Labeling Steps Research Articles

Ultra-sensitive immobilization-free homogeneous electrochemiluminescence biosensor for thrombin detection via electrostatic interaction and Exo I-powered signal amplification

Herein, we present the development of an ultra-sensitive immobilization-free homogeneous electrochemiluminescence (ECL) biosensor, leveraging the electrostatic repulsion between a negatively charged indium tin oxide (ITO) electrode and DNA and exonuclease I (Exo I)-powered signal amplification, to achieve highly efficient detection of thrombin. Specifically, the aptamer and the complementary DNA engage in the formation of a double-stranded DNA (dsDNA) complex. This negatively charged dsDNA structure subsequently associates with a positively charged ECL indicator, namely ruthenium phenanthroline (Ru(phen)32+), resulting in the generation of the dsDNA-Ru(phen)32+ ECL probe. The negatively charged dsDNA-Ru(phen)32+ experiences electrostatic repulsion from the negatively charged ITO electrode, resulting in a low ECL signal. Nonetheless, upon the addition of thrombin, the aptamer preferentially binds to thrombin, triggering the releases of the embedded Ru(phen)32+ facilitated by Exo I and hence resulting in a robust and enhanced ECL signal. The amplified ECL signal is linearly correlated with the logarithm of thrombin concentration within a detection range spanning from 10 fmol/mL to 50 pmol/mL, with a remarkable detection limit of 3.21 fmol/mL. This strategy eliminates the need for cumbersome labeling steps, avoids the electrode modification process, overcoming the low immobilization efficiency of aptamers and poor signal transduction of indicators labeled at the end of DNA.

Lateral Flow Immunoassay for the Rapid Detection of Thiamethoxam in Tea Based on a SERS Tag Constructed by Phenolic-Mediated Coating Engineering.

Sensitive and accurate detection of thiamethoxam in tea is significant to ensuring consumer health. In this study, surface-enhanced Raman scattering (SERS) tags were prepared by using a polyphenol-mediated coating engineering strategy. This approach involved the self-assembly of tannic acid (TA) and self-polymerization of benzene-1,4-dithiol (BDT) on the surface of gold nanoparticles, resulting in the formation of Au@pBDT-TA. The SERS tags possess high Raman signals and antibody adsorption properties, avoiding the complex decoration or label steps of SERS reporters. We discovered that Au@pBDT-TA, composed of gold nanoparticles at 40 nm and a pBDT-TA thickness of 10 nm, was optimal for the development of the SERS lateral flow immunoassay (LFIA). In comparison to the gold nanoparticle-based LFIA, the SERS-LFIA demonstrated 4-fold and 720-fold enhancements in visual and quantitative limits of detection in buffer solution. The SERS-LFIA demonstrated quantitative limits of detection of 0.06 and 0.1 ng/g for black and green tea, respectively, with a broader linear range spanning over 2 orders of magnitude and a short detection time of 20 min. The proposed SERS-LFIA not only offers a sensitive and reliable method for monitoring thiamethoxam in tea but also possesses a versatile potential that can be easily adapted for the trace detection of various other targets.

Setting a Successful Sorting for Extracellular Vesicle Isolation.

Extracellular vesicles (EVs) released by mesenchymal stromal cells (MSCs) contain a set of microRNAs with regenerative and anti-inflammatory roles. Therefore, purified MSC-EVs are envisioned as a next-generation therapeutic option for a wide array of diseases. In this protocol, we report the strategy for successfully sorting EVs from the supernatant of adipose-derived MSCs (ASCs), often used in orthopedics regenerative medicine applications. First, we described the sample preparation, focusing on EV isolation and labeling steps with carboxyfluorescein succinimidyl ester (CFSE) for fluorescence detection; subsequently, we detailed the sorting process, which constitutes the main part of the protocol. In addition to the rules defined by MISEV 2023 and MIFlowCyt EV guidelines, we applied specific experimental conditions concerning nozzle size, frequency, and sheath pressure. Morphological parameters are established using beads of diameters selected to cover the theoretical range of EV size. After ASC-EVs sorting, we performed a purity check of the sorted fraction by re-analyzing it with the sorter and verifying the EV size distribution with the nanoparticle tracking analysis technique. Due to the increasing importance of EVs, having a pure population to study and characterize is becoming crucial. Here, we demonstrate a winner strategy to set up sorting to achieve this goal.

A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation.

A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC). This study aims to highlight the current limitations of existing NLP algorithm development approaches that are exacerbated by NLP tasks surrounding emergent clinical concepts and to illustrate our approach to addressing these issues through the use case of developing an NLP system for the signs and symptoms of COVID-19 and PASC. We used 2 preexisting studies on PASC as a baseline to determine a set of concepts that should be extracted by NLP. This concept list was then used in conjunction with the Unified Medical Language System to autonomously generate an expanded lexicon to weakly annotate a training set, which was then reviewed by a human expert to generate a fine-tuned NLP algorithm. The annotations from a fully human-annotated test set were then compared with NLP results from the fine-tuned algorithm. The NLP algorithm was then deployed to 10 additional sites that were also running our NLP infrastructure. Of these 10 sites, 5 were used to conduct a federated evaluation of the NLP algorithm. An NLP algorithm consisting of 12,234 unique normalized text strings corresponding to 2366 unique concepts was developed to extract COVID-19 or PASC signs and symptoms. An unweighted mean dictionary coverage of 77.8% was found for the 5 sites. The evolutionary and time-critical nature of the PASC NLP task significantly complicates existing approaches to NLP algorithm development. In this work, we present a hybrid approach using the Open Health Natural Language Processing Toolkit aimed at addressing these needs with a dictionary-based weak labeling step that minimizes the need for additional expert annotation while still preserving the fine-tuning capabilities of expert involvement.

Combining Native Mass Spectrometry and Proteomics to Differentiate and Map the Metalloform Landscape in Metallothioneins.

Within the intricate landscape of the proteome, approximately 30% of all proteins bind metal ions. This repertoire is even larger when considering all the different forms of a protein, known as proteoforms. Here, we propose the term "metalloforms" to refer to different structural or functional variations of a protein resulting from the binding of various hetero- or homogeneous metal ions. Using human Cu(I)/Zn(II)-metallothionein-3 as a representative model, we developed a chemical proteomics strategy to simultaneously differentiate and map Zn(II) and Cu(I) metal binding sites. In the first labeling step, N-ethylmaleimide reacts with Cysteine (Cys), resulting in the dissociation of all Zn(II) ions while Cu(I) remains bound to the protein. In the second labeling step, iodoacetamide is utilized to label Cu(I)-bound Cys residues. Native mass spectrometry (MS) was used to determine the metal/labeling protein stoichiometries, while bottom-up/top-down MS was used to map the Cys-labeled residues. Next, we used a developed methodology to interrogate an isolated rabbit liver metallothionein fraction containing three metallothionein-2 isoforms and multiple Cd(II)/Zn(II) metalloforms. The approach detailed in this study thus holds the potential to decode the metalloproteoform diversity within other proteins.

Detection of ChatGPT fake science with the xFakeSci learning algorithm

Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that “AI generated content may exhibit a distinctive behavior that can be separated from scientific articles”. In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions. We then show how we tested this premise in two phases and prove its validity. Subsequently, we introduce xFakeSci, a novel learning algorithm, that is capable of distinguishing ChatGPT-generated articles from publications produced by scientists. The algorithm is trained using network models driven from both sources. To mitigate overfitting issues, we incorporated a calibration step that is built upon data-driven heuristics, including proximity and ratios. Specifically, from a total of a 3952 fake articles for three different medical conditions, the algorithm was trained using only 100 articles, but calibrated using folds of 100 articles. As for the classification step, it was performed using 300 articles per condition. The actual label steps took place against an equal mix of 50 generated articles and 50 authentic PubMed abstracts. The testing also spanned publication periods from 2010 to 2024 and encompassed research on three distinct diseases: cancer, depression, and Alzheimer’s. Further, we evaluated the accuracy of the xFakeSci algorithm against some of the classical data mining algorithms (e.g., Support Vector Machines, Regression, and Naive Bayes). The xFakeSci algorithm achieved F1 scores ranging from 80 to 94%, outperforming common data mining algorithms, which scored F1 values between 38 and 52%. We attribute the noticeable difference to the introduction of calibration and a proximity distance heuristic, which underscores this promising performance. Indeed, the prediction of fake science generated by ChatGPT presents a considerable challenge. Nonetheless, the introduction of the xFakeSci algorithm is a significant step on the way to combating fake science.

Proteomic Profiling of Antimalarial Plasmodione Using 3-Benz(o)ylmenadione Affinity-Based Probes.

Understanding the mechanisms of drug action in malarial parasites is crucial for the development of new drugs to combat infection and to counteract drug resistance. Proteomics is a widely used approach to study host-pathogen systems and to identify drug protein targets. Plasmodione is an antiplasmodial early-lead drug exerting potent activities against young asexual and sexual blood stages in vitro with low toxicity to host cells. To elucidate its molecular mechanisms, an affinity-based protein profiling (AfBPP) approach was applied to yeast and P. falciparum proteomes. New (pro-) AfBPP probes based on the 3-benz(o)yl-6-fluoro-menadione scaffold were synthesized. With optimized conditions of both photoaffinity labeling and click reaction steps, the AfBPP protocol was then applied to a yeast proteome, yielding 11 putative drug-protein targets. Among these, we found four proteins associated with oxidoreductase activities, the hypothesized type of targets for plasmodione and its metabolites, and other proteins associated with the mitochondria. In Plasmodium parasites, the MS analysis revealed 44 potential plasmodione targets that need to be validated in further studies. Finally, the localization of a 3-benzyl-6-fluoromenadione AfBPP probe was studied in the subcellular structures of the parasite at the trophozoite stage.

A Portable Device for in Situ Noninvasive Monitoring of Cell Secretions and Communications with Fluorescence and Nanochannel Electrochemistry.

In situ monitoring of cell secretions and communications plays a fundamental role in screening of disease diagnostic biomarkers and drugs. Quantitative detection of cell secretions and monitoring of intercellular communication have been separately reported, which often rely on target labeling or complex pretreatment steps, inevitably causing damage to the target. Simultaneous in situ noninvasive detection of cell secretions and monitoring of intercellular communication are challenging and have never been reported. Herein, we smartly developed a portable device for in situ label-free monitoring of cell secretions and communications with fluorescence and ion-transport-based nanochannel electrochemistry. Based on the dual signal mode, a series of nonelectroactive secretions were sensitively and accurately quantified. The detection limits for VEGF, MUC1, and ATP were 3.84 pg/mL, 32.7 pg/mL, and 47.4 fM (3σ/S), which were 1/3.9, 1/1.1, and 1/41 of those of commercial ELISA kits, respectively. More interestingly, under the released secretions, the gradual opening of the nanochannel connected the two cells in the left and right chambers of the device; thus, the secretion mediated intercellular communication can be monitored. The proposed platform may provide a promising tool for understanding the mechanism of intercellular communication and discovering new therapeutic targets.

Cetyltrimethylammonium-chloride assisted in situ metabolic incorporation of nano-sized ROS-generating cascade-reaction containers in Gram-positive and Gram-negative peptidoglycan layers for the control of bacterially-induced sepsis

Cascade-reaction containers generating reactive oxygen species (ROS) as an alternative for antibiotic-based strategies for bacterial infection control, require endogenous oxygen-sources and ROS-generation close to or preferably inside target bacteria. Here, this is achieved by cetyltrimethylammonium-chloride (CTAC) assisted in situ metabolic labeling and incorporation of mesoporous SiO2-nanoparticles, dual-loaded with glucose-oxidase and Fe3O4-nanoparticles as cascade-reaction containers, inside bacterial cell walls. First, azide-functionalized d-alanine (D-Ala-N3) was inserted in cell wall peptidoglycan layers of growing Gram-positive pathogens. In Gram-negatives, this could only be achieved after outer lipid-membrane permeabilization, using a low concentration of CTAC. Low concentrations of CTAC had no adverse effect on in vitro blood clotting or hemolysis nor on the health of mice when blood-injected. Next, dibenzocyclooctyne-polyethylene-glycol modified, SiO2-nanoparticles were in situ click-reacted with d-Ala-N3 in bacterial cell wall peptidoglycan layers. Herewith, a two-step cascade-reaction is facilitated inside bacteria, in which glucose-oxidase generates H2O2 at endogenously-available glucose concentrations, while subsequently Fe3O4-nanoparticles catalyze generation of •OH from the H2O2 generated. Generation of •OH inside bacterial cell walls by dual-loaded mesoporous SiO2-nanoparticles yielded more effective in vitro killing of both planktonic Gram-positive and Gram-negative bacteria suspended in 10 % plasma than SiO2-nanoparticles solely loaded with glucose-oxidase. Gram-positive or Gram-negative bacterially induced sepsis in mice could be effectively treated by in situ pre-treatment with tail-vein injected CTAC and d-Ala-N3, followed by injection of dual-loaded cascade-reaction containers without using antibiotics. This makes in situ metabolic incorporation of cascade-reaction containers as described attractive for further investigation with respect to the control of other types of infections comprising planktonic bacteria. Statement of SignificanceIn situ metabolic-incorporation of cascade-reaction-containers loaded with glucose-oxidase and Fe3O4 nanoparticles into bacterial cell-wall peptidoglycan is described, yielding ROS-generation from endogenous glucose, non-antibiotically killing bacteria before ROS inactivates. Hitherto, only Gram-positives could be metabolically-labeled, because Gram-negatives possess two lipid-membranes. The outer membrane impedes direct access to the peptidoglycan. This problem was solved by outer-membrane permeabilization using a quaternary-ammonium compound. Several studies on metabolic-labeling perform crucial labeling steps during bacterial-culturing that in real-life should be part of a treatment. In situ metabolic-incorporation as described, can be applied in well-plates during in vitro experiments or in the body as during in vivo animal experiments. Surprisingly, metabolic-incorporation proceeded unhampered in blood and a murine, bacterially-induced sepsis could be well treated.

EpHero – a tandem-fluorescent probe to track the fate of apoptotic cells during efferocytosis

The efficient removal of apoptotic cells via efferocytosis is critical for maintaining optimal tissue function. This involves the binding and engulfment of apoptotic cells by phagocytes and the subsequent maturation of the phagosome, culminating in lysosomal fusion and cargo destruction. However, current approaches to measure efferocytosis rely on labelling apoptotic targets with fluorescent dyes, which do not sufficiently distinguish between changes to the engulfment and acidification of apoptotic material. To address this limitation, we have developed a genetically coded ratiometric probe epHero which when expressed in the cytoplasm of target cells, bypasses the need for additional labelling steps. We demonstrate that epHero is a pH-sensitive reporter for efferocytosis and can be used to simultaneously track changes to apoptotic cell uptake and acidification, both in vitro and in mice. As proof-of-principle, we modify extracellular nutrition to show how epHero can distinguish between changes to cargo engulfment and acidification. Thus, tracking efferocytosis with epHero is a simple, cost-effective improvement on conventional techniques.

A semi-supervised anomaly detection approach for detecting mechanical failures

Abstract Predictive maintenance attempts to prevent unscheduled downtime by scheduling maintenance before expected failures and/or breakdowns while maximally optimizing uptime. However, this is a non-trivial problem, which requires sufficient data analytics knowledge and labeled data, either to design supervised fault detection models or to evaluate the performance of unsupervised models. While today most companies collect data by adding sensors to their machinery, the majority of this data is unfortunately not labeled. Moreover, labeling requires expert knowledge and is very cumbersome. To solve this mismatch, we present an architecture that guides experts, only requiring them to label a very small subset of the data compared to today’s standard labeling campaigns that are used when designing predictive maintenance solutions. We use auto-encoders to highlight potential anomalies and clustering approaches to group these anomalies into (potential) failure types. The accompanied dashboard then presents the anomalies to domain experts for labeling. In this way, we enable domain experts to enrich routinely collected machine data with business intelligence via a user-friendly hybrid model, combining auto-encoder models with labeling steps and supervised models. Ultimately, the labeled failure data allows for creating better failure prediction models, which in turn enables more effective predictive maintenance. More specifically, our architecture gets rid of cumbersome labeling tasks, allowing companies to make maximum use of their data and expert knowledge to ultimately increase their profit. Using our methodology, we achieve a labeling gain of 90% at best compared to standard labeling tasks.

Colloidal self-assembly in biosensing strategies for biomarkers diagnosis

<p>Rapid and high-throughput detection of biomarkers plays an important role in disease diagnosis, prognosis, and monitoring treatment. Existing diagnostic tools that rely on genetic testing and antibody recognition can provide the required sensitivity and specificity. However, these methods need multiple labeling steps for the signal amplification, which are tedious and time-consuming. To address these issues, self-assembled colloidal superstructures, especially for the nanophotonic structures, exhibit many optical sensing properties in response to external stimuli, which lays the foundation for developing the next-generation biosensors. Herein, we discuss the potential of self-assembled nanophotonic-based biosensors for achieving low-cost, high-sensitive, multiplexed detection of various biomarkers, including nucleic acids, proteins, and exosomes. We conclude the current challenges in optical colloidal biosensors and offer insights into their further advances towards the smart healthcare.</p>

2-Aminopurine-based quencher-free DNA tweezers with fluorescence properties well tuned by surrounding bases.

Reversible structural changes in DNA nanomachines have great potential in the field of bioanalysis. Here, we demonstrate an assembly strategy for quencher-free and tunable DNA tweezers based on 2-aminopurine (2-AP), avoiding the tedious fluorescence labelling step. The conformational state of the tweezers could be controlled by specific oligonucleotides (fuel or anti-fuel). Taking advantage of the local environmental sensitivity of 2-AP, the structural changes of the tweezers were easily tracked, and multiple cyclic switching of the tweezers between the open and closed states was achieved. In addition, the influence of oligonucleotide structure on the fluorescence properties of 2-AP was deeply explored. We figured out that the fluorescence of 2-AP was highly quenched by the base-stacking of natural bases in DNA oligonucleotides. Moreover, by comprehensively regulating the type of bases surrounding the inserted 2-AP site, a sensitive fluorescence response towards dynamic change can be obtained. This principle of quencher-free nanodevices based on 2-AP provides a convenient method for monitoring the structural changes of DNA nanomachines.

Analysis of Caenorhabditis Protein Glycosylation.

Glycoproteins result from post-translational modification of proteins by glycans attached to certain side chains, with possible heterogeneity due to different structures being possible at the same glycosylation site.In contrast to the mammalian systems, analysis of invertebrate glycans presents a challenge in analysis as there exist unfamiliar epitopes and a high degree of structural and isomeric variation between different species-Caenorhabditis elegans is no exception. Simple screening using lectins and antibodies can yield hints regarding which glycan epitopes are present in wild-type and mutant strains, but detailed analysis is necessary for determining more exact glycomic information. Here, our analytical approach is to analyze N- and O-glycans involving "off-line" RP-HPLC MALDI-TOF MS/MS. Enrichment and labeling steps facilitate the analysis of single structures and provide isomeric separation. Thereby, the "simple" worm expresses over 200N-glycan structures varying depending on culture conditions or the genetic background.

Development of a CNN-based fault detection system for a real water injection centrifugal pump

Large-sized centrifugal pumps play a major role in produced water injection systems in oil and gas production. Monitoring this equipment operation is vital to guarantee its efficiency and to reduce the occurrence of unplanned downtimes. The main goal of this work was to develop a fault detection system based on artificial intelligence (AI) algorithms for a water injection centrifugal pump located at an oil and gas company’s offshore platform. The convolutional neural network (CNN) was the main algorithm investigated in this work. However, other machine learning techniques (i.e., support vector machines, random forest, and multilayer perceptrons) were used to compare against the results achieved by the CNN. A data-driven methodology was proposed for the stages of exploratory data analysis (EDA), data labeling, automatic hyperparameter optimization, training and testing of the chosen models. The results showed that the proposed methodology was effective and applicable. The CNN and the support vector machine (SVM) models presented interesting performances. The CNN model returned 93.7% precision and 59.8% recall, whereas the SVM model returned 84.6% precision and 66.7% recall. The challenges related to the use of a real dataset were also discussed, emphasizing the data labeling step. Applying the k-means clustering technique proved to be useful for labeling the data instances.

Radiolabeling of Platelets with 99mTc-HYNIC-Duramycin for In Vivo Imaging Studies

Following the in vivo biodistribution of platelets can contribute to a better understanding of their physiological and pathological roles, and nuclear imaging methods, such as single photon emission tomography (SPECT), provide an excellent method for that. SPECT imaging needs stable labeling of the platelets with a radioisotope. In this study, we report a new method to label platelets with 99mTc, the most frequently used isotope for SPECT in clinical applications. The proposed radiolabeling procedure uses a membrane-binding peptide, duramycin. Our results show that duramycin does not cause significant platelet activation, and radiolabeling can be carried out with a procedure utilizing a simple labeling step followed by a size-exclusion chromatography-based purification step. The in vivo application of the radiolabeled human platelets in mice yielded quantitative biodistribution images of the spleen and liver and no accumulation in the lungs. The performed small-animal SPECT/CT in vivo imaging investigations revealed good in vivo stability of the labeling, which paves the way for further applications of 99mTc-labeled-Duramycin in platelet imaging.

Offsetting Low-Affinity Carbohydrate Binding with Covalency to Engage Sugar-Specific Proteins for Tumor-Immune Proximity Induction.

Carbohydrate-binding receptors are often used by the innate immune system to potentiate inflammation, target endocytosis/destruction, and adaptive immunity (e.g., CD206, DC-SIGN, MBL, and anticarbohydrate antibodies). To access this class of receptors for cancer immunotherapy, a growing repertoire of bifunctional proximity-inducing therapeutics use high-avidity multivalent carbohydrate binding domains to offset the intrinsically low affinity associated with monomeric carbohydrate-protein binding interactions (Kd ≈ 10-3-10-6 M). For applications aimed at recruiting anticarbohydrate antibodies to tumor cells, large synthetic scaffolds are used that contain both a tumor-binding domain (TBD) and a multivalent antibody-binding domain (ABD) comprising multiple l-rhamnose monosaccharides. This allows for stable bridging between tumor cells and antibodies, which activates tumoricidal immune function. Problematically, such multivalent macromolecules can face limitations including synthetic and/or structural complexity and the potential for off-target immune engagement. We envisioned that small bifunctional "proximity-inducing" molecules containing a low-affinity monovalent ABD could efficiently engage carbohydrate-binding receptors for tumor-immune proximity by coupling weak binding with covalent engagement. Typical covalent drugs and electrophilic chimeras use high-affinity ligands to promote the fast covalent engagement of target proteins (i.e., large kinact/KI), driven by a favorably small KI for binding. We hypothesized the much less favorable KI associated with carbohydrate-protein binding interactions can be offset by a favorably large kinact for the covalent labeling step. In the current study, we test this hypothesis in the context of a model system that uses rhamnose-specific antibodies to induce tumor-immune proximity and tumoricidal function. We discovered that synthetic chimeric molecules capable of preorganizing an optimal electrophile (i.e., SuFEx vs activated ester) for protein engagement can rapidly covalently engage natural sources of antirhamnose antibody using only a single low-affinity rhamnose monosaccharide ABD. Strikingly, we observe chimeric molecules lacking an electrophile, which can only noncovalently bind the antibody, completely lack tumoricidal function. This is in stark contrast to previous work targeting small molecule hapten and peptide-specific antibodies. Our findings underscore the utility of covalency as a strategy to engage low-affinity carbohydrate-specific proteins for tumor-immune proximity induction.

Evaluation of Flaw Detection Algorithm Using Simulated X-Ray Computed Tomography of Ground Truth Data

Abstract A framework to generate simulated X-ray computed tomography (XCT) data of ground truth (denoted here as “GT”) flaws was developed for the evaluation of flaw detection algorithms using image comparison metrics. The flaws mimic some of those found in additively manufactured parts. The simulated flaw structure gave a GT data set with which to quantitatively evaluate, by calculating exact errors, the results of flaw detection algorithms applied to simulated XCT images. The simulated data avoided time-consuming manual voxel labeling steps needed for many physical data sets to generate GT images. The voxelated pore meshes that exactly match GT images were used in this study as opposed to using continuum pore meshes. The voxelated pore mesh approach avoids approximation error that occurs when converting continuum pore meshes to voxelated GT images. Spherical pores of varying sizes were randomly distributed near the surface and interior of a cylindrical part. XCT simulation was carried out on the structure at three different signal-to-noise levels by changing the number of frames integrated for each projection. Two different local thresholding algorithms (a commercial code and the Bernsen method) and a global thresholding algorithm (Otsu) were used to segment images using varying sets of algorithm parameters. The segmentation results were evaluated with various image evaluation metrics, which showed different behaviors for the three algorithms regarding “closeness” to the GT data. An approach to optimize the thresholding parameters was demonstrated for the commercial flaw detection algorithm based on semantic evaluation metrics. A framework to evaluate pore sizing error and binary probability of detection was further demonstrated to compare the optimization results.

A flow cytometry method for safe detection of bacterial viability.

Flow cytometry is a relevant tool to meet the requirements of academic and industrial research projects aimed at estimating the features of a bacterial population (e.g., quantity, viability, activity). One of the remaining challenges is now the safe assessment of bacterial viability while minimizing the risks inherent to existing protocols. In our core facility at the Paris-Saclay University, we have addressed this issue with two objectives: measuring bacterial viability in biological samples and preventing bacterial contamination and chemical exposure of the staff and cytometers used on the platform. Here, we report the development of a protocol achieving these two objectives, including a viability labeling step before bacteria fixation, which removes the risk of biological exposure, and the decrease of the use of reagents such as propidium iodide (PI), which are dangerous for health (CMR: carcinogenic, mutagenic, and reprotoxic). For this purpose, we looked for a non-CMR viability dye that can irreversibly label dead bacteria before fixation procedures and maintain intense fluorescence after further staining. We decided to test on the bacteria, eFluor Fixable Viability dyes, which are usually used on eukaryotic cells. Since the bacteria had size and granularity characteristics very similar to those associated with flow cytometry background signals, a step of bacterial DNA labeling with SYTO or DRAQ5 was necessarily added to differentiate them from the background. Three marker combinations (viability-DNA) were tested on LSR Fortessa and validated on pure bacterial populations (Gram+ , Gram- ) and polybacterial cultures. Any of the three methods can be used and adapted to the needs of each project and allow users to adapt the combination according to the configuration of their cytometer. Having been tested on six bacterial populations, validated on two cytometers, and repeated at least two times in each evaluated condition, we consider this method reliable in the context of these conditions. The reliability of the results obtained in flow cytometry was successfully validated by applying this protocol to confocal microscopy, permeabilization, and also to follow cultures over time. This flow cytometry protocol for measuring bacterial viability under safer conditions also opens the prospect of its use for further bacterial characterization.

Construction of Exploration Data for Greenhouse Gas Geologic Storage: Focusing on Geological Cross-section Data

In this study, the most basic data, underground geological structure data, that is, geological cross-section data, were established to select a candidate site for underground storage of greenhouse gases based on AI. As a target area, the Gyeongsang Basin, where a large amount of sedimentary rocks are distributed, was selected as the greenhouse gas can be stored most effectively in sedimentary rocks. To this end, the acquisition and edit step, the refinement step, and the labeling step were carried out in the order of raw data collection, source data and labeling data construction to construct the geological cross-section data. This data can be downloaded through the AI hub site (https://aihub.or.kr/aihubdata/data/view.do?curr Menu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71390) operated by the Korea Institute for Intelligent Information Society Promotion.