Rank discriminants for predicting phenotypes from RNA expression

Statistical methods for analyzing large-scale biomolecular data are commonplace in computational biology. A notable example is phenotype prediction from gene expression data, for instance, detecting human cancers, differentiating subtypes and predicting clinical outcomes. Still, clinical applications remain scarce. One reason is that the complexity of the decision rules that emerge from standard statistical learning impedes biological understanding, in particular, any mechanistic interpretation. Here we explore decision rules for binary classification utilizing only the ordering of expression among several genes; the basic building blocks are then two-gene expression comparisons. The simplest example, just one comparison, is the TSP classifier, which has appeared in a variety of cancer-related discovery studies. Decision rules based on multiple comparisons can better accommodate class heterogeneity, and thereby increase accuracy, and might provide a link with biological mechanism. We consider a general framework ("rank-in-context") for designing discriminant functions, including a data-driven selection of the number and identity of the genes in the support ("context"). We then specialize to two examples: voting among several pairs and comparing the median expression in two groups of genes. Comprehensive experiments assess accuracy relative to other, more complex, methods, and reinforce earlier observations that simple classifiers are competitive.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS738 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

An Experimental Investigation of the Integration of Smart Building Components with Building Information Model (BIM)

Building Information Modeling (BIM) is a methodology to digitally represent all the physical and functional characteristics of a building. Importantly, in smart buildings smart components that are enabled with sensing and actuation need to be modeled accurately within the BIM model. This data representation needs to include multiple status of the smart component based on their performance to guide the design and construction process. However, currently there is not a clear methodology or guideline on how to embed smart components in the BIM model. Visualization techniques have been developed based on CAD technology to integrate smart components in the building model but these techniques have not been applied to BIM environment. To accurately model smart components, the component must be more than a single status representation and must contain complete and accurate dynamic data of the smart component. In this research, data properties, visualization techniques, and categorization of smart components is investigated. Then, through an experimental investigation, nine smart components across five building disciplines are modeled and embedded in a BIM model of a smart space. The model includes parameters that facilitate the data representation of the smart components. Data properties, data organization, and simulation of the smart component within the building model is explained. Challenges and future research is discussed

Effect of two organic chemical fluids on the mechanical properties of an expansive clay soil

This is the author accepted manuscript. The final version is available from ASTM International via the DOI in this recordAn experimental study was conducted to investigate the effect of two organic chemical fluids (glycerol and acetone) on the mechanical behavior of an expansive clay soil. A number of experimental tests, including Atterberg limits, compaction, free swelling, unconfined compressive strength, California Bearing Ratio (CBR), and one-dimensional consolidation (loading and unloading) tests, were conducted on specimens of natural soil and soil contaminated with pure glycerol and acetone fluids at different percentages (10 %, 15 %, and 20 %) by weight. The results showed that the effect of pure glycerol on the behavior of the contaminated soil is different from acetone. Glycerol caused a reduction of Atterberg limits, free swelling, unconfined compressive strength, CBR, and optimum water content and an increase in maximum dry unit weight, while acetone showed the opposite effects. These variations of mechanical and physical behavior are a function of the percentage of glycerol or acetone. Furthermore, the results of the loading and unloading tests showed that the compression and swelling indexes are independent of the type of organic chemical fluids used. Results from scanning electron microscopy tests confirmed that the effect of glycerol on the behavior of soil is not the same as acetone

Impacts of heating and surfactant treatments on the geotechnical properties of a cohesive soil

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordAn experimental investigation was performed to assess the effect of heating and surfactant on treatment of a soil contaminated with gasoline. Contaminated soil samples were prepared by adding 5, 10 and 15% weight of gasoline to a cohesive soil. The contaminated soil samples were treated by applying heating at 50, 100 and 150 °C. In addition, treatment of the contaminated samples was done by using two different types of surfactant, namely SDS (Sodium Dodecyl Sulfate) and Tween 80. The physical and mechanical properties of the natural soil, contaminated soil and treated soil were determined through experimental tests including Atterberg limit, grain size distribution, compaction and unconfined compression tests. Comparison of the results showed that adding gasoline to soil changes its behavior and the amount of change was function of percent of gasoline. The results also indicated that heating can be used for treatment of the contaminated soil. Comparison of the results showed that using surfactant was more effective in treating the contaminated soil than thermal treatment and the properties of surfactant-treated soil were closer to the original condition. The results also showed that SDS surfactant was more effective in treating the contaminated soil than Tween 80

Pulse compression with minimum uncertainty: An efficient microwave medical imaging technique

A pulse compression technique that gives an optimum contrast and visibility of targets in radar-based medical imaging is presented. A smoothing window for microwave beamforming technique which more properly alleviates the effect of abrupt truncation in finite length signals with the aid of the uncertainty principle is utilized. It is found that using a closer output signal shape to the Gaussian pulse results in a lower uncertainty and ambiguity in the reconstructed images. Hence, when the back-scattered signal passes through a window whose uncertainty is the least, the visibility of the target in the imaged domain will be the highest with high signal-to-noise ratio and fine resolution in microwave medical imaging. The accumulation of the above properties together increases the chance of detecting any abnormality in the human body at early stages and thus resulting in a higher chance of survival. The idea is tested on a real-sized head model surrounded by an array of dipoles operating across the band 1.3-1.4 GHz. The results are compared with the most commonly used beamforming techniques to show the achieved improvements in practice

Janus membranes for membrane distillation: Recent advances and challenges

© 2021 Elsevier B.V. Membrane distillation (MD) is a promising hybrid thermal-membrane separation technology that can efficiently produce freshwater from seawater or contaminated wastewater. However, the relatively low flux and the presence of fouling or wetting agents in feed solution negate the applicability of MD for long term operation. In recent years, ‘two-faced’ membranes or Janus membranes have shown promising potential to decrease wetting and fouling problem of common MD system as well as enhance the flux performance. In this review, a comprehensive study was performed to investigate the various fabrication, modification, and novel design processes to prepare Janus membranes and discuss their performance in desalination and wastewater treatment utilizing MD. The promising potential, challenges and future prospects relating to the design and use of Janus membranes for MD are also tackled in this review

Korelasi Antara Kadar Total Flavonoid Dan Fenolik Dari Ekstrak Dan Fraksi Daun Jati Putih (Gmelina Arborea Roxb.) Terhadap Aktivitas Antioksidan: Correlation Between Total Phenolic and Flavonoid Contents of Jati Putih (Gmelina Arborea Roxb.) Leaves Extract and Fraction Toward Antioxidant Activity

This experiment aims to determine the correlation of total phenolic and flavonoid content of jati putih leaves fraction (Gmelina arborea Roxb.) towards Antioxidant activity .  Sample was extracted by maceration method using ethanol 70% to obtain the ethanol extract (EE), followed by liquid-liquid extraction method to obtain fraction of ethyl acetate (EA) and n-Hexane (EH). The phytochemical screening  and determination of total phenolic and flavonoid content were done by colorimetric method. Antioxidant activity were done by DPPH, FRAP and ABTS methods. Phytochemical screening showed positive results for flavonoids, phenolic and saponins.  The largest total phenolic content was found on EA (11,59 µg/ml ± 0,3 %b/b EAG) and the largest total flavonoid content was on EA (3,88 µg/ml ± 0,02 %b/b EK). The total phenolic and flavonoid content of Jati putih leaves has a correlation with antioxidant activity. The coefficient correlation of activity on reducingDPPH radical was 56,7% (total of phenolic content) and 57,8% (total of flavonoid content) and on iron reduction power in FRAP method  was 99,9% (total of phenolics and flavonoids content). The relationship with the activity in reducing radical ABTS obtained coefficient correlation of 57,0% and 58,1% for total phenolic and flavonoids contents, respectively

Toxic heavy metals and nutrient concentration in the milk of goat herds in two Iranian industrial and non-industrial zones

This work aimed to explore the concentration of nickel, manganese, iron, copper, chromium, and lead in the milk of goat herds in the industrial area of Asaluyeh (southern Iran) and the non-industrial area of Kaki. The milk of 16 goat herds (each herd had at least ten goats) was collected in several villages in each area, and at the same time, the drinking water and forage of goats were sampled. The concentration of elements in the samples was determined by ICP-OES. The mean concentrations of chromium, copper, iron, manganese, lead, and nickel in milk samples of the Asaluyeh area were 16.423 ± 0.349, 0.146 ± 0.118, 6.111 ± 0.501, 0.239 ± 0.016, 0.141 ± 0.030, and 1.447 ± 0.101 mg/kg, respectively. Concentrations of heavy metals (except for copper) in the milk of goats in the industrialized area of Asaluyeh were significantly higher than that of Kaki (P < 0.05). Also, the content of heavy metals was significantly correlated with lactose levels (P < 0.05). The hazard index for drinking the goat milk was computed to be 0.444 and 0.386 for the Asaluyeh and Kaki area, respectively, which shows a minimal effect of this exposure pathway

An Introduction to EEG Source Analysis with an illustration of a study on Error-Related Potentials

International audienceOver the last twenty years blind source separation (BSS) has become a fundamental signal processing tool in the study of human electroencephalography (EEG), other biological data, as well as in many other signal processing domains such as speech, images, geophysics and wireless communication (Comon and Jutten, 2010). Without relying on head modeling BSS aims at estimating both the waveform and the scalp spatial pattern of the intracranial dipolar current responsible of the observed EEG, increasing the sensitivity and specificity of the signal received from the electrodes on the scalp. This chapter begins with a short review of brain volume conduction theory, demonstrating that BSS modeling is grounded on current physiological knowledge. We then illustrate a general BSS scheme requiring the estimation of second-order statistics (SOS) only. A simple and efficient implementation based on the approximate joint diagonalization of covariance matrices (AJDC) is described. The method operates in the same way in the time or frequency domain (or both at the same time) and is capable of modeling explicitly physiological and experimental source of variations with remarkable flexibility. Finally, we provide a specific example illustrating the analysis of a new experimental study on error-related potentials

Differential Geometry for Model Independent Analysis of Images and Other Non-Euclidean Data: Recent Developments

This article provides an exposition of recent methodologies for nonparametric analysis of digital observations on images and other non-Euclidean objects. Fr\'echet means of distributions on metric spaces, such as manifolds and stratified spaces, have played an important role in this endeavor. Apart from theoretical issues of uniqueness of the Fr\'echet minimizer and the asymptotic distribution of the sample Fr\'echet mean under uniqueness, applications to image analysis are highlighted. In addition, nonparametric Bayes theory is brought to bear on the problems of density estimation and classification on manifolds