Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology

Mechanisms and therapeutic applications of electromagnetic therapy in Parkinson's disease

© 2015 Vadalà et al. Electromagnetic therapy is a non-invasive and safe approach for the management of several pathological conditions including neurodegenerative diseases. Parkinson's disease is a neurodegenerative pathology caused by abnormal degeneration of dopaminergic neurons in the ventral tegmental area and substantia nigra pars compacta in the midbrain resulting in damage to the basal ganglia. Electromagnetic therapy has been extensively used in the clinical setting in the form of transcranial magnetic stimulation, repetitive transcranial magnetic stimulation, high-frequency transcranial magnetic stimulation and pulsed electromagnetic field therapy which can also be used in the domestic setting. In this review, we discuss the mechanisms and therapeutic applications of electromagnetic therapy to alleviate motor and non-motor deficits that characterize Parkinson's disease

Ubiquitous molecular substrates for associative learning and activity-dependent neuronal facilitation.

Recent evidence suggests that many of the molecular cascades and substrates that contribute to learning-related forms of neuronal plasticity may be conserved across ostensibly disparate model systems. Notably, the facilitation of neuronal excitability and synaptic transmission that contribute to associative learning in Aplysia and Hermissenda, as well as associative LTP in hippocampal CA1 cells, all require (or are enhanced by) the convergence of a transient elevation in intracellular Ca2+ with transmitter binding to metabotropic cell-surface receptors. This temporal convergence of Ca2+ and G-protein-stimulated second-messenger cascades synergistically stimulates several classes of serine/threonine protein kinases, which in turn modulate receptor function or cell excitability through the phosphorylation of ion channels. We present a summary of the biophysical and molecular constituents of neuronal and synaptic facilitation in each of these three model systems. Although specific components of the underlying molecular cascades differ across these three systems, fundamental aspects of these cascades are widely conserved, leading to the conclusion that the conceptual semblance of these superficially disparate systems is far greater than is generally acknowledged. We suggest that the elucidation of mechanistic similarities between different systems will ultimately fulfill the goal of the model systems approach, that is, the description of critical and ubiquitous features of neuronal and synaptic events that contribute to memory induction

Motricite et mecanisme de regulation du myometre de la rate a mi-gestation

SIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : AR 15483 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Oil palm for biodiesel in Brazil-risks and opportunities

Although mainly used for other purposes, and historically mainly established at the expense of tropical forests, oil palm can be the most land efficient feedstock for biodiesel. Large parts of Brazil are suitable for oil palm cultivation and a series of policy initiatives have recently been launched to promote oil palm production. These initiatives are however highly debated both in the parliament and in academia. Here we present results of a high resolution modelling study of opportunities and risks associated with oil palm production for biodiesel in Brazil, under different energy, policy, and infrastructure scenarios. Oil palm was found to be profitable on extensive areas, including areas under native vegetation where establishment would cause large land use change (LUC) emissions. However, some 40-60 Mha could support profitable biodiesel production corresponding to approximately 10% of the global diesel demand, without causing direct LUC emissions or impinging on protected areas. Pricing of LUC emissions could make oil palm production unprofitable on most lands where conversion would impact on native ecosystems and carbon stocks, if the carbon price is at the level $125/tC, or higher