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Using MeerKAT, we have discovered three new millisecond pulsars (MSPs) in the bulge globular cluster M62: M62H, M62I, and M62J. All three are in binary systems, which means all ten known pulsars in the cluster are in binaries. M62H has a planetary-mass companion with a median mass Mc, med ∼ 3 MJ and a mean density of ρ ∼ 11 g cm−3. M62I has an orbital period of 0.51 d and a Mc, med ∼ 0.15 M⊙. Neither of these low-mass systems exhibit eclipses. M62J has only been detected in the two Ultra High Frequency band (816 MHz) observations with a flux density S816 = 0.08 mJy. The non-detection in the L-band (1284 MHz) indicates it has a relatively steep spectrum (β < −3.1). We also present 23-yr-long timing solutions obtained using data from the Parkes ‘Murriyang’, Effelsberg, and MeerKAT telescopes for the six previously known pulsars. For all these pulsars, we measured the second spin-period derivatives and the rate of change of orbital period caused by the gravitational field of the cluster, and their proper motions. From these measurements, we conclude that the pulsars’ maximum accelerations are consistent with the maximum cluster acceleration assuming a core-collapsed mass distribution. Studies of the eclipses of the redback M62B and the black widow M62E at four and two different frequency bands, respectively, reveal a frequency dependence with longer and asymmetric eclipses at lower frequencies. The presence of only binary MSPs in this cluster challenges models which suggest that the MSP population of core-collapsed clusters should be dominated by isolated MSPs.

 

This paper characterizes the acid and cold stress responses of the thermoacidophilic crenarchaeon Saccharolobus islandicus REY15A, showing that each stress results in impaired growth rates, altered GDGT-lipid profiles, and differences in transcriptomes and proteomes. 

This paper investigates the mechanical behaviour of a bi-layered panel containing many particles in one layer and demonstrates the size effect of particles on the deflection. The inclusion-based boundary element method (iBEM) considers a fully bounded bi-material system. The fundamental solution for two-jointed half spaces has been used to acquire elastic fields resulting from source fields over inclusions and boundary-avoiding multi-domain integral along the interface. Eshelby’s equivalent inclusion method is used to simulate the material mismatch with a continuously distributed eigenstrain field over the equivalent inclusion. The eigenstrain is expanded at the centre of the inclusion, which provides tailorable accuracy based on the order of the polynomial of the eigenstrain. As a single-domain approach, the iBEM algorithm is particularly suitable for conducting virtual experiments of bi-layered composites with many defects or reinforcements for both local analysis and homogenization purposes. The maximum deflection of solar panel coupons is studied under uniform vertical loading merged with inhomogeneities of different material properties, dimensions and volume fractions. The size of defects or reinforcements plays a significant role in the deflection of the panel, even with the same volume fraction, as the substrate is relatively thin. 

A longstanding goal of learner modeling and educational data min-ing is to improve the domain model of knowledge that is used to make inferences about learning and performance. In this report we present a tool for finding domain models that is built into an exist-ing modeling framework, logistic knowledge tracing (LKT). LKT allows the flexible specification of learner models in logistic re-gression by allowing the modeler to select whatever features of the data are relevant to prediction. Each of these features (such as the count of prior opportunities) is a function computed for a compo-nent of data (such as a student or knowledge component). In this context, we have developed the “autoKC” component, which clus-ters knowledge components and allows the modeler to compute features for the clustered components. For an autoKC, the input component (initial KC or item assignment) is clustered prior to computing the feature and the feature is a function of that cluster. Another recent new function for LKT, which allows us to specify interactions between the logistic regression predictor terms, is com-bined with autoKC for this report. Interactions allow us to move beyond just assuming the cluster information has additive effects to allow us to model situations where a second factor of the data mod-erates a first factor. 

Objective: Mortality-trends from alcoholic liver disease (ALD) have recently increased and they differ by various factors in the U.S. However, these trends have only been analyzed using univariate models and in reality they may be influenced by various factors. We thus examined trends in age-standardized mortality from ALD among U.S. adults for 1999-2017, using multivariable piecewise log-linear models. Methods: We collected mortality-data from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research database, using the Underlying Cause of Death. Results: We identified 296,194 deaths from ALD and 346,386 deaths indirectly attributable to ALD during the period from 1999-2017. The multivariable-adjusted, age-standardized ALD mortality was stable during 1999-2006 (annual percentage change [APC]=-2.24, P=0.24), and increased during 2006-2017 (APC=3.18, P<0.006). Their trends did not differ by sex, race, age or urbanization. Subgroup analyses revealed upward multivariable-adjusted, age-standardized mortality-trends in alcoholic fatty liver (APC=4.64, P<0.001), alcoholic hepatitis (APC=4.38, P<0.001), and alcoholic cirrhosis (APC=5.33, P<0.001), but downward mortality-trends in alcoholic hepatic failure (APC=-1.63, P=0.006) and unspecified ALD (APC=-0.86, P=0.013). Strikingly, non-alcoholic cirrhosis also had an upward multivariable-adjusted, age-standardized mortality-trend (APC=0.69, P=0.046). By contrast, recent mortality-trends were stable for all cause of deaths (APC=-0.39, P=0.379) and downward for malignant neoplasms excluding liver cancer (APC=-2.82, P<0.001), infections (APC=-2.60, P<0.001), cardiovascular disease (APC=-0.69, P=0.044) and respiratory disease (APC=-0.56, P=0.002). The adjusted mortality with ALD as a contributing cause of death also had an upward trend during 2000-2017 (APC=5.47, P<0.001). Strikingly, common comorbidities of ALD, including hepatocellular carcinoma, cerebrovascular and ischemic heart cardiovascular diseases and sepsis, had upward trends during the past 14 to 16 years. Conclusions: ALD had an upward multivariable-adjusted, age-standardized mortality-trend among U.S. adults, without significant differences by sex, race, age or urbanization. Three ALD subtypes (alcoholic fatty liver, alcoholic hepatitis and alcoholic cirrhosis) and non-alcoholic cirrhosis had upward morality-trends, while other ALD subtypes and other causes of death did not. 

The generation, manipulation and quantification of non-classical light, such as quantum-entangled photon pairs, differs significantly from methods with classical light. Thus, quantum measures could be harnessed to give new information about the interaction of light with matter. In this study we investigate if quantum entanglement can be used to diagnose disease. In particular, we test whether brain tissue from subjects suffering from Alzheimer’s disease can be distinguished from healthy tissue. We find that this is indeed the case. Polarization-entangled photons traveling through brain tissue lose their entanglement via a decohering scattering interaction that gradually renders the light in a maximally mixed state. We found that in thin tissue samples (between 120 and 600 micrometers) photons decohere to a distinguishable lesser degree in samples with Alzheimer’s disease than in healthy-control ones. Thus, it seems feasible that quantum measures of entangled photons could be used as a means to identify brain samples with the neurodegenerative disease.