Flexible Workflow Research Articles

Automated Solar-Powered Aeroponics Structure for Plant Cultivation and Monitoring

Global food security is at risk due to climate change and outdated agricultural technology. The application of readily available technology and input management increased the food supply by 10 to 30 percent, raising a need for alternative farming practices. Furthermore, this study designed, developed, tested, and evaluated an automated, solar-powered aeroponics structure for plant cultivation and monitoring. A research and development approach was used, adapting the iterative model with its four phases: design (planning the prototype), development (creating program logic and assembling components), testing (functionality testing), and evaluation (assessing strengths and weaknesses of the prototype), creating a manageable and flexible workflow, allowing the researchers to incorporate changes based on ongoing feedback. The researchers constructed a prototype with three (3) main components: a growing chamber, a control panel, and a water reservoir. This system was programmed to read and transmit sensor data to the cloud for monitoring. Testing confirmed the functionality of all parts, and evaluation revealed the prototype to be both user-friendly and adaptable to user preferences. In conclusion, the aeroponics structure's high ratings in functionality, acceptability, and adaptability demonstrate a well-performing and user-friendly prototype. This aeroponics system offers a promising and efficient way to advance agricultural practices through its high adaptability to a wide range of conditions. Farmers will be able to modify the aeroponics structure to grow a variety of crops, including vegetables, as it advances. Moreover, the system requires less manual labor due to its automated features, making it an efficient and convenient agricultural innovation.

Learning-based defect inspection of laptop covers with a laser profile sensor

Quality inspection is a crucial process in the manufacturing industry, which significantly impacts the productivity and profits of a factory. To reduce costs and enhance quality control, companies are increasingly driving automation of this process due to the limitations of manual labour. However, the lack of flexible solutions that can adapt to the changing environment in factories has been a challenge. Computer-vision-based inspection methods often require hefty mechanisms and strict lighting conditions, which limit the investment in such solutions. Furthermore, with increasing demands for precision, traditional algorithms are struggling to cope with the problem, while data-driven solutions like deep learning require significant effort in acquiring and labelling data. To address these problems, this paper proposes an eye-in-hand system, i.e., a sensor attached to a robotic manipulator, integrated with unsupervised learning algorithms. One typical product, laptop bottom covers, is tested and discussed with regard to the system to validate our method since it has tiny defects and light-absorbing coatings. With only nine samples of the product, our system solved the case and demonstrated high-precision results and a flexible workflow. In this paper, our main contributions are: 1) we propose a flexible quality inspection system and workflow that needs extremely small volumes of data with unsupervised learning algorithms; and 2) we first propose using a laser profile sensor for collecting 2D intensity images, that are resilient to ambient light disturbance, of matt laptop cover data without lighting equipment.

Artificial Intelligence and Digital Pathology: A Narrative Review on Advancements and Opportunities for Improved Diagnosis and Treatment

Recent advancements in Digital Pathology (DP) have empowered pathologists to provide more accurate diagnosis through digital means. Whole-slide Imaging (WSI) technology has enabled the digital scanning, representation, and preservation of numerous tissue slides, while Artificial Intelligence (AI), image analysis, and Machine Learning (ML) have enhanced disease diagnosis accuracy. There is a growing acceptance of the transition from traditional glass slide histopathological diagnosis to AI-assisted diagnosis using digital slides, driven by the substantial data accumulation that demands computer-aided analysis. Maximising the potential of AI breakthroughs in DP is critical, offering significant research opportunities across related fields. DP offers significant potential for telepathology, second opinions, and educational purposes. Additionally, it presents substantial research opportunities in image computing due to its vast reservoir of data. Pathologists have discerned characteristics beyond the naked eye’s perception by analysing “sub-visual” images using DP. The flexibility of workflow provided by DP will be the main reason for its widespread adaptation and acceptance. DP also has the potential to be an essential tool to maintain operations of the pathology department in case of public health emergencies, which will streamline, fast-track, and improve patient care. Given the expanding accessibility and prevalence of the internet, it is crucial to develop innovations like DP. This technology catalyses enhancing patient care, opening avenues for further advancements in healthcare delivery. The purpose of present review was to bring to light the great potential that DP encompasses to improve diagnosis and treatment planning, which will ultimately lead to better patient care. However, integrating DP systems necessitates collaboration from various stakeholders beyond the Pathology Department. Despite evident advantages, several challenges must be addressed for the successful implementation and mass acceptance. Therefore, this narrative review aimed to illuminate the substantial potential inherent in DP for enhancing both diagnosis and treatment planning processes, consequently fostering improvements in patient care and also understanding DP, highlighting its challenges and opportunities. It also delves into the role of AI, image analysis, and ML in aiding disease diagnosis and reporting. With social distancing measures in place during the Coronavirus Disease -2019 (COVID-19) pandemic, pathologists were able to remotely access and analyse DP images, further cementing the importance of DP in current scenarios.

AN INNOVATIVE METHODOLOGY TO DESIGN GYROID HEAT EXCHANGERS FOR METAL ADDITIVE MANUFACTURING

This study demonstrates an innovative approach to the automatic design of compact gyroid heat exchangers using the advanced engineering software nTopology, which is based on implicit modeling technology. The aim is to provide a modern enhancement to traditional 'Shell and Tube' type heat exchangers. Utilizing functions in implicit modeling and parametric design features, complex internal gyroid structures can be used as an alternative heat transfer interface in 'Shell and Tube' type heat exchangers. The most striking aspect of the methodology is its ability to fully automate the design process. By consolidating specific parameters into a single function block and entering scalar values, a fast and flexible workflow is activated, automatically generating the final geometry. Gyroid structures with high thermal performance and fluid dynamics are automatically adapted for various volumes and geometries. These structures can improve the overall efficiency of heat exchangers and offer significant advantages, especially in specialized application areas such as aerospace and space industries. In the design and sizing process of the gyroid heat exchanger, if manufacturing is planned, it is necessary to pay attention to the design principles for metal additive manufacturing. In conclusion, this study demonstrates that the advanced engineering software known as nTopology can create a synergistic effect in the rapid and easy design of gyroid heat exchangers and in establishing the automation of the design process.

BLR: a flexible pipeline for haplotype analysis of multiple linked-read technologies.

Linked-read sequencing promises a one-method approach for genome-wide insights including single nucleotide variants (SNVs), structural variants, and haplotyping. We introduce Barcode Linked Reads (BLR), an open-source haplotyping pipeline capable of handling millions of barcodes and data from multiple linked-read technologies including DBS, 10× Genomics, TELL-seq and stLFR. Running BLR on DBS linked-reads yielded megabase-scale phasing with low (<0.2%) switch error rates. Of 13616 protein-coding genes phased in the GIAB benchmark set (v4.2.1), 98.6% matched the BLR phasing. In addition, large structural variants showed concordance with HPRC-HG002 reference assembly calls. Compared to diploid assembly with PacBio HiFi reads, BLR phasing was more continuous when considering switch errors. We further show that integrating long reads at low coverage (∼10×) can improve phasing contiguity and reduce switch errors in tandem repeats. When compared to Long Ranger on 10× Genomics data, BLR showed an increase in phase block N50 with low switch-error rates. For TELL-Seq and stLFR linked reads, BLR generated longer or similar phase block lengths and low switch error rates compared to results presented in the original publications. In conclusion, BLR provides a flexible workflow for comprehensive haplotype analysis of linked reads from multiple platforms.

Selective Characterization of mRNA 5' End-Capping by DNA Probe-Directed Enrichment with Site-Specific Endoribonucleases.

The N7-methyl guanosine cap structure is an essential 5' end modification of eukaryotic mRNA. It plays a critical role in many aspects of the life cycle of mRNA, including nuclear export, stability, and translation. Equipping synthetic transcripts with a 5' cap is paramount to the development of effective mRNA vaccines and therapeutics. Here, we report a simple and flexible workflow to selectively isolate and analyze structural features of the 5' end of an mRNA by means of DNA probe-directed enrichment with site-specific single-strand endoribonucleases. Specifically, we showed that the RNA cleavage by site-specific RNases can be effectively steered by a complementary DNA probe to recognition sites downstream of the probe-hybridized region, utilizing a flexible range of DNA probe designs. We applied this approach using human RNase 4 to isolate well-defined cleavage products from the 5' end of diverse uridylated and N1-methylpseudouridylated mRNA 5' end transcript sequences. hRNase 4 increases the precision of the RNA cleavage, reducing product heterogeneity while providing comparable estimates of capped products and their intermediaries relative to the widely used RNase H. Collectively, we demonstrated that this workflow ensures well-defined and predictable 5' end cleavage products suitable for analysis and relative quantitation of synthetic mRNA 5' cap structures by UHPLC-MS/MS.

FLARE: a fast and flexible workflow for identifying RNA editing foci

BackgroundFusion of RNA-binding proteins (RBPs) to RNA base-editing enzymes (such as APOBEC1 or ADAR) has emerged as a powerful tool for the discovery of RBP binding sites. However, current methods that analyze sequencing data from RNA-base editing experiments are vulnerable to false positives due to off-target editing, genetic variation and sequencing errors.ResultsWe present FLagging Areas of RNA-editing Enrichment (FLARE), a Snakemake-based pipeline that builds on the outputs of the SAILOR edit site discovery tool to identify regions statistically enriched for RNA editing. FLARE can be configured to analyze any type of RNA editing, including C to U and A to I. We applied FLARE to C-to-U editing data from a RBFOX2-APOBEC1 STAMP experiment, to show that our approach attains high specificity for detecting RBFOX2 binding sites. We also applied FLARE to detect regions of exogenously introduced as well as endogenous A-to-I editing.ConclusionsFLARE is a fast and flexible workflow that identifies significantly edited regions from RNA-seq data. The FLARE codebase is available at https://github.com/YeoLab/FLARE.

GelMap: intrinsic calibration and deformation mapping for expansion microscopy

Expansion microscopy (ExM) is a powerful technique to overcome the diffraction limit of light microscopy by physically expanding biological specimen in three dimensions. Nonetheless, using ExM for quantitative or diagnostic applications requires robust quality control methods to precisely determine expansion factors and to map deformations due to anisotropic expansion. Here we present GelMap, a flexible workflow to introduce a fluorescent grid into pre-expanded hydrogels that scales with expansion and reports deformations. We demonstrate that GelMap can be used to precisely determine the local expansion factor and to correct for deformations without the use of cellular reference structures or pre-expansion ground-truth images. Moreover, we show that GelMap aids sample navigation for correlative uses of expansion microscopy. Finally, we show that GelMap is compatible with expansion of tissue and can be readily implemented as a quality control step into existing ExM workflows.

Generative pretrained autoregressive transformer graph neural network applied to the analysis and discovery of novel proteins

We report a flexible language-model-based deep learning strategy, applied here to solve complex forward and inverse problems in protein modeling, based on an attention neural network that integrates transformer and graph convolutional architectures in a causal multi-headed graph mechanism, to realize a generative pretrained model. The model is applied to predict the secondary structure content (per-residue level and overall content), protein solubility, and sequencing tasks. Further trained on inverse tasks, the model is rendered capable of designing proteins with these properties as target features. The model is formulated as a general framework, completely prompt-based, and can be adapted for a variety of downstream tasks. We find that adding additional tasks yields emergent synergies that the model exploits in improving overall performance, beyond what would be possible by training a model on each dataset alone. Case studies are presented to validate the method, yielding protein designs specifically focused on structural materials, but also exploring the applicability in the design of soluble, antimicrobial biomaterials. While our model is trained to ultimately perform eight distinct tasks, with available datasets, it can be extended to solve additional problems. In a broader sense, this study illustrates a form of multiscale modeling that relates a set of ultimate building blocks (here, byte-level utf8 characters that define the nature of the physical system at hand) to complex output. This materiomic scheme captures complex emergent relationships between universal building block and resulting properties, via a synergizing learning capacity, to express a set of potentialities embedded in the knowledge used in training via the interplay of universality and diversity. Significance statement: Predicting the properties of materials based on a flexible description of their structure, environment, or process, is a long-standing challenge in multiscale modeling. Our MaterioFormer language model, trained to solve forward and inverse tasks, incorporates a deep learning capacity through attention and graph strategies to yield a multimodal approach to model and design materials. Since our model is prompt-based and information is encoded consistently via byte-level utf8 tokenization, it can process diverse modalities of information, such as sequence data, description of tasks, and numbers, and offers a flexible workflow that integrates human intelligence and artificial intelligence. Autoregressive training, using pre-training against a large unlabeled dataset, allows for straightforward adjustment of specific objectives.

Using multiple qualitative methods to inform intervention development: Improving functional status measurement for older veterans in primary care settings.

Functional status, or the ability to perform activities of daily living, is central to older adults' health and quality of life. However, health systems have been slow to incorporate routine measurement of function into patient care. We used multiple qualitative methods to develop a patient-centered, interprofessional intervention to improve measurement of functional status for older veterans in primary care settings. We conducted semi-structured interviews with patients, clinicians, and operations staff (n = 123) from 7 Veterans Health Administration (VHA) Medical Centers. Interviews focused on barriers and facilitators to measuring function. We used concepts from the Consolidated Framework for Implementation Science and sociotechnical analysis to inform rapid qualitative analyses and a hybrid deductive/inductive approach to thematic analysis. We mapped qualitative findings to intervention components. Barriers to measurement included time pressures, cumbersome electronic tools, and the perception that measurement would not be used to improve patient care. Facilitators included a strong interprofessional environment and flexible workflows. Findings informed the development of five intervention components, including (1) an interprofessional educational session; (2) routine, standardized functional status measurement among older patients; (3) annual screening by nurses using a standardized instrument and follow-up assessment by primary care providers; (4) electronic tools and templates to facilitate increased identification and improved management of functional impairment; and (5) tailored reports on functional status for clinicians and operations leaders. These findings show how qualitative methods can be used to develop interventions that are more responsive to real-world contexts, increasing the chances of successful implementation. Using a conceptually-grounded approach to intervention development has the potential to improve patient and clinician experience with measuring function in primary care.

Interactive analysis of single-cell data using flexible workflows with SCTK2

Analysis of single-cell RNA sequencing (scRNA-seq) data can reveal novel insights into the heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analyses. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single-Cell Toolkit 2 (SCTK2), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users.

Human-cyber-physical system for post-digital design and construction of lightweight timber structures

Current digital design-to-build workflows are limited to distinct tasks and pre-programmed machine operations, and the lack of system adaptability makes it difficult to accommodate non-linear construction processes and unexpected incidents. This work questions how to address humanization in post-digital architectural practices in order to increase workflow flexibility. Taking a lightweight timber structure as a demonstrator, this work describes its design-to-build procedure embracing the idea of the human-cyber-physical system (HCPS). The complex structural geometry was realized through the combination of computational form-finding method, human-guided robotic fabrication, and mixed reality (MR)-aided assembly and analysis. The human-in-the-loop workflow discussed in this work enables bidirectional communication between the virtual and the actual, and suggests humans' intuitive intervention in the design materialization processes. Future research will focus on human augmentation in full-scale architectural practices concerning novel human-robot interactions with more accurate sensing and gesture-tracking methods.

UniDec Processing Pipeline for Rapid Analysis of Biotherapeutic Mass Spectrometry Data.

Recent advances in native mass spectrometry (MS) and denatured intact protein MS have made these techniques essential for biotherapeutic characterization. As MS analysis has increased in throughput and scale, new data analysis workflows are needed to provide rapid quantitation from large datasets. Here, we describe the UniDec processing pipeline (UPP) for the analysis of batched biotherapeutic intact MS data. UPP is built into the UniDec software package, which provides fast processing, deconvolution, and peak detection. The user and programming interfaces for UPP read a spreadsheet that contains the data file names, deconvolution parameters, and quantitation settings. After iterating through the spreadsheet and analyzing each file, it returns a spreadsheet of results and HTML reports. We demonstrate the use of UPP to measure the correct pairing percentage on a set of bispecific antibody data and to measure drug-to-antibody ratios from antibody-drug conjugates. Moreover, because the software is free and open-source, users can easily build on this platform to create customized workflows and calculations. Thus, UPP provides a flexible workflow that can be deployed in diverse settings and for a wide range of biotherapeutic applications.

Method versatility in RNA extraction-free PCR detection of SARS-CoV-2 in saliva samples

Early in the pandemic, a simple, open-source, RNA extraction-free RT-qPCR protocol for SARS-CoV-2 detection in saliva was developed and made widely available. This simplified approach (SalivaDirect) requires only sample treatment with proteinase K prior to PCR testing. However, feedback from clinical laboratories highlighted a need for a flexible workflow that can be seamlessly integrated into their current health and safety requirements for the receiving and handling of potentially infectious samples. To address these varying needs, we explored additional pre-PCR workflows. We built upon the original SalivaDirect workflow to include an initial incubation step (95 °C for 30 min, 95 °C for 5 min or 65 °C for 15 min) with or without addition of proteinase K. The limit of detection for the workflows tested did not significantly differ from that of the original SalivaDirect workflow. When tested on de-identified saliva samples from confirmed COVID-19 individuals, these workflows also produced comparable virus detection and assay sensitivities, as determined by RT-qPCR analysis. Exclusion of proteinase K did not negatively affect the sensitivity of the assay. The addition of multiple heat pretreatment options to the SalivaDirect protocol increases the accessibility of this cost-effective SARS-CoV-2 test as it gives diagnostic laboratories the flexibility to implement the workflow which best suits their safety protocols.

Scipion Flexibility Hub: an integrative framework for advanced analysis of conformational heterogeneity in cryoEM.

Understanding how structure and function meet to drive biological processes is progressively shifting the cryoEM field towards a more advanced analysis of macromolecular flexibility. Thanks to techniques such as single-particle analysis and electron tomography, it is possible to image a macromolecule in different states, information that can subsequently be extracted through advanced image-processing methods to build a richer approximation of a conformational landscape. However, the interoperability of all of these algorithms remains a challenging task that is left to users, preventing them from defining a single flexible workflow in which conformational information can be addressed by different algorithms. Therefore, in this work, a new framework integrated into Scipion is proposed called the Flexibility Hub. This framework automatically handles intercommunication between different heterogeneity software, simplifying the task of combining the software into workflows in which the quality and the amount of information extracted from flexibility analysis is maximized.

Designing an app for alternative access assessments: using interviews to uncover and define user needs

ABSTRACT To support effective alternative access assessment and service delivery for people with motor impairments, we are developing an app called Access Navigator. This paper describes the first stage of the project: the interviews we conducted as part of a discovery process to improve our understanding of user needs. We individually interviewed 8 assistive technology practitioners and 3 clients who use alternative access methods, to learn what practitioners were currently doing in their access assessments, identify opportunities for improving current practice, and understand what the assessment process was like for clients. We extracted 236 relevant quotes from interview transcripts, and clustered the quotes using the affinity wall method, yielding 42 themes. Here we present the 21 highest priority themes, along with the app requirements that support those themes. Results confirmed the need for a tool to support more effective assessments, with emphasis on taking the worry out of access assessments and supporting early success. Results also revealed specific needs such as flexible workflow, personalized test-drive tasks, and a choice of metrics (including client preference). With a richer understanding of user needs, we are in a much better position to design software that can address those needs.

The Quantification of Bacterial Cell Size: Discrepancies Arise from Varied Quantification Methods.

The robust regulation of the cell cycle is critical for the survival and proliferation of bacteria. To gain a comprehensive understanding of the mechanisms regulating the bacterial cell cycle, it is essential to accurately quantify cell-cycle-related parameters and to uncover quantitative relationships. In this paper, we demonstrate that the quantification of cell size parameters using microscopic images can be influenced by software and by the parameter settings used. Remarkably, even if the consistent use of a particular software and specific parameter settings is maintained throughout a study, the type of software and the parameter settings can significantly impact the validation of quantitative relationships, such as the constant-initiation-mass hypothesis. Given these inherent characteristics of microscopic image-based quantification methods, it is recommended that conclusions be cross-validated using independent methods, especially when the conclusions are associated with cell size parameters that were obtained under different conditions. To this end, we presented a flexible workflow for simultaneously quantifying multiple bacterial cell-cycle-related parameters using microscope-independent methods.

Spatial Optimization with Morphological Spatial Pattern Analysis for Green Space Conservation Planning

Conservation areas are essential for preserving green spaces and biological diversity. Although previous studies have demonstrated that spatial optimization techniques are effective for balancing the relationship between ecological importance and spatial pattern during conservation practices, the design of ecological corridors still requires an efficient, intelligent, and flexible workflow. In addition, functional connectivity information is usually unavailable or very difficult to obtain. To alleviate these problems, this paper has developed a new spatial optimization-based model that combines morphological spatial pattern analysis (MSPA) with ecological importance assessment. The consideration of MSPA can guarantee enough ecological corridors in the conservation plan, while the regions with higher ecological importance can be discovered through an ecological importance assessment. This method has been applied to the planning of conservation areas in a highly developed city. Several experiments have indicated that our proposed model could achieve much better performance than conventional models in terms of spatial pattern. Therefore, this new model is expected to assist decision processes during the planning and regulation of green spaces in fragmented urban ecosystems. Furthermore, it can be applied to ecological management and planning in many other aspects because the above-mentioned research gaps are not unique to only Asian or less-developed countries.

The BD Rhapsody&amp;[trade] HT Xpress System supports dynamic and high-throughput single-cell capture enabling a seamless workflow for analysis of cellular immune responses

Abstract The BD Rhapsody™ Single-Cell Analysis System is an innovative platform that aids high-throughput sequencing of mRNA and proteins in thousands of single cells. Here we present a new eight-lane cartridge design concept, the BD Rhapsody™ HT Xpress System. This novel device architecture can reduce both hands-on and assay time by enabling the users to simultaneously load multiple cell samples while preserving the flexibility to use one lane at a time. To demonstrate the increased throughput capabilities and workflow flexibility of the system, we measured in vitro T cell activation over time. To do this, peripheral blood mononuclear cells from four different subjects were prepared for cell culture in IL-2 or IL-2/anti-CD3/anti-CD28. After one day in culture, cell samples from each culture condition were barcoded using the BD® Single-Cell Multiplexing Kit. Cells from the same subject were pooled and stained with the BD® AbSeq Immune Discovery Panel. Pooled cells from each subject were loaded into the cartridge (one subject per lane, four lanes total). Remaining cells were cultured for another day and similarly barcoded, stained and loaded into the other four lanes of the cartridge. Eight different next generation sequencing libraries were generated and analyzed with the BD Rhapsody™ Analysis Pipeline and SeqGeq™ Software. This strategy enabled us to assess dynamic changes in both T cell surface protein profiles and transcriptome during cell activation while using a streamlined workflow for concomitant analysis of multiple samples. For Research Use Only. Not for use in diagnostic or therapeutic procedures. BD, the BD Logo, BD Rhapsody and SeqGeq are trademarks of Becton, Dickinson and Company or its affiliates. © 2023 BD. All rights reserved.

Assessing resource use: a case study with the Human Disease Ontology.

As a genomic resource provider, grappling with getting a handle on how your resource is utilized can be extremely challenging. At the same time, being able to thus document the plethora of use cases is vital to demonstrate sustainability. Herein, we describe a flexible workflow, built on readily available software, that the Human Disease Ontology (DO) project has utilized to transition to semi-automated methods to identify uses of the ontology in the published literature. The novel R package DO.utils (https://github.com/DiseaseOntology/DO.utils) has been devised with a small set of key functions to support our usage workflow in combination with Google Sheets. Use of this workflow has resulted in a 3-fold increase in the number of identified publications that use the DO and has provided novel usage insights that offer new research directions and reveal a clearer picture of the DO's use and scientific impact. The DO's resource use assessment workflow and the supporting software are designed to be useful to other resources, including databases, software tools, method providers and other web resources, to achieve similar results. Database URL: https://github.com/DiseaseOntology/DO.utils.