Robust Workflow Research Articles

Inward Outward Signaling in Ovarian Cancer: Morpho-Phospho-Proteomic Profiling Upon Application of Hypoxia and Shear Stress Characterizes the Adaptive Plasticity of OVCAR-3 and SKOV-3 Cells.

With the onset of resistance, ovarian cancer cells display almost unpredictable adaptive potential. This may derive from the tumor genetic ancestry and can be additionally tailored by post translational protein modifications (PTMs). In this study, we took advantage of high-end (phospho)-proteome analysis combined with multiparametric morphometric profiling in high-grade serous (OVCAR-3) and non-serous (SKOV-3) ovarian carcinoma cells. For functional experiments, we applied two different protocols, representing typical conditions of the abdominal cavity and of the growing tumor tissue: on the one side hypoxia (oxygen 1%) which develops within the tumor mass or is experienced during migration/extravasation in non-vascularized areas. On the other hand, fluid shear stress (250 rpm, 2.8 dyn/cm2) which affects tumor surface in the peritoneum or metastases in the bloodstream. After 3 hours incubation, treatment groups were clearly distinguishable by PCA analysis. Whereas basal proteome profiles of OVCAR-3 and SKOV-3 cells appeared almost unchanged, phosphoproteome analysis revealed multiple regulatory events. These affected primarily cellular structure and proliferative potential and consolidated in the proteome signature after 24h treatment. Upon oxygen reduction, metabolism switched toward glycolysis (e.g. upregulation hexokinase-2; HK2) and cell size increased, in concerted regulation of pathways related to Rho-GTPases and/or cytoskeletal elements, resembling a vasculogenic mimicry response. Shear stress regulated proteins governing cell cycle and structure, as well as the lipid metabolism machinery including the delta(14)-sterol reductase, kinesin-like proteins (KIF-22/20A) and the actin-related protein 2/3 complex. Independent microscopy-based validation experiments confirmed cell-type specific morphometric responses. In conclusion, we established a robust workflow enabling the description of the adaptive potential of ovarian cancer cells to physical and chemical stressors typical for the abdominal cavity and supporting the identification of novel molecular mechanisms sustaining tumor plasticity and pharmacologic resistance.

Standardization of Workflow and Flow Cytometry Panels for Quantitative Expression Profiling of Surface Antigens on Blood Leukocyte Subsets: An HCDM CDMaps Initiative.

BackgroundThe Human Cell Differentiation Molecules (HCDM) organizes Human Leukocyte Differentiation Antigen (HLDA) workshops to test and name clusters of antibodies that react with a specific antigen. These cluster of differentiation (CD) markers have provided the scientific community with validated antibody clones, consistent naming of targets and reproducible identification of leukocyte subsets. Still, quantitative CD marker expression profiles and benchmarking of reagents at the single-cell level are currently lacking.ObjectiveTo develop a flow cytometric procedure for quantitative expression profiling of surface antigens on blood leukocyte subsets that is standardized across multiple research laboratories.MethodsA high content framework to evaluate the titration and reactivity of Phycoerythrin (PE)-conjugated monoclonal antibodies (mAbs) was created. Two flow cytometry panels were designed: an innate cell tube for granulocytes, dendritic cells, monocytes, NK cells and innate lymphoid cells (12-color) and an adaptive lymphocyte tube for naive and memory B and T cells, including TCRγδ+, regulatory-T and follicular helper T cells (11-color). The potential of these 2 panels was demonstrated via expression profiling of selected CD markers detected by PE-conjugated antibodies and evaluated using 561 nm excitation.ResultsUsing automated data annotation and dried backbone reagents, we reached a robust workflow amenable to processing hundreds of measurements in each experiment in a 96-well plate format. The immunophenotyping panels enabled discrimination of 27 leukocyte subsets and quantitative detection of the expression of PE-conjugated CD markers of interest that could quantify protein expression above 400 units of antibody binding capacity. Expression profiling of 4 selected CD markers (CD11b, CD31, CD38, CD40) showed high reproducibility across centers, as well as the capacity to benchmark unique clones directed toward the same CD3 antigen.ConclusionWe optimized a procedure for quantitative expression profiling of surface antigens on blood leukocyte subsets. The workflow, bioinformatics pipeline and optimized flow panels enable the following: 1) mapping the expression patterns of HLDA-approved mAb clones to CD markers; 2) benchmarking new antibody clones to established CD markers; 3) defining new clusters of differentiation in future HLDA workshops.

Probing Embryonic Development Enables the Discovery of Unique Small-Molecule Bone Morphogenetic Protein Potentiators.

We report on the feasibility to harness embryonic development in vitro for the identification of small-molecule cytokine mimetics and signaling activators. Here, a phenotypic, target-agnostic, high-throughput assay is presented that probes bone morphogenetic protein (BMP) signaling during mesodermal patterning of embryonic stem cells. The temporal discrimination of BMP- and transforming growth factor-β (TGFβ)-driven stages of cardiomyogenesis underpins a selective, authentic orchestration of BMP cues that can be recapitulated for the discovery of BMP activator chemotypes. Proof of concept is shown from a chemical screen of 7000 compounds, provides a robust hit validation workflow, and afforded 2,3-disubstituted 4H-chromen-4-ones as potent BMP potentiators with osteogenic efficacy. Mechanistic studies suggest that Chromenone 1 enhances canonical BMP outputs at the expense of TGFβ-Smads in an unprecedented manner. Pharmacophoric features were defined, providing a set of novel chemical probes for various applications in (stem) cell biology, regenerative medicine, and basic research on the BMP pathway.

A robust deep learning workflow to predict multiphase flow behavior during geological [formula omitted] sequestration injection and Post-Injection periods

Simulation of multiphase flow in porous media is essential to manage the geologic CO2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO2 as a deep learning feature, leads to the most accurate prediction of CO2 saturation. For the post-injection period, it is key to use cumulative CO2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of GCS.

A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion.

Recent advances in both instrumentation and image processing software have made single-particle cryo-electron microscopy (cryo-EM) the preferred method for structural biologists to determine high-resolution structures of a wide variety of macromolecules. Multiple software suites are available to new and expert users for image processing and structure calculation, which streamline the same basic workflow: movies acquired by the microscope detectors undergo correction for beam-induced motion and contrast transfer function (CTF) estimation. Next, particle images are selected and extracted from averaged movie frames for iterative 2D and 3D classification, followed by 3D reconstruction, refinement, and validation. Because various software packages employ different algorithms and require varying levels of expertise to operate, the 3D maps they generate often differ in quality and resolution. Thus, users regularly transfer data between a variety of programs for optimal results. This paper provides a guide for users to navigate a workflow across the popular software packages: cryoSPARC v3, RELION-3, and Scipion 3 to obtain a near-atomic resolution structure of the adeno-associated virus (AAV). We first detail an image processing pipeline with cryoSPARC v3, as its efficient algorithms and easy-to-use GUI allow users to quickly arrive at a 3D map. In the next step, we use PyEM and in-house scripts to convert and transfer particle coordinates from the best quality 3D reconstruction obtained in cryoSPARC v3 to RELION-3 and Scipion 3 and recalculate 3D maps. Finally, we outline steps for further refinement and validation of the resultant structures by integrating algorithms from RELION-3 and Scipion 3. In this article, we describe how to effectively utilize three processing platforms to create a single and robust workflow applicable to a variety of data sets for high-resolution structure determination.

Identification of Master Regulators Driving Disease Progression, Relapse, and Drug Resistance in Lung Adenocarcinoma.

Backgrounds: Lung cancer is the leading cause of cancer related death worldwide. Current treatment strategies primarily involve surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, determined by TNM stages, histologic types, and genetic profiles. Plenty of studies have been trying to identify robust prognostic gene expression signatures. Even for high performance signatures, they usually have few shared genes. This is not totally unexpected, since a prognostic signature is associated with patient survival and may contain no upstream regulators. Identification of master regulators driving disease progression is a vital step to understand underlying molecular mechanisms and develop new treatments. Methods: In this study, we have utilized a robust workflow to identify potential master regulators that drive poor prognosis in patients with lung adenocarcinoma. This workflow takes gene expression signatures that are associated with poor survival of early-stage lung adenocarcinoma, EGFR-TKI resistance, and responses to immune checkpoint inhibitors, respectively, and identifies recurrent master regulators from seven public gene expression datasets by a regulatory network-based approach. Results: We have found that majority of the master regulators driving poor prognosis in early stage LUAD are cell-cycle related according to Gene Ontology annotation. However, they were demonstrated experimentally to promote a spectrum of processes such as tumor cell proliferation, invasion, metastasis, and drug resistance. Master regulators predicted from EGFR-TKI resistance signature and the EMT pathway signature are largely shared, which suggests that EMT pathway functions as a hub and interact with other pathways such as hypoxia, angiogenesis, TNF-α signaling, inflammation, TNF-β signaling, Wnt, and Notch signaling pathways. Master regulators that repress immunotherapy are enriched with MYC targets, E2F targets, oxidative phosphorylation, and mTOR signaling. Conclusion: Our study uncovered possible mechanisms underlying recurrence, resistance to targeted therapy, and immunotherapy. The predicted master regulators may serve as potential therapeutic targets in patients with lung adenocarcinoma.

High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening.

Effect-directed analysis (EDA) aims at the detection of bioactive chemicals of emerging concern (CECs) by combining toxicity testing and high-resolution mass spectrometry (HRMS). However, consolidation of toxicological and chemical analysis techniques to identify bioactive CECs remains challenging and laborious. In this study, we incorporate state-of-the-art identification approaches in EDA and propose a robust workflow for the high-throughput screening of CECs in environmental and human samples. Three different sample types were extracted and chemically analyzed using a single high-performance liquid chromatography HRMS method. Chemical features were annotated by suspect screening with several reference databases. Annotation quality was assessed using an automated scoring system. In parallel, the extracts were fractionated into 80 micro-fractions each covering a couple of seconds from the chromatogram run and tested for bioactivity in two bioassays. The EDA workflow prioritized and identified chemical features related to bioactive fractions with varying levels of confidence. Confidence levels were improved with the in silico software tools MetFrag and the retention time indices platform. The toxicological and chemical data quality was comparable between the use of single and multiple technical replicates. The proposed workflow incorporating EDA for feature prioritization in suspect and nontarget screening paves the way for the routine identification of CECs in a high-throughput manner.

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification.

Simple SummaryThe implementation of artificial intelligence in the computer-aided decision (CAD) support systems holds great promise for future cancer diagnosis. It is crucial to build these algorithms in a structured manner to ensure reproducibility and reliability. In this context, we used a dataset of breast ultrasound (US) images with 252 breast cancer and 253 benign cases to refine the CAD image analysis workflow. Various dataset preparations (i.e., pre-processing, and spatial augmentation) and machine learning algorithms were tested to establish the framework with the best performance in the detection and classification of breast lesions in US images. The efficacy of the proposed workflows was evaluated regarding accuracy, precision, specificity, and sensitivity.Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.

Application of statistical machine learning clustering algorithms to improve EUR predictions using decline curve analysis in shale-gas reservoirs

A robust machine learning workflow using two well-established statistical clustering algorithms, K-Means Clustering (K-MC) and K-Nearest Neighbors (K-NN) was developed to improve ultimate recovery (EUR) predictions of new wells in shale-gas reservoirs using a decline curve model developed by Duong (2011). These two clustering algorithms can handle big datasets with multiple well parameters with high computational efficiency. Out of a total of 55,623 dry-gas wells from seven shale gas formations, 7631 wells that fit the criteria for Duong's decline curve model were selected in this study for further analysis. K-MC and K-NN were applied to group wells with similar well characteristics using well location, well depth, well length, and production starting year as parameters. Locations of the grouped wells show that the clusters of wells with similar well characteristics identified by both clustering methods scatter over a big area. These findings clearly demonstrate that simply grouping wells by the proximity of physical locations is not a good way of identifying wells with similar well characteristics. The results from this study also suggest that the selection of optimal clustering methods is highly dependent on the shale formation and the method used for EUR prediction. Even with the limited number of well parameters available in the datasets used in this study, the machine learning clustering algorithms managed to improve the EUR prediction accuracy by around 20%. More available well information will allow the machine learning algorithms to capture more of the well characteristics, thereby further increasing the accuracy of EUR predictions.

PipeIT2: Somatic Variant Calling Workflow for Ion Torrent Sequencing Data.

The detection of somatic genetic variants forms the cornerstone of precision oncology. Molecular diagnostics laboratories require economical and rapid protocols to generate accurate variant calls for clinical purposes. The Ion Torrent sequencing platform is frequently used but lacks robust analysis workflows tailored to the needs of custom sequencing panels. We present PipeIT2, a somatic variant calling workflow for both matched tumor-germline and tumor-only analyses. PipeIT2 is enclosed in a Singularity container to ensure reproducibility and ease of deployment.

Biophysical and structural characterizations of the effects of mutations on the structure-activity relationships of SARS-CoV-2 spike protein.

Mutations on the spike (S) protein of SARS-CoV-2 could induce structural changes that help increase viral transmissibility and enhance resistance to antibody neutralization. Here, we report a robust workflow to prepare recombinant S protein variants and its host receptor angiotensin-convert enzyme 2 (ACE2) by using a mammalian cell expression system. The functional states of the S protein variants are investigated by cryo-electron microscopy (cryo-EM) and negative staining electron microscopy (NSEM) to visualize their molecular structures in response to mutations, receptor binding, antibody binding, and environmental changes. The folding stabilities of the S protein variants can be deduced from morphological changes based on NSEM imaging analysis. Differential scanning calorimetry provides thermodynamic information to complement NSEM. Impacts of the mutations on host receptor binding and antibody neutralization are in vitro by kinetic binding analyses in addition to atomic insights gleaned from cryo-electron microscopy (cryo-EM). This experimental strategy is generally applicable to studying the molecular basis of host-pathogen interactions.

A Robust Workflow Scheduling Method for Spot Instances in Clouds Based on Monte Carlo Simulation

A Robust Workflow Scheduling Method for Spot Instances in Clouds Based on Monte Carlo Simulation

Development of ONT-cappable-seq to unravel the transcriptional landscape of Pseudomonas phages

RNA sequencing has become the method of choice to study the transcriptional landscape of phage-infected bacteria. However, short-read RNA sequencing approaches generally fail to capture the primary 5′ and 3′ boundaries of transcripts, confounding the discovery of key transcription initiation and termination events as well as operon architectures. Yet, the elucidation of these elements is crucial for the understanding of the strategy of transcription regulation during the infection process, which is currently lacking beyond a handful of model phages. We developed ONT-cappable-seq, a specialized long-read RNA sequencing technique that allows end-to-end sequencing of primary prokaryotic transcripts using the Nanopore sequencing platform. We applied ONT-cappable-seq to study transcription of Pseudomonas aeruginosa phage LUZ7, obtaining a comprehensive genome-wide map of viral transcription start sites, terminators, and complex operon structures that fine-regulate gene expression. Our work provides new insights in the RNA biology of a non-model phage, unveiling distinct promoter architectures, putative small non-coding viral RNAs, and the prominent regulatory role of terminators during infection. The robust workflow presented here offers a framework to obtain a global, yet fine-grained view of phage transcription and paves the way for standardized, in-depth transcription studies for microbial viruses or bacteria in general.

Assembly and Analysis of Cell-Scale Membrane Envelopes.

The march toward exascale computing will enable routine molecular simulation of larger and more complex systems, for example, simulation of entire viral particles, on the scale of approximately billions of atoms─a simulation size commensurate with a small bacterial cell. Anticipating the future hardware capabilities that will enable this type of research and paralleling advances in experimental structural biology, efforts are currently underway to develop software tools, procedures, and workflows for constructing cell-scale structures. Herein, we describe our efforts in developing and implementing an efficient and robust workflow for construction of cell-scale membrane envelopes and embedding membrane proteins into them. A new approach for construction of massive membrane structures that are stable during the simulations is built on implementing a subtractive assembly technique coupled with the development of a structure concatenation tool (fastmerge), which eliminates overlapping elements based on volumetric criteria rather than adding successive molecules to the simulation system. Using this approach, we have constructed two "protocells" consisting of MARTINI coarse-grained beads to represent cellular membranes, one the size of a cellular organelle and another the size of a small bacterial cell. The membrane envelopes constructed here remain whole during the molecular dynamics simulations performed and exhibit water flux only through specific proteins, demonstrating the success of our methodology in creating tight cell-like membrane compartments. Extended simulations of these cell-scale structures highlight the propensity for nonspecific interactions between adjacent membrane proteins leading to the formation of protein microclusters on the cell surface, an insight uniquely enabled by the scale of the simulations. We anticipate that the experiences and best practices presented here will form the basis for the next generation of cell-scale models, which will begin to address the addition of soluble proteins, nucleic acids, and small molecules essential to the function of a cell.

A Robust Workflow for Acquiring and Preprocessing Ambient Vibration Data from Small Aperture Ocean Bottom Seismometer Arrays to Extract Scholte and Love Waves Phase-Velocity Dispersion Curves

The phase-velocity dispersion curve (DC) is an important characteristic of the propagation of surface waves in sedimentary environments. Although the procedure for DC estimation in onshore environments using ambient vibration recordings is well established, the DC estimation in offshore environments using Ocean Bottom Seismometers (OBS) array recordings of ambient vibrations presents three additional challenges: (1) the localization of sensors, (2) the orientation of the OBS horizontal components, and (3) the clock error. Here, we address these challenges in an inherent preprocessing workflow to ultimately extract the Love and Scholte wave DC from small aperture OBS array measurements performed between 2018 and 2020 in Lake Lucerne (Switzerland). The arrays have a maximum aperture of 679 m and a maximum deployment water depth of 81 m. The challenges related to the OBS location on the lake floor are addressed by combining the multibeam bathymetry map and the backscatter image for the investigated site with the differential GPS coordinates of the OBS at recovery. The OBS measurements are complemented by airgun surveys. Airgun data are first used to estimate the misorientation of the horizontal components of the OBS and second to estimate the clock error. To assess the robustness of the preprocessing workflow, we use two array processing methods, namely the three-component high-resolution frequency-wavenumber and the interferometric multichannel analysis of surface waves, to estimate the dispersion characteristics of the propagating Scholte and Love waves for one of the OBS array sites. The results show the effectiveness of the preprocessing workflow. We observe the phase-velocity dispersion curve branches in the frequency range between 1.2 and 3.2 Hz for both array processing techniques.

Assessment of potentially toxic elements (PTEs) sources on soils surrounding a fossil fuel power plant in a semi-arid/arid environment: A case study from the Sonoran Desert

Understanding the sources of potentially toxic elements (PTEs) in soils is a worldwide challenge that requires effective discrimination between geogenic and anthropogenic contributions, particularly in areas with certain geological complexity. This study aims to examine the chemical contents of 23 topsoil samples collected from the surroundings of a fossil fuel power plant in the village of Puerto Libertad (Sonoran Desert, Mexico). The study did not exclusively focus on the source identification of the priority PTEs to evaluate soil pollution. Furthermore, major oxides and immobile trace element (Zr, Hf, and REE: La→Lu) data were provided for a reliable assessment of the provenance of the soils. The relatively high SiO2 contents (65.26–75.42 wt%, anhydrous basis), the Post-Archean Australian Shale (PAAS)-normalized REE patterns, and the uniformity of the values of the Index of Compositional Variability (ICV = 1.11–2.72) and the Chemical Index of Alteration (CIA = 31.65–51.79) suggest that the soils were derived from intermediate to felsic source rocks, controlled by the local weathering of the parent bedrocks, under a low degree of chemical weathering conditions. The PTE data were treated following a robust workflow, which included the use of the enrichment factor (EF), the Spearman rank correlation (ρ), and multivariate statistical analyses allowed the generation of significant elemental associations and the identification of pools related either to the geological background or to anthropogenic activities. The results suggesting that Mo and Zn concentrations present a moderate anthropogenic influence while the concentrations of Pb, Sn, Cu, Cd, As, Cr, and Co are predominantly of geogenic origin. Vanadium (avg. EFV = 3.4) and Ni (avg. EFNi = 4.6) were the most enriched elements in the soils. Moreover, the highest values of the integrated Nemerow Pollution Index (PIN>3) were recorded at the sampling stations closer to the village, suggesting point-source pollution by the emissions of the power station. Finally, in this paper is traced the extent of the particulate released into the atmosphere, which can be dispersed in a wide area into the Sonoran Desert.

Evaluating the usefulness of the direct density reconstruction algorithm for intensity modulated and passively scattered proton therapy: Validation using an anthropomorphic phantom.

Accurate calculation of the proton beam range inside a patient is an important topic in proton therapy. In recent times, a computed tomography (CT) image reconstruction algorithm was developed for treatment planning to reduce the impact of the variation of the CT number with changes in imaging conditions. In this study, we investigated the usefulness of this new reconstruction algorithm (DirectDensity™: DD) in proton therapy based on its comparison with filtered back projection (FBP). We evaluated the effects of variations in the X-ray tube potential and target size on the FBP- and DD-image values and investigated the usefulness of the DD algorithm based on the range variations and dosimetric quantity variations. For X-ray tube potential variations, the range variation in the case of FBP was up to 12.5mm (20.8%), whereas that of DD was up to 3.3mm (5.6%). Meanwhile, for target size variations, the range variation in the case of FBP was up to 2.2mm (2.5%), whereas that of DD was up to 0.9mm (1.4%). Moreover, the variations observed in the case of DD were smaller than those of FBP for all dosimetric quantities. The dose distributions obtained using DD were more robust against variations in the CT imaging conditions (X-ray tube potential and target size) than those obtained using FBP, and the range variations were often less than the dose calculation grid (2mm). Therefore, the DD algorithm is effective in a robust workflow and reduces uncertainty in range calculations.

A multi‐tenancy and robust workflow management system

AbstractWorkflow management system (WfMS) in cloud always works as platform as a service to manage customized business processes for massive enterprises. In big data era, non‐functional guarantees of such systems are significant when facing a large number of users and concurrent requests. It is not trivial to support multi‐tenancy and hold high‐availability, because traditional architecture cannot simultaneously satisfy requirements about data isolation and runtime efficiency. In this paper, a modularized distributed workflow management system is proposed, which considers both multi‐tenancy and high‐availability in storage and engine parts of the system. A multiple‐worker‐with‐separate‐schema mechanism is defined to jointly manage the data for tenants, and a proactive strategy is presented to intelligently dispatch large concurrent requests from users to engine workers. After extensive case studies and experiments in practical scenes, our system deployed on modest machines is proved to support tens of thousands of tenants, second‐level response time for 10 K concurrency, and no‐human‐intervened failure recovery for a fail‐stop system node.

Computational screening of miRNAs and their targets in saffron (Crocus sativus L.) by transcriptome mining.

A robust workflow for the identification of miRNAs and their targets in saffron was developed. MicroRNA-mediated gene regulation in saffron is potentially involved in several biological processes, including the biosynthesis of highly valuable apocarotenoids. Saffron (Crocus sativus L.) is the most expensive spice in the world and a major source of apocarotenoids. Even though miRNAs (20-24 nt non-coding small RNAs) are important regulators of gene expression at transcriptional and post-transcriptional levels, their role in saffron has not been thoroughly investigated. As a result, a workflow for computational identification of miRNAs and their targets can be useful to uncover the regulatory networks underlying biological processes in this valuable plant. The efficiency of several assembly tools such as Trans-ABySS, Trinity, Bridger, rnaSPAdes, and EvidentialGene was evaluated based on both reference-based and reference-free metrics using transcriptome data. A reliable workflow for computational identification of miRNAs and their targets in saffron was described. The EvidentialGene was found to be the most efficient de novo transcriptome assembler for saffron as a complex triploid model, followed by the Trinity. In total, 66 miRNAs from 19 different families that target 2880 genes, including several transcription factors involved in the flowering transition, were identified. Three of the identified targets were involved in the terpenoids backbone biosynthesis. CsCCD and CsUGT genes involved in the apocarotenoids biosynthetic pathway were targeted by csa-miR156g and csa-miR156b-3p, revealing a unique post-transcriptional regulation dynamic in saffron. The identified miRNAs and their targets add to our understanding of the many biological roles of miRNAs in saffron and shed new light on the control of the apocarotenoid biosynthetic pathway in this valuable plant.