Abstract
Neutron stars are observed to undergo small, abrupt rotational speed-up. This phenomenon is known as glitch. In pulsar timing observations, detection of a neutron star glitch is constrained ...by the time of occurrence of the event relative to entire observing span and observing cadences, time of occurrence of preceding/subsequent glitches relative to observing cadences and the strength of timing noise. Using the Yu et al. data sets, in this paper, we analyse the observational selection in terms of detection probability. We define partial probabilities for the constraints and use the Monte Carlo method with assuming glitches distribute uniformly to solve the complete probability formula for both group case involving 157 pulsars and individual cases for each of the seven pulsars with glitch numbers ≥5. In the simulations, numerical Bayesian inference is used for glitch identification. With the derived detection probability density and observed results, we uncover glitch size probability distribution embedded in the data for both the group and individual cases. We find the most prominent correction occurred for PSR J1341−6220, in which the exponent of the power-law model varies from the observed
$+0.7^{+1.4}_{-0.7}$
to
$-0.4^{+1.0}_{-0.4}$
. We suggest observers determine the detection probability for glitch theories, e.g. the self-organized criticality.
By designing a structured gas density profile between the dual-stage gas jets to manipulate electron seeding and energy chirp reversal for compressing the energy spread, we have experimentally ...produced high-brightness high-energy electron beams from a cascaded laser wakefield accelerator with peak energies in the range of 200-600 MeV, 0.4%-1.2% rms energy spread, 10-80 pC charge, and ∼0.2 mrad rms divergence. The maximum six-dimensional brightness B_{6D,n} is estimated as ∼6.5×10^{15} A/m^{2}/0.1%, which is very close to the typical brightness of e beams from state-of-the-art linac drivers. These high-brightness high-energy e beams may lead to the realization of compact monoenergetic gamma-ray and intense coherent x-ray radiation sources.
Varied causative and risk factors can lead to cardiac dysfunction. Cardiac dysfunction often evolves into heart failure by cardiac remodeling due to autonomic nervous system disturbance and ...neurohumoral abnormalities, even if the detriment factors are removed. Renal sympathetic nerve activity plays a pivotal regulatory role in neurohumoral mechanisms. The present study was designed to determine the therapeutic effects of renal sympathetic denervation (RSD) on cardiac dysfunction, fibrosis, and neurohumoral response in transverse aortic constriction (TAC) rats with chronic pressure overload. The present study demonstrated that RSD attenuated myocardial fibrosis and hypertrophy, and structural remodeling of the left atrium and ventricle, up-regulated cardiac beta adrenoceptor (beta-AR, including beta(1)AR and beta(2)AR) and sarco-endoplasmic reticulum Ca(2+)-ATPase (SERCA) while down-regulated angiotensin II type 1 receptor (AT(1)R), and decreased plasma B-type natriuretic peptide (BNP), norepinephrine (NE), angiotensin II (Ang II), and arginine vasopressin (AVP) levels in TAC rats with chronic pressure overload. We conclude that RSD attenuates myocardial fibrosis, the left atrial enlargement, and the left ventricular wall hypertrophy; inhibits the overdrive of the sympathetic nervous system (SNS), renin-angiotensin-aldosterone system (RAAS), and AVP system in TAC rats with chronic pressure overload. RSD could be a promising non-pharmacological approach to control the progression of cardiac dysfunction.
The pentatelluridesZrTe5andHfTe5are layered compounds with one-dimensional transition-metal chains that show a not-yet-understood temperature-dependent transition in transport properties as well as ...recently discovered properties suggesting topological semimetallic behavior. Here, we report magnetotransport properties for two kinds ofZrTe5single crystals grown with the chemical vapor transport (CVT) and the flux method (Flux), respectively. They show distinct transport properties at zero field: The CVT crystal displays a metallic behavior with a pronounced resistance peak and a sudden sign reversal in thermopower at approximately 130 K, consistent with previous observations of the electronic transition; in striking contrast, the Flux crystal exhibits a semiconducting-like behavior at low temperatures and a positive thermopower over the whole temperature range. For both samples, strong effects on the transport properties are observed when the magnetic field is applied along the orthorhombicbandcaxes, i.e., perpendicular to the chain direction. Refinements on the single-crystal x-ray diffraction and the measurements of energy dispersive spectroscopy reveal the presence of noticeable Te vacancies in the CVT samples, while the Flux samples are close to the stoichiometry. Analyses on the magnetotransport properties confirm that the carrier densities of the CVT sample are about two orders higher than those of the Flux sample. Our results thus indicate that the widely observed anomalous transport behaviors in pentatellurides actually take place in the Te-deficient samples. For the stoichiometric pentatellurides, our electronic structure calculations show narrow-gap semiconducting behavior, with different transport anisotropies for holes and electrons. For the degenerately dopedn-type samples, our transport calculations can result in a resistivity peak and crossover in thermopower from negative to positive at temperatures close to those observed experimentally due to a combination of bipolar effects and different anisotropies of electrons and holes. Our present work resolves the long-standing puzzle regarding the anomalous transport behaviors of pentatellurides, as well as the electronic structure in favor of a semiconducting state.
A consensus conference on multiple system atrophy (MSA) in 1998 established criteria for diagnosis that have been accepted widely. Since then, clinical, laboratory, neuropathologic, and imaging ...studies have advanced the field, requiring a fresh evaluation of diagnostic criteria. We held a second consensus conference in 2007 and present the results here.
Experts in the clinical, neuropathologic, and imaging aspects of MSA were invited to participate in a 2-day consensus conference. Participants were divided into five groups, consisting of specialists in the parkinsonian, cerebellar, autonomic, neuropathologic, and imaging aspects of the disorder. Each group independently wrote diagnostic criteria for its area of expertise in advance of the meeting. These criteria were discussed and reconciled during the meeting using consensus methodology.
The new criteria retain the diagnostic categories of MSA with predominant parkinsonism and MSA with predominant cerebellar ataxia to designate the predominant motor features and also retain the designations of definite, probable, and possible MSA. Definite MSA requires neuropathologic demonstration of CNS alpha-synuclein-positive glial cytoplasmic inclusions with neurodegenerative changes in striatonigral or olivopontocerebellar structures. Probable MSA requires a sporadic, progressive adult-onset disorder including rigorously defined autonomic failure and poorly levodopa-responsive parkinsonism or cerebellar ataxia. Possible MSA requires a sporadic, progressive adult-onset disease including parkinsonism or cerebellar ataxia and at least one feature suggesting autonomic dysfunction plus one other feature that may be a clinical or a neuroimaging abnormality.
These new criteria have simplified the previous criteria, have incorporated current knowledge, and are expected to enhance future assessments of the disease.
Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of data sets gathered by governments and organizations. However, these data sets ...may contain lots of user's private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in recent years. Therefore, how to develop accurate traffic prediction while preserving privacy is a significant problem to be solved, and there is a tradeoff between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning (FL) and propose an FL-based gated recurrent unit neural network algorithm (FedGRU) for traffic flow prediction (TFP). FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism rather than directly sharing raw data among organizations. In the secure parameter aggregation mechanism, we adopt a federated averaging algorithm to reduce the communication overhead during the model parameter transmission process. Furthermore, we design a joint announcement protocol to improve the scalability of FedGRU. We also propose an ensemble clustering-based scheme for TFP by grouping the organizations into clusters before applying the FedGRU algorithm. Extensive case studies on a real-world data set demonstrate that FedGRU can produce predictions that are merely 0.76 km/h worse than the state of the art in terms of mean average error under the privacy preservation constraint, confirming that the proposed model develops accurate traffic predictions without compromising the data privacy.
PET imaging using (18)Ffluorodeoxyglucose (FDG) and (11)CPittsburgh compound B (PIB) have been proposed as biomarkers of Alzheimer disease (AD), as have CSF measures of the 42 amino acid beta-amyloid ...protein (Abeta(1-42)) and total and phosphorylated tau (t-tau and p-tau). Relationships between biomarkers and with disease severity are incompletely understood.
Ten subjects with AD, 11 control subjects, and 34 subjects with mild cognitive impairment from the Alzheimer's Disease Neuroimaging Initiative underwent clinical evaluation; CSF measurement of Abeta(1-42), t-tau, and p-tau; and PIB-PET and FDG-PET scanning. Data were analyzed using continuous regression and dichotomous outcomes with subjects classified as "positive" or "negative" for AD based on cutoffs established in patients with AD and controls from other cohorts.
Dichotomous categorization showed substantial agreement between PIB-PET and CSF Abeta(1-42) measures (91% agreement, kappa = 0.74), modest agreement between PIB-PET and p-tau (76% agreement, kappa = 0.50), and minimal agreement for other comparisons (kappa <0.3). Mini-Mental State Examination score was significantly correlated with FDG-PET but not with PIB-PET or CSF Abeta(1-42). Regression models adjusted for diagnosis showed that PIB-PET was significantly correlated with Abeta(1-42), t-tau, and p-tau(181p), whereas FDG-PET was correlated only with Abeta(1-42).
PET and CSF biomarkers of Abeta agree with one another but are not related to cognitive impairment. (18)Ffluorodeoxyglucose-PET is modestly related to other biomarkers but is better related to cognition. Different biomarkers for Alzheimer disease provide different information from one another that is likely to be complementary.
Lagged oceanic and atmospheric climate indices are potentially useful predictors of seasonal rainfall totals. A rigorous Bayesian joint probability modeling approach is applied to find the ...cross-validation predictive densities of gridded Australian seasonal rainfall totals using lagged climate indices as predictors over the period of 1950–2009. The evidence supporting the use of each climate index as a predictor of seasonal rainfall is quantified by the pseudo-Bayes factor based on cross-validation predictive densities. The evidence strongly supports the use of climate indices from the Pacific region with weaker, but positive, evidence for the use of climate indices from the Indian region and the extratropical region. The spatial structure and seasonal variation of the evidence for each climate index is mapped and compared. Spatially, the strongest supporting evidence is found for forecasting in northern and eastern Australia. Seasonally, the strongest evidence is found from August–October to November–January and the weakest evidence is found from March–May to May–July. In some regions and seasons, there is little evidence supporting the use of climate indices for forecasting seasonal rainfall. Climate indices derived from sea surface temperature anomalies in the Pacific region show stronger persistence in the relationship with Australian seasonal rainfall totals than climate indices derived from sea surface temperature anomalies in the Indian region. Climate indices derived from atmospheric variables are also strongly supported, provided they represent the large-scale circulation. Many climate indices are found to show similar supporting evidence for forecasting Australian seasonal rainfall, leading to the prospect of combining climate indices in multiple predictor models and/or model averaging.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Neuroimaging measures and chemical biomarkers may be important indices of clinical progression in normal aging and mild cognitive impairment (MCI) and need to be evaluated longitudinally.
To ...characterize cross-sectionally and longitudinally clinical measures in normal controls, subjects with MCI, and subjects with mild Alzheimer disease (AD) to enable the assessment of the utility of neuroimaging and chemical biomarker measures.
A total of 819 subjects (229 cognitively normal, 398 with MCI, and 192 with AD) were enrolled at baseline and followed for 12 months using standard cognitive and functional measures typical of clinical trials.
The subjects with MCI were more memory impaired than the cognitively normal subjects but not as impaired as the subjects with AD. Nonmemory cognitive measures were only minimally impaired in the subjects with MCI. The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year. Approximately 50% of the subjects with MCI were on antidementia therapies. There was minimal movement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale for the normal control subjects, slight movement for the subjects with MCI of 1.1, and a modest change for the subjects with AD of 4.3. Baseline CSF measures of Abeta-42 separated the 3 groups as expected and successfully predicted the 12-month change in cognitive measures.
The Alzheimer's Disease Neuroimaging Initiative has successfully recruited cohorts of cognitively normal subjects, subjects with mild cognitive impairment (MCI), and subjects with Alzheimer disease with anticipated baseline characteristics. The 12-month progression rate of MCI was as predicted, and the CSF measures heralded progression of clinical measures over 12 months.
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