Discrimination and numerical analysis of human pathogenic Candida albicans strains based on SDSPAGE protein profiles

In the present study, 21 Candida albicans strains were investigated using the commercial kit API 20C AUX system and the numerical analysis of whole-cell protein profiles. The results of the commercial kitconfirmed that the all the strains belonged to C. albicans species. However, the research indicated that SDS-PAGE of polypeptides of whole-cell extracts can provide more valuable taxonomic informationthan conventional yeast test kits at the subspecies level. Despite the fact that C. albicans subtypes isolated from different anatomical sites had similar protein profiles, there were some distinctive proteinbands. Numerical analysis of whole-cell protein profiles of all strains revealed 2 major clusters at similarity degrees of between 46.26 and 100%. Moreover, the results of numerical analysis confirmedthat each cluster had characteristic and distinctive protein profiles. The research showed that, the morphological examination of yeast isolates remains essential to obtaining a correct identification, boththe commercial yeast kit system and the numerical analysis of whole-cell protein patterns can be useful for the more reliable identification of C. albicans strains

Accelerated search and design of stretchable graphene kirigami using machine learning

Making kirigami-inspired cuts into a sheet has been shown to be an effective way of designing stretchable materials with metamorphic properties where the 2D shape can transform into complex 3D shapes. However, finding the optimal solutions is not straightforward as the number of possible cutting patterns grows exponentially with system size. Here, we report on how machine learning (ML) can be used to approximate the target properties, such as yield stress and yield strain, as a function of cutting pattern. Our approach enables the rapid discovery of kirigami designs that yield extreme stretchability as verified by molecular dynamics (MD) simulations. We find that convolutional neural networks, commonly used for classification in vision tasks, can be applied for regression to achieve an accuracy close to the precision of the MD simulations. This approach can then be used to search for optimal designs that maximize elastic stretchability with only 1000 training samples in a large design space of ∼4×106 candidate designs. This example demonstrates the power and potential of ML in finding optimal kirigami designs at a fraction of iterations that would be required of a purely MD or experiment-based approach, where no prior knowledge of the governing physics is known or available.P. Z. H. developed the codes, performed the simulations and data analysis, and wrote the manuscript with input from all authors. P. Z. H. and E. D. C. developed the machine learning methods. P. Z. H., D. K. C. and H. S. P. acknowledge the Hariri Institute Research Incubation Grant No. 2018-02-002 and the Boston University High Performance Shared Computing Cluster. P. Z. H. is grateful for the Hariri Graduate Fellowship. P. Z. H. thank Grace Gu and Adrian Yi for helpful discussions. (2018-02-002 - Hariri Graduate Fellowship)Published versio

Semantic analysis of field sports video using a petri-net of audio-visual concepts

The most common approach to automatic summarisation and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets which can be used for both semantic description and event detection within sports videos. Low-level algorithms for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts is formally defined to describe video content. We call this a Perception Concept Network-Petri Net (PCN-PN) model. Using PCN-PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN-PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework

The effect of CoQ10 and vitamin E on serum total sialic acid, lipid-bound sialic acid, some trace elements and minerals in rats induced with doxorubicin

This study was designed to evaluate the effect of CoQ10 and vitamin E on serum total sialic acid (TSA), lipid bound sialic acid (LSA) and some elements in rat administered doxorubicin (DXR). Cu levels were increased in the group treated with DXR + vitamin E in comparison with DXR (p<0.05) and CoQ10 groups (p = 0.001). Furthermore, copper levels were increased in the group treated with DXR + CoQ10 in comparison with CoQ10 group (p < 0.05). Zn levels were decreased in the group treated with DXR + vitamin E in comparison with CoQ10 group (p < 0.05). Mg levels were decreased in subjects treated with DXR + vitamin E in comparison with the control group values (p < 0.05). Particularly, the observed increase in Cu levels in rats from DXR + vitamin E group might be due to the decrease of vitamin E. However, the oxidative damage could be as a result of DXR occurence and may be helpful to clinicians in chemotherapy using anthracycline.Key words: Doxorubicin, total sialic acid (TSA), lipid bound sialic acid (LSA), trace elements, minerals

Video semantic content analysis framework based on ontology combined MPEG-7

The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. New multimedia standard, MPEG-7, provides the rich functionalities to enable the generation of audiovisual descriptions and is expressed solely in XML Schema which provides little support for expressing semantic knowledge. In this paper, a video semantic content analysis framework based on ontology combined MPEG-7 is presented. Domain ontology is used to define high level semantic concepts and their relations in the context of the examined domain. MPEG-7 metadata terms of audiovisual descriptions and video content analysis algorithms are expressed in this ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how low-level features and algorithms for video analysis should be applied according to different perception content. Temporal Description Logic is used to describe the semantic events, and a reasoning algorithm is proposed for events detection. The proposed framework is demonstrated in sports video domain and shows promising results

Equational characterization of Boolean function classes

Cataloged from PDF version of article.Several noteworthy classes of Boolean functions can be characterized by algebraic identities (e.g. the class of positive functions consists of all functions f satisfying the identity f(x) V f(y) V f(x V y) = f(x V y)). We give algebraic identities for several of the most frequently analyzed classes of Boolean functions (including Horn, quadratic, supermodular, and submodular functions) and proceed then to the general question of which classes of Boolean functions can be characterized by algebraic identities. We answer this question for function classes closed under addition of inessential (irrelevant) variables. Nearly all classes of interest have this property. We show that a class with this property has a characterization by algebraic identities if and only if the class is closed under the operation of variable identification. Moreover, a single identity suffices to characterize a class if and only if the number of minimal forbidden identification minors is finite. Finally, we consider characterizations by general first-order sentences, rather than just identities. We show that a class of Boolean functions can be described by an appropriate set of such first-order sentences if and only if it is closed under permutation of variables. © 2000 Elsevier Science B.V. All rights reserved