A framework for interpreting type I error rates from a product-term model of interaction applied to quantitative traits

Adequate control of type I error rates will be necessary in the increasing genome‐wide search for interactive effects on complex traits. After observing unexpected variability in type I error rates from SNP‐by‐genome interaction scans, we sought to characterize this variability and test the ability of heteroskedasticity‐consistent standard errors to correct it. We performed 81 SNP‐by‐genome interaction scans using a product‐term model on quantitative traits in a sample of 1,053 unrelated European Americans from the NHLBI Family Heart Study, and additional scans on five simulated datasets. We found that the interaction‐term genomic inflation factor (lambda) showed inflation and deflation that varied with sample size and allele frequency; that similar lambda variation occurred in the absence of population substructure; and that lambda was strongly related to heteroskedasticity but not to minor non‐normality of phenotypes. Heteroskedasticity‐consistent standard errors narrowed the range of lambda, with HC3 outperforming HC0, but in individual scans tended to create new P‐value outliers related to sparse two‐locus genotype classes. We explain the lambda variation as a result of non‐independence of test statistics coupled with stochastic biases in test statistics due to a failure of the test to reach asymptotic properties. We propose that one way to interpret lambda is by comparison to an empirical distribution generated from data simulated under the null hypothesis and without population substructure. We further conclude that the interaction‐term lambda should not be used to adjust test statistics and that heteroskedasticity‐consistent standard errors come with limitations that may outweigh their benefits in this setting

Heterogeneity of monosomy 3 in fine needle aspiration biopsy of choroidal melanoma.

PurposeTo report on the heterogeneity of monosomy 3 in a fine needle aspiration biopsy obtained transsclerally from choroidal melanoma for prognosis.MethodsAll clinical records for patients who had been diagnosed with choroidal melanoma and underwent iodine-125 plaque brachytherapy with intraoperative transscleral fine needle aspiration biopsy from January 2005 to August 20, 2011, and who had a positive result for monosomy 3 according to fluorescence in situ hybridization as reported by clinical cytogenetics testing were collected. Patient age and sex, total number of cells evaluated and number of cells positive for monosomy 3, tumor size, and metastatic outcome were recorded for each patient.ResultsA positive result for monosomy 3 was reported in 93 patients who underwent transscleral fine needle aspiration biopsy. Two patients were lost to follow-up immediately post-operatively, and the remaining 91 patients were included in this study. The mean number of cells evaluated in the biopsy was 273 (range 28 to 520). The mean percentage of cells positive for monosomy 3 was 62.9% (range 4.7%-100%). The mean tumor height was 5.91 mm (range 1.99 to 10.85 mm). Larger tumors were associated with a higher percentage of cells positive for monosomy 3. During the average follow-up interval of 28.9 months (range 3-76 months), choroidal melanoma metastasis developed in 18 (20%) patients. Patients whose tumors had 1%-33% of cells positive for monosomy 3 had a significantly lower risk of metastasis-related death compared to patients whose tumors harbored a higher percentage of monosomy 3 (p = 0.04).ConclusionsCytogenetic heterogeneity of fluorescent in situ hybridization for monosomy 3 exists in a biopsy sample. Larger tumors were more likely to have a higher percentage of monosomy 3 positive cells in the sample. Furthermore, patients whose tumors had more than 33% of cells positive for monosomy 3 had a poorer prognosis than patients whose tumors had lower percentages of monosomy 3

Multilingual Speech Recognition With A Single End-To-End Model

Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific. In contrast, sequence-to-sequence models are well suited for multilingual ASR because they encapsulate an acoustic, pronunciation and language model jointly in a single network. In this work we present a single sequence-to-sequence ASR model trained on 9 different Indian languages, which have very little overlap in their scripts. Specifically, we take a union of language-specific grapheme sets and train a grapheme-based sequence-to-sequence model jointly on data from all languages. We find that this model, which is not explicitly given any information about language identity, improves recognition performance by 21% relative compared to analogous sequence-to-sequence models trained on each language individually. By modifying the model to accept a language identifier as an additional input feature, we further improve performance by an additional 7% relative and eliminate confusion between different languages.Comment: Accepted in ICASSP 201

DPFC Performance with the Comparison of PI and ANN Controller

Modern power systems demand the active control of power flow and for this purpose Power flow controlling devices (PFCDs) are required. Distributed FACTS Controller (DPFC) is a part of FACTS family. DPFC offers equal control ability same as UPFC, comprising the adjustment of the internal angle of the machine and bus voltage includes line impedance. In addition to UPFC a new device evolved known as DPFC in which common DC link is eliminated that enables the exclusive working between the two converters which are shunt and the series. The Distributed-FACTS (D- FACTS) idea is adopt in the series converter scheme. The replacement of the high rating three phase series converter  with the multiple low rating single phase converters results in cost reduction and increases reliability greatly. The useful power transfer between the two converters which are shunt and series through common dc link in UPFC where as in DPFC in this the required power is transferred in the transmission line with three times of natural fundamental frequency. Where as in the new device no need of large voltage separation between the line and PFC Device is no requirement of high voltage isolation between because D-FACTS converters which are 1-ᴓ floating device with respect to the ground. Accordingly, In this paper we bring out the DPFC performance differences with different control techniques which are PI and Artificial Neural Network Controllers and bring with conclusion that ANN is a better control strategy compared to PI

Improved Performance of DPFC Using Sliding Mode Controller Method

Modern power systems demand the need of active power flow with the help of Power Electronics control devices is needed. In the family of Flexible AC Transmission devices (FACTS), Dynamic PFC (DPFC) offers the same controlling function as Unified PFC (UPFC), comprising the control of transmission angle, bus voltage and line impedance. A technical modification of UPFC is DPFC in which fluctuations of voltage at DC link is eliminated that enables the individual operation as series and parallel controllers. The concept of DFACTS is used in design of the series converter. The replacement of  the  high  rating  three  phase  series  converter with  the multiple low rating single phase converters results in cost reduction and increases reliability greatly. This DC Link is used to transfer the real power between two converters in UPFC such as in DPFC which eliminates the 3rd harmonic frequencies at transmission lines. D-FACTS converters are acting as insulation between high voltage phases acts as 1-ᴓ floating with respect to ground. These results in lower cost for the DPFC system compared to the UPFC. This paper describes the comparison of PI and Sliding Mode Controllers which conclude that SMC is a better control strategy compared to PI

Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model

Sequence-to-sequence models provide a simple and elegant solution for building speech recognition systems by folding separate components of a typical system, namely acoustic (AM), pronunciation (PM) and language (LM) models into a single neural network. In this work, we look at one such sequence-to-sequence model, namely listen, attend and spell (LAS), and explore the possibility of training a single model to serve different English dialects, which simplifies the process of training multi-dialect systems without the need for separate AM, PM and LMs for each dialect. We show that simply pooling the data from all dialects into one LAS model falls behind the performance of a model fine-tuned on each dialect. We then look at incorporating dialect-specific information into the model, both by modifying the training targets by inserting the dialect symbol at the end of the original grapheme sequence and also feeding a 1-hot representation of the dialect information into all layers of the model. Experimental results on seven English dialects show that our proposed system is effective in modeling dialect variations within a single LAS model, outperforming a LAS model trained individually on each of the seven dialects by 3.1 ~ 16.5% relative.Comment: submitted to ICASSP 201

Efficacy, safety and cost effectiveness of levocetrizine and ebastine in allergic rhinitis: a comparative study

Background: Allergic rhinitis is a ubiquitous aliment affecting a large population of individuals. The mainstay of treatment includes antihistamines and topical steroids. With a large assortment available, the choice of the drugs needs to be non-random and evidenced based. Hence this study was undertaken to compare Levocetrezine and Ebastine in terms of efficacy, safety and cost effectiveness in treatment of allergic rhinitis.Methods: Newly diagnosed patients with allergic rhinitis were categorized into two groups and treated with Levocetrizine 5mg and Ebastine 20mg per day respectively. Severity of the symptoms at the commencement and at the end of second and the fourth weeks of therapy were assessed using a four-point Likert scale and assigning a Total Nasal Symptoms Score (TNSS). The primary efficacy measure was mean change from baseline TNSS at each follow up visit. Change in TNSS was compared using Independent sample test. Adverse effects in both the groups were compared using Chi square test. Cost effectiveness was inferred by calculation of the Average Cost Effectiveness Ratio.Results: A total of 159 patients 84 from Levocetrizine group (L group) and 75 from the Ebastine group (E group) were available for study. At the end of second week, the E group showed a better reduction in TNSS(p-0.04). However, both the groups showed similar reduction in TNSS at the concluding visit (p-value of 0.09). The incidence of adverse effects was significantly higher in the L than in E. Levocetrezine was found to be more cost effective than Ebastine.Conclusions: Levocterezine and Ebastine are equally efficacious in treatment of allergic rhinitis. Hence treatment will have to be personalized to the individual patients based on other factors such as adverse drug effects and cost effectiveness

Analysis and Design of Visualization of Educational Institution database using Power BI Tool

Visualization of data set is a process of making understand the significance of data through visual context and part of data analytics where it2019;s executed after the data correction. Nowadays visualization is more useful in business intelligence and Analytics in every field, There are different techniques for visualizing the datasets, it may be in dynamic or interactive nature, and datasets can be visualized in different types of visuals insights, This paper deals with the interactive visualization of educational institution database using Microsoft Power BI Tool with different modules and this paper focuses on process model, operations of Microsoft Power BI, types of data sources available in Tool and its different related types of visual insights or context

Cell-extracellular matrix interactions regulate neural differentiation of human embryonic stem cells

<p>Abstract</p> <p>Background</p> <p>Interactions of cells with the extracellular matrix (ECM) are critical for the establishment and maintenance of stem cell self-renewal and differentiation. However, the ECM is a complex mixture of matrix molecules; little is known about the role of ECM components in human embryonic stem cell (hESC) differentiation into neural progenitors and neurons.</p> <p>Results</p> <p>A reproducible protocol was used to generate highly homogenous neural progenitors or a mixed population of neural progenitors and neurons from hESCs. This defined adherent culture system allowed us to examine the effect of ECM molecules on neural differentiation of hESCs. hESC-derived differentiating embryoid bodies were plated on Poly-D-Lysine (PDL), PDL/fibronectin, PDL/laminin, type I collagen and Matrigel, and cultured in neural differentiation medium. We found that the five substrates instructed neural progenitors followed by neuronal differentiation to differing degrees. Glia did not appear until 4 weeks later. Neural progenitor and neuronal generation and neurite outgrowth were significantly greater on laminin and laminin-rich Matrigel substrates than on other 3 substrates. Laminin stimulated hESC-derived neural progenitor expansion and neurite outgrowth in a dose-dependent manner. The laminin-induced neural progenitor expansion was partially blocked by the antibody against integrin α6 or β1 subunit.</p> <p>Conclusion</p> <p>We defined laminin as a key ECM molecule to enhance neural progenitor generation, expansion and differentiation into neurons from hESCs. The cell-laminin interactions involve α6β1 integrin receptors implicating a possible role of laminin/α6β1 integrin signaling in directed neural differentiation of hESCs. Since laminin acts in concert with other ECM molecules <it>in vivo</it>, evaluating cellular responses to the composition of the ECM is essential to clarify further the role of cell-matrix interactions in neural derivation of hESCs.</p

State-of-the-art Speech Recognition With Sequence-to-Sequence Models

Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In previous work, we have shown that such architectures are comparable to state-of-theart ASR systems on dictation tasks, but it was not clear if such architectures would be practical for more challenging tasks such as voice search. In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance. On the structural side, we show that word piece models can be used instead of graphemes. We also introduce a multi-head attention architecture, which offers improvements over the commonly-used single-head attention. On the optimization side, we explore synchronous training, scheduled sampling, label smoothing, and minimum word error rate optimization, which are all shown to improve accuracy. We present results with a unidirectional LSTM encoder for streaming recognition. On a 12, 500 hour voice search task, we find that the proposed changes improve the WER from 9.2% to 5.6%, while the best conventional system achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to 5% for the conventional system.Comment: ICASSP camera-ready versio