Cooperativity in Proteasome Core Particle Maturation

This work is licensed under a Creative Commons Attribution 4.0 International License.Proteasomes are multi-subunit protease complexes found in all domains of life. The maturation of the core particle (CP), which harbors the active sites, involves dimerization of two half CPs (HPs) and an autocatalytic cleavage that removes β propeptides. How these steps are regulated remains poorly understood. Here, we used the Rhodococcus erythropolis CP to dissect this process in vitro. Our data show that propeptides regulate the dimerization of HPs through flexible loops we identified. Furthermore, N-terminal truncations of the propeptides accelerated HP dimerization and decelerated CP auto-activation. We identified cooperativity in autocatalysis and found that the propeptide can be partially cleaved by adjacent active sites, potentially aiding an otherwise strictly autocatalytic mechanism. We propose that cross-processing during bacterial CP maturation is the underlying mechanism leading to the observed cooperativity of activation. Our work suggests that the bacterial β propeptide plays an unexpected and complex role in regulating dimerization and autocatalytic activation

Spin- and energy relaxation of hot electrons at GaAs surfaces

The mechanisms for spin relaxation in semiconductors are reviewed, and the mechanism prevalent in p-doped semiconductors, namely spin relaxation due to the electron-hole exchange interaction, is presented in some depth. It is shown that the solution of Boltzmann-type kinetic equations allows one to obtain quantitative results for spin relaxation in semiconductors that go beyond the original Bir-Aronov-Pikus relaxation-rate approximation. Experimental results using surface sensitive two-photon photoemission techniques show that the spin relaxation-time of electrons in p-doped GaAs at a semiconductor/metal surface is several times longer than the corresponding bulk spin relaxation-times. A theoretical explanation of these results in terms of the reduced density of holes in the band-bending region at the surface is presented.Comment: 33 pages, 12 figures; earlier submission replaced by corrected and expanded version; eps figures now included in the tex

Does reductive metabolism predict response to tirapazamine (SR 4233) in human non-small-cell lung cancer cell lines?

The bioreductive drug tirapazamine (TPZ, SR 4233, WIN 59075) is a lead compound in a series of potent cytotoxins that selectively kill hypoxic rodent and human solid tumour cells in vitro and in vivo. Phases II and III trials have demonstrated its efficacy in combination with both fractionated radiotherapy and some chemotherapy. We have evaluated the generality of an enzyme-directed approach to TPZ toxicity by examining the importance of the one-electron reducing enzyme NADPH:cytochrome P450 reductase (P450R) in the metabolism and toxicity of this lead prodrug in a panel of seven human non-small-cell lung cancer cell lines. We relate our findings on TPZ sensitivity in these lung lines with our previously published results on TPZ sensitivity in six human breast cancer cell lines (Patterson et al (1995) Br J Cancer 72: 1144–1150) and with the sensitivity of all these cell types to eight unrelated cancer chemotherapeutic agents with diverse modes of action. Our results demonstrate that P450R plays a significant role in the activation of TPZ in this panel of lung lines, which is consistent with previous observations in a panel of breast cancer cell lines (Patterson et al (1995) Br J Cancer 72: 1144–1150; Patterson et al (1997) Br J Cancer 76: 1338–1347). However, in the lung lines it is likely that it is the inherent ability of these cells to respond to multiple forms of DNA damage, including that arising from P450R-dependent TPZ metabolism, that underlies the ultimate expression of toxicity. © 1999 Cancer Research Campaig

Children and older adults exhibit distinct sub-optimal cost-benefit functions when preparing to move their eyes and hands

"© 2015 Gonzalez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited"Numerous activities require an individual to respond quickly to the correct stimulus. The provision of advance information allows response priming but heightened responses can cause errors (responding too early or reacting to the wrong stimulus). Thus, a balance is required between the online cognitive mechanisms (inhibitory and anticipatory) used to prepare and execute a motor response at the appropriate time. We investigated the use of advance information in 71 participants across four different age groups: (i) children, (ii) young adults, (iii) middle-aged adults, and (iv) older adults. We implemented 'cued' and 'non-cued' conditions to assess age-related changes in saccadic and touch responses to targets in three movement conditions: (a) Eyes only; (b) Hands only; (c) Eyes and Hand. Children made less saccade errors compared to young adults, but they also exhibited longer response times in cued versus non-cued conditions. In contrast, older adults showed faster responses in cued conditions but exhibited more errors. The results indicate that young adults (18 -25 years) achieve an optimal balance between anticipation and execution. In contrast, children show benefits (few errors) and costs (slow responses) of good inhibition when preparing a motor response based on advance information; whilst older adults show the benefits and costs associated with a prospective response strategy (i.e., good anticipation)

The influence of C3 and C4 vegetation on soil organic matter dynamics in contrasting semi-natural tropical ecosystems

Variations in the carbon isotopic composition of soil organic matter (SOM) in bulk and fractionated samples were used to assess the influence of C3 and C4 vegetation on SOM dynamics in semi-natural tropical ecosystems sampled along a precipitation gradient in West Africa. Differential patterns in SOM dynamics in C3/C4 mixed ecosystems occurred at various spatial scales. Relative changes in C=N ratios between two contrasting SOM fractions were used to evaluate potential site-scale differences in SOM dynamics between C3- and C4-dominated locations. These differences were strongly controlled by soil texture across the precipitation gradient, with a function driven by bulk 13C and sand content explaining 0.63 of the observed variability. The variation of 13C with soil depth indicated a greater accumulation of C3-derived carbon with increasing precipitation, with this trend also being strongly dependant on soil characteristics. The influence of vegetation thickening on SOM dynamics was also assessed in two adjacent, but structurally contrasting, transitional ecosystems occurring on comparable soils to minimise the confounding effects posed by climatic and edaphic factors. Radiocarbon analyses of sand-size aggregates yielded relatively short mean residence times ( ) even in deep soil layers, while the most stable SOM fraction associated with silt and clay exhibited shorter in the savanna woodland than in the neighbouring forest stand. These results, together with the vertical variation observed in 13C values, strongly suggest that both ecosystems are undergoing a rapid transition towards denser closed canopy formations.However, vegetation thickening varied in intensity at each site and exerted contrasting effects on SOM dynamics. Thisstudy shows that the interdependence between biotic and abiotic factors ultimately determine whether SOM dynamics of C3- and C4-derived vegetation are at variance in ecosystems where both vegetation types coexist. The results highlight the far-reaching implications that vegetation thickening may have for the stability of deep SOM. © 2015, Copernicus Publications

TMFoldRec: a statistical potential-based transmembrane protein fold recognition tool.

BACKGROUND: Transmembrane proteins (TMPs) are the key components of signal transduction, cell-cell adhesion and energy and material transport into and out from the cells. For the deep understanding of these processes, structure determination of transmembrane proteins is indispensable. However, due to technical difficulties, only a few transmembrane protein structures have been determined experimentally. Large-scale genomic sequencing provides increasing amounts of sequence information on the proteins and whole proteomes of living organisms resulting in the challenge of bioinformatics; how the structural information should be gained from a sequence. RESULTS: Here, we present a novel method, TMFoldRec, for fold prediction of membrane segments in transmembrane proteins. TMFoldRec based on statistical potentials was tested on a benchmark set containing 124 TMP chains from the PDBTM database. Using a 10-fold jackknife method, the native folds were correctly identified in 77 % of the cases. This accuracy overcomes the state-of-the-art methods. In addition, a key feature of TMFoldRec algorithm is the ability to estimate the reliability of the prediction and to decide with an accuracy of 70 %, whether the obtained, lowest energy structure is the native one. CONCLUSION: These results imply that the membrane embedded parts of TMPs dictate the TM structures rather than the soluble parts. Moreover, predictions with reliability scores make in this way our algorithm applicable for proteome-wide analyses. AVAILABILITY: The program is available upon request for academic use

Photo-Induced Spin Dynamics in Semiconductor Quantum Wells

We experimentally investigate the dynamics of spins in GaAs quantum wells under applied electric bias by photoluminescence (PL) measurements excited with circularly polarized light. The bias-dependent circular polarization of PL (PPL) with and without magnetic field is studied. ThePPLwithout magnetic field is found to be decayed with an enhancement of increasing the strength of the negative bias. However,PPLin a transverse magnetic field shows oscillations under an electric bias, indicating that the precession of electron spin occurs in quantum wells. The results are discussed based on the electron–hole exchange interaction in the electric field

A Standardised Procedure for Evaluating Creative Systems: Computational Creativity Evaluation Based on What it is to be Creative

Computational creativity is a flourishing research area, with a variety of creative systems being produced and developed. Creativity evaluation has not kept pace with system development with an evident lack of systematic evaluation of the creativity of these systems in the literature. This is partially due to difficulties in defining what it means for a computer to be creative; indeed, there is no consensus on this for human creativity, let alone its computational equivalent. This paper proposes a Standardised Procedure for Evaluating Creative Systems (SPECS). SPECS is a three-step process: stating what it means for a particular computational system to be creative, deriving and performing tests based on these statements. To assist this process, the paper offers a collection of key components of creativity, identified empirically from discussions of human and computational creativity. Using this approach, the SPECS methodology is demonstrated through a comparative case study evaluating computational creativity systems that improvise music

Chapter 11: Challenges in and Principles for Conducting Systematic Reviews of Genetic Tests used as Predictive Indicators

In this paper, we discuss common challenges in and principles for conducting systematic reviews of genetic tests. The types of genetic tests discussed are those used to 1). determine risk or susceptibility in asymptomatic individuals; 2). reveal prognostic information to guide clinical management in those with a condition; or 3). predict response to treatments or environmental factors. This paper is not intended to provide comprehensive guidance on evaluating all genetic tests. Rather, it focuses on issues that have been of particular concern to analysts and stakeholders and on areas that are of particular relevance for the evaluation of studies of genetic tests. The key points include:The general principles that apply in evaluating genetic tests are similar to those for other prognostic or predictive tests, but there are differences in how the principles need to be applied or the degree to which certain issues are relevant.A clear definition of the clinical scenario and an analytic framework is important when evaluating any test, including genetic tests.Organizing frameworks and analytic frameworks are useful constructs for approaching the evaluation of genetic tests.In constructing an analytic framework for evaluating a genetic test, analysts should consider preanalytic, analytic, and postanalytic factors; such factors are useful when assessing analytic validity.Predictive genetic tests are generally characterized by a delayed time between testing and clinically important events.Finding published information on the analytic validity of some genetic tests may be difficult. Web sites (FDA or diagnostic companies) and gray literature may be important sources.In situations where clinical factors associated with risk are well characterized, comparative effectiveness reviews should assess the added value of using genetic testing along with known factors compared with using the known factors alone.For genome-wide association studies, reviewers should determine whether the association has been validated in multiple studies to minimize both potential confounding and publication bias. In addition, reviewers should note whether appropriate adjustments for multiple comparisons were used

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics