Development Of Workflow Research Articles

Development of a field-deployable analytical workflow for determining current status and indicative human health risks at a historic dl-POPs hotspot

This paper introduces an integrated workflow that effectively evaluates environmental and health risks of dioxin-like Persistent Organic Pollutants (dl-POPs) at industrial hotspot regions. The developments of validated, cost effective and user-friendly analytical strategies which can be field deployable are quintessential for routine monitoring of dl-POPs, particularly in developing countries. This study addresses the lacunae by enabling an exclusive gas chromatography triple quadrupole mass spectrometer based analytical workflow substituting conventional magnetic sector high resolution mass spectrometer technique and validated the methodology as per the European Union regulation 644/2017. The viable monitoring utility of the methodology for predicting enviro-food-health nexus was field-tested by analyzing fish and sediment samples from the Eloor-Edayar industrial belt, a solitary POPs hotspot in India. The profiles of congeners indicate that dl-POPs were formed through precursor pathways, suggesting the potential release of chlorinated precursor species from surrounding industrial area as the root cause. Fish samples from hotspots were observed to have 8 times higher levels of polychlorinated dibenzo-p-dioxin/furans (PCDD/Fs) and 30 times higher levels of polychlorinated biphenyls (PCBs) than the control sites. A strong statistically significant (p < 0.05) positive correlation was observed between dl-POPs levels in fish and sediment samples at the study site and the Biota sediment accumulation factors for PCDD/Fs and dl-PCBs ranged from 0.019 to 0.092 and 0.004 to 0.671 respectively. The estimated weekly intake from fish consumption in the study region was observed to be 3 to 24 times higher than the maximum levels set by the European food safety authority (2 pgTEQ kg−1bwweek−1). Hence, the periodic surveillance of dl-POPs employing user friendly/validated confirmatory tools stands highly imperative to safeguard human health and environment. Keywords: Dioxin and PCBs, GC-MS/MS, POPs Hotspot, Biota-sediment accumulation factor, Correlation analysis, Health risk assessment.

Surrogate model development using simulation data to predict weld residual stress: A case study based on the NeT-TG1 benchmark

Surrogate models have been used as a substitute for traditional engineering simulation to get target simulation results faster. These surrogate models usually need sufficient data to fulfill their optimum performance. However, the acquisition of high quality datasets for cases like welding residual stress is both difficult and costly. This work developed a workflow that can rapidly develop machine learning based surrogate models to predict post-weld residual stresses using finite element (FE) simulation data. The workflow comprises five steps: (i) baseline FE model creation, (ii) FE model processing (iii) scripting (iv) data generation and (v) surrogate model development. The workflow presented in the paper automates all the steps involved in generating, calculating, and extracting results from FE simulations, with the aim of reducing the time and cost required for data acquisition. In addition, to minimize the possibility of FE analyses not completing successfully during the automated data generation process and to ensure that the training data can be obtained without any manual intervention, the FE model was simplified without significant loss of accuracy. A single weld bead-on-plate structure, taken from the NeT project Task Group 1, was used as a case study to validate the developed workflow. The overall aim of this work was to validate the feasibility of developing a surrogate model for specific welding conditions with no experimental dataset and with the aid of simulations, and to investigate the predictive performance of an Artificial Neural Network based surrogate model. Four welding parameters, arc advance speed, net energy input rate, arc travel length and position, were considered as the input for the surrogate model to predict the longitudinal stress along the line running across the sample thickness in the mid-length of the specimen. The relative root mean square error of the predictive surrogate model for the test dataset was 0.0024. Residual stress prediction based on the NeT-TG1 benchmark welding case met the R6 validation criteria and the developed model generates predictions that are in line with other methods used by contributors to the NeT-TG1 round robin.

Advancing parcel-level hurricane regional loss assessments using open data and the regional resilience determination tool

Hurricanes are a major driver of losses in the United States and thus are the focus of risk assessment capacity building efforts in the public and private sectors, as well as in the scholarly community. Capabilities for loss modeling have been particularly advanced through the development of open-source scientific workflows that conduct site-specific, building-specific, and even component-level loss assessments across entire regions. Notable among these is the Natural Hazards Engineering Research Infrastructure's Computational Modeling and Simulation Center's (NHERI SimCenter) Regional Resilience Determination (R2D) tool. However, the modular architecture of R2D's computational scaffolding has only been described and illustrated through testbed applications thus far. This study presents the first replication and extension of the R2D tool to conduct parcel-level and component-level hurricane performance assessments outside of the SimCenter's testbed locations. The study first details how building inventories that capture time-evolving building characteristics and regional construction practices can be generated using updated heuristic rulesets that guide the integration of tax assessor data with other open data sources. These rulesets and supporting data are then utilized to generate building inventory information for a set of single family homes located in Florida's Bay County, the landfall site of Hurricane Michael in 2018. HAZUS-compatible, parcel-level damage and loss assessments are then conducted, considering Hurricane Michael's peak gust wind speeds. Finally, a set of custom fragilities, empirically-derived from multiple regional post-disaster datasets, are incorporated into R2D to conduct the first component-level damage assessment of buildings under hurricanes using the SimCenter's regional loss modeling workflows. In total, this represents an important first step in operationalizing replicable regional risk assessments down to the parcel level to provide more granular risk information to key stakeholders.

Development of patient-specific osteosynthesis including 3D-printed drilling guides for medial tibial plateau fracture surgery

PurposeA substantial proportion of conventional tibial plateau plates have a poor fit, which may result in suboptimal fracture reduction due to applied -uncontrolled- compression on the bone. This study aimed to assess whether patient-specific osteosyntheses could facilitate proper fracture reduction in medial tibial plateau fractures.MethodsIn three Thiel embalmed human cadavers, a total of six tibial plateau fractures (three Schatzker 4, and three Schatzker 6) were created and CT scans were made. A 3D surgical plan was created and a patient-specific implant was designed and fabricated for each fracture. Drilling guides that fitted on top of the customized plates were designed and 3D printed in order to assist the surgeon in positioning the plate and steering the screws in the preplanned direction. After surgery, a postoperative CT scan was obtained and outcome was compared with the preoperative planning in terms of articular reduction, plate positioning, and screw direction.ResultsA total of six patient-specific implants including 41 screws were used to operate six tibial plateau fractures. Three fractures were treated with single plating, and three fractures with dual plating. The median intra-articular gap was reduced from 6.0 (IQR 4.5–9.5) to 0.9 mm (IQR 0.2–1.4), whereas the median step-off was reduced from 4.8 (IQR 4.1–5.3) to 1.3 mm (IQR 0.9–1.5). The median Euclidean distance between the centre of gravity of the planned and actual implant was 3.0 mm (IQR: 2.8–3.7). The lengths of the screws were according to the predetermined plan. None of the screws led to screw penetration. The median difference between the planned and actual screw direction was 3.3° (IQR: 2.5–5.1).ConclusionThis feasibility study described the development and implementation of a patient-specific workflow for medial tibial plateau fracture surgery that facilitates proper fracture reduction, tibial alignment and accurately placed screws by using custom-made osteosynthesis plates with drilling guides.

Time-dependent sorption behavior of lentiviral vectors during anion-exchange chromatography.

Use of lentiviral vectors (LVs) in clinical Cell and Gene Therapy applications is growing. However, functional product loss during capture chromatography, typically anion-exchange (AIEX), remains a significant unresolved challenge for the design of economic processes. Despite AIEX's extensive use, variable performance and generally low recovery is reported. This poor understanding of product loss mechanisms highlights a significant gap in our knowledge of LV adsorption and other types of vector delivery systems. This work demonstrates HIV-1-LV recovery over quaternary-amine membrane adsorbents is a function of time in the adsorbed state. Kinetic data for product loss in the column bound state was generated. Fitting a second order-like rate model, we observed a rapid drop in functional recovery due to increased irreversible binding for vectors encoding two separate transgenes ( = 12.7 and 18.7 min). Upon gradient elution, a two-peak elution profile implicating the presence of two distinct binding subpopulations is observed. Characterizing the loss kinetics of these two subpopulations showed a higher rate of vector loss in the weaker binding peak. This work highlights time spent in the adsorbed state as a critical factor impacting LV product loss and the need for consideration in LV AIEX process development workflows.

Seeing the Forest for the Trees: Mapping Cover and Counting Trees from Aerial Images of a Mangrove Forest Using Artificial Intelligence

Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks.

A chemistry-informed hybrid machine learning approach to predict metal adsorption onto mineral surfaces

Historically, surface complexation model (SCM) constants and distribution coefficients (Kd) have been employed to quantify mineral-based retardation effects controlling the fate of metals in subsurface geologic systems. Our recent SCM development workflow, based on the Lawrence Livermore National Laboratory Surface Complexation/Ion Exchange (L-SCIE) database, illustrated a community FAIR data approach to SCM development by predicting uranium(VI)-quartz adsorption for a large number of literature-mined data. Here, we present an alternative hybrid machine learning (ML) approach that shows promise in achieving equivalent high-quality predictions compared to traditional surface complexation models. At its core, the hybrid random forest (RF) ML approach is motivated by the proliferation of incongruent SCMs in the literature that limit their applicability in reactive transport models. Our hybrid ML approach implements PHREEQC-based aqueous speciation calculations; values from these simulations are automatically used as input features for a random forest (RF) algorithm to quantify adsorption and avoid SCM modeling constraints entirely. Named the LLNL Speciation Updated Random Forest (L-SURF) model, this hybrid approach is shown to have applicability to U(VI) sorption cases driven by both ion-exchange and surface complexation, as is shown for quartz and montmorillonite cases. The approach can be applied to reactive transport modeling and may provide an alternative to the costly development of self-consistent SCM reaction databases.

Comprehensive HRMS Chemical Characterization of Pomegranate-Based Antioxidant Drinks via a Newly Developed Suspect and Target Screening Workflow.

Antioxidants play a significant role in human health, protecting against a variety of diseases. Therefore, the development of products with antioxidant activity is becoming increasingly prominent in the human lifestyle. New antioxidant drinks containing different percentages of pomegranate, blackberries, red grapes, and aronia have been designed, developed, and manufactured by a local industry. The comprehensive characterization of the drinks' constituents has been deemed necessary to evaluate their bioactivity. Thus, LC-qTOFMS has been selected, due to its sensitivity and structure identification capability. Both data-dependent and -independent acquisition modes have been utilized. The data have been treated according to a novel, newly designed workflow based on MS-DIAL and MZmine for suspect, as well as target screening. The classical MS-DIAL workflow has been modified to perform suspect and target screening in an automatic way. Furthermore, a novel methodology based on a compiled bioactivity-driven suspect list was developed and expanded with combinatorial enumeration to include metabolism products of the highlighted metabolites. Compounds belonging to ontologies with possible antioxidant capacity have been identified, such as flavonoids, amino acids, and fatty acids, which could be beneficial to human health, revealing the importance of the produced drinks as well as the efficacy of the new in-house developed workflow.

Review on Geopolymers as Wellbore Sealants: State of the Art Optimization for CO2 Exposure and Perspectives.

Wellbores used in underground production and storage activities, including carbon capture and storage (CCS), are typically sealed using sealants based on Ordinary Portland Cement (OPC). However, leakage along these seals or through them during CCS operations can pose a significant threat to long-term storage integrity. In this review paper, we explore the potential of geopolymer (GP) systems as alternative sealants in wells exposed to CO2 during CCS. First, we discuss how key parameters control the mechanical properties, permeability, and chemical durability of GPs based on different starting materials as well as their optimum values. These parameters include the chemical and mineralogical composition, particle size, and particle shape of the precursor materials; the composition of the hardener; the chemistry of the full system (particularly the Si/Al, Si/(Na+K), Si/Ca, Si/Mg, and Si/Fe ratios); the water content of the mix; and the conditions under which curing occurs. Next, we review existing knowledge on the use of GPs as wellbore sealants to identify key knowledge gaps and challenges and the research needed to address these challenges. Our review shows the great potential of GPs as alternative wellbore sealant materials in CCS (as well as other applications) due to their high corrosion durability, low matrix permeability, and good mechanical properties. However, important challenges are identified that require further research, such as mix optimization, taking into account curing and exposure conditions and available starting materials; the development of optimalization workflows, along with building larger data sets on how the identified parameters affect GP properties, can streamline this optimization for future applications.

DESIGN THINKING IN DATA-INTENSIVE HEALTHCARE IMPROVEMENT: LESSONS FROM A PERIOPERATIVE CASE STUDY

AbstractHealthcare generates vast quantities of ‘routinely collected’ data that is recognised as a valuable substrate to drive improvement. Realising this benefit however, requires the sequential distillation of new knowledge before analytical findings are used to inform real-world change. This dichotomy requires the combination of techniques from data science (to derive meaningful knowledge) and improvement (to deliver change). Recognising this transdisciplinary need and the complexity of modern healthcare, we developed an improvement project to incorporate a ‘systems approach’ into the analysis of pseudonymised perioperative data for the purpose of redesigning the systems that deliver surgical care to older patients. This required the development of novel mixed-methods workflows combining tools used to realise a systems approach in practice and to support meaningful analysis, and to translate these findings towards ‘better’ care systems. This paper recounts the incorporation of these tools into ‘dataintensive improvement’ and reflects on the relevance of design thinking to improve the conduct of the necessary data science to achieve our ultimate aim, using data to improve services for older surgical patients.

A novel agile ontology engineering methodology for supporting organizations in collaborative ontology development

Ontologies can represent technological enablers for knowledge elicitation and management in different kinds of organizations, especially with the exponential growth of sources and types of data fostered by digital transformation. However, their adoption in business applications is still limited, with existing Ontology Engineering Methodologies (OEMs) lacking adequate support during knowledge elicitation, authoring and reuse phases. This paper introduces a novel agile ontology engineering methodology (AgiSCOnt) to support ontologists (especially novice ones) in ontology development workflow, fostering collaboration with domain experts in an iterative, flexible and customizable approach. AgiSCOnt combines macro-level instructions with micro-level guidance, leveraging existing techniques and a management framework to help novice ontologists throughout the whole ontology engineering process. The methodology is compared to existing OEMs and assessed with three other agile methodologies (UPONLite, SAMOD, and RapidOWL). The evaluation is conducted with a sample of novice ontologists in a learning environment on Industry 4.0 technologies. Both the development process with a methodology from a user perspective and the quality of the developed ontologies were considered in the evaluation. Preliminary results show that AgiSCOnt effectively supports authoring and reuse, with developed ontologies of good quality. It is perceived as clear and simple, while being flexible and adaptable enough, thus supporting knowledge management and sharing in industrial organizations through the documentation of the ontologies.

Three-Dimensional Printing of Living Mycelium-Based Composites: Material Compositions, Workflows, and Ways to Mitigate Contamination.

The construction industry makes a significant contribution to global CO2 emissions. Material extraction, processing, and demolition account for most of its environmental impact. As a response, there is an increasing interest in developing and implementing innovative biomaterials that support a circular economy, such as mycelium-based composites. The mycelium is the network of hyphae of fungi. Mycelium-based composites are renewable and biodegradable biomaterials obtained by ceasing mycelial growth on organic substrates, including agricultural waste. Cultivating mycelium-based composites within molds, however, is often wasteful, especially if molds are not reusable or recyclable. Shaping mycelium-based composites using 3D printing can minimize mold waste while allowing intricate forms to be fabricated. In this research, we explore the use of waste cardboard as a substrate for cultivating mycelium-based composites and the development of extrudable mixtures and workflows for 3D-printing mycelium-based components. In this paper, existing research on the use of mycelium-based material in recent 3D printing efforts was reviewed. This review is followed by the MycoPrint experiments that we conducted, and we focus on the main challenges that we faced (i.e., contamination) and the ways in which we addressed them. The results of this research demonstrate the feasibility of using waste cardboard as a substrate for cultivating mycelia and the potential for developing extrudable mixtures and workflows for 3D-printing mycelium-based components.

The 3D emergence profile on implant-supported restorations: A method for evaluating restorative angles.

Emerging evidence indicates that the emergence profile and subsequent restorative angle play a crucial role in clinical outcomes and can potentially affect the development and progression of peri-implant diseases. However, the traditional evaluation of the emergence profile and angle has been limited to mesial and distal sites using periapical x-rays, without considering the buccal sites. To describe a novel 3D method to estimate the emergence profile and restorative angles around single implant-supported crowns including buccal sites. A total of 30 implant-supported crowns (11 molars, 8 premolars, 8 central incisors and 1 canine) were extra-orally scanned using an intraoral scanner and the STL files produced were imported into a 3D software. The crown/abutment interface of each crown was delineated, and apico-coronal lines were automatically drawn following the shape of the crown. Three reference points were defined on the apico-coronal lines at the transition edge of the biological (BC) and the esthetic zone (EC) and the resulting angles were then calculated. The reliability of the measurements (2D and 3D) were assessed using the intraclass correlation coefficient (ICC). In anterior restorations, the mean angle of the esthetic zone amounted to 162 ± 14° at mesial sites, to 140 ± 10° at buccal sites and to 163 ± 11° at distal sites. The corresponding angles at the biological zones, amounted to 155 ± 13° at mesial sites, 139 ± 15° at buccal sites and 157 ± 5° at distal sites. In posterior restorations, the mean angle of the esthetic zone amounted to 162 ± 12° at mesial sites, to 157 ± 13 at buccal sites and to 162 ± 11 at distal sites. The corresponding angles at the biological zone, amounted to 158 ± 8 at mesial sites, 150 ± 15° at buccal sites and 156 ± 10 at distal sites. The ICC for all measurements ranged between 0.77 and 0.99 indicating a good intra-examiner reliability. Within the limitations of the present study, the 3D analysis seems to be a reliable and applicable method for the quantitative evaluation of the emergence profile in daily practice. Future randomized clinical trials are needed to assess whether a 3D analysis with the ensuing the emergence profile serves as a predictor for clinical outcomes. The development and implementation of a 3D workflow will provide technicians and dentists with the ability to assess the restorative angle of implant-supported restorations during the provisional phase and the final restoration. This approach may help achieve an aesthetically pleasing restoration while minimizing potential clinical complications.

Towards AI-assisted digital twins for smart railways: preliminary guideline and reference architecture

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.

Enabling robust environmental DNA assay design with “unikseq” for the identification of taxon‐specific regions within whole mitochondrial genomes

AbstractEnvironmental DNA (eDNA) is revolutionizing species monitoring in nature. At the heart of any eDNA approach is the reliance upon sufficient DNA sequence information to satisfy the demands of eDNA assay specificity and sensitivity. The most common source of this information has been restricted to short barcoding regions of the mitochondrial genome (mitogenome) and marker genes. The use of these limited regions for assay design has often resulted in substantial trade‐offs in assay performance. With increased accessibility of full mitogenome assemblies, the potential for designing more robust eDNA assays is considerably enhanced. However, this also poses a new challenge to effectively identify suitable regions for assay design using considerably larger sequences. We present unikseq, a utility that uses words of length k (k‐mers) to identify unique regions in a reference sequence relative to tolerated (ingroup) and not‐tolerated (outgroup or non‐target) sequence sets, quickly and with low memory that can yield highly specific assays. We illustrate its application within an assay development workflow through use‐case examples for the design and validation of four quantitative real‐time polymerase chain reaction (qPCR)‐based assays selective for American bullfrog (Rana [Lithobates] catesbeiana), Burbot (Lota lota), Lake trout (Salvelinus namaycush), and Quillback rockfish (Sebastes maliger). The chosen target species vary in range, habitat, and degree of relatedness to their sympatric species that, consequently, impact eDNA assay design difficulty. We demonstrate the effectiveness of unikseq through assay validation and characterization using DNA from voucher specimens, synthetic DNA, and, where possible, field samples, to verify the specificity and sensitivity of the newly designed assays. By facilitating whole mitogenome sequence comparison, the creation of high‐performing eDNA assays is substantially enhanced. Having several adjustable parameters for specifying user requirements within unikseq, this approach can facilitate the identification of suitable regions for a broad range of applications requiring nucleotide sequence comparisons.

PyCHARMM: Embedding CHARMM Functionality in a Python Framework.

CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. We anticipate that pyCHARMM will be a robust platform for the development of comprehensive and complex workflows utilizing Python and its extensive functionality as well as an optimal platform for users to learn molecular modeling methods and practices within a Python-friendly environment such as Jupyter Notebooks.

Development of analytical methods fort he quantitative analysis of the enzyme abundance and activity in the arachidonic acid cascade

The arachidonic acid (ARA) cascade is a key regulatory pathway where eicosanoids and other oxylipins are formed via the oxidation of polyunsaturated fatty acids. These oxylipins are involved in controlling physiological functions such as vascular tone, blood clotting and immune regulation. Changes in the oxylipin pattern can be caused by direct enzyme inhibition or modulated gene expression. In order to understand the effect mechanisms modulating the ARA cascade it is essential to not only investigate the metabolite, i.e. oxylipin, concentrations but also the abundances of the enzymes involved in their formation. For this reason, a quantitative targeted proteomics method for the ARA cascade was developed within this thesis.A detailed workflow for method development was established where enzyme/protein levels are measured via sequence‐specific, representative peptides chosen on the basis of several criteria. With liquid chromatography‐tandem mass spectrometry‐based analysis, the cyclooxygenase (COX) pathway of the ARA cascade was investigated in human colon carcinoma cell lines and primary macrophages where strong correlations were found between the oxylipins formed via the COX pathway and the COX‐2 abundance.The modulating effects of polyphenols on the 5‐lipoxygenase (LOX) pathway of the ARA cascade were investigated in cell‐free enzyme assays and human neutrophils. Resveratrol, its dimer ɛ‐viniferin and a resveratrol imine analogue directly inhibited the 5‐LOX with micromolar potencies and modulated the total oxylipin pattern in the neutrophils with remarkably different effects. Genistein did not act as a 5‐LOX inhibitor in the cell‐free assay but the modulated oxylipin pattern in the cells suggests a global cellular interference.The enzymes of the LOX pathway were included in the targeted proteomics method which together with an extended targeted oxylipin metabolomics platform enables a sensitive multi‐ omics analysis of the ARA cascade in a single sample. The comparison of the multiple reaction monitoring (MRM) detection and the MRM^‐based approach with an additional fragmentation step demonstrated that MRM is more favorable in targeted proteomics due to its higher sensitivity, greater linear range and higher multiplexing capacities. The analysis with the multi‐ omics approach showed that the 5 LOX protein is induced together with its product formation during the differentiation of human monocytic THP 1 cells to macrophages. M1‐like human primary macrophages were also characterized by the abundance of 5‐LOX and its activating protein (FLAP), while 15‐LOX and 15‐LOX‐2 dominated the protein pattern of the M2 like macrophages accompanied by high levels of the oxylipins formed via these enzymes. The developed methodology allows mechanistic investigation of a modulation of the ARA cascade as demonstrated by lipo‐poly‐saccharide stimulation as well as pharmaceutical treatment of the cells.Overall, with this thesis, a new quantitative proteomics strategy for the enzymes of the ARA cascade is established which can be easily extended to further proteins. The use of this technique in combination with targeted oxylipin metabolomics has provided new insights into the mechanisms involved in the modulation of the ARA cascade in human immune cells.

FRI-160 - Development of a rapid workflow for detecting HCV RNA from whole blood

FRI-160 - Development of a rapid workflow for detecting HCV RNA from whole blood

Evaluating the effect of setup uncertainty reduction and adaptation to geometric changes on normal tissue complication probability using online adaptive head and neck intensity modulated proton therapy

Objective. To evaluate the impact of setup uncertainty reduction (SUR) and adaptation to geometrical changes (AGC) on normal tissue complication probability (NTCP) when using online adaptive head and neck intensity modulated proton therapy (IMPT). Approach. A cohort of ten retrospective head and neck cancer patients with daily scatter corrected cone-beam CT (CBCT) was studied. For each patient, two IMPT treatment plans were created: one with a 3 mm setup uncertainty robustness setting and one with no explicit setup robustness. Both plans were recalculated on the daily CBCT considering three scenarios: the robust plan without adaptation, the non-robust plan without adaptation and the non-robust plan with daily online adaptation. Online-adaptation was simulated using an in-house developed workflow based on GPU-accelerated Monte Carlo dose calculation and partial spot-intensity re-optimization. Dose distributions associated with each scenario were accumulated on the planning CT, where NTCP models for six toxicities were applied. NTCP values from each scenario were intercompared to quantify the reduction in toxicity risk induced by SUR alone, AGC alone and SUR and AGC combined. Finally, a decision tree was implemented to assess the clinical significance of the toxicity reduction associated with each mechanism. Main results. For most patients, clinically meaningful NTCP reductions were only achieved when SUR and AGC were performed together. In these conditions, total reductions in NTCP of up to 30.48 pp were obtained, with noticeable NTCP reductions for aspiration, dysphagia and xerostomia (mean reductions of 8.25, 5.42 and 5.12 pp respectively). While SUR had a generally larger impact than AGC on NTCP reductions, SUR alone did not induce clinically meaningful toxicity reductions in any patient, compared to only one for AGC alone. Significance Online adaptive head and neck proton therapy can only yield clinically significant reductions in the risk of long-term side effects when combining the benefits of SUR and AGC.

Implementation of a Continuous Integration and Deployment Pipeline for Containerized Applications in Amazon Web Services Using Jenkins

This project focuses on streamlining the software development process by integrating various tools and automating key tasks. Developers contribute code changes to a Git repository hosted on GitHub. These changes trigger Jenkins, a continuous integration and delivery tool, to build the code using Maven, a build automation tool. Maven compiles the code and executes unit tests to ensure its correctness. To access code quality, security vulnerabilities, and bugs, sonarqube, a static code analysis tool, is employed. As part of the automation process, the built code is packaged into a Docker image. This image is then automatically pushed to dockerhub, a container registry, with a new version identifier. Additionally, a shell script is utilized to update the manifest deployment file, which specifies the version and configuration details for deployment, with the newly created version. This ensures that the deployment process is seamlessly synchronized with the latest code changes. By integrating these tools and automating the necessary steps, this project enhances the efficiency and reliability of the software development pipeline. It facilitates rapid code testing, quality analysis, and version management, thereby improving the overall development workflow.