Continuous Regional Arterial Infusion with Fluorouracil and Octreotide Attenuates Severe Acute Pancreatitis in a Canine Model

Aim: To investigate the therapeutic effects of fluorouracil (5-Fu) and octreotide (Oct) continuous regional arterial infusion (CRAI,) alone or in combination, was administered in a canine model of severe acute pancreatitis (SAP). Materials and Methods: The animals were divided into five groups; group A (Sham), group B (SAP), group C (SAP and 5-Fu), group D (SAP and Oct), and group E (SAP and 5-Fu + Oct). Levels of amylase, alpha-tumor necrosis factor (TNF-alpha), blood urea nitrogen (BUN), creatinine, thromboxane B2 and 6-keto-prostaglandin F1 alpha were measured both before and after the induction of SAP. Pathologic examination of the pancreas and kidneys was performed after termination of the study. Results: Pathologic changes noted in the pancreas in SAP significantly improved following CRAI with either single or combined administration of 5-Fu and Oct, where combination therapy demonstrated the lowest injury score. All treatment groups had significantly lower levels of serum TNF-alpha and amylase activity (P<0.05), though only groups D and E had a lower BUN level as compared to group B. The plasma thromboxane B-2 level increased in SAP, but the ratio of thromboxane B-2/6-keto-prostaglandin F-1 alpha decreased in the treatment groups, with the combination therapy (group E) demonstrating the lowest ratio as compared to the other 3 experimental groups (P<0.05). Conclusions: The findings in the present study demonstrate an attenuation of SAP in a canine model following CRAI administration with 5-Fu or Oct, alone or in combination

Associating Facial Expressions and Upper-Body Gestures with Learning Tasks for Enhancing Intelligent Tutoring Systems

Learning involves a substantial amount of cognitive, social and emotional states. Therefore, recognizing and understanding these states in the context of learning is key in designing informed interventions and addressing the needs of the individual student to provide personalized education. In this paper, we explore the automatic detection of learner’s nonverbal behaviors involving hand-over-face gestures, head and eye movements and emotions via facial expressions during learning. The proposed computer vision-based behavior monitoring method uses a low-cost webcam and can easily be integrated with modern tutoring technologies. We investigate these behaviors in-depth over time in a classroom session of 40 minutes involving reading and problem-solving exercises. The exercises in the sessions are divided into three categories: an easy, medium and difficult topic within the context of undergraduate computer science. We found that there is a significant increase in head and eye movements as time progresses, as well as with the increase of difficulty level. We demonstrated that there is a considerable occurrence of hand-over-face gestures (on average 21.35%) during the 40 minutes session and is unexplored in the education domain. We propose a novel deep learning approach for automatic detection of hand-over-face gestures in images with a classification accuracy of 86.87%. There is a prominent increase in hand-over-face gestures when the difficulty level of the given exercise increases. The hand-over-face gestures occur more frequently during problem-solving (easy 23.79%, medium 19.84% and difficult 30.46%) exercises in comparison to reading (easy 16.20%, medium 20.06% and difficult 20.18%)

Topological optimization of patterned silicon anode by finite element analysis

© 2019 Elsevier Ltd A silicon-based anode in lithium-ion battery exhibits several times higher gravimetric energy storage capacity compared to an established carbon-based anode. However, the cycling performance of the silicon anode is poor due to the extremely large volume variation during the intercalation of lithium ions. The micro-structuring of silicon facilitates cycling performance. In particular, patterned microstructures are discussed as a possible solution. The large volumetric change can be adopted in such structures by bending walls and rotation around fixed vertexes. Nevertheless, the cycling performance of known patterned anodes remains poor due to plastic deformations. In this paper, a new square-based-patterned silicon anode is proposed and analyzed using the finite element method. The maximal stress in the topologically optimized structure is below the yield strength of lithiated silicon. In contrast to known structures, the deformed pattern of the new structure is explicitly defined by its initial geometry. A similar modification of the honeycomb-based-patterned anode leads to a slightly larger bending stress, but still below the yield stress of lithiated silicon. The related pure elastic deformation behavior is favorable to a prolonged cycling life of the micro-structured silicon anode. The developed approach can be applied for analysis of other severely swelling metamaterials

Modeling large patterned deflection during lithiation of micro-structured silicon

The application of silicon (Si) as potential anode material in Li-ion batteries provides a more than nine-fold increase in gravimetric storage capacity compared to conventional graphite anodes. However, full lithiation of Si induces the volume to increase by approximately 300%. Such enormous volume expansion causes large mechanical stress, resulting in non-elastic deformation and crack formation. This ultimately leads to anode failure and strong decrease in cycle life. This problem can be resolved by making use of structured anodes with small dimensions. Particularly honeycomb-shaped microstructures turned out to be beneficial in this respect. In the present paper, finite element modeling was applied to describe the experimentally observed mechanical deformation of honeycomb-structured Si anodes upon lithiation. A close agreement between simulated and experimentally observed shape changes is observed in all cases. The predictive ability of the model was further exploited by investigating alternative geometries, such as square-based microstructure. Strikingly, dimension and pattern optimization shows that the stress levels can be reduced even below the yield strength, while maintaining the footprint-area-specific storage capacity of the microstructures. The pure elastic deformation is highly beneficial for the fatigue resistance of optimized silicon structures. The obtained results are directly applicable for other (de)lithiating materials, such as mixed ionic–electronic conductors (MIEC) widely applied in Li-ion and future Na-ion batteries

Bags of Graphs for Human Action Recognition

International audienceBags of visual words are a well known approach for images classification that also has been used in human action recognition. This model proposes to represent images or videos in a structure referred to as bag of visual words before classifying. The process of representing a video in a bag of visual words is known as the encoding process and is based on mapping the interest points detected in the scene into the new structure by means of a codebook. In this paper we propose to improve the representativeness of this model including the structural relations between the interest points using graph sequences. The proposed model achieves very competitive results for human action recognition and could also be applied to solve graph sequences classification problems