Arabidopsis thaliana J-class heat shock proteins: cellular stress sensors

Plants are sessile organisms that have evolved a variety of mechanisms to maintain their cellular homeostasis under stressful environmental conditions. Survival of plants under abiotic stress conditions requires specialized group of heat shock protein machinery, belonging to Hsp70:J-protein family. These heat shock proteins are most ubiquitous types of chaperone machineries involved in diverse cellular processes including protein folding, translocation across cell membranes, and protein degradation. They play a crucial role in maintaining the protein homeostasis by reestablishing functional native conformations under environmental stress conditions, thus providing protection to the cell. J-proteins are co-chaperones of Hsp70 machine, which play a critical role by stimulating Hsp70s ATPase activity, thereby stabilizing its interaction with client proteins. Using genome-wide analysis of Arabidopsis thaliana, here we have outlined identification and systematic classification of J-protein co-chaperones which are key regulators of Hsp70s function. In comparison with Saccharomyces cerevisiae model system, a comprehensive domain structural organization, cellular localization, and functional diversity of A. thaliana J-proteins have also been summarized. Electronic supplementary material The online version of this article (doi:10.1007/s10142-009-0132-0) contains supplementary material, which is available to authorized users.

A potent nonporphyrin class of photodynamic therapeutic agent: Cellular localisation, cytotoxic potential and influence of hypoxia

We have developed a totally new class of nonporphyrin photodynamic therapeutic agents with a specific focus on two lead candidates azadipyrromethene (ADPM)01 and ADPM06. Confocal laser scanning microscopy imaging showed that these compounds are exclusively localised to the cytosolic compartment, with specific accumulation in the endoplasmic reticulum and to a lesser extent in the mitochondria. Light-induced toxicity assays, carried out over a broad range of human tumour cell lines, displayed EC50 values in the micro-molar range for ADPM01 and nano-molar range for ADPM06, with no discernable activity bias for a specific cell type. Strikingly, the more active agent, ADPM06, even retained significant activity under hypoxic conditions. Both photosensitisers showed low to nondeterminable dark toxicity. Flow cytometric analysis revealed that ADPM01 and ADPM06 were highly effective at inducing apoptosis as a mode of cell death. The photophysical and biological characteristics of these PDT agents suggest that they have potential for the development of new anticancer therapeutics. © 2005 Cancer Research UK.

The curvature invariant for a class of homogeneous operators

For an operator T in the class B-n(), introduced by Cowen and Douglas, the simultaneous unitary equivalence class of the curvature and the covariant derivatives up to a certain order of the corresponding bundle E-T determine the unitary equivalence class of the operator T. In a subsequent paper, the authors ask if the simultaneous unitary equivalence class of the curvature and these covariant derivatives are necessary to determine the unitary equivalence class of the operator T is an element of B-n(). Here we show that some of the covariant derivatives are necessary. Our examples consist of homogeneous operators in B-n(). For homogeneous operators, the simultaneous unitary equivalence class of the curvature and all its covariant derivatives at any point w in the unit disc are determined from the simultaneous unitary equivalence class at 0. This shows that it is enough to calculate all the invariants and compare them at just one point, say 0. These calculations are then carried out in number of examples. One of our main results is that the curvature along with its covariant derivative of order (0, 1) at 0 determines the equivalence class of generic homogeneous Hermitian holomorphic vector bundles over the unit disc.

The Noether-Lefschetz theorem for the divisor class group

Let X be a normal projective threefold over a field of characteristic zero and vertical bar L vertical bar be a base-point free, ample linear system on X. Under suitable hypotheses on (X, vertical bar L vertical bar), we prove that for a very general member Y is an element of vertical bar L vertical bar, the restriction map on divisor class groups Cl(X) -> Cl(Y) is an isomorphism. In particular, we are able to recover the classical Noether-Lefschetz theorem, that a very general hypersurface X subset of P-C(3) of degree >= 4 has Pic(X) congruent to Z.

Synthesis of new 4-isopropylthiazole hydrazide analogs and some derived clubbed triazole, oxadiazole ring systems - A novel class of potential antibacterial, antifungal and antitubercular agents

In the present study a series of 4-isopropylthiazole-2-carbohydrazide analogs, derived clubbed oxadiazole-thiazole and triazole-thiazole derivatives have been synthesized and characterized by IR, H-1 NMR, C-13 NMR, elemental and mass spectral analyses. The synthesized compounds were evaluated for their preliminary in vitro antibacterial, antifungal and antitubercular activity against Mycobacterium tuberculosis H(37)Rv strain by broth dilution assay method. The synthesized compounds 7a, 7b, 7d and 4 showed an antitubercular efficacy considerably greater than that of the parent 4-isopropyl-1,3-thiazole-2-carbohydrazide 1, suggesting that the substituted 4-isopropylthiazole-2-carbohydrazide moiety plays an important role in enhancing the antitubercular properties of this class of compounds. Compounds 2c, 3, 4, 6d, 7a and 7b exhibited good or moderate antibacterial and antifungal activity. Compounds 4 and 7b showed appreciable cytotoxicity at a concentration of 250 mu M.

Synthesis and Evaluation of a Novel Class of G-Quadruplex-Stabilizing Small Molecules Based on the 1,3-Phenylene-Bis(piperazinyl benzimidazole) System

Achieving stabilization of telomeric DNA in G-quadruplex conformation by Various organic compounds has been an important goal for the medicinal chemists seeking to develop new anticancer agents. Several compounds are known to stabilize G-quadruplexes. However, relatively few are known to induce their formation and/or alter the topology, of the preformed quadruplex DNA. Herein, four compounds having the 1,3-phenylene-bis(piperazinyl benzimidazole) unit as a basic skeleton have been synthesized, and their interactions with the 24-mer telomeric DNA sequences from Tetrahymena thermophilia d(T(2)G(4))(4) have been investigated using high-resolution techniques Such as circular dichroism (CD) spectropolarimetry, CD melting, emission spectroscopy, and polyacrylamide gel electrophoresis. The data obtained, in the presence of one of three ions (Li+, Na+, or K+), indicate that all the new compounds have a high affinity for G-quadruplex DNA, and the strength of the binding with G-quadruplex depends on (1) phenyl ring substitution, (ii) the piperazinyl side chain, and (iii) the type of monovalent cation present in the buffer. Results further Suggest that these compounds are able to abet the conversion of the Intramolecular quadruplex into parallel stranded intermolecular G-quadruplex DNA. Notably, these compounds are also capable of inducing and stabilizing the parallel stranded quadruplex from randomly structured DNA in the absence of any stabilizing cation. The kinetics of the structural changes Induced by these compounds could be followed by recording the changes in the CD signal as a function of time. The implications of the findings mentioned above are discussed in this paper.

Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs

State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar´ f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

Shortcomings of discontinuous-pressure finite element methods on a class of transient problems

Past studies that have compared LBB stable discontinuous- and continuous-pressure finite element formulations on a variety of problems have concluded that both methods yield Solutions of comparable accuracy, and that the choice of interpolation is dictated by which of the two is more efficient. In this work, we show that using discontinuous-pressure interpolations can yield inaccurate solutions at large times on a class of transient problems, while the continuous-pressure formulation yields solutions that are in good agreement with the analytical Solution.

A Bayesian incentive compatible mechanism for decentralized supply chain formation

In this paper we consider a decentralized supply chain formation problem for linear multi-echelon supply chains when the managers of the individual echelons are autonomous, rational, and intelligent. At each echelon, there is a choice of service providers and the specific problem we solve is that of determining a cost-optimal mix of service providers so as to achieve a desired level of end-to-end delivery performance. The problem can be broken up into two sub-problems following a mechanism design approach: (1) Design of an incentive compatible mechanism to elicit the true cost functions from the echelon managers; (2) Formulation and solution of an appropriate optimization problem using the true cost information. In this paper we propose a novel Bayesian incentive compatible mechanism for eliciting the true cost functions. This improves upon existing solutions in the literature which are all based on the classical Vickrey-Clarke-Groves mechanisms, requiring significant incentives to be paid to the echelon managers for achieving dominant strategy incentive compatibility. The proposed solution, which we call SCF-BIC (Supply Chain Formation with Bayesian Incentive Compatibility), significantly reduces the cost of supply chain formation. We illustrate the efficacy of the proposed methodology using the example of a three echelon manufacturing supply chain.

Broadcast in Ad hoc Wireless Networks with Selfish Nodes: A Bayesian Incentive Compatibility Approach

We consider the incentive compatible broadcast (ICB) problem in ad hoc wireless networks with selfish nodes. We design a Bayesian incentive compatible Broadcast (BIC-B) protocol to address this problem. VCG mechanism based schemes have been popularly used in the literature to design dominant strategy incentive compatible (DSIC) protocols for ad hoe wireless networks. VCG based mechanisms have two critical limitations: (i) the network is required to he bi-connected, (ii) the resulting protocol is not budget balanced. Our proposed BIC-B protocol overcomes these difficulties. We also prove the optimality of the proposed scheme.

Synthesis and bioassay of a new class of heterocycles pyrrolyl oxadiazoles/thiadiazoles/triazoles

A new class of heterocycles pyrrolyl thiadiazoles, pyrrolyl oxadiazoles and pyrrolyl triazoles were prepared from arylsulfonylethenesulfonylacetic acid methyl ester and tested for their antimicrobial and cytotoxic activities. (C) 2008 Elsevier Masson SAS. All rights reserved.

Facile one-pot synthesis of thio and selenourea derivatives: A new class of potent urease inhibitors

A facile, one-pot synthesis of thio and selenourea derivatives from amines using tetrathiomolybdate 1 and tetraseleno-tungstate 2 as sulfur and selenium transfer reagents, respectively, is reported. The compounds were tested for their activity as urease inhibitors and some of the compounds showed potent activity in the nanomolar range towards jack bean urease. (C) 2007 Elsevier Ltd. All rights reserved.