An optimally concentrated Gabor transform for localized time-frequency components

Gabor analysis is one of the most common instances of time-frequency signal analysis. Choosing a suitable window for the Gabor transform of a signal is often a challenge for practical applications, in particular in audio signal processing. Many time-frequency (TF) patterns of different shapes may be present in a signal and they can not all be sparsely represented in the same spectrogram. We propose several algorithms, which provide optimal windows for a user-selected TF pattern with respect to different concentration criteria. We base our optimization algorithm on $l^p$-norms as measure of TF spreading. For a given number of sampling points in the TF plane we also propose optimal lattices to be used with the obtained windows. We illustrate the potentiality of the method on selected numerical examples

HMMSplicer: A Tool for Efficient and Sensitive Discovery of Known and Novel Splice Junctions in RNA-Seq Data

Background: High-throughput sequencing of an organism’s transcriptome, or RNA-Seq, is a valuable and versatile new strategy for capturing snapshots of gene expression. However, transcriptome sequencing creates a new class of alignment problem: mapping short reads that span exon-exon junctions back to the reference genome, especially in the case where a splice junction is previously unknown. Methodology/Principal Findings: Here we introduce HMMSplicer, an accurate and efficient algorithm for discovering canonical and non-canonical splice junctions in short read datasets. HMMSplicer identifies more splice junctions than currently available algorithms when tested on publicly available A. thaliana, P. falciparum, and H. sapiens datasets without a reduction in specificity. Conclusions/Significance: HMMSplicer was found to perform especially well in compact genomes and on genes with low expression levels, alternative splice isoforms, or non-canonical splice junctions. Because HHMSplicer does not rely on prebuilt gene models, the products of inexact splicing are also detected. For H. sapiens, we find 3.6 % of 39 splice sites and 1.4% of 59 splice sites are inexact, typically differing by 3 bases in either direction. In addition, HMMSplicer provides a score for every predicted junction allowing the user to set a threshold to tune false positive rates depending on the needs of the experiment. HMMSplicer is implemented in Python. Code and documentation are freely available a

The KELT Follow-Up Network And Transit False-Positive Catalog: Pre-Vetted False Positives For TESS

The Kilodegree Extremely Little Telescope (KELT) project has been conducting a photometric survey of transiting planets orbiting bright stars for over 10 years. The KELT images have a pixel scale of ~23\u27\u27 pixel⁻¹—very similar to that of NASA\u27s Transiting Exoplanet Survey Satellite (TESS)—as well as a large point-spread function, and the KELT reduction pipeline uses a weighted photometric aperture with radius 3\u27. At this angular scale, multiple stars are typically blended in the photometric apertures. In order to identify false positives and confirm transiting exoplanets, we have assembled a follow-up network (KELT-FUN) to conduct imaging with spatial resolution, cadence, and photometric precision higher than the KELT telescopes, as well as spectroscopic observations of the candidate host stars. The KELT-FUN team has followed-up over 1600 planet candidates since 2011, resulting in more than 20 planet discoveries. Excluding ~450 false alarms of non-astrophysical origin (i.e., instrumental noise or systematics), we present an all-sky catalog of the 1128 bright stars (6 \u3c V \u3c 13) that show transit-like features in the KELT light curves, but which were subsequently determined to be astrophysical false positives (FPs) after photometric and/or spectroscopic follow-up observations. The KELT-FUN team continues to pursue KELT and other planet candidates and will eventually follow up certain classes of TESS candidates. The KELT FP catalog will help minimize the duplication of follow-up observations by current and future transit surveys such as TESS

Active Singularities for Multivehicle Motion Planning in an N-Vortex System

Abstract. This paper presents a path-planning paradigm for distributed control of multiple sensor platforms in a geophysical flow well-approximated by a point-vortex model. We utilize Hamiltonian dynamics to generate control vector fields for vehicle motion in N-vortex flows using the con-cept of an active singularity whose strength is a tunable control input. We introduce active singularities that are virtual point vortices possibly collocated with virtual point sources or sinks. We provide a principled method to stabilize relative equilibria of these virtual vortices in the presence of the actual point vortices, which represent the underlying geo-physical flow. We illustrate how these relative equilibria may be useful for vehicle path planning and sampling in a geophysical flow. Preliminary results presented here are based on an adaptive control design

ReCombine: A Suite of Programs for Detection and Analysis of Meiotic Recombination in Whole-Genome Datasets

In meiosis, the exchange of DNA between chromosomes by homologous recombination is a critical step that ensures proper chromosome segregation and increases genetic diversity. Products of recombination include reciprocal exchanges, known as crossovers, and non-reciprocal gene conversions or non-crossovers. The mechanisms underlying meiotic recombination remain elusive, largely because of the difficulty of analyzing large numbers of recombination events by traditional genetic methods. These traditional methods are increasingly being superseded by high-throughput techniques capable of surveying meiotic recombination on a genome-wide basis. Next-generation sequencing or microarray hybridization is used to genotype thousands of polymorphic markers in the progeny of hybrid yeast strains. New computational tools are needed to perform this genotyping and to find and analyze recombination events. We have developed a suite of programs, ReCombine, for using short sequence reads from next-generation sequencing experiments to genotype yeast meiotic progeny. Upon genotyping, the program CrossOver, a component of ReCombine, then detects recombination products and classifies them into categories based on the features found at each location and their distribution among the various chromatids. CrossOver is also capable of analyzing segregation data from microarray experiments or other sources. This package of programs is designed to allow even researchers without computational expertise to use high-throughput, whole-genome methods to study the molecular mechanisms of meiotic recombination

CUT-PCR: CRISPR-mediated, ultrasensitive detection of target DNA using PCR

Circulating tumor DNA (ctDNA) has emerged as a tumor-specific biomarker for the early detection of various cancers. To date, several techniques have been devised to enrich the extremely small amounts of ctDNA present in plasma, but they are still insufficient for cancer diagnosis, especially at the early stage. Here, we developed a novel method, CUT (CRISPR-mediated, Ultrasensitive detection of Target DNA)-PCR, which uses CRISPR endonucleases to enrich and detect the extremely small amounts of tumor DNA fragments among the much more abundant wild-type DNA fragments by specifically eliminating the wild-type sequences. We computed that by using various orthologonal CRISPR endonucleases such as SpCas9 and FnCpf1, the CUT-PCR method would be applicable to 80% of known cancer-linked substitution mutations registered in the COSMIC database. We further verified that CUT-PCR together with targeted deep sequencing enables detection of a broad range of oncogenes with high sensitivity (<0.01%) and accuracy, which is superior to conventional targeted deep sequencing. In the end, we successfully applied CUT-PCR to detect sequences with oncogenic mutations in the ctDNA of colorectal cancer patients' blood, suggesting that our technique could be adopted for diagnosing various types of cancer at early stages

Transcriptome Analysis of the Model Protozoan, Tetrahymena thermophila, Using Deep RNA Sequencing

Background: The ciliated protozoan Tetrahymena thermophila is a well-studied single-celled eukaryote model organism for cellular and molecular biology. However, the lack of extensive T. thermophila cDNA libraries or a large expressed sequence tag (EST) database limited the quality of the original genome annotation. Methodology/Principal Findings: This RNA-seq study describes the first deep sequencing analysis of the T. thermophila transcriptome during the three major stages of the life cycle: growth, starvation and conjugation. Uniquely mapped reads covered more than 96 % of the 24,725 predicted gene models in the somatic genome. More than 1,000 new transcribed regions were identified. The great dynamic range of RNA-seq allowed detection of a nearly six order-of-magnitude range of measurable gene expression orchestrated by this cell. RNA-seq also allowed the first prediction of transcript untranslated regions (UTRs) and an updated (larger) size estimate of the T. thermophila transcriptome: 57 Mb, or about 55 % of the somatic genome. Our study identified nearly 1,500 alternative splicing (AS) events distributed over 5.2 % of T. thermophila genes. This percentage represents a two order-of-magnitude increase over previous EST-based estimates in Tetrahymena. Evidence of stage-specific regulation of alternative splicing was also obtained. Finally, our study allowed us to completely confirm about 26.8 % of the genes originally predicted by the gene finder, to correct coding sequence boundaries an

Cross-Species Transmission of a Novel Adenovirus Associated with a Fulminant Pneumonia Outbreak in a New World Monkey Colony

Adenoviruses are DNA viruses that naturally infect many vertebrates, including humans and monkeys, and cause a wide range of clinical illnesses in humans. Infection from individual strains has conventionally been thought to be species-specific. Here we applied the Virochip, a pan-viral microarray, to identify a novel adenovirus (TMAdV, titi monkey adenovirus) as the cause of a deadly outbreak in a closed colony of New World monkeys (titi monkeys; Callicebus cupreus) at the California National Primate Research Center (CNPRC). Among 65 titi monkeys housed in a building, 23 (34%) developed upper respiratory symptoms that progressed to fulminant pneumonia and hepatitis, and 19 of 23 monkeys, or 83% of those infected, died or were humanely euthanized. Whole-genome sequencing of TMAdV revealed that this adenovirus is a new species and highly divergent, sharing <57% pairwise nucleotide identity with other adenoviruses. Cultivation of TMAdV was successful in a human A549 lung adenocarcinoma cell line, but not in primary or established monkey kidney cells. At the onset of the outbreak, the researcher in closest contact with the monkeys developed an acute respiratory illness, with symptoms persisting for 4 weeks, and had a convalescent serum sample seropositive for TMAdV. A clinically ill family member, despite having no contact with the CNPRC, also tested positive, and screening of a set of 81 random adult blood donors from the Western United States detected TMAdV-specific neutralizing antibodies in 2 individuals (2/81, or 2.5%). These findings raise the possibility of zoonotic infection by TMAdV and human-to-human transmission of the virus in the population. Given the unusually high case fatality rate from the outbreak (83%), it is unlikely that titi monkeys are the native host species for TMAdV, and the natural reservoir of the virus is still unknown. The discovery of TMAdV, a novel adenovirus with the capacity to infect both monkeys and humans, suggests that adenoviruses should be monitored closely as potential causes of cross-species outbreaks