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Systems for identifying objects by means of x-ray fluorescence (XRF) of encased labeling elements have been developed. The XRF spectra of objects so labeled would be analogous to the external bar code labels now used to track objects in everyday commerce. In conjunction with computer-based tracking systems, databases, and labeling conventions, the XRF labels could be used in essentially the same manner as that of bar codes to track inventories and to record and process commercial transactions. In addition, as summarized briefly below, embedded XRF labels could be used to verify the authenticity of products, thereby helping to deter counterfeiting and fraud. A system, as described above, is called an encased core product identification and authentication system (ECPIAS). The ECPIAS concept is a modified version of that of a related recently initiated commercial development of handheld XRF spectral scanners that would identify alloys or detect labeling elements deposited on the surfaces of objects. In contrast, an ECPIAS would utilize labeling elements encased within the objects of interest. The basic ECPIAS concept is best illustrated by means of an example of one of several potential applications: labeling of cultured pearls by labeling the seed particles implanted in oysters to grow the pearls. Each pearl farmer would be assigned a unique mixture of labeling elements that could be distinguished from the corresponding mixtures of other farmers. The mixture would be either incorporated into or applied to the surfaces of the seed prior to implantation in the oyster. If necessary, the labeled seed would be further coated to make it nontoxic to the oyster. After implantation, the growth of layers of mother of pearl on the seed would encase the XRF labels, making these labels integral, permanent parts of the pearls that could not be removed without destroying the pearls themselves. The XRF labels would be read by use of XRF scanners, the spectral data outputs of which would be converted to alphanumeric data in a digital equivalent data system (DEDS), which is the subject of the previous article. These alphanumeric data would be used to track the pearls through all stages of commerce, from the farmer to the retail customer.
In this CIF project, we worked toward semi-automating knowledge discovery from anomaly detection algorithms through the use of active learning. Active learning is an area of research within machine learning that uses an "expert in the loop" to learn from large data sets that have very few annotations or labels available, and where providing such labels is expensive. In our case, the task can be defined as the identification of safety events from flight operational data. Since traditional anomaly detection algorithms cannot differentiate between operationally relevant and irrelevant statistical anomalies, Subject Matter Experts (SMEs) have a lengthy and expensive burden of investigating every example identified by the detection algorithm, classifying and labeling them as relevant or irrelevant. Active learningidentifies the unlabeled example for which a label would most improve the classifier, asks the domain expert for a label, and repeats this process until there are no more resources (time, budget) available for labeling or a minimum required performance is reached. A positive label indicates an operationally significant safety event whereas a negative label indicates otherwise. Based on these few labels we propose to build an active learning system that utilizes the SME's time in the most effective manner by iteratively asking for labels for as few informative instances as possible. Our work was proposed to be a stepping stone toward implementation and deployment of the system with user interface to be pursued by the Aviation Operations and Safety Program (AOSP) given its interest in safety monitoring and discovery of safety incidents.
This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. We used a DenseNet121 model pre-trained MIMIC-CXR dataset for deep learning-based multi-label classification using ground truth labels from radiology reports extracted using the CheXpert and CheXbert Labeler. We compared the performance of the 14 chest X-ray labels on the MIMIC-CXR and Veterans Healthcare Administration chest X-ray dataset (VA-CXR). The validation of ground truth and the assessment of multi-label classification performance across various NLP extraction tools revealed that the VA-CXR dataset exhibited lower disagreement rates than the MIMIC-CXR datasets. Additionally, there were notable differences in AUC scores between models utilizing CheXpert and CheXbert. When evaluating multi-label classification performance across different datasets, minimal domain shift was observed in the unseen VA dataset, except for the label “Enlarged Cardiomediastinum.” The subgroup with the most significant variations in multi-label classification performance was study year. These findings underscore the importance of considering domain shift in chest X-ray classification tasks, paying particular attention to the temporality of the exam. Our study reveals the significant impact of domain shift and demographic factors on chest X-ray classification, emphasizing the need for improved transfer learning and robust model development. Addressing these challenges is crucial for advancing medical imaging research and improving patient care.
Hydroxyl radical protein footprinting (HRPF) using synchrotron radiation is a well-validated method to assess protein structure in the native solution state. In this method, X-ray radiolysis of water generates hydroxyl radicals that can react with solvent accessible side chains of proteins, with mass spectrometry used to detect the resulting labeled products. An ideal footprinting dose provides sufficient labeling to measure the structure but not so much as to influence the results. The optimization of hydroxyl radical dose is typically performed using an indirect Alexa488 fluorescence assay sensitive to hydroxyl radical concentration, but full evaluation of the experiment's outcome relies upon bottom-up liquid chromatography mass spectrometry (LC-MS) measurements to directly determine sites and extent of oxidative labeling at the peptide and protein level. A direct evaluation of the extent of labeling to provide direct and absolute measurements of dose and “safe” dose ranges in terms of, for example, average numbers of labels per protein, would provide immediate feedback on experimental outcomes prior to embarking on detailed LC-MS analyses. To this end, we describe an approach to integrate intact MS screening of labeled samples immediately following exposure, along with metrics to quantify the extent of observed labeling from the intact mass spectra. Intact MS results on the model protein lysozyme were evaluated in the context of Alexa488 assay results and a bottom-up LC-MS analysis of the same samples. In conclusion, this approach provides a basis for placing delivered hydroxyl radical dose metrics on firmer technical grounds for synchrotron X-ray footprinting of proteins, with explicit parameters to increase the likelihood of a productive experimental outcome. Further, the method directs approaches to provide absolute and direct dosimetry for all types of labeling for protein footprinting.
The Auger electron-emitting radionuclide 191 Pt is a promising candidate for radiopharmaceutical therapy. Herein, we explored novel labeling methods for 191 Pt using thiol-containing ligands to improve the in vivo stability and targeting ability of 191 Pt-labeled complexes. We synthesized dithiol-containing N 2 S 2 and NS 2 ligands, and a trithiol ligand, and then compared their radiochemical reactivity with 191 Pt. [ 191 Pt]Pt-trithiol was synthesized and its biodistribution was evaluated in mice and compared with free 191 Pt. Finally, a 191 Pt-trithiol complex targeting prostate-specific membrane antigen (PSMA): [ 191 Pt]Pt-trithiol-PSMA was developed and evaluated in mice bearing tumor xenografts and compared with a 191 Pt-complex labeled via monothiol-containing Cys ([ 191 Pt]Pt-Cys-PSMA). A comparison of N 2 S 2 , NS 2 , and trithiol showed that the trithiol ligand is the best for producing 191 Pt-labeled compounds in high yield and as a single peak in preparative HPLC. Notably, the trithiol ligand made 191 Pt-labeled compounds and precursors separatable, achieving 191 Pt-labeled products with a high molar activity: 200–400 mCi/μmol (7.4–14.8 GBq/μmol) at EOS. Additionally, [ 191 Pt]Pt-trithiol and [ 191 Pt]Pt-trithiol-PSMA were stable in vivo with rapid clearance compared with free 191 Pt and [ 191 Pt]Pt-Cys-PSMA. [ 191 Pt]Pt-trithiol-PSMA resulted in a low uptake in most normal organs and a high uptake in the kidneys and prostate cancer with PSMA expression. Furthermore, this study demonstrated that a labeling method with trithiol for Pt radionuclides achieves 191 Pt-labeled products with high molar activity. 191 Pt-trithiol-PSMA showed promising in vivo stability and tumor-targeting specificity, which should facilitate the pharmaceutical development of Pt radionuclides for radiopharmaceutical therapy, especially Auger electron cancer therapy.
Stable isotope tracers are a powerful tool for the quantitative analysis of microbial metabolism, enabling pathway elucidation, metabolic flux quantification, and assessment of reaction and pathway thermodynamics. 13 C and 2 H metabolic flux analysis commonly relies on isotopically labeled carbon substrates, such as glucose. However, the use of 2 H-labeled nutrient substrates faces limitations due to their high cost and limited availability in comparison to 13 C-tracers. Furthermore, isotope tracer studies in industrially relevant bacteria that metabolize complex substrates such as cellulose, hemicellulose, or lignocellulosic biomass, are challenging given the difficulty in obtaining these as isotopically labeled substrates. In this study, we examine the potential of deuterated water ( 2 H 2 O) as an affordable, substrate-neutral isotope tracer for studying central carbon metabolism. We apply 2 H 2 O labeling to investigate the reversibility of glycolytic reactions across three industrially relevant bacterial species -C. thermocellum, Z. mobilis, and E. coli-harboring distinct glycolytic pathways with unique thermodynamics. We demonstrate that 2 H 2 O labeling recapitulates previous reversibility and thermodynamic findings obtained with established 13 C and 2 H labeled nutrient substrates. Furthermore, we exemplify the utility of this 2 H 2 O labeling approach by applying it to high-substrate C. thermocellum fermentations -a setting in which the use of conventional tracers is impractical-thereby identifying the glycolytic enzyme phosphofructokinase as a major bottleneck during high-substrate fermentations and unveiling critical insights that will steer future engineering efforts to enhance ethanol production in this cellulolytic organism. This study demonstrates the utility of deuterated water as a substrate-agnostic isotope tracer for examining flux and reversibility of central carbon metabolic reactions, which yields biological insights comparable to those obtained using costly 2 H-labeled nutrient substrates.
Abstract Microalgae accumulate triacylglycerol (TAG) during nutrient deprivation and break it down after nutrient resupply, and these processes involve dramatic shifts in cellular carbon allocation. Due to the importance of algae in the global carbon cycle, and the potential of algal lipids as feedstock for chemical and fuel production, these processes are of both ecophysiological and biotechnological importance. However, the metabolism of TAG is not well understood, particularly the contributions of fatty acids (FAs) from different membrane lipids to TAG accumulation and the fate of TAG FAs during degradation. Here, we used isotopic labeling time course experiments on Chlamydomonas reinhardtii to track FA synthesis and transfer between lipid pools during nitrogen (N)-deprivation and resupply. When cells were labeled before N-deprivation, total levels of label in cellular FAs were unchanged during subsequent N-deprivation and later resupply, despite large fluxes into and out of TAG and membrane lipid pools. Detailed analyses of FA levels and labeling revealed that about one-third of acyl chains accumulating in TAG during N-deprivation derive from preexisting membrane lipids, and in total, at least 45% of TAG FAs passed through membrane lipids at one point. Notably, most acyl chains in membrane lipids during recovery after N-resupply come from TAG. Fluxes of polyunsaturated FAs from plastidic membranes into TAG during N-deprivation were particularly noteworthy. These findings demonstrate a high degree of integration of TAG and membrane lipid metabolism and highlight a role for TAG in storage and supply of membrane lipid components.
Abstract Background Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model. Results We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.
In the Multiple Instance Learning scenario, the training data consists of instances grouped into bags, and each bag is labelled with whether it is positive, i.e. contains at least one positive instance. First, Active Learning, in which additional labels can be iteratively requested, has the potential to allow more accurate classifiers to be learned with less labels. Active Learning has been applied to the Multiple Instance Learning under two settings: when bag labels of unlabelled bags can be requested, and when instance labels within bags known to be positive can be requested. Second, Bayesian Active learning methods have the potential to learn accurate classifiers with few labels, because they explicitly track the classifier uncertainty and can thus address its knowledge gaps. Yet, there does not exist any Bayesian Active Learning method for the Multiple Instance Learning Scenario. In this work, we develop the first such method. We develop a Bayesian classifier for the Multiple Instance Learning scenario, show how it can be efficiently used for Bayesian Active Learning, and perform experiments assessing its performance. While its performance exceeds that when no Active Learning is used, it is sometimes better, sometimes worse than the naive baseline of uncertainty sampling, depending on the situation. This suggests future work: building more customizable Bayesian Active Learning methods for the Multiple Instance Scenario, customizable to whether bag or instance label accuracy is targeted, and the labeling budget.
This report presents the developments and results of MindSynchro project as part of DOE OE FOA 1861. DOE and Pacific Northwest National Laboratory (PNNL) have made available to FOA awardees datasets containing years of real historical data recorded from various phasor measurement units (PMUs) which are installed in three large US interconnections: Texas (IC A), Western (IC B), and Eastern (IC C). The main goal of the project, which was successfully achieved, was to develop methods for detection and identification of events which are relevant for power grid operation. Tasks performed for achieving the project goals included data exploration and pre-processing, the development and application of physics-based features, data analysis and labeling based on unsupervised learning approaches, training and testing of DSSL models for classification of events which are relevant for power grid operation, and deployment of solutions to cloud environments. The methods developed in the project can potentially provide relevant benefits to power grid asset owners/operators in general in terms of situational awareness. Two main types of outcomes can be provided by these tools: Identification of specific relevant power grid event types: Semi-supervised ML methods developed in the project can adequately employ not only the relatively scarce labeled data but also the large amount of available unlabeled data to train models for detection of specific event types. Such methods enable the application of trained models for the detection of events in a population of PMUs much larger than that associated to the labeled events. Support in data labeling / label validation: Labels are critical for training of models for identification of specific types of events. However, labeling large amounts of data is a manual and tedious process. This means that such process is error prone and is not scalable. Methods developed in the project, based on ensembles of clustering models, have been successfully employed for turning manual labeling into a scalable process. Accurate identification of specific relevant events can provide the operators with immediate situational awareness that could otherwise require hours or days of analysis from domain experts. We envision that such methods could be initially employed in support of post-mortem analysis of events and, as confidence is gained, they could be employed for online/real-time support, providing, among other benefits, insights for avoiding major events which could happen due to a combination of smaller ones. On the longer term, related methods could potentially be employed to improve protection and control.
In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.
Previous methods of identifying cells undergoing DNA synthesis (S-phase) utilized H-3 thymidine (3HT) autoradiography. 5-Bromo-2'-deoxyuridine (BrdU) immunohistochemistry is a nonradioactive alternative method. This experiment compared the two methods using the nuclear volume model for osteoblast histogenesis in two different embedding media. Twenty Sprague-Dawley rats were used, with half receiving 3HT (1 micro Ci/g) and the other half BrdU (50 microgram/g). Condyies were embedded (one side in paraffin, the other in plastic) and S-phase nuclei were identified using either autoradiography or immunohistochemistry. The fractional distribution of preosteoblast cell types and the percentage of labeled cells (within each cell fraction and label index) were calculated and expressed as mean q standard error. Chi-Square analysis showed only a minor difference in the fractional distribution of cell types. However, there were significant differences (p less than 0.05) by ANOVA, in the nuclear labeling of specific cell types. With the exception of the less-differentiated A+A'cells, more BrdU label was consistently detected in paraffin than in plastic-embedded sections. In general, more nuclei were labeled with 3H-thymidine than with BrdU in both types of embedding media. Labeling index data (labeled cells/total cells sampled x 100) indicated that BrdU in paraffin, but not plastic gave the same results as 3HT in either embedding method. Thus, we conclude that the two labeling methods do not yield the same results for the nuclear volume model and that embedding media is an important factor whenusing BrdU. As a result of this work, 3HT was chosen for used in the PARE.03 flight experiments.
Previous methods of identifying cells undergoing DNA synthesis (S-phase) utilized 3H-thymidine (3HT) autoradiography. 5-Bromo-2'-deoxyuridine (BrdU) immunohistochemistry is a nonradioactive alternative method. This experiment compared the two methods using the nuclear volume model for osteoblast histogenesis in two different embedding media. Twenty Sprague-Dawley rats were used, with half receiving 3HT (1 micro-Ci/g) and the other half BrdU (50 micro-g/g). Condyles were embedded (one side in paraffin, the other in plastic) and S-phase nuclei were identified using either autoradiography or immunohistochemistry. The fractional distribution of preosteoblast cell types and the percentage of labeled cells (within each cell fraction and label index) were calculated and expressed as mean +/- standard error. Chi-Square analysis showed only a minor difference in the fractional distribution of cell types. However, there were,significant differences (p less than 0.05) by ANOVA, in the nuclear labeling of specific cell types. With the exception of the less-differentiated A+A' cells, more BrdU label was consistently detected in paraffin than in plastic-embedded sections. In general, more nuclei were labeled with 3H-thymidine than with BrdU in both types of embedding media (Fig 2.). Labeling index data (labeled cells/total cells sampled x 100) indicated that BrdU in paraffin, but not plastic gave the same results as 3HT in either embedding method. Thus, we conclude that the two labeling methods do not yield the same results.
The effect of Ca on the polar movement of [3H]indoleacetic acid ([3H]IAA) in gravistimulated roots was examined using 3-day-old seedlings of maize (Zea mays L.). Transport of label was measured by placing an agar donor block containing [3H]IAA on one side of the elongation zone and measuring movement of label across the root into an agar receiver block on the opposite side. In vertically oriented roots, movement of label across the elongation zone into the receiver was slight and was not enhanced by incorporating 10 millimolar CaCl2 into the receiver block. In horizontally oriented roots, movement of label across the root was readily detectable and movement to a receiver on the bottom was about 3-fold greater than movement in the opposite direction. This polarity was abolished in roots from which the caps were removed prior to gravistimulation. When CaCl2 was incorporated into the receivers, movement of label across horizontally oriented intact roots was increased about 3-fold in both the downward and upward direction. The ability of Ca to enhance the movement of label from [3H]IAA increased with increasing Ca concentration in the receiver up to 5 to 10 millimolar CaCl2. With the inclusion of CaCl2 in the receiver blocks, gravity-induced polar movement of label into receiver blocks from applied [3H]IAA was detectable within 30 minutes, and asymmetric distribution of label within the tissue was detectable within 20 minutes. The results indicate that gravistimulation induces a physiological asymmetry in the auxin transport system of maize roots and that Ca increases the total transport of auxin across the root.
Two S-phase markers for in vivo studies of cell proliferation in the developing central nervous system, tritiated thymidine ((3)H-TdR) and bromodeoxyuridine (BUdR), were compared using double-labeling techniques in the developing mouse cortex at embryonic day 14 (E14). The labeling efficiencies and detectability of the two tracers were approximately equivalent, and there was no evidence of significant tracer interactions that depend on order of administration. For both tracers, the loading time needed to label an S-phase cell to detectability is estimated at <0.2 h shortly after the injection of the label, but, as the concentration of the label falls, it increases to approximately 0.65 h after about 30 min. Thereafter, cells that enter the S-phase continue to become detectably labeled for approximately 5-6 h. The approximate equivalence of these two tracers was exploited to observe directly the numbers and positions of nuclei entering (labeled with the second tracer only) and leaving (labeled with the first tracer only) the S-phase. As expected, the numbers of nuclei entering and leaving the S-phase both increased as the interval between the two injections lengthened. Also, nuclei leaving the S-phase rapidly move towards the ventricular surface during G2, but, unexpectedly, the distribution of the entering nuclei does not differ significantly from the distribution of the nuclei in the S-phase. This indicates that: (1) the extent and rate of abventricular nuclear movement during G1 is variable, such that not all nuclei traverse the entire width of the ventricular zone, and (2) interkinetic nuclear movements are minimal during S-phase. Copyright 2000 S. Karger AG, Basel.
Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.