Active IR location as the roadway profilometry method

This article observe a using of active infrared beam location as roadway surface quality control. Changes in the spatial structure of the emitted IR radiation by surfaces within the capture scene allow creating a depth map of this scene. An optical camera makes it possible to use classical computer vision methods for stitching a depth map. For testing the possibility of using this approach, we made statistical studies on a multiple sample of distance measurements. Here we explain two experimental schemes with a programmable mechanical scanning system. The first one, we had determined the distance, which the image is capture accurately. The second, we measure the planar resolution, a minimum size of the defect that recognize by the infrared beam location system.

Tree-based Correlation Screen and Visualization for Exploring Phenotype-Cell Type Association in Multiple Sample Single-Cell RNA-Sequencing Experiments

Single-cell RNA-seq experiments with multiple samples are increasingly used to discover cell types and their molecular features that may influence sample phenotype (e.g. disease). However, analyzing and visualizing the complex cell type-phenotype association remains nontrivial. TreeCorTreat is an open source R package that tackles this problem by using a tree-based correlation screen to analyze and visualize the association between phenotype and transcriptomic features and cell types at multiple cell type resolution levels. With TreeCorTreat, one can conveniently explore and compare different feature types, phenotypic traits, analysis protocols and datasets, and evaluate the impacts of potential confounders.

Improving Estimates of Natural Resources Using Model-Based Estimators: Impacts of Sample Design, Estimation Technique, and Strengths of Association

Natural resource managers need accurate depictions of existing resources to make informed decisions. The classical approach to describing resources for a given area in a quantitative manner uses probabilistic sampling and design-based inference to estimate population parameters. While probabilistic designs are accepted as being necessary for design-based inference, many recent studies have adopted non-probabilistic designs that do not include elements of random selection or balance and have relied on models to justify inferences. While common, model-based inference alone assumes that a given model accurately depicts the relationship between response and predictors across all populations. Within complex systems, this assumption can be difficult to justify. Alternatively, models can be trained to a given population by adopting design-based principles such as balance and spread. Through simulation, we compare estimates of population totals and pixel-level values using linear and nonlinear model-based estimators for multiple sample designs that balance and spread sample units. The findings indicate that model-based estimators derived from samples spread and balanced across predictor variable space reduce the variability of population and unit-level estimators. Moreover, if samples achieve approximate balance over feature space, then model-based estimates of population totals approached simple expansion-based estimates of totals. Finally, in all comparisons made, improvements in estimation were achieved using model-based estimation over design-based estimation alone. Our simulations suggest that samples drawn from a probabilistic design, that are spread and balanced across predictor variable space, improve estimation accuracy.

Measured Glomerular Filtration Rate: The Query for a Workable Golden Standard Technique

Inulin clearance has, for a long time, been considered as the reference method to determine measured glomerular filtration rates (mGFRs). However, given the known limitations of the standard marker, serum creatinine, and of inulin itself, and the frequent need for accurate GFR estimations, several other non-radioactive (iohexol and iothalamate) and radioactive (51Cr-EDTA, 99mTc-DTPA, 125I iothalamate) exogenous mGFR filtration markers are nowadays considered the most accurate options to evaluate GFR. The availability of 51Cr-EDTA is limited, and all methods using radioactive tracers necessitate specific safety precautions. Serum- or plasma-based certified reference materials for iohexol and iothalamate and evidence-based protocols to accurately and robustly measure GFR (plasma vs. urinary clearance, single-sample vs. multiple-sample strategy, effect of sampling time delay) are lacking. This leads to substantial variation in reported mGFR results across studies and questions the scientific reliability of the alternative mGFR methods as the gold standard to evaluate kidney function. On top of the scientific discussion, regulatory issues are further narrowing the clinical use of mGFR methods. Therefore, this review is a call for standardization of mGFR in terms of three aspects: the marker, the analytical method to assess concentrations of that marker, and the procedure to determine GFR in practice. Moreover, there is also a need for an endogenous filtration marker or a panel of filtration markers from a single blood draw that would allow estimation of GFR as accurately as mGFR, and without the need for application of anthropometric, clinical, and demographic characteristics.

SCIGA: Software for large-scale, single-cell immunoglobulin repertoire analysis

Background B-cell immunoglobulin repertoires with paired heavy and light chain can be determined by means of 10X single-cell V(D)J sequencing. Precise and quick analysis of 10X single-cell immunoglobulin repertoires remains a challenge owing to the high diversity of immunoglobulin repertoires and a lack of specialized software that can analyze such diverse data. Findings In this study, specialized software for 10X single-cell immunoglobulin repertoire analysis was developed. SCIGA (Single-Cell Immunoglobulin Repertoire Analysis) is an easy-to-use pipeline that performs read trimming, immunoglobulin sequence assembly and annotation, heavy and light chain pairing, statistical analysis, visualization, and multiple sample integration analysis, which is all achieved by using a 1-line command. Then SCIGA was used to profile the single-cell immunoglobulin repertoires of 9 patients with coronavirus disease 2019 (COVID-19). Four neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were identified from these repertoires. Conclusions SCIGA provides a complete and quick analysis for 10X single-cell V(D)J sequencing datasets. It can help researchers to interpret B-cell immunoglobulin repertoires with paired heavy and light chain.

Step 2: RNA extraction and RT-qPCR v1

STRIP is a start-to-end streamlined and automated procedure for COVID-19 testing, centering on a single Tecan Fluent liquid-handling robot that can process over 14,000 samples per day. Key features of the customized Tecan Fluent robot are (1) on-board 1D and 2D barcode scanners, (2) an automated tube decapper, (3) two robotic arms for simultaneous processing of different procedural steps, (4) a newly-designed spin vessel to keep magnetic beads in solution and immediately transferable to 384-well plates, (5) a built-in magnetic deck and a 384-channel pipetting head for rapid RNA isolation, (6) a heating device for fast drying of RNA prior to elution, (7) a built-in plate sealer and (8) a plate storage system to allow processing of multiple sample plates in a single run (See Materials). Here we describe the RNA extraction and RT-qPCR protocol as currently applied in STRIP.

Optimization of Sample Preparation for Metabolomics Exploration of Urine, Feces, Blood and Saliva in Humans Using Combined NMR and UHPLC-HRMS Platforms

Currently, most clinical studies in metabolomics only consider a single type of sample such as urine, plasma, or feces and use a single analytical platform, either NMR or MS. Although some studies have already investigated metabolomics data from multiple fluids, the information is limited to a unique analytical platform. On the other hand, clinical studies investigating the human metabolome that combine multi-analytical platforms have focused on a single biofluid. Combining data from multiple sample types for one patient using a multimodal analytical approach (NMR and MS) should extend the metabolome coverage. Pre-analytical and analytical phases are time consuming. These steps need to be improved in order to move into clinical studies that deal with a large number of patient samples. Our study describes a standard operating procedure for biological specimens (urine, blood, saliva, and feces) using multiple platforms (1H-NMR, RP-UHPLC-MS, and HILIC-UHPLC-MS). Each sample type follows a unique sample preparation procedure for analysis on a multi-platform basis. Our method was evaluated for its robustness and was able to generate a representative metabolic map.