Efficiency of liquid culture systems over conventional micropropagation: A progress towards commercialization

The most common methods of micropropagation involve the proliferation of shoots via a semi solid system. While such semi solid systems have been moderately to highly successful in terms ofmultiplication yields, it has become increasingly important to improve productivity and reduce the time taken to multiply commercially important material. Micropropagation by conventional techniques istypically a labor intensive time taking means of clonal propagation. To overcome this, the use of shake cultures utilizing liquid culture medium has been promoted. The liquid medium allows the close contactwith the tissue which stimulates and facilitates the uptake of nutrients and phytohormones, leading to better shoot and root growth. Continuous shaking promotes lesser expression of apical dominance which generally leads to induction and proliferation of numerous axillary buds. Further, with in the shake culture conditions, the growth and multiplication rate of shoots is enhanced by forced aeration,since continuous shaking of medium provides ample oxygen supply to the tissue which ultimately leads to their faster growth. Bioreactor provides a rapid and efficient clonal propagation systemutilizing liquid medium to avoid intensive manual handling. Automation of micropropagation in bioreactors has been advanced by several authors as a possible way of reducing cost of micropropagation. Micropropagation in bioreactors for optimal plant production depends upon better understanding of physiological and biochemical responses of plant to the signals of culture microenvironment and an optimization of specific physical and chemical culture conditions to controlthe morphogenesis of plants in liquid culture systems

Examining the Heterogeneous Genome Content of Multipartite Viruses BMV and CCMV by Native Mass Spectrometry

Since the concept was first introduced by Brian Chait and co-workers in 1991, mass spectrometry of proteins and protein complexes under non-denaturing conditions (native MS) has strongly developed, through parallel advances in instrumentation, sample preparation, and data analysis tools. However, the success rate of native MS analysis, particularly in heterogeneous mega-Dalton (MDa) protein complexes, still strongly depends on careful instrument modification. Here, we further explore these boundaries in native mass spectrometry, analyzing two related endogenous multipartite viruses: the Brome Mosaic Virus (BMV) and the Cowpea Chlorotic Mottle Virus (CCMV). Both CCMV and BMV are approximately 4.6 megadalton (MDa) in mass, of which approximately 1 MDA originates from the genomic content of the virion. Both viruses are produced as mixtures of three particles carrying different segments of the genome, varying by approximately 0.1 MDA in mass (~2%). This mixture of particles poses a challenging analytical problem for high-resolution native MS analysis, given the large mass scales involved. We attempt to unravel the particle heterogeneity using both Q-TOF and Orbitrap mass spectrometers extensively modified for analysis of very large assemblies. We show that manipulation of the charging behavior can provide assistance in assigning the correct charge states. Despite their challenging size and heterogeneity, we obtained native mass spectra with resolved series of charge states for both BMV and CCMV, demonstrating that native MS of endogenous multipartite virions is feasible. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s13361-016-1348-6) contains supplementary material, which is available to authorized users

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Feature Selection Improves Tree-based Classification for Wireless Intrusion Detection

With the growth of 5G wireless technologies and IoT, it become urgent to develop robust network security systems, such as intrusions detection systems (IDS) to keep the networks secure. These IDS systems need to detect unauthorized access and attacks in real-time. However, most of the modern IDS are built based on complex machine learning models that are time-consuming to train. In this work, we propose a methodology using the SHapley Additive exPlanations (SHAP) in combination with tree-based classifiers. SHAP can be used to select consistent and small feature subsets to reduce the execution time and improve classification accuracy. We demonstrate the proposed approach with the Aegean Wi-Fi Intrusion Dataset (AWID) dataset in a series of multi-class classification experiments. Among the four classes ("normal", "injection", "flooding"and "impersonation"), it is well-known that the class impersonation is hard to be classified accurately. Tests show that we can use about 10% of the initial feature set without reducing the overall prediction accuracy. With this reduced set of features, the training time could be reduced as much as a factor of four, while slightly improving the discriminating ability to identify impersonation instances. This study suggests that by reducing the number of features, the classification algorithms are able to focus on key trends that differentiates the "attacks"classes from the "normal"class. Using a reduces subset of features improves IDS's accuracy and performance. Also, SHAP dependence plots capture the relationship between individual features and the classification decision