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Breast cancer is a heterogeneous disease composed of various biologically distinct subtypes, each characterized by unique molecular features. Its formation and progression involve a complex, multistep process that includes the accumulation of numerous genetic and epigenetic alterations. Although integrating RNA-seq transcriptome data with ATAC-seq epigenetic information provides a more comprehensive understanding of gene regulation and its impact across different conditions, no classification model has yet been developed for breast cancer intrinsic subtypes based on such integrative analyses. In this study, we employed machine learning algorithms to predict intrinsic subtypes through the integrative analysis of ATAC-seq and RNA-seq data. We identified 10 signature genes ( CDH3 , ERBB2 , TYMS , GREB1 , OSR1 , MYBL2 , FAM83D , ESR1 , FOXC1 , and NAT1 ) using recursive feature elimination with cross-validation (RFECV) and a support vector machine (SVM) based on SHAP (SHapley Additive exPlanations) feature importance. Furthermore, we found that these genes were primarily associated with immune responses, hormone signaling, cancer progression, and cellular proliferation.

Parvalbumin expressing interneurons (PV INs) are key players in the local inhibitory circuits and their developmental maturation coincides with the onset of adult-type network dynamics in the brain. Glutamatergic signaling regulates emergence of the unique PV IN phenotype, yet the receptor mechanisms involved are not fully understood. Here we show that GluK1 subunit containing kainate receptors (KARs) are necessary for development and maintenance of the neurochemical and functional properties of PV INs in the lateral and basal amygdala (BLA). Ablation of GluK1 expression specifically from PV INs resulted in low parvalbumin expression and loss of characteristic high firing rate throughout development. In addition, we observed reduced spontaneous excitatory synaptic activity at adult GluK1 lacking PV INs. Intriguingly, inactivation of GluK1 expression in adult PV INs was sufficient to abolish their high firing rate and to reduce PV expression levels, suggesting a role for GluK1 in dynamic regulation of PV IN maturation state. The PV IN dysfunction in the absence of GluK1 perturbed the balance between evoked excitatory vs. inhibitory synaptic inputs and long-term potentiation (LTP) in LA principal neurons, and resulted in aberrant development of the resting-state functional connectivity between mPFC and BLA. Behaviorally, the absence of GluK1 from PV INs associated with hyperactivity and increased fear of novelty. These results indicate a critical role for GluK1 KARs in regulation of PV IN function across development and suggest GluK1 as a potential therapeutic target for pathologies involving PV IN malfunction., (© 2024. The Author(s).)

This study focuses on improving the 3D printability of pea protein with the help of food inks designed for jet-type 3D printers. Initially, the food ink base was formulated using nanocellulose-alginate with a gradient of native potato starch and its 3D printability was evaluated. The 3D-printed structures using only candidates for the food ink base formulated with or without potato starch exhibited dimensional accuracy exceeding 95% on both the X and Y axes. However, the accuracy of stacking on the Z-axis was significantly affected by the ink composition. Food ink with 1% potato starch closely matched the CAD design, with an accuracy of approximately 99% on the Z-axis. Potato starch enhanced the stacking of 3D-printed structures by improving the electrostatic repulsion, viscoelasticity, and thixotropic behavior of the food ink base. The 3D printability of pea protein was evaluated using the selected food ink base, showing a 46% improvement in dimensional accuracy on the Z-axis compared to the control group printed with a food ink base lacking potato starch. These findings suggest that starch can serve as an additive support for high-resolution 3D jet-type printing of food ink material.

Predicting clinical responses to tumor immunotherapy is essential to reduce side effects and the potential for sustained clinical responses. Nevertheless, preselecting patients who are likely to respond to such treatments remains highly challenging. Here, we explored the potential of microRNAs (miRNAs) as predictors of immune checkpoint blockade responses using a machine learning approach. First, we constructed random forest models to predict the response to tumor ICB therapy using miRNA expression profiles across 19 cancer types. The contribution of individual miRNAs to each prediction process was determined by employing SHapley Additive exPlanations (SHAP) for model interpretation. Remarkably, the predictive performance achieved by using a small number of miRNAs with high feature importance was similar to that achieved by using the entire miRNA set. Additionally, the genes targeted by these miRNAs were closely associated with tumor- and immune-related pathways. In conclusion, this study demonstrates the potential of miRNA expression data for assessing tumor immunotherapy responses. Furthermore, we confirmed the potential of informative miRNAs as biomarkers for the prediction of immunotherapy response, which will advance our understanding of tumor immunotherapy mechanisms., (© 2024. The Author(s).)

The realization of quantum advantage with noisy-intermediate-scale quantum (NISQ) machines has become one of the major challenges in computational sciences. Maintaining coherence of a physical system with more than ten qubits is a critical challenge that motivates research on compact system representations to reduce algorithm complexity. Toward this end, the variational quantum eigensolver (VQE) used to perform quantum simulations is considered to be one of the most promising algorithms for quantum chemistry in the NISQ era. We investigate reduced mapping of one spatial orbital to a single qubit to analyze the ground state energy in a way that the Pauli operators of qubits are mapped to the creation/annihilation of singlet pairs of electrons. To include the effect of non-bosonic (or non-paired) excitations, we introduce a simple correction scheme in the electron correlation model approximated by the geometrical mean of the bosonic (or paired) terms. Employing it in a VQE algorithm, we assess ground state energies of H 2 O, N 2 , and Li 2 O in good agreement with full configuration interaction (FCI) models respectively, using only 6, 8, and 12 qubits with quantum gate depths proportional to the squares of the qubit counts. With the adopted seniority-zero approximation that uses only one half of the qubit counts of a conventional VQE algorithm, we find that our non-bosonic correction method reaches reliable quantum chemistry simulations at least for the tested systems.

Quantum computers offer significant potential for complex system analysis, yet their application in large systems is hindered by limitations such as qubit availability and quantum hardware noise. While the variational quantum eigensolver (VQE) was proposed to address these issues, its scalability remains limited. Many efforts, including new ansätze and Hamiltonian modifications, have been made to overcome these challenges. In this work, we introduced the novel Fragment Molecular Orbital/Variational Quantum Eigensolver (FMO/VQE) algorithm. This method combines the fragment molecular orbital (FMO) approach with VQE and efficiently utilizes qubits for quantum chemistry simulations. Employing the UCCSD ansatz, the FMO/VQE achieved an absolute error of just 0.053 mHa with 8 qubits in a [Formula: see text] system using the STO-3G basis set, and an error of 1.376 mHa with 16 qubits in a [Formula: see text] system with the 6-31G basis set. These results indicated a significant advancement in scalability over conventional VQE, maintaining accuracy with fewer qubits. Therefore, our FMO/VQE method exemplifies how integrating fragment-based quantum chemistry with quantum algorithms can enhance scalability, facilitating more complex molecular simulations and aligning with quantum computing advancements., (© 2024. The Author(s).)

We propose a novel heterodyne detection scheme for continuous-variable quantum key distribution (CVQKD), which measures both quadrature components of a quantum signal encoded in optical phase space. The proposed method uses time division to achieve identical performance to conventional heterodyne detection with only a single homodyne detection system. Our method also uses a Faraday-Michelson interferometer to make it independent of polarization drift and eliminate the need for dynamic polarization control. Our method is experimentally demonstrated using the Gaussian-modulated coherent-states (GMCS) protocol over a 20.06 km optical fiber channel, achieving an expected secret key rate of up to 0.187 Mbps.

Inspired by classical graph neural networks, we discuss a novel quantum graph neural network (QGNN) model to predict the chemical and physical properties of molecules and materials. QGNNs were investigated to predict the energy gap between the highest occupied and lowest unoccupied molecular orbitals of small organic molecules. The models utilize the equivariantly diagonalizable unitary quantum graph circuit (EDU-QGC) framework to allow discrete link features and minimize quantum circuit embedding. The results show QGNNs can achieve lower test loss compared to classical models if a similar number of trainable variables are used, and converge faster in training. This paper also provides a review of classical graph neural network models for materials research and various QGNNs.

Background: Adequate resection of normal surgical margins is important. However, the clear distinction between the normal surgical margins and tumor tissues is still difficult., Objective: Here, this study analyzed the variety of cell types in tumors and the normal surgical margins using a computational approach., Methods: The composition of cell types was compared between the two tissues by statistical and machine learning approaches., Results: The results showed the distinct cellular composition between tumor-adjacent and tumor tissues. In particular, endothelial cells were highly represented and macrophages were underrepresented at the normal surgical margin. Moreover, the normal surgical margin and tumor tissues could be discriminated using a machine learning algorithm., Conclusion: The results will help to understand cellular differences between normal surgical margins and tumor tissues and to provide potentials for tumor detection and treatment., (© 2023. The Author(s) under exclusive licence to The Genetics Society of Korea.)

A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability., (© 2023. The Author(s).)

In this study, we investigated the optimal conditions for 3D structure printing of alternative fats that have the textural properties of lard using beeswax (BW)-based oleogel by a statistical analysis. Products printed with over 15% BW oleogel at 50% and 75% infill level (IL) showed high printing accuracy with the lowest dimensional printing deviation for the designed model. The hardness, cohesion, and adhesion of printed samples were influenced by BW concentration and infill level. For multi-response optimization, fixed target values (hardness, adhesiveness, and cohesiveness) were applied with lard printed at 75% IL. The preparation parameters obtained as a result of multiple reaction prediction were 58.9% IL and 16.0% BW, and printing with this oleogel achieved fixed target values similar to those of lard. In conclusion, our study shows that 3D printing based on the BW oleogel system produces complex internal structures that allow adjustment of the textural properties of the printed samples, and BW oleogels could potentially serve as an excellent replacement for fat.

We propose a photon-counting-statistics-based imaging process for quantum imaging where background photon noise can be distinguished and eliminated by photon mode estimation from the multi-mode Bose-Einstein distribution. Photon-counting statistics show multi-mode behavior in a practical, low-cost single-photon-level quantum imaging system with a short coherence time and a long measurement time interval. Different mode numbers in photon-counting probability distributions from single-photon illumination and background photon noise can be classified by a machine learning technique such as a support vector machine (SVM). The proposed photon-counting statistics-based support vector machine (PSSVM) learns the difference in the photon-counting distribution of each pixel to distinguish between photons from the source and the background photon noise to improve the image quality. We demonstrated quantum imaging of a binary-image object with photon illumination from a spontaneous parametric down-conversion (SPDC) source. The experiment results show that the PSSVM applied quantum image improves a peak signal-to-noise ratio (PSNR) gain of 2.89dB and a structural similarity index measure (SSIM) gain of 27.7% compared to the conventional direct single-photon imaging., (© 2022. The Author(s).)

The aberrant expression of cancer-related genes can lead to colorectal cancer (CRC) carcinogenesis, and DNA methylation is one of the causes of abnormal expression. Although many studies have been conducted to reveal how DNA methylation affects transcription regulation, the ways in which it modulates gene expression and the regions that significantly affect DNA methylation-mediated gene regulation remain unclear. In this study, we investigated how DNA methylation in specific genomic areas can influence gene expression. Several regression models were constructed for gene expression prediction based on DNA methylation. Among these models, ElasticNet, which had the best performance, was chosen for further analysis. DNA methylation near transcription start sites (TSS), especially from 2 kb upstream to 7 kb downstream of TSS, had an essential regulatory role in gene expression. Moreover, methylation-affected and survival-associated genes were compiled and found to be mainly enriched in immune-related pathways. This study investigated genomic regions in which methylation changes can affect gene expression. In addition, this study proposed that aberrantly expressed genes due to DNA methylation can lead to CRC pathogenesis by the immune system., Competing Interests: The authors declare no conflicts of interest.

Tumor purity refers to the proportion of tumor cells in tumor tissue samples. This value plays an important role in understanding the mechanisms of the tumor microenvironment. Although various attempts have been made to predict tumor purity, attempts to predict tumor purity using miRNAs are still lacking. We predicted tumor purity using miRNA expression data for 16 TCGA tumor types using random forest regression. In addition, we identified miRNAs with high feature-importance scores and examined the extent of the change in predictive performance using informative miRNAs. The predictive performance obtained using only 10 miRNAs with high feature importance was close to the result obtained using all miRNAs. Furthermore, we also found genes targeted by miRNAs and confirmed that these genes were mainly related to immune and cancer pathways. Therefore, we found that the miRNA expression data could predict tumor purity well, and the results suggested the possibility that 10 miRNAs with high feature importance could be used as potential markers to predict tumor purity and to help improve our understanding of the tumor microenvironment.

Quantum computing is expected to play an important role in solving the problem of huge computational costs in various applications by utilizing the collective properties of quantum states, including superposition, interference, and entanglement, to perform computations. Quantum mechanical (QM) methods are candidates for various applications and can provide accurate absolute energy calculations in structure-based methods. QM methods are powerful tools for describing reaction pathways and their potential energy surfaces (PES). In this study, we applied quantum computing to describe the PES of the bimolecular nucleophilic substitution (S N 2) reaction between chloromethane and chloride ions. We performed noiseless and noise simulations using quantum algorithms and compared the accuracy and noise effects of the ansatzes. In noiseless simulations, the results from UCCSD and k-UpCCGSD are similar to those of full configurational interaction (FCI) with the same active space, which indicates that quantum algorithms can describe the PES of the S N 2 reaction. In noise simulations, UCCSD is more susceptible to quantum noise than k-UpCCGSD. Therefore, k-UpCCGSD can serve as an alternative to UCCSD to reduce quantum noisy effects in the noisy intermediate-scale quantum era, and k-UpCCGSD is sufficient to describe the PES of the S N 2 reaction in this work. The results showed the applicability of quantum computing to the S N 2 reaction pathway and provided valuable information for structure-based molecular simulations with quantum computing., (© 2022. The Author(s).)

Motivation: Bulk tumor samples used for high-throughput molecular profiling are often an admixture of cancer cells and non-cancerous cells, which include immune and stromal cells. The mixed composition can confound the analysis and affect the biological interpretation of the results, and thus, accurate prediction of tumor purity is critical. Although several methods have been proposed to predict tumor purity using high-throughput molecular data, there has been no comprehensive study on machine learning-based methods for the estimation of tumor purity., Results: We applied various machine learning models to estimate tumor purity. Overall, the models predicted the tumor purity accurately and showed a high correlation with well-established gold standard methods. In addition, we identified a small group of genes and demonstrated that they could predict tumor purity well. Finally, we confirmed that these genes were mainly involved in the immune system., Availability: The machine learning models constructed for this study are available at https://github.com/BonilKoo/ML_purity., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Genetically encoded voltage indicators (GEVIs) expressed pan-neuronally were able to optically resolve bicuculline induced spontaneous oscillations in brain slices of the mouse motor cortex. Three GEVIs were used that differ in their timing of response to voltage transients as well as in their voltage ranges. The duration, number of cycles, and frequency of the recorded oscillations reflected the characteristics of each GEVI used. Multiple oscillations imaged in the same slice never originated at the same location, indicating the lack of a "hot spot" for induction of the voltage changes. Comparison of pan-neuronal, Ca 2+ /calmodulin-dependent protein kinase II α restricted, and parvalbumin restricted GEVI expression revealed distinct profiles for the excitatory and inhibitory cells in the spontaneous oscillations of the motor cortex. Resolving voltage fluctuations across space, time, and cell types with GEVIs represent a powerful approach to dissecting neuronal circuit activity., Competing Interests: YI was employed by SPEC Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Rhee, Iwamoto and Baker.)

Kimchi is a fermented food prepared via spontaneous fermentation by lactic acid bacteria originating from raw ingredients. To investigate the effect of these ingredients on food fermentation, four types of food that differed only in their main raw ingredients (kimchi cabbage, green onion, leaf mustard, and young radish) were evaluated. The major microorganisms were Leuconostoc gelidum, Weissella kandleri, and Lactobacillus sakei groups. The distribution of these species depended on the sample type. All three species were primarily distributed in the food prepared from kimchi cabbage and young radish; however, the Lac. sakei group was hardly found in the food prepared using green onion and leaf mustard. Metabolite analysis results showed that the free sugar, organic acid, ethanol, and amino acid profiles differed with the sample type. This study indicates that the main ingredients could be an important factor in determining the composition of the microbial community and the metabolite composition., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Hyperventilation-induced downbeat nystagmus (HV-DBN) has been reported in cerebellar disorders and explained by a loss of the inhibitory cerebellar output via a metabolic effect on cerebellar Ca 2+ channels. The aim of this study was to determine the clinical characteristics and underlying pathogenesis of episodic vestibular syndrome (EVS) with HV-DBN. Of 667 patients with EVS, we recruited 22 with HV-DBN and assessed their clinical characteristics, video-oculographic findings, and the results of molecular genetic analyses. The age at symptom onset was 47.5 ± 13.0 years (mean ± SD), and there was a female preponderance (n = 15, 68%). The duration of vertigo/dizziness attacks ranged from minutes to a few days, and 11 patients (50%) fulfilled the diagnostic criteria for vestibular migraine. HV-induced new-onset DBN in 8 patients, while the remaining 14 showed augmentation of spontaneous DBN by HV. The maximum slow-phase velocity of HV-DBN ranged from 2.2 to 11.9°/s, which showed a statistical difference with that of spontaneous DBN (median = 4.95, IQR = 3.68-6.55 vs. median = 1.25, IQR = 0.20-2.15, p < 0.001). HV-DBN was either purely downbeat (n = 11) or accompanied with small horizontal components (n = 11). Other neuro-otologic findings included perverted head-shaking nystagmus (n = 11), central positional nystagmus (n = 7), saccadic pursuit (n = 3), and horizontal gaze-evoked nystagmus (n = 1). Gene expression profiling with a bioinformatics analysis identified 43 upregulated and 49 downregulated differentially expressed genes (DEGs) in patients with EVS and HV-DBN and revealed that the downregulated DEGs were significantly enriched in terms related to the ribosome pathway. Our results suggest that the underlying cerebellar dysfunction would be responsible for paroxysmal attacks of vertigo in patients with EVS and HV-DBN., (© 2020. Springer Science+Business Media, LLC, part of Springer Nature.)

The CA1 area in the mammalian hippocampus is essential for spatial learning. Pyramidal cells are the hippocampus output neurons and their activities are regulated by inhibition exerted by a diversified population of interneurons. Lateral inhibition has been suggested as the mechanism enabling the reconfiguration of pyramidal cell assembly activity observed during spatial learning tasks in rodents. However, lateral inhibition in the CA1 lacks the overwhelming evidence reported in other hippocampal areas such as the CA3 and the dentate gyrus. The use of genetically encoded voltage indicators and fast optical recordings permits the construction of cell-type specific response maps of neuronal activity. Here, we labelled mouse CA1 pyramidal neurons with the genetically encoded voltage indicator ArcLight and optically recorded their response to Schaffer Collaterals stimulation in vitro. By undertaking a manifold learning approach, we report a hyperpolarization-dominated area focused in the perisomatic region of pyramidal cells receiving late excitatory synaptic input. Functional network organization metrics revealed that information transfer was higher in this area. The localized hyperpolarization disappeared when GABA A receptors were pharmacologically blocked. This is the first report where the spatiotemporal pattern of lateral inhibition is visualized in the CA1 by expressing a genetically encoded voltage indicator selectively in principal neurons. Our analysis suggests a fundamental role of lateral inhibition in CA1 information processing., (© 2021 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Cerebellar granule cells (GCs) relay mossy fiber (MF) inputs to Purkinje cell dendrites via their axons, the parallel fibers (PFs), which are individually located at a given sublayer of the molecular layer (ML). Although a certain degree of heterogeneity among GCs has been recently reported, variability of GC responses to MF inputs has never been associated with their most notable structural variability, location of their projecting PFs in the ML. Here, we utilize an adeno-associated virus (AAV)-mediated labeling technique that enables us to categorize GCs according to the location of their PFs, and compare the Ca 2+ responses to MF stimulations between three groups of GCs, consisting of either GCs having PFs at the deep (D-GCs), middle (M-GCs), or superficial (S-GCs) sublayer. Our structural analysis revealed that there was no correlation between position of GC soma in the GC layer and location of its PF in the ML, confirming that our AAV-mediated labeling was important to test the projection-dependent variability of the Ca 2+ responses in GCs. We then found that the Ca 2+ responses of D-GCs differed from those of M-GCs. Pharmacological experiments implied that the different Ca 2+ responses were mainly attributable to varied distributions of GABA A receptors (GABA A Rs) at the synaptic and extrasynaptic regions of GC dendrites. In addition to GABA A R distributions, amounts of extrasynaptic NMDA receptors appear to be also varied, because Ca 2+ responses were different between D-GCs and M-GCs when glutamate spillover was enhanced. Whereas the Ca 2+ responses of S-GCs were mostly equivalent to those of D-GCs and M-GCs, the blockade of GABA uptake resulted in larger Ca 2+ responses in S-GCs compared with D-GCs and M-GCs, implying existence of mechanisms leading to more excitability in S-GCs with increased GABA release. Thus, this study reveals MF stimulation-mediated non-uniform Ca 2+ responses in the cerebellar GCs associated with the location of their PFs in the ML, and raises a possibility that combination of inherent functional variability of GCs and their specific axonal projection contributes to the information processing through the GCs.

The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis., (© 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.)

Extremely halophilic archaea (haloarchaea) belonging to the phylum Euryarchaeota have been found in high-salinity environments. In this study, Halarchaeum sp. CBA1220, Halorubrum sp. CBA1229, and Halolamina sp. CBA1230, which are facultatively oligotrophic haloarchaea, were isolated from solar salt by culture under oligotrophic culture conditions. The complete genomes of strains CBA1220, CBA1229, and CBA1230 were sequenced and were found to contain 3,175,875, 3,582,278, and 3,465,332 bp, with a G + C content of 68.25, 67.66, and 66.75 mol %, respectively. In total, 60, 36, and 33 carbohydrate-active enzyme genes were determined in the respective strains. The strains harbored various genes encoding stress-tolerance proteins, including universal stress proteins, cold-shock proteins, and rubrerythrin and rubrerythrin-related proteins. The genome data produced in this study will facilitate further research to improve our understanding of other halophilic strains and promote their industrial application.

The objective of this study was to optimize the conditions for enhancing the antioxidant properties of sword bean ( Canavalia gladiata ) as a coffee substitute in two processing methods, roasting and grinding. The optimum conditions for removing off-flavor of the bean and maximizing functionality and efficiency were light roasting and cryogenic grinding (< 53 μm). In these conditions, extraction yield was 16.75%, total phenolic content (TPC) was 69.82 ± 0.35 mg gallic acid equivalents/g, and total flavonoid content (TFC) was 168.81 ± 1.64 mg quercetin equivalents/100 g. The antioxidant properties were 77.58 ± 0.27% for DPPH radical scavenging activity and 58.02 ± 0.76 mg Trolox equivalents/g for ABTS radical scavenging activity. The values for TFC and ABTS radical scavenging activity were significantly higher ( p < 0.05) than in other conditions, and TPC and DPPH radical scavenging activity were second highest in lightly roasted beans, following raw beans. HS-SPME/GCMS analysis confirmed that the amino acids and carbohydrates, which are the main components of sword bean, were condensed into other volatile flavor compounds, such as derivatives of furan, pyrazine, and pyrrole during roasting. Roasted and cryogenically ground (cryo-ground) sword beans showed higher functionality in terms of TFC, DPPH, and ABTS radical scavenging activities compared to those of coffee. Overall results showed that light roasting and cryogenic grinding are the most suitable processing conditions for enhancing the bioactivity of sword beans.

Fermented foods constitute hubs of microbial consortia differentially affecting nutritional and organoleptic properties, quality, and safety. Here we show the origin source of fermentative microbes and fermentation dynamics of kimchi. We partitioned microbiota by raw ingredient (kimchi cabbage, garlic, ginger, and red pepper) to render kimchi fermented by each source-originated microbe pool and applied multi-omics (metataxonomics and metabolomics), bacterial viability, and physiochemical analyses to longitudinally collected samples. Only kimchi cabbage- and garlic-derived microbial inoculums yielded successful kimchi fermentations. The dominant fermentative microbial taxa and subsequent metabolic outputs differed by raw ingredient type: the genus Leuconostoc, Weissella, and Lactobacillus for all non-sterilized ingredients, garlic, and kimchi cabbage, respectively. Gnotobiotic kimchi inoculated by mono-, di-, and tri- isolated fermentative microbe combinations further revealed W. koreensis-mediated reversible microbial metabolic outputs. The results suggest that the raw ingredient microbial habitat niches selectively affect microbial community assembly patterns and processes during kimchi fermentation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Genetically encoded voltage indicators (GEVIs) continue to evolve, resulting in many different probes with varying strengths and weaknesses. Developers of new GEVIs tend to highlight their positive features. A recent article from an independent laboratory has compared the signal/noise ratios of a number of GEVIs. Such a comparison can be helpful to investigators eager to try to image the voltage of excitable cells. In this perspective, we will present examples of how the biophysical features of GEVIs affect the imaging of excitable cells in an effort to assist researchers when considering probes for their specific needs., (Copyright © 2020 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

A novel, facultatively anaerobic actinobacterium, designated strain CBA3103 T , was isolated from sediment of the Geum River in South Korea. Phylogenetic analysis indicated that strain CBA3103 T is most closely related to Raineyella antarctica LZ-22 T (98.47 % 16S rRNA gene sequence similarity). The genome of strain CBA3103 T was 3 649 865 bp with a DNA G+C content of 69.6 mol%. The average nucleotide identity value between strain CBA3103 T and R. antarctica LZ-22 T was 79.22 %. Cells of strain CBA3103 T were Gram-positive, rod-shaped, 0.6-0.9 µm wide and 1.4-2.4 µm long. Growth occurred at 15-40 °C (optimum, 35 °C), at pH 6.0-7.0 (optimum, pH 7.0) and with 0-2 % NaCl (w/v) (optimum, 0-1 %, w/v). The major cellular fatty acids in strain CBA3103 T were anteiso-C 15 : 0 , anteiso-C 15 : 1 A and iso-C 14 : 0 . The major respiratory quinone was menaquinone-9(H 4 ). The polar lipids of strain CBA3103 T were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, five unidentified glycolipids and three unidentified phospholipids. Based on the genotypic, phenotypic and chemotaxonomic analyses, strain CBA3103 T represents a novel species of the genus Raineyella , for which the name Raineyella fluvialis sp. nov. (type strain CBA3103 T =KACC 21446 T =DSM 110288 T ) is proposed.

Two extremely halophilic archaea strains, CBA1112 T and CBA1113, were isolated from solar salt in Korea. The genome sizes and G+C content of CBA1112 T and CBA1113 were 3.77 and 3.53Mb, and 66.0 and 66.5mol%, respectively. Phylogenetic analysis based on closely related taxa and environmental Haloplanus sequences indicated that both CBA1112 T and CBA1113 strains are grouped within the genus Haloplanus. OrthoANI and in silico DNA-DNA hybridization values were below the species delineation threshold. Pan-genomic analysis showed that the two novel strains and four reference strains had 6203 pan-orthologous groups in total. Six Haloplanus strains shared 1728 core pan-genome orthologous groups, which were mainly associated with amino acid transport and metabolism and translation, ribosomal structure and biogenesis categories, and amino acid metabolism and carbohydrate metabolism related categories. The novel strain-specific pan-genome orthologous groups were mainly involved with replication, recombination and repair category and replication and repair pathway or amino acid metabolism pathway. Cells of both strains were Gram-negative and pleomorphic, and colonies were red-pigmented. The major polar lipids of both strains were phosphatidylglycerol, phosphatidylglycerol phosphate methyl ester, phosphatidylglycerol sulfate, and one glycolipid, sulfated mannosyl glucosyl diether. Based on genomic, phylogenetic, phenotypic, and chemotaxonomic features, strains CBA1112 T and CBA1113 are described as novel species of the genus Haloplanus. Thus, we propose the name Haloplanus rubicundus sp. nov. The type strain is CBA1112 T (=KCCM 43224 T =JCM 30475 T )., (Copyright © 2020 Elsevier GmbH. All rights reserved.)

Background: Pheochromocytoma and paraganglioma (PPGL) are tumours that arise from chromaffin cells. Some genetic mutations influence PPGL, among which, those in genes encoding subunits of succinate dehydrogenase (SDHA, SDHB, SDHC and SDHD) and assembly factor (SDHAF2) are the most relevant. However, the risk of metastasis posed by these mutations is not reported except for SDHB and SDHD mutations. This study aimed to update the metastatic risks, considering prevalence and incidence of each SDHx mutation, which were dealt formerly all together., Methods: We searched EMBASE and MEDLINE and selected 27 articles. The patients included in the studies were divided into three groups depending on the presence of PPGL. We checked the heterogeneity between studies and performed a meta-analysis using Hartung-Knapp-Sidik-Jonkman method based on a random effect model., Results: The highest PPGL prevalence was for SDHB mutation, ranging from 23% to 31%, and for SDHC mutation (23%), followed by that for SDHA mutation (16%). The lowest prevalence was for SDHD mutation, ranging from 6% to 8%. SDHAF2 mutation showed no metastatic events. The PPGL incidence showed a tendency similar to that of its prevalence with the highest risk of metastasis posed by SDHB mutation (12%-41%) and the lowest risk by SDHD mutation (~4%)., Conclusion: There was no integrated evidence of how SDHx mutations are related to metastatic PPGL. However, these findings suggest that SDHA, SDHB and SDHC mutations are highly associated and should be tested as indicators of metastasis in patients with PPGL., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: The cause of Meniere's disease (MD) is unclear but likely involves genetic and environmental factors. The aim of this study was to investigate the genetic basis underlying MD by screening putative candidate genes for MD. Methods: Sixty-eight patients who met the diagnostic criteria for MD of the Barany Society were included. We performed targeted gene sequencing using next generation sequencing (NGS) panel composed of 45 MD-associated genes. We identified the rare variants causing non-synonymous amino acid changes, stop codons, and insertions/deletions in the coding regions, and excluded the common variants with minor allele frequency >0.01 in public databases. The pathogenicity of the identified variants was analyzed by various predictive tools and protein structural modeling. Results: The average read depth for the targeted regions was 1446.3-fold, and 99.4% of the targeted regions were covered by 20 or more reads, achieving the high quality of the sequencing. After variant filtering, annotation, and interpretation, we identified a total of 15 rare heterozygous variants in 12 (17.6%) sporadic patients. Among them, four variants were detected in familial MD genes ( DTNA, FAM136A, DPT ), and the remaining 11 in MD-associated genes ( PTPN22, NFKB1, CXCL10, TLR2, MTHFR, SLC44A2, NOS3, NOTCH2 ). Three patients had the variants in two or more genes. All variants were not detected in our healthy controls ( n = 100). No significant differences were observed between patients with and without a genetic variant in terms of sex, mean age of onset, bilaterality, the type of MD, and hearing threshold at diagnosis. Conclusions: Our study identified rare variants of putative candidate genes in some of MD patients. The genes were related to the formation of inner ear structures, the immune-associated process, or systemic hemostasis derangement, suggesting the multiple genetic predispositions in the development of MD., (Copyright © 2020 Oh, Shin, Kim, Cho, Choi, Choi, Rhee, Lee, Lee and Choi.)

Objective: This study aimed to investigate the underlying pathogenesis of acute unilateral vestibulopathy (AUV) using gene expression profiling combined with bioinformatics analysis., Methods: Total RNA was extracted from the peripheral blood mononuclear cells of ten AUV patients in the acute phase and from ten controls. The differentially expressed genes (DEGs) between these two groups were screened using microarray analysis with the cut-off criteria (|fold changes| > 1.5 and p-value < 0.05). Functional enrichment analysis of DEGs was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, and the protein-protein interaction (PPI) network was constructed using the STRING (Search Tool for the Retrieval of Interacting Genes) database., Results: There were 57 DEGs (50 up-regulated and 7 down-regulated) identified in the AUV group. Functional enrichment analysis showed that most of the up-regulated DEGs were significantly enriched in terms related to the neutrophil-mediated immune pathway. From the PPI network, the top ten hub genes were extracted by calculating four topological properties, and most of them were related to the innate immune system, inflammatory processes and vascular disorders. The complete blood count tests showed that the neutrophil-to-lymphocyte ratio was significantly higher in the 72 AUV patients than in the age-matched controls (2.93±2.25 vs 1.54±0.61, p < 0.001)., Conclusions: This study showed that the neutrophil-mediated immune pathway may contribute to the development of AUV by mediating inflammatory and thrombotic changes in the vestibular organ.

Background: Most bioinformatic tools for next generation sequencing (NGS) data are computationally intensive, requiring a large amount of computational power for processing and analysis. Here the utility of graphic processing units (GPUs) for NGS data computation is assessed., Method: In a previous study, we developed a probabilistic evolutionary algorithm with toggling for haplotyping (PEATH) method based on the estimation of distribution algorithm and toggling heuristic. Here, we parallelized the PEATH method (PEATH/G) using general-purpose computing on GPU (GPGPU)., Results: The PEATH/G runs approximately 46.8 times and 25.4 times faster than PEATH on the NA12878 fosmid-sequencing dataset and the HuRef dataset, respectively, with an NVIDIA GeForce GTX 1660Ti. Moreover, the PEATH/G is approximately 13.3 times faster on the fosmid-sequencing dataset, even with an inexpensive conventional GPGPU (NVIDIA GeForce GTX 950)., Conclusions: PEATH/G can be a practical single individual haplotyping tool in terms of both its accuracy and speed. GPGPU can help reduce the running time of NGS analysis tools., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Mutation hotspots are either solitary amino acid residues or stretches of amino acids that show elevated mutation frequency in cancer-related genes, but their prevalence and biological relevance are not completely understood. Here, we developed a Smith-Waterman algorithm-based mutation hotspot discovery method, MutClustSW, to identify mutation hotspots of either single or clustered amino acid residues. We identified 181 missense mutation hotspots from COSMIC and TCGA mutation databases. In addition to 77 single amino acid residue hotspots (42.5 percent) including well-known mutation hotspots such as IDH1 (p.R132) and BRAF (p.V600), we identified 104 mutation hotspots (57.5 percent) as clusters or stretches of multiple amino acids, and the hotspots on MUC2, EPPK1, KMT2C, and TP53 were larger than 50 amino acids. Twelve of 27 nonsense mutation hotspots (44.4 percent) were observed in four cancer-related genes, TP53, ARID1A, CDKN2A, and PTEN, suggesting that truncating mutations on some tumor suppressor genes are not randomly distributed as previously assumed. We also show that hotspot mutations have higher mutation allele frequency than non-hotspots, and the hotspot information can be used to prioritize the cancer drivers. Together, the proposed algorithm and the mutation hotspot information can serve as valuable resources in the selection of functional driver mutations and associated genes.

A prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition states. In this work, we present a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way. For registering or updating classical data consisting of M entries, each represented by n bits, the method requires O(n) qubits and O(Mn) steps. With post-selection at an additional cost, our method can also store continuous data as probability amplitudes. As an example, we present a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Further improvements can be achieved by reducing the number of state preparation queries with the introduction of quantum forking.

To extract pectin in food industry, HCl is generally used as the major extracting solvent for releasing the pectin from the plant tissues, however it has an environmental issue to use. In this study, food-grade tartaric-, malic, and citric acids were used to produce apple peel pectin as an eco-friendly protocol instead of HCl. Finely-ground lyophilized apple peel was applied as the raw material, and the pectin was extracted by organic acids at 85 °C. The pectin extracted with citric acid displayed greater molecular weight and apparent viscosity compared to other organic acid treatments. Analysis of degree of methyl esterification revealed that the pectins extracted with organic acids were highly methoxylated. From these results, it was suggested that organic acids could be utilized to extract apple peel pectin effectively as a green process. Especially, the extraction process with citric acid as the solvent showed great potential to produce high-viscosity apple peel pectin., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Pancreatic cancer is one of the most difficult cancers to cure due to the lack of early diagnostic tools and effective therapeutic agents. In this study, we aimed to isolate new bioactive compounds that effectively kill pancreatic ductal adenocarcinoma (PDAC) cells, but not untransformed, human pancreatic ductal epithelial (HPDE) cells. To this end, we established four primary PDAC cell lines and screened 4141 compounds from four bioactive-compound libraries. Initial screening yielded 113 primary hit compounds that caused over a 50% viability reduction in all tested PDAC cells. Subsequent triplicate, dose-dependent analysis revealed three compounds with a tumor cell-specific cytotoxic effect. We found that these three compounds fall into a single category of thiopurine biogenesis. Among them, 6-thioguanine (6-TG) showed an IC 50 of 0.39-1.13 μm toward PDAC cells but had no effect on HPDE cells. We propose that this cancer selectivity is due to differences in thiopurine methyltransferase (TPMT) expression between normal and cancer cells. This enzyme is responsible for methylation of thiopurine, which reduces its cytotoxicity. We found that TPMT levels were lower in all four PDAC cell lines than in HPDE or Panc1 cells, and that knockdown of TPMT in HPDE or Panc1 cells sensitized them to 6-TG. Lastly, we used a patient-derived xenograft model to confirm that 6-TG has a significant antitumor effect in combination with gemcitabine. Overall, our study presents 6-TG as a strong candidate for use as a therapeutic agent against PDAC with low levels of TPMT., (© 2018 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.)

Orthogonal frequency division multiple access (OFDMA) uplink in a passive optical network (PON) requires the delay alignment for OFDMA symbols from remotely distributed optical network units (ONUs). In this paper, we experimentally demonstrate and analyze the performance of a Zadoff-Chu (ZC) sequence-based upstream ranging scheme in an intensity modulation/direct detection (IM/DD)-based OFDMA-PON. The experimental results show that the proposed scheme can achieve upstream synchronization with only marginal inter-carrier interference (ICI) and requires no additional bandwidths in a typical OFDMA transmission with cyclic prefix (CP).

Motivation: Single-individual haplotyping (SIH) is critical in genomic association studies and genetic diseases analysis. However, most genomic analysis studies do not perform haplotype-phasing analysis due to its complexity. Several computational methods have been developed to solve the SIH problem, but these approaches have not generated sufficiently reliable haplotypes., Results: Here, we propose a novel SIH algorithm, called PEATH (Probabilistic Evolutionary Algorithm with Toggling for Haplotyping), to achieve more accurate and reliable haplotyping. The proposed PEATH method was compared to the most recent algorithms in terms of the phased length, N50 length, switch error rate and minimum error correction. The PEATH algorithm consistently provides the best phase and N50 lengths, as long as possible, given datasets. In addition, verification of the simulation data demonstrated that the PEATH method outperforms other methods on high noisy data. Additionally, the experimental results of a real dataset confirmed that the PEATH method achieved comparable or better accuracy., Availability and Implementation: Source code of PEATH is available at https://github.com/jcna99/PEATH., Contact: jkrhee@catholic.ac.kr or sooyong.shin@gmail.com., Supplementary Information: Supplementary data are available at Bioinformatics online.

Environmental temperature is one of the most important factors for the growth and survival of microorganisms. Here we describe a novel extremely halophilic archaeon (haloarchaea) designated as strain CBA1119 T isolated from solar salt. Strain CBA1119 T had the highest maximum and optimal growth temperatures (66 °C and 55 °C, respectively) and one of the largest genome sizes among haloarchaea (5.1 Mb). It also had the largest number of strain-specific pan-genome orthologous groups and unique pathways among members of the genus Natrinema in the class Halobacteria. A dendrogram based on the presence/absence of genes and a phylogenetic tree constructed based on OrthoANI values highlighted the particularities of strain CBA1119 T as compared to other Natrinema species and other haloarchaea members. The large genome of strain CBA1119 T may provide information on genes that confer tolerance to extreme environmental conditions, which may lead to the discovery of other thermophilic strains with potential applications in industrial biotechnology.

BRCA1 is a multifunctional tumor suppressor involved in several essential cellular processes. Although many of these functions are driven by or related to its transcriptional/epigenetic regulator activity, there has been no genome-wide study to reveal the transcriptional/epigenetic targets of BRCA1. Therefore, we conducted a comprehensive analysis of genomics/transcriptomics data to identify novel BRCA1 target genes. We first analyzed ENCODE data with BRCA1 chromatin immunoprecipitation (ChIP)-sequencing results and identified a set of genes with a promoter occupied by BRCA1. We collected 3085 loci with a BRCA1 ChIP signal from four cell lines and calculated the distance between the loci and the nearest gene transcription start site (TSS). Overall, 66.5% of the BRCA1-bound loci fell into a 2-kb region around the TSS, suggesting a role in transcriptional regulation. We selected 45 candidate genes based on gene expression correlation data, obtained from two GEO (Gene Expression Omnibus) datasets and TCGA data of human breast cancer, compared to BRCA1 expression levels. Among them, we further tested three genes ( MEIS2 , CKS1B and FADD ) and verified FADD as a novel direct target of BRCA1 by ChIP, RT-PCR, and a luciferase reporter assay. Collectively, our data demonstrate genome-wide transcriptional regulation by BRCA1 and suggest target genes as biomarker candidates for BRCA1-associated breast cancer.

Background: It is well recognized that accumulation of somatic mutations in cancer genomes plays a role in carcinogenesis; however, the temporal sequence and evolutionary relationship of somatic mutations remain largely unknown., Methods: In this study, we built a population-based statistical framework to infer the temporal sequence of acquisition of somatic mutations. Using the model, we analyzed the mutation profiles of 1954 tumor specimens across eight tumor types., Results: As a result, we identified tumor type-specific directed networks composed of 2-15 cancer-related genes (nodes) and their mutational orders (edges). The most common ancestors identified in pairwise comparison of somatic mutations were TP53 mutations in breast, head/neck, and lung cancers. The known relationship of KRAS to TP53 mutations in colorectal cancers was identified, as well as potential ancestors of TP53 mutation such as NOTCH1, EGFR, and PTEN mutations in head/neck, lung and endometrial cancers, respectively. We also identified apoptosis-related genes enriched with ancestor mutations in lung cancers and a relationship between APC hotspot mutations and TP53 mutations in colorectal cancers., Conclusion: While evolutionary analysis of cancers has focused on clonal versus subclonal mutations identified in individual genomes, our analysis aims to further discriminate ancestor versus descendant mutations in population-scale mutation profiles that may help select cancer drivers with clinical relevance.

Drug metabolites have been monitored with various types of newly developed techniques and/or combination of common analytical methods, which could provide a great deal of information on metabolite profiling. Because it is not easy to analyze whole drug metabolites qualitatively and quantitatively, a single solution of analytical techniques is combined in a multilateral manner to cover the widest range of drug metabolites. Mass-based spectroscopic analysis of drug metabolites has been expanded with the help of other parameter-based methods. The current development of metabolism studies through contemporary pharmaceutical research are reviewed with an overview on conventionally used spectroscopic methods. Several technical approaches for conducting drug metabolic profiling through spectroscopic methods are discussed in depth.

Rhodosporidium toruloides , an oleaginous yeast, can be used as a fast and reliable evaluation tool to screen new natural lipid-lowering agents. Herein, we showed that triglyceride (TG) accumulation was inhibited by 42.6% in 0.1% red radish coral sprout extract (RRSE)-treated R. toruloides . We also evaluated the anti-obesity effect of the RRSE in a mouse model. The body weight gain of mice fed a high-fat diet (HFD) with 0.1% RRSE (HFD-RRSE) was significantly decreased by 60% compared with that mice fed the HFD alone after the 8-week experimental period. Body fat of the HFD-RRSE-fed group was dramatically reduced by 38.3% compared with that of the HFD-fed group.

Surgical archives of tumor specimens are often impure. The presence of RNA transcripts from nontumor cells, such as immune and stromal cells, can impede analyses of cancer expression profiles. To systematically analyze the impact of tumor purity, the gene expression profiles and tumor purities were obtained for 7,794 tumor specimens across 21 tumor types (available in The Cancer Genome Atlas consortium). First, we observed that genes with roles in immunity and oxidative phosphorylation were significantly inversely correlated and correlated with the tumor purity, respectively. The expression of genes implicated in immunotherapy and specific immune cell genes, along with the abundance of immune cell infiltrates, was substantially inversely correlated with tumor purity. This relationship may explain the correlation between immune gene expression and mutation burden, highlighting the need to account for tumor purity in the evaluation of expression markers obtained from bulk tumor transcriptome data. Second, examination of cluster membership of gene pairs, with or without controlling for tumor purity, revealed that tumor purity may have a substantial impact on gene clustering across tumor types. Third, feature genes for molecular taxonomy were analyzed for correlation with tumor purity, and for some tumor types, feature genes representing the mesenchymal and classical subtypes were inversely correlated and correlated with tumor purity, respectively. Our findings indicate that tumor purity is an important confounder in evaluating the correlation between gene expression and clinicopathologic features such as mutation burden, as well as gene clustering and molecular taxonomy. Cancer Immunol Res; 6(1); 87-97. ©2017 AACR ., (©2017 American Association for Cancer Research.)

The primary aim of this study was to investigate the genetic predisposition of Internet gaming disorder (IGD), and the secondary aim was to compare the results to those of alcohol dependence (AD). Two independent case-control studies were conducted. A total of 30 male participants with IGD, diagnosed according to the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria, and 30 sex-matched controls participated in study 1. We designed targeted exome sequencing (TES) to test for 72 candidate genes that have been implicated in the pathogenesis of addiction. The genes included seven neurotransmitter (dopamine, serotonin, glutamate, r-aminobutyric acid (GABA), norepinephrine, acetylcholine, and opioid) system genes. A total of 31 male in-patients with AD and 29 normal male controls (NC) were enrolled in study 2. The same 72 genes included in study 1 and ten additional genes related to alcohol-metabolic enzyme were selected as the target genes, and we identified the genetic variants using the same method (TES). The IGD group had a lower frequency of the T allele of rs1044396 in the nicotinic acetylcholine receptor alpha 4 subunit (CHRNA4), and this variant represents a protective allele against IGD. However, we did not find a significant difference in the polymorphisms of the 72 genes that encode neurotransmitter systems between the AD and NC groups. This study demonstrated that rs1044396 of CHRNA4 was significantly associated with IGD.

The anticoagulant activity of heparin administered during medical interventions must be reversed to restore normal clotting, typically by titrating with protamine. Given the acute toxicity associated with protamine, we endeavored to generate safer heparin antagonists by engineering bacteriophage Qβ virus-like particles (VLPs) to display motifs that bind heparin. A particle bearing a single amino acid change from wild-type (T18R) was identified as a promising candidate for heparin antagonism. Surface potential maps generated through molecular modeling reveal that the T18R mutation adds synergistically to adjacent positive charges on the particle surface, resulting in a large solvent-accessible cationic region that is replicated 180 times over the capsid. Chromatography using a heparin-sepharose column confirmed a strong interaction between heparin and the T18R particle. Binding studies using fluorescein-labeled heparin (HepFL) resulted in a concentration-dependent change in fluorescence intensity, which could be perturbed by the addition of unlabeled heparin. Analysis of the fluorescence data yielded a dissociation constant of approximately 1 nM and a 1:1 binding stoichiometry for HepFL:VLP. Dynamic light scattering (DLS) experiments suggested that T18R forms discrete complexes with heparin when the VLP:heparin molar ratios are equivalent, and in vitro clotting assays confirmed the 1:1 binding stoichiometry as full antagonism of heparin is achieved. Biolayer interferometry and backscattering interferometry corroborated the strong interaction of T18R with heparin, yielding K d ∼ 1-10 nM. These biophysical measurements further validated T18R, and VLPs in general, for potential clinical use as effective, nontoxic heparin antagonists.