Bacterial spores in spices and dried herbs: The risks for processed food

Production and world consumption of spices are constantly increasing. Although the antimicrobial properties of some spices are well documented, their use in the agri-food industry is also responsible for microbial contamination and spoilage. Bacterial spores introduced by spices can withstand different preparation processes, particularly thermal treatments, leading to food alterations during storage. This review brings together data from the literature about the prevalence and concentrations of spore-forming bacteria in all commercially available spices. The sporeformers found in spices belong mainly to the genera Bacillus and Clostridium. Such contaminations are very common and sometimes reach high levels, as in pepper and turmeric. Bacillus licheniformis and Bacillus cereus are the most frequently detected species. Studying the harvesting, processing, and storage procedures for spices provides elements to explain why high prevalence and concentrations are observed. Spices are mostly produced in developing countries on small farms using traditional production methods. Spices become contaminated by bacterial spores in two main ways: by contact with soil during harvesting or drying, as for pepper, or by cross-contamination during the water-cooking step, as for turmeric. From these observations, we propose some recommendations. Different methods that can be used to eliminate bacterial spores from spices are presented indicating their efficiency and the limitations of their use.     

Adventitial Tertiary Lymphoid Organs as Potential Source of MicroRNA Biomarkers for Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is an inflammatory disease associated with marked changes in the cellular composition of the aortic wall. This study aims to identify microRNA (miRNA) expression in aneurysmal inflammatory cells isolated by laser microdissection from human tissue samples. The distribution of inflammatory cells (neutrophils, B and T lymphocytes, mast cells) was evaluated in human AAA biopsies. We observed in half of the samples that adventitial tertiary lymphoid organs (ATLOs) with a thickness from 0.5 to 2 mm were located exclusively in the adventitia. Out of the 850 miRNA that were screened by microarray in isolated ATLOs (n = 2), 164 miRNAs were detected in ATLOs. The three miRNAs (miR-15a-3p, miR-30a-5p and miR-489-3p) with the highest expression levels were chosen and their expression quantified by RT-PCR in isolated ATLOs (n = 4), M1 (n = 2) and M2 macrophages (n = 2) and entire aneurysmal biopsies (n = 3). Except for the miR-30a-5p, a similar modulation was found in ATLOs and the two subtypes of macrophages. The modulated miRNAs were then evaluated in the plasma of AAA patients for their potential as AAA biomarkers. Our data emphasize the potential of miR-15a-3p and miR-30a-5p as biomarkers of AAA but also as triggers of ATLO evolution. Further investigations will be required to evaluate their targets in order to better understand AAA pathophysiology.     

Trends in family psychology.

Presents an overview of the recent past, the present, and anticipated future trends in the field of family psychology (i.e., the scientific study of the family in terms of its historical forms and variations; structure and functioning across time, space, culture, and generations; and idiosyncratic and systems attributes). Areas discussed include availability of graduate education in family psychology, internship availability, postgraduate training opportunities, clinical practice, research and publications, ethical and legal concerns, credentialing, and impact on other family therapy organizations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Novel optimized tree-based stack-type architecture for 2n-bit comparator at nanoscale with energy dissipation analysis

Comparator is an essential building block in many digital circuits such as biometric authentication, data sorting, and exponents comparison in floating-point architectures among others. Quantum-dot Cellular Automata (QCA) is a latest nanotechnology that overcomes the drawbacks of Complementary Metal Oxide Semiconductor (CMOS) technology. In this paper, novel area optimized 2n-bit comparator architecture is proposed. To achieve the objective, 1-bit stack-type and 4-bit tree-based stack-type (TB-ST) comparators are proposed using QCA. Then, two tree-based architectures of 4-bit comparators are arranged in two layers to optimize the number of quantum cells and area of an 8-bit comparator. Thus, this design can be extended to any 2n-bit comparator. Simulation results of 4-bit and 8-bit comparators using QCADesigner 2.0.3 show that there is a significant improvement in the number of quantum cells and area occupancy. The proposed TB-ST 8-bit comparator uses 2.5 clock cycles and 622 quantum cells with area occupancy of 0.49 µm² which is an improvement by 10.5% and 38%, respectively, compared to existing designs. Scaling it to a 32-bit comparator, the proposed architecture requires only 2675 quantum cells in an area of 2.05 µm² with a delay of 3.5 clock cycles, indicating 9.35% and 28.8% improvements, respectively, demonstrating the merit of the proposed architecture. Besides, energy dissipation analysis of the proposed TB-ST 8-bit comparator is simulated on QCADesigner-E tool, indicating average energy dissipation reduction of 17.3% compared to existing works.

     

In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer

The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.     

A study of the plasma lipoproteins and the tissue lipids of the migrating lamprey,Mordacia mordax

Sea lampreys,Mordacia mordax, were collected in spring from the Yarra River, Victoria, during their upstream spawning migration, to study the lipid composition in their tissues and plasma and their lipid transport system. Plasma lipoproteins were isolated by preparative ultracentrifugation and their chemical compositions were analyzed. The major classes of lipoproteins were found to be similar to those of man and higher animals. Lipids from the muscle and liver were analyzed for fatty acids. The striking feature of the lipids in the migrating lamprey is the presence of very high levels of cholesterol in both plasma and muscle. The possible metabolic roles of cholesterol have been discussed.     

Linking the proteins--elucidation of proteome-scale networks using mass spectrometry

Proteomes are intricate. Typically, thousands of proteins interact through physical association and post-translational modifications (PTMs) to give rise to the emergent functions of cells. Understanding these functions requires one to study proteomes as "systems" rather than collections of individual protein molecules. The abstraction of the interacting proteome to "protein networks" has recently gained much attention, as networks are effective representations, that lose specific molecular details, but provide the ability to see the proteome as a whole. Mostly two aspects of the proteome have been represented by network models: proteome-wide physical protein-protein-binding interactions organized into Protein Interaction Networks (PINs), and proteome-wide PTM relations organized into Protein Signaling Networks (PSNs). Mass spectrometry (MS) techniques have been shown to be essential to reveal both of these aspects on a proteome-wide scale. Techniques such as affinity purification followed by MS have been used to elucidate protein-protein interactions, and MS-based quantitative phosphoproteomics is critical to understand the structure and dynamics of signaling through the proteome. We here review the current state-of-the-art MS-based analytical pipelines for the purpose to characterize proteome-scale networks.     

An enhanced encryption algorithm for video based on multiple Huffman tables

Encryption is one of the fundamental technologies that is used in the security of multimedia data. Unlike ordinary computer applications, multimedia applications generate large amount of data that has to be processed in real time. This work investigates the problem of efficient multimedia data encryption. A scheme known as the Randomized Huffman Table scheme was recently proposed to achieve encryption along with compression. Though this scheme has several advantages it cannot overcome the chosen plaintext attack. An enhancement of this Huffman scheme is proposed in this work which essentially overcomes the attack and improves the security. The proposed encryption approach consists of two modules. The first module is the Randomized Huffman Table module, the output of which is fed to the second XOR module to enhance the performance. Security analysis shows that the proposed scheme can withstand the chosen plaintext attack. The efficiency and security of the proposed scheme makes it an ideal choice for real time secure multimedia applications.     

Dynamic and scalable multi-level trust management model for Social Internet of Things

The Internet of Things (IoT) is a paradigm that has made everyday objects intelligent by offering them the ability to connect to the Internet and communicate. Integrating the social component into IoT gave rise to the Social Internet of Things (SIoT), which has helped overcome various issues such as heterogeneity and navigability. In this kind of environment, participants compete to offer a variety of attractive services. Nevertheless, some of them resort to malicious behaviour to spread poor-quality services. They perform so-called Trust-Attacks and break the basic functionality of the system. Trust management mechanisms aim to counter these attacks and provide the user with an estimate of the trust degree they can place in other users, thus ensuring reliable and qualified exchanges and interactions. Several works in literature have interfered with this problem and have proposed different Trust-Models. The majority tried to adapt and reapply Trust-Models designed for common social networks or peer-to-peer ones. That is, despite the similarities between these types of networks, SIoT ones present specific peculiarities. In SIoT, users, devices and services are collaborating. Devices entities can present constrained computing and storage capabilities, and their number can reach some millions. The resulting network is complex, constrained and highly dynamic, and the attacks-implications can be more significant. In this paper, we propose DSL-STM a new dynamic and scalable multi-level Trust-Model, specifically designed for SIoT environments. We propose multidimensional metrics to describe and SIoT entities behaviours. The latter are aggregated via a Machine Learning-based method, allowing classifying users, detecting attack types and countering them. Finally, a hybrid propagation method is suggested to spread trust values in the network, while minimizing resource consumption and preserving scalability and dynamism. Experimentation made on various simulated scenarios allows us to prove the resilience and performance of DSL-STM.

     

Ferritin Protein Regulates the Degradation of Iron Oxide Nanoparticles

Proteins implicated in iron homeostasis are assumed to be also involved in the cellular processing of iron oxide nanoparticles. In this work, the role of an endogenous iron storage protein—namely the ferritin—is examined in the remediation and biodegradation of magnetic iron oxide nanoparticles. Previous in vivo studies suggest the intracellular transfer of the iron ions released during the degradation of nanoparticles to endogenous protein cages within lysosomal compartments. Here, the capacity of ferritin cages to accommodate and store the degradation products of nanoparticles is investigated in vitro in the physiological acidic environment of the lysosomes. Moreover, it is questioned whether ferritin proteins can play an active role in the degradation of the nanoparticles. The magnetic, colloidal, and structural follow‐up of iron oxide nanoparticles and proteins in lysosome‐like medium confirms the efficient remediation of potentially harmful iron ions generated by nanoparticles within ferritins. The presence of ferritins, however, delays the degradation of particles due to a complex colloidal behavior of the mixture in acidic medium. This study exemplifies the important implications of intracellular proteins in processes of degradation and metabolization of iron oxide nanoparticles.