Background: Astrocytomas are tumors occurring in young adulthood. Astrocytic tumors can be classified into four grades according to histologic features: grade I, grade II, grade III and grade IV. ...Malignant tumors, those of grade III and IV, are characterized by uncontrolled proliferation, which is known to be regulated by the family of Rho GTPases. StarD13, a GAP for Rho GTPases, has been described as a tumor suppressor in hepatocellular carcinoma. Materials and Methods: In the present study, we used immunohistochemistry on tissues taken from human patients of different grade astrocytomas. We also used astrocytoma cell lines. We knocked down Stardi 3 by transfecting the cells with StarDI 3 siRNA and we overexpressed Stard13 by transfecting the cells with a GFP-Stard13 construct. We measured cell proliferation and cell death using the MTT and WST kits and doing cell cycle analysis by flow cytometry. Results: In the present study, IHC analysis on Grade I-IV brain tissues from patients showed StarDI3 to be overexpressed in grade III and IV astrocytoma tumors when compared to grade I and II. However, when we mined the REMBRANDT data, we found that the mRNA levels of StarDI 3 are indeed higher in the higher grades but much lower than the normal tissues. The overexpression of a GFP-StarD13 construct in astrocytoma cells led to the increase in cell death and a decrease of cell viability. Knocking down StarD13 using siRNA led to a decrease in cell death and an increase in cell viability. When looking at the mechanism, we found that the tumor suppressor effect of StarDI 3 is through the inhibition of the cell cycle and not through the activation of apoptosis. When knocking down StarDI 3, we also saw an increase in p-ERK, uncovering a potential link between Rho GTPases and ERK activation. Conclusion: In conclusion, we found StarDI3 to be a tumor suppressor in astrocytoma. It is underexpressed in comparison to normal brain and when knocked down in astrocytoma cells, this leads to a decrease in cell proliferation.
The photon spectrum in the inclusive electromagnetic radiative decays of the B meson, B → X(s)γ plus B → X(d)γ, is studied using a data sample of (382.8 ± 4.2) × 10(6)Υ(4S) → BB decays collected by ...the BABAR experiment at SLAC. The spectrum is used to extract the branching fraction B(B → X(s)γ) = (3.21 ± 0.33) × 10(-4) for E(γ) >1.8 GeV and the direct CP asymmetry A(CP) (B → X(s+d)γ) = 0.057 ± 0.063. The effects of detector resolution and Doppler smearing are unfolded to measure the photon energy spectrum in the B meson rest frame.
Phys. Rev. D 89, 051101 (2014) We describe searches for B meson decays to the charmless vector-vector final
states omega omega and omega phi with 471 x 10^6 B Bbar pairs produced in e+ e-
...annihilation at sqrt(s) = 10.58 GeV using the BABAR detector at the PEP-II
collider at the SLAC National Accelerator Laboratory. We measure the branching
fraction B(B0 --> omega omega) = (1.2 +- 0.3 +0.3-0.2) x 10^-6, where the first
uncertainty is statistical and the second is systematic, corresponding to a
significance of 4.4 standard deviations. We also determine the upper limit B(B0
--> omega phi) < 0.7 x 10^-6 at 90% confidence level. These measurements
provide the first evidence for the decay B0 --> omega omega, and an improvement
of the upper limit for the decay B0 --> omega phi.
We study the decay
$\bar{B}^{0}\rightarrow\Lambda_{c}^{+}\bar{p}\pi^{+}\pi^{-}$, reconstructing
the \Lambda_{c}^{+} baryon in the $p K^{-}\pi^{+}$ mode, using a data sample of
$467\times 10^{6}$ ...$B\bar{B}$ pairs collected with the BaBar detector at the
PEP-2 storage rings at SLAC. We measure branching fractions for decays with
intermediate $\Sigma_{c}$ baryons to be ${\cal
B}\bar{B}^{0}\rightarrow\Sigma_{c}(2455)^{++}\bar{p}\pi^{-}=(21.3 \pm 1.0 \pm
1.0 \pm 5.5) \times 10^{-5}$, ${\cal
B}\bar{B}^{0}\rightarrow\Sigma_{c}(2520)^{++}\bar{p}\pi^{-}=(11.5\pm 1.0 \pm
0.5 \pm 3.0)\times 10^{-5}$, ${\cal
B}\bar{B}^{0}\rightarrow\Sigma_{c}(2455)^{0}\bar{p}\pi^{+}=(9.1 \pm 0.7 \pm
0.4 \pm 2.4)\times10^{-5}$, and ${\cal
B}\bar{B}^{0}\rightarrow\Sigma_{c}(2520)^{0}\bar{p}\pi^{+}= (2.2 \pm 0.7 \pm
0.1\pm 0.6) \times 10^{-5}$, where the uncertainties are statistical,
systematic, and due to the uncertainty on the
$\Lambda_{c}^{+}\rightarrow\proton\Km\pi^{+}$ branching fraction, respectively.
For decays without $\Sigma_{c}(2455)$ or $\Sigma_{c}(2520)$ resonances, we
measure ${\cal
B}\bar{B}^{0}\rightarrow\Lambda_{c}^{+}\bar{p}\pi^{+}\pi^{-}_{\mathrm{non-\Sigma_{c}}}=(79
\pm 4 \pm 4 \pm 20)\times10^{-5}$. The total branching fraction is determined
to be ${\cal
B}\bar{B}^{0}\rightarrow\Lambda_{c}^{+}\bar{p}\pi^{+}\pi^{-}_{\mathrm{total}}=(123
\pm 5 \pm 7 \pm 32)\times10^{-5}$. We examine multibody mass combinations in
the resonant three-particle $\Sigma_{c}\bar{p}\pi$ final states and in the
four-particle $\Lambda_{c}^{+}\bar{p}\pi^{+}\pi^{-}$ final state, and observe
different characteristics for the $\bar{p}\pi$ combination in neutral versus
doubly-charged $\Sigma_{c}$ decays.
We study the decay \(\bar{B}^{0}\rightarrow\Lambda_{c}^{+}\bar{p}\pi^{+}\pi^{-}\), reconstructing the \Lambda_{c}^{+} baryon in the \(p K^{-}\pi^{+}\) mode, using a data sample of \(467\times ...10^{6}\) \(B\bar{B}\) pairs collected with the BaBar detector at the PEP-2 storage rings at SLAC. We measure branching fractions for decays with intermediate \(\Sigma_{c}\) baryons to be \({\cal B}\bar{B}^{0}\rightarrow\Sigma_{c}(2455)^{++}\bar{p}\pi^{-}=(21.3 \pm 1.0 \pm 1.0 \pm 5.5) \times 10^{-5}\), \({\cal B}\bar{B}^{0}\rightarrow\Sigma_{c}(2520)^{++}\bar{p}\pi^{-}=(11.5\pm 1.0 \pm 0.5 \pm 3.0)\times 10^{-5}\), \({\cal B}\bar{B}^{0}\rightarrow\Sigma_{c}(2455)^{0}\bar{p}\pi^{+}=(9.1 \pm 0.7 \pm 0.4 \pm 2.4)\times10^{-5}\), and \({\cal B}\bar{B}^{0}\rightarrow\Sigma_{c}(2520)^{0}\bar{p}\pi^{+}= (2.2 \pm 0.7 \pm 0.1\pm 0.6) \times 10^{-5}\), where the uncertainties are statistical, systematic, and due to the uncertainty on the \(\Lambda_{c}^{+}\rightarrow\proton\Km\pi^{+}\) branching fraction, respectively. For decays without \(\Sigma_{c}(2455)\) or \(\Sigma_{c}(2520)\) resonances, we measure \({\cal B}\bar{B}^{0}\rightarrow\Lambda_{c}^{+}\bar{p}\pi^{+}\pi^{-}_{\mathrm{non-\Sigma_{c}}}=(79 \pm 4 \pm 4 \pm 20)\times10^{-5}\). The total branching fraction is determined to be \({\cal B}\bar{B}^{0}\rightarrow\Lambda_{c}^{+}\bar{p}\pi^{+}\pi^{-}_{\mathrm{total}}=(123 \pm 5 \pm 7 \pm 32)\times10^{-5}\). We examine multibody mass combinations in the resonant three-particle \(\Sigma_{c}\bar{p}\pi\) final states and in the four-particle \(\Lambda_{c}^{+}\bar{p}\pi^{+}\pi^{-}\) final state, and observe different characteristics for the \(\bar{p}\pi\) combination in neutral versus doubly-charged \(\Sigma_{c}\) decays.
Neuronal theories of conscious access tentatively relate conscious perception to the integration and global broadcasting of information across distant cortical and thalamic areas 1–6. Experiments ...contrasting visible and invisible stimuli support this view and suggest that global neuronal communication may be detectable using scalp electroencephalography (EEG) 3, 5–11. However, whether global information sharing across brain areas also provides a specific signature of conscious state in awake but noncommunicating patients remains an active topic of research 12–15. We designed a novel measure termed “weighted symbolic mutual information” (wSMI) and applied it to 181 high-density EEG recordings of awake patients recovering from coma and diagnosed in various states of consciousness. The results demonstrate that this measure of information sharing systematically increases with consciousness state, particularly across distant sites. This effect sharply distinguishes patients in vegetative state (VS), minimally conscious state (MCS), and conscious state (CS) and is observed regardless of etiology and delay since insult. The present findings support distributed theories of conscious processing and open up the possibility of an automatic detection of conscious states, which may be particularly important for the diagnosis of awake but noncommunicating patients.
•Theories of consciousness link conscious access to global information integration•181 EEG recordings were acquired, including 143 from VS and MCS patients•Information sharing across current sources was estimated with a new measure•The results suggest that unconscious patients have lower global information sharing
See Sokoliuk and Cruse (doi:10.1093/brain/awy267) for a scientific commentary on this article.
Detecting awareness in patients who recover from coma but remain behaviourally unresponsive is a major ...challenge. Engemann, Raimondo et al. demonstrate that machine learning on EEG signals enables robust classification of state-of-consciousness in data from brain-injured patients in multiple hospitals.
Abstract
Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.
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