Interactive correction of mislabeled training data

In this paper, we develop a visual analysis method for interactively improving the quality of labeled data, which is essential to the success of supervised and semi-supervised learning. The quality improvement is achieved through the use of user-selected trusted items. We employ a bi-level optimization model to accurately match the labels of the trusted items and to minimize the training loss. Based on this model, a scalable data correction algorithm is developed to handle tens of thousands of labeled data efficiently. The selection of the trusted items is facilitated by an incremental tSNE with improved computational efficiency and layout stability to ensure a smooth transition between different levels. We evaluated our method on real-world datasets through quantitative evaluation and case studies, and the results were generally favorable

Interactive correction of mislabeled training data

In this paper, we develop a visual analysis method for interactively improving the quality of labeled data, which is essential to the success of supervised and semi-supervised learning. The quality improvement is achieved through the use of user-selected trusted items. We employ a bi-level optimization model to accurately match the labels of the trusted items and to minimize the training loss. Based on this model, a scalable data correction algorithm is developed to handle tens of thousands of labeled data efficiently. The selection of the trusted items is facilitated by an incremental tSNE with improved computational efficiency and layout stability to ensure a smooth transition between different levels. We evaluated our method on real-world datasets through quantitative evaluation and case studies, and the results were generally favorable

A multi-purpose, multi-rotor drone system for long-range and high-altitude volcanic gas plume measurements

A multi-rotor drone has been adapted for studies of volcanic gas plumes. This adaptation includes improved capacity for high-altitude and long-range, real-time SO2 concentration monitoring, long-range manual control, remotely activated bag sampling and plume speed measurement capability. The drone is capable of acting as a stable platform for various instrument configurations, including multi-component gas analysis system (MultiGAS) instruments for in situ measurements of SO2, H2S, and CO2 concentrations in the gas plume and portable differential optical absorption spectrometer (MobileDOAS) instruments for spectroscopic measurement of total SO2 emission rate, remotely controlled gas sampling in bags and sampling with gas denuders for posterior analysis on the ground of isotopic composition and halogens. The platform we present was field-tested during three campaigns in Papua New Guinea: in 2016 at Tavurvur, Bagana and Ulawun volcanoes, in 2018 at Tavurvur and Langila volcanoes and in 2019 at Tavurvur and Manam volcanoes, as well as in Mt. Etna in Italy in 2017. This paper describes the drone platform and the multiple payloads, the various measurement strategies and an algorithm to correct for different response times of MultiGAS sensors. Specifically, we emphasize the need for an adaptive flight path, together with live data transmission of a plume tracer (such as SO2 concentration) to the ground station, to ensure optimal plume interception when operating beyond the visual line of sight. We present results from a comprehensive plume characterization obtained during a field deployment at Manam volcano in May 2019. The Papua New Guinea region, and particularly Manam volcano, has not been extensively studied for volcanic gases due to its remote location, inaccessible summit region and high level of volcanic activity. We demonstrate that the combination of a multi-rotor drone with modular payloads is a versatile solution to obtain the flux and composition of volcanic plumes, even for the case of a highly active volcano with a high-altitude plume such as Manam. Drone-based measurements offer a valuable solution to volcano research and monitoring applications and provide an alternativespan idCombining double low line"page4256"/> and complementary method to ground-based and direct sampling of volcanic gases

Novel tumor necrosis factor-related long non-coding RNAs signature for risk stratification and prognosis in glioblastoma

BackgroundTumor necrosis factor (TNF) is an inflammatory cytokine that can coordinate tissue homeostasis by co-regulating the production of cytokines, cell survival, or death. It widely expresses in various tumor tissues and correlates with the malignant clinical features of patients. As an important inflammatory factor, the role of TNFα is involved in all steps of tumorigenesis and development, including cell transformation, survival, proliferation, invasion and metastasis. Recent research has showed that long non-coding RNAs (lncRNAs), defined as RNA transcripts >200 nucleotides that do not encode a protein, influence numerous cellular processes. However, little is known about the genomic profile of TNF pathway related-lncRNAs in GBM. This study investigated the molecular mechanism of TNF related-lncRNAs and their immune characteristics in glioblastoma multiforme (GBM) patients.MethodsTo identify TNF associations in GBM patients, we performed bioinformatics analysis of public databases - The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). The ConsensusClusterPlus, CIBERSORT, Estimate, GSVA and TIDE and first-order bias correlation and so on approaches were conducted to comprehensively characterize and compare differences among TNF-related subtypes.ResultsBased on the comprehensive analysis of TNF-related lncRNAs expression profiles, we constructed six TNF-related lncRNAs (C1RL-AS1, LINC00968, MIR155HG, CPB2-AS1, LINC00906, and WDR11-AS1) risk signature to determine the role of TNF-related lncRNAs in GBM. This signature could divide GBM patients into subtypes with distinct clinical and immune characteristics and prognoses. We identified three molecular subtypes (C1, C2, and C3), with C2 showing the best prognosis; otherwise, C3 showing the worst prognosis. Moreover, we assessed the prognostic value, immune infiltration, immune checkpoints, chemokines cytokines and enrichment analysis of this signature in GBM. The TNF-related lncRNA signature was tightly associated with the regulation of tumor immune therapy and could serve as an independent prognostic biomarker in GBM.ConclusionThis analysis provides a comprehensive understanding of the role of TNF-related characters, which may improve the clinical outcome of GBM patients

Development and validation of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video

BackgroundThe performance of existing image-based training models in evaluating bowel preparation on colonoscopy videos was relatively low, and only a few models used external data to prove their generalization. Therefore, this study attempted to develop a more precise and stable AI system for assessing bowel preparation of colonoscopy video.MethodsWe proposed a system named ViENDO to assess the bowel preparation quality, including two CNNs. First, Information-Net was used to identify and filter out colonoscopy video frames unsuitable for Boston bowel preparation scale (BBPS) scoring. Second, BBPS-Net was trained and tested with 5,566 suitable short video clips through three-dimensional (3D) convolutional neural network (CNN) technology to detect BBPS-based insufficient bowel preparation. Then, ViENDO was applied to complete withdrawal colonoscopy videos from multiple centers to predict BBPS segment scores in clinical settings. We also conducted a human-machine contest to compare its performance with endoscopists.ResultsIn video clips, BBPS-Net for determining inadequate bowel preparation generated an area under the curve of up to 0.98 and accuracy of 95.2%. When applied to full-length withdrawal colonoscopy videos, ViENDO assessed bowel cleanliness with an accuracy of 93.8% in the internal test set and 91.7% in the external dataset. The human-machine contest demonstrated that the accuracy of ViENDO was slightly superior compared to most endoscopists, though no statistical significance was found.ConclusionThe 3D-CNN-based AI model showed good performance in evaluating full-length bowel preparation on colonoscopy video. It has the potential as a substitute for endoscopists to provide BBPS-based assessments during daily clinical practice

Targeting dithiol-disulfide switches in cells

Many proteins have pairs of spatially proximal cysteine residues which act as redox switches via disulfide bond formation/reduction. These switches can often have profound effects on the biological activity of the proteins. Dibromomaleimide and dibromopyridazinedione derivatives were previously reported to be effective in crosslinking two cysteines. Here, the selective targeting of dithiols over single cysteines was attempted using two approaches. One approach used a bicyclic electrophile which it was hoped would give direct selective reaction. The second strategy was to effect non-selective cysteine functionalization followed by selective cleavage with thiol of adducts on isolated cysteines. Unfortunately, the bicyclic electrophiles examined failed to label the protein models, whilst in the second strategy, thiol cleavage removed both crosslinked adducts and non-crosslinked single cysteine adducts. Interestingly, monobromopyridazinedione was found to react with lysine. It labelled the lysines in somatostatin and this lysine modification was stable under thiol cleavage conditions. Thus, these experiments revealed a potential strategy for selective labelling of lysine residues in the presence of competing cysteines. Covalent modification of proteins was also explored using a chemical probe based on a series of inhibitors previously reported to inhibit NF-κB expression. These inhibitors were previously reported to covalently bind to proteins via a Michael receptor warhead and they had been shown in our lab to prevent cyclin D1 down-regulation after DNA damaging treatment of mammalian cells. An alkyne handle was introduced to the molecule previously synthesized in our group to allow “click reaction” with an azide-tagged biotin/fluorophore to isolate targets for subsequent identification. Competition assays between the probe and the parental inhibitors in both cell lysates and cells were conducted. The inhibitors and the probe acted in competition in cell lysates, but had a synergistic effect on labelling in cell.Open Acces