A randomized optimal k-mer indexing approach for efficient parallel genome sequence compression.

Next Generation Sequencing (NGS) technology generates massive amounts of genome sequence that increases rapidly over time. As a result, there is a growing need for efficient compression algorithms to facilitate the processing, storage, transmission, and analysis of large-scale genome sequences. Over the past 31 years, numerous state-of-the-art compression algorithms have been developed. The performance of any compression algorithm is measured by three main compression metrics: compression ratio, time, and memory usage. Existing k-mer hash indexing systems take more time, due to the decision-making process based on compression results. In this paper, we propose a two-phase reference genome compression algorithm using optimal k-mer length (RGCOK). Reference-based compression takes advantage of the inter-similarity between chromosomes of the same species. RGCOK achieves this by finding the optimal k-mer length for matching, using a randomization method and hashing. The performance of RGCOK was evaluated on three different benchmark data sets: novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Homo sapiens, and other species sequences using an Amazon AWS virtual cloud machine. Experiments showed that the optimal k-mer finding time by RGCOK is around 45.28 min, whereas the time for existing state-of-the-art algorithms HiRGC, SCCG, and HRCM ranges from 58 min to 8.97 h.

Defect resonance frequency of spherical pore within isotropic elastic solid.

Ultrasound based porosity imaging in metals, composite or additive manufactured structures pose a challenge due to various absorbing media. This paper addresses this problem by developing a defect resonance frequency equation that will be used for individual porosity imaging. An analytical model is developed for closed spherical shell supported on elastic foundation based on the six mode shell theory. The defect resonance frequency is calculated from the breathing mode, corresponding to maximum out-of-plane displacement, when supporting stiffness tending towards rigid condition. Thereafter, steady state and explicit dynamic analysis are carried out for a mild steel specimen with single and multiple pores to detect the defect resonance frequency along with defect mode. Further, the defect resonance frequency is validated with experiment involving a mild steel specimen with single spherical pore. The numerical and experimental results are good agreement with the results obtained from defect resonance frequency equation. This defect resonance frequency equation can be used for high resolution imaging of porosities.

MRI-negative myeloradiculoneuropathy following Covid-19 infection: An index case.

Covid-19 associated several neurological manifestation in the form of Post-infectious transverse myelitis(TM) and para-infectious TM has been reported. A 54 years old female patient presented to us with acute retention of urine and upper motor neuron type of bilateral lower limb weakness in shock stage, after 12 days of covid-19 infection. MRI (3T) brain and spine showed no abnormality and Nerve conduction study showed acquired motor axonal polyradiculoneuropathy in bilateral lower limbs. We herein present an index case of MRI-negative myeloradiculoneuropathy following covid-19 infection.

A Lamin-Associated Chromatin Model for Chromosome Organization.

We propose a simple model for chromatin organization based on the interaction of the chromatin fibers with lamin proteins along the nuclear membrane. Lamin proteins are known to be a major factor that influences chromatin organization and hence gene expression in the cells. We provide a quantitative understanding of lamin-associated chromatin organization in a crowded macromolecular environment by systematically varying the heteropolymer segment distribution and the strength of the lamin-chromatin attractive interaction. Our minimal polymer model reproduces the formation of lamin-associated-domains and provides an in silico tool for quantifying domain length distributions for different distributions of heteropolymer segments. We show that a Gaussian distribution of heteropolymer segments, coupled with strong lamin-chromatin interactions, can qualitatively reproduce observed length distributions of lamin-associated-domains. Further, lamin-mediated interaction can enhance the formation of chromosome territories as well as the organization of chromatin into tightly packed heterochromatin and the loosely packed gene-rich euchromatin regions.

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound.

Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

Resonant activation of different intermodulation frequencies using dual excitations.

In this paper, an analytical framework is presented to understand the generation of different intermodulation frequencies under dual excitation which is also verified with experiments. One single periodic frequency (SPF) excitation along with one variable frequency excitation, varying from local defect resonance (LDR) frequency and its superharmonics to subharmonic, are used. The flexural wave propagation in an orthotropic composite plate containing a delamination is analytically derived considering quadratic nonlinearity. Both analytical and experimental results are presented for glass fibre reinforced plastic (GFRP) plates with single and double delaminations. Two different types of intermodulation peaks are observed, that is local defect resonance intermodulation (LDRI) and single periodic frequency intermodulation (SPFI), out of which SPFI is found to be generated only during superharmonics and subharmonic excitation. At LDR frequency excitation, only its higher harmonics are found and no SPFI peaks are observed. The acoustic nonlinearity parameters are found increase with order of harmonics and delamination diameter.