The consumption of design drugs, frequently known as new psychoactive substances (NPS), has increased considerably worldwide, becoming a severe issue for the responsible governmental agencies. These ...illicit substances can be defined as synthetic compounds produced in clandestine laboratories in order to act as analogs of schedule drugs mimetizing its chemical structure and improving its pharmacological effects while hampering the control and making regulation more complicated. In this way, the development of new methodologies for chemical analysis of NPS drugs is indispensable to determine a novel class of drugs arising from the underground market. Therefore, this work shows the use of high‐resolution mass spectrometry Fourier transform ion cyclotron resonance mass spectrometry (FT‐ICR MS) applying different ionization sources such as paper spray ionization (PSI) and electrospray ionization (ESI) in the evaluation of miscellaneous of seized drugs samples as blotter paper (n = 79) and tablet (n = 100). Also, an elucidative analysis was performed by ESI(+)MS/MS experiments, and fragmentation mechanisms were proposed to confirm the chemical structure of compounds identified. Besides, the results of ESI(+) and PSI(+)‐FT‐ICR MS were compared with those of GC–MS, revealing that ESI(+)MS showed greater detection efficiency among the methodologies employed in this study. Moreover, this study stands out as a guide for the chemical analysis of NPS drugs, highlighting the differences between the techniques of ESI(+)‐FT‐ICR MS, PSI(+)‐FT‐ICR MS, and GC–MS.
On ψ-Learning Shen, Xiaotong; Tseng, George C; Zhang, Xuegong ...
Journal of the American Statistical Association,
09/2003, Letnik:
98, Številka:
463
Journal Article
Recenzirano
The concept of large margins have been recognized as an important principle in analyzing learning methodologies, including boosting, neural networks, and support vector machines (SVMs). However, this ...concept alone is not adequate for learning in nonseparable cases. We propose a learning methodology, called ψ-learning, that is derived from a direct consideration of generalization errors. We provide a theory for ψ-learning and show that it essentially attains the optimal rates of convergence in two learning examples. Finally, results from simulation studies and from breast cancer classification confirm the ability of ψ-learning to outperform SVM in generalization.
Operational status of the Magnum-PSI linear plasma device Scholten, John; Zeijlmans van Emmichoven, P.A.; van Eck, H.J.N. ...
Fusion engineering and design,
October 2013, 2013-10-00, 20131001, Letnik:
88, Številka:
9-10
Journal Article
Recenzirano
Odprti dostop
•High heat flux, high density plasmas in a highly accessible linear plasma device.•Plasma exposure of targets of different sizes under selectable plasma beam angles.•Dedicated plasma and surface ...diagnostics.•Differential vacuum pumping system.
The construction phase of the linear plasma generator Magnum-PSI at the FOM institute DIFFER has been completed and the facility has been officially opened in March 2012. The scientific program to gain more insight in the plasma–wall interactions relevant for ITER and future fusion reactors has started.
In Magnum-PSI, targets of a wide range of materials and shapes can be exposed to high particle, high heat flux plasmas (>1024ionsm−2s−1; >10MW/m2). For magnetization of the plasma, oil-cooled electromagnets are temporarily installed to enable pulsed operation until the device is upgraded with a superconducting magnet. The magnets generate a field of up to 1.9T close to the plasma source for a duration of 6s. Longer exposure times are available for lower field settings.
Plasma characterizations were done with a variety of gases (H, D, He, Ne and Ar) to determine the machine performance and prepare for subsequent scientific experiments. Thomson scattering and optical emission spectroscopy were used to determine the plasma parameters while infrared thermography and target calorimetry were used to determine the power loads to the surface.
This paper reports on the status of Magnum-PSI and its diagnostic systems. In addition, an overview of the plasma parameters that can be achieved in the present state will be given.
There are many challenges associated with analysing gas chromatography - mass
spectrometry (GC-MS) data. Many of these challenges stem from the fact that
electron ionisation can make it difficult to ...recover molecular information due
to the high degree of fragmentation with concomitant loss of molecular ion
signal. With GC-MS data there are often many common fragment ions shared among
closely-eluting peaks, necessitating sophisticated methods for analysis. Some
of these methods are fully automated, but make some assumptions about the data
which can introduce artifacts during the analysis. Chemometric methods such as
Multivariate Curve Resolution, or Parallel Factor Analysis are particularly
attractive, since they are flexible and make relatively few assumptions about
the data - ideally resulting in fewer artifacts. These methods do require
expert user intervention to determine the most relevant regions of interest and
an appropriate number of components, $k$, for each region. Automated region of
interest selection is needed to permit automated batch processing of
chromatographic data with advanced signal deconvolution. Here, we propose a new
method for automated, untargeted region of interest selection that accounts for
the multivariate information present in GC-MS data to select regions of
interest based on the ratio of the squared first, and second singular values
from the Singular Value Decomposition of a window that moves across the
chromatogram. Assuming that the first singular value accounts largely for
signal, and that the second singular value accounts largely for noise, it is
possible to interpret the relationship between these two values as a
probabilistic distribution of Fisher Ratios. The sensitivity of the algorithm
was tested by investigating the concentration at which the algorithm can no
longer pick out chromatographic regions known to contain signal.
South American oil-palm (Elaeis oleifera) is not cultivated in tropical countries like Malaysia on large scale due to low yield of palm oil derived from its fruit mesocarp. However, its fruit ...mesocarp oil contains about 68.6 % oleic acid (C sub(18:1)) which is more than double in comparison to commercially cultivated oil-palm, E. guineensis Jacq Tenera (hybrid of Dura x Pisifera). It is also known that E. oleifera is a good source of tocotrienols and carotenoids. Therefore, it is of interest to know the genome sequence of E. oleifera. The objective of this study is to generate genome survey sequences (GSS) to get GC content insight in the E. oleifera genome. The nuclear genomic DNA isolated from young leaf-tissues was digested with EcoRI and NdeI/DraI restriction enzymes; and three genomic DNA libraries were constructed using Lambda ZAP-II, pGEM+-T Easy, and pDONR 222 as cloning vectors. Generated 76 GSSs were analyzed by using Bioinformatics tools. The analysis result indicates that the adenine, cytosine, guanine and thymine content in generated GSSs are 30%, 20%, 20%, and 30% respectively. In conclusion, based on the precise GC content analysis of the randomly isolated 76 GSSs by using Bioinformatics tools we hypothesize that GC content in E. oleifera genome is 40%. The hypothesized 40% GC content in E. oleifera genome is expected to remain close to the GC content based on the whole genome analysis.
Information flow between brain areas is difficult to estimate from EEG measurements due to the presence of noise as well as due to volume conduction. We here test the ability of popular measures of ...effective connectivity to detect an underlying neuronal interaction from simulated EEG data, as well as the ability of commonly used inverse source reconstruction techniques to improve the connectivity estimation. We find that volume conduction severely limits the neurophysiological interpretability of sensor-space connectivity analyses. Moreover, it may generally lead to conflicting results depending on the connectivity measure and statistical testing approach used. In particular, we note that the application of Granger-causal (GC) measures combined with standard significance testing leads to the detection of spurious connectivity regardless of whether the analysis is performed on sensor-space data or on sources estimated using three different established inverse methods. This empirical result follows from the definition of GC. The phase-slope index (PSI) does not suffer from this theoretical limitation and therefore performs well on our simulated data.
We develop a theoretical framework to characterize artifacts of volume conduction, which may still be present even in reconstructed source time series as zero-lag correlations, and to distinguish their time-delayed brain interaction. Based on this theory we derive a procedure which suppresses the influence of volume conduction, but preserves effects related to time-lagged brain interaction in connectivity estimates. This is achieved by using time-reversed data as surrogates for statistical testing. We demonstrate that this robustification makes Granger-causal connectivity measures applicable to EEG data, achieving similar results as PSI. Integrating the insights of our study, we provide a guidance for measuring brain interaction from EEG data. Software for generating benchmark data is made available.
► We assess methods for EEG-based connectivity analysis on realistically simulated data. ► We demonstrate a number of pitfalls occurring depending on the method used. ► Granger-causal approaches are obscured by so-called weak data asymmetries. ► We propose a simple strategy for alleviating the impact of weak asymmetries. ► Code for data generation and analysis is provided for benchmarking purposes.
The Ψ-RNA packaging signal of the human immunodeficiency virus type-1 (HIV-1) genome contains a 35 nucleotide stem-loop, termed SL1, which is important for efficient genome packaging during virus ...assembly and for reverse transcription during infectivity. The predicted secondary structure of SL1 consists of an upper stem with a GC-rich loop that facilitates dimerization, a lower stem, and an intervening bulge (G5, A24–G25–G26) that is both strictly conserved and essential for efficient packaging of the viral genome. The structure of the upper stem in both the kissing and duplex dimer forms have been determined recently. Here, we report the structure of an engineered form of SL1 (SL1
m) that contains a GAGA tetraloop substituted for the GC-rich loop. This construct does not aggregate and remains monomeric at concentrations up to 1
mM, enabling structural studies of the intact stems and bulge. The structure was refined using
1H–
13C residual dipolar couplings. The upper stem (C6–G12, C17–G23) is in close agreement with X-ray structures of kissing and duplex dimer forms of related oligoribonucleotides, and nucleotides C1–G4 and C27–G30 form the expected
A-helical lower stem. Residues G5 and A24 of the predicted bulge form a G–A mismatch that stacks with the upper stem, and residues G25 and G26 stack between the G–A mismatch and the lower stem in a manner that produces a hole in the center of the bulge and a 25(±4)° bend between the upper and lower stems. SL1
m exhibits relatively poor affinity for the HIV-1 nucleocapsid protein, suggesting that the bulge plays other roles in genome packaging.
There are many challenges associated with analysing gas chromatography - mass spectrometry (GC-MS) data. Many of these challenges stem from the fact that electron ionisation can make it difficult to ...recover molecular information due to the high degree of fragmentation with concomitant loss of molecular ion signal. With GC-MS data there are often many common fragment ions shared among closely-eluting peaks, necessitating sophisticated methods for analysis. Some of these methods are fully automated, but make some assumptions about the data which can introduce artifacts during the analysis. Chemometric methods such as Multivariate Curve Resolution, or Parallel Factor Analysis are particularly attractive, since they are flexible and make relatively few assumptions about the data - ideally resulting in fewer artifacts. These methods do require expert user intervention to determine the most relevant regions of interest and an appropriate number of components, \(k\), for each region. Automated region of interest selection is needed to permit automated batch processing of chromatographic data with advanced signal deconvolution. Here, we propose a new method for automated, untargeted region of interest selection that accounts for the multivariate information present in GC-MS data to select regions of interest based on the ratio of the squared first, and second singular values from the Singular Value Decomposition of a window that moves across the chromatogram. Assuming that the first singular value accounts largely for signal, and that the second singular value accounts largely for noise, it is possible to interpret the relationship between these two values as a probabilistic distribution of Fisher Ratios. The sensitivity of the algorithm was tested by investigating the concentration at which the algorithm can no longer pick out chromatographic regions known to contain signal.
Display omitted
► Dibenzofuran is widely distributed in environment. ► It is direct precursor molecule of extremely toxic halogenated dibenzofuran. ► The mechanism of dibenzofuran induced toxicity is ...proposed. ► The mechanism explains dibenzofuran forms quinone metabolite after its metabolism in HepG2 cells. ► It potentiates ROS formation, G-1 arrest and mitochondrial membrane potential dip and hence cell death.
Dioxins are a class of extremely toxic environmentally persistent pollutant, comprised of halogenated dibenzo-p-dioxins, dibenzofurans and biphenyls. Despite significant human exposure via multiple routes, very little is known about toxicity induced by dibenzofuran (DF). Current study shed lights on the potential toxicity mechanism of DF using human hepatoma cell line (HepG2). It was observed that the exposure to DF potentiate oxidative stress, apoptosis and necrosis at 10μM within 8h in HepG2 cells. Interestingly, when we pre-incubated the cells with α-NF (1nM) for 12h, an aromatic hydrocarbon receptor antagonist, the IC50 of DF increased by 14 folds indicating the cytoprotective ability of α-NF from DF induced toxicity. Furthermore, three additional metabolites were observed while studying the metabolic profile of DF in HepG2 cells with and without pre-incubation with α-NF using chromatography–mass spectroscopy (GC–MS). Of these, two metabolites were characterized as dihydroxylated derivative of DF and third metabolite was characterized as quinone derivative of DF. By flow cytometry and confocal laser microscopy analysis we followed the ROS formation after DF (10μM) exposure for 3h. Significantly low ROS was generated in cells which were pre-incubated with α-NF than cells which were not pre-incubated with α-NF underlining the importance of metabolism in DF toxicity. The same pattern of protection was consistent while measuring mitochondrial membrane potential (MMP), i.e., less MMP dip was observed in ‘with α-NF pre-incubated and DF (10μM) exposed cells’ than ‘without α-NF pre-incubated but DF exposed cells’. In cell cycle studies, it was confirmed that cell population of HepG2 at G1 stage progressively increased in number (∼74%) within 24h. Thus, DF and its metabolites induce significantly higher cytotoxicity after metabolism in HepG2 cells than its parent compound (DF) by ROS formation, MMP dip and impaired cell cycle.
This brief report is a response to the article by Peter Bancel entitled “Searching for Global Consciousness: A Seventeen Year Exploration” in which he compares a goal orientation (GO) model with a ...field-like model he refers to as global consciousness (GC). He first attempts to exclude the latter, and then presents selected tests that compare the models. While the article appears to provide support for Bancel׳s conclusion that GC cannot explain the data and must be supplanted by GO, there are good reasons to believe this conclusion is premature at best. I address the vulnerable assumptions underlying Bancel׳s rejection of GC, and then provide multiple examples of parametric structure in the data, which cannot be attributed to GO, but are amenable to explanation by field-like models.
PDF
