Learning Nonstationary Time-Series With Dynamic Pattern Extractions

Copyright © The authors 2021. The era of information explosion had prompted the accumulation of a tremendous amount of time-series data, including stationary and non-stationary time-series data. State-of-the-art algorithms have achieved a decent performance in dealing with stationary temporal data. However, traditional algorithms that tackle stationary time-series do not apply to non-stationary series like Forex trading. This paper investigates applicable models that can improve the accuracy of forecasting future trends of non-stationary time-series sequences. In particular, we focus on identifying potential models and investigate the effects of recognizing patterns from historical data. We propose a combination of \rebuttal{the} seq2seq model based on RNN, along with an attention mechanism and an enriched set features extracted via dynamic time warping and zigzag peak valley indicators. Customized loss functions and evaluating metrics have been designed to focus more on the predicting sequence's peaks and valley points. Our results show that our model can predict 4-hour future trends with high accuracy in the Forex dataset, which is crucial in realistic scenarios to assist foreign exchange trading decision making. We further provide evaluations of the effects of various loss functions, evaluation metrics, model variants, and components on model performance

Thioglycosides Are Efficient Metabolic Decoys of Glycosylation that Reduce Selectin Dependent Leukocyte Adhesion

© 2018 Elsevier Ltd Small-molecule inhibitors of glycosylation can be applied in basic science studies, and clinical investigations as anti-inflammatory, anti-metastatic, and anti-viral therapies. This article demonstrates that thioglycosides represent a class of potent metabolic decoys that resist hydrolysis, and block E-selectin-dependent leukocyte adhesion in models of inflammation

Development of an Automatic Translate Real-Time Voice to Sign Language Conversion for Deaf and Dumb People

Sign Language Recognition is one of the most growing fields of research area. Many new techniques have been developed recently in this area. The sign language is mainly used for communication of deaf-dumb people. The study proposed a design and initial implementation of a robust system which can automatically translates voice into text and text to sign language animations. Sign Language Translation Systems could significantly improve deaf lives especially in communications, exchange of information and employment of machine for translation conversations from one language to another. Therefore, considering these points, it seems necessary to study the speech recognition. Usually, the voice recognition algorithms address three major challenges. The first is extracting feature form speech; second is when limited sound gallery are available for recognition; and the final challenge is to improve speaker dependent to speaker independent voice recognition. Extracting feature form speech is an important stage in the method. Different procedures are available for extracting feature form speech. One of the commonest used in speech recognition systems is Mel-Frequency Cepstral Coefficients (MFCCs). The algorithm starts with preprocessing and signal conditioning. The next is extracting feature form speech using Cepstral coefficients. Then the result sends to segmentation part. Finally, recognition part recognizes the words and then converting word recognized to facial animation. The project is still in progress and some new interesting methods are described in the current report. The system will perform the recognition process through matching the parameter set of the input speech with the stored templates to finally display the sign language in caption of video on the screen of computer/mobile etc. So, Deaf and Dumb people or students easily learn the subject through the online YouTube video

Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein

Cell division (mitosis) results in the equal segregation of chromosomes between two daughter cells. The mitotic spindle plays a pivotal role in chromosome alignment and segregation during metaphase and anaphase. Structural or functional errors of this spindle can cause aneuploidy, a hallmark of many cancers. To investigate if a given protein associates with the mitotic spindle and regulates its assembly, stability, or function, fluorescence microscopy can be performed to determine if disruption of that protein induces phenotypes indicative of spindle dysfunction. Importantly, functional disruption of proteins with specific roles during mitosis can lead to cancer cell death by inducing mitotic insult. However, there is a lack of automated computational tools to detect and quantify the effects of such disruption on spindle integrity.We developed the image analysis software tool MatQuantify, which detects both large-scale and subtle structural changes in the spindle or DNA and can be used to statistically compare the effects of different treatments. MatQuantify can quantify various physical properties extracted from fluorescence microscopy images, such as area, lengths of various components, perimeter, eccentricity, fractal dimension, satellite objects and orientation. It can also measure textual properties including entropy, intensities and the standard deviation of intensities. Using MatQuantify, we studied the effect of knocking down the protein clathrin heavy chain (CHC) on the mitotic spindle. We analysed 217 microscopy images of untreated metaphase cells, 172 images of metaphase cells transfected with small interfering RNAs targeting the luciferase gene (as a negative control), and 230 images of metaphase cells depleted of CHC. Using the quantified data, we trained 23 supervised machine learning classification algorithms. The Support Vector Machine learning algorithm was the most accurate method (accuracy: 85.1%; area under the curve: 0.92) for classifying a spindle image. The Kruskal-Wallis and Tukey-Kramer tests demonstrated that solidity, compactness, eccentricity, extent, mean intensity and number of satellite objects (multipolar spindles) significantly differed between CHC-depleted cells and untreated/luciferase-knockdown cells.MatQuantify enables automated quantitative analysis of images of mitotic spindles. Using this tool, researchers can unambiguously test if disruption of a protein-of-interest changes metaphase spindle maintenance and thereby affects mitosis

Assessment of vitamin D among male adolescents and young adults hospitalized with eating disorders.

PurposeMedical complications of eating disorders in males are understudied compared to females, as is the case of vitamin D deficiency. The aim of this study was to assess vitamin D levels among male and female adolescents and young adults hospitalized for medical complications of eating disorders.MethodsWe retrospectively reviewed electronic medical records of patients aged 9-25 years (N = 565) admitted to the University of California, San Francisco Eating Disorders Program for medical instability, between May 2012 and August 2020. Serum vitamin D (25-hydroxy) level was assessed at admission as was history of prior calcium, vitamin D, or multivitamin supplementation. Linear regression was used to assess factors associated with vitamin D levels.ResultsA total of 93 males and 472 females met eligibility criteria (age 15.5 ± 2.8, 58.8% anorexia nervosa; admission body mass index 17.6 ± 2.91). Among male participants, 44.1% had 25-hydroxyvitamin D levels < 30 ng/mL, 18.3% had 25-hydroxyvitamin D levels < 20 ng/mL, and 8.6% had 25-hydroxyvitamin D levels < 12 ng/mL. There were no significant differences in 25-hydroxyvitamin D levels in males compared to females, except that a lower proportion (1.9%) of female participants had 25-hydroxyvitamin D levels < 12 ng/mL (p = 0.001). Only 3.2% of males reported calcium or vitamin D-specific supplementation prior to hospital admission, while 8.6% reported taking multivitamins. White race, prior calcium/vitamin D supplementation, and higher calcium levels were associated with higher vitamin D levels on admission.ConclusionsNearly half of patients admitted to the hospital for malnutrition secondary to eating disorders presented with low 25-hydroxyvitamin D levels; males were more likely than females to have severe vitamin D deficiency. These findings support vitamin D assessment as part of the routine medical/nutritional evaluation for hospitalized eating disorder patients, with particular attention on male populations