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Mixed disulfide derivatives of bovine beta-lactoglobulin (BLG) were studied by circular dichroism (CD), gel-permeation HPLC and high-sensitivity differential scanning calorimetry (HS-DSC). It was shown that modification of Cys121 with mercaptopropionic acid and mercaptoethanol does not affect the secondary structure of BLG, but results instead in tertiary and quaternary structure changes. At neutral pH, the equilibrium dimer<==>monomer of modified beta-lactoglobulin is shifted towards monomeric form. In contrast to native BLG, thermal denaturation of modified beta-lactoglobulin is fully reversible in neutral and acidic pH as demonstrated by CD and HS-DSC measurements. Modification of Cys121 results in a significant decrease of transition temperature (-6 degrees C) and enthalpy (-106 kJ/mol) at pH 2.05 while unfolding heat capacity increment remains unchanged. Thermal unfolding transitions of native and modified beta-lactoglobulin at pH 2.05 are well approximated by a two-state model suggesting that no intermediate states appear after modification. The difference in Gibbs energy of denaturation between native and modified beta-lactoglobulin, 8.5 kJ/mol at 37 degrees C and pH 2.05, does not depend on the nature of the introduced group (charged or neutral). Computer analysis of possible interactions involving Cys121 in a three-dimensional structure of beta-lactoglobulin revealed that the thiol group is too far away from neighboring residues to form side-chain hydrogen bonds. This suggests that the sulfhydryl group of Cys121 may contribute to the maintenance of BLG tertiary structure via water mediated H-bonding.

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The increase in the volume of user-generated content on Twitter has resulted in tweet sentiment analysis becoming an essential tool for the extraction of information about Twitter users' emotional state. Consequently, there has been a rapid growth of tweet sentiment analysis in the area of natural language processing. Tweet sentiment analysis is increasingly applied in many areas, such as decision support systems and recommendation systems. Therefore, improving the accuracy of tweet sentiment analysis has become practical and an area of interest for many researchers. Many approaches have tried to improve the performance of tweet sentiment analysis methods by using the feature ensemble method. However, most of the previous methods attempted to model the syntactic information of words without considering the sentiment context of these words. Besides, the positioning of words and the impact of phrases containing fuzzy sentiment have not been mentioned in many studies. This study proposed a new approach based on a feature ensemble model related to tweets containing fuzzy sentiment by taking into account elements such as lexical, word-type, semantic, position, and sentiment polarity of words. The proposed method has been experimented on with real data, and the result proves effective in improving the performance of tweet sentiment analysis in terms of the F 1 score. INDEX TERMS Feature ensemble model, fuzzy sentiment, tweet embeddings, tweet sentiment analysis.

Abstract-Automatic perception of human posture and gesture from vision input has an important role in developing intelligent video systems. In this paper, we present a novel gesture recognition approach for human computer interactivity based on marker-less upper body pose tracking in 3-D with multiple cameras. To achieve the robustness and real-time performance required for practical applications, the idea is to break the exponentially large search problem of upper body pose into two steps: first, the 3-D movements of upper body extremities (i.e., head and hands) are tracked. Then using knowledge of upper body model constraints, these extremities movements are used to infer the whole 3-D upper body motion as an inverse kinematics problem. Since the head and hand regions are typically well defined and undergo less occlusion, tracking is more reliable and could enable more robust upper body pose determination. Moreover, by breaking the problem of upper body pose tracking into two steps, the complexity is reduced considerably. Using pose tracking output, the gesture recognition is then done based on longest common subsequence similarity measurement of upper body joint angles dynamics. In our experiment, we provide an extensive validation of the proposed upper body pose tracking from 3-D extremity movement which showed good results with various subjects in different environments. Regarding the gesture recognition based on joint angles dynamics, our experimental evaluation of five subjects doing six upper body gestures with average classification accuracies over 90% indicates the promise and feasibility of the proposed system. Index Terms-Head and hands tracking, human activity analysis, inverse kinematics, smart environment, upper body motion tracking.

Abstract-Driver's body posture in 3D contains information potentially related to driver intent, driver affective state, and driver distraction. In this paper, we discuss issues and possibilities in developing a vision-based, markerless system to systematically explore the role of 3D driver posture dynamics for driver assistance. At high level, two main emphases in the proposed system are: (i) The coordination between real world driving testbed and simulation environment and (ii) The usefulness of driver posture dynamics is studied not only as an individual cue but also in relation with other contextual information (e.g. head dynamics, facial features, and vehicle dynamics). Some initial results in our experiment following these guidelines show the feasibility and promise of extracting and using 3D driver posture dynamics for driver assistance.

Abstract-It has become increasingly important to monitor the state of roadways in order to better manage traffic congestion. Sophisticated traffic management systems are in development to process both the static and mobile sensor data that provide traffic information for the roadway network. In addition to typical traffic data such as flow, density, and average traffic speed, there is now strong interest in environmental factors such as greenhouse gases, pollutant emissions, and fuel consumption from traffic. It is now possible to combine high resolution real-time traffic data with instantaneous emission models to estimate these environmental measures in real-time. In this paper, a system is described that estimates average traffic fuel economy, CO2, CO, HC, and NOx emissions using a computer vision-based methodology in combination with vehicle specific power based energy and emission models. The CalSentry system provides not only the typical traffic measures, but also gives individual vehicle trajectories (instantaneous dynamics) and recognizes vehicle categories which are used in the emission models to predict environmental parameters. This estimation process provides far more dynamic and accurate environmental information compared to static emission inventory estimation models.

Motivated from a basic tip for safe driving, "Keeping Hands on the Wheel and Eyes on the Road", this paper introduces a vision based system for driver activity analysis by observing 3D movement of driver's head and hands from multiview video. From the results of upper body and head pose tracking, semantic descriptions of driver activities are extracted in two steps: First, we determine basic activities of each upper body part (e.g. Two, one, or no hand is on the steering wheel; Head is looking left, straight, or right). Then these basic activities are combined in a fusion step to extract higher level of semantic description of driver activities (e.g. whether the driver is following the above safety tip or not). Our experimental evaluation with real-world street driving shows the promise of applying the proposed system for both post analysis of captured driving data as well as for real-time driver assistance.