Velocity-selective direct frequency-comb spectroscopy of atomic vapors

We present an experimental and theoretical investigation of two-photon direct frequency-comb spectroscopy performed through velocity-selective excitation. In particular, we explore the effect of repetition rate on the $\textrm{5S}_{1/2}\rightarrow \textrm{5D}_{3/2, 5/2}$ two-photon transitions excited in a rubidium atomic vapor cell. The transitions occur via step-wise excitation through the $\textrm{5P}_{1/2, 3/2}$ states by use of the direct output of an optical frequency comb. Experiments were performed with two different frequency combs, one with a repetition rate of $\approx 925$ MHz and one with a repetition rate of $\approx 250$ MHz. The experimental spectra are compared to each other and to a theoretical model.Comment: 10 pages, 7 figure

Deep Learning Techniques for Geospatial Data Analysis

Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such as object recognition, image classification, and scene understanding. The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data. The chapter is organized as below: (i) A brief overview of deep learning algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques for RFID data analytics.Comment: This is a pre-print of the following chapter: Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam, {\em Deep Learning Techniques for Geospatial Data Analysis}, published in {\bf Machine Learning Paradigms}, edited by George A. TsihrintzisLakhmi C. Jain, 2020, publisher Springer, Cham reproduced with permission of publisher Springer, Cha

Gene- and variant-specific efficacy of serum/glucocorticoid-regulated kinase 1 inhibition in long QT syndrome types 1 and 2.

AIMS Current long QT syndrome (LQTS) therapy, largely based on beta-blockade, does not prevent arrhythmias in all patients; therefore, novel therapies are warranted. Pharmacological inhibition of the serum/glucocorticoid-regulated kinase 1 (SGK1-Inh) has been shown to shorten action potential duration (APD) in LQTS type 3. We aimed to investigate whether SGK1-Inh could similarly shorten APD in LQTS types 1 and 2. METHODS AND RESULTS Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and hiPSC-cardiac cell sheets (CCS) were obtained from LQT1 and LQT2 patients; CMs were isolated from transgenic LQT1, LQT2, and wild-type (WT) rabbits. Serum/glucocorticoid-regulated kinase 1 inhibition effects (300 nM-10 µM) on field potential durations (FPD) were investigated in hiPSC-CMs with multielectrode arrays; optical mapping was performed in LQT2 CCS. Whole-cell and perforated patch clamp recordings were performed in isolated LQT1, LQT2, and WT rabbit CMs to investigate SGK1-Inh (3 µM) effects on APD. In all LQT2 models across different species (hiPSC-CMs, hiPSC-CCS, and rabbit CMs) and independent of the disease-causing variant (KCNH2-p.A561V/p.A614V/p.G628S/IVS9-28A/G), SGK1-Inh dose-dependently shortened FPD/APD at 0.3-10 µM (by 20-32%/25-30%/44-45%). Importantly, in LQT2 rabbit CMs, 3 µM SGK1-Inh normalized APD to its WT value. A significant FPD shortening was observed in KCNQ1-p.R594Q hiPSC-CMs at 1/3/10 µM (by 19/26/35%) and in KCNQ1-p.A341V hiPSC-CMs at 10 µM (by 29%). No SGK1-Inh-induced FPD/APD shortening effect was observed in LQT1 KCNQ1-p.A341V hiPSC-CMs or KCNQ1-p.Y315S rabbit CMs at 0.3-3 µM. CONCLUSION A robust SGK1-Inh-induced APD shortening was observed across different LQT2 models, species, and genetic variants but less consistently in LQT1 models. This suggests a genotype- and variant-specific beneficial effect of this novel therapeutic approach in LQTS

CpG-creating mutations are costly in many human viruses.

Mutations can occur throughout the virus genome and may be beneficial, neutral or deleterious. We are interested in mutations that yield a C next to a G, producing CpG sites. CpG sites are rare in eukaryotic and viral genomes. For the eukaryotes, it is thought that CpG sites are rare because they are prone to mutation when methylated. In viruses, we know less about why CpG sites are rare. A previous study in HIV suggested that CpG-creating transition mutations are more costly than similar non-CpG-creating mutations. To determine if this is the case in other viruses, we analyzed the allele frequencies of CpG-creating and non-CpG-creating mutations across various strains, subtypes, and genes of viruses using existing data obtained from Genbank, HIV Databases, and Virus Pathogen Resource. Our results suggest that CpG sites are indeed costly for most viruses. By understanding the cost of CpG sites, we can obtain further insights into the evolution and adaptation of viruses

An information fusion approach for filtering GNSS data sets collected during construction operations

Global Navigation Satellite Systems (GNSS) are widely used to document the on- and off-site trajectories of construction equipment. Before analyzing the collected data for better understanding and improving construction operations, the data need to be freed from outliers. Eliminating outliers is challenging. While manually identifying outliers is a time-consuming and error-prone process, automatic filtering is exposed to false positives errors, which can lead to eliminating accurate trajectory segments. This paper addresses this issue by proposing a hybrid filtering method, which integrates experts’ decisions. The decisions are operationalized as parameters to search for next outliers and are based on visualization of sensor readings and the human-generated notes that describe specifics of the construction project. A specialized open-source software prototype was developed and applied by the authors to illustrate the proposed approach. The software was utilized to filter outliers in sensor readings collected during earthmoving and asphalt paving projects that involved five different types of common construction equipmen

Plasmid Location and Molecular Heterogeneity of the L1 and L2 β-Lactamase Genes of Stenotrophomonas maltophilia

An approximately 200-kb plasmid has been purified from clinical isolates of Stenotrophomonas maltophilia. This plasmid was found in all of the 10 isolates examined and contains both the L1 and the L2 β-lactamase genes. The location of L1 and L2 on a plasmid makes it more likely that they could spread to other gram-negative bacteria, potentially causing clinical problems. Sequence analysis of the 10 L1 genes revealed three novel genes, L1c, L1d, and L1e, with 8, 12, and 20% divergence from the published strain IID 1275 L1 (L1a), respectively. The most unusual L1 enzyme (L1e) displayed markedly different kinetic properties, with respect to hydrolysis of nitrocefin and imipenem, compared to those of L1a (250- and 100-fold lower k(cat)/K(m) ratios respectively). L1c and L1d, in contrast, displayed levels of hydrolysis very similar to that of L1a. Several nonconservative amino acid differences with respect to L1a, L1b, L1c, and L1d were observed in the substrate binding-catalytic regions of L1e, and this could explain the kinetic differences. Three novel L2 genes (L2b, L2c, and L2d) were sequenced from the same isolates, and their sequences diverge from the published sequence of strain IID 1275 L2 (L2a) by 4, 9, and 25%, respectively. Differences in L1 and L2 gene sequences were not accompanied by similar divergences in 16S rRNA gene sequences, for which differences of <1% were found. It is therefore apparent that the L1 and L2 genes have evolved relatively quickly, perhaps because of their presence on a plasmid