It's a Bird it's a Plane it's a Meteor

<p>Meteor showers are some of the most dazzling and memorable events occuring in the night sky. Caused by bits of celestial debris from comets and asteroids entering Earth’s atmosphere at astronomical speeds, meteors are bright streaks of light in the night sky, sometimes called shooting stars. Those meteors are recorded, tracked and triangulated by low-light surveillance cameras in a project called CAMS: Cameras for Allsky Meteor Surveillance. CAMS offers insights into a universe of otherwise invisible solar system bodies, but that task has proven difficult due to the lack of automated supervision. Until recently, much of the data control was done by hand. Necessary to build supervised classification models,  labeled training data is essential because other man-made objects such as airplanes and satellites can be mistaken for meteors. To address this issue, we leverage one year's worth of meteor activity data from CAMS to provide weak supervision for over a decade of collected data, drastically reducing the amount of manual annotation necessary and expanding available labelled meteor training data.</p><p> </p><p>Founded in 2010, CAMS aims to automate video surveillance of the night sky to validate the International Astronomical Union’s Working List of Meteor Showers, discover new meteor showers, and predict future meteor showers. Since 2010, CAMS has collected a decade's worth of night sky activity data in the form of astrometric tracks and brightness profiles, a year of which has been manually annotated. We utilize this one year's labelled data to train a high confidence LSTM meteor classifier to generate low confidence labels for the remaining decade’s worth of meteor data. Our classifier yields confidence levels for each prediction, and when the confidence lies above a statistically significant threshold, predicted labels can be treated as weak supervision for future training runs. Remaining predictions below the threshold can be manually annotated. Using a high threshold minimizes label noise and ensures instances are correctly labeled while considerably reducing the  amount of data that needs to be annotated. Weak supervision can be confirmed by checking date ranges and data distributions for known meteor showers to verify predicted labels.</p><p> </p><p>To encourage discovery and distribution of training data and models, we additionally provide scripts to automate data ingestion and model training from raw camera data files. The data scripts handle processing of CAMS data, providing a pipeline to encourage open sharing and reproduction of our research. Additionally, we provide code for a LSTM classifier baseline model which can identify probable meteors. This baseline model script allows further exploration of CAMS data and an opportunity to experiment with other model types.  </p><p> </p><p>In conclusion, our contributions are (1) a weak supervision method utilizing a year’s worth of labelled CAMS data to generate labels for a decade’s worth of data, along with (2) baseline data processing and model scripts to encourage open discovery and distribution. Our unique contributions expand access to labeled training meteor data and make the data globally and publicly accessible thorough daily generated maps of meteor shower activity posted at http://cams.seti.org/FDL/. </p>

Meteors with extreme beginning heights from observations with high-sensitivity super-isocon TV systems

ABSTRACT Meteors with extremely high altitudes are considered. Parameters of seven meteors having anomalous beginning heights recorded with highly sensitive super-Isocon TV systems are presented. One 1993 Perseid meteor, one 2001 sporadic meteor and five meteors from the 2002 Leonid storm had beginning heights in the range 135–145 km. The sporadic meteor is used to demonstrate the methods of data processing and observation precision results. The original TV meteor images, photometric calibration curves and meteor light curve are shown. Light curves are shown for the Leonid shower meteors as well. Based on the sporadic meteor and the 2002 Leonid shower meteor data, mass-loss curves were calculated as functions of height and time: the maximum rates of mass loss were 0.14 and 0.20 g s−1, respectively. Using the classic equation for partially isothermal stone particle heating, the detected beginning heights of most meteors considered (136–135 km) are shown to possibly be related to blowing the molten layer off from a meteoroid surface and most segments of the light curves (below 124 km) show intensive evaporation. For some Leonid meteors, appearing higher than 145–140 km, energy exchange of atmosphere molecules and atoms with the ‘cold’ meteoroid surface can also be assumed. Another possible explanation lies in the low melting temperature of 1500–1600 K for Leonid meteors.

Long-period comet C/1963 A1 (Ikeya), the probable parent body of π-Hydrids, δ-Corvids, November α-Sextantids, and ϑ-Leonids

Aims. We study the meteoroid stream of the long-period comet C/1963 A1 (Ikeya) to predict the meteor showers originating in this comet. We also aim to identify the predicted showers with their real counterparts. Methods. We modeled 23 parts of a theoretical meteoroid stream of the parent comet considered. Each of our models is characterized by a single value of the evolutionary time and a single value of the strength of the Poynting–Robertson effect. The evolutionary time is defined as the time before the present when the stream is modeled and when we start to follow its dynamical evolution. This period ranges from 10 000 to 80 000 yr. In each model, we considered a stream consisting of 10 000 test particles that dynamically evolve, and their dynamics is followed via a numerical integration up to the present. At the end of the integration, we analyzed the mean orbital characteristics of particles in the orbits approaching Earth’s orbit, which thus enabled us to predict a shower related to the parent comet. We attempted to identify each predicted shower with a shower recorded in the International Astronomical Union Meteor Data Center list of all showers. In addition, we tried to separate, often successfully, a real counterpart of each predicted shower from the databases of real meteors. Results. Many modeled parts of the stream of comet C/1963 A1 are identified with the corresponding real showers in three video-meteor databases. No real counterpart is found in the IAU MDC photographic or radio-meteor data. Specifically, we predict five showers related to C/1963 A1. Two predicted showers are identified with π-Hydrids #101 and δ-Corvids #729. The third predicted shower is only vaguely similar to November α-Sextantids #483, when its mean orbit is compared with the mean orbit of the November α-Sextantids in the IAU MDC list of all showers. However, the prediction is very consistent with the corresponding showers newly separated from three video databases. Another predicted shower has no counterpart in the IAU MDC list, but there is a good match of the prediction and a shower that we separated from the Cameras for Allsky Meteor Surveillance video data. We name this new shower ϑ-Leonids. The last of the predicted showers should be relatively low in number and, hence, no real counterparts were either found in the IAU MDC list or separated from any considered database.

Modeling of the meteoroid stream of comet C/1975 T2 and λ-Ursae Majorids

Aims. We study the meteoroid stream of the long-period comet C/1975 T2 (Suzuki-Saigusa-Mori). This comet was suggested as the parent body of the established λ-Ursae Majorid meteor shower, No. 524. Methods. We modeled 32 parts of a theoretical meteoroid stream of the parent comet considered. Each of our models is characterized with a single value of the evolutionary time and a single value of the strength of Poynting-Robertson effect. The evolutionary time ranges from 10 000 to 80 000 yr. It is the period during which the evolution of the stream part is followed. In each model, the dynamical evolution of 10 000 test particles was then followed, via a numerical integration, from the time of the modeling up to the present. At the end of the integration, we analyzed the mean orbital characteristics of particles in the orbits that approach the Earth’s orbit, which thus enabled us to predict a shower related to the parent comet. The predicted shower was subsequently compared with its observed counterparts. We separated the latter from the databases of real meteors. As well, we attempted to identify the predicted shower to a shower recorded in the International Astronomical Union Meteor Data Center (IAU MDC) list of all showers. Results. Almost all modeled parts of the stream of comet C/1975 T2 are identified with the corresponding real shower in three video-meteor databases. No real counterpart is found in the IAU MDC photographic or radio-meteor data. In the IAU MDC list of showers and in our current study, this shower is identified with the established λ-Ursae Majorid shower, No. 524. Hence, our modeling confirms the results of previous authors. At the same time we exclude an existence of other meteor shower associated with C/1975 T2.

Meteor Databases in Astronomy

There are specific problems of databases in meteor science such as making meteor databases into the modern research tools. Special institutes and virtual observatories exist for the meteor data storage where the data is online and in open access. However, there are also numerous databases without the open access, such as for example, three radar databases: Kharkiv database with 250,000 meteor orbits in Ukraine, New Zealand database with 500,000 meteor orbits, and Canadian database with more than 3 million meteor orbits. One of the reasons the open access is absent for these databases could be the complexity in the copyright compliance. In the framework of the creation of the modern effective research tool in the meteor science, we discuss here the case of the Kharkiv meteor database.

Systematic Drug Repositioning By Integrating Transcriptome and Historical Clinical Data, Identification of Digoxin As a Novel Drug Reposition Candidate for High-Risk Myelodysplastic Syndromes

Background: Traditional drug development typically takes more than 15 years and costs 1 billions to bring a new drug to market. Drug repositioning, the application of established drug compounds to new therapeutic indications can hasten effective therapies at a lower cost. Although a number of treatment options are available, there is currently no proven cure for Myelodysplastic syndromes (MDS) besides bone marrow transplantation. We employed a novel two stages, transdisciplinary method to identify potential drug reposition candidates for MDS with commonly prescribed drugs. We found Digoxin, a drug prescribed for heart failure, as the leading candidate. Methods: After we downloaded all human mRNA microarray platforms and their associated annotation files from NCBI GEO database (as Jul 2014), we retained 145 microarray platforms from more than 4k unique Gene IDs. We then used a text mining tool, MetaMap (http://metamap.nlm.nih.gov/), to map text to Unified Medical Language System (UMLS) Concept Unique Identifier (CUI). Only concepts belong to Anatomical Abnormality, Disease or Syndrome and Neoplastic Process were kept. Then we manually grouped microarray samples into healthy and pathological conditions and susing R package, Limma, we generated diseases signatures by systematically comparing with 1.3M drug response gene expression signatures of 20K drugs/compounds and using pattern matching algorithm. In the second stage, drug repositioning candidates for MDS were further analyzed by integrating historical clinical data warehouse, METEOR (M ethodist E nvironment for T ranslational E nhancement and O utcomes R esearch). METEOR data warehouse contains clinical records of Houston Methodist Hospital System and external data dating from Jan 1st 2006 to present (2015). There are over 1 million unique patients and over 4 million unique patient encounters in METEOR. Selected drug repositioning candidates predicted by transcriptome data in the first stage were tested for their effects on MDS patients using METEOR data. Age, gender, race matched myelodysplastic syndrome (MDS) patients that have taken or not taken a specific drug were compared for their overall survival. Results: 1174 diseases signatures for 301 human diseases were generated; for each the signatures, we computed it's repositioning score in CMap and LINCS database by non-parametric rank-ordered kolmogorov-Smirnov statistics. The overall disease-drug reposition map is presented in Figure 1. Many FDA approved indications were recovered by our disease-drug reposition map including 5'-azacitidine which was at top 1% of leading hints in the disease-drug reposition map (Figure 1B). We also find some potential new indication for old drugs such as cardiac glycosides (Digoxin, Digitoxin, Lanatoside C and others) for MDS (Figure 1D). In the second stage we found Digoxin, had a significant positive effect on high risk patients (Figure 2) unlike those for low risk MDS. Conclusions: We have generated 1174 gene expression signatures for 301 human diseases including MDS and systematically compared them with gene expression signatures of 20K drug/compounds. By integrating clinical data warehouse, METEOR, we further verified one potential drug repositioning candidate, Digoxin for high-risk MDS. We are currently testing Digoxin like compounds for its effects in short-term bone marrow culture from high-risk MDS patients. Preliminary results show encouraging results which will be reported at the Annual ASH meeting 2015. Disclosures No relevant conflicts of interest to declare.