High Temperature in Combination with UV Irradiation Enhances Horizontal Transfer of stx2 Gene from E. coli O157:H7 to Non-Pathogenic E. coli

Background: Shiga toxin (stx) genes have been transferred to numerous bacteria, one of which is E. coli O157:H7. It is a common belief that stx gene is transferred by bacteriophages, because stx genes are located on lambdoid prophages in the E. coli O157:H7 genome. Both E. coli O157:H7 and non-pathogenic E. coli are highly enriched in cattle feedlots. We hypothesized that strong UV radiation in combination with high temperature accelerates stx gene transfer into nonpathogenic E. coli in feedlots. Methodology/Principal Findings: E. coli O157:H7 EDL933 strain were subjected to different UV irradiation (0 or 0.5 kJ/m 2) combination with different temperature (22, 28, 30, 32, and 37uC) treatments, and the activation of lambdoid prophages was analyzed by plaque forming unit while induction of Stx2 prophages was quantified by quantitative real-time PCR. Data showed that lambdoid prophages in E. coli O157:H7, including phages carrying stx2, were activated under UV radiation, a process enhanced by elevated temperature. Consistently, western blotting analysis indicated that the production of Shiga toxin 2 was also dramatically increased by UV irradiation and high temperature. In situ colony hybridization screening indicated that these activated Stx2 prophages were capable of converting laboratory strain of E. coli K12 into new Shiga toxigenic E. coli, which were further confirmed by PCR and ELISA analysis. Conclusions/Significance: These data implicate that high environmental temperature in combination with UV irradiatio

Fusion de données complémentaires en vue de l'amélioration de la dynamique des systèmes d'imagerie

Le but de cette communication consiste à montrer que la théorie de l'évidence de Dempster-Shafer peut être appliquée au problème de fusion dans le domaine de l'amélioration de la dynamique d'un système d'imagerie à rayons X. Une hypothèse étant définie comme une épaisseur d'objet, l'incertitude et l'imprécision sur les épaisseurs sont réduites grâce à la gestion des hypothèses composées ainsi qu'à la définition de leur fonctions de masse à partir de la modélisation des histogrammes des niveaux de gris. La prise de décision est basée sur le critère de maximum de crédibilité sur des hypothèses simples (singletons) ou composées (union des hypothèses). L'augmentation de la dynamique du système d'imagerie à rayons X est réalisée en combinant chaque couple de pixels correspondant au même point physique de l'objet grâce à la règle de Dempster et à la consultation d'une table de fusion

Selection of Peptide Inhibitor to Matrix Metalloproteinase-2 Using Phage Display and Its Effects on Pancreatic Cancer Cell lines PANC-1 and CFPAC-1

Despite tremendous advances in cancer treatment and survival rates, pancreatic cancer remains one of the most deadly afflictions and the fourth leading cause of cancer deaths in the world. Matrix Metalloproteinases (MMPs) are thought to be involved in cancer progression. Matrix metalloproteinase (MMP)-2 is known to play a pivotal role in tumor invasion, metastasis and angiogenesis, and validated to be the anticancer target. Inhibition of MMP-2 activity is able to reduce the cancer cell invasion and suppress tumor growth in vivo. Two novel peptides, M204C4 and M205C4, which could specially inhibit MMP-2 activity, were identified by a phage display library screening. We showed that M204C4 and M205C4 inhibited the activity of MMP-2 in a dose dependent manner in vitro. Two peptides reduced MMP-2 mediated invasion of the pancreatic cancer cell lines PANC-1 and CFPAC-1, but not affected the expression and release of MMP-2. Furthermore, these two peptides could suppress tumor growth in vivo. Our results indicated that two peptides selected by phase display technology may be used as anticancer drugs in the future

AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention

Multi-modal medical image fusion is essential for the precise clinical diagnosis and surgical navigation since it can merge the complementary information in multi-modalities into a single image. The quality of the fused image depends on the extracted single modality features as well as the fusion rules for multi-modal information. Existing deep learning-based fusion methods can fully exploit the semantic features of each modality, they cannot distinguish the effective low and high frequency information of each modality and fuse them adaptively. To address this issue, we propose AdaFuse, in which multimodal image information is fused adaptively through frequency-guided attention mechanism based on Fourier transform. Specifically, we propose the cross-attention fusion (CAF) block, which adaptively fuses features of two modalities in the spatial and frequency domains by exchanging key and query values, and then calculates the cross-attention scores between the spatial and frequency features to further guide the spatial-frequential information fusion. The CAF block enhances the high-frequency features of the different modalities so that the details in the fused images can be retained. Moreover, we design a novel loss function composed of structure loss and content loss to preserve both low and high frequency information. Extensive comparison experiments on several datasets demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics. The ablation experiments also validate the effectiveness of the proposed loss and fusion strategy

Détection de défauts par fusion de signaux ultrasonores et d'images radiographiques

Cette étude porte sur la détection automatique de défauts par fusion de haut niveau des données ultrasonores et radiographiques. L'amélioration de la fiabilité des deux contrôles nécessite une prise en compte des caractères incertains et imprécis des informations. Nous avons choisi d'utiliser la théorie de l'évidence de Dempster-Shafer (DS) pour sa souplesse de modélisation et pour sa puissance de combinaison d'informations. Toutefois, la modélisation des informations sous forme de fonction de masses est un problème délicat. Nous proposons dans cet article une méthode permettant de déterminer ces fonctions. A l'issue de chaque contrôle, une phase de traitement permet tout d'abord d'extraire les informations nécessaires à l'interprétation. La transposition de ces informations en terme de fonctions de masses s'effectue grâce à la définition de propositions élémentaires représentant différents niveaux d'incertitude. L'introduction de la logique floue permet de tenir compte de l'imprécision liée à cette attribution

DiffSeer: Difference-based Dynamic Weighted Graph Visualization

Existing dynamic weighted graph visualization approaches rely on users' mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based node reordering strategy is developed to group nodes with similar evolution patterns and highlight interesting graph structure details in each timeslice. We conducted two case studies on real-world graph datasets and in-depth interviews with 12 target users to evaluate DiffSeer. The results demonstrate its effectiveness in visualizing dynamic weighted graphs

Local spatial-frequency analysis of images using Wigner-Ville distribution

Local spectrum analysis is an interesting method to extract pertinent features of an image . This paper proposes a new local spectrum analysis method allowing to accurately characterize the local spatial frequency content of an image. It is based on the use of the two-dimensional Wigner-Ville distribution (2D WVD), which permits to separately control spatial and frequential analysis resolutions. The application of this method to texture feature extraction and discrimination is illustrated, and a comparison with the classical 2D spectrogram method is also given.L'analyse spectrale locale est une méthode intéressante pour obtenir des caractéristiques pertinentes d'une image. Cet article propose une nouvelle méthode d'analyse spectrale locale permettant de caractériser de manière précise les propriétés fréquentielles locales d'une image. Cette méthode est basée sur la transformation de Wigner-Ville bidimensionnelle (TWV 2D), qui permet de contrôler, de façon souple, séparement les résolutions spatiale et fréquentielle. L'application de cette technique à la caractérisation d'images de textures est illustrée, et une comparaison de sa performance par rapport à la méthode classique de spectrogramme 2D est également montré