ALBERT

Description: Motivation: Most data analysis tools for high-throughput screening (HTS) seek to uncover interesting hits for further analysis. They typically assume a low hit rate per plate. Hit rates can be dramatically higher in secondary screening, RNAi screening and in drug sensitivity testing using biologically active drugs. In particular, drug sensitivity testing on primary cells is often based on dose–response experiments, which pose a more stringent requirement for data quality and for intra- and inter-plate variation. Here, we compared common plate normalization and noise-reduction methods, including the B -score and the Loess a local polynomial fit method under high hit-rate scenarios of drug sensitivity testing. We generated simulated 384-well plate HTS datasets, each with 71 plates having a range of 20 (5%) to 160 (42%) hits per plate, with controls placed either at the edge of the plates or in a scattered configuration. Results: We identified 20% (77/384) as the critical hit-rate after which the normalizations started to perform poorly. Results from real drug testing experiments supported this estimation. In particular, the B -score resulted in incorrect normalization of high hit-rate plates, leading to poor data quality, which could be attributed to its dependency on the median polish algorithm. We conclude that a combination of a scattered layout of controls per plate and normalization using a polynomial least squares fit method, such as Loess helps to reduce column, row and edge effects in HTS experiments with high hit-rates and is optimal for generating accurate dose–response curves. Contact: john.mpindi@helsinki.fi Availability and implementation, Supplementary information: R code and Supplementary data are available at Bioinformatics online.

Description: Motivation: Matrix Assisted Laser Desorption Ionization-Imaging Mass Spectrometry (MALDI-IMS) in ‘omics’ data acquisition generates detailed information about the spatial distribution of molecules in a given biological sample. Various data processing methods have been developed for exploring the resultant high volume data. However, most of these methods process data in the spectral domain and do not make the most of the important spatial information available through this technology. Therefore, we propose a novel streamlined data analysis pipeline specifically developed for MALDI-IMS data utilizing significant spatial information for identifying hidden significant molecular distribution patterns in these complex datasets. Methods: The proposed unsupervised algorithm uses Sliding Window Normalization (SWN) and a new spatial distribution based peak picking method developed based on Gray level Co-Occurrence (GCO) matrices followed by clustering of biomolecules. We also use gist descriptors and an improved version of GCO matrices to extract features from molecular images and minimum medoid distance to automatically estimate the number of possible groups. Results: We evaluated our algorithm using a new MALDI-IMS metabolomics dataset of a plant (Eucalypt) leaf. The algorithm revealed hidden significant molecular distribution patterns in the dataset, which the current Component Analysis and Segmentation Map based approaches failed to extract. We further demonstrate the performance of our peak picking method over other traditional approaches by using a publicly available MALDI-IMS proteomics dataset of a rat brain. Although SWN did not show any significant improvement as compared with using no normalization, the visual assessment showed an improvement as compared to using the median normalization. Availability and implementation: The source code and sample data are freely available at http://exims.sourceforge.net/ . Contact: awgcdw@student.unimelb.edu.au or chalini_w@live.com Supplementary information : Supplementary data are available at Bioinformatics online.

Description: An approach to infer the unknown microbial population structure within a metagenome is to cluster nucleotide sequences based on common patterns in base composition, otherwise referred to as binning. When functional roles are assigned to the identified populations, a deeper understanding of microbial communities can be attained, more so than gene-centric approaches that explore overall functionality. In this study, we propose an unsupervised, model-based binning method with two clustering tiers, which uses a novel transformation of the oligonucleotide frequency-derived error gradient and GC content to generate coarse groups at the first tier of clustering; and tetranucleotide frequency to refine these groups at the secondary clustering tier. The proposed method has a demonstrated improvement over PhyloPythia, S-GSOM, TACOA and TaxSOM on all three benchmarks that were used for evaluation in this study. The proposed method is then applied to a pyrosequenced metagenomic library of mud volcano sediment sampled in southwestern Taiwan, with the inferred population structure validated against complementary sequencing of 16S ribosomal RNA marker genes. Finally, the proposed method was further validated against four publicly available metagenomes, including a highly complex Antarctic whale-fall bone sample, which was previously assumed to be too complex for binning prior to functional analysis.

Description: Motivation : Modern proteomics studies utilize high-throughput mass spectrometers which can produce data at an astonishing rate. These big mass spectrometry (MS) datasets can easily reach peta-scale level creating storage and analytic problems for large-scale systems biology studies. Each spectrum consists of thousands of peaks which have to be processed to deduce the peptide. However, only a small percentage of peaks in a spectrum are useful for peptide deduction as most of the peaks are either noise or not useful for a given spectrum. This redundant processing of non-useful peaks is a bottleneck for streaming high-throughput processing of big MS data. One way to reduce the amount of computation required in a high-throughput environment is to eliminate non-useful peaks. Existing noise removing algorithms are limited in their data-reduction capability and are compute intensive making them unsuitable for big data and high-throughput environments. In this paper we introduce a novel low-complexity technique based on classification, quantization and sampling of MS peaks. Results : We present a novel data-reductive strategy for analysis of Big MS data. Our algorithm, called MS-REDUCE, is capable of eliminating noisy peaks as well as peaks that do not contribute to peptide deduction before any peptide deduction is attempted. Our experiments have shown up to 100 x speed up over existing state of the art noise elimination algorithms while maintaining comparable high quality matches. Using our approach we were able to process a million spectra in just under an hour on a moderate server. Availability and implementation: The developed tool and strategy has been made available to wider proteomics and parallel computing community and the code can be found at https://github.com/pcdslab/MSREDUCE Contact : fahad.saeed@wmich.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Eccentricity is defined as deviation from the circular path and it occurs due to clamping deviation or geometric imperfection when machining elements revolves around the line parallel to the machining element axis, but not around the machining element axis. In this paper, the variation of offset values along the central axial line of different small sections of spindle, tool holder and milling cutter was explored. This work was undertaken by the observation that eccentricity implies adverse impact on the material properties, tooling properties, machining elements, machine system etc. The problem solution was found out by measuring the whole body of spindle, tool holder and milling cutter with Swiss Dial Gauge after dividing them into small sections along the z level axis. After measurement, analysis was done of the recorded data on software’s by illustrating their actual profiles and variation along the Centre line of small divided sections at different orientations. After analy...

Description: Concrete chemical admixtures are used to improve the fresh and hardened properties of mortar or concrete in different applications. Their addition can allow for the reduction of the water to cement ratio (w/c), without affecting the workability of the mixture. By reducing the amount of water, the cement paste will have higher density. However, the reduction of the water content in a concrete mixture should be done in such a way that complete cement hydration take place and sufficient workability is achieved. In order to maintain the workability of the concrete mix, the dosage of the admixture must be carefully calculated and must be taken into account in the calculation of the w/c ratio. In the present study, three types of chemical admixtures; Type A water-reducing, Type D water-reducing and retarding and Type F high range, water-reducing admixtures conforming to ASTM C 494/C 494M – 04 standards, were used to optimize the percentage of the admixture with respect to the w/c rati...

Description: Based on the radar field environment, the detection process is generally based on the adaptive threshold required to detect the received radar cell signal. Many algorithms are used to design this adaptive threshold to satisfy a Constant False Alarm Rate (CFAR) in line with detection criteria in non-homogenous environments. Although CFAR algorithms have increasingly become a vital factor in the detection process, the performance of these algorithms differs according to their treatment of the received radar bins. In cases of targets in a clutter edge and multi-targets, the performance of the GO-, VI-, OSSO- and OSGO-CFAR algorithms are better than the performance of the CA-CFAR algorithm, while the CA-CFAR and OS-CFAR algorithms lose such targets. In this context, the SO-CFAR has a higher P fa than the above algorithms for small targets in a clutter edge. OSSO-, OSGO, VI, and CMLD also have higher P D S based on their outcomes than the other algorithms i...

Description: The Kut Barrage is one of the most important hydraulic structures in Iraq, which is in place to regulate the flow from the Tigris River and distribute it between Graf River, Dujelah Channel, Dalmch Channels, and other irrigation projects. The barrage includes 56 sluice gates, each 6 m wide, which control the discharge of the Tigris River at the individual locations. According to observations and measurements, about 90% of the flow at the downstream has been modular (free flow) within the last five years. In the present study, a trial was attempted to calibrate some universal formulas that would offer a reliable means of determining the discharge under gates such as those presented by Rajaratnam and Subramanya, Swamme, and Ferro, which would make them suitable for Kut Barrage discharge calculation over a wide range of scenarios in terms of gate openings. A recent formula presented by Maatooq for canal operation was also adopted in order to test whether it was appropriate for appl...

Description: This paper presents simulation very simple structure of multi band metamaterial absorber at the microwave frequency band. The unit cell of the proposed structure consists of two copper rings at different radius, loaded onto FR4 substrate with 2.5 mm thick in order to achieve perfect multi absorption bands. The asymmetrical ring structure generally makes it sensitive to electromagnetic (EM) waves polarization. But the Penta-Perfect Metamaterial Absorber PPMMA proposed structure is capable to absorbs electromagnetic wave at wide range of incident EM wave. The CST simulated result of proposed PPMMA structure shows that the five-band realizes perfect metamaterial absorber for normal incident electromagnetic waves of 91.3%, 99.3%, 93.7%, 98.2%, and 97.4% at 12.25 GHz, 12.9 GHz, 16.15 GHz, 16.78 GHz, and 18.44 GHz of absorbance value respectively.

Description: Changes in the hydrological cycle are among the aspects of climate change most relevant for human systems and ecosystems. Besides trends in overall wetting or drying, changes in temporal characteristics of wetting and drying are of crucial importance in determining the climate hazard posed by such changes. This is particularly the case for tropical regions, where most precipitation occurs during the rainy season and changes in rainy season onset and length have substantial consequences. Here we present projections for changes in tropical rainy season lengths for mean temperature increase of 1.5 °C and 2 °C above pre-industrial levels. Based on multi-ensemble quasi-stationary simulations at these warming levels, our analysis indicates robust changes in rainy season characteristics in large parts of the tropics despite substantial natural variability. Specifically, we report a robust shortening of the rainy season for all of tropical Africa as well as north-east Brazil. About 27% ...