Effective Data Interpretation Research Articles

Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data

BackgroundIn recent years, Single-cell RNA sequencing (scRNA-seq) is increasingly accessible to researchers of many fields. However, interpreting its data demands proficiency in multiple programming languages and bioinformatic skills, which limited researchers, without such expertise, exploring information from scRNA-seq data. Therefore, there is a tremendous need to develop easy-to-use software, covering all the aspects of scRNA-seq data analysis.ResultsWe proposed a clear analysis framework for scRNA-seq data, which emphasized the fundamental and crucial roles of cell identity annotation, abstracting the analysis process into three stages: upstream analysis, cell annotation and downstream analysis. The framework can equip researchers with a comprehensive understanding of the analysis procedure and facilitate effective data interpretation. Leveraging the developed framework, we engineered Shaoxia, an analysis platform designed to democratize scRNA-seq analysis by accelerating processing through high-performance computing capabilities and offering a user-friendly interface accessible even to wet-lab researchers without programming expertise.ConclusionShaoxia stands as a powerful and user-friendly open-source software for automated scRNA-seq analysis, offering comprehensive functionality for streamlined functional genomics studies. Shaoxia is freely accessible at http://www.shaoxia.cloud, and its source code is publicly available at https://github.com/WiedenWei/shaoxia.

Hecatomb: an integrated software platform for viral metagenomics.

Modern sequencing technologies offer extraordinary opportunities for virus discovery and virome analysis. Annotation of viral sequences from metagenomic data requires a complex series of steps to ensure accurate annotation of individual reads and assembled contigs. In addition, varying study designs will require project-specific statistical analyses. Here we introduce Hecatomb, a bioinformatic platform coordinating commonly used tasks required for virome analysis. Hecatomb means "a great sacrifice." In this setting, Hecatomb is "sacrificing" false-positive viral annotations using extensive quality control and tiered-database searches. Hecatomb processes metagenomic data obtained from both short- and long-read sequencing technologies, providing annotations to individual sequences and assembled contigs. Results are provided in commonly used data formats useful for downstream analysis. Here we demonstrate the functionality of Hecatomb through the reanalysis of a primate enteric and a novel coral reef virome. Hecatomb provides an integrated platform to manage many commonly used steps for virome characterization, including rigorous quality control, host removal, and both read- and contig-based analysis. Each step is managed using the Snakemake workflow manager with dependency management using Conda. Hecatomb outputs several tables properly formatted for immediate use within popular data analysis and visualization tools, enabling effective data interpretation for a variety of study designs. Hecatomb is hosted on GitHub (github.com/shandley/hecatomb) and is available for installation from Bioconda and PyPI.

SATELLITES TO AGRICULTURAL FIELDS: THE ROLE OF REMOTE SENSING IN PRECISION AGRICULTURE

Precision agriculture, driven by the growing demand for sustainable farming practices, relies heavily on technologies such as remote sensing. Despite its critical role, a comprehensive review of remote sensing within the context of precision agriculture remains sparse. This paper aims to bridge this gap by providing a thorough overview of remote sensing technologies, their applications, challenges, future trends, and potential impact on precision agriculture. Employing a literature review methodology, we analyzed key studies to comprehend precision agriculture's evolution and remote sensing technologies' significant role. Our examination encompassed various remote sensing applications, from crop health monitoring to yield estimation, soil mapping, irrigation management, and pest and disease detection. We also evaluated emerging trends and identified challenges such as the need for high-resolution data, atmospheric disturbances, and requisite technical expertise for effective data interpretation. Despite these challenges, the review underscores the transformative potential of remote sensing technologies in advancing precision agriculture. Future research should prioritize addressing these challenges and strive to make these technologies more accessible and affordable. Moreover, integrating remote sensing with artificial intelligence and machine learning in interdisciplinary research could further bolster the efficacy and potential of precision agriculture.

A Model for Improved Knowledge Management Performance in Higher Education Institutions in Developing Countries: A Case of Zambia

Knowledge management is an organizational process of creating a centralized knowledge source that enables acquiring, assimilating, distributing, integrating, sharing, retrieving and reuse of internal and external, explicit and tacit knowledge to promote innovation in the organization. Higher education institutions are not an exception and must be at the center of knowledge management practices as they are the major producers of a country’s workforce. The knowledge from individuals, systems and collaborations with other institutions can only be properly appreciated by way of having a well-executed knowledge management system in place. Whilst knowledge management is seen to be a very important practice to have in a higher education institution, in Zambia, knowledge management systems do not seem to be given the deserved importance and consideration by higher education institutions. The primary aim of this study is to propose a model that would assist with the knowledge management implementation process in higher education institutions in Zambia. Four higher education institutions were purposively selected to give an overview of the status of KM practice, namely: the University of Zambia, Mulungushi University, Chalimbana University and the National Institute of Public Administration (NIPA). This study adopted a mixed method approach, that is, qualitative and quantitative research design for analyzing research questions and for effective interpretation of data from the questionnaire. Both online and hardcopy questionnaires were distributed among the respondents. IBM Statistical Package for Social Sciences (IBM SPSS) version 20 was used to analyze the data. For data analysis, descriptive statistics and inferential statistical tests (Pearson Chi-square) were used. The results revealed that the majority of the respondents have a good level of knowledge management awareness; however, it is not commonly practiced in the institutions. These institutions do not have knowledge management policies or strategies in place and there is no presence of a KM department. If there is one, probably on paper but not established nor practiced. The study proposed a model that would guide the adoption process of knowledge management practices in higher education institutions in Zambia, by highlighting some critical success factors from the four categories of CSF as proposed by the Heisig (2009) model.

Using Gene Ontology to describe the role of the neurexin-neuroligin-SHANK complex in human, mouse and rat and its relevance to autism.

BackgroundPeople with an autistic spectrum disorder (ASD) display a variety of characteristic behavioral traits, including impaired social interaction, communication difficulties and repetitive behavior. This complex neurodevelopment disorder is known to be associated with a combination of genetic and environmental factors. Neurexins and neuroligins play a key role in synaptogenesis and neurexin-neuroligin adhesion is one of several processes that have been implicated in autism spectrum disorders.ResultsIn this report we describe the manual annotation of a selection of gene products known to be associated with autism and/or the neurexin-neuroligin-SHANK complex and demonstrate how a focused annotation approach leads to the creation of more descriptive Gene Ontology (GO) terms, as well as an increase in both the number of gene product annotations and their granularity, thus improving the data available in the GO database.ConclusionsThe manual annotations we describe will impact on the functional analysis of a variety of future autism-relevant datasets. Comprehensive gene annotation is an essential aspect of genomic and proteomic studies, as the quality of gene annotations incorporated into statistical analysis tools affects the effective interpretation of data obtained through genome wide association studies, next generation sequencing, proteomic and transcriptomic datasets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0622-0) contains supplementary material, which is available to authorized users.

Techniques for the Analysis of Cysteine Sulfhydryls and Oxidative Protein Folding

Modification of cysteine thiols dramatically affects protein function and stability. Hence, the abilities to quantify specific protein sulfhydryl groups within complex biological samples and map disulfide bond structures are crucial to gaining greater insights into how proteins operate in human health and disease. Many different molecular probes are now commercially available to label and track cysteine residues at great sensitivity. Coupled with mass spectrometry, stable isotope-labeled sulfhydryl-specific reagents can provide previously unprecedented molecular insights into the dynamics of cysteine modification. Likewise, the combined application of modern mass spectrometers with improved sample preparation techniques and novel data mining algorithms is beginning to routinize the analysis of complex protein disulfide structures. Proper application of these modern tools and techniques, however, still requires fundamental understanding of sulfhydryl chemistry as well as the assumptions that accompany sample preparation and underlie effective data interpretation. The continued development of tools, technical approaches, and corresponding data processing algorithms will, undoubtedly, facilitate site-specific protein sulfhydryl quantification and disulfide structure analysis from within complex biological mixtures with ever-improving accuracy and sensitivity. Fully routinizing disulfide structure analysis will require an equal but balanced focus on sample preparation and corresponding mass spectral dataset reproducibility.

Urine Stability Studies for Novel Biomarkers of Acute Kidney Injury

The study of novel urinary biomarkers of acute kidney injury has expanded exponentially. Effective interpretation of data and meaningful comparisons between studies require awareness of factors that can adversely affect measurement. We examined how variations in short-term storage and processing might affect the measurement of urine biomarkers. Cross-sectional prospective. Hospitalized patients from 2 sites: Yale New Haven Hospital (n=50) and University of California, San Francisco Medical Center (n=36). We tested the impact of 3 urine processing conditions on these biomarkers: (1) centrifugation and storage at 4°C for 48 hours before freezing at -80°C, (2) centrifugation and storage at 25°C for 48 hours before freezing at -80°C, and (3) uncentrifuged samples immediately frozen at -80°C. Urine concentrations of 5 biomarkers: neutrophil gelatinase-associated lipocalin (NGAL), interleukin 18 (IL-18), kidney injury molecule 1 (KIM-1), liver-type fatty acid-binding protein (L-FABP), and cystatin C. We measured urine biomarkers by established enzyme-linked immunosorbent assay methods. Biomarker values were log-transformed, and agreement with a reference standard of immediate centrifugation and storage at -80°C was compared using concordance correlation coefficients (CCCs). Neither storing samples at 4°C for 48 hours nor centrifugation had a significant effect on measured levels, with CCCs higher than 0.9 for all biomarkers tested. For samples stored at 25°C for 48 hours, excellent CCC values (>0.9) also were noted between the test sample and the reference standard for NGAL, cystatin C, L-FABP and KIM-1. However, the CCC for IL-18 between samples stored at 25°C for 48 hours and the reference standard was 0.81 (95% CI, 0.66-0.96). No comparisons to fresh, unfrozen samples; no evaluation of the effect of protease inhibitors. All candidate markers tested using the specified assays showed high stability with both short-term storage at 4°C and without centrifugation prior to freezing. For optimal fidelity, urine for IL-18 measurement should not be stored at 25°C before long-term storage or analysis.

CWords - systematic microRNA regulatory motif discovery from mRNA expression data

BackgroundPost-transcriptional regulation of gene expression by small RNAs and RNA binding proteins is of fundamental importance in development of complex organisms, and dysregulation of regulatory RNAs can influence onset, progression and potentially be target for treatment of many diseases. Post-transcriptional regulation by small RNAs is mediated through partial complementary binding to messenger RNAs leaving nucleotide signatures or motifs throughout the entire transcriptome. Computational methods for discovery and analysis of sequence motifs in high-throughput mRNA expression profiling experiments are becoming increasingly important tools for the identification of post-transcriptional regulatory motifs and the inference of the regulators and their targets.ResultscWords is a method designed for regulatory motif discovery in differential case–control mRNA expression datasets. We have improved the algorithms and statistical methods of cWords, resulting in at least a factor 100 speed gain over the previous implementation. On a benchmark dataset of 19 microRNA (miRNA) perturbation experiments cWords showed equal or better performance than two comparable methods, miReduce and Sylamer. We have developed rigorous motif clustering and visualization that accompany the cWords analysis for more intuitive and effective data interpretation. To demonstrate the versatility of cWords we show that it can also be used for identification of potential siRNA off-target binding. Moreover, cWords analysis of an experiment profiling mRNAs bound by Argonaute ribonucleoprotein particles discovered endogenous miRNA binding motifs.ConclusionscWords is an unbiased, flexible and easy-to-use tool designed for regulatory motif discovery in differential case–control mRNA expression datasets. cWords is based on rigorous statistical methods that demonstrate comparable or better performance than other existing methods. Rich visualization of results promotes intuitive and efficient interpretation of data. cWords is available as a stand-alone Open Source program at Github https://github.com/simras/cWords and as a web-service at: http://servers.binf.ku.dk/cwords/.

Counting the homeless: the culture of quantification in American social policy

This commentary reflects on the practice of counting the homeless within political jurisdictions (i.e., homeless counts) within the United States to satisfy a cultural practice of quantification within American social policy. It discusses the problems arising from the cultural preference for quantification as the means of identifying and addressing a social problem with specific examples from US homeless assistance programs and participant observation in Long Beach, California. The culture of quantification is apparent in explanations for why counts are done, the disproportionate attention paid to measurement methods over reflection on interpretation of and responses to the numbers, and the general failure to effectively link the practice of counting to the causes of and solutions to homelessness. This focus diverts attention from real data needs and effective interpretation of data, consumes resources better used elsewhere, and generates among well-meaning people, particularly count volunteers, a false sense that they are addressing a problem simply by quantifying it. The commentary concludes with suggestions for examining how counts are actually used in modifying existing programs to evaluate the effectiveness of these quantification practices.

Ligand fishing with functionalized magnetic nanoparticles coupled with mass spectrometry for herbal medicine analysis: ligand fishing for herbal medicine analysis.

The chemical composition of herbal medicines is very complex, and their therapeutic effects are determined by multi-components with sophisticated synergistic and/or suppressive actions. Therefore, quality control of herbal medicines has been a formidable challenge. In this work, we describe a fast analytical method that can be used for quality assessment of herbal medicines. The method is based on ligand fishing using human-serum-albumin-functionalized magnetic nanoparticles (HSA-MNPs) and mass spectrometry. To demonstrate the applicability of the proposed method, eight samples of Dioscorea panthaica were analyzed. The sampled plants were of both wild and cultivated origins. They grew at different geographical locations and were harvested at different times. The ligands bound to HSA-MNPs were isolated from the plant extracts and detected by using direct infusion electrospray ionization mass spectrometry (DI-ESI-MS). Chemical identity has been confirmed for five of the ligands isolated. From more than 15 peaks in the ESI-MS spectrum, 11 common peaks were selected for calculating the correlation coefficient and cosine ratio. The values of correlation coefficient and cosine ratio were >0.9824 and >0.9988, respectively, for all the samples tested. The results indicated a high level of similarity among the eight D. panthaica samples. Compared with chromatographic fingerprint analysis, the proposed HSA-MNP-based DI-ESI-MS/MS approach was not only fast and easy to carry out but also biological-activity-oriented, promising a more effective data interpretation and thus reliable assessment conclusions.

PRECLINICAL MODELS OF SHOCK AND SEPSIS: WHAT CAN THEY TELL US?

The goal of translational research is to transform biologic knowledge into new treatments for human disease. Although preclinical models replicate some of the features of the disease process modeled, they invariably fail to reproduce the complexity of human illness, and by their very experimental nature, they are readily manipulated to maximize evidence of efficacy. The result is that successful translation from preclinical models to clinically effective therapy is uncommon, and that clinical trials are often undertaken without a comprehensive and realistic preclinical portfolio of studies to optimize their design. The lethal and morbid human conditions of sepsis and shock are attractive targets for new therapies and enormously challenging processes to translate because they entail considerable clinical heterogeneity, require emergent effective intervention, and are shaped not only by the initial insult, but by approaches to subsequent resuscitation and support. A colloquium jointly sponsored by the Shock Society and the International Sepsis Forum in June 2004 addressed the challenges of translational research in shock and sepsis. Through a comprehensive review of a broad variety of model approaches, and vigorous debate about the merits of differing strategies, a series of common themes emerged. We concluded that there is no single ideal model of shock or sepsis, but rather a large number of complementary models that recapitulate some discrete features of the disorders while minimizing others. Consequently, successful preclinical investigation mandates the use of a panel of preclinical studies consciously designed to address specific questions of relevance to the clinical setting. A corollary of this conclusion is that preclinical studies can shape concepts of disease and can be used to refine decisions regarding optimal patient populations for therapeutic interventional trials. We further recognized that the design and reporting of preclinical studies is highly variable, thereby limiting effective data interpretation and integration between studies. Hence, greater model standardization would aid in interpreting data and in pooling results into systematic data syntheses: such efforts should be promoted and undertaken.

The Double Star magnetic field investigation: instrument design, performance and highlights of the first year's observations

Abstract. One of the primary objectives of the Double Star mission is the accurate measurement of the magnetic field vector along the orbits of the two spacecraft. The magnetic field is an essential parameter for the understanding of space plasma processes and is also required for the effective interpretation of data from the other instruments on the spacecraft. We present the design of the magnetic field instrument onboard both of the Double Star spacecraft and an overview of the performance as measured first on-ground and then in-orbit. We also report the results of in-flight calibration of the magnetometers, and the processing methods employed to produce the final data products which are provided to Double Star investigators, and the wider community in general. Particular attention is paid to the techniques developed for removing magnetic interference generated by the solar arrays on the first (equatorial orbiting) spacecraft. Results from the first year of operations are reviewed in the context of combined observations by Double Star and Cluster, and examples given from the different regions visited by the spacecraft to date.

Improving the Iron Oxide Sink Method for Extracting Soil Phosphorus

AbstractIron oxide coated filter paper can estimate labile P in soil by acting as a P sink. Variations in procedures and contamination from soil adhering to the sinks limit effective data interpretation, however. We evaluated two papers with pores <5.0 µm and one with pores 20 to 25 µm. Iron oxide coatings were formed by exposing FeCl3‐treated papers to NH3 vapor or 2.7 M NH4OH. Coated papers were either fixed in a screen assembly or allowed to move freely during shaking. The screen assembly eliminated abrasion loss of the FeO coating. Paper circles (5.5 cm) proved satisfactory and eliminated the need to cut 2 by 10 cm strips from coated papers. Fixed, small‐pore circles had less soil contamination (0.8–1.9 mg) than fixed, large‐pore circles (4.7–8.1 mg), a condition confirmed by x‐ray diffraction. Similarly, free, small‐pore circles retained less soil contamination (3.5–9.0 mg) than free, large‐pore circles (16.1–21.7 mg). Two sources of acid‐extractable P inflated the labile P pool. First, acid‐extractable P from soil adhering to papers increased sink‐extractable P by 7.5 to 24.1 mg P kg−1, an error of 28 to 65%. Also, paper prepared in NH3 vapor acidified the extracting solution with Kahola (fine‐silty, mixed, mesic Cumulic Hapludoll) and Haynie (coarse‐silty, mixed [calcareous], mesic Typic Udifluvent) soils and increased their extractable P an average of 13 mg P kg−1, or 56%. To eliminate some analytical problems associated with the FeO‐sink extraction of soil P, we recommend preparing small‐pore circles (<5 µm) in NH4OH solution and securing them during shaking.

Measurement, Models and Medicine: Computer Modelling in hEalth Care Delivery

Computational modelling is becoming of increasing importance in assisting with, and enhancing, the processes of decision-making in clinical medicine and in health care delivery, processes which depend upon the effective interpretation of data yielded by measurement practice. Using conceptual models of health care delivery, a framework can be established within which the role of specific computational modelling paradigms can be clearly identified. Some of the methodological issues which underpin computer modelling are considered across the range of realizations (mathematical, logical, statistical and graphical) that find application in the clinical and health care domains. Examples are given of both quantitative modelling and qualitative model-based reasoning as important adjuncts to measurement practice.

Variables in the use and design of acoustic emission transducers

Previously, ∗1 we presented a theory to explain the resonant behaviour of a couplant/multilayer acoustic emission transducer system. Here we present experiments used to verify the applicability of the theory in describing the behaviour of such a system. An acoustic emission transducer/couplant system is typically five layers; but to obtain agreement between theory and experiment, it was necessary to extend the theory to six layers and carry out six-layer measurements. Agreement between theory and experiment is quite good and we believe that this theory provides a basis for a better understanding of the effect of couplant thickness on measured acoustic emission parameters. In the longer term, this will produce greater measurement precision and more effective interpretation of data.