Generation Of High-quality Data Research Articles

Continuous Glucose, Insulin and Lifestyle Data Augmentation in Artificial Pancreas Using Adaptive Generative and Discriminative Models.

Artificial pancreas requires data from multiple sources for accurate insulin dose estimation. These include data from continuous glucose sensors, past insulin dosage information, meal quantity and time and physical activity data. The effectiveness of closed-loop diabetes management systems might be hampered by the absence of these data caused by device error or lack of compliance by patients. In this study, we demonstrate the effect of output sequence length-driven generative and discriminative model selection in high quality data generation and augmentation. This novel generative adversarial network (GAN) based architecture automatically selects the generator and discriminator architecture based on the desired output sequence length. The proposed model is able to generate glucose, physical activity, meal information data for individual patients. The discriminative scores for Ohio T1DM (2018) dataset were 0.17 ±0.03 (Inputs: CGM, CHO, Insulin) and 0.15 ±0.02 (Inputs: CGM, CHO, Insulin, Heart Rate, Steps) and for Ohio T1D (2020) dataset was 0.16 ±0.02 (Inputs: CGM, CHO, Insulin) and 0.15 ±0.02 (Inputs: CGM, CHO, Insulin, acceleration). A mixture of generated and real data was used to test predictive scores for glucose forecasting models. The best RMSE and MARD achieved for OhioT1DM patients were 17.19 ±3.22 and 7.14 ±1.76 for PH=30 min with CGM, CHO, Insulin, heartrate and steps as inputs. Similarly, the RMSE and MARD for real+synthetic data were 15.63 ±2.57 and 5.86 ±1.69 respectively. Compared to existing generative models, we demonstrate that sequence length based architecture selection leads to better synthetic data generation for multiple output sequences (CGM, CHO, Insulin) and forecasting accuracy.

Enhanced classification of hydraulic testing of directional control valves with synthetic data generation

Production environments bring inherent system challenges that are reflected in the high-dimensional production data. The data is often nonstationary, is not available in sufficient size and quality, and is class imbalanced due to the predominance of good parts. Data-driven manufacturing analytics requires data of sufficient quantity and quality. In order to predict quality characteristics, production data is collected across processes in the industrial use case at Bosch Rexroth AG for the purpose of inferring results in hydraulic final inspection using machine learning methods. Since high quality data generation is costly, synthetic data generation methodologies offer a promising alternative to improve prediction models and thus generate safer, more accurate predictions for manufacturing companies. Among the synthetic data generation methodologies used, variational autoencoders compared to generative adversarial networks and synthetic minority oversampling technique methods are best suited to synthesize the feature with highest feature importance from a small sample data set compared to the production data and improve the prediction for the target variable.

Full-Length Single-Cell RNA-Sequencing with FLASH-seq.

The single-cell RNA-sequencing (scRNA-seq) field has evolved tremendously since the first paper was published back in 2009 (Tang et al. Nat Methods 6:377-382, 2009). While the first methods analyzed just a handful of cells, the throughput and performance rapidly increased over a very short time span. However, it was not until the introduction of emulsion droplets methods, such as the well-known kits commercialized by 10x Genomics, that the robust and reproducible analysis of thousands of cells became feasible (Zheng et al Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049, 2017). Despite generating data at a speed and a cost per cell that remains unmatched for full-length protocols like Smart-seq (Hagemann-Jensen et al Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat Biotechnol 38:708-714, 2020; Picelli et al Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10:1096-1098, 2013), scRNA-seq in droplets still comes with the drawback of addressing only the terminal portion of the transcripts, thus lacking the required sensitivity for comprehensively analyzing the entire transcriptome.Building upon the existing Smart-seq2/3 workflows (Hagemann-Jensen et al Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat Biotechnol 38:708-714, 2020; Picelli et al Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10:1096-1098, 2013), we developed FLASH-seq (FS), a new full-length scRNA-seq method capable of detecting a significantly higher number of genes than previous versions, requiring limited hands-on time and with a great potential for customization (Hahaut et al. Lightning Fast and Highly Sensitive Full-Length Single-cell sequencing using FLASH-Seq. http://biorxiv.org/lookup/doi/10.1101/2021.07.14.452217. https://doi.org/10.1101/2021.07.14.452217, 2021). Here, we present three variants of the FS protocol.Standard FLASH-seq (FS), which builds upon Smart-seq2 developed in the past, is non-stranded and does not use unique molecular identifiers (UMIs) but still remains the easiest method to measure gene expression in a cell population.FLASH-seq low-amplification (FS-LA) represents the fastest method, which generates sequencing-ready libraries in 4.5 h, without sacrificing performance.FLASH-seq with UMIs (FS-UMI) builds upon the same principle as Smart-seq3 and introduces UMIs for molecule counting and isoform reconstruction. The newly designed template-switching oligonucleotide (TSO) contains a 5-bp spacer, which allows the generation of high-quality data while minimizing the amount of strand-invasion artifacts.

Time-Series Regeneration With Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation

For health prognostic task, ever-increasing efforts have been focused on machine learning based methods, which are capable of yielding accurate remaining useful life (RUL) estimation for industrial equipment or components without exploring the degradation mechanism. A prerequisite ensuring the success of these methods depends on a wealth of run-to-failure data; however, run-to-failure data may be insufficient in practice. That is, conducting a substantial amount of destructive experiments not only is of high cost but also may cause catastrophic consequences. Out of this consideration, an enhanced RUL framework focusing on data self-generation is put forward for both noncyclic and cyclic degradation patterns for the first time. It is designed to enrich data from a data-driven way, generating realistic-like time-series to enhance current RUL methods. First, high-quality data generation is ensured through the proposed convolutional recurrent generative adversarial network, which adopts a two-channel fusion convolutional recurrent neural network. Next, a hierarchical framework is proposed to combine generated data into current RUL estimation methods. Finally, in this article the efficacy of the proposed method is verified through both noncyclic and cyclic degradation systems. With the enhanced RUL framework, an aero-engine system following noncyclic degradation has been tested using three typical RUL models. State-of-the-art RUL estimation results are achieved by enhancing capsule network with generated time-series. Specifically, estimation errors evaluated by the index score function have been reduced by 21.77$\%$ and 32.67$\%$ for the two employed operating conditions, respectively. Besides, the estimation error is reduced to zero for the lithium-ion battery system, which presents cyclic degradation.

Approaches to management of cardiovascular morbidity in adult cancer patients \u2013 cross-sectional survey among cardio-oncology experts

BackgroundIn cardio-oncology, a range of clinical dilemmas can be identified where high-quality evidence for management is still lacking. The aim of this project was to study clinical practices and expert approaches to several clinical cardio-oncological dilemmas regarding prediction, prevention and treatment of cardiovascular disease in adult cancer patients.MethodsA cross-sectional online survey was sent out to internationally renowned experts in the field of cardio-oncology. Participants were selected based on being first or last authors of papers in the field of cardio-oncology, or principal investigators to trials in this field.ResultsTopics discussed include, among others, the use of biomarkers for subclinical cardiovascular toxicity, approaches towards primary prevention and follow-up with medication and life-style recommendations, and management of fluoropyrimidine-vasospasm, QTc-prolongation and asymptomatic declines in left ventricular ejection fraction.ConclusionThe answers provided in this survey have shed light on expert-based practices in cardio-oncologic dilemmas. Attitudes towards, as well as discrepancies in those dilemmas are presented. Existing discrepancies clearly indicate the need for generation of high-quality data that allows for more evidence-based recommendations in the future.

An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks

Deep generative neural networks (DGNNs) have achieved realistic and high-quality data generation. In particular, the adversarial training scheme has been applied to many DGNNs and has exhibited powerful performance. Despite of recent advances in generative networks, identifying the image generation mechanism still remains challenging. In this paper, we present an explorative sampling algorithm to analyze generation mechanism of DGNNs. Our method efficiently obtains samples with identical attributes from a query image in a perspective of the trained model. We define generative boundaries which determine the activation of nodes in the internal layer and probe inside the model with this information. To handle a large number of boundaries, we obtain the essential set of boundaries using optimization. By gathering samples within the region surrounded by generative boundaries, we can empirically reveal the characteristics of the internal layers of DGNNs. We also demonstrate that our algorithm can find more homogeneous, the model specific samples compared to the variations of ϵ-based sampling method.

Safeguarding continuing cardiovascular research excellence and quality publications in France: A working document from the French Society of Cardiology

France has a long history of successful cardiovascular research and scientific innovations, but its continued success cannot be taken for granted. To identify current obstacles to cardiovascular research in France and to crystallize the analysis into recommendations for maintained and enhanced research excellence in the future. The French Society of Cardiology set up seven Working Groups, each comprising four to eight cardiologists, covering a spectrum of research institutes, hospitals, specialties, ages and research experience. The Working Groups met regularly in person or by conference call to analyse experiences, refine situation assessments and formulate recommendations for improvements. Results and suggestions were presented to a Core Team, which worked to synthesize, prioritize and organize the findings into a consolidated situation assessment and generate a set of action-orientated recommendations. Four key areas of action were identified: stronger focus on the generation of high-quality data; facilitation of future cardiovascular research; greater promotion and support for research among young cardiologists; and increased focus and support for communications. Most recommendations targeted structural shortcomings and may be implemented at low additional financial cost. It is possible to maintain, and even increase, the quality of cardiovascular research in France and to boost the conversion of successful projects into high-impact publications, without major increases in funding. Intense collaboration between specialties and organizations is necessary to achieve sustainable results.

The Big Data Deluge in Biology: Challenges and Solutions

Over the years, continuous efforts in understanding the complex multi-step processes at different levels have transformed biology from a qualitative to a more quantitative subject, resulting an enormous generation of information at diverse scales from molecular/genome to ecological as well as epidemiological/clinical. Biological data is highly overrepresented with respect to its quantity, diversity and analysis. Advances in high throughput experimental techniques have expanded the lengths and scales of biology by providing a large amount of diverse biological data with relatively less input costs. Its heterogenous and complex nature poses a great challenge for both scientists and technicians in high quality data generation, its management, accessibility, handling and integration.

Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma

There is an increasing need in biology and clinical medicine to robustly and reliably measure tens to hundreds of peptides and proteins in clinical and biological samples with high sensitivity, specificity, reproducibility, and repeatability. Previously, we demonstrated that LC-MRM-MS with isotope dilution has suitable performance for quantitative measurements of small numbers of relatively abundant proteins in human plasma and that the resulting assays can be transferred across laboratories while maintaining high reproducibility and quantitative precision. Here, we significantly extend that earlier work, demonstrating that 11 laboratories using 14 LC-MS systems can develop, determine analytical figures of merit, and apply highly multiplexed MRM-MS assays targeting 125 peptides derived from 27 cancer-relevant proteins and seven control proteins to precisely and reproducibly measure the analytes in human plasma. To ensure consistent generation of high quality data, we incorporated a system suitability protocol (SSP) into our experimental design. The SSP enabled real-time monitoring of LC-MRM-MS performance during assay development and implementation, facilitating early detection and correction of chromatographic and instrumental problems. Low to subnanogram/ml sensitivity for proteins in plasma was achieved by one-step immunoaffinity depletion of 14 abundant plasma proteins prior to analysis. Median intra- and interlaboratory reproducibility was <20%, sufficient for most biological studies and candidate protein biomarker verification. Digestion recovery of peptides was assessed and quantitative accuracy improved using heavy-isotope-labeled versions of the proteins as internal standards. Using the highly multiplexed assay, participating laboratories were able to precisely and reproducibly determine the levels of a series of analytes in blinded samples used to simulate an interlaboratory clinical study of patient samples. Our study further establishes that LC-MRM-MS using stable isotope dilution, with appropriate attention to analytical validation and appropriate quality control measures, enables sensitive, specific, reproducible, and quantitative measurements of proteins and peptides in complex biological matrices such as plasma.

Identification of microorganisms by FTIR spectroscopy: perspectives and limitations of the method

Fourier transform infrared (FTIR) spectroscopy was introduced in 1991 as a technique to identify and classify microbes. Since then, it has gained growing interest and has undergone a remarkable evolution. Highly sophisticated spectrometers have been developed, enabling a high sample throughput. Today, the generation of high-quality data in a short time and the application of the technique for rapid and reliable identification of microbes to the species level are well documented. What makes FTIR spectroscopy even more attractive is the fact that spectral information can also be exploited for strain typing purposes, which is particularly important for epidemiological analyses and some technological applications. Accordingly, in recent years, FTIR spectroscopy has been increasingly used for typing and classifying microorganisms below the species level. The intention of this review is to give an overview over current knowledge and strategies of using FTIR spectroscopy for species identification and to describe different approaches for strain typing.

Addressing challenges in the production and analysis of illumina sequencing data

Advances in DNA sequencing technologies have made it possible to generate large amounts of sequence data very rapidly and at substantially lower cost than capillary sequencing. These new technologies have specific characteristics and limitations that require either consideration during project design, or which must be addressed during data analysis. Specialist skills, both at the laboratory and the computational stages of project design and analysis, are crucial to the generation of high quality data from these new platforms. The Illumina sequencers (including the Genome Analyzers I/II/IIe/IIx and the new HiScan and HiSeq) represent a widely used platform providing parallel readout of several hundred million immobilized sequences using fluorescent-dye reversible-terminator chemistry. Sequencing library quality, sample handling, instrument settings and sequencing chemistry have a strong impact on sequencing run quality. The presence of adapter chimeras and adapter sequences at the end of short-insert molecules, as well as increased error rates and short read lengths complicate many computational analyses. We discuss here some of the factors that influence the frequency and severity of these problems and provide solutions for circumventing these. Further, we present a set of general principles for good analysis practice that enable problems with sequencing runs to be identified and dealt with.

Events Data as Bismarck's Sausages? Intercoder Reliability, Coders' Selection, and Data Quality

Precise measurement is difficult but essential in the generation of high-quality data, and it is therefore remarkable that often so little attention is paid to intercoder reliability. It is commonly recognized that poor validity leads to systematic errors and biased inference. In contrast, low reliability is generally assumed to be a lesser concern, leading only to random errors and inefficiency. We evaluate the intercoder reliability of our recently collected data on governance events in UN peacekeeping and show how poor coding and low intercoder reliability can produce systematic errors and even biased inference. We also show how intercoder reliability checks are useful to improve data quality. Continuous testing for intercoder reliability ex post enables researchers to create better data and ultimately improves the quality of their analyses.

Large-scale protein interactome mapping: strategies and opportunities

Interactions between proteins are central to any cellular process, and mapping these into a protein network is informative both for the function of individual proteins and the functional organization of the cell as a whole. Many strategies have been developed that are up to this task, and the last 10 years have seen the high-throughput application of a number of those in large-scale, sometimes proteome-wide, interactome mapping efforts. Although initially the quality of the data produced in these screening campaigns has been questioned, quality standards and empirical validation schemes are now in place to ensure high-quality data generation. Through their integration with other ‘omics’ data, interactomics datasets have proven highly valuable towards applications in different areas of clinical importance.

PhRMA BioResearch Monitoring Committee Perspective on Acceptable Approaches for Clinical Trial Monitoring

The integrity of clinical research data is critical for making sound regulatory decisions regarding the approval and use of medications. Equally important is the safeguarding of human subjects in clinical trials. A key element in the generation of high-quality data and protection of research participants during clinical research is sponsor monitoring of the clinical investigator site. This article provides the current thinking of the PhRMA BioResearch Monitoring Committee on acceptable approaches to clinical trial monitoring, taking advantage of risk-based decision making and modem technological tools. The intent is to provide flexibility in approaches—given the broad diversity of types of clinical research—while maintaining high standards for quality and integrity. The PhRMA BioResearch Monitoring Committee believes such approaches are applicable to all types of clinical research, regardless of who is sponsoring or conducting the research (industry, academia, or government agency).

High-Throughput Biochemical Kinase Selectivity Assays: Panel Development and Screening Applications

Kinases represent attractive targets for drug discovery. Eight small-molecule kinase inhibitors are currently marketed in the area of oncology, and numerous others are in clinical trials. Characterization of the selectivity profiles of these compounds is important to target appropriate patient populations and to reduce the potential of toxicity due to off-target effects. The authors describe the development, validation, and utilization of a biochemical kinase assay panel for the selectivity profiling of inhibitors. The panel was developed as 29 radiometric Flashplate assays, and then an initial 13 were transitioned to a nonradiometric Caliper mobility shift assay format. Generation of high-quality data from the panel is detailed along with a comparison of the assay formats. Both assay technologies were found to be suitable for panel screening, but mobility shift assays yielded higher data quality. The selectivity data generated here should be useful in computational modeling and help facilitate, in conjunction with sequence and structural information, the rational design of inhibitors with well-defined selectivity profiles.

Assistência obstétrica e complicações graves da gestação na América Latina e Caribe: análise das informações obtidas a partir de inquéritos demográficos de saúde

To compile, consolidate, and analyze information obtained in surveys conducted by the MEASURE DHS [Demographic and Health Surveys] program, concerning obstetric care and pregnancy complications for women in Latin America and the Caribbean, in the five years before the survey. This exploratory study utilized data from demographic surveys carried out in the 1990s in seven countries of Latin America: Bolivia, Brazil, Colombia, the Dominican Republic, Guatemala, Nicaragua, and Peru. The study describes the characteristics of the women who were interviewed and of the obstetric care that they received in the five years before the respective survey, and it also estimates the occurrence of prolonged labor and of hemorrhagic, hypertensive, and infectious complications in those five years. The median number of prenatal consultations ranged from 4.7 in Bolivia to 6.6 in the Dominican Republic. More than 40% of deliveries in Guatemala, Peru, and Bolivia were attended by traditional midwives, relatives, or other persons without formal training. The highest rates of deliveries performed in health care facilities (> 90%) were in the Dominican Republic and Brazil. In Guatemala, Peru, and Bolivia more than 45% of deliveries were at home. The highest rate of cesarean delivery was in Brazil (36.4%), and the lowest rates (< 12%) were in Peru and Guatemala. The rate of pregnancy complications reported by the women surveyed was 16.7% in Brazil, 17.9% in Guatemala, 42.1% in Colombia, 42.5% in Nicaragua, 43.0% in the Dominican Republic, 51.7% in Bolivia, and 51.8% in Peru. The reported occurrence of severe pregnancy complications in the surveys we examined was well above the 15% rate reported in other scientific literature, suggesting that these complications may have been overestimated in the MEASURE DHS surveys. Prior validation of the questionnaires used for data collection is extremely important in the generation of high-quality data.

Investigator and site selection and performing GCP clinical studies in India

The optimum site and investigator selection process remains a closely guarded confidential matter and an essential part of development expertise of big pharmaceutical companies and CROs. The right and careful selection and evaluation of investigators and site is critical for successful completion of the trial within budget, timelines and generation of high quality data. The criteria for site and investigator selection in India for Good Clinical Practices (GCP) clinical trials are described for a start up company/CRO and can be applied to any country in Asia and Africa. Foreign sponsors doing clinical studies in India should pay close attention to site and investigator selection. The first GCP study in India was done only in 1995. At the dawn of 21st century, India is at the take off stage in clinical trials now. GCP studies can be done in India, as the quality of data is good, costs are lower and patient enrolment is much faster resulting in early completion of studies.

The development and implementation of bioanalytical methods using LC-MS to support ADME studies in early drug discovery and candidate selection.

The dramatic improvement in bioanalytical method development and application within the pharmaceutical industry began in the early 1990s with the commercial availability of liquid chromatography-mass spectrometry (LC-MS) systems. As the power of this type of detection was realized, research groups [drug metabolism, safety assessment, product research and development (PR&D), pharmacology, medicinal chemistry] throughout the industry acquired this technology insatiably. Companies purchased LC-MS systems from a variety of vendors and incorporated this technology into their respective areas of research in a number of innovated ways. Clearly, this technology has become the primary analytical tool within the pharmaceutical industry and its incorporation and efficient use have helped to accelerate the generation of high-quality data in support of research and development. By the end of the last century, essentially every bioanalytical department in a pharmaceutical company possessed a number of mass spectrometers, including single-stage, triple quadrupole, time-of-flight (TOF) and ion trap instruments. In addition, many of these groups also purchased automated liquid handling instruments and incorporated them into a variety of sample preparation procedures that are carried out daily in laboratories around the world.

Analytical methods for a national study of chemical residues in fish: II: Pesticides and polychlorinated biphenyls

Analytical methods and a quality assurance plan have been developed to determine the concentration of a select group of bioaccumulatable chemicals in fish tissue. The analytes include PCBs and 21 pesticides and industrial chemicals. The methodology has been used to conduct a survey of chemical contaminants in fish from nearly 400 major watersheds in the United States. The methodology consists of the preparation of a single extract via soxhlet extraction, gel permeation and silica gel chromatography and quantification by HRGC/LRMS. The minimum level of detection for most analytes is near 1 ng/g. Rigorous quality assurance/quality control criteria have been developed to assure the generation of high quality data.