Multiple Sample Sets Research Articles

Multiphase Tectonic Processes in the Mesozoic and Formation of Early Regional Geomorphic Pattern in the Qilian Orogenic Belt, Northeastern Tibetan Plateau

Abstract Conventional orogenic settings are largely confined to plate boundaries and their immediate vicinity. However, there is growing recognition that deformation of the continental lithosphere may extend for considerable distances away from active plate boundaries. The Mesozoic Qilian orogenic belt occupies a key position in East Asia and thus is important to consider when interpreting intracontinental deformation there. To determine when and how the North Qilian orogenic belt developed, multiple sets of samples collected from the Dahuang Mountain of the North Qilian orogenic belt were analyzed for apatite (U-Th)/He and zircon (U-Th)/He thermochronology. The results show that the study area experienced multiphase tectonic exhumation events that took place in the Early-Middle Triassic (250–235 Ma), Middle-Late Jurassic (170–150 Ma), and Early Cretaceous (130–110 Ma). This study reveals that the generation deformation and stress distribution in the Dahuang Mountain fold-fault system have the characteristics of temporal–spatial migration from N to S. Results indicate widespread, continuous exhumation and deposition in the Qilian Mountain due to multiplate interaction since the Mesozoic, persisting till at least late Miocene. Multiphase intracontinental deformation is driven by stresses at plate boundaries, with lithosphere serving as stress pathway.

Improved reservoir quality assessment by evaluating illite grain coatings, quartz cementation, and compaction – Case study from the Buntsandstein, Upper Rhine Graben, Germany

Reservoir quality (RQ) in the Buntsandstein of the Upper Rhine Graben is in the center of attention as it is a possible target formation for geothermal energy production and hydrocarbon exploration surrounding existing fields. An understanding of properties affecting reservoir quality of the target lithology is still fairly poor and the success of accurately targeting high RQ intervals is limited. This is due to the fact, that the effect of compaction and the interplay between enhanced chemical compaction (i.e. pressure dissolution of quartz grains), illitic contact coatings and quartz cementation in the lithology has been underestimated. The understanding and quantification of controlling factors on reservoir qualities in fluvio-eolian sedimentary rocks, deposited in a (semi-)arid climate has been improved in recent years and this case study highlights the benefits of detailed petrographic analyses in understanding diagenetic and compactional processes in this lithology. This is especially relevant in the observation of grain coating clay minerals, whose effect depends on their specific location, i.e. either at grain contacts between quartz grains (GTG coating) or at the interface between detrital quartz grains and the intergranular volume (GTI coating).Illitic GTI coatings affect syntaxial quartz overgrowth precipitation, as precipitation sites are locally blocked. The negative correlation between the grain coating coverage and quartz cement volumes support these findings across multiple sample sets, and they may locally preserve intergranular porosity. Illitic GTG coatings on the other hand enhance chemical compaction (i.e. pressure dissolution) and will reduce the IGV. The negative correlation between these two properties again underlines the negative effect of this process on reservoir properties. Studied samples from a deep Buntsandstein well in the central URG show low reservoir quality due to either intense quartz cementation (0.7–31.7%) or a high degree of mechanical and chemical compaction (IGV: 2.3–38.0%). Higher illitic GTG and GTI coating coverages play a substantial role in controlling reservoir quality development, as demonstrated by comparing data from other fluvio-eolian lithologies (Triassic Buntsandstein and Permian Rotliegendes) to results of this study. In relation to their respective burial histories, higher illitic GTI coating coverages always correlate with smaller syntaxial quartz cement volumes. Similarly, higher illitic GTG coating coverages always correlate with lower IGV values in the three compared sample series.As both, the precipitation of quartz cements and compaction, are a function of the burial history, i.e. effective stresses and experienced temperatures, understanding the interaction of both these processes may enable the prediction of reservoir properties in undrilled areas.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery.

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

Accurate method based on data filtering for quantitative multi-element analysis of soils using CF-LIBS

To obtain more stable spectral data for accurate quantitative analysis of multi-element, especially for the large-area in-situ elements detection of soils, we propose a method for a multi-element quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I 324.75 nm for filtering the experimental data of multiple sample sets. Pre- and post- data filtering, the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection (LOD) values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post- data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis.

Multiple augmented reduced rank regression for pan-cancer analysis.

Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (ie, cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variations. We consider a structured nuclear norm objective that is motivated by random matrix theory, in which the regression or factorization terms may be shared or specific to any number of cohorts. Our framework subsumes several existing methods, such as reduced rank regression and unsupervised multimatrix factorization approaches, and includes a promising novel approach to regression and factorization of a single dataset (aRRR) as a special case. Simulations demonstrate substantial gains in power from combining multiple datasets, and from parsimoniously accounting for all structured variations. We apply maRRR to gene expression data from multiple cancer types (ie, pan-cancer) from The Cancer Genome Atlas, with somatic mutations as covariates. The method performs well with respect to prediction and imputation of held-out data, and provides new insights into mutation-driven and auxiliary variations that are shared or specific to certain cancer types.

Customized meta-dataset for automatic classifier accuracy evaluation

Automatic classifier accuracy evaluation (ACAEval) on unlabeled test sets is critical for unseen real-world environments. The use of dataset-level regression on synthesized meta-datasets (comprised of many sample sets) has shown promising results for ACAEval. However, the existing meta-dataset for ACAEval is created using simple image transformations such as rotation and background substitution, which can make it difficult to ensure a reasonable distribution shift between the sample set and the test set. When the distribution shift is large, it becomes challenging to estimate the classifier accuracy on the test set using those sample sets. To ensure more robust ACAEval, this paper attempts to customize a meta-dataset in which each sample set has a reasonable distribution shift to the test set. An intra-class cycle-consistent adversarial learning (ICAL) method is introduced to transfer the style of a labeled training set to the style of the test set, by jointly considering the domain shift issue, the label flipping issue (the semantic information may be changed after style transformation), and the diversity of multiple sample sets in the meta-dataset. Experiments validate that under the same experimental setup, our method outperforms the existing ACAEval methods by a good margin, and achieves state-of-the-art performance on several standard benchmark datasets, including digit classification and natural image classification.

An improved integration strategy for prediction of shear wave velocity using petrophysical logs: Integration of spatiotemporal and small sample nonlinear feature

Shear wave velocity (Vs) plays an important role in seismic data inversion and accurately determining petrophysical parameters. However, owing to the high acquisition cost of S-wave logging, Vs-related data cannot be obtained from all wells, especially the older ones. Therefore, developing an efficient and fast intelligent prediction method for Vs using existing petrophysical logging data is necessary. Owing to the advantages of conventional machine learning (CML) and deep learning (DL) methods, this study developed a data-driven integrated learning (IL) method for Vs prediction, which uses sensitive logging data as input, builds multiple sample sets, analyzes the performance differences of the CML and DL methods for predicting Vs in each dataset, and optimizes the support vector machine (SVM) in the CML method and convolutional neural network-long short-term memory (CNN-LSTM) in the DL method to construct an integrated CNN-LSTM-SVM neural network (ICLSNN). The spatiotemporal feature extraction capability of CNN-LSTM was used to effectively extract the deposition information of the formation from the logging data, and the small sample nonlinear feature extraction capability of SVM was used to alleviate the limitations of overfitting in DL for data-driven intelligent Vs prediction. The ICLSNN showed a higher Vs prediction accuracy than SVM or CNN-LSTM alone. The Vs prediction results of the single well in the discovery without training were better than those of the other IL and empirical models, proving the strong generalization applicability of the proposed model. This method compensates for the limitations of individual machine learning or DL models for Vs prediction and provides a novel research idea. The effective implementation of this scheme will facilitate the efficient prediction of Vs in areas with few wells.

A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data

BackgroundPan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior.ResultsWe found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not.ConclusionsWe address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach.

On the Influence of the Sample Properties on the Measurement of the Implied Open-Circuit Voltage

The implied voltage iV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> is a popular parameter for the electronic quality of solar cell test samples. It is used to characterize properties like the passivation quality of surface coatings. While iV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> fundamentally depends on the sample properties besides the electronic quality of the interface, such influences have not been systematically quantified and are usually not stated, which questions the comparability of iV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> across multiple sample sets. This article is dedicated to study the influence of the wafer doping and thickness, surface passivation and reflection as well as light trapping on iV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> by device simulations using Quokka3, supported by experimental data. It is shown that, even moderate changes in these sample properties can result in a significant deviation in iV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> . This is emphasized if multiple influences are combined, as shown on two samples featuring an iV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> of 737 and 754 mV. This difference in iV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> can be broken down into individual contributions, demonstrating how the aforementioned influences can quickly add up to 10-15 mV if combined. From this perspective, it is difficult to compare their respective surface passivation performance, which only accounts for less than 10% of the total change in iV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> . Therefore, we recommend a precise description of the aforementioned sample properties when reporting iV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> values in publication. To quantify the surface recombination, we recommend the use of J <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0s</sub> instead of iV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> , since it specifically describes surface recombination and is usually independent from the discussed sample properties.

Collaborative-Set Measurement for ECG-Based Human Identification

Electrocardiogram (ECG) has attracted intense research interests and contests due to its great value in biometric applications, especially with the technological progress of smart instrumentation and artificial intelligence nowadays. For the issue of ECG-based human identification, distance measure in a suitable data space can be an effective solution. Almost all conventional distance measures rely on the independent data samples for classification. However, such measuring mechanism is vulnerable to the variation and bias of data distribution, which are easily caused by the noisy artifacts in this issue. To tackle this problem, we suggest doing distance measurement at the level of multiple-set bundle that consists of multiple sample sets obtained under different conditions. More specifically, we propose a novel method, “Collaborative-Set Measurement (CSM),” which creatively extends distance measure from the sample level through the set level to the bundle level. CSM not only captures the representative information from within-set distribution but also exploits the discriminative information from between-set collaboration, and meanwhile incorporates the robust information from in-bundle fusion. Attributing to these information, this method can largely enhance the identification performance in this issue despite the serious noisy artifacts influencing the data samples. The experimental results have demonstrated the capability of CSM for ECG-based human identification, by means of the public challenging database, DREAMER, on which the proposed method produces an accuracy of 91.30%. In summary, this proposal has initially brought forward a new thought of “collaborative sets.” This thought may provide inspirations for further researches on biometric recognition and general signal classification topics.

Abstract 4430: Quantification of tumor-infiltrating immune cell populations with an augmented transcriptome

Abstract Comprehensive characterization of the tumor and tumor microenvironment (TME) can improve our understanding of tumor progression and treatment outcomes. For example, quantification of the immune infiltrate can inform mechanisms of immune escape and predict response to checkpoint blockade. Standard experimental approaches exist to enumerate tumor-infiltrating immune cells, but they can have practical limitations of throughput, number of markers, or sample requirements. RNA sequencing can be used as a scalable solution to comprehensively profile the immune cell composition of the TME. However, care must be taken to ensure that the computational analysis accurately reflects the underlying immune cell composition. To address these challenges, we developed ImmunoID NeXT, an augmented, immuno-oncology-optimized exome/transcriptome platform designed to provide comprehensive information regarding the tumor and TME from a single FFPE tumor sample. This includes the quantification of tumor-infiltrating immune cells using RNA-seq analysis, which we compare to quantification by orthogonal methods. To generate our reference data, we profiled the transcriptomes of eight purified immune cell types using ImmunoID NeXT. Then, we analyzed multiple sample types and orthogonally quantified immune cells in each. These include profiling of healthy donor PBMCs with cytometry by time of flight (CyTOF), and immunofluorescence (IF) characterization of FFPE tumor samples. We also used ImmunoID NeXT to profile the immune infiltrate of over 500 tumor samples across 13 cancer types. Finally, we created a set of in vitro cell mixtures and profiled them by flow cytometry. We utilized the transcriptome profiles of eight purified immune cell types to develop reference expression signatures specific for each cell type. Then, we compared ImmunoID NeXT's transcriptome-based approach to CyTOF results of healthy donor PBMCs, showing accuracy in real samples with diverse immune populations. We also compared to FFPE tumor samples with IF, ensuring that our approach is able to profile the immune composition in tumor samples. Next, we highlight the diversity of immune populations across cancer types by applying ImmunoID NeXT to over 500 tumor samples. Finally, to demonstrate concordance across the eight cell types, we compared to flow cytometry results of in vitro cell mixtures. Analysis of the immune infiltrate of tumor samples can add to our understanding of the tumor-immune interaction, with potential applications including studies of response to immunotherapy. RNA sequencing can be utilized as a scalable approach for such analysis. Here, we test the accuracy of our approach using multiple sample sets with orthogonal profiling. We demonstrate that ImmunoID NeXT can accurately evaluate the composition of infiltrating immune cells in tumor samples. Citation Format: Eric Levy, Pamela Milani, Charles W. Abbott, Manxia Lee, Robert Power, John West, Richard Chen, Sean M. Boyle. Quantification of tumor-infiltrating immune cell populations with an augmented transcriptome [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4430.

A Multiplexed Microarray Platform for Targeted Proteomics

A Multiplexed Microarray Platform for Targeted Proteomics

Erosion studies of the iron boride coatings for protection of tubing components in oil production, mineral processing and engineering applications

Heavy oil production, oil sand and mineral processing require protection of steel components against sand erosion and erosion-corrosion. Boronized coatings on carbon steels obtained through thermal diffusion process were evaluated in the dry sand and slurry erosion conditions at low impingement angles commonly occurred in industrial applications. The specially designed system allowed testing either multiple sets of samples or full-size tubular sections with diameters of 180–220 mm. The boronized coatings consisted of dual iron boride layers FeB and Fe2B with a total thickness of ~200 μm demonstrated significantly higher erosion resistance over carbon steel commonly used in industry. High performance of the boride coatings is defined by their high hardness, dual protective layer with a well-consolidated structure and diffusion related bonding with steels. Erosion wear mechanism is discussed based on examination of coating's structure and composition and analysis of testing conditions. The components and tubing with inner or inner and outer protective iron boride coatings can be successfully employed in downhole oil production conditions, mineral processing and various engineering applications.

Dual L1-Normalized Context Aware Tensor Power Iteration and Its Applications to Multi-object Tracking and Multi-graph Matching

The multi-dimensional assignment problem is universal for data association analysis such as data association-based visual multi-object tracking and multi-graph matching. In this paper, multi-dimensional assignment is formulated as a rank-1 tensor approximation problem. A dual L1-normalized context/hyper-context aware tensor power iteration optimization method is proposed. The method is applied to multi-object tracking and multi-graph matching. In the optimization method, tensor power iteration with the dual unit norm enables the capture of information across multiple sample sets. Interactions between sample associations are modeled as contexts or hyper-contexts which are combined with the global affinity into a unified optimization. The optimization is flexible for accommodating various types of contextual models. In multi-object tracking, the global affinity is defined according to the appearance similarity between objects detected in different frames. Interactions between objects are modeled as motion contexts which are encoded into the global association optimization. The tracking method integrates high order motion information and high order appearance variation. The multi-graph matching method carries out matching over graph vertices and structure matching over graph edges simultaneously. The matching consistency across multi-graphs is based on the high-order tensor optimization. Various types of vertex affinities and edge/hyper-edge affinities are flexibly integrated. Experiments on several public datasets, such as the MOT16 challenge benchmark, validate the effectiveness of the proposed methods.

Center Settled Multiple-Coil Spring Model to Improve Facial Recognition Under Various Complexities

A facial extracted image does not have the equalized distribution of features over the complete image. Instead, most striking features are located within the core of the facial part. As the distance increases from the core part, the strength of these features faded and its impact on the recognition model reduces. In this paper, a coil spring structured model is presented to generate the selective features based on structured weights. These weights are assigned under the pressure, position, direction and coverage parameters of magnetic coils. The magnetic coil effect is applied to extract the facial features. These features are collected and mapped with dataset images with region consideration. This mapping is done for the individual region with physical features and coil-spring based evaluation. As the method is center settled, so that the effective recognition rate is achieved missing facial information or the wrong captured images. The experimentation is applied to the complete facial image sets as well as improper, occluded and irregular captured facial images. The comparative analysis is provided on Aberdeen, Stirling, Iranian, ORL, FERET and LFW databases. The proportionate observations are taken against six different algorithms, including LDA, PCA, ICA, LDA–PCA, SVM and PNN classifiers. Multiple sample sets are considered over each dataset under distinctive variation aspects. These variations include expression, pose, illumination, occlusion, etc. The analytical evaluation is also taken for CNN and landmark based methods. The extensive experimentation shows that model has improved the accuracy and robustness up to an extent. The recognition rate for each variation aspect is improved.

Rigor and Reproducibility of Cytometry Practices for Immuno-Oncology: A multifaceted challenge.

The rapid advancement of immunotherapy strategies has created a need for technologies that can reliably and reproducibly identify rare populations, detect subtle changes in modulatory signals, and assess antigenic expression patterns that are time-sensitive. Accomplishing these tasks requires careful planning and the employment of tools that provide greater sensitivity and specificity without demanding extensive time. Flow Cytometry has earned its place as a preferred analysis platform. This technology offers a flexible path to the interrogation of protein expression patterns and detection of functional properties in cell populations of interest. Mass Cytometry is a newcomer technology that has generated significant interest in the field. By incorporating mass spectrometry analysis to the traditional principles of flow cytometry, this innovative tool promises to significantly expand the ability to detect multiple proteins on a single cell. The use of these technologies in a manner that is consistent and reproducible through multiple sample sets demands careful attention to experiment design, reagent selection, and instrumentation. Whether applying flow or mass cytometry, reaching successful, reliable results involves many factors. Sample preparation, antibody titrations, and appropriate controls are major biological considerations that impact cytometric analysis. Additionally, instrument voltages, lasers, and run quality assessments are essential for ensuring comparability and reproducibility between analyses. In this article, we aim to discuss the critical aspects that impact flow cytometry, and will touch on important considerations for mass cytometry as well. Focusing on their relevance to immunotherapy studies, we will address the importance of appropriate sample processing and will discuss how selection of suitable panels, controls, and antibodies must follow a carefully designed plan. We will also comment on how educated use of instrumentation plays a significant role in the reliability and reproducibility of results.Through this work, we hope to contribute to the effort toward establishing higher standards for rigor and reproducibility of cytometry practices by researchers, operators, and general cytometry users employing cytometry-based assays in their work. © 2019 International Society for Advancement of Cytometry.

ScRNABatchQC: multi-samples quality control for single cell RNA-seq data.

SummarySingle cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers.Availability and implementationscRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package.Supplementary information Supplementary data are available at Bioinformatics online.

Leukemia Inhibitory Factor Promotes Castration-resistant Prostate Cancer and Neuroendocrine Differentiation by Activated ZBTB46.

The molecular targets for castration-resistant prostate cancer (CRPC) are unknown because the disease inevitably recurs, and therapeutic approaches for patients with CRPC remain less well understood. We sought to investigate regulatory mechanisms that result in increased therapeutic resistance, which is associated with neuroendocrine differentiation of prostate cancer and linked to dysregulation of the androgen-responsive pathway. The underlying intracellular mechanism that sustains the oncogenic network involved in neuroendocrine differentiation and therapeutic resistance of prostate cancer was evaluated to investigate and identify effectors. Multiple sets of samples with prostate adenocarcinomas and CRPC were assessed via IHC and other assays. We demonstrated that leukemia inhibitory factor (LIF) was induced by androgen deprivation therapy (ADT) and was upregulated by ZBTB46 in prostate cancer to promote CRPC and neuroendocrine differentiation. LIF was found to be induced in patients with prostate cancer after ADT and was associated with enriched nuclear ZBTB46 staining in high-grade prostate tumors. In prostate cancer cells, high ZBTB46 output was responsible for the activation of LIF-STAT3 signaling and neuroendocrine-like features. The abundance of LIF was mediated by ADT-induced ZBTB46 through a physical interaction with the regulatory sequence of LIF. Analysis of serum from patients showed that cases of higher tumor grade and metastatic prostate cancer exhibited higher LIF titers. Our findings suggest that LIF is a potent serum biomarker for diagnosing advanced prostate cancer and that targeting the ZBTB46-LIF axis may therefore inhibit CRPC development and neuroendocrine differentiation after ADT.

Holographic Ensemble Forecasting Method for Short-Term Power Load

In this paper, we newly propose a holographic ensemble forecasting method (HEFM). First, we use the mutual information and statistical method to select feature variables, which is an ensemble of information about the cross-border multi-source data at the dataset level. Then, we generate multiple training sets by performing diversity sampling with bootstrap, which is an ensemble of information about multiple sample sets at the sampling space level. Next, we construct a multi-model using different artificial intelligence and machine-learning algorithms, which is an ensemble of information about multiple nonlinear heterogeneous models at the forecasting model level. Finally, we use the original features, the forecasting load which is output of the multiple heterogeneous models trained in the first learning, and the actual load of the recent period before each forecasted time to generate a new training set, which is used for the online second learning and final forecasting. This is an ensemble of information about online second learning at the decision level. The ensemble of multi-category multi-state information for four levels (dataset, sampling space, forecasting model, and decision) constitutes the framework of HEFM, whose essence is a forecasting method with comprehensive information integration for the whole life cycle of the forecasting process. We study the load in Guangzhou, China, and New England, USA. Compared to the state-of-the-art forecasting methods, the MAPE of HEFM is reduced by 7.69%–65.77%. The results demonstrate that the forecasting performance may not be improved with the number of algorithms, and that there is a need to understand the positive and negative fusion effect between different algorithms and data characteristics.

An Indoor Variance-Based Localization Technique Utilizing the UWB Estimation of Geometrical Propagation Parameters

A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays.The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim–Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values.