11 human subjects. We could decode object category information from human visual cortex in single trials as early as 100 ms post-stimulus. Decoding performance was robust to depth rotation and scale changes. The results suggest that physiological activity in the temporal lobe can account for key properties of visual recognition. The fast decoding in single trials is compatible with feed-forward theories and provides strong constraints for computational models of human vision. PMID:19409272

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition.

PubMed

Fong, Simon; Song, Wei; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2017-02-27

In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called 'shadow features' are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

PubMed Central

Fong, Simon; Song, Wei; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K. L.

2017-01-01

In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research. PMID:28264470

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

PubMed

Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

2015-09-30

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ("face patches") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. Significance statement: We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. Copyright © 2015 the authors 0270-6474/15/3513402-17$15.00/0.

Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning.

PubMed

Miller, Vonda H; Jansen, Ben H

2008-12-01

Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.

Human NOD2 Recognizes Structurally Unique Muramyl Dipeptides from Mycobacterium leprae

PubMed Central

Schenk, Mirjam; Mahapatra, Sebabrata; Le, Phuonganh; Kim, Hee Jin; Choi, Aaron W.; Brennan, Patrick J.; Belisle, John T.

2016-01-01

The innate immune system recognizes microbial pathogens via pattern recognition receptors. One such receptor, NOD2, via recognition of muramyl dipeptide (MDP), triggers a distinct network of innate immune responses, including the production of interleukin-32 (IL-32), which leads to the differentiation of monocytes into dendritic cells (DC). NOD2 has been implicated in the pathogenesis of human leprosy, yet it is not clear whether Mycobacterium leprae, which has a distinct MDP structure, can activate this pathway. We investigated the effect of MDP structure on the innate immune response, finding that infection of monocytes with M. leprae induces IL-32 and DC differentiation in a NOD2-dependent manner. The presence of the proximal l-Ala instead of Gly in the common configuration of the peptide side chain of M. leprae did not affect recognition by NOD2 or cytokine production. Furthermore, amidation of the d-Glu residue did not alter NOD2 activation. These data provide experimental evidence that NOD2 recognizes naturally occurring structural variants of MDP. PMID:27297389

Improving human activity recognition and its application in early stroke diagnosis.

PubMed

Villar, José R; González, Silvia; Sedano, Javier; Chira, Camelia; Trejo-Gabriel-Galan, Jose M

2015-06-01

The development of efficient stroke-detection methods is of significant importance in today's society due to the effects and impact of stroke on health and economy worldwide. This study focuses on Human Activity Recognition (HAR), which is a key component in developing an early stroke-diagnosis tool. An overview of the proposed global approach able to discriminate normal resting from stroke-related paralysis is detailed. The main contributions include an extension of the Genetic Fuzzy Finite State Machine (GFFSM) method and a new hybrid feature selection (FS) algorithm involving Principal Component Analysis (PCA) and a voting scheme putting the cross-validation results together. Experimental results show that the proposed approach is a well-performing HAR tool that can be successfully embedded in devices.

M. tuberculosis-Initiated Human Mannose Receptor Signaling Regulates Macrophage Recognition and Vesicle Trafficking by FcRγ-Chain, Grb2, and SHP-1.

PubMed

Rajaram, Murugesan V S; Arnett, Eusondia; Azad, Abul K; Guirado, Evelyn; Ni, Bin; Gerberick, Abigail D; He, Li-Zhen; Keler, Tibor; Thomas, Lawrence J; Lafuse, William P; Schlesinger, Larry S

2017-10-03

Despite its prominent role as a C-type lectin (CTL) pattern recognition receptor, mannose receptor (MR, CD206)-specific signaling molecules and pathways are unknown. The MR is highly expressed on human macrophages, regulating endocytosis, phagocytosis, and immune responses and mediating Mycobacterium tuberculosis (M.tb) phagocytosis by human macrophages, thereby limiting phagosome-lysosome (P-L) fusion. We identified human MR-associated proteins using phosphorylated and non-phosphorylated MR cytoplasmic tail peptides. We found that MR binds FcRγ-chain, which is required for MR plasma membrane localization and M.tb cell association. Additionally, we discovered that MR-mediated M.tb association triggers immediate MR tyrosine residue phosphorylation and Grb2 recruitment, activating the Rac/Pak/Cdc-42 signaling cascade important for M.tb uptake. MR activation subsequently recruits SHP-1 to the M.tb-containing phagosome, where its activity limits PI(3)P generation at the phagosome and M.tb P-L fusion and promotes M.tb growth. In sum, we identify human MR signaling pathways that temporally regulate phagocytosis and P-L fusion during M.tb infection. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Recognition of DHN-melanin by a C-type lectin receptor is required for immunity to Aspergillus.

PubMed

Stappers, Mark H T; Clark, Alexandra E; Aimanianda, Vishukumar; Bidula, Stefan; Reid, Delyth M; Asamaphan, Patawee; Hardison, Sarah E; Dambuza, Ivy M; Valsecchi, Isabel; Kerscher, Bernhard; Plato, Anthony; Wallace, Carol A; Yuecel, Raif; Hebecker, Betty; da Glória Teixeira Sousa, Maria; Cunha, Cristina; Liu, Yan; Feizi, Ten; Brakhage, Axel A; Kwon-Chung, Kyung J; Gow, Neil A R; Zanda, Matteo; Piras, Monica; Zanato, Chiara; Jaeger, Martin; Netea, Mihai G; van de Veerdonk, Frank L; Lacerda, João F; Campos, António; Carvalho, Agostinho; Willment, Janet A; Latgé, Jean-Paul; Brown, Gordon D

2018-03-15

Resistance to infection is critically dependent on the ability of pattern recognition receptors to recognize microbial invasion and induce protective immune responses. One such family of receptors are the C-type lectins, which are central to antifungal immunity. These receptors activate key effector mechanisms upon recognition of conserved fungal cell-wall carbohydrates. However, several other immunologically active fungal ligands have been described; these include melanin, for which the mechanism of recognition is hitherto undefined. Here we identify a C-type lectin receptor, melanin-sensing C-type lectin receptor (MelLec), that has an essential role in antifungal immunity through recognition of the naphthalene-diol unit of 1,8-dihydroxynaphthalene (DHN)-melanin. MelLec recognizes melanin in conidial spores of Aspergillus fumigatus as well as in other DHN-melanized fungi. MelLec is ubiquitously expressed by CD31 + endothelial cells in mice, and is also expressed by a sub-population of these cells that co-express epithelial cell adhesion molecule and are detected only in the lung and the liver. In mouse models, MelLec was required for protection against disseminated infection with A. fumigatus. In humans, MelLec is also expressed by myeloid cells, and we identified a single nucleotide polymorphism of this receptor that negatively affected myeloid inflammatory responses and significantly increased the susceptibility of stem-cell transplant recipients to disseminated Aspergillus infections. MelLec therefore recognizes an immunologically active component commonly found on fungi and has an essential role in protective antifungal immunity in both mice and humans.

ɣδ T cell receptor ligands and modes of antigen recognition

PubMed Central

Champagne, Eric

2011-01-01

T lymphocytes expressing the γδ-type of T cell receptors for antigens contribute to all aspects of immune responses, including defenses against viruses, bacteria, parasites and tumors, allergy and autoimmunity. Multiple subsets have been individualized in humans as well as in mice and they appear to recognize in a TCR-dependent manner antigens as diverse as small non-peptidic molecules, soluble or membrane-anchored polypeptides and molecules related to MHC antigens on cell surfaces, implying diverse modes of antigen recognition. We review here the γδ TCR ligands which have been identified along the years and their characteristics, with emphasis on a few systems which have been extensively studied such as human γδ T cells responding to phosphoantigens or murine γδ T cells activated by allogeneic MHC antigens. We discuss a speculative model of antigen recognition involving simultaneous TCR recognition of MHC-like and non-MHC ligands which could fit with most available data and shares many similarities with the classical model of MHC-restricted antigen recognition for peptides or lipids by T cells subsets with αβ-type TCRs. PMID:21298486

γδ T cell receptor ligands and modes of antigen recognition.

PubMed

Champagne, Eric

2011-04-01

T lymphocytes expressing the γδ-type of T cell receptors (TCRs) for antigens contribute to all aspects of immune responses, including defenses against viruses, bacteria, parasites and tumors, allergy and autoimmunity. Multiple subsets have been individualized in humans as well as in mice and they appear to recognize in a TCR-dependent manner antigens as diverse as small non-peptidic molecules, soluble or membrane-anchored polypeptides and molecules related to MHC antigens on cell surfaces, implying diverse modes of antigen recognition. We review here the γδ TCR ligands which have been identified along the years and their characteristics, with emphasis on a few systems which have been extensively studied such as human γδ T cells responding to phosphoantigens or murine γδ T cells activated by allogeneic MHC antigens. We discuss a speculative model of antigen recognition involving simultaneous TCR recognition of MHC-like and non-MHC ligands which could fit with most available data and shares many similarities with the classical model of MHC-restricted antigen recognition for peptides or lipids by T cells subsets with αβ-type TCRs.

Human movement activity classification approaches that use wearable sensors and mobile devices

NASA Astrophysics Data System (ADS)

Kaghyan, Sahak; Sarukhanyan, Hakob; Akopian, David

2013-03-01

Cell phones and other mobile devices become part of human culture and change activity and lifestyle patterns. Mobile phone technology continuously evolves and incorporates more and more sensors for enabling advanced applications. Latest generations of smart phones incorporate GPS and WLAN location finding modules, vision cameras, microphones, accelerometers, temperature sensors etc. The availability of these sensors in mass-market communication devices creates exciting new opportunities for data mining applications. Particularly healthcare applications exploiting build-in sensors are very promising. This paper reviews different approaches of human activity recognition.

Understanding the molecular differential recognition of muramyl peptide ligands by LRR domains of human NOD receptors.

PubMed

Vijayrajratnam, Sukhithasri; Pushkaran, Anju Choorakottayil; Balakrishnan, Aathira; Vasudevan, Anil Kumar; Biswas, Raja; Mohan, Chethampadi Gopi

2017-07-27

Human nucleotide-binding oligomerization domain proteins, hNOD1 and hNOD2, are host intracellular receptors with C-terminal leucine-rich repeat (LRR) domains, which recognize specific bacterial peptidoglycan (PG) fragments as their ligands. The specificity of this recognition is dependent on the third amino acid of the stem peptide of the PG ligand, which is usually meso -diaminopimelic acid ( meso DAP) or l-lysine (l-Lys). Since the LRR domains of hNOD receptors had been experimentally shown to confer the PG ligand-sensing specificity, we developed three-dimensional structures of hNOD1-LRR and the hNOD2-LRR to understand the mechanism of differential recognition of muramyl peptide ligands by hNOD receptors. The hNOD1-LRR and hNOD2-LRR receptor models exhibited right-handed curved solenoid shape. The hot-spot residues experimentally proved to be critical for ligand recognition were located in the concavity of the NOD-LRR and formed the recognition site. Our molecular docking analyses and molecular electrostatic potential mapping studies explain the activation of hNOD-LRRs, in response to effective molecular interactions of PG ligands at the recognition site; and conversely, the inability of certain PG ligands to activate hNOD-LRRs, by deviations from the recognition site. Based on molecular docking studies using PG ligands, we propose few residues - G825, D826 and N850 in hNOD1-LRR and L904, G905, W931, L932 and S933 in hNOD2-LRR, evolutionarily conserved across different host species, which may play a major role in ligand recognition. Thus, our integrated experimental and computational approach elucidates the molecular basis underlying the differential recognition of PG ligands by hNOD receptors. © 2017 The Author(s); published by Portland Press Limited on behalf of the Biochemical Society.

Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex.

PubMed Central

Malach, R; Reppas, J B; Benson, R R; Kwong, K K; Jiang, H; Kennedy, W A; Ledden, P J; Brady, T J; Rosen, B R; Tootell, R B

1995-01-01

The stages of integration leading from local feature analysis to object recognition were explored in human visual cortex by using the technique of functional magnetic resonance imaging. Here we report evidence for object-related activation. Such activation was located at the lateral-posterior aspect of the occipital lobe, just abutting the posterior aspect of the motion-sensitive area MT/V5, in a region termed the lateral occipital complex (LO). LO showed preferential activation to images of objects, compared to a wide range of texture patterns. This activation was not caused by a global difference in the Fourier spatial frequency content of objects versus texture images, since object images produced enhanced LO activation compared to textures matched in power spectra but randomized in phase. The preferential activation to objects also could not be explained by different patterns of eye movements: similar levels of activation were observed when subjects fixated on the objects and when they scanned the objects with their eyes. Additional manipulations such as spatial frequency filtering and a 4-fold change in visual size did not affect LO activation. These results suggest that the enhanced responses to objects were not a manifestation of low-level visual processing. A striking demonstration that activity in LO is uniquely correlated to object detectability was produced by the "Lincoln" illusion, in which blurring of objects digitized into large blocks paradoxically increases their recognizability. Such blurring led to significant enhancement of LO activation. Despite the preferential activation to objects, LO did not seem to be involved in the final, "semantic," stages of the recognition process. Thus, objects varying widely in their recognizability (e.g., famous faces, common objects, and unfamiliar three-dimensional abstract sculptures) activated it to a similar degree. These results are thus evidence for an intermediate link in the chain of processing stages leading to object recognition in human visual cortex. Images Fig. 1 Fig. 2 Fig. 3 PMID:7667258

Dissociation of Active Working Memory and Passive Recognition in Rhesus Monkeys

ERIC Educational Resources Information Center

Basile, Benjamin M.; Hampton, Robert R.

2013-01-01

Active cognitive control of working memory is central in most human memory models, but behavioral evidence for such control in nonhuman primates is absent and neurophysiological evidence, while suggestive, is indirect. We present behavioral evidence that monkey memory for familiar images is under active cognitive control. Concurrent cognitive…

Innate Immune Response to Streptococcus pyogenes Depends on the Combined Activation of TLR13 and TLR2

PubMed Central

Fieber, Christina; Janos, Marton; Koestler, Tina; Gratz, Nina; Li, Xiao-Dong; Castiglia, Virginia; Aberle, Marion; Sauert, Martina; Wegner, Mareike; Alexopoulou, Lena; Kirschning, Carsten J.; Chen, Zhijian J.; von Haeseler, Arndt; Kovarik, Pavel

2015-01-01

Innate immune recognition of the major human-specific Gram-positive pathogen Streptococcus pyogenes is not understood. Here we show that mice employ Toll-like receptor (TLR) 2- and TLR13-mediated recognition of S. pyogenes. These TLR pathways are non-redundant in the in vivo context of animal infection, but are largely redundant in vitro, as only inactivation of both of them abolishes inflammatory cytokine production by macrophages and dendritic cells infected with S. pyogenes. Mechanistically, S. pyogenes is initially recognized in a phagocytosis-independent manner by TLR2 and subsequently by TLR13 upon internalization. We show that the TLR13 response is specifically triggered by S. pyogenes rRNA and that Tlr13 −/− cells respond to S. pyogenes infection solely by engagement of TLR2. TLR13 is absent from humans and, remarkably, we find no equivalent route for S. pyogenes RNA recognition in human macrophages. Phylogenetic analysis reveals that TLR13 occurs in all kingdoms but only in few mammals, including mice and rats, which are naturally resistant against S. pyogenes. Our study establishes that the dissimilar expression of TLR13 in mice and humans has functional consequences for recognition of S. pyogenes in these organisms. PMID:25756897

MDMA (Ecstasy) use is associated with reduced BOLD signal change during semantic recognition in abstinent human polydrug users: a preliminary fMRI study

PubMed Central

Raj, Vidya; Liang, Han-Chun; Woodward, Neil D.; Bauernfeind, Amy L.; Lee, Junghee; Dietrich, Mary; Park, Sohee; Cowan, Ronald L.

2011-01-01

Objectives MDMA users have impaired verbal memory, and voxel-based morphometry has demonstrated decreased gray matter in Brodmann area (BA) 18, 21 and 45. Because these regions play a role in verbal memory, we hypothesized that MDMA users would show altered brain activation in these areas during performance of an fMRI task that probed semantic verbal memory. Methods Polysubstance users enriched for MDMA exposure participated in a semantic memory encoding and recognition fMRI task that activated left BA 9, 18, 21/22 and 45. Primary outcomes were percent BOLD signal change in left BA 9, 18, 21/22 and 45, accuracy and response time. Results During semantic recognition, lifetime MDMA use was associated with decreased activation in left BA 9, 18 and 21/22 but not 45. This was partly influenced by contributions from cannabis and cocaine use. MDMA exposure was not associated with accuracy or response time during the semantic recognition task. Conclusions During semantic recognition, MDMA exposure is associated with reduced regional brain activation in regions mediating verbal memory. These findings partially overlap with prior structural evidence for reduced gray matter in MDMA users and may, in part, explain the consistent verbal memory impairments observed in other studies of MDMA users. PMID:19304866

Human action recognition based on kinematic similarity in real time

PubMed Central

Chen, Longting; Luo, Ailing; Zhang, Sicong

2017-01-01

Human action recognition using 3D pose data has gained a growing interest in the field of computer robotic interfaces and pattern recognition since the availability of hardware to capture human pose. In this paper, we propose a fast, simple, and powerful method of human action recognition based on human kinematic similarity. The key to this method is that the action descriptor consists of joints position, angular velocity and angular acceleration, which can meet the different individual sizes and eliminate the complex normalization. The angular parameters of joints within a short sliding time window (approximately 5 frames) around the current frame are used to express each pose frame of human action sequence. Moreover, three modified KNN (k-nearest-neighbors algorithm) classifiers are employed in our method: one for achieving the confidence of every frame in the training step, one for estimating the frame label of each descriptor, and one for classifying actions. Additional estimating of the frame’s time label makes it possible to address single input frames. This approach can be used on difficult, unsegmented sequences. The proposed method is efficient and can be run in real time. The research shows that many public datasets are irregularly segmented, and a simple method is provided to regularize the datasets. The approach is tested on some challenging datasets such as MSR-Action3D, MSRDailyActivity3D, and UTD-MHAD. The results indicate our method achieves a higher accuracy. PMID:29073131

Dissecting out conscious and unconscious memory (sub)processes within the human medial temporal lobe.

PubMed

Grunwald, T; Pezer, N; Münte, T F; Kurthen, M; Lehnertz, K; Van Roost, D; Fernández, G; Kutas, M; Elger, C E

2003-11-01

The human medial temporal lobe (MTL) system mediates memories that can be consciously recollected. However, the specific natures of the individual contributions of its various subregions to conscious memory processes remain equivocal. Here we show a functional dissociation between the hippocampus proper and the parahippocampal region in conscious and unconscious memory as revealed by invasive recordings of limbic event-related brain potentials recorded during explicit and implicit word recognition: Only hippocampal and not parahippocampal neural activity exhibits a sensitivity to the implicit versus explicit nature of the recognition memory task. Moreover, only within the hippocampus proper do the neural responses to repeated words differ not only from those to new words but also from each other as a function of recognition success. By contrast parahippocampal (rhinal) responses are sensitive to repetition independent of conscious recognition. These findings thus demonstrate that it is the hippocampus proper among the MTL structures that is specifically engaged during conscious memory processes.

Robot Command Interface Using an Audio-Visual Speech Recognition System

NASA Astrophysics Data System (ADS)

Ceballos, Alexánder; Gómez, Juan; Prieto, Flavio; Redarce, Tanneguy

In recent years audio-visual speech recognition has emerged as an active field of research thanks to advances in pattern recognition, signal processing and machine vision. Its ultimate goal is to allow human-computer communication using voice, taking into account the visual information contained in the audio-visual speech signal. This document presents a command's automatic recognition system using audio-visual information. The system is expected to control the laparoscopic robot da Vinci. The audio signal is treated using the Mel Frequency Cepstral Coefficients parametrization method. Besides, features based on the points that define the mouth's outer contour according to the MPEG-4 standard are used in order to extract the visual speech information.

Static facial expression recognition with convolution neural networks

NASA Astrophysics Data System (ADS)

Zhang, Feng; Chen, Zhong; Ouyang, Chao; Zhang, Yifei

2018-03-01

Facial expression recognition is a currently active research topic in the fields of computer vision, pattern recognition and artificial intelligence. In this paper, we have developed a convolutional neural networks (CNN) for classifying human emotions from static facial expression into one of the seven facial emotion categories. We pre-train our CNN model on the combined FER2013 dataset formed by train, validation and test set and fine-tune on the extended Cohn-Kanade database. In order to reduce the overfitting of the models, we utilized different techniques including dropout and batch normalization in addition to data augmentation. According to the experimental result, our CNN model has excellent classification performance and robustness for facial expression recognition.

Evidence for the Activation of Sensorimotor Information during Visual Word Recognition: The Body-Object Interaction Effect

ERIC Educational Resources Information Center

Siakaluk, Paul D.; Pexman, Penny M.; Aguilera, Laura; Owen, William J.; Sears, Christopher R.

2008-01-01

We examined the effects of sensorimotor experience in two visual word recognition tasks. Body-object interaction (BOI) ratings were collected for a large set of words. These ratings assess perceptions of the ease with which a human body can physically interact with a word's referent. A set of high BOI words (e.g., "mask") and a set of low BOI…

Human behavior recognition using a context-free grammar

NASA Astrophysics Data System (ADS)

Rosani, Andrea; Conci, Nicola; De Natale, Francesco G. B.

2014-05-01

Automatic recognition of human activities and behaviors is still a challenging problem for many reasons, including limited accuracy of the data acquired by sensing devices, high variability of human behaviors, and gap between visual appearance and scene semantics. Symbolic approaches can significantly simplify the analysis and turn raw data into chains of meaningful patterns. This allows getting rid of most of the clutter produced by low-level processing operations, embedding significant contextual information into the data, as well as using simple syntactic approaches to perform the matching between incoming sequences and models. We propose a symbolic approach to learn and detect complex activities through the sequences of atomic actions. Compared to previous methods based on context-free grammars, we introduce several important novelties, such as the capability to learn actions based on both positive and negative samples, the possibility of efficiently retraining the system in the presence of misclassified or unrecognized events, and the use of a parsing procedure that allows correct detection of the activities also when they are concatenated and/or nested one with each other. An experimental validation on three datasets with different characteristics demonstrates the robustness of the approach in classifying complex human behaviors.

Human facial neural activities and gesture recognition for machine-interfacing applications.

PubMed

Hamedi, M; Salleh, Sh-Hussain; Tan, T S; Ismail, K; Ali, J; Dee-Uam, C; Pavaganun, C; Yupapin, P P

2011-01-01

The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.

Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data

PubMed Central

Munoz-Organero, Mario; Lotfi, Ahmad

2016-01-01

Human activity recognition algorithms based on information obtained from wearable sensors are successfully applied in detecting many basic activities. Identified activities with time-stationary features are characterised inside a predefined temporal window by using different machine learning algorithms on extracted features from the measured data. Better accuracy, precision and recall levels could be achieved by combining the information from different sensors. However, detecting short and sporadic human movements, gestures and actions is still a challenging task. In this paper, a novel algorithm to detect human basic movements from wearable measured data is proposed and evaluated. The proposed algorithm is designed to minimise computational requirements while achieving acceptable accuracy levels based on characterising some particular points in the temporal series obtained from a single sensor. The underlying idea is that this algorithm would be implemented in the sensor device in order to pre-process the sensed data stream before sending the information to a central point combining the information from different sensors to improve accuracy levels. Intra- and inter-person validation is used for two particular cases: single step detection and fall detection and classification using a single tri-axial accelerometer. Relevant results for the above cases and pertinent conclusions are also presented. PMID:27618063

Conserved water mediated recognition and the dynamics of active site Cys 331 and Tyr 411 in hydrated structure of human IMPDH-II.

PubMed

Bairagya, Hridoy R; Mukhopadhyay, Bishnu P; Bera, Asim K

2011-01-01

Inosine monophosphate dehydrogenase (IMPDH) of human is involved in GMP biosynthesis pathway, increased level of IMPDH-II (an isoform of enzyme) activity have found in leukemic and sarcoma cells. Modeling and extensive molecular dynamics simulation (15 ns) studies of IMPDH-II (1B3O PDB structure) have indicated the intricate involvement of four conserved water molecules (W 1, W 2, W 3, and W 4) in the conformational transition or the mobilities of "flap" (residues 400-450) and "loop" (residues 325-342) regions in enzyme. The stabilization of active site residues Asn 303, Gly 324, Ser 329, Cys 331, Asp 364, and Tyr 411 through variable H-bonding coordination from the conserved water molecular center seems interesting in the uninhibited hydrated form of human IMPDH-II structures. This conformational transition or the flexibility of mobile regions, water molecular recognition to active site residues Cys 331 and Tyr 411, and the presence of a hydrophilic cavity approximately 540 Å(3) (enclaved by the loop and flap region) near the C-terminal surface of this enzyme may explore a rational hope toward the water mimic inhibitor or anticancer agent design for human. 2010 John Wiley & Sons, Ltd.

CARMA2sh and ULK2 control pathogen-associated molecular patterns recognition in human keratinocytes: psoriasis-linked CARMA2sh mutants escape ULK2 censorship.

PubMed

Scudiero, Ivan; Mazzone, Pellegrino; D'Andrea, Luca E; Ferravante, Angela; Zotti, Tiziana; Telesio, Gianluca; De Rubis, Gabriele; Reale, Carla; Pizzulo, Maddalena; Muralitharan, Shanmugakonar; Vito, Pasquale; Stilo, Romania

2017-02-23

The molecular complexes formed by specific members of the family of CARMA proteins, the CARD domain-containing adapter molecule BCL10 and MALT1 (CBM complex) represent a central hub in regulating activation of the pleiotropic transcription factor NF-κB. Recently, missense mutations in CARMA2sh have been shown to cause psoriasis in a dominant manner and with high penetrancy. Here, we demonstrate that in human keratinocytes CARMA2sh plays an essential role in the signal transduction pathway that connects pathogen-associated molecular patterns recognition to NF-κB activation. We also find that the serine/threonine kinase ULK2 binds to and phosphorylates CARMA2sh, thereby inhibiting its capacity to activate NF-κB by promoting lysosomal degradation of BCL10, which is essential for CARMA2sh-mediated NF-κB signaling. Remarkably, CARMA2sh mutants associated with psoriasis escape ULK2 inhibition. Finally, we show that a peptide blocking CARD-mediated BCL10 interactions reduces the capacity of psoriasis-linked CARMA2sh mutants to activate NF-κB. Our work elucidates a fundamental signaling mechanism operating in human keratinocytes and opens to novel potential tools for the therapeutical treatment of human skin disorders.

Structure of the active form of human origin recognition complex and its ATPase motor module

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tocilj, Ante; On, Kin Fan; Yuan, Zuanning

Binding of the Origin Recognition Complex (ORC) to origins of replication marks the first step in the initiation of replication of the genome in all eukaryotic cells. Here, we report the structure of the active form of human ORC determined by X-ray crystallography and cryo-electron microscopy. The complex is composed of an ORC1/4/5 motor module lobe in an organization reminiscent of the DNA polymerase clamp loader complexes. A second lobe contains the ORC2/3 subunits. The complex is organized as a double-layered shallow corkscrew, with the AAA+ and AAA+-like domains forming one layer, and the winged-helix domains (WHDs) forming a topmore » layer. CDC6 fits easily between ORC1 and ORC2, completing the ring and the DNA-binding channel, forming an additional ATP hydrolysis site. Analysis of the ATPase activity of the complex provides a basis for understanding ORC activity as well as molecular defects observed in Meier-Gorlin Syndrome mutations.« less

Human-inspired sound environment recognition system for assistive vehicles

NASA Astrophysics Data System (ADS)

González Vidal, Eduardo; Fredes Zarricueta, Ernesto; Auat Cheein, Fernando

2015-02-01

Objective. The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct line-of-view is necessary to identify objects, in the environment recognition process. This work focuses on implementing human reaction to sound stimuli and environment recognition on assistive robotic devices, such as robotic wheelchairs or robotized cars. These vehicles need environment information to ensure safe navigation. Approach. In the field of environment recognition, range sensors (such as LiDAR and ultrasonic systems) and artificial vision devices are widely used; however, these sensors depend on environment constraints (such as lighting variability or color of objects), and sound can provide important information for the characterization of an environment. In this work, we propose a sound-based approach to enhance the environment recognition process, mainly for cases that compromise human integrity, according to the International Classification of Functioning (ICF). Our proposal is based on a neural network implementation that is able to classify up to 15 different environments, each selected according to the ICF considerations on environment factors in the community-based physical activities of people with disabilities. Main results. The accuracy rates in environment classification ranges from 84% to 93%. This classification is later used to constrain assistive vehicle navigation in order to protect the user during daily activities. This work also includes real-time outdoor experimentation (performed on an assistive vehicle) by seven volunteers with different disabilities (but without cognitive impairment and experienced in the use of wheelchairs), statistical validation, comparison with previously published work, and a discussion section where the pros and cons of our system are evaluated. Significance. The proposed sound-based system is very efficient at providing general descriptions of the environment. Such descriptions are focused on vulnerable situations described by the ICF. The volunteers answered a questionnaire regarding the importance of constraining the vehicle velocities in risky environments, showing that all the volunteers felt comfortable with the system and its performance.

Human-inspired sound environment recognition system for assistive vehicles.

PubMed

Vidal, Eduardo González; Zarricueta, Ernesto Fredes; Cheein, Fernando Auat

2015-02-01

The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct line-of-view is necessary to identify objects, in the environment recognition process. This work focuses on implementing human reaction to sound stimuli and environment recognition on assistive robotic devices, such as robotic wheelchairs or robotized cars. These vehicles need environment information to ensure safe navigation. In the field of environment recognition, range sensors (such as LiDAR and ultrasonic systems) and artificial vision devices are widely used; however, these sensors depend on environment constraints (such as lighting variability or color of objects), and sound can provide important information for the characterization of an environment. In this work, we propose a sound-based approach to enhance the environment recognition process, mainly for cases that compromise human integrity, according to the International Classification of Functioning (ICF). Our proposal is based on a neural network implementation that is able to classify up to 15 different environments, each selected according to the ICF considerations on environment factors in the community-based physical activities of people with disabilities. The accuracy rates in environment classification ranges from 84% to 93%. This classification is later used to constrain assistive vehicle navigation in order to protect the user during daily activities. This work also includes real-time outdoor experimentation (performed on an assistive vehicle) by seven volunteers with different disabilities (but without cognitive impairment and experienced in the use of wheelchairs), statistical validation, comparison with previously published work, and a discussion section where the pros and cons of our system are evaluated. The proposed sound-based system is very efficient at providing general descriptions of the environment. Such descriptions are focused on vulnerable situations described by the ICF. The volunteers answered a questionnaire regarding the importance of constraining the vehicle velocities in risky environments, showing that all the volunteers felt comfortable with the system and its performance.

Design of a Digital Library for Human Movement.

ERIC Educational Resources Information Center

Ben-Arie, Jezekiel; Pandit, Purvin; Rajaram, ShyamSundar

This paper is focused on a central aspect in the design of a planned digital library for human movement, i.e. on the aspect of representation and recognition of human activity from video data. The method of representation is important since it has a major impact on the design of all the other building blocks of the system such as the user…

A modified active appearance model based on an adaptive artificial bee colony.

PubMed

Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

Haemophilus ducreyi infection induces activation of the NLRP3 inflammasome in nonpolarized but not in polarized human macrophages.

PubMed

Li, Wei; Katz, Barry P; Bauer, Margaret E; Spinola, Stanley M

2013-08-01

Recognition of microbial infection by certain intracellular pattern recognition receptors leads to the formation of a multiprotein complex termed the inflammasome. Inflammasome assembly activates caspase-1 and leads to cleavage and secretion of the proinflammatory cytokines interleukin-1 beta (IL-1β) and IL-18, which help control many bacterial pathogens. However, excessive inflammation mediated by inflammasome activation can also contribute to immunopathology. Here, we investigated whether Haemophilus ducreyi, a Gram-negative bacterium that causes the genital ulcer disease chancroid, activates inflammasomes in experimentally infected human skin and in monocyte-derived macrophages (MDM). Although H. ducreyi is predominantly extracellular during human infection, several inflammasome-related components were transcriptionally upregulated in H. ducreyi-infected skin. Infection of MDM with live, but not heat-killed, H. ducreyi induced caspase-1- and caspase-5-dependent processing and secretion of IL-1β. Blockage of H. ducreyi uptake by cytochalasin D significantly reduced the amount of secreted IL-1β. Knocking down the expression of the inflammasome components NLRP3 and ASC abolished IL-1β production. Consistent with NLRP3-dependent inflammasome activation, blocking ATP signaling, K(+) efflux, cathepsin B activity, and lysosomal acidification all inhibited IL-1β secretion. However, inhibition of the production and function of reactive oxygen species did not decrease IL-1β production. Polarization of macrophages to classically activated M1 or alternatively activated M2 cells abrogated IL-1β secretion elicited by H. ducreyi. Our study data indicate that H. ducreyi induces NLRP3 inflammasome activation via multiple mechanisms and suggest that the heterogeneity of macrophages within human lesions may modulate inflammasome activation during human infection.

Human NOD2 Recognizes Structurally Unique Muramyl Dipeptides from Mycobacterium leprae.

PubMed

Schenk, Mirjam; Mahapatra, Sebabrata; Le, Phuonganh; Kim, Hee Jin; Choi, Aaron W; Brennan, Patrick J; Belisle, John T; Modlin, Robert L

2016-09-01

The innate immune system recognizes microbial pathogens via pattern recognition receptors. One such receptor, NOD2, via recognition of muramyl dipeptide (MDP), triggers a distinct network of innate immune responses, including the production of interleukin-32 (IL-32), which leads to the differentiation of monocytes into dendritic cells (DC). NOD2 has been implicated in the pathogenesis of human leprosy, yet it is not clear whether Mycobacterium leprae, which has a distinct MDP structure, can activate this pathway. We investigated the effect of MDP structure on the innate immune response, finding that infection of monocytes with M. leprae induces IL-32 and DC differentiation in a NOD2-dependent manner. The presence of the proximal l-Ala instead of Gly in the common configuration of the peptide side chain of M. leprae did not affect recognition by NOD2 or cytokine production. Furthermore, amidation of the d-Glu residue did not alter NOD2 activation. These data provide experimental evidence that NOD2 recognizes naturally occurring structural variants of MDP. Copyright © 2016 Schenk et al.

On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition

PubMed Central

Banos, Oresti; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

2012-01-01

The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise) imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered. PMID:22969386

Human brain regions involved in recognizing environmental sounds.

PubMed

Lewis, James W; Wightman, Frederic L; Brefczynski, Julie A; Phinney, Raymond E; Binder, Jeffrey R; DeYoe, Edgar A

2004-09-01

To identify the brain regions preferentially involved in environmental sound recognition (comprising portions of a putative auditory 'what' pathway), we collected functional imaging data while listeners attended to a wide range of sounds, including those produced by tools, animals, liquids and dropped objects. These recognizable sounds, in contrast to unrecognizable, temporally reversed control sounds, evoked activity in a distributed network of brain regions previously associated with semantic processing, located predominantly in the left hemisphere, but also included strong bilateral activity in posterior portions of the middle temporal gyri (pMTG). Comparisons with earlier studies suggest that these bilateral pMTG foci partially overlap cortex implicated in high-level visual processing of complex biological motion and recognition of tools and other artifacts. We propose that the pMTG foci process multimodal (or supramodal) information about objects and object-associated motion, and that this may represent 'action' knowledge that can be recruited for purposes of recognition of familiar environmental sound-sources. These data also provide a functional and anatomical explanation for the symptoms of pure auditory agnosia for environmental sounds reported in human lesion studies.

On the use of sensor fusion to reduce the impact of rotational and additive noise in human activity recognition.

PubMed

Banos, Oresti; Damas, Miguel; Pomares, Hector; Rojas, Ignacio

2012-01-01

The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise) imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered.

Human-Computer Interface Controlled by Horizontal Directional Eye Movements and Voluntary Blinks Using AC EOG Signals

NASA Astrophysics Data System (ADS)

Kajiwara, Yusuke; Murata, Hiroaki; Kimura, Haruhiko; Abe, Koji

As a communication support tool for cases of amyotrophic lateral sclerosis (ALS), researches on eye gaze human-computer interfaces have been active. However, since voluntary and involuntary eye movements cannot be distinguished in the interfaces, their performance is still not sufficient for practical use. This paper presents a high performance human-computer interface system which unites high quality recognitions of horizontal directional eye movements and voluntary blinks. The experimental results have shown that the number of incorrect inputs is decreased by 35.1% in an existing system which equips recognitions of horizontal and vertical directional eye movements in addition to voluntary blinks and character inputs are speeded up by 17.4% from the existing system.

Role of a disulfide-bonded peptide loop within human complement C9 in the species-selectivity of complement inhibitor CD59.

PubMed

Husler, T; Lockert, D H; Sims, P J

1996-03-12

CD59 antigen is a membrane glycoprotein that inhibits the activity of the C9 component of the C5b-9 membrane attack complex (MAC), thereby protecting human cells from lysis by human complement. The complement-inhibitory activity of CD59 is species-selective, and is most effective toward C9 derived from human or other primate plasma. The species-selective activity of CD59 was recently used to map the segment of human C9 that is recognized by this MAC inhibitor, using recombinant rabbit/human C9 chimeras that retain lytic function within the MAC [Husler, T., Lockert, D. H., Kaufman, K. M., Sodetz, J. M., & Sims, P. J. (1995) J. Biol. Chem. 270,3483-3486]. These experiments suggested that the CD59 recognition domain was contained between residues 334 and 415 in human C9. By analyzing the species-selective lytic activity of recombinant C9 with chimeric substitutions internal to this segment, we now demonstrate that the site in human C9 uniquely recognized by CD59 is centered on those residues contained between C9 Cys359/Cys384, with an additional contribution by residues C-terminal to this segment. Consistent with its role as a CD59 recognition domain, CD59 specifically bound a human C9-derived peptide corresponding to residues 359-384, and antibody (Fab) raised against this C9-derived peptide inhibited the lytic activity of human MAC. Mutant human C9 in which Ala was substituted for Cys359/384 was found to express normal lytic activity and to be fully inhibited by CD59. This suggests that the intrachain Cys359/Cys384 disulfide bond within C9 is not required to maintain the conformation of this segment of C9 for interaction with CD59.

Cross spectral, active and passive approach to face recognition for improved performance

NASA Astrophysics Data System (ADS)

Grudzien, A.; Kowalski, M.; Szustakowski, M.

2017-08-01

Biometrics is a technique for automatic recognition of a person based on physiological or behavior characteristics. Since the characteristics used are unique, biometrics can create a direct link between a person and identity, based on variety of characteristics. The human face is one of the most important biometric modalities for automatic authentication. The most popular method of face recognition which relies on processing of visual information seems to be imperfect. Thermal infrared imagery may be a promising alternative or complement to visible range imaging due to its several reasons. This paper presents an approach of combining both methods.

Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

NASA Astrophysics Data System (ADS)

Nikiforov, M. P.; Reukov, V. V.; Thompson, G. L.; Vertegel, A. A.; Guo, S.; Kalinin, S. V.; Jesse, S.

2009-10-01

Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

Emerging Bordetella pertussis Strains Induce Enhanced Signaling of Human Pattern Recognition Receptors TLR2, NOD2 and Secretion of IL-10 by Dendritic Cells

PubMed Central

Hovingh, Elise S.; van Gent, Marjolein; Hamstra, Hendrik-Jan; Demkes, Marc; Mooi, Frits R.; Pinelli, Elena

2017-01-01

Vaccines against pertussis have been available for more than 60 years. Nonetheless, this highly contagious disease is reemerging even in countries with high vaccination coverage. Genetic changes of Bordetella pertussis over time have been suggested to contribute to the resurgence of pertussis, as these changes may favor escape from vaccine-induced immunity. Nonetheless, studies on the effects of these bacterial changes on the immune response are limited. Here, we characterize innate immune recognition and activation by a collection of genetically diverse B. pertussis strains isolated from Dutch pertussis patients before and after the introduction of the pertussis vaccines. For this purpose, we used HEK-Blue cells transfected with human pattern recognition receptors TLR2, TLR4, NOD2 and NOD1 as a high throughput system for screening innate immune recognition of more than 90 bacterial strains. Physiologically relevant human monocyte derived dendritic cells (moDC), purified from peripheral blood of healthy donors were also used. Findings indicate that, in addition to inducing TLR2 and TLR4 signaling, all B. pertussis strains activate the NOD-like receptor NOD2 but not NOD1. Furthermore, we observed a significant increase in TLR2 and NOD2, but not TLR4, activation by strains circulating after the introduction of pertussis vaccines. When using moDC, we observed that the recently circulating strains induced increased activation of these cells with a dominant IL-10 production. In addition, we observed an increased expression of surface markers including the regulatory molecule PD-L1. Expression of PD-L1 was decreased upon blocking TLR2. These in vitro findings suggest that emerging B. pertussis strains have evolved to dampen the vaccine-induced inflammatory response, which would benefit survival and transmission of this pathogen. Understanding how this disease has resurged in a highly vaccinated population is crucial for the design of improved vaccines against pertussis. PMID:28076445

Real-time Human Activity Recognition

NASA Astrophysics Data System (ADS)

Albukhary, N.; Mustafah, Y. M.

2017-11-01

The traditional Closed-circuit Television (CCTV) system requires human to monitor the CCTV for 24/7 which is inefficient and costly. Therefore, there’s a need for a system which can recognize human activity effectively in real-time. This paper concentrates on recognizing simple activity such as walking, running, sitting, standing and landing by using image processing techniques. Firstly, object detection is done by using background subtraction to detect moving object. Then, object tracking and object classification are constructed so that different person can be differentiated by using feature detection. Geometrical attributes of tracked object, which are centroid and aspect ratio of identified tracked are manipulated so that simple activity can be detected.

Self-organized Evaluation of Dynamic Hand Gestures for Sign Language Recognition

NASA Astrophysics Data System (ADS)

Buciu, Ioan; Pitas, Ioannis

Two main theories exist with respect to face encoding and representation in the human visual system (HVS). The first one refers to the dense (holistic) representation of the face, where faces have "holon"-like appearance. The second one claims that a more appropriate face representation is given by a sparse code, where only a small fraction of the neural cells corresponding to face encoding is activated. Theoretical and experimental evidence suggest that the HVS performs face analysis (encoding, storing, face recognition, facial expression recognition) in a structured and hierarchical way, where both representations have their own contribution and goal. According to neuropsychological experiments, it seems that encoding for face recognition, relies on holistic image representation, while a sparse image representation is used for facial expression analysis and classification. From the computer vision perspective, the techniques developed for automatic face and facial expression recognition fall into the same two representation types. Like in Neuroscience, the techniques which perform better for face recognition yield a holistic image representation, while those techniques suitable for facial expression recognition use a sparse or local image representation. The proposed mathematical models of image formation and encoding try to simulate the efficient storing, organization and coding of data in the human cortex. This is equivalent with embedding constraints in the model design regarding dimensionality reduction, redundant information minimization, mutual information minimization, non-negativity constraints, class information, etc. The presented techniques are applied as a feature extraction step followed by a classification method, which also heavily influences the recognition results.

Sticky-flares for in situ monitoring of human telomerase RNA in living cells.

PubMed

Wu, Qilong; Liu, Zhengjie; Su, Lei; Han, Guangmei; Liu, Renyong; Zhao, Jun; Zhao, Tingting; Jiang, Changlong; Zhang, Zhongping

2018-05-17

Human telomerase RNA (hTR), a template of telomerase for telomeric repeat synthesis, was used to reflect the telomerase activity and act as a potential target of antitumor therapy. Here, we report a novel DNA-conjugated AuNP probe termed sticky-flares for the in situ detection of intracellular human telomerase RNA. The sticky-flares probe is capable of entering living cells directly without any auxiliary and recognizing the binding domain of human telomerase RNA. On recognition, the fluorophore-modified recognition flares can specifically bind to the target, separate from the sticky-flares and act as a fluorescent reporter to quantify and dynamically profile human telomerase RNA in living cells. We envision that the sticky-flares probe would be a valuable platform to investigate the function and regulation of hTR in antitumor therapy and hTR-related drug invention.

21 CFR 26.8 - Other transition activities.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 1 2010-04-01 2010-04-01 false Other transition activities. 26.8 Section 26.8 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL MUTUAL RECOGNITION OF PHARMACEUTICAL GOOD MANUFACTURING PRACTICE REPORTS, MEDICAL DEVICE QUALITY SYSTEM AUDIT REPORTS...

Package Design Affects Accuracy Recognition for Medications.

PubMed

Endestad, Tor; Wortinger, Laura A; Madsen, Steinar; Hortemo, Sigurd

2016-12-01

Our aim was to test if highlighting and placement of substance name on medication package have the potential to reduce patient errors. An unintentional overdose of medication is a large health issue that might be linked to medication package design. In two experiments, placement, background color, and the active ingredient of generic medication packages were manipulated according to best human factors guidelines to reduce causes of labeling-related patient errors. In two experiments, we compared the original packaging with packages where we varied placement of the name, dose, and background of the active ingredient. Age-relevant differences and the effect of color on medication recognition error were tested. In Experiment 1, 59 volunteers (30 elderly and 29 young students), participated. In Experiment 2, 25 volunteers participated. The most common error was the inability to identify that two different packages contained the same active ingredient (young, 41%, and elderly, 68%). This kind of error decreased with the redesigned packages (young, 8%, and elderly, 16%). Confusion errors related to color design were reduced by two thirds in the redesigned packages compared with original generic medications. Prominent placement of substance name and dose with a band of high-contrast color support recognition of the active substance in medications. A simple modification including highlighting and placing the name of the active ingredient in the upper right-hand corner of the package helps users realize that two different packages can contain the same active substance, thus reducing the risk of inadvertent medication overdose. © 2016, Human Factors and Ergonomics Society.

Molecular mechanism of peroxisome proliferator-activated receptor α activation by WY14643: a new mode of ligand recognition and receptor stabilization.

PubMed

Bernardes, Amanda; Souza, Paulo C T; Muniz, João R C; Ricci, Clarisse G; Ayers, Stephen D; Parekh, Nili M; Godoy, André S; Trivella, Daniela B B; Reinach, Peter; Webb, Paul; Skaf, Munir S; Polikarpov, Igor

2013-08-23

Peroxisome proliferator-activated receptors (PPARs) are members of a superfamily of nuclear transcription factors. They are involved in mediating numerous physiological effects in humans, including glucose and lipid metabolism. PPARα ligands effectively treat dyslipidemia and have significant antiinflammatory and anti-atherosclerotic activities. These effects and their ligand-dependent activity make nuclear receptors obvious targets for drug design. Here, we present the structure of the human PPARα in complex with WY14643, a member of fibrate class of drug, and a widely used PPAR activator. The crystal structure of this complex suggests that WY14643 induces activation of PPARα in an unusual bipartite mechanism involving conventional direct helix 12 stabilization and an alternative mode that involves a second ligand in the pocket. We present structural observations, molecular dynamics and activity assays that support the importance of the second site in WY14643 action. The unique binding mode of WY14643 reveals a new pattern of nuclear receptor ligand recognition and suggests a novel basis for ligand design, offering clues for improving the binding affinity and selectivity of ligand. We show that binding of WY14643 to PPARα was associated with antiinflammatory disease in a human corneal cell model, suggesting possible applications for PPARα ligands. Copyright © 2013 Elsevier Ltd. All rights reserved.

A dinucleotide motif in oligonucleotides shows potent immunomodulatory activity and overrides species-specific recognition observed with CpG motif.

PubMed

Kandimalla, Ekambar R; Bhagat, Lakshmi; Zhu, Fu-Gang; Yu, Dong; Cong, Yan-Ping; Wang, Daqing; Tang, Jimmy X; Tang, Jin-Yan; Knetter, Cathrine F; Lien, Egil; Agrawal, Sudhir

2003-11-25

Bacterial and synthetic DNAs containing CpG dinucleotides in specific sequence contexts activate the vertebrate immune system through Toll-like receptor 9 (TLR9). In the present study, we used a synthetic nucleoside with a bicyclic heterobase [1-(2'-deoxy-beta-d-ribofuranosyl)-2-oxo-7-deaza-8-methyl-purine; R] to replace the C in CpG, resulting in an RpG dinucleotide. The RpG dinucleotide was incorporated in mouse- and human-specific motifs in oligodeoxynucleotides (oligos) and 3'-3-linked oligos, referred to as immunomers. Oligos containing the RpG motif induced cytokine secretion in mouse spleen-cell cultures. Immunomers containing RpG dinucleotides showed activity in transfected-HEK293 cells stably expressing mouse TLR9, suggesting direct involvement of TLR9 in the recognition of RpG motif. In J774 macrophages, RpG motifs activated NF-kappa B and mitogen-activated protein kinase pathways. Immunomers containing the RpG dinucleotide induced high levels of IL-12 and IFN-gamma, but lower IL-6 in time- and concentration-dependent fashion in mouse spleen-cell cultures costimulated with IL-2. Importantly, immunomers containing GTRGTT and GARGTT motifs were recognized to a similar extent by both mouse and human immune systems. Additionally, both mouse- and human-specific RpG immunomers potently stimulated proliferation of peripheral blood mononuclear cells obtained from diverse vertebrate species, including monkey, pig, horse, sheep, goat, rat, and chicken. An immunomer containing GTRGTT motif prevented conalbumin-induced and ragweed allergen-induced allergic inflammation in mice. We show that a synthetic bicyclic nucleotide is recognized in the C position of a CpG dinucleotide by immune cells from diverse vertebrate species without bias for flanking sequences, suggesting a divergent nucleotide motif recognition pattern of TLR9.

[Comparative studies of face recognition].

PubMed

Kawai, Nobuyuki

2012-07-01

Every human being is proficient in face recognition. However, the reason for and the manner in which humans have attained such an ability remain unknown. These questions can be best answered-through comparative studies of face recognition in non-human animals. Studies in both primates and non-primates show that not only primates, but also non-primates possess the ability to extract information from their conspecifics and from human experimenters. Neural specialization for face recognition is shared with mammals in distant taxa, suggesting that face recognition evolved earlier than the emergence of mammals. A recent study indicated that a social insect, the golden paper wasp, can distinguish their conspecific faces, whereas a closely related species, which has a less complex social lifestyle with just one queen ruling a nest of underlings, did not show strong face recognition for their conspecifics. Social complexity and the need to differentiate between one another likely led humans to evolve their face recognition abilities.

Human Activity Recognition Supported on Indoor Localization: A Systematic Review.

PubMed

Cerón, Jesús; López, Diego M

2018-01-01

The number of older adults is growing worldwide. This has a social and economic impact in all countries because of the increased number of older adults affected by chronic diseases, health emergencies, and disabilities, representing at the end high cost for the health system. To face this problem, the Ambient Assisted Living (AAL) domain has emerged. Its main objective is to extend the time that older adults can live independently in their homes. AAL is supported by different fields and technologies, being Human Activity Recognition (HAR), control of vital signs and location tracking the three of most interest during the last years. To perform a systematic review about Human Activity Recognition (HAR) approaches supported on Indoor Localization (IL) and vice versa, describing the methods they have used, the accuracy they have obtained and whether they have been directed towards the AAL domain or not. A systematic review of six databases was carried out (ACM, IEEE Xplore, PubMed, Science Direct and Springer). 27 papers were found. They were categorised into three groups according their approach: paper focus on 1. HAR, 2. IL, 3. HAR and IL. A detailed analysis of the following factors was performed: type of methods and technologies used for HAR, IL and data fusion, as well as the precision obtained for them. This systematic review shows that the relationship between HAR and IL has been very little studied, therefore providing insights of its potential mutual support to provide AAL solutions.

Intelligent Automatic Right-Left Sign Lamp Based on Brain Signal Recognition System

NASA Astrophysics Data System (ADS)

Winda, A.; Sofyan; Sthevany; Vincent, R. S.

2017-12-01

Comfort as a part of the human factor, plays important roles in nowadays advanced automotive technology. Many of the current technologies go in the direction of automotive driver assistance features. However, many of the driver assistance features still require physical movement by human to enable the features. In this work, the proposed method is used in order to make certain feature to be functioning without any physical movement, instead human just need to think about it in their mind. In this work, brain signal is recorded and processed in order to be used as input to the recognition system. Right-Left sign lamp based on the brain signal recognition system can potentially replace the button or switch of the specific device in order to make the lamp work. The system then will decide whether the signal is ‘Right’ or ‘Left’. The decision of the Right-Left side of brain signal recognition will be sent to a processing board in order to activate the automotive relay, which will be used to activate the sign lamp. Furthermore, the intelligent system approach is used to develop authorized model based on the brain signal. Particularly Support Vector Machines (SVMs)-based classification system is used in the proposed system to recognize the Left-Right of the brain signal. Experimental results confirm the effectiveness of the proposed intelligent Automatic brain signal-based Right-Left sign lamp access control system. The signal is processed by Linear Prediction Coefficient (LPC) and Support Vector Machines (SVMs), and the resulting experiment shows the training and testing accuracy of 100% and 80%, respectively.

Irradiation, microwave and alternative energy-based treatments for low water activity foods

USDA-ARS?s Scientific Manuscript database

There is an increasing recognition of low water activity foods as vectors for human pathogens. Partially or fully dried agricultural commodities, along with modern formulated dried food products, are complex, and designed to meet a variety of nutritional, sensory, and market-oriented goal. This comp...

C1q-Mediated Complement Activation and C3 Opsonization Trigger Recognition of Stealth Poly(2-methyl-2-oxazoline)-Coated Silica Nanoparticles by Human Phagocytes.

PubMed

Tavano, Regina; Gabrielli, Luca; Lubian, Elisa; Fedeli, Chiara; Visentin, Silvia; Polverino De Laureto, Patrizia; Arrigoni, Giorgio; Geffner-Smith, Alessandra; Chen, Fangfang; Simberg, Dmitri; Morgese, Giulia; Benetti, Edmondo M; Wu, Linping; Moghimi, Seyed Moein; Mancin, Fabrizio; Papini, Emanuele

2018-05-23

Poly(2-methyl-2-oxazoline) (PMOXA) is an alternative promising polymer to poly(ethylene glycol) (PEG) for design and engineering of macrophage-evading nanoparticles (NPs). Although PMOXA-engineered NPs have shown comparable pharmacokinetics and in vivo performance to PEGylated stealth NPs in the murine model, its interaction with elements of the human innate immune system has not been studied. From a translational angle, we studied the interaction of fully characterized PMOXA-coated vinyltriethoxysilane-derived organically modified silica NPs (PMOXA-coated NPs) of approximately 100 nm in diameter with human complement system, blood leukocytes, and macrophages and compared their performance with PEGylated and uncoated NP counterparts. Through detailed immunological and proteomic profiling, we show that PMOXA-coated NPs extensively trigger complement activation in human sera exclusively through the classical pathway. Complement activation is initiated by the sensing molecule C1q, where C1q binds with high affinity ( K d = 11 ± 1 nM) to NP surfaces independent of immunoglobulin binding. C1q-mediated complement activation accelerates PMOXA opsonization with the third complement protein (C3) through the amplification loop of the alternative pathway. This promoted NP recognition by human blood leukocytes and monocyte-derived macrophages. The macrophage capture of PMOXA-coated NPs correlates with sera donor variability in complement activation and opsonization but not with other major corona proteins, including clusterin and a wide range of apolipoproteins. In contrast to these observations, PMOXA-coated NPs poorly activated the murine complement system and were marginally recognized by mouse macrophages. These studies provide important insights into compatibility of engineered NPs with elements of the human innate immune system for translational steps.

Sudden Event Recognition: A Survey

PubMed Central

Suriani, Nor Surayahani; Hussain, Aini; Zulkifley, Mohd Asyraf

2013-01-01

Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1) the importance of a sudden event over a general anomalous event; (2) frameworks used in sudden event recognition; (3) the requirements and comparative studies of a sudden event recognition system and (4) various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition. PMID:23921828

Biological activity of natural flavonoids as impacted by protein flexibility: an example of flavanones.

PubMed

Ding, Fei; Peng, Wei

2015-04-01

Naturally multifunctional Rutaceae hesperidin and its aglycone hesperetin have a great variety of biopharmaceutical activities, e.g. anti-cancer, anti-inflammatory, antioxidant and antitumor; however, the influence of the molecular structures of hesperidin and hesperetin, and in particular, the structural properties such as flexibility and dynamic features of protein on the biological activities of these bioactive compounds remains ambiguous. In the present study, the biomolecular recognition of crucial biopolymer - albumin from human serum (HSA) with Rutaceae, the recognition differences between HSA-hesperidin and HSA-hesperetin, the key elements that lead to the discrepancies as well as the structural characters of protein to the recognition processes were comparatively examined by employing biophysical approaches at the molecular scale. The results illustrated distinctly that (1) aglycone hesperetin can form stronger noncovalent bonds with HSA and possess higher recognition stability as compared with hesperidin. This phenomenon suggest that the introduction of glycoside structure into flavanone may possibly not be able to increase the noncovalent recognition of flavanone by a biopolymer, and conversely, this event will probably decrease the recognition capacity. (2) Although hesperidin and hesperetin can be located within subdomains IIA and IIIA, respectively, the conformational stability of flavanones in subdomain IIA is greater than subdomain IIIA; as a result, the recognition ability of subdomain IIIA with flavanones is patently lesser than subdomain IIA. These discrepancies likely originate from the unique characteristics of the respective cavity, or more specifically, subdomain IIA is basically a closed space, whereas subdomain IIIA is a semi-open region. Meanwhile, the detailed analyses of root-mean-square fluctuation interpreted the recognition of flavanones by subdomain IIA on HSA, which would evoke larger conformational alterations in several amino acid residues, and the similar phenomenon also resides in subdomain IIIA, which signifies that the flexible characteristics of different binding patches in protein may possess fairly notable effects on the HSA-flavanones recognition. Moreover, the integral structural changes of HSA exhibit some disparities on account of the dissimilarities of recognition capability to the protein-flavanone biointeractions, and all these conclusions received further forceful supports from fluorescence and circular dichroism experiments in solution. Perhaps the work emerged herein could not only help us to better evaluate the bioavailability of natural flavanones with or without glycoside, but to understand the sketches of the three-dimensional structure trait of certain biomacromolecules for the medicinal properties of flavonoids in the human body.

Skeleton-based human action recognition using multiple sequence alignment

NASA Astrophysics Data System (ADS)

Ding, Wenwen; Liu, Kai; Cheng, Fei; Zhang, Jin; Li, YunSong

2015-05-01

Human action recognition and analysis is an active research topic in computer vision for many years. This paper presents a method to represent human actions based on trajectories consisting of 3D joint positions. This method first decompose action into a sequence of meaningful atomic actions (actionlets), and then label actionlets with English alphabets according to the Davies-Bouldin index value. Therefore, an action can be represented using a sequence of actionlet symbols, which will preserve the temporal order of occurrence of each of the actionlets. Finally, we employ sequence comparison to classify multiple actions through using string matching algorithms (Needleman-Wunsch). The effectiveness of the proposed method is evaluated on datasets captured by commodity depth cameras. Experiments of the proposed method on three challenging 3D action datasets show promising results.

Metric invariance in object recognition: a review and further evidence.

PubMed

Cooper, E E; Biederman, I; Hummel, J E

1992-06-01

Phenomenologically, human shape recognition appears to be invariant with changes of orientation in depth (up to parts occlusion), position in the visual field, and size. Recent versions of template theories (e.g., Ullman, 1989; Lowe, 1987) assume that these invariances are achieved through the application of transformations such as rotation, translation, and scaling of the image so that it can be matched metrically to a stored template. Presumably, such transformations would require time for their execution. We describe recent priming experiments in which the effects of a prior brief presentation of an image on its subsequent recognition are assessed. The results of these experiments indicate that the invariance is complete: The magnitude of visual priming (as distinct from name or basic level concept priming) is not affected by a change in position, size, orientation in depth, or the particular lines and vertices present in the image, as long as representations of the same components can be activated. An implemented seven layer neural network model (Hummel & Biederman, 1992) that captures these fundamental properties of human object recognition is described. Given a line drawing of an object, the model activates a viewpoint-invariant structural description of the object, specifying its parts and their interrelations. Visual priming is interpreted as a change in the connection weights for the activation of: a) cells, termed geon feature assemblies (GFAs), that conjoin the output of units that represent invariant, independent properties of a single geon and its relations (such as its type, aspect ratio, relations to other geons), or b) a change in the connection weights by which several GFAs activate a cell representing an object.

The episodic engram transformed: Time reduces retrieval-related brain activity but correlates it with memory accuracy.

PubMed

Furman, Orit; Mendelsohn, Avi; Dudai, Yadin

2012-11-15

We took snapshots of human brain activity with fMRI during retrieval of realistic episodic memory over several months. Three groups of participants were scanned during a memory test either hours, weeks, or months after viewing a documentary movie. High recognition accuracy after hours decreased after weeks and remained at similar levels after months. In contrast, BOLD activity in a retrieval-related set of brain areas during correctly remembered events was similar after hours and weeks but significantly declined after months. Despite this reduction, BOLD activity in retrieval-related regions was positively correlated with recognition accuracy only after months. Hippocampal engagement during retrieval remained similar over time during recall but decreased in recognition. Our results are in line with the hypothesis that hippocampus subserves retrieval of real-life episodic memory long after encoding, its engagement being dependent on retrieval demands. Furthermore, our findings suggest that over time episodic engrams are transformed into a parsimonious form capable of supporting accurate retrieval of the crux of events, arguably a critical goal of memory, with only minimal network activation.

The Value of Humans in the Biological Exploration of Space

NASA Astrophysics Data System (ADS)

Cockell, C. S.

2004-06-01

Regardless of the discovery of life on Mars, or of "no apparent life" on Mars, the questions that follow will provide a rich future for biological exploration. Extraordinary pattern recognition skills, decadal assimilation of data and experience, and rapid sample acquisition are just three of the characteristics that make humans the best means we have to explore the biological potential of Mars and other planetary surfaces. I make the case that instead of seeing robots as in conflict, or even in support, of human exploration activity, from the point of view of scientific data gathering and analysis, we should view humans as the most powerful robots we have, thus removing the separation that dogs discussions on the exploration of space. The narrow environmental requirements of humans, although imposing constraints on the life support systems required, is more than compensated for by their capabilities in biological exploration. I support this view with an example of the "Christmas present effect," a simple demonstration of human data and pattern recognition capabilities.

Computer-aided active-site-directed modeling of the Herpes Simplex Virus 1 and human thymidine kinase

NASA Astrophysics Data System (ADS)

Folkers, Gerd; Trumpp-Kallmeyer, Susanne; Gutbrod, Oliver; Krickl, Sabine; Fetzer, Jürgen; Keil, Günther M.

1991-10-01

Thymidine kinase (TK), which is induced by Herpes Simplex Virus 1 (HSV1), plays a key role in the antiviral activity of guanine derivatives such as aciclovir (ACV). In contrast, ACV shows only low affinity to the corresponding host cell enzyme. In order to define the differences in substrate binding of the two enzymes on molecular level, models for the three-dimensional (3-D) structures of the active sites of HSV1-TK and human TK were developed. The reconstruction of the active sites started from primary and secondary structure analysis of various kinases. The results were validated to homologous enzymes with known 3-D structures. The models predict that both enzymes consist of a central core β-sheet structure, connected by loops and α-helices very similar to the overall structure of other nucleotide binding enzymes. The phosphate binding is made up of a highly conserved glycine-rich loop at the N-terminus of the proteins and a conserved region at the C-terminus. The thymidine recognition site was found about 100 amino acids downstream from the phosphate binding loop. The differing substrate specificity of human and HSV1-TK can be explained by amino-acid substitutions in the homologous regions. To achieve a better understanding of the structure of the active site and how the thymidine kinase proteins interact with their substrates, the corresponding complexes of thymidine and dihydroxypropoxyguanine (DHPG) with HSV1 and human TK were built. For the docking of the guanine derivative, the X-ray structure of Elongation Factor Tu (EF-Tu), co-crystallized with guanosine diphosphate, was taken as reference. Fitting of thymidine into the active sites was done with respect to similar interactions found in thymidylate kinase. To complement the analysis of the 3-D structures of the two kinases and the substrate enzyme interactions, site-directed mutagenesis of the thymidine recognition site of HSV1-TK has been undertaken, changing Asp162 in the thymidine recognition site into Asn. First investigations reveal that the enzymatic activity of the mutant protein is destroyed.

Face Recognition in Humans and Machines

NASA Astrophysics Data System (ADS)

O'Toole, Alice; Tistarelli, Massimo

The study of human face recognition by psychologists and neuroscientists has run parallel to the development of automatic face recognition technologies by computer scientists and engineers. In both cases, there are analogous steps of data acquisition, image processing, and the formation of representations that can support the complex and diverse tasks we accomplish with faces. These processes can be understood and compared in the context of their neural and computational implementations. In this chapter, we present the essential elements of face recognition by humans and machines, taking a perspective that spans psychological, neural, and computational approaches. From the human side, we overview the methods and techniques used in the neurobiology of face recognition, the underlying neural architecture of the system, the role of visual attention, and the nature of the representations that emerges. From the computational side, we discuss face recognition technologies and the strategies they use to overcome challenges to robust operation over viewing parameters. Finally, we conclude the chapter with a look at some recent studies that compare human and machine performances at face recognition.

A computational feedforward model predicts categorization of masked emotional body language for longer, but not for shorter, latencies.

PubMed

Stienen, Bernard M C; Schindler, Konrad; de Gelder, Beatrice

2012-07-01

Given the presence of massive feedback loops in brain networks, it is difficult to disentangle the contribution of feedforward and feedback processing to the recognition of visual stimuli, in this case, of emotional body expressions. The aim of the work presented in this letter is to shed light on how well feedforward processing explains rapid categorization of this important class of stimuli. By means of parametric masking, it may be possible to control the contribution of feedback activity in human participants. A close comparison is presented between human recognition performance and the performance of a computational neural model that exclusively modeled feedforward processing and was engineered to fulfill the computational requirements of recognition. Results show that the longer the stimulus onset asynchrony (SOA), the closer the performance of the human participants was to the values predicted by the model, with an optimum at an SOA of 100 ms. At short SOA latencies, human performance deteriorated, but the categorization of the emotional expressions was still above baseline. The data suggest that, although theoretically, feedback arising from inferotemporal cortex is likely to be blocked when the SOA is 100 ms, human participants still seem to rely on more local visual feedback processing to equal the model's performance.

Arousal Rather than Basic Emotions Influence Long-Term Recognition Memory in Humans

PubMed Central

Marchewka, Artur; Wypych, Marek; Moslehi, Abnoos; Riegel, Monika; Michałowski, Jarosław M.; Jednoróg, Katarzyna

2016-01-01

Emotion can influence various cognitive processes, however its impact on memory has been traditionally studied over relatively short retention periods and in line with dimensional models of affect. The present study aimed to investigate emotional effects on long-term recognition memory according to a combined framework of affective dimensions and basic emotions. Images selected from the Nencki Affective Picture System were rated on the scale of affective dimensions and basic emotions. After 6 months, subjects took part in a surprise recognition test during an fMRI session. The more negative the pictures the better they were remembered, but also the more false recognitions they provoked. Similar effects were found for the arousal dimension. Recognition success was greater for pictures with lower intensity of happiness and with higher intensity of surprise, sadness, fear, and disgust. Consecutive fMRI analyses showed a significant activation for remembered (recognized) vs. forgotten (not recognized) images in anterior cingulate and bilateral anterior insula as well as in bilateral caudate nuclei and right thalamus. Further, arousal was found to be the only subjective rating significantly modulating brain activation. Higher subjective arousal evoked higher activation associated with memory recognition in the right caudate and the left cingulate gyrus. Notably, no significant modulation was observed for other subjective ratings, including basic emotion intensities. These results emphasize the crucial role of arousal for long-term recognition memory and support the hypothesis that the memorized material, over time, becomes stored in a distributed cortical network including the core salience network and basal ganglia. PMID:27818626

Arousal Rather than Basic Emotions Influence Long-Term Recognition Memory in Humans.

PubMed

Marchewka, Artur; Wypych, Marek; Moslehi, Abnoos; Riegel, Monika; Michałowski, Jarosław M; Jednoróg, Katarzyna

2016-01-01

Emotion can influence various cognitive processes, however its impact on memory has been traditionally studied over relatively short retention periods and in line with dimensional models of affect. The present study aimed to investigate emotional effects on long-term recognition memory according to a combined framework of affective dimensions and basic emotions. Images selected from the Nencki Affective Picture System were rated on the scale of affective dimensions and basic emotions. After 6 months, subjects took part in a surprise recognition test during an fMRI session. The more negative the pictures the better they were remembered, but also the more false recognitions they provoked. Similar effects were found for the arousal dimension. Recognition success was greater for pictures with lower intensity of happiness and with higher intensity of surprise, sadness, fear, and disgust. Consecutive fMRI analyses showed a significant activation for remembered (recognized) vs. forgotten (not recognized) images in anterior cingulate and bilateral anterior insula as well as in bilateral caudate nuclei and right thalamus. Further, arousal was found to be the only subjective rating significantly modulating brain activation. Higher subjective arousal evoked higher activation associated with memory recognition in the right caudate and the left cingulate gyrus. Notably, no significant modulation was observed for other subjective ratings, including basic emotion intensities. These results emphasize the crucial role of arousal for long-term recognition memory and support the hypothesis that the memorized material, over time, becomes stored in a distributed cortical network including the core salience network and basal ganglia.

Transgenerational Effects of Prenatal Bisphenol A on Social Recognition

PubMed Central

Wolstenholme, Jennifer T.; Goldsby, Jessica A.; Rissman, Emilie F.

2014-01-01

Bisphenol A (BPA) is a man-made endocrine disrupting compound used to manufacture polycarbonate plastics. It is found in plastic bottles, canned food linings, thermal receipts and other commonly used items. Over 93% of people have detectable BPA levels in their urine. Epidemiological studies report correlations between BPA levels during pregnancy and activity, anxiety, and depression in children. We fed female mice control or BPA–containing diets that produced plasma BPA concentrations similar to concentrations in humans. Females were mated and at birth, pups were fostered to control dams to limit BPA exposure to gestation in the first generation. Sibling pairs were bred to the third generation with no further BPA exposure. First (F1) and third (F3) generation juveniles were tested for social recognition and in the open field. Adult F3 mice were tested for olfactory discrimination. In both generations, BPA exposed juvenile mice displayed higher levels of investigation than controls in a social recognition task. In F3 BPA exposed mice, dishabituation to a novel female was impaired. In the open field, no differences were noted in F1 mice, while in F3, BPA lineage mice were more active than controls. No impairments were detected in F3 mice, all were able to discriminate different male urine pools and urine from water. No sex differences were found in any task. These results demonstrate that BPA exposure during gestation has long lasting, transgenerational effects on social recognition and activity in mice. These findings show that BPA exposure has transgenerational actions on behavior and have implications for human neurodevelopmental behavioral disorders. PMID:24100195

Self-face recognition in social context.

PubMed

Sugiura, Motoaki; Sassa, Yuko; Jeong, Hyeonjeong; Wakusawa, Keisuke; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta

2012-06-01

The concept of "social self" is often described as a representation of the self-reflected in the eyes or minds of others. Although the appearance of one's own face has substantial social significance for humans, neuroimaging studies have failed to link self-face recognition and the likely neural substrate of the social self, the medial prefrontal cortex (MPFC). We assumed that the social self is recruited during self-face recognition under a rich social context where multiple other faces are available for comparison of social values. Using functional magnetic resonance imaging (fMRI), we examined the modulation of neural responses to the faces of the self and of a close friend in a social context. We identified an enhanced response in the ventral MPFC and right occipitoparietal sulcus in the social context specifically for the self-face. Neural response in the right lateral parietal and inferior temporal cortices, previously claimed as self-face-specific, was unaffected for the self-face but unexpectedly enhanced for the friend's face in the social context. Self-face-specific activation in the pars triangularis of the inferior frontal gyrus, and self-face-specific reduction of activation in the left middle temporal gyrus and the right supramarginal gyrus, replicating a previous finding, were not subject to such modulation. Our results thus demonstrated the recruitment of a social self during self-face recognition in the social context. At least three brain networks for self-face-specific activation may be dissociated by different patterns of response-modulation in the social context, suggesting multiple dynamic self-other representations in the human brain. Copyright © 2011 Wiley-Liss, Inc.

Transgenerational effects of prenatal bisphenol A on social recognition.

PubMed

Wolstenholme, Jennifer T; Goldsby, Jessica A; Rissman, Emilie F

2013-11-01

Bisphenol A (BPA) is a man-made endocrine disrupting compound used to manufacture polycarbonate plastics. It is found in plastic bottles, canned food linings, thermal receipts and other commonly used items. Over 93% of people have detectable BPA levels in their urine. Epidemiological studies report correlations between BPA levels during pregnancy and activity, anxiety, and depression in children. We fed female mice control or BPA-containing diets that produced plasma BPA concentrations similar to concentrations in humans. Females were mated and at birth, pups were fostered to control dams to limit BPA exposure to gestation in the first generation. Sibling pairs were bred to the third generation with no further BPA exposure. First (F1) and third (F3) generation juveniles were tested for social recognition and in the open field. Adult F3 mice were tested for olfactory discrimination. In both generations, BPA exposed juvenile mice displayed higher levels of investigation than controls in a social recognition task. In F3 BPA exposed mice, dishabituation to a novel female was impaired. In the open field, no differences were noted in F1 mice, while in F3, BPA lineage mice were more active than controls. No impairments were detected in F3 mice, all were able to discriminate different male urine pools and urine from water. No sex differences were found in any task. These results demonstrate that BPA exposure during gestation has long lasting, transgenerational effects on social recognition and activity in mice. These findings show that BPA exposure has transgenerational actions on behavior and have implications for human neurodevelopmental behavioral disorders. © 2013.

Dissociating Medial Temporal and Striatal Memory Systems With a Same/Different Matching Task: Evidence for Two Neural Systems in Human Recognition.

PubMed

Sinha, Neha; Glass, Arnold Lewis

2017-01-01

The medial temporal lobe and striatum have both been implicated as brain substrates of memory and learning. Here, we show dissociation between these two memory systems using a same/different matching task, in which subjects judged whether four-letter strings were the same or different. Different RT was determined by the left-to-right location of the first letter different between the study and test string, consistent with a left-to-right comparison of the study and test strings, terminating when a difference was found. This comparison process results in same responses being slower than different responses. Nevertheless, same responses were faster than different responses. Same responses were associated with hippocampus activation. Different responses were associated with both caudate and hippocampus activation. These findings are consistent with the dual-system hypothesis of mammalian memory and extend the model to human visual recognition.

Neural microgenesis of personally familiar face recognition

PubMed Central

Ramon, Meike; Vizioli, Luca; Liu-Shuang, Joan; Rossion, Bruno

2015-01-01

Despite a wealth of information provided by neuroimaging research, the neural basis of familiar face recognition in humans remains largely unknown. Here, we isolated the discriminative neural responses to unfamiliar and familiar faces by slowly increasing visual information (i.e., high-spatial frequencies) to progressively reveal faces of unfamiliar or personally familiar individuals. Activation in ventral occipitotemporal face-preferential regions increased with visual information, independently of long-term face familiarity. In contrast, medial temporal lobe structures (perirhinal cortex, amygdala, hippocampus) and anterior inferior temporal cortex responded abruptly when sufficient information for familiar face recognition was accumulated. These observations suggest that following detailed analysis of individual faces in core posterior areas of the face-processing network, familiar face recognition emerges categorically in medial temporal and anterior regions of the extended cortical face network. PMID:26283361

Neural microgenesis of personally familiar face recognition.

PubMed

Ramon, Meike; Vizioli, Luca; Liu-Shuang, Joan; Rossion, Bruno

2015-09-01

Despite a wealth of information provided by neuroimaging research, the neural basis of familiar face recognition in humans remains largely unknown. Here, we isolated the discriminative neural responses to unfamiliar and familiar faces by slowly increasing visual information (i.e., high-spatial frequencies) to progressively reveal faces of unfamiliar or personally familiar individuals. Activation in ventral occipitotemporal face-preferential regions increased with visual information, independently of long-term face familiarity. In contrast, medial temporal lobe structures (perirhinal cortex, amygdala, hippocampus) and anterior inferior temporal cortex responded abruptly when sufficient information for familiar face recognition was accumulated. These observations suggest that following detailed analysis of individual faces in core posterior areas of the face-processing network, familiar face recognition emerges categorically in medial temporal and anterior regions of the extended cortical face network.

Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

PubMed

Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

2015-01-01

Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients

PubMed Central

2015-01-01

Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations. PMID:25885272

[Influence of music different in volume and style on human recognition activity].

PubMed

Pavlygina, R A; Sakharov, D S; Davydov, V I; Avdonkin, A V

2009-01-01

The efficiency of recognition of masked visual images (Arabic numerals) under conditions of listening to classical (intensity 62 dB) or rock music (25 dB) increased. Coherence of potential in the frontal cortical region characteristic of the masked image recognition increased under conditions of listening to music. The changes in intercenter EEG relations were correlated with the formation of "the recognition dominant" at the behavioral level. Such behavioral and EEG changes were not observed during listening to louder music (85 dB) and listening to music of other styles, however, the coherence between potentials of the temporal and motor areas of the right hemisphere increased, and the latency of hand motor reactions decreased. The results suggest that the "recognition dominant" is formed under conditions of establishment of certain relations between the levels of excitation in the corresponding centers. These findings should be taken into consideration in case if it were necessary to increase the efficiency of the recognition.

Structure of the active form of human origin recognition complex and its ATPase motor module

PubMed Central

Tocilj, Ante; On, Kin Fan; Yuan, Zuanning; Sun, Jingchuan; Elkayam, Elad; Li, Huilin; Stillman, Bruce; Joshua-Tor, Leemor

2017-01-01

Binding of the Origin Recognition Complex (ORC) to origins of replication marks the first step in the initiation of replication of the genome in all eukaryotic cells. Here, we report the structure of the active form of human ORC determined by X-ray crystallography and cryo-electron microscopy. The complex is composed of an ORC1/4/5 motor module lobe in an organization reminiscent of the DNA polymerase clamp loader complexes. A second lobe contains the ORC2/3 subunits. The complex is organized as a double-layered shallow corkscrew, with the AAA+ and AAA+-like domains forming one layer, and the winged-helix domains (WHDs) forming a top layer. CDC6 fits easily between ORC1 and ORC2, completing the ring and the DNA-binding channel, forming an additional ATP hydrolysis site. Analysis of the ATPase activity of the complex provides a basis for understanding ORC activity as well as molecular defects observed in Meier-Gorlin Syndrome mutations. DOI: http://dx.doi.org/10.7554/eLife.20818.001 PMID:28112645

Crystal structure of a gammadelta T-cell receptor specific for the human MHC class I homolog MICA.

PubMed

Xu, Bin; Pizarro, Juan C; Holmes, Margaret A; McBeth, Christine; Groh, Veronika; Spies, Thomas; Strong, Roland K

2011-02-08

γδ T cells play important roles in bridging innate and adaptive immunity, but their recognition mechanisms remain poorly understood. Human γδ T cells of the V(δ)1 subset predominate in intestinal epithelia and respond to MICA and MICB (MHC class I chain-related, A and B; MIC) self-antigens, mediating responses to tumorigenesis or viral infection. The crystal structure of an MIC-reactive V(δ)1 γδ T-cell receptor (TCR) showed expected overall structural homology to antibodies, αβ, and other γδ TCRs, but complementary determining region conformations and conservation of V(δ)1 use revealed an uncharacteristically flat potential binding surface. MIC, likewise, serves as a ligand for the activating immunoreceptor natural killer group 2, D (NKG2D), also expressed on γδ T cells. Although MIC recognition drives both the TCR-dependent stimulatory and NKG2D-dependent costimulatory signals necessary for activation, interaction analyses showed that MIC binding by the two receptors was mutually exclusive. Analysis of relative binding kinetics suggested sequential recognition, defining constraints for the temporal organization of γδ T-cell/target cell interfaces.

A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

PubMed Central

Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748

Direction of Magnetoencephalography Sources Associated with Feedback and Feedforward Contributions in a Visual Object Recognition Task

PubMed Central

Ahlfors, Seppo P.; Jones, Stephanie R.; Ahveninen, Jyrki; Hämäläinen, Matti S.; Belliveau, John W.; Bar, Moshe

2014-01-01

Identifying inter-area communication in terms of the hierarchical organization of functional brain areas is of considerable interest in human neuroimaging. Previous studies have suggested that the direction of magneto- and electroencephalography (MEG, EEG) source currents depends on the layer-specific input patterns into a cortical area. We examined the direction in MEG source currents in a visual object recognition experiment in which there were specific expectations of activation in the fusiform region being driven by either feedforward or feedback inputs. The source for the early non-specific visual evoked response, presumably corresponding to feedforward driven activity, pointed outward, i.e., away from the white matter. In contrast, the source for the later, object-recognition related signals, expected to be driven by feedback inputs, pointed inward, toward the white matter. Associating specific features of the MEG/EEG source waveforms to feedforward and feedback inputs could provide unique information about the activation patterns within hierarchically organized cortical areas. PMID:25445356

One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM.

PubMed

Rodriguez, Mario; Orrite, Carlos; Medrano, Carlos; Makris, Dimitrios

2016-05-10

This paper presents a novel activity class representation using a single sequence for training. The contribution of this representation lays on the ability to train an one-shot learning recognition system, useful in new scenarios where capturing and labeling sequences is expensive or impractical. The method uses a universal background model of local descriptors obtained from source databases available on-line and adapts it to a new sequence in the target scenario through a maximum a posteriori adaptation. Each activity sample is encoded in a sequence of normalized bag of features and modeled by a new hidden Markov model formulation, where the expectation-maximization algorithm for training is modified to deal with observations consisting in vectors in a unit simplex. Extensive experiments in recognition have been performed using one-shot learning over the public datasets Weizmann, KTH, and IXMAS. These experiments demonstrate the discriminative properties of the representation and the validity of application in recognition systems, achieving state-of-the-art results.

RNA chaperone activity of human La protein is mediated by variant RNA recognition motif.

PubMed

Naeeni, Amir R; Conte, Maria R; Bayfield, Mark A

2012-02-17

La proteins are conserved factors in eukaryotes that bind and protect the 3' trailers of pre-tRNAs from exonuclease digestion via sequence-specific recognition of UUU-3'OH. La has also been hypothesized to assist pre-tRNAs in attaining their native fold through RNA chaperone activity. In addition to binding polymerase III transcripts, human La has also been shown to enhance the translation of several internal ribosome entry sites and upstream ORF-containing mRNA targets, also potentially through RNA chaperone activity. Using in vitro FRET-based assays, we show that human and Schizosaccharomyces pombe La proteins harbor RNA chaperone activity by enhancing RNA strand annealing and strand dissociation. We use various RNA substrates and La mutants to show that UUU-3'OH-dependent La-RNA binding is not required for this function, and we map RNA chaperone activity to its RRM1 motif including a noncanonical α3-helix. We validate the importance of this α3-helix by appending it to the RRM of the unrelated U1A protein and show that this fusion protein acquires significant strand annealing activity. Finally, we show that residues required for La-mediated RNA chaperone activity in vitro are required for La-dependent rescue of tRNA-mediated suppression via a mutated suppressor tRNA in vivo. This work delineates the structural elements required for La-mediated RNA chaperone activity and provides a basis for understanding how La can enhance the folding of its various RNA targets.

A strip chart recorder pattern recognition tool kit for Shuttle operations

NASA Technical Reports Server (NTRS)

Hammen, David G.; Moebes, Travis A.; Shelton, Robert O.; Savely, Robert T.

1993-01-01

During Space Shuttle operations, Mission Control personnel monitor numerous mission-critical systems such as electrical power; guidance, navigation, and control; and propulsion by means of paper strip chart recorders. For example, electrical power controllers monitor strip chart recorder pen traces to identify onboard electrical equipment activations and deactivations. Recent developments in pattern recognition technologies coupled with new capabilities that distribute real-time Shuttle telemetry data to engineering workstations make it possible to develop computer applications that perform some of the low-level monitoring now performed by controllers. The number of opportunities for such applications suggests a need to build a pattern recognition tool kit to reduce software development effort through software reuse. We are building pattern recognition applications while keeping such a tool kit in mind. We demonstrated the initial prototype application, which identifies electrical equipment activations, during three recent Shuttle flights. This prototype was developed to test the viability of the basic system architecture, to evaluate the performance of several pattern recognition techniques including those based on cross-correlation, neural networks, and statistical methods, to understand the interplay between an advanced automation application and human controllers to enhance utility, and to identify capabilities needed in a more general-purpose tool kit.

The human RNA-binding protein and E3 ligase MEX-3C binds the MEX-3-recognition element (MRE) motif with high affinity.

PubMed

Yang, Lingna; Wang, Chongyuan; Li, Fudong; Zhang, Jiahai; Nayab, Anam; Wu, Jihui; Shi, Yunyu; Gong, Qingguo

2017-09-29

MEX-3 is a K-homology (KH) domain-containing RNA-binding protein first identified as a translational repressor in Caenorhabditis elegans , and its four orthologs (MEX-3A-D) in human and mouse were subsequently found to have E3 ubiquitin ligase activity mediated by a RING domain and critical for RNA degradation. Current evidence implicates human MEX-3C in many essential biological processes and suggests a strong connection with immune diseases and carcinogenesis. The highly conserved dual KH domains in MEX-3 proteins enable RNA binding and are essential for the recognition of the 3'-UTR and post-transcriptional regulation of MEX-3 target transcripts. However, the molecular mechanisms of translational repression and the consensus RNA sequence recognized by the MEX-3C KH domain are unknown. Here, using X-ray crystallography and isothermal titration calorimetry, we investigated the RNA-binding activity and selectivity of human MEX-3C dual KH domains. Our high-resolution crystal structures of individual KH domains complexed with a noncanonical U-rich and a GA-rich RNA sequence revealed that the KH1/2 domains of human MEX-3C bound MRE10, a 10-mer RNA (5'-CAGAGUUUAG-3') consisting of an eight-nucleotide MEX-3-recognition element (MRE) motif, with high affinity. Of note, we also identified a consensus RNA motif recognized by human MEX-3C. The potential RNA-binding sites in the 3'-UTR of the human leukocyte antigen serotype ( HLA-A2 ) mRNA were mapped with this RNA-binding motif and further confirmed by fluorescence polarization. The binding motif identified here will provide valuable information for future investigations of the functional pathways controlled by human MEX-3C and for predicting potential mRNAs regulated by this enzyme. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

LL37 and Cationic Peptides Enhance TLR3 Signaling by Viral Double-stranded RNAs

PubMed Central

Lai, Yvonne; Adhikarakunnathu, Sreedevi; Bhardwaj, Kanchan; Ranjith-Kumar, C. T.; Wen, Yahong; Jordan, Jarrat L.; Wu, Linda H.; Dragnea, Bogdan; Mateo, Lani San; Kao, C. Cheng

2011-01-01

Background Toll-like Receptor 3 (TLR3) detects viral dsRNA during viral infection. However, most natural viral dsRNAs are poor activators of TLR3 in cell-based systems, leading us to hypothesize that TLR3 needs additional factors to be activated by viral dsRNAs. The anti-microbial peptide LL37 is the only known human member of the cathelicidin family of anti-microbial peptides. LL37 complexes with bacterial lipopolysaccharide (LPS) to prevent activation of TLR4, binds to ssDNA to modulate TLR9 and ssRNA to modulate TLR7 and 8. It synergizes with TLR2/1, TLR3 and TLR5 agonists to increase IL8 and IL6 production. This work seeks to determine whether LL37 enhances viral dsRNA recognition by TLR3. Methodology/Principal Findings Using a human bronchial epithelial cell line (BEAS2B) and human embryonic kidney cells (HEK 293T) transiently transfected with TLR3, we found that LL37 enhanced poly(I:C)-induced TLR3 signaling and enabled the recognition of viral dsRNAs by TLR3. The presence of LL37 also increased the cytokine response to rhinovirus infection in BEAS2B cells and in activated human peripheral blood mononuclear cells. Confocal microscopy determined that LL37 could co-localize with TLR3. Electron microscopy showed that LL37 and poly(I:C) individually formed globular structures, but a complex of the two formed filamentous structures. To separate the effects of LL37 on TLR3 and TLR4, other peptides that bind RNA and transport the complex into cells were tested and found to activate TLR3 signaling in response to dsRNAs, but had no effect on TLR4 signaling. This is the first demonstration that LL37 and other RNA-binding peptides with cell penetrating motifs can activate TLR3 signaling and facilitate the recognition of viral ligands. Conclusions/Significance LL37 and several cell-penetrating peptides can enhance signaling by TLR3 and enable TLR3 to respond to viral dsRNA. PMID:22039520

Purified monomeric ligand.MD-2 complexes reveal molecular and structural requirements for activation and antagonism of TLR4 by Gram-negative bacterial endotoxins.

PubMed

Gioannini, Theresa L; Teghanemt, Athmane; Zhang, DeSheng; Esparza, Gregory; Yu, Liping; Weiss, Jerrold

2014-08-01

A major focus of work in our laboratory concerns the molecular mechanisms and structural bases of Gram-negative bacterial endotoxin recognition by host (e.g., human) endotoxin-recognition proteins that mediate and/or regulate activation of Toll-like receptor (TLR) 4. Here, we review studies of wild-type and variant monomeric endotoxin.MD-2 complexes first produced and characterized in our laboratories. These purified complexes have provided unique experimental reagents, revealing both quantitative and qualitative determinants of TLR4 activation and antagonism. This review is dedicated to the memory of Dr. Theresa L. Gioannini (1949-2014) who played a central role in many of the studies and discoveries that are reviewed.

Viewpoint Integration for Hand-Based Recognition of Social Interactions from a First-Person View.

PubMed

Bambach, Sven; Crandall, David J; Yu, Chen

2015-11-01

Wearable devices are becoming part of everyday life, from first-person cameras (GoPro, Google Glass), to smart watches (Apple Watch), to activity trackers (FitBit). These devices are often equipped with advanced sensors that gather data about the wearer and the environment. These sensors enable new ways of recognizing and analyzing the wearer's everyday personal activities, which could be used for intelligent human-computer interfaces and other applications. We explore one possible application by investigating how egocentric video data collected from head-mounted cameras can be used to recognize social activities between two interacting partners (e.g. playing chess or cards). In particular, we demonstrate that just the positions and poses of hands within the first-person view are highly informative for activity recognition, and present a computer vision approach that detects hands to automatically estimate activities. While hand pose detection is imperfect, we show that combining evidence across first-person views from the two social partners significantly improves activity recognition accuracy. This result highlights how integrating weak but complimentary sources of evidence from social partners engaged in the same task can help to recognize the nature of their interaction.

Viewpoint Integration for Hand-Based Recognition of Social Interactions from a First-Person View

PubMed Central

Bambach, Sven; Crandall, David J.; Yu, Chen

2016-01-01

Wearable devices are becoming part of everyday life, from first-person cameras (GoPro, Google Glass), to smart watches (Apple Watch), to activity trackers (FitBit). These devices are often equipped with advanced sensors that gather data about the wearer and the environment. These sensors enable new ways of recognizing and analyzing the wearer’s everyday personal activities, which could be used for intelligent human-computer interfaces and other applications. We explore one possible application by investigating how egocentric video data collected from head-mounted cameras can be used to recognize social activities between two interacting partners (e.g. playing chess or cards). In particular, we demonstrate that just the positions and poses of hands within the first-person view are highly informative for activity recognition, and present a computer vision approach that detects hands to automatically estimate activities. While hand pose detection is imperfect, we show that combining evidence across first-person views from the two social partners significantly improves activity recognition accuracy. This result highlights how integrating weak but complimentary sources of evidence from social partners engaged in the same task can help to recognize the nature of their interaction. PMID:28966999

Differential amygdala response during facial recognition in patients with schizophrenia: an fMRI study.

PubMed

Kosaka, H; Omori, M; Murata, T; Iidaka, T; Yamada, H; Okada, T; Takahashi, T; Sadato, N; Itoh, H; Yonekura, Y; Wada, Y

2002-09-01

Human lesion or neuroimaging studies suggest that amygdala is involved in facial emotion recognition. Although impairments in recognition of facial and/or emotional expression have been reported in schizophrenia, there are few neuroimaging studies that have examined differential brain activation during facial recognition between patients with schizophrenia and normal controls. To investigate amygdala responses during facial recognition in schizophrenia, we conducted a functional magnetic resonance imaging (fMRI) study with 12 right-handed medicated patients with schizophrenia and 12 age- and sex-matched healthy controls. The experiment task was a type of emotional intensity judgment task. During the task period, subjects were asked to view happy (or angry/disgusting/sad) and neutral faces simultaneously presented every 3 s and to judge which face was more emotional (positive or negative face discrimination). Imaging data were investigated in voxel-by-voxel basis for single-group analysis and for between-group analysis according to the random effect model using Statistical Parametric Mapping (SPM). No significant difference in task accuracy was found between the schizophrenic and control groups. Positive face discrimination activated the bilateral amygdalae of both controls and schizophrenics, with more prominent activation of the right amygdala shown in the schizophrenic group. Negative face discrimination activated the bilateral amygdalae in the schizophrenic group whereas the right amygdala alone in the control group, although no significant group difference was found. Exaggerated amygdala activation during emotional intensity judgment found in the schizophrenic patients may reflect impaired gating of sensory input containing emotion. Copyright 2002 Elsevier Science B.V.

Effect of speech-intrinsic variations on human and automatic recognition of spoken phonemes.

PubMed

Meyer, Bernd T; Brand, Thomas; Kollmeier, Birger

2011-01-01

The aim of this study is to quantify the gap between the recognition performance of human listeners and an automatic speech recognition (ASR) system with special focus on intrinsic variations of speech, such as speaking rate and effort, altered pitch, and the presence of dialect and accent. Second, it is investigated if the most common ASR features contain all information required to recognize speech in noisy environments by using resynthesized ASR features in listening experiments. For the phoneme recognition task, the ASR system achieved the human performance level only when the signal-to-noise ratio (SNR) was increased by 15 dB, which is an estimate for the human-machine gap in terms of the SNR. The major part of this gap is attributed to the feature extraction stage, since human listeners achieve comparable recognition scores when the SNR difference between unaltered and resynthesized utterances is 10 dB. Intrinsic variabilities result in strong increases of error rates, both in human speech recognition (HSR) and ASR (with a relative increase of up to 120%). An analysis of phoneme duration and recognition rates indicates that human listeners are better able to identify temporal cues than the machine at low SNRs, which suggests incorporating information about the temporal dynamics of speech into ASR systems.

From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems

PubMed Central

Yildiz, Izzet B.; von Kriegstein, Katharina; Kiebel, Stefan J.

2013-01-01

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments. PMID:24068902

From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

PubMed

Yildiz, Izzet B; von Kriegstein, Katharina; Kiebel, Stefan J

2013-01-01

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents-an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.

Computer-assisted visual interactive recognition and its prospects of implementation over the Internet

NASA Astrophysics Data System (ADS)

Zou, Jie; Gattani, Abhishek

2005-01-01

When completely automated systems don't yield acceptable accuracy, many practical pattern recognition systems involve the human either at the beginning (pre-processing) or towards the end (handling rejects). We believe that it may be more useful to involve the human throughout the recognition process rather than just at the beginning or end. We describe a methodology of interactive visual recognition for human-centered low-throughput applications, Computer Assisted Visual InterActive Recognition (CAVIAR), and discuss the prospects of implementing CAVIAR over the Internet. The novelty of CAVIAR is image-based interaction through a domain-specific parameterized geometrical model, which reduces the semantic gap between humans and computers. The user may interact with the computer anytime that she considers its response unsatisfactory. The interaction improves the accuracy of the classification features by improving the fit of the computer-proposed model. The computer makes subsequent use of the parameters of the improved model to refine not only its own statistical model-fitting process, but also its internal classifier. The CAVIAR methodology was applied to implement a flower recognition system. The principal conclusions from the evaluation of the system include: 1) the average recognition time of the CAVIAR system is significantly shorter than that of the unaided human; 2) its accuracy is significantly higher than that of the unaided machine; 3) it can be initialized with as few as one training sample per class and still achieve high accuracy; and 4) it demonstrates a self-learning ability. We have also implemented a Mobile CAVIAR system, where a pocket PC, as a client, connects to a server through wireless communication. The motivation behind a mobile platform for CAVIAR is to apply the methodology in a human-centered pervasive environment, where the user can seamlessly interact with the system for classifying field-data. Deploying CAVIAR to a networked mobile platform poses the challenge of classifying field images and programming under constraints of display size, network bandwidth, processor speed, and memory size. Editing of the computer-proposed model is performed on the handheld while statistical model fitting and classification take place on the server. The possibility that the user can easily take several photos of the object poses an interesting information fusion problem. The advantage of the Internet is that the patterns identified by different users can be pooled together to benefit all peer users. When users identify patterns with CAVIAR in a networked setting, they also collect training samples and provide opportunities for machine learning from their intervention. CAVIAR implemented over the Internet provides a perfect test bed for, and extends, the concept of Open Mind Initiative proposed by David Stork. Our experimental evaluation focuses on human time, machine and human accuracy, and machine learning. We devoted much effort to evaluating the use of our image-based user interface and on developing principles for the evaluation of interactive pattern recognition system. The Internet architecture and Mobile CAVIAR methodology have many applications. We are exploring in the directions of teledermatology, face recognition, and education.

Using GOMS and Bayesian plan recognition to develop recognition models of operator behavior

NASA Astrophysics Data System (ADS)

Zaientz, Jack D.; DeKoven, Elyon; Piegdon, Nicholas; Wood, Scott D.; Huber, Marcus J.

2006-05-01

Trends in combat technology research point to an increasing role for uninhabited vehicles in modern warfare tactics. To support increased span of control over these vehicles human responsibilities need to be transformed from tedious, error-prone and cognition intensive operations into tasks that are more supervisory and manageable, even under intensely stressful conditions. The goal is to move away from only supporting human command of low-level system functions to intention-level human-system dialogue about the operator's tasks and situation. A critical element of this process is developing the means to identify when human operators need automated assistance and to identify what assistance they need. Toward this goal, we are developing an unmanned vehicle operator task recognition system that combines work in human behavior modeling and Bayesian plan recognition. Traditionally, human behavior models have been considered generative, meaning they describe all possible valid behaviors. Basing behavior recognition on models designed for behavior generation can offers advantages in improved model fidelity and reuse. It is not clear, however, how to reconcile the structural differences between behavior recognition and behavior modeling approaches. Our current work demonstrates that by pairing a cognitive psychology derived human behavior modeling approach, GOMS, with a Bayesian plan recognition engine, ASPRN, we can translate a behavior generation model into a recognition model. We will discuss the implications for using human performance models in this manner as well as suggest how this kind of modeling may be used to support the real-time control of multiple, uninhabited battlefield vehicles and other semi-autonomous systems.

Transparent Stretchable Self-Powered Patchable Sensor Platform with Ultrasensitive Recognition of Human Activities.

PubMed

Hwang, Byeong-Ung; Lee, Ju-Hyuck; Trung, Tran Quang; Roh, Eun; Kim, Do-Il; Kim, Sang-Woo; Lee, Nae-Eung

2015-09-22

Monitoring of human activities can provide clinically relevant information pertaining to disease diagnostics, preventive medicine, care for patients with chronic diseases, rehabilitation, and prosthetics. The recognition of strains on human skin, induced by subtle movements of muscles in the internal organs, such as the esophagus and trachea, and the motion of joints, was demonstrated using a self-powered patchable strain sensor platform, composed on multifunctional nanocomposites of low-density silver nanowires with a conductive elastomer of poly(3,4-ethylenedioxythiophene):polystyrenesulfonate/polyurethane, with high sensitivity, stretchability, and optical transparency. The ultra-low-power consumption of the sensor, integrated with both a supercapacitor and a triboelectric nanogenerator into a single transparent stretchable platform based on the same nanocomposites, results in a self-powered monitoring system for skin strain. The capability of the sensor to recognize a wide range of strain on skin has the potential for use in new areas of invisible stretchable electronics for human monitoring. A new type of transparent, stretchable, and ultrasensitive strain sensor based on a AgNW/PEDOT:PSS/PU nanocomposite was developed. The concept of a self-powered patchable sensor system integrated with a supercapacitor and a triboelectric nanogenerator that can be used universally as an autonomous invisible sensor system was used to detect the wide range of strain on human skin.

CUS2, a Yeast Homolog of Human Tat-SF1, Rescues Function of Misfolded U2 through an Unusual RNA Recognition Motif

PubMed Central

Yan, Dong; Perriman, Rhonda; Igel, Haller; Howe, Kenneth J.; Neville, Megan; Ares, Manuel

1998-01-01

A screen for suppressors of a U2 snRNA mutation identified CUS2, an atypical member of the RNA recognition motif (RRM) family of RNA binding proteins. CUS2 protein is associated with U2 RNA in splicing extracts and interacts with PRP11, a subunit of the conserved splicing factor SF3a. Absence of CUS2 renders certain U2 RNA folding mutants lethal, arguing that a normal activity of CUS2 is to help refold U2 into a structure favorable for its binding to SF3b and SF3a prior to spliceosome assembly. Both CUS2 function in vivo and the in vitro RNA binding activity of CUS2 are disrupted by mutation of the first RRM, suggesting that rescue of misfolded U2 involves the direct binding of CUS2. Human Tat-SF1, reported to stimulate Tat-specific, transactivating region-dependent human immunodeficiency virus transcription in vitro, is structurally similar to CUS2. Anti-Tat-SF1 antibodies coimmunoprecipitate SF3a66 (SAP62), the human homolog of PRP11, suggesting that Tat-SF1 has a parallel function in splicing in human cells. PMID:9710584

Recognition of complex human behaviours using 3D imaging for intelligent surveillance applications

NASA Astrophysics Data System (ADS)

Yao, Bo; Lepley, Jason J.; Peall, Robert; Butler, Michael; Hagras, Hani

2016-10-01

We introduce a system that exploits 3-D imaging technology as an enabler for the robust recognition of the human form. We combine this with pose and feature recognition capabilities from which we can recognise high-level human behaviours. We propose a hierarchical methodology for the recognition of complex human behaviours, based on the identification of a set of atomic behaviours, individual and sequential poses (e.g. standing, sitting, walking, drinking and eating) that provides a framework from which we adopt time-based machine learning techniques to recognise complex behaviour patterns.

The structural basis for receptor recognition of human interleukin-18

DOE PAGES

Tsutsumi, Naotaka; Kimura, Takeshi; Arita, Kyohei; ...

2014-12-15

Interleukin (IL)-18 is a proinflammatory cytokine that belongs to the IL-1 family and plays an important role in inflammation. The uncontrolled release of this cytokine is associated with severe chronic inflammatory disease. IL-18 forms a signalling complex with the IL-18 receptor α (Rα) and β (Rβ) chains at the plasma membrane, which induces multiple inflammatory cytokines. Here, we present a crystal structure of human IL-18 bound to the two receptor extracellular domains. Generally, the receptors’ recognition mode for IL-18 is similar to IL-1β; however, certain notable differences were observed. The architecture of the IL-18 receptor second domain (D2) is uniquemore » among the other IL-1R family members, which presumably distinguishes them from the IL-1 receptors that exhibit a more promiscuous ligand recognition mode. The structures and associated biochemical and cellular data should aid in developing novel drugs to neutralize IL-8 activity.« less

The structural basis for receptor recognition of human interleukin-18

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsutsumi, Naotaka; Kimura, Takeshi; Arita, Kyohei

Interleukin (IL)-18 is a proinflammatory cytokine that belongs to the IL-1 family and plays an important role in inflammation. The uncontrolled release of this cytokine is associated with severe chronic inflammatory disease. IL-18 forms a signalling complex with the IL-18 receptor α (Rα) and β (Rβ) chains at the plasma membrane, which induces multiple inflammatory cytokines. Here, we present a crystal structure of human IL-18 bound to the two receptor extracellular domains. Generally, the receptors’ recognition mode for IL-18 is similar to IL-1β; however, certain notable differences were observed. The architecture of the IL-18 receptor second domain (D2) is uniquemore » among the other IL-1R family members, which presumably distinguishes them from the IL-1 receptors that exhibit a more promiscuous ligand recognition mode. The structures and associated biochemical and cellular data should aid in developing novel drugs to neutralize IL-8 activity.« less

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras.

PubMed

Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

2017-03-16

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

Heat Shock Protein-90 Inhibitors Enhance Antigen Expression on Melanomas and Increase T Cell Recognition of Tumor Cells

PubMed Central

Haggerty, Timothy J.; Dunn, Ian S.; Rose, Lenora B.; Newton, Estelle E.; Pandolfi, Franco; Kurnick, James T.

2014-01-01

In an effort to enhance antigen-specific T cell recognition of cancer cells, we have examined numerous modulators of antigen-expression. In this report we demonstrate that twelve different Hsp90 inhibitors (iHsp90) share the ability to increase the expression of differentiation antigens and MHC Class I antigens. These iHsp90 are active in several molecular and cellular assays on a series of tumor cell lines, including eleven human melanomas, a murine B16 melanoma, and two human glioma-derived cell lines. Intra-cytoplasmic antibody staining showed that all of the tested iHsp90 increased expression of the melanocyte differentiation antigens Melan-A/MART-1, gp100, and TRP-2, as well as MHC Class I. The gliomas showed enhanced gp100 and MHC staining. Quantitative analysis of mRNA levels showed a parallel increase in message transcription, and a reporter assay shows induction of promoter activity for Melan-A/MART-1 gene. In addition, iHsp90 increased recognition of tumor cells by T cells specific for Melan-A/MART-1. In contrast to direct Hsp90 client proteins, the increased levels of full-length differentiation antigens that result from iHsp90 treatment are most likely the result of transcriptional activation of their encoding genes. In combination, these results suggest that iHsp90 improve recognition of tumor cells by T cells specific for a melanoma-associated antigen as a result of increasing the expressed intracellular antigen pool available for processing and presentation by MHC Class I, along with increased levels of MHC Class I itself. As these Hsp90 inhibitors do not interfere with T cell function, they could have potential for use in immunotherapy of cancer. PMID:25503774

Vimentin is an endogenous ligand for the pattern recognition receptor Dectin-1.

PubMed

Thiagarajan, Praveena S; Yakubenko, Valentin P; Elsori, Deena H; Yadav, Satya P; Willard, Belinda; Tan, Carmela D; Rodriguez, E René; Febbraio, Maria; Cathcart, Martha K

2013-08-01

Atherosclerosis is a chronic inflammatory disorder of cholesterol deposition in monocyte-derived macrophages (MDM) within the arterial wall leading to impingement on the lumen of the vessel. In atherosclerotic lesions, MDM are the primary source of NADPH oxidase-derived superoxide anion (O₂⁻) inducing low-density lipoprotein (LDL) oxidation leading to their unregulated uptake of oxidized LDL and foam cell formation. We recently discovered that zymosan potently activates monocyte NADPH oxidase via the non-toll pattern recognition receptor (PRR), Dectin-1. Other PRRs bind endogenous human ligands, yet no such ligands have been identified for Dectin-1. Our hypothesis was that inflammation generates endogenous ligands for Dectin-1 that activate O₂⁻ production and thereby contributes to atherogenesis. Human: anti-zymosan antibodies were used to identify similar, cross-reactive epitopes in human atherosclerotic tissue extracts. Immunoblot analysis revealed consistent antibody reactive protein bands on one- and two-dimensional gel electrophoreses. Vimentin was identified by mass spectrometry in the immunoreactive bands across different tissue samples. Direct binding of vimentin to Dectin-1 was observed using BIACORE. Further data revealed that vimentin induces O₂⁻ production by human monocytes. Analysis of human atherosclerotic lesions revealed that vimentin was detected extracellularly in the necrotic core and in areas of active inflammation. Vimentin also co-localized with Dectin-1 in macrophage-rich regions where O₂⁻ is produced. We conclude that vimentin is an endogenous, activating ligand for Dectin-1. Its presence in areas of artery wall inflammation and O₂⁻ production suggests that vimentin activates Dectin-1 and contributes to the oxidation of lipids and cholesterol accumulation in atherosclerosis.

"Who" is saying "what"? Brain-based decoding of human voice and speech.

PubMed

Formisano, Elia; De Martino, Federico; Bonte, Milene; Goebel, Rainer

2008-11-07

Can we decipher speech content ("what" is being said) and speaker identity ("who" is saying it) from observations of brain activity of a listener? Here, we combine functional magnetic resonance imaging with a data-mining algorithm and retrieve what and whom a person is listening to from the neural fingerprints that speech and voice signals elicit in the listener's auditory cortex. These cortical fingerprints are spatially distributed and insensitive to acoustic variations of the input so as to permit the brain-based recognition of learned speech from unknown speakers and of learned voices from previously unheard utterances. Our findings unravel the detailed cortical layout and computational properties of the neural populations at the basis of human speech recognition and speaker identification.

Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body.

PubMed

Nguyen, Dat Tien; Park, Kang Ryoung

2016-07-21

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.

Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body

PubMed Central

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-01

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images. PMID:27455264

Decoding ensemble activity from neurophysiological recordings in the temporal cortex.

PubMed

Kreiman, Gabriel

2011-01-01

We study subjects with pharmacologically intractable epilepsy who undergo semi-chronic implantation of electrodes for clinical purposes. We record physiological activity from tens to more than one hundred electrodes implanted in different parts of neocortex. These recordings provide higher spatial and temporal resolution than non-invasive measures of human brain activity. Here we discuss our efforts to develop hardware and algorithms to interact with the human brain by decoding ensemble activity in single trials. We focus our discussion on decoding visual information during a variety of visual object recognition tasks but the same technologies and algorithms can also be directly applied to other cognitive phenomena.

Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

PubMed Central

Munoz-Organero, Mario; Ruiz-Blazquez, Ramona

2017-01-01

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware. PMID:28208736

Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition.

PubMed

Munoz-Organero, Mario; Ruiz-Blazquez, Ramona

2017-02-08

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates ( F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware.

Bordetella pertussis Naturally Occurring Isolates with Altered Lipooligosaccharide Structure Fail To Fully Mature Human Dendritic Cells

PubMed Central

Brummelman, Jolanda; Veerman, Rosanne E.; Hamstra, Hendrik Jan; Deuss, Anna J. M.; Schuijt, Tim J.; Sloots, Arjen; Kuipers, Betsy; van Els, Cécile A. C. M.; van der Ley, Peter; Mooi, Frits R.; Han, Wanda G. H.

2014-01-01

Bordetella pertussis is a Gram-negative bacterium and the causative agent of whooping cough. Despite high vaccination coverage, outbreaks are being increasingly reported worldwide. Possible explanations include adaptation of this pathogen, which may interfere with recognition by the innate immune system. Here, we describe innate immune recognition and responses to different B. pertussis clinical isolates. By using HEK-Blue cells transfected with different pattern recognition receptors, we found that 3 out of 19 clinical isolates failed to activate Toll-like receptor 4 (TLR4). These findings were confirmed by using the monocytic MM6 cell line. Although incubation with high concentrations of these 3 strains resulted in significant activation of the MM6 cells, it was found to occur mainly through interaction with TLR2 and not through TLR4. When using live bacteria, these 3 strains also failed to activate TLR4 on HEK-Blue cells, and activation of MM6 cells or human monocyte-derived dendritic cells was significantly lower than activation induced by the other 16 strains. Mass spectrum analysis of the lipid A moieties from these 3 strains indicated an altered structure of this molecule. Gene sequence analysis revealed mutations in genes involved in lipid A synthesis. Findings from this study indicate that B. pertussis isolates that do not activate TLR4 occur naturally and that this phenotype may give this bacterium an advantage in tempering the innate immune response and establishing infection. Knowledge on the strategies used by this pathogen in evading the host immune response is essential for the improvement of current vaccines or for the development of new ones. PMID:25348634

Asteroids in the service of humanity

NASA Astrophysics Data System (ADS)

Crawford, Ian A.

2013-07-01

There are at least three compelling reasons for the human race to initiate a major programme to explore and better understand the 'minor planets' of the Solar System: (1) Enhancing scientific knowledge; (2) Mitigating the impact hazard; and (3) Utilizing extraterrestrial resources. Strong synergies exist between all three. Moreover, all these activities would benefit from greater international cooperation in space exploration by the World's space agencies, and the recognition that asteroids are important targets for human and robotic exploration.

Recognition of Mannosylated Ligands and Influenza A Virus by Human Surfactant Protein D: Contributions of an Extended Site and Residue 343

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crouch, E.; Hartshorn, K; Horlacher, T

2009-01-01

Surfactant protein D (SP-D) plays important roles in antiviral host defense. Although SP-D shows a preference for glucose/maltose, the protein also recognizes d-mannose and a variety of mannose-rich microbial ligands. This latter preference prompted an examination of the mechanisms of mannose recognition, particularly as they relate to high-mannose viral glycans. Trimeric neck plus carbohydrate recognition domains from human SP-D (hNCRD) preferred ?1-2-linked dimannose (DM) over the branched trimannose (TM) core, ?1-3 or ?1-6 DM, or d-mannose. Previous studies have shown residues flanking the carbohydrate binding site can fine-tune ligand recognition. A mutant with valine at 343 (R343V) showed enhanced bindingmore » to mannan relative to wild type and R343A. No alteration in affinity was observed for d-mannose or for ?1-3- or ?1-6-linked DM; however, substantially increased affinity was observed for ?1-2 DM. Both proteins showed efficient recognition of linear and branched subdomains of high-mannose glycans on carbohydrate microarrays, and R343V showed increased binding to a subset of the oligosaccharides. Crystallographic analysis of an R343V complex with 1,2-DM showed a novel mode of binding. The disaccharide is bound to calcium by the reducing sugar ring, and a stabilizing H-bond is formed between the 2-OH of the nonreducing sugar ring and Arg349. Although hNCRDs show negligible binding to influenza A virus (IAV), R343V showed markedly enhanced viral neutralizing activity. Hydrophobic substitutions for Arg343 selectively blocked binding of a monoclonal antibody (Hyb 246-05) that inhibits IAV binding activity. Our findings demonstrate an extended ligand binding site for mannosylated ligands and the significant contribution of the 343 side chain to specific recognition of multivalent microbial ligands, including high-mannose viral glycans.« less

Human brain activity with functional NIR optical imager

NASA Astrophysics Data System (ADS)

Luo, Qingming

2001-08-01

In this paper we reviewed the applications of functional near infrared optical imager in human brain activity. Optical imaging results of brain activity, including memory for new association, emotional thinking, mental arithmetic, pattern recognition ' where's Waldo?, occipital cortex in visual stimulation, and motor cortex in finger tapping, are demonstrated. It is shown that the NIR optical method opens up new fields of study of the human population, in adults under conditions of simulated or real stress that may have important effects upon functional performance. It makes practical and affordable for large populations the complex technology of measuring brain function. It is portable and low cost. In cognitive tasks subjects could report orally. The temporal resolution could be millisecond or less in theory. NIR method will have good prospects in exploring human brain secret.

RNA Chaperone Activity of Human La Protein Is Mediated by Variant RNA Recognition Motif*

PubMed Central

Naeeni, Amir R.; Conte, Maria R.; Bayfield, Mark A.

2012-01-01

La proteins are conserved factors in eukaryotes that bind and protect the 3′ trailers of pre-tRNAs from exonuclease digestion via sequence-specific recognition of UUU-3′OH. La has also been hypothesized to assist pre-tRNAs in attaining their native fold through RNA chaperone activity. In addition to binding polymerase III transcripts, human La has also been shown to enhance the translation of several internal ribosome entry sites and upstream ORF-containing mRNA targets, also potentially through RNA chaperone activity. Using in vitro FRET-based assays, we show that human and Schizosaccharomyces pombe La proteins harbor RNA chaperone activity by enhancing RNA strand annealing and strand dissociation. We use various RNA substrates and La mutants to show that UUU-3′OH-dependent La-RNA binding is not required for this function, and we map RNA chaperone activity to its RRM1 motif including a noncanonical α3-helix. We validate the importance of this α3-helix by appending it to the RRM of the unrelated U1A protein and show that this fusion protein acquires significant strand annealing activity. Finally, we show that residues required for La-mediated RNA chaperone activity in vitro are required for La-dependent rescue of tRNA-mediated suppression via a mutated suppressor tRNA in vivo. This work delineates the structural elements required for La-mediated RNA chaperone activity and provides a basis for understanding how La can enhance the folding of its various RNA targets. PMID:22203678

Control of antiviral immunity by pattern recognition and the microbiome

PubMed Central

Pang, Iris K.; Iwasaki, Akiko

2013-01-01

Summary Human skin and mucosal surfaces are in constant contact with resident and invasive microbes. Recognition of microbial products by receptors of the innate immune system triggers rapid innate defense and transduces signals necessary for initiating and maintaining the adaptive immune responses. Microbial sensing by innate pattern recognition receptors is not restricted to pathogens. Rather, proper development, function, and maintenance of innate and adaptive immunity rely on continuous recognition of products derived from the microorganisms indigenous to the internal and external surfaces of mammalian host. Tonic immune activation by the resident microbiota governs host susceptibility to intestinal and extra-intestinal infections including those caused by viruses. This review highlights recent developments in innate viral recognition leading to adaptive immunity, and discusses potential link between viruses, microbiota and the host immune system. Further, we discuss the possible roles of microbiome in chronic viral infection and pathogenesis of autoimmune disease, and speculate on the benefit for probiotic therapies against such diseases. PMID:22168422

Darwin revisited: The vagus nerve is a causal element in controlling recognition of other's emotions.

PubMed

Colzato, Lorenza S; Sellaro, Roberta; Beste, Christian

2017-07-01

Charles Darwin proposed that via the vagus nerve, the tenth cranial nerve, emotional facial expressions are evolved, adaptive and serve a crucial communicative function. In line with this idea, the later-developed polyvagal theory assumes that the vagus nerve is the key phylogenetic substrate that regulates emotional and social behavior. The polyvagal theory assumes that optimal social interaction, which includes the recognition of emotion in faces, is modulated by the vagus nerve. So far, in humans, it has not yet been demonstrated that the vagus plays a causal role in emotion recognition. To investigate this we employed transcutaneous vagus nerve stimulation (tVNS), a novel non-invasive brain stimulation technique that modulates brain activity via bottom-up mechanisms. A sham/placebo-controlled, randomized cross-over within-subjects design was used to infer a causal relation between the stimulated vagus nerve and the related ability to recognize emotions as indexed by the Reading the Mind in the Eyes Test in 38 healthy young volunteers. Active tVNS, compared to sham stimulation, enhanced emotion recognition for easy items, suggesting that it promoted the ability to decode salient social cues. Our results confirm that the vagus nerve is causally involved in emotion recognition, supporting Darwin's argumentation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Impairments in Monkey and Human Face Recognition in 2-Year-Old Toddlers with Autism Spectrum Disorder and Developmental Delay

ERIC Educational Resources Information Center

Chawarska, Katarzyna; Volkmar, Fred

2007-01-01

Face recognition impairments are well documented in older children with Autism Spectrum Disorders (ASD); however, the developmental course of the deficit is not clear. This study investigates the progressive specialization of face recognition skills in children with and without ASD. Experiment 1 examines human and monkey face recognition in…

Functional Connectivity of Multiple Brain Regions Required for the Consolidation of Social Recognition Memory.

PubMed

Tanimizu, Toshiyuki; Kenney, Justin W; Okano, Emiko; Kadoma, Kazune; Frankland, Paul W; Kida, Satoshi

2017-04-12

Social recognition memory is an essential and basic component of social behavior that is used to discriminate familiar and novel animals/humans. Previous studies have shown the importance of several brain regions for social recognition memories; however, the mechanisms underlying the consolidation of social recognition memory at the molecular and anatomic levels remain unknown. Here, we show a brain network necessary for the generation of social recognition memory in mice. A mouse genetic study showed that cAMP-responsive element-binding protein (CREB)-mediated transcription is required for the formation of social recognition memory. Importantly, significant inductions of the CREB target immediate-early genes c-fos and Arc were observed in the hippocampus (CA1 and CA3 regions), medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), and amygdala (basolateral region) when social recognition memory was generated. Pharmacological experiments using a microinfusion of the protein synthesis inhibitor anisomycin showed that protein synthesis in these brain regions is required for the consolidation of social recognition memory. These findings suggested that social recognition memory is consolidated through the activation of CREB-mediated gene expression in the hippocampus/mPFC/ACC/amygdala. Network analyses suggested that these four brain regions show functional connectivity with other brain regions and, more importantly, that the hippocampus functions as a hub to integrate brain networks and generate social recognition memory, whereas the ACC and amygdala are important for coordinating brain activity when social interaction is initiated by connecting with other brain regions. We have found that a brain network composed of the hippocampus/mPFC/ACC/amygdala is required for the consolidation of social recognition memory. SIGNIFICANCE STATEMENT Here, we identify brain networks composed of multiple brain regions for the consolidation of social recognition memory. We found that social recognition memory is consolidated through CREB-meditated gene expression in the hippocampus, medial prefrontal cortex, anterior cingulate cortex (ACC), and amygdala. Importantly, network analyses based on c-fos expression suggest that functional connectivity of these four brain regions with other brain regions is increased with time spent in social investigation toward the generation of brain networks to consolidate social recognition memory. Furthermore, our findings suggest that hippocampus functions as a hub to integrate brain networks and generate social recognition memory, whereas ACC and amygdala are important for coordinating brain activity when social interaction is initiated by connecting with other brain regions. Copyright © 2017 the authors 0270-6474/17/374103-14$15.00/0.

The Episodic Engram Transformed: Time Reduces Retrieval-Related Brain Activity but Correlates It with Memory Accuracy

ERIC Educational Resources Information Center

Furman, Orit; Mendelsohn, Avi; Dudai, Yadin

2012-01-01

We took snapshots of human brain activity with fMRI during retrieval of realistic episodic memory over several months. Three groups of participants were scanned during a memory test either hours, weeks, or months after viewing a documentary movie. High recognition accuracy after hours decreased after weeks and remained at similar levels after…

Increasing trend of wearables and multimodal interface for human activity monitoring: A review.

PubMed

Kumari, Preeti; Mathew, Lini; Syal, Poonam

2017-04-15

Activity recognition technology is one of the most important technologies for life-logging and for the care of elderly persons. Elderly people prefer to live in their own houses, within their own locality. If, they are capable to do so, several benefits can follow in terms of society and economy. However, living alone may have high risks. Wearable sensors have been developed to overcome these risks and these sensors are supposed to be ready for medical uses. It can help in monitoring the wellness of elderly persons living alone by unobtrusively monitoring their daily activities. The study aims to review the increasing trends of wearable devices and need of multimodal recognition for continuous or discontinuous monitoring of human activity, biological signals such as Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG) and parameters along with other symptoms. This can provide necessary assistance in times of ominous need, which is crucial for the advancement of disease-diagnosis and treatment. Shared control architecture with multimodal interface can be used for application in more complex environment where more number of commands is to be used to control with better results in terms of controlling. Copyright © 2016 Elsevier B.V. All rights reserved.

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

PubMed Central

Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

2017-01-01

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body. PMID:28300783

Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment.

PubMed

Qiao, Hong; Xi, Xuanyang; Li, Yinlin; Wu, Wei; Li, Fengfu

2015-11-01

Recently, many computational models have been proposed to simulate visual cognition process. For example, the hierarchical Max-Pooling (HMAX) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex, which could achieve position- and scale-tolerant recognition. In our previous work, we have introduced memory and association into the HMAX model to simulate visual cognition process. In this paper, we improve our theoretical framework by mimicking a more elaborate structure and function of the primate visual cortex. We will mainly focus on the new formation of memory and association in visual processing under different circumstances as well as preliminary cognition and active adjustment in the inferior temporal cortex, which are absent in the HMAX model. The main contributions of this paper are: 1) in the memory and association part, we apply deep convolutional neural networks to extract various episodic features of the objects since people use different features for object recognition. Moreover, to achieve a fast and robust recognition in the retrieval and association process, different types of features are stored in separated clusters and the feature binding of the same object is stimulated in a loop discharge manner and 2) in the preliminary cognition and active adjustment part, we introduce preliminary cognition to classify different types of objects since distinct neural circuits in a human brain are used for identification of various types of objects. Furthermore, active cognition adjustment of occlusion and orientation is implemented to the model to mimic the top-down effect in human cognition process. Finally, our model is evaluated on two face databases CAS-PEAL-R1 and AR. The results demonstrate that our model exhibits its efficiency on visual recognition process with much lower memory storage requirement and a better performance compared with the traditional purely computational methods.

Modulation of Human Leukocyte Antigen-C by Human Cytomegalovirus Stimulates KIR2DS1 Recognition by Natural Killer Cells

PubMed Central

van der Ploeg, Kattria; Chang, Chiwen; Ivarsson, Martin A.; Moffett, Ashley; Wills, Mark R.; Trowsdale, John

2017-01-01

The interaction of inhibitory killer cell Ig-like receptors (KIRs) with human leukocyte antigen (HLA) class I molecules has been characterized in detail. By contrast, activating members of the KIR family, although closely related to inhibitory KIRs, appear to interact weakly, if at all, with HLA class I. KIR2DS1 is the best studied activating KIR and it interacts with C2 group HLA-C (C2-HLA-C) in some assays, but not as strongly as KIR2DL1. We used a mouse 2B4 cell reporter system, which carries NFAT-green fluorescent protein with KIR2DS1 and a modified DAP12 adaptor protein. KIR2DS1 reporter cells were not activated upon coculture with 721.221 cells transfected with different HLA-C molecules, or with interferon-γ stimulated primary dermal fibroblasts. However, KIR2DS1 reporter cells and KIR2DS1+ primary natural killer (NK) cells were activated by C2-HLA-C homozygous human fetal foreskin fibroblasts (HFFFs) but only after infection with specific clones of a clinical strain of human cytomegalovirus (HCMV). Active viral gene expression was required for activation of both cell types. Primary NKG2A−KIR2DS1+ NK cell subsets degranulated after coculture with HCMV-infected HFFFs. The W6/32 antibody to HLA class I blocked the KIR2DS1 reporter cell interaction with its ligand on HCMV-infected HFFFs but did not block interaction with KIR2DL1. This implies a differential recognition of HLA-C by KIR2DL1 and KIR2DS1. The data suggest that modulation of HLA-C by HCMV is required for a potent KIR2DS1-mediated NK cell activation. PMID:28424684

Multisensor data fusion for physical activity assessment.

PubMed

Liu, Shaopeng; Gao, Robert X; John, Dinesh; Staudenmayer, John W; Freedson, Patty S

2012-03-01

This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multisensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which activity type and energy expenditure are derived. The results show that the method correctly recognized the 13 activity types 88.1% of the time, which is 12.3% higher than using a hip accelerometer alone. Also, the method predicted energy expenditure with a root mean square error of 0.42 METs, 22.2% lower than using a hip accelerometer alone. Furthermore, the fusion method was effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition, especially when data from the ventilation sensor were added to the fusion model. These results demonstrate that the multisensor fusion technique presented is more effective in identifying activity type and energy expenditure than the traditional accelerometer-alone-based methods.

Interaction in Spoken Word Recognition Models: Feedback Helps.

PubMed

Magnuson, James S; Mirman, Daniel; Luthra, Sahil; Strauss, Ted; Harris, Harlan D

2018-01-01

Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis.

Interaction in Spoken Word Recognition Models: Feedback Helps

PubMed Central

Magnuson, James S.; Mirman, Daniel; Luthra, Sahil; Strauss, Ted; Harris, Harlan D.

2018-01-01

Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis. PMID:29666593

Bridging automatic speech recognition and psycholinguistics: Extending Shortlist to an end-to-end model of human speech recognition (L)

NASA Astrophysics Data System (ADS)

Scharenborg, Odette; ten Bosch, Louis; Boves, Lou; Norris, Dennis

2003-12-01

This letter evaluates potential benefits of combining human speech recognition (HSR) and automatic speech recognition by building a joint model of an automatic phone recognizer (APR) and a computational model of HSR, viz., Shortlist [Norris, Cognition 52, 189-234 (1994)]. Experiments based on ``real-life'' speech highlight critical limitations posed by some of the simplifying assumptions made in models of human speech recognition. These limitations could be overcome by avoiding hard phone decisions at the output side of the APR, and by using a match between the input and the internal lexicon that flexibly copes with deviations from canonical phonemic representations.

Feedforward object-vision models only tolerate small image variations compared to human

PubMed Central

Ghodrati, Masoud; Farzmahdi, Amirhossein; Rajaei, Karim; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi

2014-01-01

Invariant object recognition is a remarkable ability of primates' visual system that its underlying mechanism has constantly been under intense investigations. Computational modeling is a valuable tool toward understanding the processes involved in invariant object recognition. Although recent computational models have shown outstanding performances on challenging image databases, they fail to perform well in image categorization under more complex image variations. Studies have shown that making sparse representation of objects by extracting more informative visual features through a feedforward sweep can lead to higher recognition performances. Here, however, we show that when the complexity of image variations is high, even this approach results in poor performance compared to humans. To assess the performance of models and humans in invariant object recognition tasks, we built a parametrically controlled image database consisting of several object categories varied in different dimensions and levels, rendered from 3D planes. Comparing the performance of several object recognition models with human observers shows that only in low-level image variations the models perform similar to humans in categorization tasks. Furthermore, the results of our behavioral experiments demonstrate that, even under difficult experimental conditions (i.e., briefly presented masked stimuli with complex image variations), human observers performed outstandingly well, suggesting that the models are still far from resembling humans in invariant object recognition. Taken together, we suggest that learning sparse informative visual features, although desirable, is not a complete solution for future progresses in object-vision modeling. We show that this approach is not of significant help in solving the computational crux of object recognition (i.e., invariant object recognition) when the identity-preserving image variations become more complex. PMID:25100986

Speech Recognition for Medical Dictation: Overview in Quebec and Systematic Review.

PubMed

Poder, Thomas G; Fisette, Jean-François; Déry, Véronique

2018-04-03

Speech recognition is increasingly used in medical reporting. The aim of this article is to identify in the literature the strengths and weaknesses of this technology, as well as barriers to and facilitators of its implementation. A systematic review of systematic reviews was performed using PubMed, Scopus, the Cochrane Library and the Center for Reviews and Dissemination through August 2017. The gray literature has also been consulted. The quality of systematic reviews has been assessed with the AMSTAR checklist. The main inclusion criterion was use of speech recognition for medical reporting (front-end or back-end). A survey has also been conducted in Quebec, Canada, to identify the dissemination of this technology in this province, as well as the factors leading to the success or failure of its implementation. Five systematic reviews were identified. These reviews indicated a high level of heterogeneity across studies. The quality of the studies reported was generally poor. Speech recognition is not as accurate as human transcription, but it can dramatically reduce turnaround times for reporting. In front-end use, medical doctors need to spend more time on dictation and correction than required with human transcription. With speech recognition, major errors occur up to three times more frequently. In back-end use, a potential increase in productivity of transcriptionists was noted. In conclusion, speech recognition offers several advantages for medical reporting. However, these advantages are countered by an increased burden on medical doctors and by risks of additional errors in medical reports. It is also hard to identify for which medical specialties and which clinical activities the use of speech recognition will be the most beneficial.

Novel images and novel locations of familiar images as sensitive translational cognitive tests in humans.

PubMed

Raber, Jacob

2015-05-15

Object recognition is a sensitive cognitive test to detect effects of genetic and environmental factors on cognition in rodents. There are various versions of object recognition that have been used since the original test was reported by Ennaceur and Delacour in 1988. There are nonhuman primate and human primate versions of object recognition as well, allowing cross-species comparisons. As no language is required for test performance, object recognition is a very valuable test for human research studies in distinct parts of the world, including areas where there might be less years of formal education. The main focus of this review is to illustrate how object recognition can be used to assess cognition in humans under normal physiological and neurological conditions. Copyright © 2015 Elsevier B.V. All rights reserved.

Posture recognition based on fuzzy logic for home monitoring of the elderly.

PubMed

Brulin, Damien; Benezeth, Yannick; Courtial, Estelle

2012-09-01

We propose in this paper a computer vision-based posture recognition method for home monitoring of the elderly. The proposed system performs human detection prior to the posture analysis; posture recognition is performed only on a human silhouette. The human detection approach has been designed to be robust to different environmental stimuli. Thus, posture is analyzed with simple and efficient features that are not designed to manage constraints related to the environment but only designed to describe human silhouettes. The posture recognition method, based on fuzzy logic, identifies four static postures and is robust to variation in the distance between the camera and the person, and to the person's morphology. With an accuracy of 74.29% of satisfactory posture recognition, this approach can detect emergency situations such as a fall within a health smart home.

Pervasive influence of idiosyncratic associative biases during facial emotion recognition.

PubMed

El Zein, Marwa; Wyart, Valentin; Grèzes, Julie

2018-06-11

Facial morphology has been shown to influence perceptual judgments of emotion in a way that is shared across human observers. Here we demonstrate that these shared associations between facial morphology and emotion coexist with strong variations unique to each human observer. Interestingly, a large part of these idiosyncratic associations does not vary on short time scales, emerging from stable inter-individual differences in the way facial morphological features influence emotion recognition. Computational modelling of decision-making and neural recordings of electrical brain activity revealed that both shared and idiosyncratic face-emotion associations operate through a common biasing mechanism rather than an increased sensitivity to face-associated emotions. Together, these findings emphasize the underestimated influence of idiosyncrasies on core social judgments and identify their neuro-computational signatures.

Association of enhanced limbic response to threat with decreased cortical facial recognition memory response in schizophrenia

PubMed Central

Satterthwaite, Theodore D.; Wolf, Daniel H.; Loughead, James; Ruparel, Kosha; Valdez, Jeffrey N.; Siegel, Steven J.; Kohler, Christian G.; Gur, Raquel E.; Gur, Ruben C.

2014-01-01

Objective Recognition memory of faces is impaired in patients with schizophrenia, as is the neural processing of threat-related signals, but how these deficits interact to produce symptoms is unclear. Here we used an affective face recognition paradigm to examine possible interactions between cognitive and affective neural systems in schizophrenia. Methods fMRI (3T) BOLD response was examined in 21 controls and 16 patients during a two-choice recognition task using images of human faces. Each target face had previously been displayed with a threatening or non-threatening affect, but here were displayed with neutral affect. Responses to successful recognition and for the effect of previously threatening vs. non-threatening affect were evaluated, and correlations with total BPRS examined. Functional connectivity analyses examined the relationship between activation in the amygdala and cortical regions involved in recognition memory. Results Patients performed the task more slowly than controls. Controls recruited the expected cortical regions to a greater degree than patients, and patients with more severe symptoms demonstrated proportionally less recruitment. Increased symptoms were also correlated with augmented amygdala and orbitofrontal cortex response to threatening faces. Controls exhibited a negative correlation between activity in the amygdala and cortical regions involved in cognition, while patients showed a weakening of that relationship. Conclusions Increased symptoms were related to an enhanced threat response in limbic regions and a diminished recognition memory response in cortical regions, supporting a link between two brain systems often examined in isolation. This finding suggests that abnormal processing of threat-related signals in the environment may exacerbate cognitive impairment in schizophrenia. PMID:20194482

Effects of acute psychosocial stress on neural activity to emotional and neutral faces in a face recognition memory paradigm.

PubMed

Li, Shijia; Weerda, Riklef; Milde, Christopher; Wolf, Oliver T; Thiel, Christiane M

2014-12-01

Previous studies have shown that acute psychosocial stress impairs recognition of declarative memory and that emotional material is especially sensitive to this effect. Animal studies suggest a central role of the amygdala which modulates memory processes in hippocampus, prefrontal cortex and other brain areas. We used functional magnetic resonance imaging (fMRI) to investigate neural correlates of stress-induced modulation of emotional recognition memory in humans. Twenty-seven healthy, right-handed, non-smoker male volunteers performed an emotional face recognition task. During encoding, participants were presented with 50 fearful and 50 neutral faces. One hour later, they underwent either a stress (Trier Social Stress Test) or a control procedure outside the scanner which was followed immediately by the recognition session inside the scanner, where participants had to discriminate between 100 old and 50 new faces. Stress increased salivary cortisol, blood pressure and pulse, and decreased the mood of participants but did not impact recognition memory. BOLD data during recognition revealed a stress condition by emotion interaction in the left inferior frontal gyrus and right hippocampus which was due to a stress-induced increase of neural activity to fearful and a decrease to neutral faces. Functional connectivity analyses revealed a stress-induced increase in coupling between the right amygdala and the right fusiform gyrus, when processing fearful as compared to neutral faces. Our results provide evidence that acute psychosocial stress affects medial temporal and frontal brain areas differentially for neutral and emotional items, with a stress-induced privileged processing of emotional stimuli.

Individual differences in cortical face selectivity predict behavioral performance in face recognition

PubMed Central

Huang, Lijie; Song, Yiying; Li, Jingguang; Zhen, Zonglei; Yang, Zetian; Liu, Jia

2014-01-01

In functional magnetic resonance imaging studies, object selectivity is defined as a higher neural response to an object category than other object categories. Importantly, object selectivity is widely considered as a neural signature of a functionally-specialized area in processing its preferred object category in the human brain. However, the behavioral significance of the object selectivity remains unclear. In the present study, we used the individual differences approach to correlate participants' face selectivity in the face-selective regions with their behavioral performance in face recognition measured outside the scanner in a large sample of healthy adults. Face selectivity was defined as the z score of activation with the contrast of faces vs. non-face objects, and the face recognition ability was indexed as the normalized residual of the accuracy in recognizing previously-learned faces after regressing out that for non-face objects in an old/new memory task. We found that the participants with higher face selectivity in the fusiform face area (FFA) and the occipital face area (OFA), but not in the posterior part of the superior temporal sulcus (pSTS), possessed higher face recognition ability. Importantly, the association of face selectivity in the FFA and face recognition ability cannot be accounted for by FFA response to objects or behavioral performance in object recognition, suggesting that the association is domain-specific. Finally, the association is reliable, confirmed by the replication from another independent participant group. In sum, our finding provides empirical evidence on the validity of using object selectivity as a neural signature in defining object-selective regions in the human brain. PMID:25071513

Human sperm bind to the N-terminal domain of ZP2 in humanized zonae pellucidae in transgenic mice

PubMed Central

Baibakov, Boris; Boggs, Nathan A.; Yauger, Belinda; Baibakov, Galina

2012-01-01

Fertilization requires taxon-specific gamete recognition, and human sperm do not bind to zonae pellucidae (ZP1–3) surrounding mouse eggs. Using transgenesis to replace endogenous mouse proteins with human homologues, gain-of-function sperm-binding assays were established to evaluate human gamete recognition. Human sperm bound only to zonae pellucidae containing human ZP2, either alone or coexpressed with other human zona proteins. Binding to the humanized matrix was a dominant effect that resulted in human sperm penetration of the zona pellucida and accumulation in the perivitelline space, where they were unable to fuse with mouse eggs. Using recombinant peptides, the site of gamete recognition was located to a defined domain in the N terminus of ZP2. These results provide experimental evidence for the role of ZP2 in mediating sperm binding to the zona pellucida and support a model in which human sperm–egg recognition is dependent on an N-terminal domain of ZP2, which is degraded after fertilization to provide a definitive block to polyspermy. PMID:22734000

Self-Recognition Sensitizes Mouse and Human Regulatory T Cells to Low-Dose CD28 Superagonist Stimulation.

PubMed

Langenhorst, Daniela; Tabares, Paula; Gulde, Tobias; Becklund, Bryan R; Berr, Susanne; Surh, Charles D; Beyersdorf, Niklas; Hünig, Thomas

2017-01-01

In rodents, low doses of CD28-specific superagonistic monoclonal antibodies (CD28 superagonists, CD28SA) selectively activate regulatory T cells (Treg). This observation has recently been extended to humans, suggesting an option for the treatment of autoimmune and inflammatory diseases. However, a mechanistic explanation for this phenomenon is still lacking. Given that CD28SA amplify T cell receptor (TCR) signals, we tested the hypothesis that the weak tonic TCR signals received by conventional CD4 + T cells (Tconv) in the absence of cognate antigen require more CD28 signaling input for full activation than the stronger TCR signals received by self-reactive Treg. We report that in vitro , the response of mouse Treg and Tconv to CD28SA strongly depends on MHC class II expression by antigen-presenting cells. To separate the effect of tonic TCR signals from self-peptide recognition, we compared the response of wild-type Treg and Tconv to low and high CD28SA doses upon transfer into wild-type or H-2M knockout mice, which lack a self-peptide repertoire. We found that the superior response of Treg to low CD28SA doses was lost in the absence of self-peptide presentation. We also tested if potentially pathogenic autoreactive Tconv would benefit from self-recognition-induced sensitivity to CD28SA stimulation by transferring TCR transgenic OVA-specific Tconv into OVA-expressing mice and found that low-dose CD28SA application inhibited, rather than supported, their expansion, presumably due to the massive concomitant activation of Treg. Finally, we report that also in the in vitro response of human peripheral blood mononuclear cells to CD28SA, HLA II blockade interferes with the expansion of Treg by low-dose CD28SA stimulation. These results provide a rational basis for the further development of low-dose CD28SA therapy for the improvement of Treg activity.

Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

NASA Astrophysics Data System (ADS)

Sanal Kumar, K. P.; Bhavani, R., Dr.

2017-08-01

Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

Effect of aging on microRNAs and regulation of pathogen recognition receptors

PubMed Central

Olivieri, Fabiola; Procopio, Antonio Dormenico

2014-01-01

Immunosenescence is the multifactorial age-associated immune deteriorization that leads to increased susceptibility to infections and decreased responses to vaccines. Recent studies have shown a fundamental role for microRNAs (miRNAs) in regulating immune responses, and nearly all the miRNAs involved in immune regulation show modulation during aging. Aging-associated miRNAs are largely negative regulators of the immune innate response and target central nodes of aging-associated networks, in particular, NF-κB, the downstream effector of TLR signals that leads to induction of proinflammatory responses. Multiple miRNAs have been reported to share similar regulatory activity. Here we review miRNA regulation of human innate immune recognition in aging, including both activation and resolution of inflammation, critical issues in detection, and areas of active investigation into our understanding of immunosenescence. PMID:24769423

Acoustic Event Detection and Classification

NASA Astrophysics Data System (ADS)

Temko, Andrey; Nadeu, Climent; Macho, Dušan; Malkin, Robert; Zieger, Christian; Omologo, Maurizio

The human activity that takes place in meeting rooms or classrooms is reflected in a rich variety of acoustic events (AE), produced either by the human body or by objects handled by humans, so the determination of both the identity of sounds and their position in time may help to detect and describe that human activity. Indeed, speech is usually the most informative sound, but other kinds of AEs may also carry useful information, for example, clapping or laughing inside a speech, a strong yawn in the middle of a lecture, a chair moving or a door slam when the meeting has just started. Additionally, detection and classification of sounds other than speech may be useful to enhance the robustness of speech technologies like automatic speech recognition.

User-Independent Motion State Recognition Using Smartphone Sensors.

PubMed

Gu, Fuqiang; Kealy, Allison; Khoshelham, Kourosh; Shang, Jianga

2015-12-04

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users' data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people's motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human's motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

Left-Lateralized Contributions of Saccades to Cortical Activity During a One-Back Word Recognition Task.

PubMed

Chang, Yu-Cherng C; Khan, Sheraz; Taulu, Samu; Kuperberg, Gina; Brown, Emery N; Hämäläinen, Matti S; Temereanca, Simona

2018-01-01

Saccadic eye movements are an inherent component of natural reading, yet their contribution to information processing at subsequent fixation remains elusive. Here we use anatomically-constrained magnetoencephalography (MEG) to examine cortical activity following saccades as healthy human subjects engaged in a one-back word recognition task. This activity was compared with activity following external visual stimulation that mimicked saccades. A combination of procedures was employed to eliminate saccadic ocular artifacts from the MEG signal. Both saccades and saccade-like external visual stimulation produced early-latency responses beginning ~70 ms after onset in occipital cortex and spreading through the ventral and dorsal visual streams to temporal, parietal and frontal cortices. Robust differential activity following the onset of saccades vs. similar external visual stimulation emerged during 150-350 ms in a left-lateralized cortical network. This network included: (i) left lateral occipitotemporal (LOT) and nearby inferotemporal (IT) cortex; (ii) left posterior Sylvian fissure (PSF) and nearby multimodal cortex; and (iii) medial parietooccipital (PO), posterior cingulate and retrosplenial cortices. Moreover, this left-lateralized network colocalized with word repetition priming effects. Together, results suggest that central saccadic mechanisms influence a left-lateralized language network in occipitotemporal and temporal cortex above and beyond saccadic influences at preceding stages of information processing during visual word recognition.

Left-Lateralized Contributions of Saccades to Cortical Activity During a One-Back Word Recognition Task

PubMed Central

Chang, Yu-Cherng C.; Khan, Sheraz; Taulu, Samu; Kuperberg, Gina; Brown, Emery N.; Hämäläinen, Matti S.; Temereanca, Simona

2018-01-01

Saccadic eye movements are an inherent component of natural reading, yet their contribution to information processing at subsequent fixation remains elusive. Here we use anatomically-constrained magnetoencephalography (MEG) to examine cortical activity following saccades as healthy human subjects engaged in a one-back word recognition task. This activity was compared with activity following external visual stimulation that mimicked saccades. A combination of procedures was employed to eliminate saccadic ocular artifacts from the MEG signal. Both saccades and saccade-like external visual stimulation produced early-latency responses beginning ~70 ms after onset in occipital cortex and spreading through the ventral and dorsal visual streams to temporal, parietal and frontal cortices. Robust differential activity following the onset of saccades vs. similar external visual stimulation emerged during 150–350 ms in a left-lateralized cortical network. This network included: (i) left lateral occipitotemporal (LOT) and nearby inferotemporal (IT) cortex; (ii) left posterior Sylvian fissure (PSF) and nearby multimodal cortex; and (iii) medial parietooccipital (PO), posterior cingulate and retrosplenial cortices. Moreover, this left-lateralized network colocalized with word repetition priming effects. Together, results suggest that central saccadic mechanisms influence a left-lateralized language network in occipitotemporal and temporal cortex above and beyond saccadic influences at preceding stages of information processing during visual word recognition. PMID:29867372

Pedestrian dead reckoning employing simultaneous activity recognition cues

NASA Astrophysics Data System (ADS)

Altun, Kerem; Barshan, Billur

2012-02-01

We consider the human localization problem using body-worn inertial/magnetic sensor units. Inertial sensors are characterized by a drift error caused by the integration of their rate output to obtain position information. Because of this drift, the position and orientation data obtained from inertial sensors are reliable over only short periods of time. Therefore, position updates from externally referenced sensors are essential. However, if the map of the environment is known, the activity context of the user can provide information about his position. In particular, the switches in the activity context correspond to discrete locations on the map. By performing localization simultaneously with activity recognition, we detect the activity context switches and use the corresponding position information as position updates in a localization filter. The localization filter also involves a smoother that combines the two estimates obtained by running the zero-velocity update algorithm both forward and backward in time. We performed experiments with eight subjects in indoor and outdoor environments involving walking, turning and standing activities. Using a spatial error criterion, we show that the position errors can be decreased by about 85% on the average. We also present the results of two 3D experiments performed in realistic indoor environments and demonstrate that it is possible to achieve over 90% error reduction in position by performing localization simultaneously with activity recognition.

Episodic Reasoning for Vision-Based Human Action Recognition

PubMed Central

Martinez-del-Rincon, Jesus

2014-01-01

Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning. PMID:24959602

Identification of full length bovine TLR1 and functional characterization of lipopeptide recognition by bovine TLR2/1 heterodimer

PubMed Central

Farhat, Katja; Riekenberg, Sabine; Jung, Günther; Wiesmüller, Karl-Heinz; Jungi, Thomas W.; Ulmer, Artur J.

2010-01-01

Toll-like receptors (TLR) are highly conserved pattern recognition receptors of the innate immune system. Toll-like receptor 2 (TLR2) recognizes bacterial lipopeptides in a heterodimeric complex with TLR6 or TLR1, thereby discriminating between di- or triacylated lipopeptides, respectively. Previously, we found that HEK293 cells transfected with bovine TLR2 (boTLR2) were able to respond to diacylated lipopeptides but did not recognize triacylated lipopeptides, even after cotransfection with the so far published sequence of boTLR1. In this study we now could show that primary bovine cells were in general able to detect triacylated lipopetides. A closer investigation of the boTLR1 gene locus revealed an additional ATG 195 base pairs upstream from the published start codon. Its transcription would result in an N-terminus with high identity to human and murine TLR1 (huTLR1, muTLR1). Cloning and cotransfection of this longer boTLR1 with boTLR2 now resulted in the recognition of triacylated lipopeptides by HEK293 cells, thereby resembling the ex vivo observation. Analysis of the structure-activity relationship showed that the ester-bound acid chains of these lipopeptides need to consist of at least 12 carbon atoms to activate the bovine heterodimer showing similarity to the recognition by huTLR2/huTLR1. In contrast, HEK293 cell cotransfected with muTLR2 and muTLR1 could already be activated by lipopeptides with shorter fatty acids of only 6 carbon atoms. Thus, our data indicate that the additional N-terminal nucleotides belong to the full length and functionally active boTLR1 (boTLR1-fl) which participates in a species-specific recognition of bacterial lipopeptides. PMID:20167196

Multimodal Neuroelectric Interface Development

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Wheeler, Kevin R.; Jorgensen, Charles C.; Totah, Joseph (Technical Monitor)

2001-01-01

This project aims to improve performance of NASA missions by developing multimodal neuroelectric technologies for augmented human-system interaction. Neuroelectric technologies will add completely new modes of interaction that operate in parallel with keyboards, speech, or other manual controls, thereby increasing the bandwidth of human-system interaction. We recently demonstrated the feasibility of real-time electromyographic (EMG) pattern recognition for a direct neuroelectric human-computer interface. We recorded EMG signals from an elastic sleeve with dry electrodes, while a human subject performed a range of discrete gestures. A machine-teaming algorithm was trained to recognize the EMG patterns associated with the gestures and map them to control signals. Successful applications now include piloting two Class 4 aircraft simulations (F-15 and 757) and entering data with a "virtual" numeric keyboard. Current research focuses on on-line adaptation of EMG sensing and processing and recognition of continuous gestures. We are also extending this on-line pattern recognition methodology to electroencephalographic (EEG) signals. This will allow us to bypass muscle activity and draw control signals directly from the human brain. Our system can reliably detect P-rhythm (a periodic EEG signal from motor cortex in the 10 Hz range) with a lightweight headset containing saline-soaked sponge electrodes. The data show that EEG p-rhythm can be modulated by real and imaginary motions. Current research focuses on using biofeedback to train of human subjects to modulate EEG rhythms on demand, and to examine interactions of EEG-based control with EMG-based and manual control. Viewgraphs on these neuroelectric technologies are also included.

The nuclear bile acid receptor FXR controls the liver derived tumor suppressor histidine-rich glycoprotein.

PubMed

Deuschle, Ulrich; Birkel, Manfred; Hambruch, Eva; Hornberger, Martin; Kinzel, Olaf; Perović-Ottstadt, Sanja; Schulz, Andreas; Hahn, Ulrike; Burnet, Michael; Kremoser, Claus

2015-06-01

The nuclear bile acid receptor Farnesoid X receptor (FXR) is strongly expressed in liver and intestine, controls bile acid and lipid homeostasis and exerts tumor-protective functions in liver and intestine. Histidine-rich glycoprotein (HRG) is an abundant plasma protein produced by the liver with the proposed function as a pattern recognition molecule involved in the clearance of immune complexes, necrotic cells and pathogens, the modulation of angiogenesis, the normalization of deranged endothelial vessel structure in tumors and tumor suppression. FXR recognition sequences were identified within a human HRG promoter fragment that mediated FXR/FXR-agonist dependent reporter gene activity in vitro. We show that HRG is a novel transcriptional target gene of FXR in human hepatoma cells, human upcyte® primary hepatocytes and 3D human liver microtissues in vitro and in mouse liver in vivo. Prolonged administration of the potent nonsteroidal FXR agonist PX20606 increases HRG levels in mouse plasma. Finally, daily oral administration of this FXR agonist for seven days resulted in a significant increase of HRG levels in the plasma of healthy human male volunteers during a clinical Phase I safety study. HRG might serve as a surrogate marker indicative of liver-specific FXR activation in future human clinical studies. Furthermore, potent FXR agonists might be beneficial in serious health conditions where HRG is reduced, for example, in hepatocellular carcinoma but also other solid cancers, liver failure, sepsis and pre-eclampsia. © 2014 UICC.

Hierarchical classifier approach to physical activity recognition via wearable smartphone tri-axial accelerometer.

PubMed

Yusuf, Feridun; Maeder, Anthony; Basilakis, Jim

2013-01-01

Physical activity recognition has emerged as an active area of research which has drawn increasing interest from researchers in a variety of fields. It can support many different applications such as safety surveillance, fraud detection, and clinical management. Accelerometers have emerged as the most useful and extensive tool to capture and assess human physical activities in a continuous, unobtrusive and reliable manner. The need for objective physical activity data arises strongly in health related research. With the shift to a sedentary lifestyle, where work and leisure tend to be less physically demanding, research on the health effects of low physical activity has become a necessity. The increased availability of small, inexpensive components has led to the development of mobile devices such as smartphones, providing platforms for new opportunities in healthcare applications. In this study 3 subjects performed directed activity routines wearing a smartphone with a built in tri-axial accelerometer, attached on a belt around the waist. The data was collected to classify 11 basic physical activities such as sitting, lying, standing, walking, and the transitions in between them. A hierarchical classifier approach was utilised with Artificial Neural Networks integrated in a rule-based system, to classify the activities. Based on our evaluation, recognition accuracy of over 89.6% between subjects and over 91.5% within subject was achieved. These results show that activities such as these can be recognised with a high accuracy rate; hence the approach is promising for use in future work.

A preliminary analysis of human factors affecting the recognition accuracy of a discrete word recognizer for C3 systems

NASA Astrophysics Data System (ADS)

Yellen, H. W.

1983-03-01

Literature pertaining to Voice Recognition abounds with information relevant to the assessment of transitory speech recognition devices. In the past, engineering requirements have dictated the path this technology followed. But, other factors do exist that influence recognition accuracy. This thesis explores the impact of Human Factors on the successful recognition of speech, principally addressing the differences or variability among users. A Threshold Technology T-600 was used for a 100 utterance vocubalary to test 44 subjects. A statistical analysis was conducted on 5 generic categories of Human Factors: Occupational, Operational, Psychological, Physiological and Personal. How the equipment is trained and the experience level of the speaker were found to be key characteristics influencing recognition accuracy. To a lesser extent computer experience, time or week, accent, vital capacity and rate of air flow, speaker cooperativeness and anxiety were found to affect overall error rates.

Modal-Power-Based Haptic Motion Recognition

NASA Astrophysics Data System (ADS)

Kasahara, Yusuke; Shimono, Tomoyuki; Kuwahara, Hiroaki; Sato, Masataka; Ohnishi, Kouhei

Motion recognition based on sensory information is important for providing assistance to human using robots. Several studies have been carried out on motion recognition based on image information. However, in the motion of humans contact with an object can not be evaluated precisely by image-based recognition. This is because the considering force information is very important for describing contact motion. In this paper, a modal-power-based haptic motion recognition is proposed; modal power is considered to reveal information on both position and force. Modal power is considered to be one of the defining features of human motion. A motion recognition algorithm based on linear discriminant analysis is proposed to distinguish between similar motions. Haptic information is extracted using a bilateral master-slave system. Then, the observed motion is decomposed in terms of primitive functions in a modal space. The experimental results show the effectiveness of the proposed method.

Spirituality, Mysticism, the Arts, and Education. On Balance.

ERIC Educational Resources Information Center

Willis, George

2000-01-01

Arts educators can learn the following from persons who have pursued spirituality and mysticism: awareness of the limits of the human condition, the necessity of metaphor to describe the ineffable, recognition that we are all potentially artists and mystics, and the call to active life in the world. (SK)

Recognition of Typhus Group Rickettsia-Infected Targets by Human Lymphokine-Activated Killer Cells

DTIC Science & Technology

1988-09-01

rick- Similar problems in detection of antigens of Theileria parva ettsia-specific cell surface antigens by performing polyacryl- (7) or influenza virus...infected with the protozoan parasite Theileria parva: workers in our laboratory are now in the process of cloning parasite strain specificity and class I

Pose-variant facial expression recognition using an embedded image system

NASA Astrophysics Data System (ADS)

Song, Kai-Tai; Han, Meng-Ju; Chang, Shuo-Hung

2008-12-01

In recent years, one of the most attractive research areas in human-robot interaction is automated facial expression recognition. Through recognizing the facial expression, a pet robot can interact with human in a more natural manner. In this study, we focus on the facial pose-variant problem. A novel method is proposed in this paper to recognize pose-variant facial expressions. After locating the face position in an image frame, the active appearance model (AAM) is applied to track facial features. Fourteen feature points are extracted to represent the variation of facial expressions. The distance between feature points are defined as the feature values. These feature values are sent to a support vector machine (SVM) for facial expression determination. The pose-variant facial expression is classified into happiness, neutral, sadness, surprise or anger. Furthermore, in order to evaluate the performance for practical applications, this study also built a low resolution database (160x120 pixels) using a CMOS image sensor. Experimental results show that the recognition rate is 84% with the self-built database.

Human detection and motion analysis at security points

NASA Astrophysics Data System (ADS)

Ozer, I. Burak; Lv, Tiehan; Wolf, Wayne H.

2003-08-01

This paper presents a real-time video surveillance system for the recognition of specific human activities. Specifically, the proposed automatic motion analysis is used as an on-line alarm system to detect abnormal situations in a campus environment. A smart multi-camera system developed at Princeton University is extended for use in smart environments in which the camera detects the presence of multiple persons as well as their gestures and their interaction in real-time.

Culture but not gender modulates amygdala activation during explicit emotion recognition.

PubMed

Derntl, Birgit; Habel, Ute; Robinson, Simon; Windischberger, Christian; Kryspin-Exner, Ilse; Gur, Ruben C; Moser, Ewald

2012-05-29

Mounting evidence indicates that humans have significant difficulties in understanding emotional expressions from individuals of different ethnic backgrounds, leading to reduced recognition accuracy and stronger amygdala activation. However, the impact of gender on the behavioral and neural reactions during the initial phase of cultural assimilation has not been addressed. Therefore, we investigated 24 Asians students (12 females) and 24 age-matched European students (12 females) during an explicit emotion recognition task, using Caucasian facial expressions only, on a high-field MRI scanner. Analysis of functional data revealed bilateral amygdala activation to emotional expressions in Asian and European subjects. However, in the Asian sample, a stronger response of the amygdala emerged and was paralleled by reduced recognition accuracy, particularly for angry male faces. Moreover, no significant gender difference emerged. We also observed a significant inverse correlation between duration of stay and amygdala activation. In this study we investigated the "alien-effect" as an initial problem during cultural assimilation and examined this effect on a behavioral and neural level. This study has revealed bilateral amygdala activation to emotional expressions in Asian and European females and males. In the Asian sample, a stronger response of the amygdala bilaterally was observed and this was paralleled by reduced performance, especially for anger and disgust depicted by male expressions. However, no gender difference occurred. Taken together, while gender exerts only a subtle effect, culture and duration of stay as well as gender of poser are shown to be relevant factors for emotion processing, influencing not only behavioral but also neural responses in female and male immigrants.

Culture but not gender modulates amygdala activation during explicit emotion recognition

PubMed Central

2012-01-01

Background Mounting evidence indicates that humans have significant difficulties in understanding emotional expressions from individuals of different ethnic backgrounds, leading to reduced recognition accuracy and stronger amygdala activation. However, the impact of gender on the behavioral and neural reactions during the initial phase of cultural assimilation has not been addressed. Therefore, we investigated 24 Asians students (12 females) and 24 age-matched European students (12 females) during an explicit emotion recognition task, using Caucasian facial expressions only, on a high-field MRI scanner. Results Analysis of functional data revealed bilateral amygdala activation to emotional expressions in Asian and European subjects. However, in the Asian sample, a stronger response of the amygdala emerged and was paralleled by reduced recognition accuracy, particularly for angry male faces. Moreover, no significant gender difference emerged. We also observed a significant inverse correlation between duration of stay and amygdala activation. Conclusion In this study we investigated the “alien-effect” as an initial problem during cultural assimilation and examined this effect on a behavioral and neural level. This study has revealed bilateral amygdala activation to emotional expressions in Asian and European females and males. In the Asian sample, a stronger response of the amygdala bilaterally was observed and this was paralleled by reduced performance, especially for anger and disgust depicted by male expressions. However, no gender difference occurred. Taken together, while gender exerts only a subtle effect, culture and duration of stay as well as gender of poser are shown to be relevant factors for emotion processing, influencing not only behavioral but also neural responses in female and male immigrants. PMID:22642400

Binding Affinity of Glycoconjugates to BACILLUS Spores and Toxins

NASA Astrophysics Data System (ADS)

Rasol, Aveen; Eassa, Souzan; Tarasenko, Olga

2010-04-01

Early recognition of Bacillus cereus group species is important since they can cause food-borne illnesses and deadly diseases in humans. Glycoconjugates (GCs) are carbohydrates covalently linked to non-sugar moieties including lipids, proteins or other entities. GCs are involved in recognition and signaling processes intrinsic to biochemical functions in cells. They also stimulate cell-cell adhesion and subsequent recognition and activation of receptors. We have demonstrated that GCs are involved in Bacillus cereus spore recognition. In the present study, we have investigated whether GCs possess the ability to bind and recognize B. cereus spores and Bacillus anthracis recombinant single toxins (sTX) and complex toxins (cTX). The affinity of GCs to spores + sTX and spores + cTX toxins was studied in the binding essay. Our results demonstrated that GC9 and GC10 were able to selectively bind to B. cereus spores and B. anthracis toxins. Different binding affinities for GCs were found toward Bacillus cereus spores + sTX and spores + cTX. Dilution of GCs does not impede the recognition and binding. Developed method provides a tool for simultaneous recognition and targeting of spores, bacteria toxins, and/or other entities.

Extending the imaging volume for biometric iris recognition.

PubMed

Narayanswamy, Ramkumar; Johnson, Gregory E; Silveira, Paulo E X; Wach, Hans B

2005-02-10

The use of the human iris as a biometric has recently attracted significant interest in the area of security applications. The need to capture an iris without active user cooperation places demands on the optical system. Unlike a traditional optical design, in which a large imaging volume is traded off for diminished imaging resolution and capacity for collecting light, Wavefront Coded imaging is a computational imaging technology capable of expanding the imaging volume while maintaining an accurate and robust iris identification capability. We apply Wavefront Coded imaging to extend the imaging volume of the iris recognition application.

NK1 receptor antagonism and emotional processing in healthy volunteers.

PubMed

Chandra, P; Hafizi, S; Massey-Chase, R M; Goodwin, G M; Cowen, P J; Harmer, C J

2010-04-01

The neurokinin-1 (NK(1)) receptor antagonist, aprepitant, showed activity in several animal models of depression; however, its efficacy in clinical trials was disappointing. There is little knowledge of the role of NK(1) receptors in human emotional behaviour to help explain this discrepancy. The aim of the current study was to assess the effects of a single oral dose of aprepitant (125 mg) on models of emotional processing sensitive to conventional antidepressant drug administration in 38 healthy volunteers, randomly allocated to receive aprepitant or placebo in a between groups double blind design. Performance on measures of facial expression recognition, emotional categorisation, memory and attentional visual-probe were assessed following the drug absorption. Relative to placebo, aprepitant improved recognition of happy facial expressions and increased vigilance to emotional information in the unmasked condition of the visual probe task. In contrast, aprepitant impaired emotional memory and slowed responses in the facial expression recognition task suggesting possible deleterious effects on cognition. These results suggest that while antagonism of NK(1) receptors does affect emotional processing in humans, its effects are more restricted and less consistent across tasks than those of conventional antidepressants. Human models of emotional processing may provide a useful means of assessing the likely therapeutic potential of new treatments for depression.

Physical Human Activity Recognition Using Wearable Sensors.

PubMed

Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

2015-12-11

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

Physical Human Activity Recognition Using Wearable Sensors

PubMed Central

Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

2015-01-01

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject. PMID:26690450

Human caspase-4 detects tetra-acylated LPS and cytosolic Francisella and functions differently from murine caspase-11.

PubMed

Lagrange, Brice; Benaoudia, Sacha; Wallet, Pierre; Magnotti, Flora; Provost, Angelina; Michal, Fanny; Martin, Amandine; Di Lorenzo, Flaviana; Py, Bénédicte F; Molinaro, Antonio; Henry, Thomas

2018-01-16

Caspase-4/5 in humans and caspase-11 in mice bind hexa-acylated lipid A, the lipid moeity of lipopolysaccharide (LPS), to induce the activation of non-canonical inflammasome. Pathogens such as Francisella novicida express an under-acylated lipid A and escape caspase-11 recognition in mice. Here, we show that caspase-4 drives inflammasome responses to F. novicida infection in human macrophages. Caspase-4 triggers F. novicida-mediated, gasdermin D-dependent pyroptosis and activates the NLRP3 inflammasome. Inflammasome activation could be recapitulated by transfection of under-acylated LPS from different bacterial species or synthetic tetra-acylated lipid A into cytosol of human macrophage. Our results indicate functional differences between human caspase-4 and murine caspase-11. We further establish that human Guanylate-binding proteins promote inflammasome responses to under-acylated LPS. Altogether, our data demonstrate a broader reactivity of caspase-4 to under-acylated LPS than caspase-11, which may have important clinical implications for management of sepsis.

Recognition of Aspergillus fumigatus hyphae by human plasmacytoid dendritic cells is mediated by dectin-2 and results in formation of extracellular traps.

PubMed

Loures, Flávio V; Röhm, Marc; Lee, Chrono K; Santos, Evelyn; Wang, Jennifer P; Specht, Charles A; Calich, Vera L G; Urban, Constantin F; Levitz, Stuart M

2015-02-01

Plasmacytoid dendritic cells (pDCs) were initially considered as critical for innate immunity to viruses. However, our group has shown that pDCs bind to and inhibit the growth of Aspergillus fumigatus hyphae and that depletion of pDCs renders mice hypersusceptible to experimental aspergillosis. In this study, we examined pDC receptors contributing to hyphal recognition and downstream events in pDCs stimulated by A. fumigatus hyphae. Our data show that Dectin-2, but not Dectin-1, participates in A. fumigatus hyphal recognition, TNF-α and IFN-α release, and antifungal activity. Moreover, Dectin-2 acts in cooperation with the FcRγ chain to trigger signaling responses. In addition, using confocal and electron microscopy we demonstrated that the interaction between pDCs and A. fumigatus induced the formation of pDC extracellular traps (pETs) containing DNA and citrullinated histone H3. These structures closely resembled those of neutrophil extracellular traps (NETs). The microarray analysis of the pDC transcriptome upon A. fumigatus infection also demonstrated up-regulated expression of genes associated with apoptosis as well as type I interferon-induced genes. Thus, human pDCs directly recognize A. fumigatus hyphae via Dectin-2; this interaction results in cytokine release and antifungal activity. Moreover, hyphal stimulation of pDCs triggers a distinct pattern of pDC gene expression and leads to pET formation.

Action Categorization in Rhesus Monkeys: discrimination of grasping from non-grasping manual motor acts.

PubMed

Nelissen, Koen; Vanduffel, Wim

2017-11-08

The ability to recognize others' actions is an important aspect of social behavior. While neurophysiological and behavioral research in monkeys has offered a better understanding of how the primate brain processes this type of information, further insight with respect to the neural correlates of action recognition requires tasks that allow recording of brain activity or perturbing brain regions while monkeys simultaneously make behavioral judgements about certain aspects of observed actions. Here we investigated whether rhesus monkeys could actively discriminate videos showing grasping or non-grasping manual motor acts in a two-alternative categorization task. After monkeys became proficient in this task, we tested their ability to generalize to a number of untrained, novel videos depicting grasps or other manual motor acts. Monkeys generalized to a wide range of novel human or conspecific grasping and non-grasping motor acts. They failed, however, for videos showing unfamiliar actions such as a non-biological effector performing a grasp, or a human hand touching an object with the back of the hand. This study shows the feasibility of training monkeys to perform active judgements about certain aspects of observed actions, instrumental for causal investigations into the neural correlates of action recognition.

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem

PubMed Central

Liu, Xunying; Zhang, Chao; Woodland, Phil; Fonteneau, Elisabeth

2017-01-01

There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental ‘machine states’, generated as the ASR analysis progresses over time, to the incremental ‘brain states’, measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain. PMID:28945744

F-box proteins Pof3 and Pof1 regulate Wee1 degradation and mitotic entry in fission yeast.

PubMed

Qiu, Cui; Yi, Yuan-Yuan; Lucena, Rafael; Wu, Meng-Juan; Sun, Jia-Hao; Wang, Xi; Jin, Quan-Wen; Wang, Yamei

2018-02-02

The key cyclin-dependent kinase Cdk1 (Cdc2) promotes irreversible mitotic entry, mainly by activating the phosphatase Cdc25 while suppressing the tyrosine kinase Wee1. Wee1 needs to be downregulated at the onset of mitosis to ensure rapid activation of Cdk1. In human somatic cells, one mechanism of suppressing Wee1 activity is mediated by ubiquitylation-dependent proteolysis through the Skp1/Cul1/F-box protein (SCF) ubiquitin E3 ligase complex. This mechanism is believed to be conserved from yeasts to humans. So far, the best-characterized human F-box proteins involved in recognition of Wee1 are β-TrCP (BTRCP) and Tome-1 (CDCA3). Although fission yeast Wee1 was the first identified member of its conserved kinase family, the F-box proteins involved in recognition and ubiquitylation of Wee1 have not been identified in this organism. In this study, our screen using Wee1- Renilla luciferase as the reporter revealed that two F-box proteins, Pof1 and Pof3, are required for downregulating Wee1 and are possibly responsible for recruiting Wee1 to SCF. Our genetic analyses supported a functional relevance between Pof1 and Pof3 and the rate of mitotic entry, and Pof3 might play a major role in this process. © 2018. Published by The Company of Biologists Ltd.

Familiar Person Recognition: Is Autonoetic Consciousness More Likely to Accompany Face Recognition Than Voice Recognition?

NASA Astrophysics Data System (ADS)

Barsics, Catherine; Brédart, Serge

2010-11-01

Autonoetic consciousness is a fundamental property of human memory, enabling us to experience mental time travel, to recollect past events with a feeling of self-involvement, and to project ourselves in the future. Autonoetic consciousness is a characteristic of episodic memory. By contrast, awareness of the past associated with a mere feeling of familiarity or knowing relies on noetic consciousness, depending on semantic memory integrity. Present research was aimed at evaluating whether conscious recollection of episodic memories is more likely to occur following the recognition of a familiar face than following the recognition of a familiar voice. Recall of semantic information (biographical information) was also assessed. Previous studies that investigated the recall of biographical information following person recognition used faces and voices of famous people as stimuli. In this study, the participants were presented with personally familiar people's voices and faces, thus avoiding the presence of identity cues in the spoken extracts and allowing a stricter control of frequency exposure with both types of stimuli (voices and faces). In the present study, the rate of retrieved episodic memories, associated with autonoetic awareness, was significantly higher from familiar faces than familiar voices even though the level of overall recognition was similar for both these stimuli domains. The same pattern was observed regarding semantic information retrieval. These results and their implications for current Interactive Activation and Competition person recognition models are discussed.

Understanding Decision Making in Critical Care

PubMed Central

Lighthall, Geoffrey K.; Vazquez-Guillamet, Cristina

2015-01-01

Background Human decision making involves the deliberate formulation of hypotheses and plans as well as the use of subconscious means of judging probability, likely outcome, and proper action. Rationale There is a growing recognition that intuitive strategies such as use of heuristics and pattern recognition described in other industries are applicable to high-acuity environments in medicine. Despite the applicability of theories of cognition to the intensive care unit, a discussion of decision-making strategies is currently absent in the critical care literature. Content This article provides an overview of known cognitive strategies, as well as a synthesis of their use in critical care. By understanding the ways by which humans formulate diagnoses and make critical decisions, we may be able to minimize errors in our own judgments as well as build training activities around known strengths and limitations of cognition. PMID:26387708

[The complexity of articulating rights: nutrition and care].

PubMed

Pautassi, Laura Cecilia

2016-01-01

This article analyzes the existing tensions between the recognition of human rights - especially the right to adequate food as it is defined in international agreements and treaties - and the insufficient connection made with care, understood as the set of activities necessary to satisfy the basic needs of existence and human and social reproduction. Applying a methodological approach based in rights and gender, the article analyzes, on one hand, the scope of the right to food and its impact at the level of public institutionality, and on the other, the recent recognition of care as a right at a regional level and its persistent invisibilization in public policies. The results obtained allow for a research and action agenda that identifies tensions and opportunities to achieve universalization in the exercise of rights based in comprehensive and interdependent public policies.

The Complex Action Recognition via the Correlated Topic Model

PubMed Central

Tu, Hong-bin; Xia, Li-min; Wang, Zheng-wu

2014-01-01

Human complex action recognition is an important research area of the action recognition. Among various obstacles to human complex action recognition, one of the most challenging is to deal with self-occlusion, where one body part occludes another one. This paper presents a new method of human complex action recognition, which is based on optical flow and correlated topic model (CTM). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms of an occlusion state variable. Secondly, the structure from motion (SFM) is used for reconstructing the missing data of point trajectories. Then, we can extract the key frame based on motion feature from optical flow and the ratios of the width and height are extracted by the human silhouette. Finally, we use the topic model of correlated topic model (CTM) to classify action. Experiments were performed on the KTH, Weizmann, and UIUC action dataset to test and evaluate the proposed method. The compared experiment results showed that the proposed method was more effective than compared methods. PMID:24574920

Association of enhanced limbic response to threat with decreased cortical facial recognition memory response in schizophrenia.

PubMed

Satterthwaite, Theodore D; Wolf, Daniel H; Loughead, James; Ruparel, Kosha; Valdez, Jeffrey N; Siegel, Steven J; Kohler, Christian G; Gur, Raquel E; Gur, Ruben C

2010-04-01

Recognition memory of faces is impaired in patients with schizophrenia, as is the neural processing of threat-related signals, but how these deficits interact to produce symptoms is unclear. The authors used an affective face recognition paradigm to examine possible interactions between cognitive and affective neural systems in schizophrenia. Blood-oxygen-level-dependent response was examined by means of functional magnetic resonance imaging (3 Tesla) in healthy comparison subjects (N=21) and in patients with schizophrenia (N=12) or schizoaffective disorder, depressed type (N=4), during a two-choice recognition task that used images of human faces. Each target face, previously displayed with a threatening or nonthreatening affect, was displayed with neutral affect. Responses to successful recognition and responses to the effect of previously threatening versus nonthreatening affect were evaluated, and correlations with symptom severity (total Brief Psychiatric Rating Scale score) were examined. Functional connectivity analyses examined the relationship between activation in the amygdala and cortical regions involved in recognition memory. Patients performed the task more slowly than healthy comparison subjects. Comparison subjects recruited the expected cortical regions to a greater degree than patients, and patients with more severe symptoms demonstrated proportionally less recruitment. Increased symptoms were also correlated with augmented amygdala and orbitofrontal cortex response to threatening faces. Comparison subjects exhibited a negative correlation between activity in the amygdala and cortical regions involved in cognition, while patients showed weakening of this relationship. Increased symptoms were related to an enhanced threat response in limbic regions and a diminished recognition memory response in cortical regions, supporting a link between these two brain systems that are often examined in isolation. This finding suggests that abnormal processing of threat-related signals in the environment may exacerbate cognitive impairment in schizophrenia.

Recognizing Spoken Words: The Neighborhood Activation Model

PubMed Central

Luce, Paul A.; Pisoni, David B.

2012-01-01

Objective A fundamental problem in the study of human spoken word recognition concerns the structural relations among the sound patterns of words in memory and the effects these relations have on spoken word recognition. In the present investigation, computational and experimental methods were employed to address a number of fundamental issues related to the representation and structural organization of spoken words in the mental lexicon and to lay the groundwork for a model of spoken word recognition. Design Using a computerized lexicon consisting of transcriptions of 20,000 words, similarity neighborhoods for each of the transcriptions were computed. Among the variables of interest in the computation of the similarity neighborhoods were: 1) the number of words occurring in a neighborhood, 2) the degree of phonetic similarity among the words, and 3) the frequencies of occurrence of the words in the language. The effects of these variables on auditory word recognition were examined in a series of behavioral experiments employing three experimental paradigms: perceptual identification of words in noise, auditory lexical decision, and auditory word naming. Results The results of each of these experiments demonstrated that the number and nature of words in a similarity neighborhood affect the speed and accuracy of word recognition. A neighborhood probability rule was developed that adequately predicted identification performance. This rule, based on Luce's (1959) choice rule, combines stimulus word intelligibility, neighborhood confusability, and frequency into a single expression. Based on this rule, a model of auditory word recognition, the neighborhood activation model, was proposed. This model describes the effects of similarity neighborhood structure on the process of discriminating among the acoustic-phonetic representations of words in memory. The results of these experiments have important implications for current conceptions of auditory word recognition in normal and hearing impaired populations of children and adults. PMID:9504270

Younger and Older Users’ Recognition of Virtual Agent Facial Expressions

PubMed Central

Beer, Jenay M.; Smarr, Cory-Ann; Fisk, Arthur D.; Rogers, Wendy A.

2015-01-01

As technology advances, robots and virtual agents will be introduced into the home and healthcare settings to assist individuals, both young and old, with everyday living tasks. Understanding how users recognize an agent’s social cues is therefore imperative, especially in social interactions. Facial expression, in particular, is one of the most common non-verbal cues used to display and communicate emotion in on-screen agents (Cassell, Sullivan, Prevost, & Churchill, 2000). Age is important to consider because age-related differences in emotion recognition of human facial expression have been supported (Ruffman et al., 2008), with older adults showing a deficit for recognition of negative facial expressions. Previous work has shown that younger adults can effectively recognize facial emotions displayed by agents (Bartneck & Reichenbach, 2005; Courgeon et al. 2009; 2011; Breazeal, 2003); however, little research has compared in-depth younger and older adults’ ability to label a virtual agent’s facial emotions, an import consideration because social agents will be required to interact with users of varying ages. If such age-related differences exist for recognition of virtual agent facial expressions, we aim to understand if those age-related differences are influenced by the intensity of the emotion, dynamic formation of emotion (i.e., a neutral expression developing into an expression of emotion through motion), or the type of virtual character differing by human-likeness. Study 1 investigated the relationship between age-related differences, the implication of dynamic formation of emotion, and the role of emotion intensity in emotion recognition of the facial expressions of a virtual agent (iCat). Study 2 examined age-related differences in recognition expressed by three types of virtual characters differing by human-likeness (non-humanoid iCat, synthetic human, and human). Study 2 also investigated the role of configural and featural processing as a possible explanation for age-related differences in emotion recognition. First, our findings show age-related differences in the recognition of emotions expressed by a virtual agent, with older adults showing lower recognition for the emotions of anger, disgust, fear, happiness, sadness, and neutral. These age-related difference might be explained by older adults having difficulty discriminating similarity in configural arrangement of facial features for certain emotions; for example, older adults often mislabeled the similar emotions of fear as surprise. Second, our results did not provide evidence for the dynamic formation improving emotion recognition; but, in general, the intensity of the emotion improved recognition. Lastly, we learned that emotion recognition, for older and younger adults, differed by character type, from best to worst: human, synthetic human, and then iCat. Our findings provide guidance for design, as well as the development of a framework of age-related differences in emotion recognition. PMID:25705105

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barnes, N.M.; Costall, B.; Egli, P.

The angiotensin converting enzyme (ACE) inhibitor ({sup 3}H)SQ29,852 identified a single high affinity recognition site (defined by 10.0 microM captopril) in the human temporal cortex (pKD 8.62 +/- 0.03; Bmax 248 +/- 24 fmol mg-1 protein, mean +/- S.E.M., n = 4). ACE inhibitors and thiorphan competed to a similar level for the ({sup 3}H)SQ29,852 binding site in the human temporal cortex with a rank order of affinity (pKi values mean +/- S.E.M., n = 3), lisinopril (9.49 +/- 0.02), captopril (9.16 +/- 0.08), SQ29,852 (8.58 +/- 0.04), epicaptopril (7.09 +/- 0.08), fosinopril (7.08 +/- 0.05) and thiorphan (6.40 +/-more » 0.04). Since this rank order of affinity is similar to the affinity of these compounds to inhibit brain ACE activity it is concluded that ({sup 3}H)SQ29,852 selectively labels the inhibitor recognition site of ACE in the human temporal cortex.« less

Guidelines for the recognition of cemetery remains in Greece.

PubMed

Eliopoulos, Constantine; Moraitis, Konstantinos; Reyes, Federico; Spiliopoulou, Chara; Manolis, Sotiris

2011-06-01

Forensic pathologists frequently consult anthropologists for the identification of skeletonized human remains. These remains may be the result of criminal activity or remains that were unearthed because of erosion, or during construction projects. In some cases, human remains that had been previously buried in a cemetery may be the subject of a forensic investigation. Early recognition of cemetery remains prevents unnecessary efforts and conserves precious resources. One of the key characteristics of cemetery remains is the presence of embalmed tissue. However, there are countries where embalming is not a common practice, and other clues must be sought for identifying previously buried remains. Current funerary customs in Greece and, in particular, the tradition of exhumations result in a large number of misplaced human remains. The present study presents examples of cemetery remains from Greece and offers guidelines for recognizing changes on skeletal remains that may be indicative of a cemetery origin. Location of discovery, condition of the remains, and the types of associated artifacts are all factors that aid forensic anthropologists in identifying cemetery remains.

Sensor-based activity recognition using extended belief rule-based inference methodology.

PubMed

Calzada, A; Liu, J; Nugent, C D; Wang, H; Martinez, L

2014-01-01

The recently developed extended belief rule-based inference methodology (RIMER+) recognizes the need of modeling different types of information and uncertainty that usually coexist in real environments. A home setting with sensors located in different rooms and on different appliances can be considered as a particularly relevant example of such an environment, which brings a range of challenges for sensor-based activity recognition. Although RIMER+ has been designed as a generic decision model that could be applied in a wide range of situations, this paper discusses how this methodology can be adapted to recognize human activities using binary sensors within smart environments. The evaluation of RIMER+ against other state-of-the-art classifiers in terms of accuracy, efficiency and applicability was found to be significantly relevant, specially in situations of input data incompleteness, and it demonstrates the potential of this methodology and underpins the basis to develop further research on the topic.

Capturing specific abilities as a window into human individuality: the example of face recognition.

PubMed

Wilmer, Jeremy B; Germine, Laura; Chabris, Christopher F; Chatterjee, Garga; Gerbasi, Margaret; Nakayama, Ken

2012-01-01

Proper characterization of each individual's unique pattern of strengths and weaknesses requires good measures of diverse abilities. Here, we advocate combining our growing understanding of neural and cognitive mechanisms with modern psychometric methods in a renewed effort to capture human individuality through a consideration of specific abilities. We articulate five criteria for the isolation and measurement of specific abilities, then apply these criteria to face recognition. We cleanly dissociate face recognition from more general visual and verbal recognition. This dissociation stretches across ability as well as disability, suggesting that specific developmental face recognition deficits are a special case of a broader specificity that spans the entire spectrum of human face recognition performance. Item-by-item results from 1,471 web-tested participants, included as supplementary information, fuel item analyses, validation, norming, and item response theory (IRT) analyses of our three tests: (a) the widely used Cambridge Face Memory Test (CFMT); (b) an Abstract Art Memory Test (AAMT), and (c) a Verbal Paired-Associates Memory Test (VPMT). The availability of this data set provides a solid foundation for interpreting future scores on these tests. We argue that the allied fields of experimental psychology, cognitive neuroscience, and vision science could fuel the discovery of additional specific abilities to add to face recognition, thereby providing new perspectives on human individuality.

Face recognition in the thermal infrared domain

NASA Astrophysics Data System (ADS)

Kowalski, M.; Grudzień, A.; Palka, N.; Szustakowski, M.

2017-10-01

Biometrics refers to unique human characteristics. Each unique characteristic may be used to label and describe individuals and for automatic recognition of a person based on physiological or behavioural properties. One of the most natural and the most popular biometric trait is a face. The most common research methods on face recognition are based on visible light. State-of-the-art face recognition systems operating in the visible light spectrum achieve very high level of recognition accuracy under controlled environmental conditions. Thermal infrared imagery seems to be a promising alternative or complement to visible range imaging due to its relatively high resistance to illumination changes. A thermal infrared image of the human face presents its unique heat-signature and can be used for recognition. The characteristics of thermal images maintain advantages over visible light images, and can be used to improve algorithms of human face recognition in several aspects. Mid-wavelength or far-wavelength infrared also referred to as thermal infrared seems to be promising alternatives. We present the study on 1:1 recognition in thermal infrared domain. The two approaches we are considering are stand-off face verification of non-moving person as well as stop-less face verification on-the-move. The paper presents methodology of our studies and challenges for face recognition systems in the thermal infrared domain.

The special role of item-context associations in the direct-access region of working memory.

PubMed

Campoy, Guillermo

2017-09-01

The three-embedded-component model of working memory (WM) distinguishes three representational states corresponding to three WM regions: activated long-term memory, direct-access region (DAR), and focus of attention. Recent neuroimaging research has revealed that access to the DAR is associated with enhanced hippocampal activity. Because the hippocampus mediates the encoding and retrieval of item-context associations, it has been suggested that this hippocampal activation is a consequence of the fact that item-context associations are particularly strong and accessible in the DAR. This study provides behavioral evidence for this view using an item-recognition task to assess the effect of non-intentional encoding and maintenance of item-location associations across WM regions. Five pictures of human faces were sequentially presented in different screen locations followed by a recognition probe. Visual cues immediately preceding the probe indicated the location thereof. When probe stimuli appeared in the same location that they had been presented within the memory set, the presentation of the cue was expected to elicit the activation of the corresponding WM representation through the just-established item-location association, resulting in faster recognition. Results showed this same-location effect, but only for items that, according to their serial position within the memory set, were held in the DAR.

Resolving human object recognition in space and time

PubMed Central

Cichy, Radoslaw Martin; Pantazis, Dimitrios; Oliva, Aude

2014-01-01

A comprehensive picture of object processing in the human brain requires combining both spatial and temporal information about brain activity. Here, we acquired human magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) responses to 92 object images. Multivariate pattern classification applied to MEG revealed the time course of object processing: whereas individual images were discriminated by visual representations early, ordinate and superordinate category levels emerged relatively later. Using representational similarity analysis, we combine human fMRI and MEG to show content-specific correspondence between early MEG responses and primary visual cortex (V1), and later MEG responses and inferior temporal (IT) cortex. We identified transient and persistent neural activities during object processing, with sources in V1 and IT., Finally, human MEG signals were correlated to single-unit responses in monkey IT. Together, our findings provide an integrated space- and time-resolved view of human object categorization during the first few hundred milliseconds of vision. PMID:24464044

Moving human full body and body parts detection, tracking, and applications on human activity estimation, walking pattern and face recognition

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2016-05-01

We have developed a new way for detection and tracking of human full-body and body-parts with color (intensity) patch morphological segmentation and adaptive thresholding for security surveillance cameras. An adaptive threshold scheme has been developed for dealing with body size changes, illumination condition changes, and cross camera parameter changes. Tests with the PETS 2009 and 2014 datasets show that we can obtain high probability of detection and low probability of false alarm for full-body. Test results indicate that our human full-body detection method can considerably outperform the current state-of-the-art methods in both detection performance and computational complexity. Furthermore, in this paper, we have developed several methods using color features for detection and tracking of human body-parts (arms, legs, torso, and head, etc.). For example, we have developed a human skin color sub-patch segmentation algorithm by first conducting a RGB to YIQ transformation and then applying a Subtractive I/Q image Fusion with morphological operations. With this method, we can reliably detect and track human skin color related body-parts such as face, neck, arms, and legs. Reliable body-parts (e.g. head) detection allows us to continuously track the individual person even in the case that multiple closely spaced persons are merged. Accordingly, we have developed a new algorithm to split a merged detection blob back to individual detections based on the detected head positions. Detected body-parts also allow us to extract important local constellation features of the body-parts positions and angles related to the full-body. These features are useful for human walking gait pattern recognition and human pose (e.g. standing or falling down) estimation for potential abnormal behavior and accidental event detection, as evidenced with our experimental tests. Furthermore, based on the reliable head (face) tacking, we have applied a super-resolution algorithm to enhance the face resolution for improved human face recognition performance.

Effects of sex and gonadectomy on social investigation and social recognition in mice.

PubMed

Karlsson, Sara A; Haziri, Kaltrina; Hansson, Evelyn; Kettunen, Petronella; Westberg, Lars

2015-11-25

An individual's ability to recognise and pay attention to others is crucial in order to behave appropriately in various social situations. Studies in humans have shown a sex bias in sociability as well as social memory, indicating that females have better face memory and gaze more at the eyes of others, but information about the factors that underpin these differences is sparse. Our aim was therefore to investigate if sociability and social recognition differ between female and male mice, and if so, to what extent gonadal hormones may be involved. Intact and gonadectomised male and female mice were assessed for sociability and social recognition using the three-chambered sociability paradigm, as well as the social discrimination test. Furthermore, we conducted a novel object recognition test, a locomotor activity test and an odour habituation/dishabituation test. The present study showed that the ability to recognise other individuals is intact in males with and without gonads, as well as in intact females, whereas it is hampered in gonadectomised females. Additionally, intact male mice displayed more persistent investigatory behaviour compared to the other groups, although the intact females showed elevated basal locomotor activity. In addition, all groups had intact object memory and habituated to odours. Our results suggest that intact male mice investigate conspecifics more than females do, and these differences seem to depend upon circulating hormones released from the testis. As these results seem to contrast what is known from human studies, they should be taken into consideration when using the three-chambered apparatus, and similar paradigms as animal models of social deficits in e.g. autism. Other behavioural tests, and animal models, may be more suitable for translational studies between patients and experimental animals.

Morphological and population genomic evidence that human faces have evolved to signal individual identity.

PubMed

Sheehan, Michael J; Nachman, Michael W

2014-09-16

Facial recognition plays a key role in human interactions, and there has been great interest in understanding the evolution of human abilities for individual recognition and tracking social relationships. Individual recognition requires sufficient cognitive abilities and phenotypic diversity within a population for discrimination to be possible. Despite the importance of facial recognition in humans, the evolution of facial identity has received little attention. Here we demonstrate that faces evolved to signal individual identity under negative frequency-dependent selection. Faces show elevated phenotypic variation and lower between-trait correlations compared with other traits. Regions surrounding face-associated single nucleotide polymorphisms show elevated diversity consistent with frequency-dependent selection. Genetic variation maintained by identity signalling tends to be shared across populations and, for some loci, predates the origin of Homo sapiens. Studies of human social evolution tend to emphasize cognitive adaptations, but we show that social evolution has shaped patterns of human phenotypic and genetic diversity as well.

Identifying typical physical activity on smartphone with varying positions and orientations.

PubMed

Miao, Fen; He, Yi; Liu, Jinlei; Li, Ye; Ayoola, Idowu

2015-04-13

Traditional activity recognition solutions are not widely applicable due to a high cost and inconvenience to use with numerous sensors. This paper aims to automatically recognize physical activity with the help of the built-in sensors of the widespread smartphone without any limitation of firm attachment to the human body. By introducing a method to judge whether the phone is in a pocket, we investigated the data collected from six positions of seven subjects, chose five signals that are insensitive to orientation for activity classification. Decision trees (J48), Naive Bayes and Sequential minimal optimization (SMO) were employed to recognize five activities: static, walking, running, walking upstairs and walking downstairs. The experimental results based on 8,097 activity data demonstrated that the J48 classifier produced the best performance with an average recognition accuracy of 89.6% during the three classifiers, and thus would serve as the optimal online classifier. The utilization of the built-in sensors of the smartphone to recognize typical physical activities without any limitation of firm attachment is feasible.

SVM-based multi-sensor fusion for free-living physical activity assessment.

PubMed

Liu, Shaopeng; Gao, Robert X; John, Dinesh; Staudenmayer, John; Freedson, Patty S

2011-01-01

This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on the support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multi-sensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which the activity types and related energy expenditures are derived. The result shows that the method correctly recognized the 13 activity types 84.7% of the time, which is 26% higher than using a hip accelerometer alone. Also, the method predicted the associated energy expenditure with a root mean square error of 0.43 METs, 43% lower than using a hip accelerometer alone. Furthermore, the fusion method was effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition, especially when data from the ventilation sensor was added to the fusion model. These results demonstrate that the multi-sensor fusion technique presented is more effective in assessing activities of varying intensities than the traditional accelerometer-alone based methods.

Sensor fusion II: Human and machine strategies; Proceedings of the Meeting, Philadelphia, PA, Nov. 6-9, 1989

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1990-01-01

Various papers on human and machine strategies in sensor fusion are presented. The general topics addressed include: active vision, measurement and analysis of visual motion, decision models for sensor fusion, implementation of sensor fusion algorithms, applying sensor fusion to image analysis, perceptual modules and their fusion, perceptual organization and object recognition, planning and the integration of high-level knowledge with perception, using prior knowledge and context in sensor fusion.

Multimodal neuroelectric interface development

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Wheeler, Kevin R.; Jorgensen, Charles C.; Rosipal, Roman; Clanton, Sam T.; Matthews, Bryan; Hibbs, Andrew D.; Matthews, Robert; Krupka, Michael

2003-01-01

We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies for computer interfaces using electroencephalogram (EEG) signals; 3) a flexible computation framework for neuroelectric interface research; and d) noncontact sensors, which measure electromyogram or EEG signals without resistive contact to the body.

Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.

PubMed

Davila, Juan Carlos; Cretu, Ana-Maria; Zaremba, Marek

2017-06-07

The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

Assessing Metacognition in an Online Community of Inquiry

ERIC Educational Resources Information Center

Akyol, Zehra; Garrison, D. Randy

2011-01-01

Metacognition is an important aspect of human intelligence and higher learning. There is the recognition that metacognition is not just a private internal activity but also socially situated. In this context, the purpose of this research is to develop and validate a metacognitive construct that provides the opportunity to assess metacognition in…

Can Market Capitalism Be Greened? Environmental Education Revisited

ERIC Educational Resources Information Center

Hill, Deb J.; Tulloch, Lynley

2013-01-01

Widespread recognition of the detrimental effects that human activities have had on nature and its ecosystems can now be found in every domain of public policy. Since the inception of international accords in the 1970s provoked greater engagement by nations in environmental amelioration measures, "education" has been lauded as an important panacea…

Probabilistic image modeling with an extended chain graph for human activity recognition and image segmentation.

PubMed

Zhang, Lei; Zeng, Zhi; Ji, Qiang

2011-09-01

Chain graph (CG) is a hybrid probabilistic graphical model (PGM) capable of modeling heterogeneous relationships among random variables. So far, however, its application in image and video analysis is very limited due to lack of principled learning and inference methods for a CG of general topology. To overcome this limitation, we introduce methods to extend the conventional chain-like CG model to CG model with more general topology and the associated methods for learning and inference in such a general CG model. Specifically, we propose techniques to systematically construct a generally structured CG, to parameterize this model, to derive its joint probability distribution, to perform joint parameter learning, and to perform probabilistic inference in this model. To demonstrate the utility of such an extended CG, we apply it to two challenging image and video analysis problems: human activity recognition and image segmentation. The experimental results show improved performance of the extended CG model over the conventional directed or undirected PGMs. This study demonstrates the promise of the extended CG for effective modeling and inference of complex real-world problems.

Post interaural neural net-based vowel recognition

NASA Astrophysics Data System (ADS)

Jouny, Ismail I.

2001-10-01

Interaural head related transfer functions are used to process speech signatures prior to neural net based recognition. Data representing the head related transfer function of a dummy has been collected at MIT and made available on the Internet. This data is used to pre-process vowel signatures to mimic the effects of human ear on speech perception. Signatures representing various vowels of the English language are then presented to a multi-layer perceptron trained using the back propagation algorithm for recognition purposes. The focus in this paper is to assess the effects of human interaural system on vowel recognition performance particularly when using a classification system that mimics the human brain such as a neural net.

Support vector machine-based facial-expression recognition method combining shape and appearance

NASA Astrophysics Data System (ADS)

Han, Eun Jung; Kang, Byung Jun; Park, Kang Ryoung; Lee, Sangyoun

2010-11-01

Facial expression recognition can be widely used for various applications, such as emotion-based human-machine interaction, intelligent robot interfaces, face recognition robust to expression variation, etc. Previous studies have been classified as either shape- or appearance-based recognition. The shape-based method has the disadvantage that the individual variance of facial feature points exists irrespective of similar expressions, which can cause a reduction of the recognition accuracy. The appearance-based method has a limitation in that the textural information of the face is very sensitive to variations in illumination. To overcome these problems, a new facial-expression recognition method is proposed, which combines both shape and appearance information, based on the support vector machine (SVM). This research is novel in the following three ways as compared to previous works. First, the facial feature points are automatically detected by using an active appearance model. From these, the shape-based recognition is performed by using the ratios between the facial feature points based on the facial-action coding system. Second, the SVM, which is trained to recognize the same and different expression classes, is proposed to combine two matching scores obtained from the shape- and appearance-based recognitions. Finally, a single SVM is trained to discriminate four different expressions, such as neutral, a smile, anger, and a scream. By determining the expression of the input facial image whose SVM output is at a minimum, the accuracy of the expression recognition is much enhanced. The experimental results showed that the recognition accuracy of the proposed method was better than previous researches and other fusion methods.

Movement Contributes to Infants' Recognition of the Human Form

ERIC Educational Resources Information Center

Christie, Tamara; Slaughter, Virginia

2010-01-01

Three experiments demonstrate that biological movement facilitates young infants' recognition of the whole human form. A body discrimination task was used in which 6-, 9-, and 12-month-old infants were habituated to typical human bodies and then shown scrambled human bodies at the test. Recovery of interest to the scrambled bodies was observed in…

BLT-humanized C57BL/6 Rag2-/-γc-/-CD47-/- mice are resistant to GVHD and develop B- and T-cell immunity to HIV infection.

PubMed

Lavender, Kerry J; Pang, Wendy W; Messer, Ronald J; Duley, Amanda K; Race, Brent; Phillips, Katie; Scott, Dana; Peterson, Karin E; Chan, Charles K; Dittmer, Ulf; Dudek, Timothy; Allen, Todd M; Weissman, Irving L; Hasenkrug, Kim J

2013-12-12

The use of C57BL/6 Rag2(-/-)γc(-/-) mice as recipients for xenotransplantation with human immune systems (humanization) has been problematic because C57BL/6 SIRPα does not recognize human CD47, and such recognition is required to suppress macrophage-mediated phagocytosis of transplanted human hematopoietic stem cells (HSCs). We show that genetic inactivation of CD47 on the C57BL/6 Rag2(-/-)γc(-/-) background negates the requirement for CD47-signal recognition protein α (SIRPα) signaling and induces tolerance to transplanted human HSCs. These triple-knockout, bone marrow, liver, thymus (TKO-BLT) humanized mice develop organized lymphoid tissues including mesenteric lymph nodes, splenic follicles and gut-associated lymphoid tissue that demonstrate high levels of multilineage hematopoiesis. Importantly, these mice have an intact complement system and showed no signs of graft-versus-host disease (GVHD) out to 29 weeks after transplantation. Sustained, high-level HIV-1 infection was observed via either intrarectal or intraperitoneal inoculation. TKO-BLT mice exhibited hallmarks of human HIV infection including CD4(+) T-cell depletion, immune activation, and development of HIV-specific B- and T-cell responses. The lack of GVHD makes the TKO-BLT mouse a significantly improved model for long-term studies of pathogenesis, immune responses, therapeutics, and vaccines to human pathogens.

Comparison of Object Recognition Behavior in Human and Monkey

PubMed Central

Rajalingham, Rishi; Schmidt, Kailyn

2015-01-01

Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize “pooled human” object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception. SIGNIFICANCE STATEMENT To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to further the goal of the field of translating knowledge gained from animal models to humans. To the best of our knowledge, this study is the first systematic attempt at comparing a high-level visual behavior of humans and macaque monkeys. PMID:26338324

Recognition of Daily Activity in Living Space based on Indoor Ambient Atmosphere and Acquiring Localized Information for Improvement of Recognition Accuracy

NASA Astrophysics Data System (ADS)

Hirasawa, Kazuki; Sawada, Shinya; Saitoh, Atsushi

The system watching over elder's life is very important in a super-aged society Japan. In this paper, we describe a method to recognize resident's daily activities by means of using the information of indoor ambient atmosphere changes. The measuring targets of environmental changes are of gas and smell, temperature, humidity, and brightness. Those changes have much relation with resident's daily activities. The measurement system with 7 sensors (4 gas sensors, a thermistor, humidity sensor, and CdS light sensor) was developed for getting indoor ambient atmosphere changes. Some measurements were done in a one-room type residential space. 21 dimensional activity vectors were composed for each daily activity from acquired data. Those vectors were classified into 9 categories that were main activities by using Self-Organizing Map (SOM) method. From the result, it was found that the recognition of main daily activities based on information on indoor ambient atmosphere changes is possible. Moreover, we also describe the method for getting information of local gas and smell environmental changes. Gas and smell environmental changes are related with daily activities, especially very important action, eating and drinking. And, local information enables the relation of the place and the activity. For such a purpose, a gas sensing module with the operation function that synchronizes with human detection signal was developed and evaluated. From the result, the sensor module had the ability to acquire and to emphasize local gas environment changes caused by the person's activity.

Structural basis for signal recognition and transduction by platelet-activating-factor receptor.

PubMed

Cao, Can; Tan, Qiuxiang; Xu, Chanjuan; He, Lingli; Yang, Linlin; Zhou, Ye; Zhou, Yiwei; Qiao, Anna; Lu, Minmin; Yi, Cuiying; Han, Gye Won; Wang, Xianping; Li, Xuemei; Yang, Huaiyu; Rao, Zihe; Jiang, Hualiang; Zhao, Yongfang; Liu, Jianfeng; Stevens, Raymond C; Zhao, Qiang; Zhang, Xuejun C; Wu, Beili

2018-06-01

Platelet-activating-factor receptor (PAFR) responds to platelet-activating factor (PAF), a phospholipid mediator of cell-to-cell communication that exhibits diverse physiological effects. PAFR is considered an important drug target for treating asthma, inflammation and cardiovascular diseases. Here we report crystal structures of human PAFR in complex with the antagonist SR 27417 and the inverse agonist ABT-491 at 2.8-Å and 2.9-Å resolution, respectively. The structures, supported by molecular docking of PAF, provide insights into the signal-recognition mechanisms of PAFR. The PAFR-SR 27417 structure reveals an unusual conformation showing that the intracellular tips of helices II and IV shift outward by 13 Å and 4 Å, respectively, and helix VIII adopts an inward conformation. The PAFR structures, combined with single-molecule FRET and cell-based functional assays, suggest that the conformational change in the helical bundle is ligand dependent and plays a critical role in PAFR activation, thus greatly extending knowledge about signaling by G-protein-coupled receptors.

Invariant recognition drives neural representations of action sequences

PubMed Central

Poggio, Tomaso

2017-01-01

Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs), that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences. PMID:29253864

Vision based assistive technology for people with dementia performing activities of daily living (ADLs): an overview

NASA Astrophysics Data System (ADS)

As'ari, M. A.; Sheikh, U. U.

2012-04-01

The rapid development of intelligent assistive technology for replacing a human caregiver in assisting people with dementia performing activities of daily living (ADLs) promises in the reduction of care cost especially in training and hiring human caregiver. The main problem however, is the various kinds of sensing agents used in such system and is dependent on the intent (types of ADLs) and environment where the activity is performed. In this paper on overview of the potential of computer vision based sensing agent in assistive system and how it can be generalized and be invariant to various kind of ADLs and environment. We find that there exists a gap from the existing vision based human action recognition method in designing such system due to cognitive and physical impairment of people with dementia.

VideoWeb Dataset for Multi-camera Activities and Non-verbal Communication

NASA Astrophysics Data System (ADS)

Denina, Giovanni; Bhanu, Bir; Nguyen, Hoang Thanh; Ding, Chong; Kamal, Ahmed; Ravishankar, Chinya; Roy-Chowdhury, Amit; Ivers, Allen; Varda, Brenda

Human-activity recognition is one of the most challenging problems in computer vision. Researchers from around the world have tried to solve this problem and have come a long way in recognizing simple motions and atomic activities. As the computer vision community heads toward fully recognizing human activities, a challenging and labeled dataset is needed. To respond to that need, we collected a dataset of realistic scenarios in a multi-camera network environment (VideoWeb) involving multiple persons performing dozens of different repetitive and non-repetitive activities. This chapter describes the details of the dataset. We believe that this VideoWeb Activities dataset is unique and it is one of the most challenging datasets available today. The dataset is publicly available online at http://vwdata.ee.ucr.edu/ along with the data annotation.

Implications of Animal Object Memory Research for Human Amnesia

ERIC Educational Resources Information Center

Winters, Boyer D.; Saksida, Lisa M.; Bussey, Timothy J.

2010-01-01

Damage to structures in the human medial temporal lobe causes severe memory impairment. Animal object recognition tests gained prominence from attempts to model "global" human medial temporal lobe amnesia, such as that observed in patient HM. These tasks, such as delayed nonmatching-to-sample and spontaneous object recognition, for assessing…

Arginine Vasopressin selectively enhances recognition of sexual cues in male humans.

PubMed

Guastella, Adam J; Kenyon, Amanda R; Unkelbach, Christian; Alvares, Gail A; Hickie, Ian B

2011-02-01

Arginine Vasopressin modulates complex social and sexual behavior by enhancing social recognition, pair bonding, and aggression in non-human mammals. The influence of Arginine Vasopressin in human social and sexual behavior is, however, yet to be fully understood. We evaluated whether Arginine Vasopressin nasal spray facilitated recognition of positive and negative social and sexual stimuli over non-social stimuli. We used a recognition task that has already been shown to be sensitive to the influence of Oxytocin nasal spray (Unkelbach et al., 2008). In a double-blind, randomized, placebo-controlled, between-subjects design, 41 healthy male volunteers were administered Arginine Vasopressin (20 IU) or a placebo nasal spray after a 45 min wait period and then completed the recognition task. Results showed that the participants administered Arginine Vasopressin nasal spray were faster to detect sexual words over other types of words. This effect appeared for both positively and negatively valenced words. Results demonstrate for the first time that Arginine Vasopressin selectively enhances human cognition for sexual stimuli, regardless of valence. They further extend animal and human genetic studies linking Arginine Vasopressin to sexual behavior in males. Findings suggest an important cognitive mechanism that could enhance sexual behaviors in humans. Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.

A sensor and video based ontology for activity recognition in smart environments.

PubMed

Mitchell, D; Morrow, Philip J; Nugent, Chris D

2014-01-01

Activity recognition is used in a wide range of applications including healthcare and security. In a smart environment activity recognition can be used to monitor and support the activities of a user. There have been a range of methods used in activity recognition including sensor-based approaches, vision-based approaches and ontological approaches. This paper presents a novel approach to activity recognition in a smart home environment which combines sensor and video data through an ontological framework. The ontology describes the relationships and interactions between activities, the user, objects, sensors and video data.

Neural network face recognition using wavelets

NASA Astrophysics Data System (ADS)

Karunaratne, Passant V.; Jouny, Ismail I.

1997-04-01

The recognition of human faces is a phenomenon that has been mastered by the human visual system and that has been researched extensively in the domain of computer neural networks and image processing. This research is involved in the study of neural networks and wavelet image processing techniques in the application of human face recognition. The objective of the system is to acquire a digitized still image of a human face, carry out pre-processing on the image as required, an then, given a prior database of images of possible individuals, be able to recognize the individual in the image. The pre-processing segment of the system includes several procedures, namely image compression, denoising, and feature extraction. The image processing is carried out using Daubechies wavelets. Once the images have been passed through the wavelet-based image processor they can be efficiently analyzed by means of a neural network. A back- propagation neural network is used for the recognition segment of the system. The main constraints of the system is with regard to the characteristics of the images being processed. The system should be able to carry out effective recognition of the human faces irrespective of the individual's facial-expression, presence of extraneous objects such as head-gear or spectacles, and face/head orientation. A potential application of this face recognition system would be as a secondary verification method in an automated teller machine.

A neuroanatomical predictor of mirror self-recognition in chimpanzees.

PubMed

Hecht, E E; Mahovetz, L M; Preuss, T M; Hopkins, W D

2017-01-01

The ability to recognize one's own reflection is shared by humans and only a few other species, including chimpanzees. However, this ability is highly variable across individual chimpanzees. In humans, self-recognition involves a distributed, right-lateralized network including frontal and parietal regions involved in the production and perception of action. The superior longitudinal fasciculus (SLF) is a system of white matter tracts linking these frontal and parietal regions. The current study measured mirror self-recognition (MSR) and SLF anatomy in 60 chimpanzees using diffusion tensor imaging. Successful self-recognition was associated with greater rightward asymmetry in the white matter of SLFII and SLFIII, and in SLFIII's gray matter terminations in Broca's area. We observed a visible progression of SLFIII's prefrontal extension in apes that show negative, ambiguous, and compelling evidence of MSR. Notably, SLFIII's terminations in Broca's area are not right-lateralized or particularly pronounced at the population level in chimpanzees, as they are in humans. Thus, chimpanzees with more human-like behavior show more human-like SLFIII connectivity. These results suggest that self-recognition may have co-emerged with adaptations to frontoparietal circuitry. © The Author (2016). Published by Oxford University Press.

Perception of biological motion from size-invariant body representations.

PubMed

Lappe, Markus; Wittinghofer, Karin; de Lussanet, Marc H E

2015-01-01

The visual recognition of action is one of the socially most important and computationally demanding capacities of the human visual system. It combines visual shape recognition with complex non-rigid motion perception. Action presented as a point-light animation is a striking visual experience for anyone who sees it for the first time. Information about the shape and posture of the human body is sparse in point-light animations, but it is essential for action recognition. In the posturo-temporal filter model of biological motion perception posture information is picked up by visual neurons tuned to the form of the human body before body motion is calculated. We tested whether point-light stimuli are processed through posture recognition of the human body form by using a typical feature of form recognition, namely size invariance. We constructed a point-light stimulus that can only be perceived through a size-invariant mechanism. This stimulus changes rapidly in size from one image to the next. It thus disrupts continuity of early visuo-spatial properties but maintains continuity of the body posture representation. Despite this massive manipulation at the visuo-spatial level, size-changing point-light figures are spontaneously recognized by naive observers, and support discrimination of human body motion.

A neuroanatomical predictor of mirror self-recognition in chimpanzees

PubMed Central

Mahovetz, L. M.; Preuss, T. M.; Hopkins, W. D.

2017-01-01

Abstract The ability to recognize one’s own reflection is shared by humans and only a few other species, including chimpanzees. However, this ability is highly variable across individual chimpanzees. In humans, self-recognition involves a distributed, right-lateralized network including frontal and parietal regions involved in the production and perception of action. The superior longitudinal fasciculus (SLF) is a system of white matter tracts linking these frontal and parietal regions. The current study measured mirror self-recognition (MSR) and SLF anatomy in 60 chimpanzees using diffusion tensor imaging. Successful self-recognition was associated with greater rightward asymmetry in the white matter of SLFII and SLFIII, and in SLFIII’s gray matter terminations in Broca’s area. We observed a visible progression of SLFIII’s prefrontal extension in apes that show negative, ambiguous, and compelling evidence of MSR. Notably, SLFIII’s terminations in Broca’s area are not right-lateralized or particularly pronounced at the population level in chimpanzees, as they are in humans. Thus, chimpanzees with more human-like behavior show more human-like SLFIII connectivity. These results suggest that self-recognition may have co-emerged with adaptations to frontoparietal circuitry. PMID:27803287

Capturing specific abilities as a window into human individuality: The example of face recognition

PubMed Central

Wilmer, Jeremy B.; Germine, Laura; Chabris, Christopher F.; Chatterjee, Garga; Gerbasi, Margaret; Nakayama, Ken

2013-01-01

Proper characterization of each individual's unique pattern of strengths and weaknesses requires good measures of diverse abilities. Here, we advocate combining our growing understanding of neural and cognitive mechanisms with modern psychometric methods in a renewed effort to capture human individuality through a consideration of specific abilities. We articulate five criteria for the isolation and measurement of specific abilities, then apply these criteria to face recognition. We cleanly dissociate face recognition from more general visual and verbal recognition. This dissociation stretches across ability as well as disability, suggesting that specific developmental face recognition deficits are a special case of a broader specificity that spans the entire spectrum of human face recognition performance. Item-by-item results from 1,471 web-tested participants, included as supplementary information, fuel item analyses, validation, norming, and item response theory (IRT) analyses of our three tests: (a) the widely used Cambridge Face Memory Test (CFMT); (b) an Abstract Art Memory Test (AAMT), and (c) a Verbal Paired-Associates Memory Test (VPMT). The availability of this data set provides a solid foundation for interpreting future scores on these tests. We argue that the allied fields of experimental psychology, cognitive neuroscience, and vision science could fuel the discovery of additional specific abilities to add to face recognition, thereby providing new perspectives on human individuality. PMID:23428079

Preserved Haptic Shape Processing after Bilateral LOC Lesions.

PubMed

Snow, Jacqueline C; Goodale, Melvyn A; Culham, Jody C

2015-10-07

The visual and haptic perceptual systems are understood to share a common neural representation of object shape. A region thought to be critical for recognizing visual and haptic shape information is the lateral occipital complex (LOC). We investigated whether LOC is essential for haptic shape recognition in humans by studying behavioral responses and brain activation for haptically explored objects in a patient (M.C.) with bilateral lesions of the occipitotemporal cortex, including LOC. Despite severe deficits in recognizing objects using vision, M.C. was able to accurately recognize objects via touch. M.C.'s psychophysical response profile to haptically explored shapes was also indistinguishable from controls. Using fMRI, M.C. showed no object-selective visual or haptic responses in LOC, but her pattern of haptic activation in other brain regions was remarkably similar to healthy controls. Although LOC is routinely active during visual and haptic shape recognition tasks, it is not essential for haptic recognition of object shape. The lateral occipital complex (LOC) is a brain region regarded to be critical for recognizing object shape, both in vision and in touch. However, causal evidence linking LOC with haptic shape processing is lacking. We studied recognition performance, psychophysical sensitivity, and brain response to touched objects, in a patient (M.C.) with extensive lesions involving LOC bilaterally. Despite being severely impaired in visual shape recognition, M.C. was able to identify objects via touch and she showed normal sensitivity to a haptic shape illusion. M.C.'s brain response to touched objects in areas of undamaged cortex was also very similar to that observed in neurologically healthy controls. These results demonstrate that LOC is not necessary for recognizing objects via touch. Copyright © 2015 the authors 0270-6474/15/3513745-16$15.00/0.

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments.

PubMed

Al-Nawashi, Malek; Al-Hazaimeh, Obaida M; Saraee, Mohamad

2017-01-01

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function. Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e., human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups: normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval. Finally, a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention.

Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors

PubMed Central

Augustyniak, Piotr; Smoleń, Magdalena; Mikrut, Zbigniew; Kańtoch, Eliasz

2014-01-01

This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system. PMID:24787640

Modulation of α power and functional connectivity during facial affect recognition.

PubMed

Popov, Tzvetan; Miller, Gregory A; Rockstroh, Brigitte; Weisz, Nathan

2013-04-03

Research has linked oscillatory activity in the α frequency range, particularly in sensorimotor cortex, to processing of social actions. Results further suggest involvement of sensorimotor α in the processing of facial expressions, including affect. The sensorimotor face area may be critical for perception of emotional face expression, but the role it plays is unclear. The present study sought to clarify how oscillatory brain activity contributes to or reflects processing of facial affect during changes in facial expression. Neuromagnetic oscillatory brain activity was monitored while 30 volunteers viewed videos of human faces that changed their expression from neutral to fearful, neutral, or happy expressions. Induced changes in α power during the different morphs, source analysis, and graph-theoretic metrics served to identify the role of α power modulation and cross-regional coupling by means of phase synchrony during facial affect recognition. Changes from neutral to emotional faces were associated with a 10-15 Hz power increase localized in bilateral sensorimotor areas, together with occipital power decrease, preceding reported emotional expression recognition. Graph-theoretic analysis revealed that, in the course of a trial, the balance between sensorimotor power increase and decrease was associated with decreased and increased transregional connectedness as measured by node degree. Results suggest that modulations in α power facilitate early registration, with sensorimotor cortex including the sensorimotor face area largely functionally decoupled and thereby protected from additional, disruptive input and that subsequent α power decrease together with increased connectedness of sensorimotor areas facilitates successful facial affect recognition.

Combining heterogenous features for 3D hand-held object recognition

NASA Astrophysics Data System (ADS)

Lv, Xiong; Wang, Shuang; Li, Xiangyang; Jiang, Shuqiang

2014-10-01

Object recognition has wide applications in the area of human-machine interaction and multimedia retrieval. However, due to the problem of visual polysemous and concept polymorphism, it is still a great challenge to obtain reliable recognition result for the 2D images. Recently, with the emergence and easy availability of RGB-D equipment such as Kinect, this challenge could be relieved because the depth channel could bring more information. A very special and important case of object recognition is hand-held object recognition, as hand is a straight and natural way for both human-human interaction and human-machine interaction. In this paper, we study the problem of 3D object recognition by combining heterogenous features with different modalities and extraction techniques. For hand-craft feature, although it reserves the low-level information such as shape and color, it has shown weakness in representing hiconvolutionalgh-level semantic information compared with the automatic learned feature, especially deep feature. Deep feature has shown its great advantages in large scale dataset recognition but is not always robust to rotation or scale variance compared with hand-craft feature. In this paper, we propose a method to combine hand-craft point cloud features and deep learned features in RGB and depth channle. First, hand-held object segmentation is implemented by using depth cues and human skeleton information. Second, we combine the extracted hetegerogenous 3D features in different stages using linear concatenation and multiple kernel learning (MKL). Then a training model is used to recognize 3D handheld objects. Experimental results validate the effectiveness and gerneralization ability of the proposed method.

Computer Recognition of Facial Profiles

DTIC Science & Technology

1974-08-01

facial recognition 20. ABSTRACT (Continue on reverse side It necessary and Identify by block number) A system for the recognition of human faces from...21 2.6 Classification Algorithms ........... ... 32 III FACIAL RECOGNITION AND AUTOMATIC TRAINING . . . 37 3.1 Facial Profile Recognition...provide a fair test of the classification system. The work of Goldstein, Harmon, and Lesk [81 indicates, however, that for facial recognition , a ten class

Package Design Affects Accuracy Recognition for Medications

PubMed Central

Endestad, Tor; Wortinger, Laura A.; Madsen, Steinar; Hortemo, Sigurd

2016-01-01

Objective: Our aim was to test if highlighting and placement of substance name on medication package have the potential to reduce patient errors. Background: An unintentional overdose of medication is a large health issue that might be linked to medication package design. In two experiments, placement, background color, and the active ingredient of generic medication packages were manipulated according to best human factors guidelines to reduce causes of labeling-related patient errors. Method: In two experiments, we compared the original packaging with packages where we varied placement of the name, dose, and background of the active ingredient. Age-relevant differences and the effect of color on medication recognition error were tested. In Experiment 1, 59 volunteers (30 elderly and 29 young students), participated. In Experiment 2, 25 volunteers participated. Results: The most common error was the inability to identify that two different packages contained the same active ingredient (young, 41%, and elderly, 68%). This kind of error decreased with the redesigned packages (young, 8%, and elderly, 16%). Confusion errors related to color design were reduced by two thirds in the redesigned packages compared with original generic medications. Conclusion: Prominent placement of substance name and dose with a band of high-contrast color support recognition of the active substance in medications. Application: A simple modification including highlighting and placing the name of the active ingredient in the upper right-hand corner of the package helps users realize that two different packages can contain the same active substance, thus reducing the risk of inadvertent medication overdose. PMID:27591209

The unique role of the visual word form area in reading.

PubMed

Dehaene, Stanislas; Cohen, Laurent

2011-06-01

Reading systematically activates the left lateral occipitotemporal sulcus, at a site known as the visual word form area (VWFA). This site is reproducible across individuals/scripts, attuned to reading-specific processes, and partially selective for written strings relative to other categories such as line drawings. Lesions affecting the VWFA cause pure alexia, a selective deficit in word recognition. These findings must be reconciled with the fact that human genome evolution cannot have been influenced by such a recent and culturally variable activity as reading. Capitalizing on recent functional magnetic resonance imaging experiments, we provide strong corroborating evidence for the hypothesis that reading acquisition partially recycles a cortical territory evolved for object and face recognition, the prior properties of which influenced the form of writing systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Neural Mechanism for Nonconscious Activation of Conditioned Placebo and Nocebo Responses.

PubMed

Jensen, Karin B; Kaptchuk, Ted J; Chen, Xiaoyan; Kirsch, Irving; Ingvar, Martin; Gollub, Randy L; Kong, Jian

2015-10-01

Fundamental aspects of human behavior operate outside of conscious awareness. Yet, theories of conditioned responses in humans, such as placebo and nocebo effects on pain, have a strong emphasis on conscious recognition of contextual cues that trigger the response. Here, we investigated the neural pathways involved in nonconscious activation of conditioned pain responses, using functional magnetic resonance imaging in healthy participants. Nonconscious compared with conscious activation of conditioned placebo analgesia was associated with increased activation of the orbitofrontal cortex, a structure with direct connections to affective brain regions and basic reward processing. During nonconscious nocebo, there was increased activation of the thalamus, amygdala, and hippocampus. In contrast to previous assumptions about conditioning in humans, our results show that conditioned pain responses can be elicited independently of conscious awareness and our results suggest a hierarchical activation of neural pathways for nonconscious and conscious conditioned responses. Demonstrating that the human brain has a nonconscious mechanism for responding to conditioned cues has major implications for the role of associative learning in behavioral medicine and psychiatry. Our results may also open up for novel approaches to translational animal-to-human research since human consciousness and animal cognition is an inherent paradox in all behavioral science. © The Author 2014. Published by Oxford University Press.

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

PubMed Central

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

2016-01-01

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

PubMed

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

2016-06-10

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.

Recognition of Aspergillus fumigatus Hyphae by Human Plasmacytoid Dendritic Cells Is Mediated by Dectin-2 and Results in Formation of Extracellular Traps

PubMed Central

Loures, Flávio V.; Röhm, Marc; Lee, Chrono K.; Santos, Evelyn; Wang, Jennifer P.; Specht, Charles A.; Calich, Vera L. G.; Urban, Constantin F.; Levitz, Stuart M.

2015-01-01

Plasmacytoid dendritic cells (pDCs) were initially considered as critical for innate immunity to viruses. However, our group has shown that pDCs bind to and inhibit the growth of Aspergillus fumigatus hyphae and that depletion of pDCs renders mice hypersusceptible to experimental aspergillosis. In this study, we examined pDC receptors contributing to hyphal recognition and downstream events in pDCs stimulated by A. fumigatus hyphae. Our data show that Dectin-2, but not Dectin-1, participates in A. fumigatus hyphal recognition, TNF-α and IFN-α release, and antifungal activity. Moreover, Dectin-2 acts in cooperation with the FcRγ chain to trigger signaling responses. In addition, using confocal and electron microscopy we demonstrated that the interaction between pDCs and A. fumigatus induced the formation of pDC extracellular traps (pETs) containing DNA and citrullinated histone H3. These structures closely resembled those of neutrophil extracellular traps (NETs). The microarray analysis of the pDC transcriptome upon A. fumigatus infection also demonstrated up-regulated expression of genes associated with apoptosis as well as type I interferon-induced genes. Thus, human pDCs directly recognize A. fumigatus hyphae via Dectin-2; this interaction results in cytokine release and antifungal activity. Moreover, hyphal stimulation of pDCs triggers a distinct pattern of pDC gene expression and leads to pET formation. PMID:25659141

Course of Relational and Non-Relational Recognition Memory across the Adult Lifespan

ERIC Educational Resources Information Center

Soei, Eleonore; Daum, Irene

2008-01-01

Human recognition memory shows a decline during normal ageing, which is thought to be related to age-associated dysfunctions of mediotemporal lobe structures. Whether the hippocampus is critical for human general relational memory or for spatial relational memory only is still disputed. The human perirhinal cortex is thought to be critically…

Face Recognition Is Shaped by the Use of Sign Language

ERIC Educational Resources Information Center

Stoll, Chloé; Palluel-Germain, Richard; Caldara, Roberto; Lao, Junpeng; Dye, Matthew W. G.; Aptel, Florent; Pascalis, Olivier

2018-01-01

Previous research has suggested that early deaf signers differ in face processing. Which aspects of face processing are changed and the role that sign language may have played in that change are however unclear. Here, we compared face categorization (human/non-human) and human face recognition performance in early profoundly deaf signers, hearing…

The effects of acute social isolation on long-term social recognition memory.

PubMed

Leser, Noam; Wagner, Shlomo

2015-10-01

The abilities to recognize individual animals of the same species and to distinguish them from other individuals are the basis for all mammalian social organizations and relationships. These abilities, termed social recognition memory, can be explored in mice and rats using their innate tendency to investigate novel social stimuli more persistently than familiar ones. Using this methodology it was found that social recognition memory is mediated by a specific neural network in the brain, the activity of which is modulated by several molecules, such the neuropeptides oxytocin and vasopressin. During the last 15 years several independent studies have revealed that social recognition memory of mice and rats depends upon their housing conditions. Specifically, long-term social recognition memory cannot be formed as shortly as few days following social isolation of the animal. This rapid and reversible impairment caused by acute social isolation seems to be specific to social memory and has not been observed in other types of memory. Here we review these studies and suggest that this unique system may serve for exploring of the mechanisms underlying the well-known negative effects of partial or perceived social isolation on human mental health. Copyright © 2015 Elsevier Inc. All rights reserved.

When fear forms memories: threat of shock and brain potentials during encoding and recognition.

PubMed

Weymar, Mathias; Bradley, Margaret M; Hamm, Alfons O; Lang, Peter J

2013-03-01

The anticipation of highly aversive events is associated with measurable defensive activation, and both animal and human research suggests that stress-inducing contexts can facilitate memory. Here, we investigated whether encoding stimuli in the context of anticipating an aversive shock affects recognition memory. Event-related potentials (ERPs) were measured during a recognition test for words that were encoded in a font color that signaled threat or safety. At encoding, cues signaling threat of shock, compared to safety, prompted enhanced P2 and P3 components. Correct recognition of words encoded in the context of threat, compared to safety, was associated with an enhanced old-new ERP difference (500-700 msec; centro-parietal), and this difference was most reliable for emotional words. Moreover, larger old-new ERP differences when recognizing emotional words encoded in a threatening context were associated with better recognition, compared to words encoded in safety. Taken together, the data indicate enhanced memory for stimuli encoded in a context in which an aversive event is merely anticipated, which could assist in understanding effects of anxiety and stress on memory processes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Multimodal approaches for emotion recognition: a survey

NASA Astrophysics Data System (ADS)

Sebe, Nicu; Cohen, Ira; Gevers, Theo; Huang, Thomas S.

2004-12-01

Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing-emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer interaction that enable the computer to be more aware of the user's emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and physiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.

Multimodal approaches for emotion recognition: a survey

NASA Astrophysics Data System (ADS)

Sebe, Nicu; Cohen, Ira; Gevers, Theo; Huang, Thomas S.

2005-01-01

Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing-emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer interaction that enable the computer to be more aware of the user's emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and physiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.

Ebselen Reversibly Inhibits Human Glutamate Dehydrogenase at the Catalytic Site.

PubMed

Jin, Yanhong; Li, Di; Lu, Shiying; Zhao, Han; Chen, Zhao; Hou, Wei; Ruan, Benfang Helen

Human glutamate dehydrogenase (GDH) plays an important role in neurological diseases, tumor metabolism, and hyperinsulinism-hyperammonemia syndrome (HHS). However, there are very few inhibitors known for human GDH. Recently, Ebselen was reported to crosslink with Escherichia coli GDH at the active site cysteine residue (Cys321), but the sequence alignment showed that the corresponding residue is Ala329 in human GDH. To investigate whether Ebselen could be an inhibitor for human GDH, we cloned and expressed an N-terminal His-tagged human GDH in E. coli. The recombinant human GDH enzyme showed expected properties such as adenosine diphosphate activation and nicotinamide adenine dinucleotide/nicotinamide adenine dinucleotide phosphate dual recognition. Further, we developed a 2-(3-(2-methoxy-4-nitrophenyl)-2-(4-nitrophenyl)-2H-tetrazol-3-ium-5-yl) benzenesulfonate sodium salt (EZMTT)-based assay for human GDH, which was highly sensitive and is suitable for high-throughput screening for potent GDH inhibitors. In addition, ForteBio binding assays demonstrated that Ebselen is a reversible active site inhibitor for human GDH. Since Ebselen is a multifunctional organoselenium compound in Phase III clinical trials for inflammation, an Ebselen-based GDH inhibitor might be valuable for future drug discovery for HHS patients.

Fungal Chitin Dampens Inflammation through IL-10 Induction Mediated by NOD2 and TLR9 Activation

PubMed Central

Wagener, Jeanette; Malireddi, R. K. Subbarao; Lenardon, Megan D.; Köberle, Martin; Vautier, Simon; MacCallum, Donna M.; Biedermann, Tilo; Schaller, Martin; Netea, Mihai G.; Kanneganti, Thirumala-Devi; Brown, Gordon D.; Brown, Alistair J. P.; Gow, Neil A. R.

2014-01-01

Chitin is an essential structural polysaccharide of fungal pathogens and parasites, but its role in human immune responses remains largely unknown. It is the second most abundant polysaccharide in nature after cellulose and its derivatives today are widely used for medical and industrial purposes. We analysed the immunological properties of purified chitin particles derived from the opportunistic human fungal pathogen Candida albicans, which led to the selective secretion of the anti-inflammatory cytokine IL-10. We identified NOD2, TLR9 and the mannose receptor as essential fungal chitin-recognition receptors for the induction of this response. Chitin reduced LPS-induced inflammation in vivo and may therefore contribute to the resolution of the immune response once the pathogen has been defeated. Fungal chitin also induced eosinophilia in vivo, underpinning its ability to induce asthma. Polymorphisms in the identified chitin receptors, NOD2 and TLR9, predispose individuals to inflammatory conditions and dysregulated expression of chitinases and chitinase-like binding proteins, whose activity is essential to generate IL-10-inducing fungal chitin particles in vitro, have also been linked to inflammatory conditions and asthma. Chitin recognition is therefore critical for immune homeostasis and is likely to have a significant role in infectious and allergic disease. Authors Summary Chitin is the second most abundant polysaccharide in nature after cellulose and an essential component of the cell wall of all fungal pathogens. The discovery of human chitinases and chitinase-like binding proteins indicates that fungal chitin is recognised by cells of the human immune system, shaping the immune response towards the invading pathogen. We show that three immune cell receptors– the mannose receptor, NOD2 and TLR9 recognise chitin and act together to mediate an anti-inflammatory response via secretion of the cytokine IL-10. This mechanism may prevent inflammation-based damage during fungal infection and restore immune balance after an infection has been cleared. By increasing the chitin content in the cell wall pathogenic fungi may influence the immune system in their favour, by down-regulating protective inflammatory immune responses. Furthermore, gene mutations and dysregulated enzyme activity in the described chitin recognition pathway are implicated in inflammatory conditions such as Crohn's Disease and asthma, highlighting the importance of the discovered mechanism in human health. PMID:24722226

Word Recognition in Auditory Cortex

ERIC Educational Resources Information Center

DeWitt, Iain D. J.

2013-01-01

Although spoken word recognition is more fundamental to human communication than text recognition, knowledge of word-processing in auditory cortex is comparatively impoverished. This dissertation synthesizes current models of auditory cortex, models of cortical pattern recognition, models of single-word reading, results in phonetics and results in…

Mycobacterium tuberculosis inhibits human innate immune responses via the production of TLR2 antagonist glycolipids.

PubMed

Blanc, Landry; Gilleron, Martine; Prandi, Jacques; Song, Ok-Ryul; Jang, Mi-Seon; Gicquel, Brigitte; Drocourt, Daniel; Neyrolles, Olivier; Brodin, Priscille; Tiraby, Gérard; Vercellone, Alain; Nigou, Jérôme

2017-10-17

Mycobacterium tuberculosis is a major human pathogen that is able to survive inside host cells and resist immune clearance. Most particularly, it inhibits several arms of the innate immune response, including phagosome maturation or cytokine production. To better understand the molecular mechanisms by which M. tuberculosis circumvents host immune defenses, we used a transposon mutant library generated in a virulent clinical isolate of M. tuberculosis of the W/Beijing family to infect human macrophages, utilizing a cell line derivative of THP-1 cells expressing a reporter system for activation of the transcription factor NF-κB, a key regulator of innate immunity. We identified several M. tuberculosis mutants inducing a NF-κB activation stronger than that of the wild-type strain. One of these mutants was found to be deficient for the synthesis of cell envelope glycolipids, namely sulfoglycolipids, suggesting that the latter can interfere with innate immune responses. Using natural and synthetic molecular variants, we determined that sulfoglycolipids inhibit NF-κB activation and subsequent cytokine production or costimulatory molecule expression by acting as competitive antagonists of Toll-like receptor 2, thereby inhibiting the recognition of M. tuberculosis by this receptor. Our study reveals that producing glycolipid antagonists of pattern recognition receptors is a strategy used by M. tuberculosis to undermine innate immune defense. Sulfoglycolipids are major and specific lipids of M. tuberculosis , considered for decades as virulence factors of the bacilli. Our study uncovers a mechanism by which they may contribute to M. tuberculosis virulence.

Rationally designed mutations convert complexes of human recombinant T cell receptor ligands into monomers that retain biological activity

PubMed Central

Huan, Jianya Y; Meza-Romero, Roberto; Mooney, Jeffery L; Chou, Yuan K; Edwards, David M; Rich, Cathleen; Link, Jason M; Vandenbark, Arthur A; Bourdette, Dennis N; Bächinger, Hans-Peter; Burrows, Gregory G

2012-01-01

Single-chain human recombinant T cell receptor ligands derived from the peptide binding/TCR recognition domain of human HLA-DR2b (DRA*0101/DRB1*1501) produced in Escherichia coli with and without amino-terminal extensions containing antigenic peptides have been described previously. While molecules with the native sequence retained biological activity, they formed higher order aggregates in solution. In this study, we used site-directed mutagenesis to modify the β-sheet platform of the DR2-derived RTLs, obtaining two variants that were monomeric in solution by replacing hydrophobic residues with polar (serine) or charged (aspartic acid) residues. Size exclusion chromatography and dynamic light scattering demonstrated that the modified RTLs were monomeric in solution, and structural characterization using circular dichroism demonstrated the highly ordered secondary structure of the RTLs. Peptide binding to the `empty' RTLs was quantified using biotinylated peptides, and functional studies showed that the modified RTLs containing covalently tethered peptides were able to inhibit antigen-specific T cell proliferation in vitro, as well as suppress experimental autoimmune encephalomyelitis in vivo. These studies demonstrated that RTLs encoding the Ag-binding/TCR recognition domain of MHC class II molecules are innately very robust structures, capable of retaining potent biological activity separate from the Ig-fold domains of the progenitor class II structure, with prevention of aggregation accomplished by modification of an exposed surface that was buried in the progenitor structure. PMID:22973070

Two processes support visual recognition memory in rhesus monkeys.

PubMed

Guderian, Sebastian; Brigham, Danielle; Mishkin, Mortimer

2011-11-29

A large body of evidence in humans suggests that recognition memory can be supported by both recollection and familiarity. Recollection-based recognition is characterized by the retrieval of contextual information about the episode in which an item was previously encountered, whereas familiarity-based recognition is characterized instead by knowledge only that the item had been encountered previously in the absence of any context. To date, it is unknown whether monkeys rely on similar mnemonic processes to perform recognition memory tasks. Here, we present evidence from the analysis of receiver operating characteristics, suggesting that visual recognition memory in rhesus monkeys also can be supported by two separate processes and that these processes have features considered to be characteristic of recollection and familiarity. Thus, the present study provides converging evidence across species for a dual process model of recognition memory and opens up the possibility of studying the neural mechanisms of recognition memory in nonhuman primates on tasks that are highly similar to the ones used in humans.

Two processes support visual recognition memory in rhesus monkeys

PubMed Central

Guderian, Sebastian; Brigham, Danielle; Mishkin, Mortimer

2011-01-01

A large body of evidence in humans suggests that recognition memory can be supported by both recollection and familiarity. Recollection-based recognition is characterized by the retrieval of contextual information about the episode in which an item was previously encountered, whereas familiarity-based recognition is characterized instead by knowledge only that the item had been encountered previously in the absence of any context. To date, it is unknown whether monkeys rely on similar mnemonic processes to perform recognition memory tasks. Here, we present evidence from the analysis of receiver operating characteristics, suggesting that visual recognition memory in rhesus monkeys also can be supported by two separate processes and that these processes have features considered to be characteristic of recollection and familiarity. Thus, the present study provides converging evidence across species for a dual process model of recognition memory and opens up the possibility of studying the neural mechanisms of recognition memory in nonhuman primates on tasks that are highly similar to the ones used in humans. PMID:22084079

Molecular evolution of the CYP2D subfamily in primates: purifying selection on substrate recognition sites without the frequent or long-tract gene conversion.

PubMed

Yasukochi, Yoshiki; Satta, Yoko

2015-03-25

The human cytochrome P450 (CYP) 2D6 gene is a member of the CYP2D gene subfamily, along with the CYP2D7P and CYP2D8P pseudogenes. Although the CYP2D6 enzyme has been studied extensively because of its clinical importance, the evolution of the CYP2D subfamily has not yet been fully understood. Therefore, the goal of this study was to reveal the evolutionary process of the human drug metabolic system. Here, we investigate molecular evolution of the CYP2D subfamily in primates by comparing 14 CYP2D sequences from humans to New World monkey genomes. Window analysis and statistical tests revealed that entire genomic sequences of paralogous genes were extensively homogenized by gene conversion during molecular evolution of CYP2D genes in primates. A neighbor-joining tree based on genomic sequences at the nonsubstrate recognition sites showed that CYP2D6 and CYP2D8 genes were clustered together due to gene conversion. In contrast, a phylogenetic tree using amino acid sequences at substrate recognition sites did not cluster the CYP2D6 and CYP2D8 genes, suggesting that the functional constraint on substrate specificity is one of the causes for purifying selection at the substrate recognition sites. Our results suggest that the CYP2D gene subfamily in primates has evolved to maintain the regioselectivity for a substrate hydroxylation activity between individual enzymes, even though extensive gene conversion has occurred across CYP2D coding sequences. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Molecular Evolution of the CYP2D Subfamily in Primates: Purifying Selection on Substrate Recognition Sites without the Frequent or Long-Tract Gene Conversion

PubMed Central

Yasukochi, Yoshiki; Satta, Yoko

2015-01-01

The human cytochrome P450 (CYP) 2D6 gene is a member of the CYP2D gene subfamily, along with the CYP2D7P and CYP2D8P pseudogenes. Although the CYP2D6 enzyme has been studied extensively because of its clinical importance, the evolution of the CYP2D subfamily has not yet been fully understood. Therefore, the goal of this study was to reveal the evolutionary process of the human drug metabolic system. Here, we investigate molecular evolution of the CYP2D subfamily in primates by comparing 14 CYP2D sequences from humans to New World monkey genomes. Window analysis and statistical tests revealed that entire genomic sequences of paralogous genes were extensively homogenized by gene conversion during molecular evolution of CYP2D genes in primates. A neighbor-joining tree based on genomic sequences at the nonsubstrate recognition sites showed that CYP2D6 and CYP2D8 genes were clustered together due to gene conversion. In contrast, a phylogenetic tree using amino acid sequences at substrate recognition sites did not cluster the CYP2D6 and CYP2D8 genes, suggesting that the functional constraint on substrate specificity is one of the causes for purifying selection at the substrate recognition sites. Our results suggest that the CYP2D gene subfamily in primates has evolved to maintain the regioselectivity for a substrate hydroxylation activity between individual enzymes, even though extensive gene conversion has occurred across CYP2D coding sequences. PMID:25808902

Songbirds use spectral shape, not pitch, for sound pattern recognition

PubMed Central

Bregman, Micah R.; Patel, Aniruddh D.; Gentner, Timothy Q.

2016-01-01

Humans easily recognize “transposed” musical melodies shifted up or down in log frequency. Surprisingly, songbirds seem to lack this capacity, although they can learn to recognize human melodies and use complex acoustic sequences for communication. Decades of research have led to the widespread belief that songbirds, unlike humans, are strongly biased to use absolute pitch (AP) in melody recognition. This work relies almost exclusively on acoustically simple stimuli that may belie sensitivities to more complex spectral features. Here, we investigate melody recognition in a species of songbird, the European Starling (Sturnus vulgaris), using tone sequences that vary in both pitch and timbre. We find that small manipulations altering either pitch or timbre independently can drive melody recognition to chance, suggesting that both percepts are poor descriptors of the perceptual cues used by birds for this task. Instead we show that melody recognition can generalize even in the absence of pitch, as long as the spectral shapes of the constituent tones are preserved. These results challenge conventional views regarding the use of pitch cues in nonhuman auditory sequence recognition. PMID:26811447

Surface imprinted beads for the recognition of human serum albumin.

PubMed

Bonini, Francesca; Piletsky, Sergey; Turner, Anthony P F; Speghini, Adolfo; Bossi, Alessandra

2007-04-15

The synthesis of poly-aminophenylboronic acid (ABPA) imprinted beads for the recognition of the protein human serum albumin (HSA) is reported. In order to create homogeneous recognition sites, covalent immobilisation of the template HSA was exploited. The resulting imprinted beads were selective for HSA. The indirect imprinting factor (IF) calculated from supernatant was 1.6 and the direct IF, evaluated from the protein recovered from the beads, was 1.9. The binding capacity was 1.4 mg/g, which is comparable to commercially available affinity materials. The specificity of the HSA recognition was evaluated with competitive experiments, indicating a molar ratio 4.5/1 of competitor was necessary to displace half of the bound HSA. The recognition and binding of the imprinted beads was also tested with a complex sample, human serum and targeted removal of HSA without a loss of the other protein components was demonstrated. The easy preparation protocol of derivatised beads and a good protein recognition properties make the approach an attractive solution to analytical and bio-analytical problems in the field of biotechnology.

Human striatal activation during adjustment of the response criterion in visual word recognition.

PubMed

Kuchinke, Lars; Hofmann, Markus J; Jacobs, Arthur M; Frühholz, Sascha; Tamm, Sascha; Herrmann, Manfred

2011-02-01

Results of recent computational modelling studies suggest that a general function of the striatum in human cognition is related to shifting decision criteria in selection processes. We used functional magnetic resonance imaging (fMRI) in 21 healthy subjects to examine the hemodynamic responses when subjects shift their response criterion on a trial-by-trial basis in the lexical decision paradigm. Trial-by-trial criterion setting is obtained when subjects respond faster in trials following a word trial than in trials following nonword trials - irrespective of the lexicality of the current trial. Since selection demands are equally high in the current trials, we expected to observe neural activations that are related to response criterion shifting. The behavioural data show sequential effects with faster responses in trials following word trials compared to trials following nonword trials, suggesting that subjects shifted their response criterion on a trial-by-trial basis. The neural responses revealed a signal increase in the striatum only in trials following word trials. This striatal activation is therefore likely to be related to response criterion setting. It demonstrates a role of the striatum in shifting decision criteria in visual word recognition, which cannot be attributed to pure error-related processing or the selection of a preferred response. Copyright © 2010 Elsevier Inc. All rights reserved.

Structure of Human GIVD Cytosolic Phospholipase A2 Reveals Insights into Substrate Recognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Hui; Klein, Michael G.; Snell, Gyorgy

Cytosolic phospholipases A2 (cPLA2s) consist of a family of calcium-sensitive enzymes that function to generate lipid second messengers through hydrolysis of membrane-associated glycerophospholipids. The GIVD cPLA2 (cPLA2δ) is a potential drug target for developing a selective therapeutic agent for the treatment of psoriasis. Here, we present two X-ray structures of human cPLA2δ, capturing an apo state, and in complex with a substrate-like inhibitor. Comparison of the apo and inhibitor-bound structures reveals conformational changes in a flexible cap that allows the substrate to access the relatively buried active site, providing new insight into the mechanism for substrate recognition. The cPLA2δ structuremore » reveals an unexpected second C2 domain that was previously unrecognized from sequence alignments, placing cPLA2δ into the class of membrane-associated proteins that contain a tandem pair of C2 domains. Furthermore, our structures elucidate novel inter-domain interactions and define three potential calcium-binding sites that are likely important for regulation and activation of enzymatic activity. These findings provide novel insights into the molecular mechanisms governing cPLA2's function in signal transduction.« less

Similarity-Based Fusion of MEG and fMRI Reveals Spatio-Temporal Dynamics in Human Cortex During Visual Object Recognition

PubMed Central

Cichy, Radoslaw Martin; Pantazis, Dimitrios; Oliva, Aude

2016-01-01

Every human cognitive function, such as visual object recognition, is realized in a complex spatio-temporal activity pattern in the brain. Current brain imaging techniques in isolation cannot resolve the brain's spatio-temporal dynamics, because they provide either high spatial or temporal resolution but not both. To overcome this limitation, we developed an integration approach that uses representational similarities to combine measurements of magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to yield a spatially and temporally integrated characterization of neuronal activation. Applying this approach to 2 independent MEG–fMRI data sets, we observed that neural activity first emerged in the occipital pole at 50–80 ms, before spreading rapidly and progressively in the anterior direction along the ventral and dorsal visual streams. Further region-of-interest analyses established that dorsal and ventral regions showed MEG–fMRI correspondence in representations later than early visual cortex. Together, these results provide a novel and comprehensive, spatio-temporally resolved view of the rapid neural dynamics during the first few hundred milliseconds of object vision. They further demonstrate the feasibility of spatially unbiased representational similarity-based fusion of MEG and fMRI, promising new insights into how the brain computes complex cognitive functions. PMID:27235099

A novel L-ficolin/mannose-binding lectin chimeric molecule with enhanced activity against Ebola virus.

PubMed

Michelow, Ian C; Dong, Mingdong; Mungall, Bruce A; Yantosca, L Michael; Lear, Calli; Ji, Xin; Karpel, Marshall; Rootes, Christina L; Brudner, Matthew; Houen, Gunnar; Eisen, Damon P; Kinane, T Bernard; Takahashi, Kazue; Stahl, Gregory L; Olinger, Gene G; Spear, Gregory T; Ezekowitz, R Alan B; Schmidt, Emmett V

2010-08-06

Ebola viruses constitute a newly emerging public threat because they cause rapidly fatal hemorrhagic fevers for which no treatment exists, and they can be manipulated as bioweapons. We targeted conserved N-glycosylated carbohydrate ligands on viral envelope surfaces using novel immune therapies. Mannose-binding lectin (MBL) and L-ficolin (L-FCN) were selected because they function as opsonins and activate complement. Given that MBL has a complex quaternary structure unsuitable for large scale cost-effective production, we sought to develop a less complex chimeric fusion protein with similar ligand recognition and enhanced effector functions. We tested recombinant human MBL and three L-FCN/MBL variants that contained the MBL carbohydrate recognition domain and varying lengths of the L-FCN collagenous domain. Non-reduced chimeric proteins formed predominantly nona- and dodecameric oligomers, whereas recombinant human MBL formed octadecameric and larger oligomers. Surface plasmon resonance revealed that L-FCN/MBL76 had the highest binding affinities for N-acetylglucosamine-bovine serum albumin and mannan. The same chimeric protein displayed superior complement C4 cleavage and binding to calreticulin (cC1qR), a putative receptor for MBL. L-FCN/MBL76 reduced infection by wild type Ebola virus Zaire significantly greater than the other molecules. Tapping mode atomic force microscopy revealed that L-FCN/MBL76 was significantly less tall than the other molecules despite similar polypeptide lengths. We propose that alterations in the quaternary structure of L-FCN/MBL76 resulted in greater flexibility in the collagenous or neck region. Similarly, a more pliable molecule might enhance cooperativity between the carbohydrate recognition domains and their cognate ligands, complement activation, and calreticulin binding dynamics. L-FCN/MBL chimeric proteins should be considered as potential novel therapeutics.

Episodic Short-Term Recognition Requires Encoding into Visual Working Memory: Evidence from Probe Recognition after Letter Report

PubMed Central

Poth, Christian H.; Schneider, Werner X.

2016-01-01

Human vision is organized in discrete processing episodes (e.g., eye fixations or task-steps). Object information must be transmitted across episodes to enable episodic short-term recognition: recognizing whether a current object has been seen in a previous episode. We ask whether episodic short-term recognition presupposes that objects have been encoded into capacity-limited visual working memory (VWM), which retains visual information for report. Alternatively, it could rely on the activation of visual features or categories that occurs before encoding into VWM. We assessed the dependence of episodic short-term recognition on VWM by a new paradigm combining letter report and probe recognition. Participants viewed displays of 10 letters and reported as many as possible after a retention interval (whole report). Next, participants viewed a probe letter and indicated whether it had been one of the 10 letters (probe recognition). In Experiment 1, probe recognition was more accurate for letters that had been encoded into VWM (reported letters) compared with non-encoded letters (non-reported letters). Interestingly, those letters that participants reported in their whole report had been near to one another within the letter displays. This suggests that the encoding into VWM proceeded in a spatially clustered manner. In Experiment 2, participants reported only one of 10 letters (partial report) and probes either referred to this letter, to letters that had been near to it, or far from it. Probe recognition was more accurate for near than for far letters, although none of these letters had to be reported. These findings indicate that episodic short-term recognition is constrained to a small number of simultaneously presented objects that have been encoded into VWM. PMID:27713722

Episodic Short-Term Recognition Requires Encoding into Visual Working Memory: Evidence from Probe Recognition after Letter Report.

PubMed

Poth, Christian H; Schneider, Werner X

2016-01-01

Human vision is organized in discrete processing episodes (e.g., eye fixations or task-steps). Object information must be transmitted across episodes to enable episodic short-term recognition: recognizing whether a current object has been seen in a previous episode. We ask whether episodic short-term recognition presupposes that objects have been encoded into capacity-limited visual working memory (VWM), which retains visual information for report. Alternatively, it could rely on the activation of visual features or categories that occurs before encoding into VWM. We assessed the dependence of episodic short-term recognition on VWM by a new paradigm combining letter report and probe recognition. Participants viewed displays of 10 letters and reported as many as possible after a retention interval (whole report). Next, participants viewed a probe letter and indicated whether it had been one of the 10 letters (probe recognition). In Experiment 1, probe recognition was more accurate for letters that had been encoded into VWM (reported letters) compared with non-encoded letters (non-reported letters). Interestingly, those letters that participants reported in their whole report had been near to one another within the letter displays. This suggests that the encoding into VWM proceeded in a spatially clustered manner. In Experiment 2, participants reported only one of 10 letters (partial report) and probes either referred to this letter, to letters that had been near to it, or far from it. Probe recognition was more accurate for near than for far letters, although none of these letters had to be reported. These findings indicate that episodic short-term recognition is constrained to a small number of simultaneously presented objects that have been encoded into VWM.

Evidence for the activation of sensorimotor information during visual word recognition: the body-object interaction effect.

PubMed

Siakaluk, Paul D; Pexman, Penny M; Aguilera, Laura; Owen, William J; Sears, Christopher R

2008-01-01

We examined the effects of sensorimotor experience in two visual word recognition tasks. Body-object interaction (BOI) ratings were collected for a large set of words. These ratings assess perceptions of the ease with which a human body can physically interact with a word's referent. A set of high BOI words (e.g., mask) and a set of low BOI words (e.g., ship) were created, matched on imageability and concreteness. Facilitatory BOI effects were observed in lexical decision and phonological lexical decision tasks: responses were faster for high BOI words than for low BOI words. We discuss how our findings may be accounted for by (a) semantic feedback within the visual word recognition system, and (b) an embodied view of cognition (e.g., Barsalou's perceptual symbol systems theory), which proposes that semantic knowledge is grounded in sensorimotor interactions with the environment.

Can human eyes prevent perceptual narrowing for monkey faces in human infants?

PubMed

Damon, Fabrice; Bayet, Laurie; Quinn, Paul C; Hillairet de Boisferon, Anne; Méary, David; Dupierrix, Eve; Lee, Kang; Pascalis, Olivier

2015-07-01

Perceptual narrowing has been observed in human infants for monkey faces: 6-month-olds can discriminate between them, whereas older infants from 9 months of age display difficulty discriminating between them. The difficulty infants from 9 months have processing monkey faces has not been clearly identified. It could be due to the structural characteristics of monkey faces, particularly the key facial features that differ from human faces. The current study aimed to investigate whether the information conveyed by the eyes is of importance. We examined whether the presence of Caucasian human eyes in monkey faces allows recognition to be maintained in 6-month-olds and facilitates recognition in 9- and 12-month-olds. Our results revealed that the presence of human eyes in monkey faces maintains recognition for those faces at 6 months of age and partially facilitates recognition of those faces at 9 months of age, but not at 12 months of age. The findings are interpreted in the context of perceptual narrowing and suggest that the attenuation of processing of other-species faces is not reversed by the presence of human eyes. © 2015 Wiley Periodicals, Inc.

The role of the amygdala and the basal ganglia in visual processing of central vs. peripheral emotional content.

PubMed

Almeida, Inês; van Asselen, Marieke; Castelo-Branco, Miguel

2013-09-01

In human cognition, most relevant stimuli, such as faces, are processed in central vision. However, it is widely believed that recognition of relevant stimuli (e.g. threatening animal faces) at peripheral locations is also important due to their survival value. Moreover, task instructions have been shown to modulate brain regions involved in threat recognition (e.g. the amygdala). In this respect it is also controversial whether tasks requiring explicit focus on stimulus threat content vs. implicit processing differently engage primitive subcortical structures involved in emotional appraisal. Here we have addressed the role of central vs. peripheral processing in the human amygdala using animal threatening vs. non-threatening face stimuli. First, a simple animal face recognition task with threatening and non-threatening animal faces, as well as non-face control stimuli, was employed in naïve subjects (implicit task). A subsequent task was then performed with the same stimulus categories (but different stimuli) in which subjects were told to explicitly detect threat signals. We found lateralized amygdala responses both to the spatial location of stimuli and to the threatening content of faces depending on the task performed: the right amygdala showed increased responses to central compared to left presented stimuli specifically during the threat detection task, while the left amygdala was better prone to discriminate threatening faces from non-facial displays during the animal face recognition task. Additionally, the right amygdala responded to faces during the threat detection task but only when centrally presented. Moreover, we have found no evidence for superior responses of the amygdala to peripheral stimuli. Importantly, we have found that striatal regions activate differentially depending on peripheral vs. central processing of threatening faces. Accordingly, peripheral processing of these stimuli activated more strongly the putaminal region, while central processing engaged mainly the caudate nucleus. We conclude that the human amygdala has a central bias for face stimuli, and that visual processing recruits different striatal regions, putaminal or caudate based, depending on the task and on whether peripheral or central visual processing is involved. © 2013 Elsevier Ltd. All rights reserved.

A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors

PubMed Central

Han, Manhyung; Bang, Jae Hun; Nugent, Chris; McClean, Sally; Lee, Sungyoung

2014-01-01

Activity recognition for the purposes of recognizing a user's intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user's activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%. PMID:25184486

Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation

PubMed Central

Song, Zhibin; Zhang, Songyuan

2016-01-01

Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used in the application of bilateral rehabilitation, where hemiplegic patients can train their affected limbs following the motion of unaffected limbs via some rehabilitation devices. Traditional methods to process the sEMG focused on motion pattern recognition, namely, discrete patterns, which are not satisfactory for use in bilateral rehabilitation. In order to overcome this problem, in this paper, we built a relationship between sEMG signals and human motion in elbow flexion and extension on the sagittal plane. During the conducted experiments, four participants were required to perform elbow flexion and extension on the sagittal plane smoothly with only an inertia sensor in their hands, where forearm dynamics were not considered. In these circumstances, sEMG signals were weak compared to those with heavy loads or high acceleration. The contrastive experimental results show that continuous motion can also be obtained within an acceptable precision range. PMID:27775573

Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation.

PubMed

Song, Zhibin; Zhang, Songyuan

2016-10-19

Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used in the application of bilateral rehabilitation, where hemiplegic patients can train their affected limbs following the motion of unaffected limbs via some rehabilitation devices. Traditional methods to process the sEMG focused on motion pattern recognition, namely, discrete patterns, which are not satisfactory for use in bilateral rehabilitation. In order to overcome this problem, in this paper, we built a relationship between sEMG signals and human motion in elbow flexion and extension on the sagittal plane. During the conducted experiments, four participants were required to perform elbow flexion and extension on the sagittal plane smoothly with only an inertia sensor in their hands, where forearm dynamics were not considered. In these circumstances, sEMG signals were weak compared to those with heavy loads or high acceleration. The contrastive experimental results show that continuous motion can also be obtained within an acceptable precision range.

Skin subspace color modeling for daytime and nighttime group activity recognition in confined operational spaces

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Poshtyar, Azin; Chan, Alex; Hu, Shuowen

2016-05-01

In many military and homeland security persistent surveillance applications, accurate detection of different skin colors in varying observability and illumination conditions is a valuable capability for video analytics. One of those applications is In-Vehicle Group Activity (IVGA) recognition, in which significant changes in observability and illumination may occur during the course of a specific human group activity of interest. Most of the existing skin color detection algorithms, however, are unable to perform satisfactorily in confined operational spaces with partial observability and occultation, as well as under diverse and changing levels of illumination intensity, reflection, and diffraction. In this paper, we investigate the salient features of ten popular color spaces for skin subspace color modeling. More specifically, we examine the advantages and disadvantages of each of these color spaces, as well as the stability and suitability of their features in differentiating skin colors under various illumination conditions. The salient features of different color subspaces are methodically discussed and graphically presented. Furthermore, we present robust and adaptive algorithms for skin color detection based on this analysis. Through examples, we demonstrate the efficiency and effectiveness of these new color skin detection algorithms and discuss their applicability for skin detection in IVGA recognition applications.

An electrophysiological signature of summed similarity in visual working memory.

PubMed

van Vugt, Marieke K; Sekuler, Robert; Wilson, Hugh R; Kahana, Michael J

2013-05-01

Summed-similarity models of short-term item recognition posit that participants base their judgments of an item's prior occurrence on that item's summed similarity to the ensemble of items on the remembered list. We examined the neural predictions of these models in 3 short-term recognition memory experiments using electrocorticographic/depth electrode recordings and scalp electroencephalography. On each experimental trial, participants judged whether a test face had been among a small set of recently studied faces. Consistent with summed-similarity theory, participants' tendency to endorse a test item increased as a function of its summed similarity to the items on the just-studied list. To characterize this behavioral effect of summed similarity, we successfully fit a summed-similarity model to individual participant data from each experiment. Using the parameters determined from fitting the summed-similarity model to the behavioral data, we examined the relation between summed similarity and brain activity. We found that 4-9 Hz theta activity in the medial temporal lobe and 2-4 Hz delta activity recorded from frontal and parietal cortices increased with summed similarity. These findings demonstrate direct neural correlates of the similarity computations that form the foundation of several major cognitive theories of human recognition memory. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Gesture Recognition Based on the Probability Distribution of Arm Trajectories

NASA Astrophysics Data System (ADS)

Wan, Khairunizam; Sawada, Hideyuki

The use of human motions for the interaction between humans and computers is becoming an attractive alternative to verbal media, especially through the visual interpretation of the human body motion. In particular, hand gestures are used as non-verbal media for the humans to communicate with machines that pertain to the use of the human gestures to interact with them. This paper introduces a 3D motion measurement of the human upper body for the purpose of the gesture recognition, which is based on the probability distribution of arm trajectories. In this study, by examining the characteristics of the arm trajectories given by a signer, motion features are selected and classified by using a fuzzy technique. Experimental results show that the use of the features extracted from arm trajectories effectively works on the recognition of dynamic gestures of a human, and gives a good performance to classify various gesture patterns.

Hierarchical human action recognition around sleeping using obscured posture information

NASA Astrophysics Data System (ADS)

Kudo, Yuta; Sashida, Takehiko; Aoki, Yoshimitsu

2015-04-01

This paper presents a new approach for human action recognition around sleeping with the human body parts locations and the positional relationship between human and sleeping environment. Body parts are estimated from the depth image obtained by a time-of-flight (TOF) sensor using oriented 3D normal vector. Issues in action recognition of sleeping situation are the demand of availability in darkness, and hiding of the human body by duvets. Therefore, the extraction of image features is difficult since color and edge features are obscured by covers. Thus, first in our method, positions of four parts of the body (head, torso, thigh, and lower leg) are estimated by using the shape model of bodily surface constructed by oriented 3D normal vector. This shape model can represent the surface shape of rough body, and is effective in robust posture estimation of the body hidden with duvets. Then, action descriptor is extracted from the position of each body part. The descriptor includes temporal variation of each part of the body and spatial vector of position of the parts and the bed. Furthermore, this paper proposes hierarchical action classes and classifiers to improve the indistinct action classification. Classifiers are composed of two layers, and recognize human action by using the action descriptor. First layer focuses on spatial descriptor and classifies action roughly. Second layer focuses on temporal descriptor and classifies action finely. This approach achieves a robust recognition of obscured human by using the posture information and the hierarchical action recognition.

Body-Based Gender Recognition Using Images from Visible and Thermal Cameras

PubMed Central

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-01

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems. PMID:26828487

Body-Based Gender Recognition Using Images from Visible and Thermal Cameras.

PubMed

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-27

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems.

Polymorphism in Human Cytomegalovirus UL40 Impacts on Recognition of Human Leukocyte Antigen-E (HLA-E) by Natural Killer Cells*

PubMed Central

Heatley, Susan L.; Pietra, Gabriella; Lin, Jie; Widjaja, Jacqueline M. L.; Harpur, Christopher M.; Lester, Sue; Rossjohn, Jamie; Szer, Jeff; Schwarer, Anthony; Bradstock, Kenneth; Bardy, Peter G.; Mingari, Maria Cristina; Moretta, Lorenzo; Sullivan, Lucy C.; Brooks, Andrew G.

2013-01-01

Natural killer (NK) cell recognition of the nonclassical human leukocyte antigen (HLA) molecule HLA-E is dependent on the presentation of a nonamer peptide derived from the leader sequence of other HLA molecules to CD94-NKG2 receptors. However, human cytomegalovirus can manipulate this central innate interaction through the provision of a “mimic” of the HLA-encoded peptide derived from the immunomodulatory glycoprotein UL40. Here, we analyzed UL40 sequences isolated from 32 hematopoietic stem cell transplantation recipients experiencing cytomegalovirus reactivation. The UL40 protein showed a “polymorphic hot spot” within the region that encodes the HLA leader sequence mimic. Although all sequences that were identical to those encoded within HLA-I genes permitted the interaction between HLA-E and CD94-NKG2 receptors, other UL40 polymorphisms reduced the affinity of the interaction between HLA-E and CD94-NKG2 receptors. Furthermore, functional studies using NK cell clones expressing either the inhibitory receptor CD94-NKG2A or the activating receptor CD94-NKG2C identified UL40-encoded peptides that were capable of inhibiting target cell lysis via interaction with CD94-NKG2A, yet had little capacity to activate NK cells through CD94-NKG2C. The data suggest that UL40 polymorphisms may aid evasion of NK cell immunosurveillance by modulating the affinity of the interaction with CD94-NKG2 receptors. PMID:23335510

Polymorphism in human cytomegalovirus UL40 impacts on recognition of human leukocyte antigen-E (HLA-E) by natural killer cells.

PubMed

Heatley, Susan L; Pietra, Gabriella; Lin, Jie; Widjaja, Jacqueline M L; Harpur, Christopher M; Lester, Sue; Rossjohn, Jamie; Szer, Jeff; Schwarer, Anthony; Bradstock, Kenneth; Bardy, Peter G; Mingari, Maria Cristina; Moretta, Lorenzo; Sullivan, Lucy C; Brooks, Andrew G

2013-03-22

Natural killer (NK) cell recognition of the nonclassical human leukocyte antigen (HLA) molecule HLA-E is dependent on the presentation of a nonamer peptide derived from the leader sequence of other HLA molecules to CD94-NKG2 receptors. However, human cytomegalovirus can manipulate this central innate interaction through the provision of a "mimic" of the HLA-encoded peptide derived from the immunomodulatory glycoprotein UL40. Here, we analyzed UL40 sequences isolated from 32 hematopoietic stem cell transplantation recipients experiencing cytomegalovirus reactivation. The UL40 protein showed a "polymorphic hot spot" within the region that encodes the HLA leader sequence mimic. Although all sequences that were identical to those encoded within HLA-I genes permitted the interaction between HLA-E and CD94-NKG2 receptors, other UL40 polymorphisms reduced the affinity of the interaction between HLA-E and CD94-NKG2 receptors. Furthermore, functional studies using NK cell clones expressing either the inhibitory receptor CD94-NKG2A or the activating receptor CD94-NKG2C identified UL40-encoded peptides that were capable of inhibiting target cell lysis via interaction with CD94-NKG2A, yet had little capacity to activate NK cells through CD94-NKG2C. The data suggest that UL40 polymorphisms may aid evasion of NK cell immunosurveillance by modulating the affinity of the interaction with CD94-NKG2 receptors.

Conformal Predictions in Multimedia Pattern Recognition

ERIC Educational Resources Information Center

Nallure Balasubramanian, Vineeth

2010-01-01

The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning…

Presentations of Shape in Object Recognition and Long-Term Visual Memory

DTIC Science & Technology

1994-04-05

theory of human image understanding . Psychological Review, 94, 115-147. Biederman, I., & Gerhardstein, P. C. (1993). Recognizing depth-rotated...Kybemetik. Submitted to Journal of Experimental Psychology: Human Perception and Performance. REFERENCES Biederman, I. (1987). Recognition-by-components: A

Lack of discrimination between DNA ligases I and III by two classes of inhibitors, anthracyclines and distamycins.

PubMed

Montecucco, A; Lestingi, M; Rossignol, J M; Elder, R H; Ciarrocchi, G

1993-04-06

We have measured the effects of eight distamycin and two anthracycline derivatives on polynucleotide joining and self-adenylating activities of human DNA ligase I and rat DNA ligases I and III. All test drugs show good inhibitory activity against the three enzymes in the poly[d(A-T)] joining assay. Several distamycins also inhibit the DNA-independent self-adenylation reaction catalysed by the human enzyme and, to a lesser extent, by rat DNA ligases. These results confirm that anthracyclines and distamycins express their inhibitory action against DNA joining activities mainly via specific interactions with the substrate, and suggest that the three test DNA ligases utilize similar, if not identical, mechanisms of recognition and interaction with DNA-drug complexes. Our findings also indicate that distamycins have a greater affinity for human DNA ligase I than for rat enzymes, suggesting that, in this respect, rat DNA ligase I is more similar to rat DNA ligase III than to human DNA ligase I.

Fusion activation through attachment protein stalk domains indicates a conserved core mechanism of paramyxovirus entry into cells.

PubMed

Bose, Sayantan; Song, Albert S; Jardetzky, Theodore S; Lamb, Robert A

2014-04-01

Paramyxoviruses are a large family of membrane-enveloped negative-stranded RNA viruses causing important diseases in humans and animals. Two viral integral membrane glycoproteins (fusion [F] and attachment [HN, H, or G]) mediate a concerted process of host receptor recognition, followed by the fusion of viral and cellular membranes, resulting in viral nucleocapsid entry into the cytoplasm. However, the sequence of events that closely links the timing of receptor recognition by HN, H, or G and the "triggering" interaction of the attachment protein with F is unclear. F activation results in F undergoing a series of irreversible conformational rearrangements to bring about membrane merger and virus entry. By extensive study of properties of multiple paramyxovirus HN proteins, we show that key features of F activation, including the F-activating regions of HN proteins, flexibility within this F-activating region, and changes in globular head-stalk interactions are highly conserved. These results, together with functionally active "headless" mumps and Newcastle disease virus HN proteins, provide insights into the F-triggering process. Based on these data and very recently published data for morbillivirus H and henipavirus G proteins, we extend our recently proposed "stalk exposure model" to other paramyxoviruses and propose an "induced fit" hypothesis for F-HN/H/G interactions as conserved core mechanisms of paramyxovirus-mediated membrane fusion. Paramyxoviruses are a large family of membrane-enveloped negative-stranded RNA viruses causing important diseases in humans and animals. Two viral integral membrane glycoproteins (fusion [F] and attachment [HN, H, or G]) mediate a concerted process of host receptor recognition, followed by the fusion of viral and cellular membranes. We describe here the molecular mechanism by which HN activates the F protein such that virus-cell fusion is controlled and occurs at the right time and the right place. We extend our recently proposed "stalk exposure model" first proposed for parainfluenza virus 5 to other paramyxoviruses and propose an "induced fit" hypothesis for F-HN/H/G interactions as conserved core mechanisms of paramyxovirus-mediated membrane fusion.

Fusion Activation through Attachment Protein Stalk Domains Indicates a Conserved Core Mechanism of Paramyxovirus Entry into Cells

PubMed Central

Bose, Sayantan; Song, Albert S.; Jardetzky, Theodore S.

2014-01-01

ABSTRACT Paramyxoviruses are a large family of membrane-enveloped negative-stranded RNA viruses causing important diseases in humans and animals. Two viral integral membrane glycoproteins (fusion [F] and attachment [HN, H, or G]) mediate a concerted process of host receptor recognition, followed by the fusion of viral and cellular membranes, resulting in viral nucleocapsid entry into the cytoplasm. However, the sequence of events that closely links the timing of receptor recognition by HN, H, or G and the “triggering” interaction of the attachment protein with F is unclear. F activation results in F undergoing a series of irreversible conformational rearrangements to bring about membrane merger and virus entry. By extensive study of properties of multiple paramyxovirus HN proteins, we show that key features of F activation, including the F-activating regions of HN proteins, flexibility within this F-activating region, and changes in globular head-stalk interactions are highly conserved. These results, together with functionally active “headless” mumps and Newcastle disease virus HN proteins, provide insights into the F-triggering process. Based on these data and very recently published data for morbillivirus H and henipavirus G proteins, we extend our recently proposed “stalk exposure model” to other paramyxoviruses and propose an “induced fit” hypothesis for F-HN/H/G interactions as conserved core mechanisms of paramyxovirus-mediated membrane fusion. IMPORTANCE Paramyxoviruses are a large family of membrane-enveloped negative-stranded RNA viruses causing important diseases in humans and animals. Two viral integral membrane glycoproteins (fusion [F] and attachment [HN, H, or G]) mediate a concerted process of host receptor recognition, followed by the fusion of viral and cellular membranes. We describe here the molecular mechanism by which HN activates the F protein such that virus-cell fusion is controlled and occurs at the right time and the right place. We extend our recently proposed “stalk exposure model” first proposed for parainfluenza virus 5 to other paramyxoviruses and propose an “induced fit” hypothesis for F-HN/H/G interactions as conserved core mechanisms of paramyxovirus-mediated membrane fusion. PMID:24453369

Characterizing age-related decline of recognition memory and brain activation profile in mice.

PubMed

Belblidia, Hassina; Leger, Marianne; Abdelmalek, Abdelouadoud; Quiedeville, Anne; Calocer, Floriane; Boulouard, Michel; Jozet-Alves, Christelle; Freret, Thomas; Schumann-Bard, Pascale

2018-06-01

Episodic memory decline is one of the earlier deficits occurring during normal aging in humans. The question of spatial versus non-spatial sensitivity to age-related memory decline is of importance for a full understanding of these changes. Here, we characterized the effect of normal aging on both non-spatial (object) and spatial (object location) memory performances as well as on associated neuronal activation in mice. Novel-object (NOR) and object-location (OLR) recognition tests, respectively assessing the identity and spatial features of object memory, were examined at different ages. We show that memory performances in both tests were altered by aging as early as 15 months of age: NOR memory was partially impaired whereas OLR memory was found to be fully disrupted at 15 months of age. Brain activation profiles were assessed for both tests using immunohistochemical detection of c-Fos (neuronal activation marker) in 3and 15 month-old mice. Normal performances in NOR task by 3 month-old mice were associated to an activation of the hippocampus and a trend towards an activation in the perirhinal cortex, in a way that did significantly differ with 15 month-old mice. During OLR task, brain activation took place in the hippocampus in 3 month-old but not significantly in 15 month-old mice, which were fully impaired at this task. These differential alterations of the object- and object-location recognition memory may be linked to differential alteration of the neuronal networks supporting these tasks. Copyright © 2018 Elsevier Inc. All rights reserved.

Incongruence Between Observers’ and Observed Facial Muscle Activation Reduces Recognition of Emotional Facial Expressions From Video Stimuli

PubMed Central

Wingenbach, Tanja S. H.; Brosnan, Mark; Pfaltz, Monique C.; Plichta, Michael M.; Ashwin, Chris

2018-01-01

According to embodied cognition accounts, viewing others’ facial emotion can elicit the respective emotion representation in observers which entails simulations of sensory, motor, and contextual experiences. In line with that, published research found viewing others’ facial emotion to elicit automatic matched facial muscle activation, which was further found to facilitate emotion recognition. Perhaps making congruent facial muscle activity explicit produces an even greater recognition advantage. If there is conflicting sensory information, i.e., incongruent facial muscle activity, this might impede recognition. The effects of actively manipulating facial muscle activity on facial emotion recognition from videos were investigated across three experimental conditions: (a) explicit imitation of viewed facial emotional expressions (stimulus-congruent condition), (b) pen-holding with the lips (stimulus-incongruent condition), and (c) passive viewing (control condition). It was hypothesised that (1) experimental condition (a) and (b) result in greater facial muscle activity than (c), (2) experimental condition (a) increases emotion recognition accuracy from others’ faces compared to (c), (3) experimental condition (b) lowers recognition accuracy for expressions with a salient facial feature in the lower, but not the upper face area, compared to (c). Participants (42 males, 42 females) underwent a facial emotion recognition experiment (ADFES-BIV) while electromyography (EMG) was recorded from five facial muscle sites. The experimental conditions’ order was counter-balanced. Pen-holding caused stimulus-incongruent facial muscle activity for expressions with facial feature saliency in the lower face region, which reduced recognition of lower face region emotions. Explicit imitation caused stimulus-congruent facial muscle activity without modulating recognition. Methodological implications are discussed. PMID:29928240

Incongruence Between Observers' and Observed Facial Muscle Activation Reduces Recognition of Emotional Facial Expressions From Video Stimuli.

PubMed

Wingenbach, Tanja S H; Brosnan, Mark; Pfaltz, Monique C; Plichta, Michael M; Ashwin, Chris

2018-01-01

According to embodied cognition accounts, viewing others' facial emotion can elicit the respective emotion representation in observers which entails simulations of sensory, motor, and contextual experiences. In line with that, published research found viewing others' facial emotion to elicit automatic matched facial muscle activation, which was further found to facilitate emotion recognition. Perhaps making congruent facial muscle activity explicit produces an even greater recognition advantage. If there is conflicting sensory information, i.e., incongruent facial muscle activity, this might impede recognition. The effects of actively manipulating facial muscle activity on facial emotion recognition from videos were investigated across three experimental conditions: (a) explicit imitation of viewed facial emotional expressions (stimulus-congruent condition), (b) pen-holding with the lips (stimulus-incongruent condition), and (c) passive viewing (control condition). It was hypothesised that (1) experimental condition (a) and (b) result in greater facial muscle activity than (c), (2) experimental condition (a) increases emotion recognition accuracy from others' faces compared to (c), (3) experimental condition (b) lowers recognition accuracy for expressions with a salient facial feature in the lower, but not the upper face area, compared to (c). Participants (42 males, 42 females) underwent a facial emotion recognition experiment (ADFES-BIV) while electromyography (EMG) was recorded from five facial muscle sites. The experimental conditions' order was counter-balanced. Pen-holding caused stimulus-incongruent facial muscle activity for expressions with facial feature saliency in the lower face region, which reduced recognition of lower face region emotions. Explicit imitation caused stimulus-congruent facial muscle activity without modulating recognition. Methodological implications are discussed.

From Humans to Rats and Back Again: Bridging the Divide between Human and Animal Studies of Recognition Memory with Receiver Operating Characteristics

ERIC Educational Resources Information Center

Koen, Joshua D.; Yonelinas, Andrew P.

2011-01-01

Receiver operating characteristics (ROCs) have been used extensively to study the processes underlying human recognition memory, and this method has recently been applied in studies of rats. However, the extent to which the results from human and animal studies converge is neither entirely clear, nor is it known how the different methods used to…

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human-Robot Interaction.

PubMed

Gandarias, Juan M; Gómez-de-Gabriel, Jesús M; García-Cerezo, Alfonso J

2018-02-26

The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human-robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.

RNA recognition by human TLR8 can lead to autoimmune inflammation

PubMed Central

Gong, Mei; Cepika, Alma-Martina; Xu, Zhaohui; Tripodo, Claudio; Bennett, Lynda; Crain, Chad; Quartier, Pierre; Cush, John J.; Pascual, Virginia; Coffman, Robert L.; Barrat, Franck J.

2013-01-01

Studies on the role of the RNA receptor TLR8 in inflammation have been limited by its different function in human versus rodents. We have generated multiple lines of transgenic mice expressing different levels of human TLR8. The high copy number chimeras were unable to pass germline; developed severe inflammation targeting the pancreas, salivary glands, and joints; and the severity of the specific phenotypes closely correlated with the huTLR8 expression levels. Mice with relatively low expression levels survived and bred successfully but had increased susceptibility to collagen-induced arthritis, and the levels of huTLR8 correlated with proinflammatory cytokines in the joints of the animals. At the cellular level, huTLR8 signaling exerted a DC-intrinsic effect leading to up-regulation of co-stimulatory molecules and subsequent T cell activation. A pathogenic role for TLR8 in human diseases was suggested by its increased expression in patients with systemic arthritis and the correlation of TLR8 expression with the elevation of IL-1β levels and disease status. We found that the consequence of self-recognition via TLR8 results in a constellation of diseases, strikingly distinct from those related to TLR7 signaling, and points to specific inflammatory diseases that may benefit from inhibition of TLR8 in humans. PMID:24277153

Human SAP is a novel peptidoglycan recognition protein that induces complement- independent phagocytosis of Staphylococcus aureus

PubMed Central

An, Jang-Hyun; Kurokawa, Kenji; Jung, Dong-Jun; Kim, Min-Jung; Kim, Chan-Hee; Fujimoto, Yukari; Fukase, Koichi; Coggeshall, K. Mark; Lee, Bok Luel

2014-01-01

The human pathogen Staphylococcus aureus is responsible for many community-acquired and hospital-associated infections and is associated with high mortality. Concern over the emergence of multidrug-resistant strains has renewed interest in the elucidation of host mechanisms that defend against S. aureus infection. We recently demonstrated that human serum mannose-binding lectin (MBL) binds to S. aureus wall teichoic acid (WTA), a cell wall glycopolymer, a discovery that prompted further screening to identify additional serum proteins that recognize S. aureus cell wall components. In this report, we incubated human serum with 10 different S. aureus mutants and determined that serum amyloid P component (SAP) bound specifically to a WTA-deficient S. aureus ΔtagO mutant, but not to tagO-complemented, WTA-expressing cells. Biochemical characterization revealed that SAP recognizes bacterial peptidoglycan as a ligand and that WTA inhibits this interaction. Although SAP binding to peptidoglycan was not observed to induce complement activation, SAP-bound ΔtagO cells were phagocytosed by human polymorphonuclear leukocytes in an Fcγ receptor-dependent manner. These results indicate that SAP functions as a host defense factor, similar to other peptidoglycan recognition proteins and nucleotide-binding oligomerization domain (NOD)-like receptors. PMID:23966633

On the applicability of brain reading for predictive human-machine interfaces in robotics.

PubMed

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

PubMed Central

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors. PMID:24358125

Age difference in numeral recognition and calculation: an event-related potential study.

PubMed

Xuan, Dong; Wang, Suhong; Yang, Yilin; Meng, Ping; Xu, Feng; Yang, Wen; Sheng, Wei; Yang, Yuxia

2007-01-01

In this study, we investigated the age difference in numeral recognition and calculation in one group of school-aged children (n = 38) and one of undergraduate students (n = 26) using the event-related potential (ERP) methods. Consistent with previous reports, the age difference was significant in behavioral results. Both numeral recognition and calculation elicited a negativity peaking at about 170-280 ms (N2) and a positivity peaking at 200-470 ms (pSW) in raw ERPs, and a difference potential (dN3) between 360 and 450 ms. The difference between the two age groups indicated that more attention resources were devoted to arithmetical tasks in school-aged children, and that school-aged children and undergraduate students appear to use different strategies to solve arithmetical problems. The analysis of frontal negativity suggested that numeral recognition and mental calculation impose greater load on working memory and executive function in schoolchildren than in undergraduate students. The topography data determined that the parietal regions were responsible for arithmetical function in humans, and there was an age-related difference in the area of cerebral activation.

Peptidoglycan microarray as a novel tool to explore protein-ligand recognition.

PubMed

Wang, Ning; Hirata, Akiyoshi; Nokihara, Kiyoshi; Fukase, Koichi; Fujimoto, Yukari

2016-11-04

Peptidoglycan is a giant bag-shaped molecule essential for bacterial cell shape and resistance to osmotic stresses. The activity of a large number of bacterial surface proteins involved in cell growth and division requires binding to this macromolecule. Recognition of peptidoglycan by immune effectors is also crucial for the establishment of the immune response against pathogens. The availability of pure and chemically defined peptidoglycan fragments is a major technical bottleneck that has precluded systematic studies of the mechanisms underpinning protein-mediated peptidoglycan recognition. Here, we report a microarray strategy suitable to carry out comprehensive studies to characterize proteins-peptidoglycan interactions. We describe a method to introduce a functional group on peptidoglycan fragments allowing their stable immobilization on amorphous carbon chip plates to minimize nonspecific binding. Such peptidoglycan microarrays were used with a model peptidoglycan binding protein-the human peptidoglycan recognition protein-S (hPGRP-S). We propose that this strategy could be implemented to carry out high-throughput analyses to study peptidoglycan binding proteins. © 2016 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 422-429, 2016. © 2016 Wiley Periodicals, Inc.

Hippocampal gamma-band Synchrony and pupillary responses index memory during visual search.

PubMed

Montefusco-Siegmund, Rodrigo; Leonard, Timothy K; Hoffman, Kari L

2017-04-01

Memory for scenes is supported by the hippocampus, among other interconnected structures, but the neural mechanisms related to this process are not well understood. To assess the role of the hippocampus in memory-guided scene search, we recorded local field potentials and multiunit activity from the hippocampus of macaques as they performed goal-directed search tasks using natural scenes. We additionally measured pupil size during scene presentation, which in humans is modulated by recognition memory. We found that both pupil dilation and search efficiency accompanied scene repetition, thereby indicating memory for scenes. Neural correlates included a brief increase in hippocampal multiunit activity and a sustained synchronization of unit activity to gamma band oscillations (50-70 Hz). The repetition effects on hippocampal gamma synchronization occurred when pupils were most dilated, suggesting an interaction between aroused, attentive processing and hippocampal correlates of recognition memory. These results suggest that the hippocampus may support memory-guided visual search through enhanced local gamma synchrony. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Tuning sensitivity of CAR to EGFR density limits recognition of normal tissue while maintaining potent anti-tumor activity

PubMed Central

Caruso, Hillary G.; Hurton, Lenka V.; Najjar, Amer; Rushworth, David; Ang, Sonny; Olivares, Simon; Mi, Tiejuan; Switzer, Kirsten; Singh, Harjeet; Huls, Helen; Lee, Dean A.; Heimberger, Amy B.; Champlin, Richard E.; Cooper, Laurence J. N.

2015-01-01

Many tumors over express tumor-associated antigens relative to normal tissue, such as epidermal growth factor receptor (EGFR). This limits targeting by human T cells modified to express chimeric antigen receptors (CARs) due to potential for deleterious recognition of normal cells. We sought to generate CAR+ T cells capable of distinguishing malignant from normal cells based on the disparate density of EGFR expression by generating two CARs from monoclonal antibodies which differ in affinity. T cells with low affinity Nimo-CAR selectively targeted cells over-expressing EGFR, but exhibited diminished effector function as the density of EGFR decreased. In contrast, the activation of T cells bearing high affinity Cetux-CAR was not impacted by the density of EGFR. In summary, we describe the generation of CARs able to tune T-cell activity to the level of EGFR expression in which a CAR with reduced affinity enabled T cells to distinguish malignant from non-malignant cells. PMID:26330164

Science 101: How Does Speech-Recognition Software Work?

ERIC Educational Resources Information Center

Robertson, Bill

2016-01-01

This column provides background science information for elementary teachers. Many innovations with computer software begin with analysis of how humans do a task. This article takes a look at how humans recognize spoken words and explains the origins of speech-recognition software.

Improving activity recognition using temporal coherence.

PubMed

Ataya, Abbas; Jallon, Pierre; Bianchi, Pascal; Doron, Maeva

2013-01-01

Assessment of daily physical activity using data from wearable sensors has recently become a prominent research area in the biomedical engineering field and a substantial application for pattern recognition. In this paper, we present an accelerometer-based activity recognition scheme on the basis of a hierarchical structured classifier. A first step consists of distinguishing static activities from dynamic ones in order to extract relevant features for each activity type. Next, a separate classifier is applied to detect more specific activities of the same type. On top of our activity recognition system, we introduce a novel approach to take into account the temporal coherence of activities. Inter-activity transition information is modeled by a directed graph Markov chain. Confidence measures in activity classes are then evaluated from conventional classifier's outputs and coupled with the graph to reinforce activity estimation. Accurate results and significant improvement of activity detection are obtained when applying our system for the recognition of 9 activities for 48 subjects.

Unification of automatic target tracking and automatic target recognition

NASA Astrophysics Data System (ADS)

Schachter, Bruce J.

2014-06-01

The subject being addressed is how an automatic target tracker (ATT) and an automatic target recognizer (ATR) can be fused together so tightly and so well that their distinctiveness becomes lost in the merger. This has historically not been the case outside of biology and a few academic papers. The biological model of ATT∪ATR arises from dynamic patterns of activity distributed across many neural circuits and structures (including retina). The information that the brain receives from the eyes is "old news" at the time that it receives it. The eyes and brain forecast a tracked object's future position, rather than relying on received retinal position. Anticipation of the next moment - building up a consistent perception - is accomplished under difficult conditions: motion (eyes, head, body, scene background, target) and processing limitations (neural noise, delays, eye jitter, distractions). Not only does the human vision system surmount these problems, but it has innate mechanisms to exploit motion in support of target detection and classification. Biological vision doesn't normally operate on snapshots. Feature extraction, detection and recognition are spatiotemporal. When vision is viewed as a spatiotemporal process, target detection, recognition, tracking, event detection and activity recognition, do not seem as distinct as they are in current ATT and ATR designs. They appear as similar mechanism taking place at varying time scales. A framework is provided for unifying ATT and ATR.

Enhancing Primary School Students' Knowledge about Global Warming and Environmental Attitude Using Climate Change Activities

ERIC Educational Resources Information Center

Karpudewan, Mageswary; Roth, Wolff-Michael; Bin Abdullah, Mohd Nor Syahrir

2015-01-01

Climate change generally and global warming specifically have become a common feature of the daily news. Due to widespread recognition of the adverse consequences of climate change on human lives, concerted societal effort has been taken to address it (e.g. by means of the science curriculum). This study was designed to test the effect that…

2004 Environment Industry

DTIC Science & Technology

2004-01-01

the country of greatest concern in the region, has put under government protection 10% of the Amazon region, created a national water resources policy...activity. The desire for basic human security often outweighs concern about environmental impact. Logging in the Amazon region, for example, is necessary...Conservation International and the Rainforest Action Network. Now that local groups are receiving more recognition from their own governments, the

The Phenomenon of "Global Education Space" as an Object of Scientific-Pedagogical Research

ERIC Educational Resources Information Center

Avshenyuk, Natalia

2014-01-01

The characteristics of global education space as a social idea of creating a system of measures to ensure the right for education to any individual as well as its converting, that is recognition regardless of the nationality and country of study; and as a specific area of human activity, which forms the internal and external environment for…

Towards human behavior recognition based on spatio temporal features and support vector machines

NASA Astrophysics Data System (ADS)

Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

2017-03-01

Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

PubMed Central

Li, Frédéric; Nisar, Muhammad Adeel; Köping, Lukas; Grzegorzek, Marcin

2018-01-01

Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches—in particular deep-learning based—have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data. PMID:29495310

Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors.

PubMed

Li, Frédéric; Shirahama, Kimiaki; Nisar, Muhammad Adeel; Köping, Lukas; Grzegorzek, Marcin

2018-02-24

Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches-in particular deep-learning based-have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.

Targeting inflammatory monocytes in sepsis-associated encephalopathy and long-term cognitive impairment.

PubMed

Andonegui, Graciela; Zelinski, Erin L; Schubert, Courtney L; Knight, Derrice; Craig, Laura A; Winston, Brent W; Spanswick, Simon C; Petri, Björn; Jenne, Craig N; Sutherland, Janice C; Nguyen, Rita; Jayawardena, Natalie; Kelly, Margaret M; Doig, Christopher J; Sutherland, Robert J; Kubes, Paul

2018-05-03

Sepsis-associated encephalopathy manifesting as delirium is a common problem in critical care medicine. In this study, patients that had delirium due to sepsis had significant cognitive impairments at 12-18 months after hospital discharge when compared with controls and Cambridge Neuropsychological Automated Test Battery-standardized scores in spatial recognition memory, pattern recognition memory, and delayed-matching-to-sample tests but not other cognitive functions. A mouse model of S. pneumoniae pneumonia-induced sepsis, which modeled numerous aspects of the human sepsis-associated multiorgan dysfunction, including encephalopathy, also revealed similar deficits in spatial memory but not new task learning. Both humans and mice had large increases in chemokines for myeloid cell recruitment. Intravital imaging of the brains of septic mice revealed increased neutrophil and CCR2+ inflammatory monocyte recruitment (the latter being far more robust), accompanied by subtle microglial activation. Prevention of CCR2+ inflammatory monocyte recruitment, but not neutrophil recruitment, reduced microglial activation and other signs of neuroinflammation and prevented all signs of cognitive impairment after infection. Therefore, therapeutically targeting CCR2+ inflammatory monocytes at the time of sepsis may provide a novel neuroprotective clinical intervention to prevent the development of persistent cognitive impairments.

Complement in the Initiation and Evolution of Rheumatoid Arthritis

PubMed Central

Holers, V. Michael; Banda, Nirmal K.

2018-01-01

The complement system is a major component of the immune system and plays a central role in many protective immune processes, including circulating immune complex processing and clearance, recognition of foreign antigens, modulation of humoral and cellular immunity, removal of apoptotic and dead cells, and engagement of injury resolving and tissue regeneration processes. In stark contrast to these beneficial roles, however, inadequately controlled complement activation underlies the pathogenesis of human inflammatory and autoimmune diseases, including rheumatoid arthritis (RA) where the cartilage, bone, and synovium are targeted. Recent studies of this disease have demonstrated that the autoimmune response evolves over time in an asymptomatic preclinical phase that is associated with mucosal inflammation. Notably, experimental models of this disease have demonstrated that each of the three major complement activation pathways plays an important role in recognition of injured joint tissue, although the lectin and amplification pathways exhibit particularly impactful roles in the initiation and amplification of damage. Herein, we review the complement system and focus on its multi-factorial role in human patients with RA and experimental murine models. This understanding will be important to the successful integration of the emerging complement therapeutics pipeline into clinical care for patients with RA. PMID:29892280

Prediction of the thermal imaging minimum resolvable (circle) temperature difference with neural network application.

PubMed

Fang, Yi-Chin; Wu, Bo-Wen

2008-12-01

Thermal imaging is an important technology in both national defense and the private sector. An advantage of thermal imaging is its ability to be deployed while fully engaged in duties, not limited by weather or the brightness of indoor or outdoor conditions. However, in an outdoor environment, many factors, including atmospheric decay, target shape, great distance, fog, temperature out of range and diffraction limits can lead to bad image formation, which directly affects the accuracy of object recognition. The visual characteristics of the human eye mean that it has a much better capacity for picture recognition under normal conditions than artificial intelligence does. However, conditions of interference significantly reduce this capacity for picture recognition for instance, fatigue impairs human eyesight. Hence, psychological and physiological factors can affect the result when the human eye is adopted to measure MRTD (minimum resolvable temperature difference) and MRCTD (minimum resolvable circle temperature difference). This study explores thermal imaging recognition, and presents a method for effectively choosing the characteristic values and processing the images fully. Neural network technology is successfully applied to recognize thermal imaging and predict MRTD and MRCTD (Appendix A), exceeding thermal imaging recognition under fatigue and the limits of the human eye.

The Medial Dorsal Thalamic Nucleus and the Medial Prefrontal Cortex of the Rat Function Together to Support Associative Recognition and Recency but Not Item Recognition

ERIC Educational Resources Information Center

Cross, Laura; Brown, Malcolm W.; Aggleton, John P.; Warburton, E. Clea

2013-01-01

In humans recognition memory deficits, a typical feature of diencephalic amnesia, have been tentatively linked to mediodorsal thalamic nucleus (MD) damage. Animal studies have occasionally investigated the role of the MD in single-item recognition, but have not systematically analyzed its involvement in other recognition memory processes. In…

Phosphotyrosine recognition domains: the typical, the atypical and the versatile

PubMed Central

2012-01-01

SH2 domains are long known prominent players in the field of phosphotyrosine recognition within signaling protein networks. However, over the years they have been joined by an increasing number of other protein domain families that can, at least with some of their members, also recognise pTyr residues in a sequence-specific context. This superfamily of pTyr recognition modules, which includes substantial fractions of the PTB domains, as well as much smaller, or even single member fractions like the HYB domain, the PKCδ and PKCθ C2 domains and RKIP, represents a fascinating, medically relevant and hence intensely studied part of the cellular signaling architecture of metazoans. Protein tyrosine phosphorylation clearly serves a plethora of functions and pTyr recognition domains are used in a similarly wide range of interaction modes, which encompass, for example, partner protein switching, tandem recognition functionalities and the interaction with catalytically active protein domains. If looked upon closely enough, virtually no pTyr recognition and regulation event is an exact mirror image of another one in the same cell. Thus, the more we learn about the biology and ultrastructural details of pTyr recognition domains, the more does it become apparent that nature cleverly combines and varies a few basic principles to generate a sheer endless number of sophisticated and highly effective recognition/regulation events that are, under normal conditions, elegantly orchestrated in time and space. This knowledge is also valuable when exploring pTyr reader domains as diagnostic tools, drug targets or therapeutic reagents to combat human diseases. PMID:23134684

Neurofeedback Training for BCI Control

NASA Astrophysics Data System (ADS)

Neuper, Christa; Pfurtscheller, Gert

Brain-computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2-4].

Emotion Recognition in Animated Compared to Human Stimuli in Adolescents with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Brosnan, Mark; Johnson, Hilary; Grawmeyer, Beate; Chapman, Emma; Benton, Laura

2015-01-01

There is equivocal evidence as to whether there is a deficit in recognising emotional expressions in Autism spectrum disorder (ASD). This study compared emotion recognition in ASD in three types of emotion expression media (still image, dynamic image, auditory) across human stimuli (e.g. photo of a human face) and animated stimuli (e.g. cartoon…

Somatosensory Representations Link the Perception of Emotional Expressions and Sensory Experience.

PubMed

Kragel, Philip A; LaBar, Kevin S

2016-01-01

Studies of human emotion perception have linked a distributed set of brain regions to the recognition of emotion in facial, vocal, and body expressions. In particular, lesions to somatosensory cortex in the right hemisphere have been shown to impair recognition of facial and vocal expressions of emotion. Although these findings suggest that somatosensory cortex represents body states associated with distinct emotions, such as a furrowed brow or gaping jaw, functional evidence directly linking somatosensory activity and subjective experience during emotion perception is critically lacking. Using functional magnetic resonance imaging and multivariate decoding techniques, we show that perceiving vocal and facial expressions of emotion yields hemodynamic activity in right somatosensory cortex that discriminates among emotion categories, exhibits somatotopic organization, and tracks self-reported sensory experience. The findings both support embodied accounts of emotion and provide mechanistic insight into how emotional expressions are capable of biasing subjective experience in those who perceive them.

Somatosensory Representations Link the Perception of Emotional Expressions and Sensory Experience123

PubMed Central

2016-01-01

Abstract Studies of human emotion perception have linked a distributed set of brain regions to the recognition of emotion in facial, vocal, and body expressions. In particular, lesions to somatosensory cortex in the right hemisphere have been shown to impair recognition of facial and vocal expressions of emotion. Although these findings suggest that somatosensory cortex represents body states associated with distinct emotions, such as a furrowed brow or gaping jaw, functional evidence directly linking somatosensory activity and subjective experience during emotion perception is critically lacking. Using functional magnetic resonance imaging and multivariate decoding techniques, we show that perceiving vocal and facial expressions of emotion yields hemodynamic activity in right somatosensory cortex that discriminates among emotion categories, exhibits somatotopic organization, and tracks self-reported sensory experience. The findings both support embodied accounts of emotion and provide mechanistic insight into how emotional expressions are capable of biasing subjective experience in those who perceive them. PMID:27280154

Neural network application for thermal image recognition of low-resolution objects

NASA Astrophysics Data System (ADS)

Fang, Yi-Chin; Wu, Bo-Wen

2007-02-01

In the ever-changing situation on a battle field, accurate recognition of a distant object is critical to a commander's decision-making and the general public's safety. Efficiently distinguishing between an enemy's armoured vehicles and ordinary civilian houses under all weather conditions has become an important research topic. This study presents a system for recognizing an armoured vehicle by distinguishing marks and contours. The characteristics of 12 different shapes and 12 characters are used to explore thermal image recognition under the circumstance of long distance and low resolution. Although the recognition capability of human eyes is superior to that of artificial intelligence under normal conditions, it tends to deteriorate substantially under long-distance and low-resolution scenarios. This study presents an effective method for choosing features and processing images. The artificial neural network technique is applied to further improve the probability of accurate recognition well beyond the limit of the recognition capability of human eyes.

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

Biomedical wellness challenges and opportunities

NASA Astrophysics Data System (ADS)

Tangney, John F.

2012-06-01

The mission of ONR's Human and Bioengineered Systems Division is to direct, plan, foster, and encourage Science and Technology in cognitive science, computational neuroscience, bioscience and bio-mimetic technology, social/organizational science, training, human factors, and decision making as related to future Naval needs. This paper highlights current programs that contribute to future biomedical wellness needs in context of humanitarian assistance and disaster relief. ONR supports fundamental research and related technology demonstrations in several related areas, including biometrics and human activity recognition; cognitive sciences; computational neurosciences and bio-robotics; human factors, organizational design and decision research; social, cultural and behavioral modeling; and training, education and human performance. In context of a possible future with automated casualty evacuation, elements of current science and technology programs are illustrated.

The MIT Summit Speech Recognition System: A Progress Report

DTIC Science & Technology

1989-01-01

understanding of the human communication process. Despite recent development of some speech recognition systems with high accuracy, the performance of such...over the past four decades on human communication , in the hope that such systems will one day have a performance approaching that of humans. We are...optimize its use. Third, the system must have a stochastic component to deal with the present state of ignorance in our understanding of the human

A survey of online activity recognition using mobile phones.

PubMed

Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J M

2015-01-19

Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.

Multi-task learning with group information for human action recognition

NASA Astrophysics Data System (ADS)

Qian, Li; Wu, Song; Pu, Nan; Xu, Shulin; Xiao, Guoqiang

2018-04-01

Human action recognition is an important and challenging task in computer vision research, due to the variations in human motion performance, interpersonal differences and recording settings. In this paper, we propose a novel multi-task learning framework with group information (MTL-GI) for accurate and efficient human action recognition. Specifically, we firstly obtain group information through calculating the mutual information according to the latent relationship between Gaussian components and action categories, and clustering similar action categories into the same group by affinity propagation clustering. Additionally, in order to explore the relationships of related tasks, we incorporate group information into multi-task learning. Experimental results evaluated on two popular benchmarks (UCF50 and HMDB51 datasets) demonstrate the superiority of our proposed MTL-GI framework.

Rethinking the medical in the medical humanities.

PubMed

O'Neill, Desmond; Jenkins, Elinor; Mawhinney, Rebecca; Cosgrave, Ellen; O'Mahony, Sarah; Guest, Clare; Moss, Hilary

2016-06-01

To clinicians there are a number of striking features of the ever-evolving field of the medical humanities. The first is a perception of a predominantly unidirectional relationship between medicine and the humanities, generally in terms of what the arts and humanities have to offer medicine. The second is the portrayal of medical practice in terms of problems and negativities for which the medical humanities are seen to pose the solution rather than viewing medicine as an active and positive contributor to an interdisciplinary project. Paradigms that fail to recognise the contributions of medicine and its practitioners (including students) to the medical humanities, this paper argues, will continue to struggle with definition and acceptance. This paper explores the possibilities for advancing the medical humanities through recognition of the contribution of medicine to the humanities and the importance of engaging with the arts, culture and leisure pursuits of doctors and medical students. Our research shows the richness of cultural engagement of medical students, their broad range of cultural interests and their ability to contribute to research and scholarship in the medical humanities. Mutual recognition of strengths, weaknesses and differences of scholarly approach is critical to successful development of the enterprise. Recognising and building on the interests, sympathies and contributions of medicine and its practitioners to the medical humanities is a fundamental component of this task. Future directions might include introductory courses for humanities scholars in aspects of healthcare and medicine. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality.

PubMed

Mehta, Dhwani; Siddiqui, Mohammad Faridul Haque; Javaid, Ahmad Y

2018-02-01

Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human-Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam.

[October 4: World Housing Day].

PubMed

1993-08-01

World Housing Day will be celebrated October 4th, 1993. Its theme this year is women and housing development. Its purpose is to promote the recognition of women as active partners in the development of human establishments. World Housing Day is celebrated every year on the first Monday of October. The UN's Organization for Human Establishments, based in Nairobi, Kenya, organizes this day. The objective of this annual presentation is to attract the attention of the entire world to the importance of housing, which plays a determining role in health, productivity, and the feeling of social well-being.

QSAR, DFT and molecular modeling studies of peptides from HIV-1 to describe their recognition properties by MHC-I.

PubMed

Andrade-Ochoa, S; García-Machorro, J; Bello, Martiniano; Rodríguez-Valdez, L M; Flores-Sandoval, C A; Correa-Basurto, J

2017-08-03

Human immunodeficiency virus type-1 (HIV-1) has infected more than 40 million people around the world. HIV-1 treatment still has several side effects, and the development of a vaccine, which is another potential option for decreasing human infections, has faced challenges. This work presents a computational study that includes a quantitative structure activity relationship(QSAR) using density functional theory(DFT) for reported peptides to identify the principal quantum mechanics descriptors related to peptide activity. In addition, the molecular recognition properties of these peptides are explored on major histocompatibility complex I (MHC-I) through docking and molecular dynamics (MD) simulations accompanied by the Molecular Mechanics Generalized Born Surface Area (MMGBSA) approach for correlating peptide activity reported elsewhere vs. theoretical peptide affinity. The results show that the carboxylic acid and hydroxyl groups are chemical moieties that have an inverse relationship with biological activity. The number of sulfides, pyrroles and imidazoles from the peptide structure are directly related to biological activity. In addition, the HOMO orbital energy values of the total absolute charge and the Ghose-Crippen molar refractivity of peptides are descriptors directly related to the activity and affinity on MHC-I. Docking and MD simulation studies accompanied by an MMGBSA analysis show that the binding free energy without considering the entropic contribution is energetically favorable for all the complexes. Furthermore, good peptide interaction with the most affinity is evaluated experimentally for three proteins. Overall, this study shows that the combination of quantum mechanics descriptors and molecular modeling studies could help describe the immunogenic properties of peptides from HIV-1.

Atoms of recognition in human and computer vision.

PubMed

Ullman, Shimon; Assif, Liav; Fetaya, Ethan; Harari, Daniel

2016-03-08

Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkable progress and have begun to rival human performance in some challenging tasks. These models are trained on image examples and learn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models are similar to those used by the human visual system. Here we show, by introducing and using minimal recognizable images, that the human visual system uses features and processes that are not used by current models and that are critical for recognition. We found by psychophysical studies that at the level of minimal recognizable images a minute change in the image can have a drastic effect on recognition, thus identifying features that are critical for the task. Simulations then showed that current models cannot explain this sensitivity to precise feature configurations and, more generally, do not learn to recognize minimal images at a human level. The role of the features shown here is revealed uniquely at the minimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend our understanding of visual recognition and its cortical mechanisms and will enhance the capacity of computational models to learn from visual experience and to deal with recognition and detailed image interpretation.

Speech Recognition Using Multiple Features and Multiple Recognizers

DTIC Science & Technology

1991-12-03

6 2.1 Introduction ............................................... 6 2.2 Human Speech Communication Process...119 How to Setup ASRT.......................................... 119 How to Use Interactive Menus .................................. 120...recognize a word from an acoustic signal. The human ear and brain perform this type of recognition with incredible speed and precision. Even though

Modeling Human Visual Perception for Target Detection in Military Simulations

DTIC Science & Technology

2009-06-01

incorrectly, is a subject for future research. Possibly, one could exploit the Recognition-by-Components theory of Biederman (1987) and decompose the...Psychophysiscs, 55, 485-496. Biederman , I. (1987). Recognition-by-components: A theory of human image understand- ing. Psychological Review, 94, 115-147

Early prediction of student goals and affect in narrative-centered learning environments

NASA Astrophysics Data System (ADS)

Lee, Sunyoung

Recent years have seen a growing recognition of the role of goal and affect recognition in intelligent tutoring systems. Goal recognition is the task of inferring users' goals from a sequence of observations of their actions. Because of the uncertainty inherent in every facet of human computer interaction, goal recognition is challenging, particularly in contexts in which users can perform many actions in any order, as is the case with intelligent tutoring systems. Affect recognition is the task of identifying the emotional state of a user from a variety of physical cues, which are produced in response to affective changes in the individual. Accurately recognizing student goals and affect states could contribute to more effective and motivating interactions in intelligent tutoring systems. By exploiting knowledge of student goals and affect states, intelligent tutoring systems can dynamically modify their behavior to better support individual students. To create effective interactions in intelligent tutoring systems, goal and affect recognition models should satisfy two key requirements. First, because incorrectly predicted goals and affect states could significantly diminish the effectiveness of interactive systems, goal and affect recognition models should provide accurate predictions of user goals and affect states. When observations of users' activities become available, recognizers should make accurate early" predictions. Second, goal and affect recognition models should be highly efficient so they can operate in real time. To address key issues, we present an inductive approach to recognizing student goals and affect states in intelligent tutoring systems by learning goals and affect recognition models. Our work focuses on goal and affect recognition in an important new class of intelligent tutoring systems, narrative-centered learning environments. We report the results of empirical studies of induced recognition models from observations of students' interactions in narrative-centered learning environments. Experimental results suggest that induced models can make accurate early predictions of student goals and affect states, and they are sufficiently efficient to meet the real-time performance requirements of interactive learning environments.

Mutations of the central tyrosines of putative cholesterol recognition amino acid consensus (CRAC) sequences modify folding, activity, and sterol-sensing of the human ABCG2 multidrug transporter.

PubMed

Gál, Zita; Hegedüs, Csilla; Szakács, Gergely; Váradi, András; Sarkadi, Balázs; Özvegy-Laczka, Csilla

2015-02-01

Human ABCG2 is a plasma membrane glycoprotein causing multidrug resistance in cancer. Membrane cholesterol and bile acids are efficient regulators of ABCG2 function, while the molecular nature of the sterol-sensing sites has not been elucidated. The cholesterol recognition amino acid consensus (CRAC, L/V-(X)(1-5)-Y-(X)(1-5)-R/K) sequence is one of the conserved motifs involved in cholesterol binding in several proteins. We have identified five potential CRAC motifs in the transmembrane domain of the human ABCG2 protein. In order to define their roles in sterol-sensing, the central tyrosines of these CRACs (Y413, 459, 469, 570 and 645) were mutated to S or F and the mutants were expressed both in insect and mammalian cells. We found that mutation in Y459 prevented protein expression; the Y469S and Y645S mutants lost their activity; while the Y570S, Y469F, and Y645F mutants retained function as well as cholesterol and bile acid sensitivity. We found that in the case of the Y413S mutant, drug transport was efficient, while modulation of the ATPase activity by cholesterol and bile acids was significantly altered. We suggest that the Y413 residue within a putative CRAC motif has a role in sterol-sensing and the ATPase/drug transport coupling in the ABCG2 multidrug transporter. Copyright © 2014. Published by Elsevier B.V.

Structural basis of UGUA recognition by the Nudix protein CFIm25 and implications for a regulatory role in mRNA 3′ processing

PubMed Central

Yang, Qin; Gilmartin, Gregory M.; Doublié, Sylvie

2010-01-01

Human Cleavage Factor Im (CFIm) is an essential component of the pre-mRNA 3′ processing complex that functions in the regulation of poly(A) site selection through the recognition of UGUA sequences upstream of the poly(A) site. Although the highly conserved 25 kDa subunit (CFIm25) of the CFIm complex possesses a characteristic α/β/α Nudix fold, CFIm25 has no detectable hydrolase activity. Here we report the crystal structures of the human CFIm25 homodimer in complex with UGUAAA and UUGUAU RNA sequences. CFIm25 is the first Nudix protein to be reported to bind RNA in a sequence-specific manner. The UGUA sequence contributes to binding specificity through an intramolecular G:A Watson–Crick/sugar-edge base interaction, an unusual pairing previously found to be involved in the binding specificity of the SAM-III riboswitch. The structures, together with mutational data, suggest a novel mechanism for the simultaneous sequence-specific recognition of two UGUA elements within the pre-mRNA. Furthermore, the mutually exclusive binding of RNA and the signaling molecule Ap4A (diadenosine tetraphosphate) by CFIm25 suggests a potential role for small molecules in the regulation of mRNA 3′ processing. PMID:20479262

Structural basis of UGUA recognition by the Nudix protein CFI(m)25 and implications for a regulatory role in mRNA 3' processing.

PubMed

Yang, Qin; Gilmartin, Gregory M; Doublié, Sylvie

2010-06-01

Human Cleavage Factor Im (CFI(m)) is an essential component of the pre-mRNA 3' processing complex that functions in the regulation of poly(A) site selection through the recognition of UGUA sequences upstream of the poly(A) site. Although the highly conserved 25 kDa subunit (CFI(m)25) of the CFI(m) complex possesses a characteristic alpha/beta/alpha Nudix fold, CFI(m)25 has no detectable hydrolase activity. Here we report the crystal structures of the human CFI(m)25 homodimer in complex with UGUAAA and UUGUAU RNA sequences. CFI(m)25 is the first Nudix protein to be reported to bind RNA in a sequence-specific manner. The UGUA sequence contributes to binding specificity through an intramolecular G:A Watson-Crick/sugar-edge base interaction, an unusual pairing previously found to be involved in the binding specificity of the SAM-III riboswitch. The structures, together with mutational data, suggest a novel mechanism for the simultaneous sequence-specific recognition of two UGUA elements within the pre-mRNA. Furthermore, the mutually exclusive binding of RNA and the signaling molecule Ap(4)A (diadenosine tetraphosphate) by CFI(m)25 suggests a potential role for small molecules in the regulation of mRNA 3' processing.

Coordinate Transformations in Object Recognition

ERIC Educational Resources Information Center

Graf, Markus

2006-01-01

A basic problem of visual perception is how human beings recognize objects after spatial transformations. Three central classes of findings have to be accounted for: (a) Recognition performance varies systematically with orientation, size, and position; (b) recognition latencies are sequentially additive, suggesting analogue transformation…

Quest Hierarchy for Hyperspectral Face Recognition

DTIC Science & Technology

2011-03-01

numerous face recognition algorithms available, several very good literature surveys are available that include Abate [29], Samal [110], Kong [18], Zou...Perception, Japan (January 1994). [110] Samal , Ashok and P. Iyengar, Automatic Recognition and Analysis of Human Faces and Facial Expressions: A Survey

Genealogy of an ancient protein family: the Sirtuins, a family of disordered members.

PubMed

Costantini, Susan; Sharma, Ankush; Raucci, Raffaele; Costantini, Maria; Autiero, Ida; Colonna, Giovanni

2013-03-05

Sirtuins genes are widely distributed by evolution and have been found in eubacteria, archaea and eukaryotes. While prokaryotic and archeal species usually have one or two sirtuin homologs, in humans as well as in eukaryotes we found multiple versions and in mammals this family is comprised of seven different homologous proteins being all NAD-dependent de-acylases. 3D structures of human SIRT2, SIRT3, and SIRT5 revealed the overall conformation of the conserved core domain but they were unable to give a structural information about the presence of very flexible and dynamically disordered regions, the role of which is still structurally and functionally unclear. Recently, we modeled the 3D-structure of human SIRT1, the most studied member of this family, that unexpectedly emerged as a member of the intrinsically disordered proteins with its long disordered terminal arms. Despite clear similarities in catalytic cores between the human sirtuins little is known of the general structural characteristics of these proteins. The presence of disorder in human SIRT1 and the propensity of these proteins in promoting molecular interactions make it important to understand the underlying mechanisms of molecular recognition that reasonably should involve terminal segments. The mechanism of recognition, in turn, is a prerequisite for the understanding of any functional activity. Aim of this work is to understand what structural properties are shared among members of this family in humans as well as in other organisms. We have studied the distribution of the structural features of N- and C-terminal segments of sirtuins in all known organisms to draw their evolutionary histories by taking into account average length of terminal segments, amino acid composition, intrinsic disorder, presence of charged stretches, presence of putative phosphorylation sites, flexibility, and GC content of genes. Finally, we have carried out a comprehensive analysis of the putative phosphorylation sites in human sirtuins confirming those sites already known experimentally for human SIRT1 and 2 as well as extending their topology to all the family to get feedback of their physiological functions and cellular localization. Our results highlight that the terminal segments of the majority of sirtuins possess a number of structural features and chemical and physical properties that strongly support their involvement in activities of recognition and interaction with other protein molecules. We also suggest how a multisite phosphorylation provides a possible mechanism by which flexible and intrinsically disordered segments of a sirtuin supported by the presence of positively or negatively charged stretches might enhance the strength and specificity of interaction with a particular molecular partner.

21 CFR 170.30 - Eligibility for classification as generally recognized as safe (GRAS).

Code of Federal Regulations, 2013 CFR

2013-04-01

... HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION (CONTINUED) FOOD ADDITIVES Food Additive... obtain approval of a food additive regulation for the ingredient. General recognition of safety through... of scientific procedures required for approval of a food additive regulation. General recognition of...

21 CFR 170.30 - Eligibility for classification as generally recognized as safe (GRAS).

Code of Federal Regulations, 2011 CFR

2011-04-01

... HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION (CONTINUED) FOOD ADDITIVES Food Additive... obtain approval of a food additive regulation for the ingredient. General recognition of safety through... of scientific procedures required for approval of a food additive regulation. General recognition of...

21 CFR 170.30 - Eligibility for classification as generally recognized as safe (GRAS).

Code of Federal Regulations, 2012 CFR

2012-04-01

... HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION (CONTINUED) FOOD ADDITIVES Food Additive... obtain approval of a food additive regulation for the ingredient. General recognition of safety through... of scientific procedures required for approval of a food additive regulation. General recognition of...

21 CFR 170.30 - Eligibility for classification as generally recognized as safe (GRAS).

Code of Federal Regulations, 2010 CFR

2010-04-01

... HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION (CONTINUED) FOOD ADDITIVES Food Additive... obtain approval of a food additive regulation for the ingredient. General recognition of safety through... of scientific procedures required for approval of a food additive regulation. General recognition of...

Perception and Processing of Faces in the Human Brain Is Tuned to Typical Feature Locations

PubMed Central

Schwarzkopf, D. Samuel; Alvarez, Ivan; Lawson, Rebecca P.; Henriksson, Linda; Kriegeskorte, Nikolaus; Rees, Geraint

2016-01-01

Faces are salient social stimuli whose features attract a stereotypical pattern of fixations. The implications of this gaze behavior for perception and brain activity are largely unknown. Here, we characterize and quantify a retinotopic bias implied by typical gaze behavior toward faces, which leads to eyes and mouth appearing most often in the upper and lower visual field, respectively. We found that the adult human visual system is tuned to these contingencies. In two recognition experiments, recognition performance for isolated face parts was better when they were presented at typical, rather than reversed, visual field locations. The recognition cost of reversed locations was equal to ∼60% of that for whole face inversion in the same sample. Similarly, an fMRI experiment showed that patterns of activity evoked by eye and mouth stimuli in the right inferior occipital gyrus could be separated with significantly higher accuracy when these features were presented at typical, rather than reversed, visual field locations. Our findings demonstrate that human face perception is determined not only by the local position of features within a face context, but by whether features appear at the typical retinotopic location given normal gaze behavior. Such location sensitivity may reflect fine-tuning of category-specific visual processing to retinal input statistics. Our findings further suggest that retinotopic heterogeneity might play a role for face inversion effects and for the understanding of conditions affecting gaze behavior toward faces, such as autism spectrum disorders and congenital prosopagnosia. SIGNIFICANCE STATEMENT Faces attract our attention and trigger stereotypical patterns of visual fixations, concentrating on inner features, like eyes and mouth. Here we show that the visual system represents face features better when they are shown at retinal positions where they typically fall during natural vision. When facial features were shown at typical (rather than reversed) visual field locations, they were discriminated better by humans and could be decoded with higher accuracy from brain activity patterns in the right occipital face area. This suggests that brain representations of face features do not cover the visual field uniformly. It may help us understand the well-known face-inversion effect and conditions affecting gaze behavior toward faces, such as prosopagnosia and autism spectrum disorders. PMID:27605606

How should a speech recognizer work?

PubMed

Scharenborg, Odette; Norris, Dennis; Bosch, Louis; McQueen, James M

2005-11-12

Although researchers studying human speech recognition (HSR) and automatic speech recognition (ASR) share a common interest in how information processing systems (human or machine) recognize spoken language, there is little communication between the two disciplines. We suggest that this lack of communication follows largely from the fact that research in these related fields has focused on the mechanics of how speech can be recognized. In Marr's (1982) terms, emphasis has been on the algorithmic and implementational levels rather than on the computational level. In this article, we provide a computational-level analysis of the task of speech recognition, which reveals the close parallels between research concerned with HSR and ASR. We illustrate this relation by presenting a new computational model of human spoken-word recognition, built using techniques from the field of ASR that, in contrast to current existing models of HSR, recognizes words from real speech input. 2005 Lawrence Erlbaum Associates, Inc.

Voice tracking and spoken word recognition in the presence of other voices

NASA Astrophysics Data System (ADS)

Litong-Palima, Marisciel; Violanda, Renante; Saloma, Caesar

2004-12-01

We study the human hearing process by modeling the hair cell as a thresholded Hopf bifurcator and compare our calculations with experimental results involving human subjects in two different multi-source listening tasks of voice tracking and spoken-word recognition. In the model, we observed noise suppression by destructive interference between noise sources which weakens the effective noise strength acting on the hair cell. Different success rate characteristics were observed for the two tasks. Hair cell performance at low threshold levels agree well with results from voice-tracking experiments while those of word-recognition experiments are consistent with a linear model of the hearing process. The ability of humans to track a target voice is robust against cross-talk interference unlike word-recognition performance which deteriorates quickly with the number of uncorrelated noise sources in the environment which is a response behavior that is associated with linear systems.

Immunoblotting with human native antigen shows stage-related sensitivity in the serodiagnosis of hepatic cystic echinococcosis.

PubMed

Mariconti, Mara; Bazzocchi, Chiara; Tamarozzi, Francesca; Meroni, Valeria; Genco, Francesca; Maserati, Roberta; Brunetti, Enrico

2014-01-01

The diagnosis of hepatic cystic echinococcosis is based on ultrasonography and confirmed by serology. However, no biological marker of cyst viability is currently available implying years-long patient follow-up, which is not always feasible in endemic areas. We characterized the performance of an immunoblotting test based on human hydatid cyst fluid with particular regard to its ability to distinguish between cyst stages. Sera from patients with cysts in different stages showed distinctive band pattern recognition. Most importantly, the test discriminated in 80% of cases CE3a from CE3b transitional cysts, known to have different viability profiles. Interestingly, we observed a rapid change in band pattern recognition of sera from one patient at time points when his cyst passed from active to transitional to inactive stages. Further identification of different antigens expressed by different cyst stages will support the development of diagnostic tools that could early define cyst viability, to guide clinical decision making, and shorten patient follow-up.

Structural insights into the extracellular recognition of the human serotonin 2B receptor by an antibody

PubMed Central

Wacker, Daniel; Kapoor, Mili; Zhang, Ai; Han, Gye Won; Basu, Shibom; Patel, Nilkanth; Messerschmidt, Marc; Weierstall, Uwe; Liu, Wei; Katritch, Vsevolod; Roth, Bryan L.; Stevens, Raymond C.

2017-01-01

Monoclonal antibodies provide an attractive alternative to small-molecule therapies for a wide range of diseases. Given the importance of G protein-coupled receptors (GPCRs) as pharmaceutical targets, there has been an immense interest in developing therapeutic monoclonal antibodies that act on GPCRs. Here we present the 3.0-Å resolution structure of a complex between the human 5-hydroxytryptamine 2B (5-HT2B) receptor and an antibody Fab fragment bound to the extracellular side of the receptor, determined by serial femtosecond crystallography with an X-ray free-electron laser. The antibody binds to a 3D epitope of the receptor that includes all three extracellular loops. The 5-HT2B receptor is captured in a well-defined active-like state, most likely stabilized by the crystal lattice. The structure of the complex sheds light on the mechanism of selectivity in extracellular recognition of GPCRs by monoclonal antibodies. PMID:28716900

Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor.

PubMed

Kim, Dong Seop; Arsalan, Muhammad; Park, Kang Ryoung

2018-03-23

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works.

Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor

PubMed Central

Kim, Dong Seop; Arsalan, Muhammad; Park, Kang Ryoung

2018-01-01

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works. PMID:29570690

Recognition of Human Erythrocyte Receptors by the Tryptophan-Rich Antigens of Monkey Malaria Parasite Plasmodium knowlesi.

PubMed

Tyagi, Kriti; Gupta, Deepali; Saini, Ekta; Choudhary, Shilpa; Jamwal, Abhishek; Alam, Mohd Shoeb; Zeeshan, Mohammad; Tyagi, Rupesh K; Sharma, Yagya D

2015-01-01

The monkey malaria parasite Plasmodium knowlesi also infect humans. There is a lack of information on the molecular mechanisms that take place between this simian parasite and its heterologous human host erythrocytes leading to this zoonotic disease. Therefore, we investigated here the binding ability of P. knowlesi tryptophan-rich antigens (PkTRAgs) to the human erythrocytes and sharing of the erythrocyte receptors between them as well as with other commonly occurring human malaria parasites. Six PkTRAgs were cloned and expressed in E.coli as well as in mammalian CHO-K1 cell to determine their human erythrocyte binding activity by cell-ELISA, and in-vitro rosetting assay, respectively. Three of six PkTRAgs (PkTRAg38.3, PkTRAg40.1, and PkTRAg67.1) showed binding to human erythrocytes. Two of them (PkTRAg40.1 and PkTRAg38.3) showed cross-competition with each other as well as with the previously described P.vivax tryptophan-rich antigens (PvTRAgs) for human erythrocyte receptors. However, the third protein (PkTRAg67.1) utilized the additional but different human erythrocyte receptor(s) as it did not cross-compete for erythrocyte binding with either of these two PkTRAgs as well as with any of the PvTRAgs. These three PkTRAgs also inhibited the P.falciparum parasite growth in in-vitro culture, further indicating the sharing of human erythrocyte receptors by these parasite species and the biological significance of this receptor-ligand interaction between heterologous host and simian parasite. Recognition and sharing of human erythrocyte receptor(s) by PkTRAgs with human parasite ligands could be part of the strategy adopted by the monkey malaria parasite to establish inside the heterologous human host.

Recognition of Human Erythrocyte Receptors by the Tryptophan-Rich Antigens of Monkey Malaria Parasite Plasmodium knowlesi

PubMed Central

Tyagi, Kriti; Gupta, Deepali; Saini, Ekta; Choudhary, Shilpa; Jamwal, Abhishek; Alam, Mohd. Shoeb; Zeeshan, Mohammad; Tyagi, Rupesh K.; Sharma, Yagya D.

2015-01-01

Background The monkey malaria parasite Plasmodium knowlesi also infect humans. There is a lack of information on the molecular mechanisms that take place between this simian parasite and its heterologous human host erythrocytes leading to this zoonotic disease. Therefore, we investigated here the binding ability of P. knowlesi tryptophan-rich antigens (PkTRAgs) to the human erythrocytes and sharing of the erythrocyte receptors between them as well as with other commonly occurring human malaria parasites. Methods Six PkTRAgs were cloned and expressed in E.coli as well as in mammalian CHO-K1 cell to determine their human erythrocyte binding activity by cell-ELISA, and in-vitro rosetting assay, respectively. Results Three of six PkTRAgs (PkTRAg38.3, PkTRAg40.1, and PkTRAg67.1) showed binding to human erythrocytes. Two of them (PkTRAg40.1 and PkTRAg38.3) showed cross-competition with each other as well as with the previously described P.vivax tryptophan-rich antigens (PvTRAgs) for human erythrocyte receptors. However, the third protein (PkTRAg67.1) utilized the additional but different human erythrocyte receptor(s) as it did not cross-compete for erythrocyte binding with either of these two PkTRAgs as well as with any of the PvTRAgs. These three PkTRAgs also inhibited the P.falciparum parasite growth in in-vitro culture, further indicating the sharing of human erythrocyte receptors by these parasite species and the biological significance of this receptor-ligand interaction between heterologous host and simian parasite. Conclusions Recognition and sharing of human erythrocyte receptor(s) by PkTRAgs with human parasite ligands could be part of the strategy adopted by the monkey malaria parasite to establish inside the heterologous human host. PMID:26393350

Antimicrobial peptide hLF1-11 directs granulocyte-macrophage colony-stimulating factor-driven monocyte differentiation toward macrophages with enhanced recognition and clearance of pathogens.

PubMed

van der Does, Anne M; Bogaards, Sylvia J P; Ravensbergen, Bep; Beekhuizen, Henry; van Dissel, Jaap T; Nibbering, Peter H

2010-02-01

The human lactoferrin-derived peptide hLF1-11 displays antimicrobial activities in vitro and is effective against infections with antibiotic-resistant bacteria and fluconazole-resistant Candida albicans in animals. However, the mechanisms underlying these activities remain largely unclear. Since hLF1-11 is ineffective in vitro at physiological salt concentrations, we suggested modulation of the immune system as an additional mechanism of action of the peptide. We investigated whether hLF1-11 affects human monocyte-macrophage differentiation and determined the antimicrobial activities of the resulting macrophages. Monocytes were cultured for 7 days with GM-CSF in the presence of hLF1-11, control peptide, or saline for various intervals. At day 6, the cells were stimulated with lipopolysaccharide (LPS), lipoteichoic acid (LTA), or heat-killed C. albicans for 24 h. Thereafter, the levels of cytokines in the culture supernatants, the expression of pathogen recognition receptors, and the antimicrobial activities of these macrophages were determined. The results showed that a short exposure of monocytes to hLF1-11 during GM-CSF-driven differentiation is sufficient to direct differentiation of monocytes toward a macrophage subset characterized by both pro- and anti-inflammatory cytokine production and increased responsiveness to microbial structures. Moreover, these macrophages are highly effective against C. albicans and Staphylococcus aureus. In conclusion, hLF1-11 directs GM-CSF-driven differentiation of monocytes toward macrophages with enhanced effector functions.

A Survey of Online Activity Recognition Using Mobile Phones

PubMed Central

Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J.M.

2015-01-01

Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research. PMID:25608213

Molecular immune recognition of botulinum neurotoxin B. The light chain regions that bind human blocking antibodies from toxin-treated cervical dystonia patients. Antigenic structure of the entire BoNT/B molecule.

PubMed

Atassi, M Zouhair; Jankovic, Joseph; Steward, Lance E; Aoki, K Roger; Dolimbek, Behzod Z

2012-01-01

We recently mapped the regions on the heavy (H) chain of botulinum neurotoxin, type B (BoNT/B) recognized by blocking antibodies (Abs) from cervical dystonia (CD) patients who develop immunoresistance during toxin treatment. Since blocking could also be effected by Abs directed against regions on the light (L) chain, we have mapped here the L chain, using the same 30 CD antisera. We synthesized, purified and characterized 32 19-residue L chain peptides that overlapped successively by 5 residues (peptide L32 overlapped with peptide N1 of the H chain by 12 residues). In a given patient, Abs against the L chain seemed less intense than those against H chain. Most sera recognized a limited set of L chain peptides. The levels of Abs against a given region varied with the patient, consistent with immune responses to each epitope being under separate MHC control. The peptides most frequently recognized were: L13, by 30 of 30 antisera (100%); L22, by 23 of 30 (76.67%); L19, by 15 of 30 (50.00%); L26, by 11 of 30 (36.70%); and L14, by 12 of 30 (40.00%). The activity of L14 probably derives from its overlap with L13. The levels of Ab binding decreased in the following order: L13 (residues 169-187), L22 (295-313), L19 (253-271), and L26 (351-369). Peptides L12 (155-173), L18 (239-257), L15 (197-215), L1 (1-19) and L23 (309-327) exhibited very low Ab binding. The remaining peptides had little or no Ab-binding activity. The antigenic regions are analyzed in terms of their three-dimensional locations and the enzyme active site. With the previous localization of the antigenic regions on the BoNT/B H chain, the human Ab recognition of the entire BoNT/B molecule is presented and compared to the recognition of BoNT/A by human blocking Abs. Copyright © 2011. Published by Elsevier GmbH.

Monogenic IL-1 Mediated Autoinflammatory and Immunodeficiency Syndromes: Finding the Right Balance in Response to Danger Signals

PubMed Central

Henderson, Cailin; Goldbach-Mansky, Raphaela

2010-01-01

INTRODUCTION Interleukin -1 was the first cytokine identified and is a powerful inducer of fever and inflammation. The biologically active receptor for IL-1, shares signaling pathways with some pathogen recognition receptors, the toll like receptors (TLRs) which early on suggested an important role in innate immune function. DISCUSSION The discovery that some intracellular “danger receptors”, the NOD like receptors (NLRs) can assemble to form multimolecular platforms, the inflammasomes, that not only sense intracellular danger but also activate IL-1β, has provided the molecular basis for the integration of IL-1 as an early response mediator in danger recognition. The critical role of balancing IL-1 production and signaling in human disease has recently been demonstrated in rare human monogenic diseases with mutations that affect the meticulous control of IL-1 production, release and signaling by leading to decreased or increased TLR/IL-1 signaling. In diseases of decreased TLR/IL-1 signaling (IRAK-4 and MyD88 deficiencies) patients are at risk for infections with gram positive organisms; and in diseases of increased signaling, patients develop systemic autoinflammatory diseases (Cryopyrin associated periodic syndromes (CAPS), and deficiency of the IL-1 receptor antagonist (DIRA)). CONCLUSION Monogenic defects in a number of rare diseases that affect the balance of TLR/IL-1 signaling have provided us with opportunities to study the systemic effects of IL-1 in human diseases. The molecular defects in CAPS and DIRA provided a therapeutic rationale for targeting IL-1 and the impressive clinical results from IL-1 blocking therapies have undoubtedly confirmed the pivotal role of IL-1 in human disease and spurred the exploration of modifying IL-1 signaling in a number of genetically complex common human diseases. PMID:20353899

Human Properdin Opsonizes Nanoparticles and Triggers a Potent Pro-inflammatory Response by Macrophages without Involving Complement Activation

PubMed Central

Kouser, Lubna; Paudyal, Basudev; Kaur, Anuvinder; Stenbeck, Gudrun; Jones, Lucy A.; Abozaid, Suhair M.; Stover, Cordula M.; Flahaut, Emmanuel; Sim, Robert B.; Kishore, Uday

2018-01-01

Development of nanoparticles as tissue-specific drug delivery platforms can be considerably influenced by the complement system because of their inherent pro-inflammatory and tumorigenic consequences. The complement activation pathways, and its recognition subcomponents, can modulate clearance of the nanoparticles and subsequent inflammatory response and thus alter the intended translational applications. Here, we report, for the first time, that human properdin, an upregulator of the complement alternative pathway, can opsonize functionalized carbon nanotubes (CNTs) via its thrombospondin type I repeat (TSR) 4 and 5. Binding of properdin and TSR4+5 is likely to involve charge pattern/polarity recognition of the CNT surface since both carboxymethyl cellulose-coated carbon nanotubes (CMC-CNT) and oxidized (Ox-CNT) bound these proteins well. Properdin enhanced the uptake of CMC-CNTs by a macrophage cell line, THP-1, mounting a robust pro-inflammatory immune response, as revealed by qRT-PCR, multiplex cytokine array, and NF-κB nuclear translocation analyses. Properdin can be locally synthesized by immune cells in an inflammatory microenvironment, and thus, its interaction with nanoparticles is of considerable importance. In addition, recombinant TSR4+5 coated on the CMC-CNTs inhibited complement consumption by CMC-CNTs, suggesting that nanoparticle decoration with TSR4+5, can be potentially used as a complement inhibitor in a number of pathological contexts arising due to exaggerated complement activation. PMID:29483907

Early innate immune responses to Sin Nombre hantavirus occur independently of IFN regulatory factor 3, characterized pattern recognition receptors, and viral entry.

PubMed

Prescott, Joseph B; Hall, Pamela R; Bondu-Hawkins, Virginie S; Ye, Chunyan; Hjelle, Brian

2007-08-01

Sin Nombre virus (SNV) is a highly pathogenic New World virus and etiologic agent of hantavirus cardiopulmonary syndrome. We have previously shown that replication-defective virus particles are able to induce a strong IFN-stimulated gene (ISG) response in human primary cells. RNA viruses often stimulate the innate immune response by interactions between viral nucleic acids, acting as a pathogen-associated molecular pattern, and cellular pattern-recognition receptors (PRRs). Ligand binding to PRRs activates transcription factors which regulate the expression of antiviral genes, and in all systems examined thus far, IFN regulatory factor 3 (IRF3) has been described as an essential intermediate for induction of ISG expression. However, we now describe a model in which IRF3 is dispensable for the induction of ISG transcription in response to viral particles. IRF3-independent ISG transcription in human hepatoma cell lines is initiated early after exposure to SNV virus particles in an entry- and replication-independent fashion. Furthermore, using gene knockdown, we discovered that this activation is independent of the best-characterized RNA- and protein-sensing PRRs including the cytoplasmic caspase recruitment domain-containing RNA helicases and the TLRs. SNV particles engage a heretofore unrecognized PRR, likely located at the cell surface, and engage a novel IRF3-independent pathway that activates the innate immune response.

Simple thermal to thermal face verification method based on local texture descriptors

NASA Astrophysics Data System (ADS)

Grudzien, A.; Palka, Norbert; Kowalski, M.

2017-08-01

Biometrics is a science that studies and analyzes physical structure of a human body and behaviour of people. Biometrics found many applications ranging from border control systems, forensics systems for criminal investigations to systems for access control. Unique identifiers, also referred to as modalities are used to distinguish individuals. One of the most common and natural human identifiers is a face. As a result of decades of investigations, face recognition achieved high level of maturity, however recognition in visible spectrum is still challenging due to illumination aspects or new ways of spoofing. One of the alternatives is recognition of face in different parts of light spectrum, e.g. in infrared spectrum. Thermal infrared offer new possibilities for human recognition due to its specific properties as well as mature equipment. In this paper we present the scheme of subject's verification methodology by using facial images in thermal range. The study is focused on the local feature extraction methods and on the similarity metrics. We present comparison of two local texture-based descriptors for thermal 1-to-1 face recognition.

A general framework for sensor-based human activity recognition.

PubMed

Köping, Lukas; Shirahama, Kimiaki; Grzegorzek, Marcin

2018-04-01

Today's wearable devices like smartphones, smartwatches and intelligent glasses collect a large amount of data from their built-in sensors like accelerometers and gyroscopes. These data can be used to identify a person's current activity and in turn can be utilised for applications in the field of personal fitness assistants or elderly care. However, developing such systems is subject to certain restrictions: (i) since more and more new sensors will be available in the future, activity recognition systems should be able to integrate these new sensors with a small amount of manual effort and (ii) such systems should avoid high acquisition costs for computational power. We propose a general framework that achieves an effective data integration based on the following two characteristics: Firstly, a smartphone is used to gather and temporally store data from different sensors and transfer these data to a central server. Thus, various sensors can be integrated into the system as long as they have programming interfaces to communicate with the smartphone. The second characteristic is a codebook-based feature learning approach that can encode data from each sensor into an effective feature vector only by tuning a few intuitive parameters. In the experiments, the framework is realised as a real-time activity recognition system that integrates eight sensors from a smartphone, smartwatch and smartglasses, and its effectiveness is validated from different perspectives such as accuracies, sensor combinations and sampling rates. Copyright © 2018 Elsevier Ltd. All rights reserved.

Recognizing Age-Separated Face Images: Humans and Machines

PubMed Central

Yadav, Daksha; Singh, Richa; Vatsa, Mayank; Noore, Afzel

2014-01-01

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components - facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario. PMID:25474200

Recognizing age-separated face images: humans and machines.

PubMed

Yadav, Daksha; Singh, Richa; Vatsa, Mayank; Noore, Afzel

2014-01-01

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components--facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario.

HIV-1 evades innate immune recognition through specific cofactor recruitment

NASA Astrophysics Data System (ADS)

Rasaiyaah, Jane; Tan, Choon Ping; Fletcher, Adam J.; Price, Amanda J.; Blondeau, Caroline; Hilditch, Laura; Jacques, David A.; Selwood, David L.; James, Leo C.; Noursadeghi, Mahdad; Towers, Greg J.

2013-11-01

Human immunodeficiency virus (HIV)-1 is able to replicate in primary human macrophages without stimulating innate immunity despite reverse transcription of genomic RNA into double-stranded DNA, an activity that might be expected to trigger innate pattern recognition receptors. We reasoned that if correctly orchestrated HIV-1 uncoating and nuclear entry is important for evasion of innate sensors then manipulation of specific interactions between HIV-1 capsid and host factors that putatively regulate these processes should trigger pattern recognition receptors and stimulate type 1 interferon (IFN) secretion. Here we show that HIV-1 capsid mutants N74D and P90A, which are impaired for interaction with cofactors cleavage and polyadenylation specificity factor subunit 6 (CPSF6) and cyclophilins (Nup358 and CypA), respectively, cannot replicate in primary human monocyte-derived macrophages because they trigger innate sensors leading to nuclear translocation of NF-κB and IRF3, the production of soluble type 1 IFN and induction of an antiviral state. Depletion of CPSF6 with short hairpin RNA expression allows wild-type virus to trigger innate sensors and IFN production. In each case, suppressed replication is rescued by IFN-receptor blockade, demonstrating a role for IFN in restriction. IFN production is dependent on viral reverse transcription but not integration, indicating that a viral reverse transcription product comprises the HIV-1 pathogen-associated molecular pattern. Finally, we show that we can pharmacologically induce wild-type HIV-1 infection to stimulate IFN secretion and an antiviral state using a non-immunosuppressive cyclosporine analogue. We conclude that HIV-1 has evolved to use CPSF6 and cyclophilins to cloak its replication, allowing evasion of innate immune sensors and induction of a cell-autonomous innate immune response in primary human macrophages.

Performing speech recognition research with hypercard

NASA Technical Reports Server (NTRS)

Shepherd, Chip

1993-01-01

The purpose of this paper is to describe a HyperCard-based system for performing speech recognition research and to instruct Human Factors professionals on how to use the system to obtain detailed data about the user interface of a prototype speech recognition application.

Recognition of Frequency Modulated Whistle-Like Sounds by a Bottlenose Dolphin (Tursiops truncatus) and Humans with Transformations in Amplitude, Duration and Frequency.

PubMed

Branstetter, Brian K; DeLong, Caroline M; Dziedzic, Brandon; Black, Amy; Bakhtiari, Kimberly

2016-01-01

Bottlenose dolphins (Tursiops truncatus) use the frequency contour of whistles produced by conspecifics for individual recognition. Here we tested a bottlenose dolphin's (Tursiops truncatus) ability to recognize frequency modulated whistle-like sounds using a three alternative matching-to-sample paradigm. The dolphin was first trained to select a specific object (object A) in response to a specific sound (sound A) for a total of three object-sound associations. The sounds were then transformed by amplitude, duration, or frequency transposition while still preserving the frequency contour of each sound. For comparison purposes, 30 human participants completed an identical task with the same sounds, objects, and training procedure. The dolphin's ability to correctly match objects to sounds was robust to changes in amplitude with only a minor decrement in performance for short durations. The dolphin failed to recognize sounds that were frequency transposed by plus or minus ½ octaves. Human participants demonstrated robust recognition with all acoustic transformations. The results indicate that this dolphin's acoustic recognition of whistle-like sounds was constrained by absolute pitch. Unlike human speech, which varies considerably in average frequency, signature whistles are relatively stable in frequency, which may have selected for a whistle recognition system invariant to frequency transposition.

Recognition of Frequency Modulated Whistle-Like Sounds by a Bottlenose Dolphin (Tursiops truncatus) and Humans with Transformations in Amplitude, Duration and Frequency

PubMed Central

Branstetter, Brian K.; DeLong, Caroline M.; Dziedzic, Brandon; Black, Amy; Bakhtiari, Kimberly

2016-01-01

Bottlenose dolphins (Tursiops truncatus) use the frequency contour of whistles produced by conspecifics for individual recognition. Here we tested a bottlenose dolphin’s (Tursiops truncatus) ability to recognize frequency modulated whistle-like sounds using a three alternative matching-to-sample paradigm. The dolphin was first trained to select a specific object (object A) in response to a specific sound (sound A) for a total of three object-sound associations. The sounds were then transformed by amplitude, duration, or frequency transposition while still preserving the frequency contour of each sound. For comparison purposes, 30 human participants completed an identical task with the same sounds, objects, and training procedure. The dolphin’s ability to correctly match objects to sounds was robust to changes in amplitude with only a minor decrement in performance for short durations. The dolphin failed to recognize sounds that were frequency transposed by plus or minus ½ octaves. Human participants demonstrated robust recognition with all acoustic transformations. The results indicate that this dolphin’s acoustic recognition of whistle-like sounds was constrained by absolute pitch. Unlike human speech, which varies considerably in average frequency, signature whistles are relatively stable in frequency, which may have selected for a whistle recognition system invariant to frequency transposition. PMID:26863519

MK-801-induced deficits in social recognition in rats: reversal by aripiprazole, but not olanzapine, risperidone, or cannabidiol.

PubMed

Deiana, Serena; Watanabe, Akihito; Yamasaki, Yuki; Amada, Naoki; Kikuchi, Tetsuro; Stott, Colin; Riedel, Gernot

2015-12-01

Deficiencies in social activities are hallmarks of numerous brain disorders. With respect to schizophrenia, social withdrawal belongs to the category of negative symptoms and is associated with deficits in the cognitive domain. Here, we used the N-methyl-D-aspartate receptor antagonist dizocilpine (MK-801) for induction of social withdrawal in rats and assessed the efficacy of several atypical antipsychotics with different pharmacological profiles as putative treatment. In addition, we reasoned that the marijuana constituent cannabidiol (CBD) may provide benefit or could be proposed as an adjunct treatment in combination with antipsychotics. Hooded Lister rats were tested in the three-chamber version for social interaction, with an initial novelty phase, followed after 3 min by a short-term recognition memory phase. No drug treatment affected sociability. However, distinct effects on social recognition were revealed. MK-801 reduced social recognition memory at all doses (>0.03 mg/kg). Predosing with aripiprazole dose-dependently (2 or 10 mg/kg) prevented the memory decline, but doses of 0.1 mg/kg risperidone or 1 mg/kg olanzapine did not. Intriguingly, CBD impaired social recognition memory (12 and 30 mg/kg) but did not rescue the MK-801-induced deficits. When CBD was combined with protective doses of aripiprazole (CBD-aripiprazole at 12 :  or 5 : 2 mg/kg) the benefit of the antipsychotic was lost. At the same time, activity-related changes in behaviour were excluded as underlying reasons for these pharmacological effects. Collectively, the combined activity of aripiprazole on dopamine D2 and serotonin 5HT1A receptors appears to provide a significant advantage over risperidone and olanzapine with respect to the rescue of cognitive deficits reminiscent of schizophrenia. The differential pharmacological properties of CBD, which are seemingly beneficial in human patients, did not back-translate and rescue the MK-801-induced social memory deficit.

Compensatory premotor activity during affective face processing in subclinical carriers of a single mutant Parkin allele.

PubMed

Anders, Silke; Sack, Benjamin; Pohl, Anna; Münte, Thomas; Pramstaller, Peter; Klein, Christine; Binkofski, Ferdinand

2012-04-01

Patients with Parkinson's disease suffer from significant motor impairments and accompanying cognitive and affective dysfunction due to progressive disturbances of basal ganglia-cortical gating loops. Parkinson's disease has a long presymptomatic stage, which indicates a substantial capacity of the human brain to compensate for dopaminergic nerve degeneration before clinical manifestation of the disease. Neuroimaging studies provide evidence that increased motor-related cortical activity can compensate for progressive dopaminergic nerve degeneration in carriers of a single mutant Parkin or PINK1 gene, who show a mild but significant reduction of dopamine metabolism in the basal ganglia in the complete absence of clinical motor signs. However, it is currently unknown whether similar compensatory mechanisms are effective in non-motor basal ganglia-cortical gating loops. Here, we ask whether asymptomatic Parkin mutation carriers show altered patterns of brain activity during processing of facial gestures, and whether this might compensate for latent facial emotion recognition deficits. Current theories in social neuroscience assume that execution and perception of facial gestures are linked by a special class of visuomotor neurons ('mirror neurons') in the ventrolateral premotor cortex/pars opercularis of the inferior frontal gyrus (Brodmann area 44/6). We hypothesized that asymptomatic Parkin mutation carriers would show increased activity in this area during processing of affective facial gestures, replicating the compensatory motor effects that have previously been observed in these individuals. Additionally, Parkin mutation carriers might show altered activity in other basal ganglia-cortical gating loops. Eight asymptomatic heterozygous Parkin mutation carriers and eight matched controls underwent functional magnetic resonance imaging and a subsequent facial emotion recognition task. As predicted, Parkin mutation carriers showed significantly stronger activity in the right ventrolateral premotor cortex during execution and perception of affective facial gestures than healthy controls. Furthermore, Parkin mutation carriers showed a slightly reduced ability to recognize facial emotions that was least severe in individuals who showed the strongest increase of ventrolateral premotor activity. In addition, Parkin mutation carriers showed a significantly weaker than normal increase of activity in the left lateral orbitofrontal cortex (inferior frontal gyrus pars orbitalis, Brodmann area 47), which was unrelated to facial emotion recognition ability. These findings are consistent with the hypothesis that compensatory activity in the ventrolateral premotor cortex during processing of affective facial gestures can reduce impairments in facial emotion recognition in subclinical Parkin mutation carriers. A breakdown of this compensatory mechanism might lead to the impairment of facial expressivity and facial emotion recognition observed in manifest Parkinson's disease.

Compensatory premotor activity during affective face processing in subclinical carriers of a single mutant Parkin allele

PubMed Central

Sack, Benjamin; Pohl, Anna; Münte, Thomas; Pramstaller, Peter; Klein, Christine; Binkofski, Ferdinand

2012-01-01

Patients with Parkinson's disease suffer from significant motor impairments and accompanying cognitive and affective dysfunction due to progressive disturbances of basal ganglia–cortical gating loops. Parkinson's disease has a long presymptomatic stage, which indicates a substantial capacity of the human brain to compensate for dopaminergic nerve degeneration before clinical manifestation of the disease. Neuroimaging studies provide evidence that increased motor-related cortical activity can compensate for progressive dopaminergic nerve degeneration in carriers of a single mutant Parkin or PINK1 gene, who show a mild but significant reduction of dopamine metabolism in the basal ganglia in the complete absence of clinical motor signs. However, it is currently unknown whether similar compensatory mechanisms are effective in non-motor basal ganglia–cortical gating loops. Here, we ask whether asymptomatic Parkin mutation carriers show altered patterns of brain activity during processing of facial gestures, and whether this might compensate for latent facial emotion recognition deficits. Current theories in social neuroscience assume that execution and perception of facial gestures are linked by a special class of visuomotor neurons (‘mirror neurons’) in the ventrolateral premotor cortex/pars opercularis of the inferior frontal gyrus (Brodmann area 44/6). We hypothesized that asymptomatic Parkin mutation carriers would show increased activity in this area during processing of affective facial gestures, replicating the compensatory motor effects that have previously been observed in these individuals. Additionally, Parkin mutation carriers might show altered activity in other basal ganglia–cortical gating loops. Eight asymptomatic heterozygous Parkin mutation carriers and eight matched controls underwent functional magnetic resonance imaging and a subsequent facial emotion recognition task. As predicted, Parkin mutation carriers showed significantly stronger activity in the right ventrolateral premotor cortex during execution and perception of affective facial gestures than healthy controls. Furthermore, Parkin mutation carriers showed a slightly reduced ability to recognize facial emotions that was least severe in individuals who showed the strongest increase of ventrolateral premotor activity. In addition, Parkin mutation carriers showed a significantly weaker than normal increase of activity in the left lateral orbitofrontal cortex (inferior frontal gyrus pars orbitalis, Brodmann area 47), which was unrelated to facial emotion recognition ability. These findings are consistent with the hypothesis that compensatory activity in the ventrolateral premotor cortex during processing of affective facial gestures can reduce impairments in facial emotion recognition in subclinical Parkin mutation carriers. A breakdown of this compensatory mechanism might lead to the impairment of facial expressivity and facial emotion recognition observed in manifest Parkinson's disease. PMID:22434215

Human action recognition based on spatial-temporal descriptors using key poses

NASA Astrophysics Data System (ADS)

Hu, Shuo; Chen, Yuxin; Wang, Huaibao; Zuo, Yaqing

2014-11-01

Human action recognition is an important area of pattern recognition today due to its direct application and need in various occasions like surveillance and virtual reality. In this paper, a simple and effective human action recognition method is presented based on the key poses of human silhouette and the spatio-temporal feature. Firstly, the contour points of human silhouette have been gotten, and the key poses are learned by means of K-means clustering based on the Euclidean distance between each contour point and the centre point of the human silhouette, and then the type of each action is labeled for further match. Secondly, we obtain the trajectories of centre point of each frame, and create a spatio-temporal feature value represented by W to describe the motion direction and speed of each action. The value W contains the information of location and temporal order of each point on the trajectories. Finally, the matching stage is performed by comparing the key poses and W between training sequences and test sequences, the nearest neighbor sequences is found and its label supplied the final result. Experiments on the public available Weizmann datasets show the proposed method can improve accuracy by distinguishing amphibious poses and increase suitability for real-time applications by reducing the computational cost.

Why the long face? The importance of vertical image structure for biological "barcodes" underlying face recognition.

PubMed

Spence, Morgan L; Storrs, Katherine R; Arnold, Derek H

2014-07-29

Humans are experts at face recognition. The mechanisms underlying this complex capacity are not fully understood. Recently, it has been proposed that face recognition is supported by a coarse-scale analysis of visual information contained in horizontal bands of contrast distributed along the vertical image axis-a biological facial "barcode" (Dakin & Watt, 2009). A critical prediction of the facial barcode hypothesis is that the distribution of image contrast along the vertical axis will be more important for face recognition than image distributions along the horizontal axis. Using a novel paradigm involving dynamic image distortions, a series of experiments are presented examining famous face recognition impairments from selectively disrupting image distributions along the vertical or horizontal image axes. Results show that disrupting the image distribution along the vertical image axis is more disruptive for recognition than matched distortions along the horizontal axis. Consistent with the facial barcode hypothesis, these results suggest that human face recognition relies disproportionately on appropriately scaled distributions of image contrast along the vertical image axis. © 2014 ARVO.

An investigation of potential applications of OP-SAPS: Operational Sampled Analog Processors

NASA Technical Reports Server (NTRS)

Parrish, E. A.; Mcvey, E. S.

1977-01-01

The application of OP-SAP's (operational sampled analog processors) in pattern recognition system is summarized. Areas investigated include: (1) human face recognition; (2) a high-speed programmable transversal filter system; (3) discrete word (speech) recognition; and (4) a resolution enhancement system.

Current trends in small vocabulary speech recognition for equipment control

NASA Astrophysics Data System (ADS)

Doukas, Nikolaos; Bardis, Nikolaos G.

2017-09-01

Speech recognition systems allow human - machine communication to acquire an intuitive nature that approaches the simplicity of inter - human communication. Small vocabulary speech recognition is a subset of the overall speech recognition problem, where only a small number of words need to be recognized. Speaker independent small vocabulary recognition can find significant applications in field equipment used by military personnel. Such equipment may typically be controlled by a small number of commands that need to be given quickly and accurately, under conditions where delicate manual operations are difficult to achieve. This type of application could hence significantly benefit by the use of robust voice operated control components, as they would facilitate the interaction with their users and render it much more reliable in times of crisis. This paper presents current challenges involved in attaining efficient and robust small vocabulary speech recognition. These challenges concern feature selection, classification techniques, speaker diversity and noise effects. A state machine approach is presented that facilitates the voice guidance of different equipment in a variety of situations.

Optimized Periocular Template Selection for Human Recognition

PubMed Central

Sa, Pankaj K.; Majhi, Banshidhar

2013-01-01

A novel approach for selecting a rectangular template around periocular region optimally potential for human recognition is proposed. A comparatively larger template of periocular image than the optimal one can be slightly more potent for recognition, but the larger template heavily slows down the biometric system by making feature extraction computationally intensive and increasing the database size. A smaller template, on the contrary, cannot yield desirable recognition though the smaller template performs faster due to low computation for feature extraction. These two contradictory objectives (namely, (a) to minimize the size of periocular template and (b) to maximize the recognition through the template) are aimed to be optimized through the proposed research. This paper proposes four different approaches for dynamic optimal template selection from periocular region. The proposed methods are tested on publicly available unconstrained UBIRISv2 and FERET databases and satisfactory results have been achieved. Thus obtained template can be used for recognition of individuals in an organization and can be generalized to recognize every citizen of a nation. PMID:23984370

Human HDAC7 Harbors a Class IIa Histone Deacetylase-specific Zinc Binding Motif and Cryptic Deacetylase Activity*S⃞

PubMed Central

Schuetz, Anja; Min, Jinrong; Allali-Hassani, Abdellah; Schapira, Matthieu; Shuen, Michael; Loppnau, Peter; Mazitschek, Ralph; Kwiatkowski, Nick P.; Lewis, Timothy A.; Maglathin, Rebecca L.; McLean, Thomas H.; Bochkarev, Alexey; Plotnikov, Alexander N.; Vedadi, Masoud; Arrowsmith, Cheryl H.

2008-01-01

Histone deacetylases (HDACs) are protein deacetylases that play a role in repression of gene transcription and are emerging targets in cancer therapy. Here, we characterize the structure and enzymatic activity of the catalytic domain of human HDAC7 (cdHDAC7). Although HDAC7 normally exists as part of a multiprotein complex, we show that cdHDAC7 has a low level of deacetylase activity which can be inhibited by known HDAC inhibitors. The crystal structures of human cdHDAC7 and its complexes with two hydroxamate inhibitors are the first structures of the catalytic domain of class IIa HDACs and demonstrate significant differences with previously reported class I and class IIb-like HDAC structures. We show that cdHDAC7 has an additional class IIa HDAC-specific zinc binding motif adjacent to the active site which is likely to participate in substrate recognition and protein-protein interaction and may provide a site for modulation of activity. Furthermore, a different active site topology results in modified catalytic properties and in an enlarged active site pocket. Our studies provide mechanistic insights into class IIa HDACs and facilitate the design of specific modulators. PMID:18285338

Human HDAC7 harbors a class IIa histone deacetylase-specific zinc binding motif and cryptic deacetylase activity.

PubMed

Schuetz, Anja; Min, Jinrong; Allali-Hassani, Abdellah; Schapira, Matthieu; Shuen, Michael; Loppnau, Peter; Mazitschek, Ralph; Kwiatkowski, Nick P; Lewis, Timothy A; Maglathin, Rebecca L; McLean, Thomas H; Bochkarev, Alexey; Plotnikov, Alexander N; Vedadi, Masoud; Arrowsmith, Cheryl H

2008-04-25

Histone deacetylases (HDACs) are protein deacetylases that play a role in repression of gene transcription and are emerging targets in cancer therapy. Here, we characterize the structure and enzymatic activity of the catalytic domain of human HDAC7 (cdHDAC7). Although HDAC7 normally exists as part of a multiprotein complex, we show that cdHDAC7 has a low level of deacetylase activity which can be inhibited by known HDAC inhibitors. The crystal structures of human cdHDAC7 and its complexes with two hydroxamate inhibitors are the first structures of the catalytic domain of class IIa HDACs and demonstrate significant differences with previously reported class I and class IIb-like HDAC structures. We show that cdHDAC7 has an additional class IIa HDAC-specific zinc binding motif adjacent to the active site which is likely to participate in substrate recognition and protein-protein interaction and may provide a site for modulation of activity. Furthermore, a different active site topology results in modified catalytic properties and in an enlarged active site pocket. Our studies provide mechanistic insights into class IIa HDACs and facilitate the design of specific modulators.

Gender Recognition from Point-Light Walkers

ERIC Educational Resources Information Center

Pollick, Frank E.; Kay, Jim W.; Heim, Katrin; Stringer, Rebecca

2005-01-01

Point-light displays of human gait provide information sufficient to recognize the gender of a walker and are taken as evidence of the exquisite tuning of the visual system to biological motion. The authors revisit this topic with the goals of quantifying human efficiency at gender recognition. To achieve this, the authors first derive an ideal…

Development of Flexible Visual Recognition Memory in Human Infants

ERIC Educational Resources Information Center

Robinson, Astri J.; Pascalis, Olivier

2004-01-01

Research using the visual paired comparison task has shown that visual recognition memory across changing contexts is dependent on the integrity of the hippocampal formation in human adults and in monkeys. The acquisition of contextual flexibility may contribute to the change in memory performance that occurs late in the first year of life. To…

Human gait recognition by pyramid of HOG feature on silhouette images

NASA Astrophysics Data System (ADS)

Yang, Guang; Yin, Yafeng; Park, Jeanrok; Man, Hong

2013-03-01

As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

Step-by-step mechanism of DNA damage recognition by human 8-oxoguanine DNA glycosylase.

PubMed

Kuznetsova, Alexandra A; Kuznetsov, Nikita A; Ishchenko, Alexander A; Saparbaev, Murat K; Fedorova, Olga S

2014-01-01

Extensive structural studies of human DNA glycosylase hOGG1 have revealed essential conformational changes of the enzyme. However, at present there is little information about the time scale of the rearrangements of the protein structure as well as the dynamic behavior of individual amino acids. Using pre-steady-state kinetic analysis with Trp and 2-aminopurine fluorescence detection the conformational dynamics of hOGG1 wild-type (WT) and mutants Y203W, Y203A, H270W, F45W, F319W and K249Q as well as DNA-substrates was examined. The roles of catalytically important amino acids F45, Y203, K249, H270, and F319 in the hOGG1 enzymatic pathway and their involvement in the step-by-step mechanism of oxidative DNA lesion recognition and catalysis were elucidated. The results show that Tyr-203 participates in the initial steps of the lesion site recognition. The interaction of the His-270 residue with the oxoG base plays a key role in the insertion of the damaged base into the active site. Lys-249 participates not only in the catalytic stages but also in the processes of local duplex distortion and flipping out of the oxoG residue. Non-damaged DNA does not form a stable complex with hOGG1, although a complex with a flipped out guanine base can be formed transiently. The kinetic data obtained in this study significantly improves our understanding of the molecular mechanism of lesion recognition by hOGG1. © 2013.

Neuroanatomical substrates involved in unrelated false facial recognition.

PubMed

Ronzon-Gonzalez, Eliane; Hernandez-Castillo, Carlos R; Pasaye, Erick H; Vaca-Palomares, Israel; Fernandez-Ruiz, Juan

2017-11-22

Identifying faces is a process central for social interaction and a relevant factor in eyewitness theory. False recognition is a critical mistake during an eyewitness's identification scenario because it can lead to a wrongful conviction. Previous studies have described neural areas related to false facial recognition using the standard Deese/Roediger-McDermott (DRM) paradigm, triggering related false recognition. Nonetheless, misidentification of faces without trying to elicit false memories (unrelated false recognition) in a police lineup could involve different cognitive processes, and distinct neural areas. To delve into the neural circuitry of unrelated false recognition, we evaluated the memory and response confidence of participants while watching faces photographs in an fMRI task. Functional activations of unrelated false recognition were identified by contrasting the activation on this condition vs. the activations related to recognition (hits) and correct rejections. The results identified the right precentral and cingulate gyri as areas with distinctive activations during false recognition events suggesting a conflict resulting in a dysfunction during memory retrieval. High confidence suggested that about 50% of misidentifications may be related to an unconscious process. These findings add to our understanding of the construction of facial memories and its biological basis, and the fallibility of the eyewitness testimony.

Response-related fMRI of veridical and false recognition of words.

PubMed

Heun, Reinhard; Jessen, Frank; Klose, Uwe; Erb, Michael; Granath, Dirk-Oliver; Grodd, Wolfgang

2004-02-01

Studies on the relation between local cerebral activation and retrieval success usually compared high and low performance conditions, and thus showed performance-related activation of different brain areas. Only a few studies directly compared signal intensities of different response categories during retrieval. During verbal recognition, we recently observed increased parieto-occipital activation related to false alarms. The present study intends to replicate and extend this observation by investigating common and differential activation by veridical and false recognition. Fifteen healthy volunteers performed a verbal recognition paradigm using 160 learned target and 160 new distractor words. The subjects had to indicate whether they had learned the word before or not. Echo-planar MRI of blood-oxygen-level-dependent signal changes was performed during this recognition task. Words were classified post hoc according to the subjects' responses, i.e. hits, false alarms, correct rejections and misses. Response-related fMRI-analysis was used to compare activation associated with the subjects' recognition success, i.e. signal intensities related to the presentation of words were compared by the above-mentioned four response types. During recognition, all word categories showed increased bilateral activation of the inferior frontal gyrus, the inferior temporal gyrus, the occipital lobe and the brainstem in comparison with the control condition. Hits and false alarms activated several areas including the left medial and lateral parieto-occipital cortex in comparison with subjectively unknown items, i.e. correct rejections and misses. Hits showed more pronounced activation in the medial, false alarms in the lateral parts of the left parieto-occipital cortex. Veridical and false recognition show common as well as different areas of cerebral activation in the left parieto-occipital lobe: increased activation of the medial parietal cortex by hits may correspond to true recognition, increased activation of the parieto-occipital cortex by false alarms may correspond to familiarity decisions. Further studies are needed to investigate the reasons for false decisions in healthy subjects and patients with memory problems.

Spatial-frequency spectra of printed characters and human visual perception.

PubMed

Põder, Endel

2003-06-01

It is well known that certain spatial frequency (SF) bands are more important than others for character recognition. Solomon and Pelli [Nature 369 (1994) 395-397] have concluded that human pattern recognition mechanism is able to use only a narrow band from available SF spectrum of letters. However, the SF spectra of letters themselves have not been studied carefully. Here I report the results of an analysis of SF spectra of printed characters and discuss their relationship to the observed band-pass nature of letter recognition.

Model and algorithmic framework for detection and correction of cognitive errors.

PubMed

Feki, Mohamed Ali; Biswas, Jit; Tolstikov, Andrei

2009-01-01

This paper outlines an approach that we are taking for elder-care applications in the smart home, involving cognitive errors and their compensation. Our approach involves high level modeling of daily activities of the elderly by breaking down these activities into smaller units, which can then be automatically recognized at a low level by collections of sensors placed in the homes of the elderly. This separation allows us to employ plan recognition algorithms and systems at a high level, while developing stand-alone activity recognition algorithms and systems at a low level. It also allows the mixing and matching of multi-modality sensors of various kinds that go to support the same high level requirement. Currently our plan recognition algorithms are still at a conceptual stage, whereas a number of low level activity recognition algorithms and systems have been developed. Herein we present our model for plan recognition, providing a brief survey of the background literature. We also present some concrete results that we have achieved for activity recognition, emphasizing how these results are incorporated into the overall plan recognition system.

The cingulo-opercular network provides word-recognition benefit.

PubMed

Vaden, Kenneth I; Kuchinsky, Stefanie E; Cute, Stephanie L; Ahlstrom, Jayne B; Dubno, Judy R; Eckert, Mark A

2013-11-27

Recognizing speech in difficult listening conditions requires considerable focus of attention that is often demonstrated by elevated activity in putative attention systems, including the cingulo-opercular network. We tested the prediction that elevated cingulo-opercular activity provides word-recognition benefit on a subsequent trial. Eighteen healthy, normal-hearing adults (10 females; aged 20-38 years) performed word recognition (120 trials) in multi-talker babble at +3 and +10 dB signal-to-noise ratios during a sparse sampling functional magnetic resonance imaging (fMRI) experiment. Blood oxygen level-dependent (BOLD) contrast was elevated in the anterior cingulate cortex, anterior insula, and frontal operculum in response to poorer speech intelligibility and response errors. These brain regions exhibited significantly greater correlated activity during word recognition compared with rest, supporting the premise that word-recognition demands increased the coherence of cingulo-opercular network activity. Consistent with an adaptive control network explanation, general linear mixed model analyses demonstrated that increased magnitude and extent of cingulo-opercular network activity was significantly associated with correct word recognition on subsequent trials. These results indicate that elevated cingulo-opercular network activity is not simply a reflection of poor performance or error but also supports word recognition in difficult listening conditions.

In-vehicle group activity modeling and simulation in sensor-based virtual environment

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Telagamsetti, Durga; Poshtyar, Azin; Chan, Alex; Hu, Shuowen

2016-05-01

Human group activity recognition is a very complex and challenging task, especially for Partially Observable Group Activities (POGA) that occur in confined spaces with limited visual observability and often under severe occultation. In this paper, we present IRIS Virtual Environment Simulation Model (VESM) for the modeling and simulation of dynamic POGA. More specifically, we address sensor-based modeling and simulation of a specific category of POGA, called In-Vehicle Group Activities (IVGA). In VESM, human-alike animated characters, called humanoids, are employed to simulate complex in-vehicle group activities within the confined space of a modeled vehicle. Each articulated humanoid is kinematically modeled with comparable physical attributes and appearances that are linkable to its human counterpart. Each humanoid exhibits harmonious full-body motion - simulating human-like gestures and postures, facial impressions, and hands motions for coordinated dexterity. VESM facilitates the creation of interactive scenarios consisting of multiple humanoids with different personalities and intentions, which are capable of performing complicated human activities within the confined space inside a typical vehicle. In this paper, we demonstrate the efficiency and effectiveness of VESM in terms of its capabilities to seamlessly generate time-synchronized, multi-source, and correlated imagery datasets of IVGA, which are useful for the training and testing of multi-source full-motion video processing and annotation. Furthermore, we demonstrate full-motion video processing of such simulated scenarios under different operational contextual constraints.

Responses of innate immune cells to group A Streptococcus

PubMed Central

Fieber, Christina; Kovarik, Pavel

2014-01-01

Group A Streptococcus (GAS), also called Streptococcus pyogenes, is a Gram-positive beta-hemolytic human pathogen which causes a wide range of mostly self-limiting but also several life-threatening diseases. Innate immune responses are fundamental for defense against GAS, yet their activation by pattern recognition receptors (PRRs) and GAS-derived pathogen-associated molecular patterns (PAMPs) is incompletely understood. In recent years, the use of animal models together with the powerful tools of human molecular genetics began shedding light onto the molecular mechanisms of innate immune defense against GAS. The signaling adaptor MyD88 was found to play a key role in launching the immune response against GAS in both humans and mice, suggesting that PRRs of the Toll-like receptor (TLR) family are involved in sensing this pathogen. The specific TLRs and their ligands have yet to be identified. Following GAS recognition, induction of cytokines such as TNF and type I interferons (IFNs), leukocyte recruitment, phagocytosis, and the formation of neutrophil extracellular traps (NETs) have been recognized as key events in host defense. A comprehensive knowledge of these mechanisms is needed in order to understand their frequent failure against GAS immune evasion strategies. PMID:25325020

Adrenergic enhancement of consolidation of object recognition memory.

PubMed

Dornelles, Arethuza; de Lima, Maria Noemia Martins; Grazziotin, Manoela; Presti-Torres, Juliana; Garcia, Vanessa Athaide; Scalco, Felipe Siciliani; Roesler, Rafael; Schröder, Nadja

2007-07-01

Extensive evidence indicates that epinephrine (EPI) modulates memory consolidation for emotionally arousing tasks in animals and human subjects. However, previous studies have not examined the effects of EPI on consolidation of recognition memory. Here we report that systemic administration of EPI enhances consolidation of memory for a novel object recognition (NOR) task under different training conditions. Control male rats given a systemic injection of saline (0.9% NaCl) immediately after NOR training showed significant memory retention when tested at 1.5 or 24, but not 96h after training. In contrast, rats given a post-training injection of EPI showed significant retention of NOR at all delays. In a second experiment using a different training condition, rats treated with EPI, but not SAL-treated animals, showed significant NOR retention at both 1.5 and 24-h delays. We next showed that the EPI-induced enhancement of retention tested at 96h after training was prevented by pretraining systemic administration of the beta-adrenoceptor antagonist propranolol. The findings suggest that, as previously observed in experiments using aversively motivated tasks, epinephrine modulates consolidation of recognition memory and that the effects require activation of beta-adrenoceptors.

The role of the hippocampus in recognition memory.

PubMed

Bird, Chris M

2017-08-01

Many theories of declarative memory propose that it is supported by partially separable processes underpinned by different brain structures. The hippocampus plays a critical role in binding together item and contextual information together and processing the relationships between individual items. By contrast, the processing of individual items and their later recognition can be supported by extrahippocampal regions of the medial temporal lobes (MTL), particularly when recognition is based on feelings of familiarity without the retrieval of any associated information. These theories are domain-general in that "items" might be words, faces, objects, scenes, etc. However, there is mixed evidence that item recognition does not require the hippocampus, or that familiarity-based recognition can be supported by extrahippocampal regions. By contrast, there is compelling evidence that in humans, hippocampal damage does not affect recognition memory for unfamiliar faces, whilst recognition memory for several other stimulus classes is impaired. I propose that regions outside of the hippocampus can support recognition of unfamiliar faces because they are perceived as discrete items and have no prior conceptual associations. Conversely, extrahippocampal processes are inadequate for recognition of items which (a) have been previously experienced, (b) are conceptually meaningful, or (c) are perceived as being comprised of individual elements. This account reconciles findings from primate and human studies of recognition memory. Furthermore, it suggests that while the hippocampus is critical for binding and relational processing, these processes are required for item recognition memory in most situations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structure of the human protein kinase MPSK1 reveals an atypical activation loop architecture.

PubMed

Eswaran, Jeyanthy; Bernad, Antonio; Ligos, Jose M; Guinea, Barbara; Debreczeni, Judit E; Sobott, Frank; Parker, Sirlester A; Najmanovich, Rafael; Turk, Benjamin E; Knapp, Stefan

2008-01-01

The activation segment of protein kinases is structurally highly conserved and central to regulation of kinase activation. Here we report an atypical activation segment architecture in human MPSK1 comprising a beta sheet and a large alpha-helical insertion. Sequence comparisons suggested that similar activation segments exist in all members of the MPSK1 family and in MAST kinases. The consequence of this nonclassical activation segment on substrate recognition was studied using peptide library screens that revealed a preferred substrate sequence of X-X-P/V/I-phi-H/Y-T*-N/G-X-X-X (phi is an aliphatic residue). In addition, we identified the GTPase DRG1 as an MPSK1 interaction partner and specific substrate. The interaction domain in DRG1 was mapped to the N terminus, leading to recruitment and phosphorylation at Thr100 within the GTPase domain. The presented data reveal an atypical kinase structural motif and suggest a role of MPSK1 regulating DRG1, a GTPase involved in regulation of cellular growth.

Quality of life differences in patients with right- versus left-sided facial paralysis: Universal preference of right-sided human face recognition.

PubMed

Ryu, Nam Gyu; Lim, Byung Woo; Cho, Jae Keun; Kim, Jin

2016-09-01

We investigated whether experiencing right- or left-sided facial paralysis would affect an individual's ability to recognize one side of the human face using hybrid hemi-facial photos by preliminary study. Further investigation looked at the relationship between facial recognition ability, stress, and quality of life. To investigate predominance of one side of the human face for face recognition, 100 normal participants (right-handed: n = 97, left-handed: n = 3, right brain dominance: n = 56, left brain dominance: n = 44) answered a questionnaire that included hybrid hemi-facial photos developed to determine decide superiority of one side for human face recognition. To determine differences of stress level and quality of life between individuals experiencing right- and left-sided facial paralysis, 100 patients (right side:50, left side:50, not including traumatic facial nerve paralysis) answered a questionnaire about facial disability index test and quality of life (SF-36 Korean version). Regardless of handedness or hemispheric dominance, the proportion of predominance of the right side in human face recognition was larger than the left side (71% versus 12%, neutral: 17%). Facial distress index of the patients with right-sided facial paralysis was lower than that of left-sided patients (68.8 ± 9.42 versus 76.4 ± 8.28), and the SF-36 scores of right-sided patients were lower than left-sided patients (119.07 ± 15.24 versus 123.25 ± 16.48, total score: 166). Universal preference for the right side in human face recognition showed worse psychological mood and social interaction in patients with right-side facial paralysis than left-sided paralysis. This information is helpful to clinicians in that psychological and social factors should be considered when treating patients with facial-paralysis. Copyright © 2016 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Recognition Is Still a Top Motivator.

ERIC Educational Resources Information Center

Cherrington, David J.; Wixom, B. Jackson, Jr.

1983-01-01

Motivation theories can be generalized to a common principle of human behavior: people do what they are reinforced or rewarded for doing. The most successful motivational recognition programs share five key elements: a recognition symbol, an attractive means of display, a meaningful presentation, effective promotion, and periodic evaluation. (MLF)

Cognitive Processing Hardware Elements

DTIC Science & Technology

2005-01-31

characters. Results will be presented below. 1 4. Recognition of human faces. There are many other possible applications such as facial recognition and...For the experiments in facial recognition , we have used a 3-layer autoassociative neural network having the following specifications: "* The input...using the facial recognition system described in the section above as an example. This system uses an autoassociative neural network containing over 10

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

NASA Technical Reports Server (NTRS)

Tescher, Andrew G. (Editor)

1989-01-01

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

Advanced Doppler radar physiological sensing technique for drone detection

NASA Astrophysics Data System (ADS)

Yoon, Ji Hwan; Xu, Hao; Garcia Carrillo, Luis R.

2017-05-01

A 24 GHz medium-range human detecting sensor, using the Doppler Radar Physiological Sensing (DRPS) technique, which can also detect unmanned aerial vehicles (UAVs or drones), is currently under development for potential rescue and anti-drone applications. DRPS systems are specifically designed to remotely monitor small movements of non-metallic human tissues such as cardiopulmonary activity and respiration. Once optimized, the unique capabilities of DRPS could be used to detect UAVs. Initial measurements have shown that DRPS technology is able to detect moving and stationary humans, as well as largely non-metallic multi-rotor drone helicopters. Further data processing will incorporate pattern recognition to detect multiple signatures (motor vibration and hovering patterns) of UAVs.

Multi-resolution analysis for ear recognition using wavelet features

NASA Astrophysics Data System (ADS)

Shoaib, M.; Basit, A.; Faye, I.

2016-11-01

Security is very important and in order to avoid any physical contact, identification of human when they are moving is necessary. Ear biometric is one of the methods by which a person can be identified using surveillance cameras. Various techniques have been proposed to increase the ear based recognition systems. In this work, a feature extraction method for human ear recognition based on wavelet transforms is proposed. The proposed features are approximation coefficients and specific details of level two after applying various types of wavelet transforms. Different wavelet transforms are applied to find the suitable wavelet. Minimum Euclidean distance is used as a matching criterion. Results achieved by the proposed method are promising and can be used in real time ear recognition system.

Time-frequency feature representation using multi-resolution texture analysis and acoustic activity detector for real-life speech emotion recognition.

PubMed

Wang, Kun-Ching

2015-01-14

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

Memory Accumulation Mechanisms in Human Cortex Are Independent of Motor Intentions

PubMed Central

Tosoni, Annalisa; Mignogna, Valeria; McAvoy, Mark P.; Shulman, Gordon L.; Corbetta, Maurizio; Romani, Gian Luca

2014-01-01

Previous studies on perceptual decision-making have often emphasized a tight link between decisions and motor intentions. Human decisions, however, also depend on memories or experiences that are not closely tied to specific motor responses. Recent neuroimaging findings have suggested that, during episodic retrieval, parietal activity reflects the accumulation of evidence for memory decisions. It is currently unknown, however, whether these evidence accumulation signals are functionally linked to signals for motor intentions coded in frontoparietal regions and whether activity in the putative memory accumulator tracks the amount of evidence for only previous experience, as reflected in “old” reports, or for both old and new decisions, as reflected in the accuracy of memory judgments. Here, human participants used saccadic-eye and hand-pointing movements to report recognition judgments on pictures defined by different degrees of evidence for old or new decisions. A set of cortical regions, including the middle intraparietal sulcus, showed a monotonic variation of the fMRI BOLD signal that scaled with perceived memory strength (older > newer), compatible with an asymmetrical memory accumulator. Another set, including the hippocampus and the angular gyrus, showed a nonmonotonic response profile tracking memory accuracy (higher > lower evidence), compatible with a symmetrical accumulator. In contrast, eye and hand effector-specific regions in frontoparietal cortex tracked motor intentions but were not modulated by the amount of evidence for the effector outcome. We conclude that item recognition decisions are supported by a combination of symmetrical and asymmetrical accumulation signals largely segregated from motor intentions. PMID:24828652