Flexible Workflow Research Articles

Software update enhances workflow flexibility and expands manufacturing capabilities.

Software update enhances workflow flexibility and expands manufacturing capabilities.

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

This paper introduces a portable framework for developing, scaling and maintaining energy management and information systems (EMIS) applications using an ontology-based approach. Key contributions include an interoperable layer based on Brick schema, the formalization of application constraints pertaining metadata and data requirements, and a field demonstration. The framework allows for querying metadata models, fetching data, preprocessing, and analyzing data, thereby offering a modular and flexible workflow for application development. Its effectiveness is demonstrated through a case study involving the development and implementation of a data-driven anomaly detection tool for the photovoltaic systems installed at the Politecnico di Torino, Italy. During eight months of testing, the framework was used to tackle practical challenges including: (i) developing a machine learning-based anomaly detection pipeline, (ii) replacing data-driven models during operation, (iii) optimizing model deployment and retraining, (iv) handling critical changes in variable naming conventions and sensor availability (v) extending the pipeline from one system to additional ones.

A novel barcoded nanopore sequencing workflow of high-quality, full-length bacterial 16S amplicons for taxonomic annotation of bacterial isolates and complex microbial communities.

Due to recent improvements, Nanopore sequencing has become a promising method for experiments relying on amplicon sequencing. We describe a flexible workflow to generate and annotate high-quality, full-length 16S rDNA amplicons. We evaluated it for two applications, namely, (i) identification of bacterial isolates and (ii) species-level profiling of microbial communities. We assessed the identification of single bacterial isolates by sequencing, using a set of barcoded full-length 16S rRNA gene primer pairs (pair A), on 47 isolates encompassing multiple genera and compared those results with matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS)-based identification. Species-level community profiling was tested with two sets of barcoded full-length 16S primer pairs (A and B) and compared to the results obtained with shotgun Illumina sequencing using 27 stool samples. We developed a Nextflow pipeline to retain high-quality reads and taxonomically annotate them. We found high agreement between our workflow and MALDI-TOF data for isolate identification (positive predictive value = 0.90, Cramér's V = 0.857, and Theil's U = 0.316). For species-level community profiling, we found strong correlations (rs > 0.6) of alpha diversity indices between the two primer sets and Illumina sequencing. At the community level, we found significant but small differences when comparing sequencing techniques. Finally, we found a moderate to strong correlation when comparing the relative abundances of individual species (average rs = 0.6 and 0.533 for primers A and B). Despite identified shortcomings, the proposed workflow enabled accurate identification of single bacterial isolates and prominent features in microbial communities, making it a worthwhile alternative to MALDI-TOF MS and Illumina sequencing.IMPORTANCEA quick, robust, simple, and cost-effective method to identify bacterial isolates and communities in each sample is indispensable in the fields of microbiology and infection biology. Recent technological advances in Oxford Nanopore Technologies sequencing make this technique an attractive option considering the adaptability, portability, and cost-effectiveness of the platform, even with small sequencing batches. Here, we validated a flexible workflow to identify bacterial isolates and characterize bacterial communities using the Oxford Nanopore Technologies sequencing platform combined with the most recent v14 chemistry kits. For bacterial isolates, we compared our nanopore-based approach to matrix-assisted laser desorption ionization-time of flight mass spectrometry-based identification. For species-level profiling of complex bacterial communities, we compared our nanopore-based approach to Illumina shotgun sequencing. For reproducibility purposes, we wrapped the code used to process the sequencing data into a ready-to-use and self-contained Nextflow pipeline.

Insights Toward Thermal Stability and Electrolyte Distribution: A Hybrid Metrology Approach

Gas evolution in Li and Na ion batteries is a key problem as it is a sign of electrode degradation and electrolyte reduction. Gas evolution is caused by breakdown of electrolyte solvents and can release flammable gases leading to thermal runaway. As new and more stable electrodes, electrolyte, or electrolyte additives are implemented, it is crucial to monitor their performance and ensure thermal safety. Covalent Metrology is an analytical service lab based in Sunnyvale CA that serves as an extension of your battery R&D team. First, I will describe Covalent Metrology’s expertise, capabilities, and flexible workflows. I will then highlight some well-established analytical methods, such as GCMS and NMR to characterize volatile and non-volatile breakdown products of the electrolyte. Lastly, I will discuss the use of Scanning Acoustic Microscopy (SAM) and Glow Discharge Optical Emission Spectroscopy (GD-OES) to investigate the spatial distribution of electrolyte in a battery. We believe this hybrid metrology approach can provide important feedback toward developing better batteries more safely.

Evaluating flood hazards in data-sparse coastal lowlands: highlighting the Ayeyarwady Delta (Myanmar)

Coastal lowlands and river deltas worldwide are increasingly exposed to coastal, pluvial and fluvial flooding as well as relative sea-level rise (RSLR). However, information about both single and multiple flood-type hazards, their potential impact and the characteristics of areas, population and assets at risk is often still limited as high-quality data either does not exist or is not accessible. This often constitutes a main barrier for generating sound assessments, especially for scientific and public communities in the so-called Global South. We provide a standardised, integrative approach for the first-order assessment of these single and multiple flood-type hazards and show how this can be conducted for data-sparse, hardly accessible and inaccessible coastal lowlands such as the Ayeyarwady Delta in Myanmar by using only open accessible and freely available datasets of satellite imagery, global precipitation estimates, satellite-based river discharge measurements, elevation, land use, and population data. More than 70% of the delta, mainly used for agriculture, and about 40% of its present population are prone to flooding due to either monsoon precipitation and runoff, storm surge, and RSLR, or their combination, jeopardising food security and economic development in the region. The approach allows for the integration and combination of various datasets, combined in a highly flexible workflow that performs at low computational capacities, supporting the evaluation of flood-prone areas on regional and local scale for data-sparse coastal lowlands worldwide. It thereby allows to attribute different types of flood hazards, complements concepts of vulnerability and risk, and supports risk-informed decision making and development of effective multi-flooding adaptation strategies.

Gapr for large-scale collaborative single-neuron reconstruction.

Whole-brain analysis of single-neuron morphology is crucial for unraveling the complex structure of the brain. However, large-scale neuron reconstruction from terabyte and even petabyte data of mammalian brains generated by state-of-the-art light microscopy is a daunting task. Here, we developed 'Gapr' (Gapr accelerates projectome reconstruction) that streamlines deep learning-based automatic reconstruction, 'automatic proofreading' that reduces human workloads at high-confidence sites, and high-throughput collaborative proofreading by crowd users through the Internet. Furthermore, Gapr offers a seamless user interface that ensures high proofreading speed per annotator, on-demand conversion for handling large datasets, flexible workflows tailored to diverse datasets and rigorous error tracking for quality control. Finally, we demonstrated Gapr's efficacy by reconstructing over 4,000 neurons in mouse brains, revealing the morphological diversity in cortical interneurons and hypothalamic neurons. Here, we present Gapr as a solution for large-scale single-neuron reconstruction projects.

NIMO: A graphical user interface‐based R package for species distribution modelling

Abstract Species distribution modelling (SDM) is essential for understanding and predicting biodiversity patterns globally. However, the complexities of data preparation, algorithm selection and model evaluation can present significant challenges. This paper introduces NIMO, an R package with a user‐friendly graphical interface aimed at streamlining the entire SDM workflow. In SDM, data preparation is a crucial and time‐consuming process, which requires the acquisition of spatiotemporal occurrence records. The Global Biodiversity Information Facility (GBIF) database plays a key role in addressing data scarcity by providing extensive community science‐based repositories. However, compiling, aligning and verifying spatial references can be laborious. NIMO addresses these challenges by offering a flexible workflow that allows users to more easily query biodiversity data. NIMO organizes its features into three main steps: pre‐modelling, modelling and post‐modelling. It empowers users to optimize data for modelling with a variety of algorithms. NIMO allows for default hyperparameter values or extensive tuning, providing users with flexibility in selecting models. Practical implication: To demonstrate the effectiveness of NIMO, we presented a case study focusing on the critically endangered white‐backed vulture (Gyps africanus) in Kruger National Park, South Africa. We used NIMO to collect GBIF occurrence data and integrated environmental predictors to map suitable habitat (probability of occurrence). The distribution modelling workflow included area calibration, collinearity check, generation of training and testing data sets, model fitting and model combination. The results showcase the discriminative ability of fitted models, especially the generalized linear model, support vector machine and random forest, that constituted the ensemble model. Spatial prediction using the ensemble model revealed that the species has a higher occurrence probability close to roads, indicating a possible increased risk of mortality for the vultures in Kruger National Park from motor vehicle collisions.

Optimized continuous homecare provisioning through distributed data-driven semantic services and cross-organizational workflows

BackgroundIn healthcare, an increasing collaboration can be noticed between different caregivers, especially considering the shift to homecare. To provide optimal patient care, efficient coordination of data and workflows between these different stakeholders is required. To achieve this, data should be exposed in a machine-interpretable, reusable manner. In addition, there is a need for smart, dynamic, personalized and performant services provided on top of this data. Flexible workflows should be defined that realize their desired functionality, adhere to use case specific quality constraints and improve coordination across stakeholders. User interfaces should allow configuring all of this in an easy, user-friendly way.MethodsA distributed, generic, cascading reasoning reference architecture can solve the presented challenges. It can be instantiated with existing tools built upon Semantic Web technologies that provide data-driven semantic services and constructing cross-organizational workflows. These tools include RMLStreamer to generate Linked Data, DIVIDE to adaptively manage contextually relevant local queries, Streaming MASSIF to deploy reusable services, AMADEUS to compose semantic workflows, and RMLEditor and Matey to configure rules to generate Linked Data.ResultsA use case demonstrator is built on a scenario that focuses on personalized smart monitoring and cross-organizational treatment planning. The performance and usability of the demonstrator’s implementation is evaluated. The former shows that the monitoring pipeline efficiently processes a stream of 14 observations per second: RMLStreamer maps JSON observations to RDF in 13.5 ms, a C-SPARQL query to generate fever alarms is executed on a window of 5 s in 26.4 ms, and Streaming MASSIF generates a smart notification for fever alarms based on severity and urgency in 1539.5 ms. DIVIDE derives the C-SPARQL queries in 7249.5 ms, while AMADEUS constructs a colon cancer treatment plan and performs conflict detection with it in 190.8 ms and 1335.7 ms, respectively.ConclusionsExisting tools built upon Semantic Web technologies can be leveraged to optimize continuous care provisioning. The evaluation of the building blocks on a realistic homecare monitoring use case demonstrates their applicability, usability and good performance. Further extending the available user interfaces for some tools is required to increase their adoption.

Conceptual Framework for Flexible Workflow Generation in Federated ERP Systems mediated by a FERP Mall

Conceptual Framework for Flexible Workflow Generation in Federated ERP Systems mediated by a FERP Mall

A Phenomenological Inquiry of the Shift to Virtual Care Delivery: Insights from Front-Line Primary Care Providers.

The rapid deployment of virtual primary care visits served as a first-line response to COVID-19 and can now be examined for insights, particularly as virtual care is playing an ongoing role in patient care and consultations. Input from primary care providers directly responsible for virtual care delivery is needed to inform policies and strategies for quality care and interactions. The overarching goal of this research study was to examine the use of virtual care as a mechanism for primary healthcare delivery. A phenomenological approach investigated the shift in primary care service delivery as experienced by primary care providers and initiated during the COVID-19 pandemic. Focus groups were conducted with primary care providers (n = 21) recruited through email, advertisements, and professional organizations, exploring how virtual care was delivered, the benefits and challenges, workflow considerations, and recommendations for future use. Integrating virtual care was performed with a great deal of autonomy as well as responsibility, and overwhelmingly depended on the telephone. Technology, communication, and workflow flexibility are three key operational aspects of virtual care and its delivery. Providers highlighted cross-cutting themes related to the dynamics of virtual care including balancing risk for quality care, physician work/life balance, efficiency, and patient benefits. Primary care providers felt that virtual care options allowed increased flexibility to attend to the needs of patients and manage their practice workload, and a few scenarios were shared for when virtual care might be best suited. However, they also recognized the need to balance in-person and virtual visits, which may require guidelines that support navigating various levels of care. Overall, virtual care was considered a good addition to the whole 'care package' but continued development and refinement is an expectation for optimizing and sustaining future use.

Abstract 79: Enabling fast and convenient immune profiling of fresh and long-term stabilized human whole blood samples with CyTOF

Abstract Accurate phenotyping of immune cells in whole blood (WB) from patients with cancer is critical for making disease prognoses and monitoring clinical efficacy of immunotherapies. Fresh WB must be analyzed within 24 hours of collection to minimize changes in cellular composition. However, WB collection and cytometric analysis are often performed at different sites, which can result in sample processing delays. Several WB preservation reagents have been developed to address this challenge, including PROT1 (Smart Tube Inc.) and Cytodelics Whole Blood Cell Stabilizer (Cytodelics). However, not all antibody panels are compatible with these reagents. A CyTOF® panel was developed to be compatible with these commercial WB stabilizers and for use in pharmaceutical and clinical research. CyTOF flow cytometry uses metal-tagged antibodies to identify cellular and functional phenotypes. Advantages and features of CyTOF technology enable rapid design and application of 50-plus-marker panels and convenient workflows in which samples can be stained and acquired in a single tube. Compensation is not required since CyTOF flow cytometry has low signal spillover and no autofluorescence. Moreover, antibody cocktails and stained samples can be frozen for later use and acquisition. Thus, CyTOF technology overcomes major hurdles of fluorescence-based cytometry and provides a streamlined and flexible workflow in clinical research. The CyTOF panel contains 20 antibodies to identify over 30 immune cell populations. For easy customization, there are more than 30 additional open channels to analyze markers of interest. The panel works with fresh and stabilized WB samples and is amenable to different staining and acquisition workflows. To show the flexibility of sample staining and stabilization, WB from three healthy donors was assessed using two stabilization workflows. First, fresh WB samples were stained with the antibody panel, followed by PROT1 or Cytodelics stabilization and storage at -80 °C. The second workflow involved immediate stabilization/fixation of WB with PROT1 or Cytodelics and storage at -80 °C. Subsequently, the samples for the second workflow were thawed, surface stained, and acquired. To reduce technical variability from staining, the antibodies were pooled together and frozen at -80 °C as single-use aliquots. Furthermore, all samples were barcoded and acquired as a single tube to reduce variability from sample acquisition. The CyTOF panel developed for broad immune profiling is compatible with WB stabilizers, which overcomes traditional and logistical challenges with WB processing and acquisition. Furthermore, freezing antibody cocktails is a unique feature of CyTOF flow cytometry, ensuring batch-to-batch consistency in clinical research. Thus, CyTOF workflows enable swift and convenient analysis of WB samples. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Michael Cohen, Shakir Hasan, Stephen Li, Christina Loh. Enabling fast and convenient immune profiling of fresh and long-term stabilized human whole blood samples with CyTOF [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 79.

Abstract 334: Flexible and high-throughput microwell-based single-cell capture for multiomic ATAC-seq and RNA-seq profiling

Abstract The simultaneous profiling of chromatin accessibility and gene expression is an active area of interest for researchers studying how epigenetic changes regulate gene expression at the single-cell level. Here, we demonstrate the feasibility of single nuclei assay for transposase-accessible chromatin (snATAC) seq either with or without simultaneous gene expression profiling using the microwell-based BD Rhapsody™ HT Xpress System. Taking advantage of an eight-lane microwell cartridge, the BD Rhapsody™ HT Xpress System can support the snATAC seq assay with nuclei input ranging from 1,000 to 400,000 nuclei in a single experiment. To demonstrate the flexible throughput capabilities, 1,000 to 50,000 nuclei from cryopreserved peripheral blood mononuclear cells (PBMCs) were tagmented and loaded onto a single lane of the BD Rhapsody™ HT Xpress System. The snATAC assay libraries were prepared and sequenced, revealing multiple high-quality ATAC-seq metrics: high counts of unique ATAC fragments, high fraction of reads in peak regions (FRiP), and high transcriptional start site (TSS) enrichment score. The data were obtained consistently across the wide range of input nuclei with use of the gentle and robust nuclei loading capabilities of the BD Rhapsody™ HT Xpress System. Moreover, after capturing ATAC fragments, the BD Rhapsody™ Enhanced Cell Capture Beads are stable and can be stored at 4 °C for up to 3 months, allowing researchers the flexibility in sequencing subsets of nuclei or lanes for QC purposes before sequencing the entire library and returning to samples for complete sequencing. Furthermore, to demonstrate the consistency of both open chromatin profiling and mRNA gene expression as a combination (multiomic snATAC + snRNAseq), firstly, we compared snRNAseq data from a whole transcriptome assay (WTA) with snRNAseq from an ATAC+mRNA multiomic assay using the same sample. This strategy enabled us to show that there is negligible gene expression differences between snRNAseq from the BD whole transcriptome assay and snRNAseq from the multiomic scATAC and scRNA assay. Likewise, the snATACseq assay and snATACseq assay with simultaneous whole transcriptome profiling shows that chromatin accessibility profiling is equivalent with and without gene expression profiling. Enabling this new scATAC assay on the BD Rhapsody™ HT Xpress System can advance the study of epigenetic regulation of gene expression with a robust and flexible workflow. This will allow researchers to analyze both open chromatin and mRNA at varying nuclei inputs with single-cell resolution to understand the underlying molecular mechanisms in gene expression regulatory pathways. This assay can be readily extended to broader samples to gain greater understanding of disease states in immunology and immuno-oncology studies. For Research Use Only. Not for use in diagnostic or therapeutic procedures. © 2023 BD. All rights reserved. Citation Format: Rosary Nguyen, Hongduan Huang, Quyen Bao, Punya Narayan, Hye-Won Song, Elham Hatami, Zhiqi Zhang, Thomas McCarthy, Youngsook Kim, Ruifang Li, Chelsea Gordon, Larry Wang, Aruna Ayer. Flexible and high-throughput microwell-based single-cell capture for multiomic ATAC-seq and RNA-seq profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 334.

Abstract 4135: First demonstration of magnetic particle imaging for sentinel lymph node identification

Abstract Sentinel Lymph Node (LN) biopsy involves the identification and surgical removal of the first LN(s) that drain from a primary tumor to evaluate for metastasis by histopathology [1]. For several tumor types, the standard of care is to manage the regional LN basin separately from the primary tumor. For head and neck cancer, melanoma, and complex breast cancer cases, pre-surgical imaging is required to determine the number and location of LN(s) to remove. Most sentinel LN biopsies are performed with nuclear imaging, which relies on short-lived radiotracers and can have poor image quality, making it challenging to identify sentinel LNs in complex anatomies. An alternative and non-inferior workflow uses a non-radioactive iron oxide magnetic tracer (ferucarbotran) with a magnetic probe [2], however this tool can only be used intraoperatively. In this abstract we introduce magnetic particle imaging (MPI) as a pre-surgical imaging technology that detects iron oxides with high sensitivity at mm-scale resolution [3]. Our objective is to demonstrate that MPI provides sensitive and quantitative tracking of ferucarbotran pharmacokinetics from four anatomical sites to primary draining LNs in mice. Methods: Ferucarbotran was administered intradermally to C57BL/6 mice at a standard clinical dose of 0.675 mg Fe/kg to the forepaw, hindpaw, or base of tail, or tongue (n = 4). Full-body 2D and 3D imaging was performed after 20 mins, 24 h, and 48 h, and 144 h using MOMENTUM imager (Magnetic Insight Inc.). MPI signal was quantified at the injection site and draining LNs. LNs of interest were extracted to verify MPI signals ex vivo then were processed for Perl’s Prussian iron staining. Results: After 20 minutes, MPI signal was seen at the injection site and primary LNs. The pharmacokinetics of ferucarbotran to LNs varied based on administration site. For hindpaw, signal was present in the popliteal LN (1.4% of tracer). For forepaw, ferucarbotran accumulated in the primary axillary LN (9%). For base of the tail, signal was observed in inguinal LN (2%). For tongue, MPI signal was detected in cervical LNs (14%). In all mice, MPI signal at the injection site decreased over time and signal in primary LNs persisted for at least 6 days. Conclusions: We demonstrated MPI is a quantitative, hotspot imaging technique for identifying primary LNs. As iron oxide tracer pharmacokinetics varies with injection site, this imaging technique could provide fundamental information required for surgical planning. Unlike nuclear imaging, persistence of MPI signal for several days provides tremendous flexibility in clinical workflow and introduces the potential for an image-guide delayed SLN biopsy [4] . This preclinical LN imaging is timely as our team is actively building and testing the world’s first large-bore, clinical-scale MPI scanner.[1] Leong SP, Clin Exp Metastasis (2022). [2] Alvarado MD, et al. Ann Surg Oncol (2019). [3] Sarnitas EU, et al. J Magn Reson (2014). [4] Karakatsanis A, et al. Ann Surg Oncol (2023). Citation Format: Olivia C. Sehl, A. Rahman Mohtasebzadeh, Kelvin Guo, Petrina Kim, Benjamin Fellows, Marcela Weyhmiller, Paula J. Foster, Patrick W. Goodwill, Joan M. Greve. First demonstration of magnetic particle imaging for sentinel lymph node identification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4135.

Conceptual Framework for Flexible Workflow Generation in Federated ERP Systems Mediated by a FERP Mall

Conceptual Framework for Flexible Workflow Generation in Federated ERP Systems Mediated by a FERP Mall

Treemendous: an R package for integrating taxonomic information across backbones.

Standardizing and translating species names from different databases is key to the successful integration of data sources in biodiversity research. There are numerous taxonomic name-resolution applications that implement increasingly powerful name-cleaning and matching approaches, allowing the user to resolve species relative to multiple backbones simultaneously. Yet there remains no principled approach for combining information across these underlying taxonomic backbones, complicating efforts to combine and merge species lists with inconsistent and conflicting taxonomic information. Here, we present Treemendous, an open-source software package for the R programming environment that integrates taxonomic relationships across four publicly available backbones to improve the name resolution of tree species. By mapping relationships across the backbones, this package can be used to resolve datasets with conflicting and inconsistent taxonomic origins, while ensuring the resulting species are accepted and consistent with a single reference backbone. The user can chain together different functionalities ranging from simple matching to a single backbone, to graph-based iterative matching using synonym-accepted relations across all backbones in the database. In addition, the package allows users to 'translate' one tree species list into another, streamlining the assimilation of new data into preexisting datasets or models. The package provides a flexible workflow depending on the use case, and can either be used as a stand-alone name-resolution package or in conjunction with existing packages as a final step in the name-resolution pipeline. The Treemendous package is fast and easy to use, allowing users to quickly merge different data sources by standardizing their species names according to the regularly updated database. By combining taxonomic information across multiple backbones, the package increases matching rates and minimizes data loss, allowing for more efficient translation of tree species datasets to aid research into forest biodiversity and tree ecology.

Development and validation of a quick, automated, and reproducible ATR FT-IR spectroscopy machine-learning model for Klebsiella pneumoniae typing.

The reliability of Fourier-transform infrared (FT-IR) spectroscopy for Klebsiella pneumoniae typing and outbreak control has been previously assessed, but issues remain in standardization and reproducibility. We developed and validated a reproducible FT-IR with attenuated total reflectance (ATR) workflow for the identification of K. pneumoniae lineages. We used 293 isolates representing multidrug-resistant K. pneumoniae lineages causing outbreaks worldwide (2002-2021) to train a random forest classification (RF) model based on capsular (KL)-type discrimination. This model was validated with 280 contemporaneous isolates (2021-2022), using wzi sequencing and whole-genome sequencing as references. Repeatability and reproducibility were tested in different culture media and instruments throughout time. Our RF model allowed the classification of 33 capsular (KL)-types and up to 36 clinically relevant K. pneumoniae lineages based on the discrimination of specific KL- and O-type combinations. We obtained high rates of accuracy (89%), sensitivity (88%), and specificity (92%), including from cultures obtained directly from the clinical sample, allowing to obtain typing information the same day bacteria are identified. The workflow was reproducible in different instruments throughout time (>98% correct predictions). Direct colony application, spectral acquisition, and automated KL prediction through Clover MS Data analysis software allow a short time-to-result (5 min/isolate). We demonstrated that FT-IR ATR spectroscopy provides meaningful, reproducible, and accurate information at a very early stage (as soon as bacterial identification) to support infection control and public health surveillance. The high robustness together with automated and flexible workflows for data analysis provide opportunities to consolidate real-time applications at a global level. IMPORTANCE We created and validated an automated and simple workflow for the identification of clinically relevant Klebsiella pneumoniae lineages by FT-IR spectroscopy and machine-learning, a method that can be extremely useful to provide quick and reliable typing information to support real-time decisions of outbreak management and infection control. This method and workflow is of interest to support clinical microbiology diagnostics and to aid public health surveillance.

Boosting the hospital by integrating mobile robotic assistance systems: a comprehensive classification of the risks to be addressed

Mobile service robots are a promising technology for supporting workflows throughout the hospital. Combined with an understanding of the environment and the current situation, such systems have the potential to become invaluable tools for overcoming personal shortages and streamlining healthcare workflows. However, few robotic systems have actually been translated to practical application so far, which is due to many challenges centered around the strict and unique requirements imposed by the different hospital environments, which have not yet been collected and analyzed in a structured manner. To address this need, we now present a comprehensive classification of different dimensions of risk to be considered when designing mobile service robots for the hospital. Our classification consists of six risk categories – environmental complexity, hygienic requirements, interaction with persons and objects, workflow flexibility and autonomy – for each of which a scale with distinct risk levels is provided. This concept, for the first time allows for a precise classification of mobile service robots for the hospital, which can prove useful for certification and admission procedures as well as for defining architectural and safety requirements throughout the design process of such robots.

Developing the Healthy Actions and Lifestyles to Avoid Dementia or Hispanos y el ALTo a la Demencia program.

With Alzheimer's disease and related dementias (ADRD) representing an enormous public health challenge, there is a need to support individuals in learning about and addressing their modifiable risk factors (e.g., diet, sleep, and physical activity) to prevent or delay dementia onset. However, there is limited availability for evidence-informed tools that deliver both quality education and support for positive behavior change such as by increasing self-efficacy and personalizing goal setting. Tools that address the needs of Latino/a, at higher risk for ADRD, are even more scarce. We established a multidisciplinary team to develop the Healthy Actions and Lifestyles to Avoid Dementia or Hispanos y el ALTo a la Demencia (HALT-AD) program, a bilingual online personalized platform to educate and motivate participants to modify their risk factors for dementia. Grounded in social cognitive theory and following a cultural adaptation framework with guidance from a community advisory board, we developed HALT-AD iteratively through several cycles of rapid prototype development, user-centered evaluation through pilot testing and community feedback, and refinement. Using this iterative approach allowed for more than 100 improvements in the content, features, and design of HALT-AD to improve the program's usability and alignment with the interests and educational/behavior change support needs of its target audience. Illustrative examples of how pilot data and community feedback informed improvements are provided. Developing HALT-AD iteratively required learning through trial and error and flexibility in workflows, contrary to traditional program development methods that rely on rigid, pre-set requirements. In addition to efficacy trials, studies are needed to identify mechanisms for effective behavior change, which might be culturally specific. Flexible and personalized educational offerings are likely to be important in modifying risk trajectories in ADRD.

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.