Application of novel techniques for interferogram analysis to laser-plasma femtosecond probing

Recently, two novel techniques for the extraction of the phase-shift map (Tomassini {\it et.~al.}, Applied Optics {\bf 40} 35 (2001)) and the electronic density map estimation (Tomassini P. and Giulietti A., Optics Communication {\bf 199}, pp 143-148 (2001)) have been proposed. In this paper we apply both methods to a sample laser-plasma interferogram obtained with femtoseconds probe pulse, in an experimental setup devoted to laser particle acceleration studies.Comment: Submitted to Laser and Particle Beam

Analysis of space-resolved X-ray spectra from laser plasmas

High dynamic range, space-resolved X-ray spectra, obtained using a TlAP crystal and a cooled CCD camera as a detector, were used to investigate the electron density and temperature profiles of an aluminum laser plasma with micrometer resolution. The electron density profile retrieved from the measurements is compared with numerical predictions from the two hydrodynamics codes MEDUSA (1D) and POLLUX (2D). It is shown that 2D density profiles can be successfully reproduced by 1D simulations using a spherical geometry with an ad hoc initial radius, leading to similar electron temperature profiles

Dynamics of charge-displacement channeling in intense laser-plasma interactions

The dynamics of transient electric fields generated by the interaction of high intensity laser pulses with underdense plasmas has been studied experimentally with the proton projection imaging technique. The formation of a charged channel, the propagation of its front edge and the late electric field evolution have been characterised with high temporal and spatial resolution. Particle-in-cell simulations and an electrostatic, ponderomotive model reproduce the experimental features and trace them back to the ponderomotive expulsion of electrons and the subsequent ion acceleration.Comment: 5 figures, accepted for publication in New Journal of Physic

An original deconvolution approach for oil production allocation based on geochemical fingerprinting

We tackle oil commingling scenarios and develop an original deconvolution approach for geochemical production allocation. This yields robust assessment of the proportions of oils forming a mixture originating from commingling oils associated with diverse reservoirs or, wells. Our study starts from considering that production allocation performed by means of geochemical fingerprinting is relevant in the context of modern and sustainable use of georesources, with the added benefit of favoring shared facilities and production equipment. A geochemical production allocation workflow is typically structured according to two steps: (i) determination of the chromatograms associated with the mixture (and eventually with each of the End Members, EMs, constituting the fluids in the mixture), and (ii) the use of a deconvolution algorithm to estimate the mass fraction of each EM. Concerning the latter step, we introduce an original approach and the ensuing deconvolution algorithm (hereafter termed PGM) that does not require additional laboratory efforts in comparison with traditional approaches. We also present extensions of widely used deconvolution algorithms, which we frame in a (stochastic) Monte Carlo context to improve their robustness and reliability. The new PGM approach is assessed jointly with a suite of typically used approaches and algorithms against new laboratory-based commingling scenarios. The latter are based on the design and introduction of a novel and low-cost experimental method. The results of the study (i) constitute a unique and rigorous comparison of the traditionally employed production allocation deconvolution algorithms, (ii) document the critical importance of the number of features of the chromatograms used during a quantitative deconvolution, and (iii) suggest that our new PGM approach is very robust and accurate compared to existing approaches

Line spectroscopy with spatial resolution of laser-plasma X-ray emission

High dynamic range, space-resolved X-ray spectra of an aluminum laser–plasma in the 5.5–8 Å range were obtained using a TlAP crystal and a cooled CCD camera as a detector. This technique was used to investigate the emission region in the longitudinal direction over a distance of approximately 350 μm from the solid target surface. These data show that the electron density profile varies by two orders of magnitude with the temperature ranging from about 180 eV in the overdense region to about 650 eV in the underdense region. Accordingly, different equilibria take place across the explored region which can be identified with this experimental technique. Detailed studies on highly ionized atomic species in different plasma conditions can therefore be performed simultaneously under controlled conditions

A genome wide association study for backfat thickness in Italian Large White pigs highlights new regions affecting fat deposition including neuronal genes.

BACKGROUND: Carcass fatness is an important trait in most pig breeding programs. Following market requests, breeding plans for fresh pork consumption are usually designed to reduce carcass fat content and increase lean meat deposition. However, the Italian pig industry is mainly devoted to the production of Protected Designation of Origin dry cured hams: pigs are slaughtered at around 160 kg of live weight and the breeding goal aims at maintaining fat coverage, measured as backfat thickness to avoid excessive desiccation of the hams. This objective has shaped the genetic pool of Italian heavy pig breeds for a few decades. In this study we applied a selective genotyping approach within a population of ~ 12,000 performance tested Italian Large White pigs. Within this population, we selectively genotyped 304 pigs with extreme and divergent backfat thickness estimated breeding value by the Illumina PorcineSNP60 BeadChip and performed a genome wide association study to identify loci associated to this trait. RESULTS: We identified 4 single nucleotide polymorphisms with P 645.0E-07 and additional 119 ones with 5.0E-07<P 645.0E-05. These markers were located throughout all chromosomes. The largest numbers were found on porcine chromosomes 6 and 9 (n=15), 4 (n=13), and 7 (n=12) while the most significant marker was located on chromosome 18. Twenty-two single nucleotide polymorphisms were in intronic regions of genes already recognized by the Pre-Ensembl Sscrofa10.2 assembly. Gene Ontology analysis indicated an enrichment of Gene Ontology terms associated with nervous system development and regulation in concordance with results of large genome wide association studies for human obesity. CONCLUSIONS: Further investigations are needed to evaluate the effects of the identified single nucleotide polymorphisms associated with backfat thickness on other traits as a pre-requisite for practical applications in breeding programs. Reported results could improve our understanding of the biology of fat metabolism and deposition that could also be relevant for other mammalian species including humans, confirming the role of neuronal genes on obesity

Salivary biomarkers of neurodegenerative and demyelinating diseases and biosensors for their detection

Salivary analysis is gaining increasing interest as a novel and promising field of research for the diagnosis of neurodegenerative and demyelinating diseases related to aging. The collection of saliva offers several advantages, being noninvasive, stress-free, and repeatable. Moreover, the detection of biomarkers directly in saliva could allow an early diagnosis of the disease, leading to timely treatments. The aim of this manuscript is to highlight the most relevant researchers’ findings relatively to salivary biomarkers of neurodegenerative and demyelinating diseases, and to describe innovative and advanced biosensing strategies for the detection of salivary biomarkers. This review is focused on five relevant aging-related neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis, Huntington's disease, Multiple Sclerosis) and the salivary biomarkers most commonly associated with them. Advanced biosensors enabling molecular diagnostics for the detection of salivary biomarkers are presented, in order to stimulate future research in this direction and pave the way for their clinical application

Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds

Large genotyping datasets, obtained from high-density single nucleotide polymorphism (SNP) arrays, developed for different livestock species, can be used to describe and differentiate breeds or populations. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this study, we applied the Boruta algorithm, a wrapper of the machine learning random forest algorithm, on a database of 23 European pig breeds (20 autochthonous and three cosmopolitan breeds) genotyped with a 70k SNP chip, to pre-select informative SNPs. To identify different sets of SNPs, these pre-selected markers were then ranked with random forest based on their mean decrease accuracy and mean decrease gene indexes. We evaluated the efficiency of these subsets for breed classification and the usefulness of this approach to detect candidate genes affecting breed-specific phenotypes and relevant production traits that might differ among breeds. The lowest overall classification error (2.3%) was reached with a subpanel including only 398 SNPs (ranked based on their mean decrease accuracy), with no classification error in seven breeds using up to 49 SNPs. Several SNPs of these selected subpanels were in genomic regions in which previous studies had identified signatures of selection or genes associated with morphological or production traits that distinguish the analysed breeds. Therefore, even if these approaches have not been originally designed to identify signatures of selection, the obtained results showed that they could potentially be useful for this purpose

Latent class analysis variable selection

We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variable contributes information about cluster allocation beyond that contained in the already selected variables, and in the other model it does not. A headlong search algorithm is used to explore the model space and select clustering variables. In simulated datasets we found that the method selected the correct clustering variables, and also led to improvements in classification performance and in accuracy of the choice of the number of classes. In two real datasets, our method discovered the same group structure with fewer variables. In a dataset from the International HapMap Project consisting of 639 single nucleotide polymorphisms (SNPs) from 210 members of different groups, our method discovered the same group structure with a much smaller number of SNP

Carfilzomib plus dexamethasone in patients with relapsed and refractory multiple myeloma: A retro-prospective observational study

Objective: We investigate safety and efficacy in common clinical practice of the combination of carfilzomib and dexamethasone (Kd56) approved for the ENDEAVOR trial for the treatment of relapsed or refractory multiple myeloma. Methods: We retro-prospective analyzed 75 patients in three centers in Tuscany, 48 of whom had a clinically relevant comorbidity and 50 of whom were older than 65 years, treated with a median use in the fourth line of therapy. We assessed the efficacy based on the International Myeloma Working Group criteria. Results: The overall response rate was 60%. Median PFS was 10&nbsp;months in the general cohort; in patients treated for more than 1&nbsp;cycle of therapy PFS was 12 months. Quality of response to Kd56 treatment was found to positively impact PFS. Refractory status to previous line of therapy or to lenalidomide or an history of exposure to pomalidomide, seemed to have no impact on survival. We also showed a low adverse events rate, with no neuropathy events, and a relatively small number of cardiovascular events above grade 3 (10%). Conclusion: Kd56 is an effective and well tolerated regimen in highly pretreated and elderly patients with a good safety profile