1I/2017 U1 ('Oumuamua), a recently discovered asteroid in a hyperbolic orbit, is likely the first macroscopic object of extrasolar origin identified in the solar system. Here, we present imaging and ...spectroscopic observations of 'Oumuamua using the Palomar Hale Telescope as well as a search of meteor activity potentially linked to this object using the Canadian Meteor Orbit Radar. We find that 'Oumuamua exhibits a moderate spectral gradient of , a value significantly lower than that of outer solar system bodies, indicative of a formation and/or previous residence in a warmer environment. Imaging observation and spectral line analysis show no evidence that 'Oumuamua is presently active. Negative meteor observation is as expected, since ejection driven by sublimation of commonly known cometary species such as CO requires an extreme ejection speed of ∼40 m s−1 at ∼100 au in order to reach the Earth. No obvious candidate stars are proposed as the point of origin for 'Oumuamua. Given a mean free path of ∼109 ly in the solar neighborhood, 'Oumuamua has likely spent a very long time in interstellar space before encountering the solar system.
Reviewers for 2017 Krausman, Paul R.; Knipps, Anna C. S.; Cox, Allison S.
The Journal of wildlife management,
April 2018, Letnik:
82, Številka:
3
Journal Article
The recently discovered minor body 1I/2017 U1 ('Oumuamua) is the first known object in our solar system that is not bound by the Sun's gravity. Its hyperbolic orbit (eccentricity greater than unity) ...strongly suggests that it originated outside our solar system; its red color is consistent with substantial space weathering experienced over a long interstellar journey. We carry out a simple calculation of the probability of detecting such an object. We find that the observed detection rate of 1I-like objects can be satisfied if the average mass of ejected material from nearby stars during the process of planetary formation is ∼20 Earth masses, similar to the expected value for our solar system. The current detection rate of such interstellar interlopers is estimated to be 0.2 yr−1, and the expected number of detections over the past few years is almost exactly one. When the Large Synoptic Survey Telescope begins its wide, fast, deep all-sky survey, the detection rate will increase to 1 yr−1. Those expected detections will provide further constraints on nearby planetary system formation through a better estimate of the number and properties of interstellar objects.
Estimates vary regarding the number of G protein-coupled receptors (GPCRs), the largest family of membrane receptors that are targeted by approved drugs, and the number of such drugs that target ...GPCRs. We review current knowledge regarding GPCRs as drug targets by integrating data from public databases (ChEMBL, Guide to PHARMACOLOGY, and DrugBank) and from the Broad Institute Drug Repurposing Hub. To account for discrepancies among these sources, we curated a list of GPCRs currently targeted by approved drugs. As of November 2017, 134 GPCRs are targets for drugs approved in the United States or European Union; 128 GPCRs are targets for drugs listed in the Food and Drug Administration Orange Book. We estimate that ∼700 approved drugs target GPCRs, implying that approximately 35% of approved drugs target GPCRs. GPCRs and GPCR-related proteins, i.e., those upstream of or downstream from GPCRs, represent ∼17% of all protein targets for approved drugs, with GPCRs themselves accounting for ∼12%. As such, GPCRs constitute the largest family of proteins targeted by approved drugs. Drugs that currently target GPCRs and GPCR-related proteins are primarily small molecules and peptides. Since ∼100 of the ∼360 human endo-GPCRs (other than olfactory, taste, and visual GPCRs) are orphan receptors (lacking known physiologic agonists), the number of GPCR targets, the number of GPCR-targeted drugs, and perhaps the types of drugs will likely increase, thus further expanding this GPCR repertoire and the many roles of GPCR drugs in therapeutics.
This study aims to determine the 1D deep S-wave velocity structure for Çanakkale Province and the surrounding area (Biga Peninsula, NW Turkey) using the moderate (
M
≥ 4.0) earthquakes from the last ...decade. A total of 540 velocity seismograms with a high S/N ratio are obtained from 218 three-component acceleration records of the 10 earthquakes (4.0 ≤
M
w
≤ 5.3) that occurred in the areas of Ayvacık, Saros, and Çan between 2010 and 2018. A total of 34 strong ground motion stations operated by AFAD are grouped in 27 azimuthal directions, and fundamental mode surface wave group velocity dispersion curves are obtained using the multiple-filter method. First, the observed dispersion curves are utilized for the inversion application to define the 1D deep Vs model. Then they are compared with the theoretical curves of the tuned 1D deep Vs models with the trial-and-error forward method after inversion. The RMS misfits between observed and calculated surface group velocities decrease from 0.6 to 0.2 on average. The dispersion analyses allow for improved seismic velocities and thicknesses of especially the uppermost 4–5 km. The defined 1D deep Vs model of 202 source-station paths are also inferred to obtain an average pseudo-3D deep Vs model. In addition, the velocity models are verified with 1D numerical ground motion simulations for 0.05–1 Hz, including the characterized source models of the earthquakes and 1D shallow soil amplifications. The simulation results are quantitatively evaluated with goodness-of-fit measures considering different frequency bands. Fairly good agreement for waveform first arrival and spectral amplitude (0.05–1 Hz) is achieved. However, the later wave packages at the sites located on thick sediment basins cannot be modeled because of the reverberations in the sediment overlying the engineering bedrock. The test of the pseudo-3D Vs model using broadband (0.05–10 Hz) simulation of the 2017 Lesvos mainshock (
M
w
6.3) also indicates that both the phase arrival times (< 1 Hz) and the amplitude spectral decay in the high-frequency range of 1–7 Hz are well modeled.
Skin cancer diagnosis, particularly melanoma detection, is an important healthcare concern worldwide. This study uses the ISIC2017 dataset to evaluate the performance of three deep learning ...architectures, VGG16, ResNet50, and InceptionV3, for binary classification of skin lesions as benign or malignant. ResNet50 achieved the highest training-set accuracy of 81.1%, but InceptionV3 outperformed the other classifiers in generalization with a validation accuracy of 76.2%. The findings reveal the various strengths and trade-offs of alternative designs, providing important insights for the development of dermatological decision support systems. This study contributes to the progress of automated skin cancer diagnosis and establishes the framework for future studies aimed at improving classification accuracy.
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