The Roundtable on Sustainable Biofuels: plant scientist input needed.

The Energy Center at the Ecole Polytechnique Fédérale de Lausanne (Swiss federal institute of technology) is coordinating a multi-stakeholder effort, the Roundtable on Sustainable Biofuels (http://energycenter.epfl.ch/biofuels), to develop global standards for sustainable biofuels production and processing. Given that many of the aspects related to biofuel production request a high scientific level of understanding, it is crucial that scientists take part in the discussion.

Twitch potentia induced by two different modalities of neuromuscular electrical stimulation: implications for motor unit recruitment.

INTRODUCTION: We tested the hypothesis that twitch potentiation would be greater following conventional (CONV) neuromuscular electrical stimulation (50-µs pulse width and 25-Hz frequency) compared with wide-pulse high-frequency (WPHF) neuromuscular electrical stimulation (1-ms, 100-Hz) and voluntary (VOL) contractions, because of specificities in motor unit recruitment (random in CONV vs. random and orderly in WPHF vs. orderly in VOL). METHODS: A single twitch was evoked by means of tibial nerve stimulation before and 2 s after CONV, WPHF, and VOL conditioning contractions of the plantar flexors (intensity: 10% maximal voluntary contraction; duration: 10 s) in 13 young healthy subjects. RESULTS: Peak twitch increased (P<0.05) after CONV (+4.5±4.0%) and WPHF (+3.3±5.9%), with no difference between the 2 modalities, whereas no changes were observed after VOL (+0.8±2.6%). CONCLUSIONS: Our results demonstrate that presumed differences in motor unit recruitment between WPHF and CONV do not seem to influence twitch potentiation results.

Economic and environmental effects of the CO2 taxation: an input-output analysis for Spain

CO2 emissions induced by human activities are the major cause of climate change; hence, strong environmental policy that limits the growing dependence on fossil fuel is indispensable. Tradable permits and environmental taxes are the usual tools used in CO2 reduction strategies. Such economic tools provide incentives to polluting industries to reduce their emissions through market signals. The aim of this work is to investigate the direct and indirect effects of an environmental tax on Spanish products and services. We apply an environmentally extended input-output (EIO) model to identify CO2 emission intensities of products and services and, accordingly, we estimate the tax proportional to these intensities. The short-term price effects are analyzed using an input-output price model. The effect of tax introduction on consumption prices and its influence on consumers’ welfare are determined. We also quantify the environmental impacts of such taxation in terms of the reduction in CO2 emissions. The results, based on the Spanish economy for the year 2007, show that sectors with relatively poor environmental profile are subjected to high environmental tax rates. And consequently, applying a CO2 tax on these sectors, increases production prices and induces a slight increase in consumer price index and a decrease in private welfare. The revenue from the tax could be used to counter balance the negative effects on social welfare and also to stimulate the increase of renewable energy shares in the most impacting sectors. Finally, our analysis highlights that the environmental and economic goals cannot be met at the same time with the environmental taxation and this shows the necessity of finding other (complementary or alternative) measures to ensure both the economic and ecological efficiencies. Keywords: CO2 emissions; environmental tax; input-output model, effects of environmental taxation.

Modalities and future prospects of gene therapy in heart transplantation.

Heart transplantation is the treatment of choice for many patients with end-stage heart failure. Its success, however, is limited by organ shortage, side effects of immunosuppressive drugs, and chronic rejection. Gene therapy is conceptually appealing for applications in transplantation, as the donor organ is genetically manipulated ex vivo before transplantation. Localised expression of immunomodulatory genes aims to create a state of immune privilege within the graft, which could eliminate the need for systemic immunosuppression. In this review, recent advances in the development of gene therapy in heart transplantation are discussed. Studies in animal models have demonstrated that genetic modification of the donor heart with immunomodulatory genes attenuates ischaemia-reperfusion injury and rejection. Alternatively, bone marrow-derived cells genetically engineered with donor-type major histocompatibility complex (MHC) class I or II promote donor-specific hyporesponsiveness. Genetic engineering of naïve T cells or dendritic cells may induce regulatory T cells and regulatory dendritic cells. Despite encouraging results in animal models, however, clinical gene therapy trials in heart transplantation have not yet been started. The best vector and gene to be delivered remain to be identified. Pre-clinical studies in non-human primates are needed. Nonetheless, the potential of gene therapy as an adjunct therapy in transplantation is essentially intact.

Image-Derived Input Function from the Vena Cava for 18F-FDG PET Studies in Rats and Mice.

Measurement of arterial input function is a restrictive aspect for quantitative (18)F-FDG PET studies in rodents because of their small total blood volume and the related difficulties in withdrawing blood.

Optimum power allocation for parallel Gaussian channels with arbitrary input distributions

The mutual information of independent parallel Gaussian-noise channels is maximized, under an average power constraint, by independent Gaussian inputs whose power is allocated according to the waterfilling policy. In practice, discrete signalling constellations with limited peak-to-average ratios (m-PSK, m-QAM, etc) are used in lieu of the ideal Gaussian signals. This paper gives the power allocation policy that maximizes the mutual information over parallel channels with arbitrary input distributions. Such policy admits a graphical interpretation, referred to as mercury/waterfilling, which generalizes the waterfilling solution and allows retaining some of its intuition. The relationship between mutual information of Gaussian channels and nonlinear minimum mean-square error proves key to solving the power allocation problem.

Capacity-achieving input covariance for single-user multi-antenna channels

We characterize the capacity-achieving input covariance for multi-antenna channels known instantaneously at the receiver and in distribution at the transmitter. Our characterization, valid for arbitrary numbers of antennas, encompasses both the eigenvectors and the eigenvalues. The eigenvectors are found for zero-mean channels with arbitrary fading profiles and a wide range of correlation and keyhole structures. For the eigenvalues, in turn, we present necessary and sufficient conditions as well as an iterative algorithm that exhibits remarkable properties: universal applicability, robustness and rapid convergence. In addition, we identify channel structures for which an isotropic input achieves capacity.

Large-system analysis of a CDMA dynamic channel under a Markovian input process

We study the minimum mean square error (MMSE) and the multiuser efficiency η of large dynamic multiple access communication systems in which optimal multiuser detection is performed at the receiver as the number and the identities of active users is allowed to change at each transmission time. The system dynamics are ruled by a Markov model describing the evolution of the channel occupancy and a large-system analysis is performed when the number of observations grow large. Starting on the equivalent scalar channel and the fixed-point equation tying multiuser efficiency and MMSE, we extend it to the case of a dynamic channel, and derive lower and upper bounds for the MMSE (and, thus, for η as well) holding true in the limit of large signal–to–noise ratios and increasingly large observation time T.

Therapeutic PD-1 pathway blockade augments with other modalities of immunotherapy T-cell function to prevent immune decline in ovarian cancer.

The tumor microenvironment mediates induction of the immunosuppressive programmed cell death-1 (PD-1) pathway, and targeted interventions against this pathway can help restore antitumor immunity. To gain insight into these responses, we studied the interaction between PD-1 expressed on T cells and its ligands (PD-1:PD-L1, PD-1:PD-L2, and PD-L1:B7.1), expressed on other cells in the tumor microenvironment, using a syngeneic orthotopic mouse model of epithelial ovarian cancer (ID8). Exhaustion of tumor-infiltrating lymphocytes (TIL) correlated with expression of PD-1 ligands by tumor cells and tumor-derived myeloid cells, including tumor-associated macrophages (TAM), dendritic cells, and myeloid-derived suppressor cells (MDSC). When combined with GVAX or FVAX vaccination (consisting of irradiated ID8 cells expressing granulocyte macrophage colony-stimulating factor or FLT3 ligand) and costimulation by agonistic α-4-1BB or TLR 9 ligand, antibody-mediated blockade of PD-1 or PD-L1 triggered rejection of ID8 tumors in 75% of tumor-bearing mice. This therapeutic effect was associated with increased proliferation and function of tumor antigen-specific effector CD8(+) T cells, inhibition of suppressive regulatory T cells (Treg) and MDSC, upregulation of effector T-cell signaling molecules, and generation of T memory precursor cells. Overall, PD-1/PD-L1 blockade enhanced the amplitude of tumor immunity by reprogramming suppressive and stimulatory signals that yielded more powerful cancer control.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

Input chains and industrialization

A key aspect of industrialization is theadoption of increasing-returns-to-scale, industrial,technologies. Two other, well-documented aspects arethat industrial technologies are adopted throughoutintermediate-input chains and that they use intermediateinputs intensively relative to the technologies theyreplace. These features of industrial technologiescombined imply that countries with access to similartechnologies may have very different levels ofindustrialization and income, even if the degree ofincreasing returns to scale at the firm level is relativelysmall. Furthermore, a small improvement in theproductivity of industrial technologies can trigger full-scaleindustrialization and a large increase in income.

Exact representation of antenna system diversity performance from input parameter description

A simplc formulation Io compute thc envelope correlation of anantenna divemiry system is dcrired. 11 is shown how to compute theenvelope correlation hom the S-parameter descnplian of the antennasystem. This approach has the advantage that i t does not require thecomputation nor the measurement of the radiation panem of theantenna system. It also offers the advantage of providing a clcaunderstanding ofthe effects ofmutual coupling and input match on thediversity performance of the antcnnii system.