Umbilical artery Doppler indices in relation to fetal outcome in high risk pregnancy

Background: Umbilical artery Doppler indices in relation to fetal outcome in high risk pregnancy. The aim of this study was to study the umbilical artery Doppler velocimetry in predicting the fetal outcome in high risk pregnancy. This is a prospective study done over a period of 1 year in Silchar Medical College and Hospital from 1st September 2011 to 31st August 2012. 100 women with singleton pregnancy with high risk admitted in SMCH were subjected to umbilical artery Doppler along with morphology and biometry scan after fulfilling the inclusion and exclusion criteria.Methods: 100 women with high risk pregnancy were evaluated by umbilical artery velocimetry between 28 and 41 weeks of pregnancy. Outcome of pregnancy was recorded for the normal Doppler group (n = 79; 79%), the low-end diastolic flow group (n = 19; 19%) and the group with absent/reversed diastolic flow (n = 2; 2%).Results: Mothers with abnormal velocimetry had more number of caesarean sections than those with normal velocimetry. The diagnosis to delivery interval, gestational age at delivery and average birth weight were comparatively lower with higher incidence of admission to neonatal intensive care unit in foetuses with abnormal umbilical Doppler velocimetry. Sensitivity, specificity, positive and negative predictive values of Doppler for detecting abnormal fetal outcome were 43%, 83%, 33% and 88% respectively. Statistical analysis used: sensitivity, specificity and predictive values.Conclusions: Fetuses with normal flow velocimetry are at lower risk than those with abnormal velocimetry in terms of poor Apgar score and neonatal intensive care admission. The average birth weight of the neonates with abnormal Doppler studies was lower than that of neonates with normal velocimetry. Doppler velocimetry studies of umbilical artery can provide the obstetrician important information regarding fetal wellbeing to help him improve fetal outcome.

Structural Changes and Ferroelectric Properties of BiFeO₃-PbTiO₃ Thin Films Grown via a Chemical Multilayer Deposition Method

Thin films of (1-x)BiFeO3-xPbTiO3 (BF-xPT) with x ~ 0.60 were fabricated on Pt/Si substrates by chemical solution deposition of precursor BF and PT layers alternately in three different multilayer configurations. These multilayer deposited precursor films upon annealing at 700{\deg}C in nitrogen show pure perovskite phase formation. In contrast to the equilibrium tetragonal structure for the overall molar composition of BF:PT::40:60, we find monoclinic structured BF-xPT phase of MA type. Piezo-force microscopy confirmed ferroelectric switching in the films and revealed different normal and lateral domain distributions in the samples. Room temperature electrical measurements show good quality ferroelectric hysteresis loops with remanent polarization, Pr, of up to 18 {\mu}C/cm2 and leakage currents as low as 10-7 A/cm2.Comment: 14 Pages and 6 figure

GEMv2: multilingual NLG benchmarking in a single line of code.

Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. The compatibility, often facilitated through leaderboards, thus leads to outdated but standardized evaluation practices. We pose that the standardization is taking place in the wrong spot. Evaluation infrastructure should enable researchers to use the latest methods and what should be standardized instead is how to incorporate these new evaluation advances. We introduce GEMv2, the new version of the Generation, Evaluation, and Metrics Benchmark which uses a modular infrastructure for dataset, model, and metric developers to benefit from each other's work. GEMv2 supports 40 documented datasets in 51 languages, ongoing online evaluation for all datasets, and our interactive tools make it easier to add new datasets to the living benchmark

Calculation of true coincidence summing correction factor for clover detector in add-back and direct mode

The true coincidence summing effect on the full-energy peak efficiency calibration of an unsuppressed clover HPGe detector has been studied. Standard multi-energetic and mono-energetic gamma-ray sources were used to determine the full-energy peak efficiency of the detector as a function of the gamma-ray energies at different source-to-detector distances. The true coincidence summing correction factors for the full-energy peak efficiency of the detector has been determined, in the add-back and direct modes of the detector, using both experimental and analytical methods. Geant4 simulations were performed to obtain the full-energy peak efficiency and total efficiency of the detector for different gamma-ray energies. The simulated efficiencies were used to calculate the correction factors using the analytical method. The correction factors obtained from both analytical and experimental methods were found to be in good agreement with each other. The clover detector in add-back mode exhibits larger summing corrections compared to the direct mode for the same source-to-detector distances. For the add-back mode, the coincidence summing effect is not significant for source-to-detector distances ~ 13 cm or above, whereas, for the direct mode, measurements can be performed for source-to-detector distances ~ 5 cm or above without considering the coincidence summing effect

Monoamine Oxidase A (MAO-A): A Therapeutic Target in Lung Cancer

Monoamine oxidase-A (MAO-A), a pro-oxidative enzyme catalyzes the oxidative deamination of endogenous and exogenous monoamines/neurotransmitters like dopamine, serotonin, norepinephrine or tyramine and converting them into their corresponding aldehydes and reactive oxygen species (ROS). Hyperactivity of MAO-A has been shown to be involved in depression, neuro-degeneration including Parkinson’s and Alzheimer’s diseases, neuropsychiatric disorders and cardiovascular diseases. Our recent results however demonstrated the involvement of MAO-A in promoting aggressiveness of lung carcinoma. We found both constitutive and inducible expression of MAO-A in non-small cell lung cancer cells H1299 and in A549 lung epithelial carcinoma cells. By using knockout (by CRISPR-Cas9 gene editing technology) or knockdown (using MAO-A specific esiRNA) MAO-A cells we demonstrated the role of MAO-A in promoting lung cancer aggressiveness and epithelial to mesenchymal transition (EMT). From our observations, we can conclude that MAO-A may be considered as a potential therapeutic target for the intervention and treatment of lung carcinoma

Context-Aware Deep Sequence Learning with Multi-View Factor Pooling for Time Series Classification

In this paper, we propose an effective, multi-view, multivariate deep classification model for time-series data. Multi-view methods show promise in their ability to learn correlation and exclusivity properties across different independent information resources. However, most current multi-view integration schemes employ only a linear model and, therefore, do not extensively utilize the relationships observed across different view-specific representations. Moreover, the majority of these methods rely exclusively on sophisticated, handcrafted features to capture local data patterns and, thus, depend heavily on large collections of labeled data. The multi-view, multivariate deep classification model for time-series data proposed in this paper makes important contributions to address these limitations. The proposed model derives a LSTM-based, deep feature descriptor to model both the view-specific data characteristics and cross-view interaction in an integrated deep architecture while driving the learning phase in a data-driven manner. The proposed model employs a compact context descriptor to exploit view-specific affinity information to design a more insightful context representation. Finally, the model uses a multi-view factor-pooling scheme for a context-driven attention learning strategy to weigh the most relevant feature dimensions while eliminating noise from the resulting fused descriptor. As shown by experiments, compared to the existing multi-view methods, the proposed multi-view deep sequential learning approach improves classification performance by roughly 4% in the UCI multi-view activity recognition dataset, while also showing significantly robust generalized representation capacity against its single-view counterparts, in classifying several large-scale multi-view light curve collections

Regulation of Monoamine Oxidase A (MAO-A) Expression, Activity, and Function in IL-13–Stimulated Monocytes and A549 Lung Carcinoma Cells

Monoamine oxidase A (MAO-A) is a mitochondrial flavoen-zyme implicated in the pathogenesis of atherosclerosis and inflammation and also in many neurological disorders. MAO-A also has been reported as a potential therapeutic target in prostate cancer. However, the regulatory mechanisms controlling cytokine-induced MAO-A expression in immune or cancer cells remain to be identified. Here, we show that MAO-A expression is co-induced with 15-lipoxygenase (15-LO) in interleukin 13 (IL-13)-activated primary human monocytes and A549 nonsmall cell lung carcinoma cells. We present evidence that MAO-A gene expression and activity are regulated by signal transducer and activator of transcription 1, 3, and 6 (STAT1, STAT3, and STAT6), early growth response 1 (EGR1), and cAMP-responsive element– binding protein (CREB), the same transcription factors that control IL-13– dependent 15-LO expression. We further established that in both primary monocytes and in A549 cells, IL-13–stimulated MAO-A expression, activity, and function are directly governed by 15-LO. In contrast, IL-13– driven expression and activity of MAO-A was 15-LO–independent in U937 promonocytic cells. Furthermore, we demonstrate that the 15-LO– dependent transcriptional regulation of MAO-A in response to IL-13 stimulation in monocytes and in A549 cells is mediated by peroxisome proliferator–activated receptor (PPAR) and that signal transducer and activator of transcription 6 (STAT6) plays a crucial role in facilitating the transcriptional activity of PPAR. We further report that the IL-13–STAT6 – 15-LO–PPAR axis is critical for MAO-A expression, activity, and function, including migration and reactive oxygen species generation. Altogether, these results have major implications for the resolution of inflammation and indicat

Calculation of true coincidence summing correction factor for a Broad Energy Germanium (BEGe) detector using standard and fabricated sources

The true coincidence summing (TCS) correction factor for a Broad Energy Germanium (BEGe) detector has been calculated at far and close geometry measurement using multi-energetic radioactive $\gamma$-ray sources $^{60}$Co, $^{133}$Ba and $^{152}$Eu. The correction factors were calculated using experimental method and analytical method. Photopeak efficiency and total efficiency required to calculate the correction factor were obtained using Geant4 Monte Carlo simulation code. A few standard as well as fabricated mono-energetic sources were also included in the $\gamma$-ray efficiency measurements. The simulated efficiencies of mono-energetic $\gamma$-ray sources were matched to experimental $\gamma$-ray efficiencies by optimizing the detector parameters. The same parameters were used to obtain the photopeak and total efficiency for $\gamma$-ray of our interest and coincident $\gamma$-ray. Analytical correction factors and experimental correction factors were found in good agreement with each other

Context-Aware Deep Sequence Learning with Multi-View Factor Pooling for Time Series Classification

In this paper, we propose an effective, multi-view, multivariate deep classification model for time-series data. Multi-view methods show promise in their ability to learn correlation and exclusivity properties across different independent information resources. However, most current multi-view integration schemes employ only a linear model and, therefore, do not extensively utilize the relationships observed across different view-specific representations. Moreover, the majority of these methods rely exclusively on sophisticated, handcrafted features to capture local data patterns and, thus, depend heavily on large collections of labeled data. The multi-view, multivariate deep classification model for time-series data proposed in this paper makes important contributions to address these limitations. The proposed model derives a LSTM-based, deep feature descriptor to model both the view-specific data characteristics and cross-view interaction in an integrated deep architecture while driving the learning phase in a data-driven manner. The proposed model employs a compact context descriptor to exploit view-specific affinity information to design a more insightful context representation. Finally, the model uses a multi-view factor-pooling scheme for a context-driven attention learning strategy to weigh the most relevant feature dimensions while eliminating noise from the resulting fused descriptor. As shown by experiments, compared to the existing multi-view methods, the proposed multi-view deep sequential learning approach improves classification performance by roughly 4% in the UCI multi-view activity recognition dataset, while also showing significantly robust generalized representation capacity against its single-view counterparts, in classifying several large-scale multi-view light curve collections