Integration of remote sensing data and the geographical information system （GIS） for the exploration of groundwater resources has become a breakthrough in the field of groundwater research, which ...assists in assessing, monitoring, and conserving groundwater resources. In the present paper, various groundwater potential zones for the assessment of groundwater availability in Theni district have been delineated using remote sensing and GIS techniques. Survey of India toposheets and IRS-IC satel- lite imageries are used to prepare various thematic layers viz. lithology, slope, land-use, lineament, drainage, soil, and rainfall were transformed to raster data using feature to raster converter tool in ArcGIS. The raster maps of these factors are allocated a fixed score and weight computed from multi influencing factor （MIF） technique. Moreover, each weighted thematic layer is statistically computed to get the groundwater potential zones. The groundwater potential zones thus obtained were divided into four categories, viz., very poor, poor, good, and very good zones. The result depicts the groundwater potential zones in the study area and found to be helpful in better planning and management of ground- water resonrces.
For decades, mass spectrometry data has been analyzed to investigate a wide array of research interests, including disease diagnostics, biological and chemical theory, genomics, and drug development. ...Progress towards solving any of these disparate problems depends upon overcoming the common challenge of interpreting the large data sets generated. Despite interim successes, many data interpretation problems in mass spectrometry are still challenging. Further, though these challenges are inherently interdisciplinary in nature, the significant domain-specific knowledge gap between disciplines makes interdisciplinary contributions difficult.
This paper provides an introduction to the burgeoning field of computational mass spectrometry. We illustrate key concepts, vocabulary, and open problems in MS-omics, as well as provide invaluable resources such as open data sets and key search terms and references.
This paper will facilitate contributions from mathematicians, computer scientists, and statisticians to MS-omics that will fundamentally improve results over existing approaches and inform novel algorithmic solutions to open problems.
Liquid chromatography-mass spectrometry is widely used for comparative replicate sample analysis in proteomics, lipidomics and metabolomics. Before statistical comparison, registration must be ...established to match corresponding analytes from run to run. Alignment, the most popular correspondence approach, consists of constructing a function that warps the content of runs to most closely match a given reference sample. To date, dozens of correspondence algorithms have been proposed, creating a daunting challenge for practitioners in algorithm selection. Yet, existing reviews have highlighted only a few approaches. In this review, we describe 50 correspondence algorithms to facilitate practical algorithm selection. We elucidate the motivation for correspondence and analyze the limitations of current approaches, which include prohibitive runtimes, numerous user parameters, model limitations and the need for reference samples. We suggest and describe a paradigm shift for overcoming current correspondence limitations by building on known liquid chromatography-mass spectrometry behavior.
Purpose
This study aims to measure the impact of loneliness, physical activity (PA) and self-esteem on the health of retired people and also checks the moderating role of living arrangements and ...gender of the elderly people on this relationship.
Design/methodology/approach
Four standardized scales were used in the study to measure four different constructs: Self-Worth Questionnaire, UCLA Loneliness Scale Version 3, General Health Questionnaire-28 and Physical Activity Scale for Elders. Structural equation modelling was applied to the four constructs.
Findings
The study concluded that loneliness has a negative and significant impact on the health of retired people, while self-esteem and PA have a positive and significant impact on their health.
Originality/value
The study is among the few to include multiple instruments to measure abstract parameters in the field of health care. The paper brings out implications not just for academicians but also for policymakers, considering the complex situation prevailing in the emerging economy.
Ceramide is a sphingolipid that serves as an important second messenger in an increasing number of stress-induced pathways. Ceramide has long been known to affect the mitochondria, altering both ...morphology and physiology. We sought to assess the impact of ceramide on skeletal muscle mitochondrial structure and function. A primary observation was the rapid and dramatic division of mitochondria in ceramide-treated cells. This effect is likely to be a result of increased Drp1 (dynamin-related protein 1) action, as ceramide increased Drp1 expression and Drp1 inhibition prevented ceramide-induced mitochondrial fission. Further, we found that ceramide treatment reduced mitochondrial O2 consumption (i.e. respiration) in cultured myotubes and permeabilized red gastrocnemius muscle fibre bundles. Ceramide treatment also increased H2O2 levels and reduced Akt/PKB (protein kinase B) phosphorylation in myotubes. However, inhibition of mitochondrial fission via Drp1 knockdown completely protected the myotubes and fibre bundles from ceramide-induced metabolic disruption, including maintained mitochondrial respiration, reduced H2O2 levels and unaffected insulin signalling. These data suggest that the forced and sustained mitochondrial fission that results from ceramide accrual may alter metabolic function in skeletal muscle, which is a prominent site not only of energy demand (via the mitochondria), but also of ceramide accrual with weight gain.
Mass spectrometry proteomics typically relies upon analyzing outcomes of single analyses; however, comparing raw data across multiple experiments should enhance both peptide/protein identification ...and quantitation. In the absence of convincing tandem MS identifications, comparing peptide quantities between experiments (or fractions) requires the chromatographic alignment of MS signals. An extension of dynamic time warping (DTW), termed ordered bijective interpolated warping (OBI-Warp), is presented and used to align a variety of electrospray ionization liquid chromatography mass spectrometry (ESI-LC-MS) proteomics data sets. An algorithm to produce a bijective (one-to-one) function from DTW output is coupled with piecewise cubic hermite interpolation to produce a smooth warping function. Data sets were chosen to represent a broad selection of ESI-LC-MS alignment cases. High confidence, overlapping tandem mass spectra are used as standards to optimize and compare alignment parameters. We determine that Pearson's correlation coefficient as a measure of spectra similarity outperforms covariance, dot product, and Euclidean distance in its ability to produce correct alignments with optimal and suboptimal alignment parameters. We demonstrate the importance of penalizing gaps for best alignments. Using optimized parameters, we show that OBI-Warp produces alignments consistent with time standards across these data sets. The source and executables are released under MIT style license at http://obi-warp.sourceforge.net/.
Bioinformatic research has produced a large volume of proposed algorithmic solutions to a host of problems. Whether presented as a processing step in a clinical experiment or treated in a stand-alone ...publication, novel bioinformatic algorithms are often not subjected to the thorough comparative evaluation endured by their counterparts in other closely related fieldssuch as computer sciencewhere an algorithm unevaluated against extant methods is considered unpublishable. Two audiences are interested in algorithmic publications: the practitioner, who may use the algorithm, and the researcher, who will work to develop solutions superior to those extant. We argue that failure during the review/publication process to require comparative evaluation for novel algorithms is detrimental to both parties.
Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in ...medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate-to-severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate-to-severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm's ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov; Unique Identifier: NCT03458806.
Shotgun differential mass spectrometry, the untargeted discovery of statistically significant differences between two or more samples, is a popular application with potential to advance biomarker ...detection, disease diagnostics, and other health objectives. Although many methods have been proposed, few have been quantitatively evaluated. The lack of ground truth data for shotgun difference detection limits quantitative evaluation and algorithmic advancement. While public mass-spectrometry data sets of single samples abound, data sets with more than one sample are rare, and data sets with the thousands of samples necessary to capture the complexity of real world populations are nonexistent due to technological and cost limitations. We present MSabundanceSIM, novel software for simulating any number of molecular samples based on one or a few real world data sets. The software uses a probabilistic model to generate case and control populations, with intuitive user parameters for tuning. We demonstrate variability by comparing to a real world data set over a range of abundances with differing biological and experimental variation coefficients. MSabundanceSIM is implemented in Ruby, is freely available, requires no external dependencies, and is suitable for a range of applications.
Countless proteomics data processing algorithms have been proposed, yet few have been critically evaluated due to lack of labeled data (data with known identities and quantities). Although labeling ...techniques exist, they are limited in terms of confidence and accuracy. In silico simulators have recently been used to create complex data with known identities and quantities. We propose Java Mass Spectrometry Simulator (JAMSS): a fast, self-contained in silico simulator capable of generating simulated MS and LC-MS runs while providing meta information on the provenance of each generated signal. JAMSS improves upon previous in silico simulators in terms of its ease to install, minimal parameters, graphical user interface, multithreading capability, retention time shift model and reproducibility.
The simulator creates mzML 1.1.0. It is open source software licensed under the GPLv3. The software and source are available at https://github.com/optimusmoose/JAMSS.
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