A contact binary satellite of the asteroid (152830) Dinkinesh.

Asteroids with diameters less than about 5 km have complex histories because they are small enough for radiative torques (that is, YORP, short for the Yarkovsky-O'Keefe-Radzievskii-Paddack effect)1 to be a notable factor in their evolution2. (152830) Dinkinesh is a small asteroid orbiting the Sun near the inner edge of the main asteroid belt with a heliocentric semimajor axis of 2.19 AU; its S-type spectrum3,4 is typical of bodies in this part of the main belt5. Here we report observations by the Lucy spacecraft6,7 as it passed within 431 km of Dinkinesh. Lucy revealed Dinkinesh, which has an effective diameter of only 720 m, to be unexpectedly complex. Of particular note is the presence of a prominent longitudinal trough overlain by a substantial equatorial ridge and the discovery of the first confirmed contact binary satellite, now named (152830) Dinkinesh I Selam. Selam consists of two near-equal-sized lobes with diameters of 210 m and 230 m. It orbits Dinkinesh at a distance of 3.1 km with an orbital period of about 52.7 h and is tidally locked. The dynamical state, angular momentum and geomorphologic observations of the system lead us to infer that the ridge and trough of Dinkinesh are probably the result of mass failure resulting from spin-up by YORP followed by the partial reaccretion of the shed material. Selam probably accreted from material shed by this event.

The Emirates Mars Mission (EMM) Emirates Mars InfraRed Spectrometer (EMIRS) Instrument.

The Emirates Mars Mission Emirates Mars Infrared Spectrometer (EMIRS) will provide remote measurements of the martian surface and lower atmosphere in order to better characterize the geographic and diurnal variability of key constituents (water ice, water vapor, and dust) along with temperature profiles on sub-seasonal timescales. EMIRS is a FTIR spectrometer covering the range from 6.0-100+ µm (1666-100 cm-1) with a spectral sampling as high as 5 cm-1 and a 5.4-mrad IFOV and a 32.5×32.5 mrad FOV. The EMIRS optical path includes a flat 45° pointing mirror to enable one degree of freedom and has a +/- 60° clear aperture around the nadir position which is fed to a 17.78-cm diameter Cassegrain telescope. The collected light is then fed to a flat-plate based Michelson moving mirror mounted on a dual linear voice-coil motor assembly. An array of deuterated L-alanine doped triglycine sulfate (DLaTGS) pyroelectric detectors are used to sample the interferogram every 2 or 4 seconds (depending on the spectral sampling selected). A single 0.846 µm laser diode is used in a metrology interferometer to provide interferometer positional control, sampled at 40 kHz (controlled at 5 kHz) and infrared signal sampled at 625 Hz. The EMIRS beamsplitter is a 60-mm diameter, 1-mm thick 1-arcsecond wedged chemical vapor deposited diamond with an antireflection microstructure to minimize first surface reflection. EMIRS relies on an instrumented internal v-groove blackbody target for a full-aperture radiometric calibration. The radiometric precision of a single spectrum (in 5 cm-1 mode) is <3.0×10-8 W cm-2 sr-1/cm-1 between 300 and 1350 cm-1 over instrument operational temperatures (<∼0.5 K NE Δ T @ 250 K). The absolute integrated radiance error is < 2% for scene temperatures ranging from 200-340 K. The overall EMIRS envelope size is 52.9×37.5×34.6 cm and the mass is 14.72 kg including the interface adapter plate. The average operational power consumption is 22.2 W, and the standby power consumption is 18.6 W with a 5.7 W thermostatically limited, always-on operational heater. EMIRS was developed by Arizona State University and Northern Arizona University in collaboration with the Mohammed bin Rashid Space Centre with Arizona Space Technologies developing the electronics. EMIRS was integrated, tested and radiometrically calibrated at Arizona State University, Tempe, AZ.

A SNPshot of PubMed to associate genetic variants with drugs, diseases, and adverse reactions.

MOTIVATION: Genetic factors determine differences in pharmacokinetics, drug efficacy, and drug responses between individuals and sub-populations. Wrong dosages of drugs can lead to severe adverse drug reactions in individuals whose drug metabolism drastically differs from the "assumed average". Databases such as PharmGKB are excellent sources of pharmacogenetic information on enzymes, genetic variants, and drug response affected by changes in enzymatic activity. Here, we seek to aid researchers, database curators, and clinicians in their search for relevant information by automatically extracting these data from literature. APPROACH: We automatically populate a repository of information on genetic variants, relations to drugs, occurrence in sub-populations, and associations with disease. We mine textual data from PubMed abstracts to discover such genotype-phenotype associations, focusing on SNPs that can be associated with variations in drug response. The overall repository covers relations found between genes, variants, alleles, drugs, diseases, adverse drug reactions, populations, and allele frequencies. We cross-reference these data to EntrezGene, PharmGKB, PubChem, and others. RESULTS: The performance regarding entity recognition and relation extraction yields a precision of 90-92% for the major entity types (gene, drug, disease), and 76-84% for relations involving these types. Comparison of our repository to PharmGKB reveals a coverage of 93% of gene-drug associations in PharmGKB and 97% of the gene-variant mappings based on 180,000 PubMed abstracts. AVAILABILITY: http://bioai4core.fulton.asu.edu/snpshot.

Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism.

MOTIVATION: Identifying drug-drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning. RESULTS: Our approach was able to find several potential DDIs that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions.

Synthesis of pharmacokinetic pathways through knowledge acquisition and automated reasoning.

Biological pathways are seen as highly critical in our understanding of the mechanism of biological functions. To collect information about pathways, manual curation has been the most popular method. However, pathway annotation is regarded as heavily time-consuming, as it requires expert curators to identify and collect information from different sources. Even with the pieces of biological facts and interactions collected from various sources, curators have to apply their biological knowledge to arrange the acquired interactions in such a way that together they perform a common biological function as a pathway. In this paper, we propose a novel approach for automated pathway synthesis that acquires facts from hand-curated knowledge bases. To comprehend the incompleteness of the knowledge bases, our approach also obtains facts through automated extraction from Medline abstracts. An essential component of our approach is to apply logical reasoning to the acquired facts based on the biological knowledge about pathways. By representing such biological knowledge, the reasoning component is capable of assigning ordering to the acquired facts and interactions that is necessary for pathway synthesis. We demonstrate the feasibility of our approach with the development of a system that synthesizes pharmacokinetic pathways. We evaluate our approach by reconstructing the existing pharmacokinetic pathways available in PharmGKB. Our results show that not only that our approach is capable of synthesizing these pathways but also uncovering information that is not available in the manually annotated pathways.