Showing papers by "Kyungpook National University published in 2018"

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Abstract: Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The b jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV).

Large anomalous Hall current induced by topological nodal lines in a ferromagnetic van der Waals semimetal.

Abstract: Topological semimetals host electronic structures with several band-contact points or lines and are generally expected to exhibit strong topological responses. Up to now, most work has been limited to non-magnetic materials and the interplay between topology and magnetism in this class of quantum materials has been largely unexplored. Here we utilize theoretical calculations, magnetotransport and angle-resolved photoemission spectroscopy to propose Fe3GeTe2, a van der Waals material, as a candidate ferromagnetic (FM) nodal line semimetal. We find that the spin degree of freedom is fully quenched by the large FM polarization, but the line degeneracy is protected by crystalline symmetries that connect two orbitals in adjacent layers. This orbital-driven nodal line is tunable by spin orientation due to spin-orbit coupling and produces a large Berry curvature, which leads to a large anomalous Hall current, angle and factor. These results demonstrate that FM topological semimetals hold significant potential for spin- and orbital-dependent electronic functionalities.

Structural insight into molecular mechanism of poly(ethylene terephthalate) degradation

Abstract: Plastics, including poly(ethylene terephthalate) (PET), possess many desirable characteristics and thus are widely used in daily life However, non-biodegradability, once thought to be an advantage offered by plastics, is causing major environmental problem Recently, a PET-degrading bacterium, Ideonella sakaiensis, was identified and suggested for possible use in degradation and/or recycling of PET However, the molecular mechanism of PET degradation is not known Here we report the crystal structure of I sakaiensis PETase (IsPETase) at 15 A resolution IsPETase has a Ser–His-Asp catalytic triad at its active site and contains an optimal substrate binding site to accommodate four monohydroxyethyl terephthalate (MHET) moieties of PET Based on structural and site-directed mutagenesis experiments, the detailed process of PET degradation into MHET, terephthalic acid, and ethylene glycol is suggested Moreover, other PETase candidates potentially having high PET-degrading activities are suggested based on phylogenetic tree analysis of 69 PETase-like proteins

Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at s=13 TeV

Abstract: The CMS muon detector system, muon reconstruction software, and high-level trigger underwent significant changes in 2013–2014 in preparation for running at higher LHC collision energy and instantaneous luminosity. The performance of the modified system is studied using proton-proton collision data at center-of-mass energy √s=13 TeV, collected at the LHC in 2015 and 2016. The measured performance parameters, including spatial resolution, efficiency, and timing, are found to meet all design specifications and are well reproduced by simulation. Despite the more challenging running conditions, the modified muon system is found to perform as well as, and in many aspects better than, previously. We dedicate this paper to the memory of Prof. Alberto Benvenuti, whose work was fundamental for the CMS muon detector.

Enhanced Network Anomaly Detection Based on Deep Neural Networks

Abstract: Due to the monumental growth of Internet applications in the last decade, the need for security of information network has increased manifolds. As a primary defense of network infrastructure, an intrusion detection system is expected to adapt to dynamically changing threat landscape. Many supervised and unsupervised techniques have been devised by researchers from the discipline of machine learning and data mining to achieve reliable detection of anomalies. Deep learning is an area of machine learning which applies neuron-like structure for learning tasks. Deep learning has profoundly changed the way we approach learning tasks by delivering monumental progress in different disciplines like speech processing, computer vision, and natural language processing to name a few. It is only relevant that this new technology must be investigated for information security applications. The aim of this paper is to investigate the suitability of deep learning approaches for anomaly-based intrusion detection system. For this research, we developed anomaly detection models based on different deep neural network structures, including convolutional neural networks, autoencoders, and recurrent neural networks. These deep models were trained on NSLKDD training data set and evaluated on both test data sets provided by NSLKDD, namely NSLKDDTest+ and NSLKDDTest21. All experiments in this paper are performed by authors on a GPU-based test bed. Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant analysis. Both deep and conventional machine learning models were evaluated using well-known classification metrics, including receiver operating characteristics, area under curve, precision-recall curve, mean average precision and accuracy of classification. Experimental results of deep IDS models showed promising results for real-world application in anomaly detection systems.

Automated detection and classification of the proximal humerus fracture by using deep learning algorithm

Abstract: Background and purpose - We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs. Patients and methods - 1,891 images (1 image per person) of normal shoulders (n = 515) and 4 proximal humerus fracture types (greater tuberosity, 346; surgical neck, 514; 3-part, 269; 4-part, 247) classified by 3 specialists were evaluated. We trained a deep convolutional neural network (CNN) after augmentation of a training dataset. The ability of the CNN, as measured by top-1 accuracy, area under receiver operating characteristics curve (AUC), sensitivity/specificity, and Youden index, in comparison with humans (28 general physicians, 11 general orthopedists, and 19 orthopedists specialized in the shoulder) to detect and classify proximal humerus fractures was evaluated. Results - The CNN showed a high performance of 96% top-1 accuracy, 1.00 AUC, 0.99/0.97 sensitivity/specificity, and 0.97 Youden index for distinguishing normal shoulders from proximal humerus fractures. In addition, the CNN showed promising results with 65-86% top-1 accuracy, 0.90-0.98 AUC, 0.88/0.83-0.97/0.94 sensitivity/specificity, and 0.71-0.90 Youden index for classifying fracture type. When compared with the human groups, the CNN showed superior performance to that of general physicians and orthopedists, similar performance to orthopedists specialized in the shoulder, and the superior performance of the CNN was more marked in complex 3- and 4-part fractures. Interpretation - The use of artificial intelligence can accurately detect and classify proximal humerus fractures on plain shoulder AP radiographs. Further studies are necessary to determine the feasibility of applying artificial intelligence in the clinic and whether its use could improve care and outcomes compared with current orthopedic assessments.

Construction of Bi2WO6/RGO/g-C3N4 2D/2D/2D hybrid Z-scheme heterojunctions with large interfacial contact area for efficient charge separation and high-performance photoreduction of CO2 and H2O into solar fuels

Abstract: We have rationally constructed a hybrid heterojunction comprising of Bi2WO6, reduced graphene oxide, and g-C3N4 (BWO/RGO/CN) with a 2D/2D/2D configuration for efficient photoreduction to generate solar fuels. These heterojunctions displayed dramatically improved performance towards CO2 reduction to generate CO and CH4 under visible-light irradiation, compared to the base material (CN), P25 as reference, as well as binary BWO/CN and RGO/CN heterojunctions. Particularly, the BWO/RGO/CN heterojunctions with 1 wt. % RGO and 15 wt. % BWO achieved record performance in the yields of carbonaceous products (CO + CH4) compared to other synthesized catalysts, with a selectivity of 92% against H2. The remarkable photocatalytic performance was mainly attributed to the unique 2D/2D/2D architecture that creates large interfacial contact between the constituent materials for rapid charge transfer, to hinder the direct recombination of photoinduced electrons and holes. Notably, RGO played two significant roles: as a supporter to capture the electrons from CN, and as a redox mediator to promote the Z-scheme charge transfer between CN and BWO. The result is a greater extent of charge separation in the present BWO/RGO/CN heterojunction system, as evidenced by the photoluminescence, photocurrent responses, and electron microscopy findings. More importantly, the heterojunctions displayed excellent stability during recycling tests with no obvious loss in the generation of CO and CH4 from photoreduction of CO2. This interesting interfacial engineering approach presented herein offers a promising route for the rational design of a new class of layered multicomponent heterojunctions with 2D/2D/2D architecture for various applications in environmental protection and solar energy conversion.

Activation of Persulfate by Nanosized Zero-Valent Iron (NZVI): Mechanisms and Transformation Products of NZVI.

Abstract: The mechanisms involved in the activation of persulfate by nanosized zero-valent iron (NZVI) were elucidated and the NZVI transformation products identified. Two distinct reaction stages, in terms of the kinetics and radical formation mechanism, were found when phenol was oxidized by the persulfate/NZVI system. In the initial stage, lasting 10 min, Fe0(s) was consumed rapidly and sulfate radicals were produced through activation by aqueous Fe2+. The second stage was governed by Fe catalyzed activation in the presence of aqueous Fe3+ and iron (oxyhydr)oxides in the NZVI shells. The second stage was 3 orders of magnitude slower than the initial stage. An electron balance showed that the sulfate radical yield per mole of persulfate was more than two times higher in the persulfate/NZVI system than in the persulfate/Fe2+ system. Radicals were believed to be produced more efficiently in the persulfate/NZVI system because aqueous Fe2+ was supplied slowly, preventing sulfate radicals being scavenged by excess aqu...

Search for Heavy Neutral Leptons in Events with Three Charged Leptons in Proton-Proton Collisions at √s = 13 TeV

Abstract: A search for a heavy neutral lepton N of Majorana nature decaying into a W boson and a charged lepton is performed using the CMS detector at the LHC. The targeted signature consists of three prompt charged leptons in any flavor combination of electrons and muons. The data were collected in proton-proton collisions at a center-of-mass energy of 13 TeV, with an integrated luminosity of 35.9 fb^(−1). The search is performed in the N mass range between 1 GeV and 1.2 TeV. The data are found to be consistent with the expected standard model background. Upper limits are set on the values of |V_(eN)|^2and |V_(μN)|^2, where V_(lN) is the matrix element describing the mixing of N with the standard model neutrino of flavor l. These are the first direct limits for N masses above 500 GeV and the first limits obtained at a hadron collider for N masses below 40 GeV.

Observation of New Properties of Secondary Cosmic Rays Lithium, Beryllium, and Boron by the Alpha Magnetic Spectrometer on the International Space Station

Abstract: We report on the observation of new properties of secondary cosmic rays Li, Be, and B measured in the rigidity (momentum per unit charge) range 1.9 GV to 3.3 TV with a total of 5.4×10^{6} nuclei collected by AMS during the first five years of operation aboard the International Space Station. The Li and B fluxes have an identical rigidity dependence above 7 GV and all three fluxes have an identical rigidity dependence above 30 GV with the Li/Be flux ratio of 2.0±0.1. The three fluxes deviate from a single power law above 200 GV in an identical way. This behavior of secondary cosmic rays has also been observed in the AMS measurement of primary cosmic rays He, C, and O but the rigidity dependences of primary cosmic rays and of secondary cosmic rays are distinctly different. In particular, above 200 GV, the secondary cosmic rays harden more than the primary cosmic rays.

A comprehensive review on single phase heat transfer enhancement techniques in heat exchanger applications

Abstract: The objective of this paper is to review the different techniques, which have been used to enhance the heat transfer rate in heat exchanger devices such as solar air heater, cooling blades of turbine and so on using single phase heat transfer fluids. The results of recent published articles with the development of new technologies such as Electrohydrodynamic (EHD) and Magnetohydrodynamics (MHD) are also included. Enhancement of heat transfer in heat exchanger can achieved by means of several techniques. These techniques are grouped into the active and passive method. In the active methods, system need some external power, however, passive method utilize surface modification either on heated surface or insertion of swirl devices in the flow field. Active methods are very complex because of external power supply, although these methods have great potential and can control thermally. Passive methods include artificial roughness, extended surface, winglets, insertion of swirl devices in the flow which alters the flow pattern causes to disturb the thermal boundary layer, and consequently high heat transfer. Passive methods are dominant over active methods because its can easily employed in existing heat exchangers. In this paper, an effort has been made to categorize the active and passive methods and review the various heat transfer techniques applied in heat exchangers. Important results have been listed for ready reference. It has been concluded that either active or passive methods have been employed alone. Based on literature, a combined method have also been recommended which include both active and passive methods.

Targeting Glutamine Metabolism for Cancer Treatment

Abstract: Rapidly proliferating cancer cells require energy and cellular building blocks for their growth and ability to maintain redox balance. Many studies have focused on understanding how cancer cells adapt their nutrient metabolism to meet the high demand of anabolism required for proliferation and maintaining redox balance. Glutamine, the most abundant amino acid in plasma, is a well-known nutrient used by cancer cells to increase proliferation as well as survival under metabolic stress conditions. In this review, we provide an overview of the role of glutamine metabolism in cancer cell survival and growth and highlight the mechanisms by which glutamine metabolism affects cancer cell signaling. Furthermore, we summarize the potential therapeutic approaches of targeting glutamine metabolism for the treatment of numerous types of cancer.

Nonfragile Exponential Synchronization of Delayed Complex Dynamical Networks With Memory Sampled-Data Control

Abstract: This paper considers nonfragile exponential synchronization for complex dynamical networks (CDNs) with time-varying coupling delay. The sampled-data feedback control, which is assumed to allow norm-bounded uncertainty and involves a constant signal transmission delay, is constructed for the first time in this paper. By constructing a suitable augmented Lyapunov function, and with the help of introduced integral inequalities and employing the convex combination technique, a sufficient condition is developed, such that the nonfragile exponential stability of the error system is guaranteed. As a result, for the case of sampled-data control free of norm-bound uncertainties, some sufficient conditions of sampled-data synchronization criteria for the CDNs with time-varying coupling delay are presented. As the formulations are in the framework of linear matrix inequality, these conditions can be easily solved and implemented. Two illustrative examples are presented to demonstrate the effectiveness and merits of the proposed feedback control.

Investigating the relationship between supply chain innovation, risk management capabilities and competitive advantage in global supply chains

Abstract: Purpose- This study aims to propose and validate a theoretical model to investigate whether supply chain innovation positively affects risk management capabilities, such as robustness and resilience in global supply chain operations, and to examine how these capabilities may improve competitive advantage. Design/methodology/approach- A theoretical model was developed from extant studies and assessed through the development of a large-scale questionnaire survey conducted with South Korean manufacturers and logistics intermediaries involved in global supply chain operations. The data were analysed using confirmatory factor analysis (CFA) and structural equation modelling (SEM) to validate the suggested model. Findings- It was found that innovative supply chains have a discernible positive influence on all dimensions of risk management capability, which in turn has a significant impact on enhancing competitive advantage. Therefore, this work provides evidence for the importance of supply chain innovation and risk management capability in supporting competitive advantage. Research limitations/implications- This study contributes to providing an empirical understanding of the strategic retention of supply chain innovation and risk management capabilities in the supply chain management (SCM) discipline. Further, it confirms and expands existing theories about innovation and competitive advantage. Practical implications- The finding provides firm grounds for managerial decisions on investment in technology innovation and process innovation. Originality/value- This research is the first of its kind to empirically validate the relationships between supply chain innovation, risk management capabilities and competitive advantage. Keywords: Supply Chain Innovation, Robustne

Biomimetic Color Changing Anisotropic Soft Actuators with Integrated Metal Nanowire Percolation Network Transparent Heaters for Soft Robotics

Abstract: To add more functionalities and overcome the limitation in conventional soft robots, highly anisotropic soft actuators with color shifting function during actuation is demonstrated for the first time. The electrothermally operating soft actuators with installed transparent metal nanowire percolation network heater allow easy programming of their actuation direction and instantaneous visualization of temperature changes through color change. Due to the unique direction dependent coefficient of thermal expansion mismatch, the suggested actuator demonstrates a highly anisotropic and reversible behavior with very large bending curvature (2.5 cm−1) at considerably low temperature (≈40 °C) compared to the previously reported electrothermal soft actuators. The mild operating heat condition required for the maximum curvature enables the superior long-term stability during more than 10 000 operating cycles. Also, the optical transparency of the polymer bilayer and metal nanowire percolation network heater allow the incorporation of the thermochromic pigments to fabricate color-shifting actuators. As a proof-of-concept, various color-shifting biomimetic soft robots such as color-shifting blooming flower, fluttering butterfly, and color-shifting twining tendril are demonstrated. The developed color-shifting anisotropic soft actuator is expected to open new application fields and functionalities overcoming the limitation of current soft robots.

Search for narrow and broad dijet resonances in proton-proton collisions at √s=13 TeV and constraints on dark matter mediators and other new particles

Abstract: Searches for resonances decaying into pairs of jets are performed using proton-proton collision data collected at s√=13 TeV corresponding to an integrated luminosity of up to 36 fb−1. A low-mass search, for resonances with masses between 0.6 and 1.6 TeV, is performed based on events with dijets reconstructed at the trigger level from calorimeter information. A high-mass search, for resonances with masses above 1.6 TeV, is performed using dijets reconstructed offline with a particle-flow algorithm. The dijet mass spectrum is well described by a smooth parameterization and no evidence for the production of new particles is observed. Upper limits at 95% confidence level are reported on the production cross section for narrow resonances with masses above 0.6 TeV. In the context of specific models, the limits exclude string resonances with masses below 7.7 TeV, scalar diquarks below 7.2 TeV, axigluons and colorons below 6.1 TeV, excited quarks below 6.0 TeV, color-octet scalars below 3.4 TeV, W′ bosons below 3.3 TeV, Z′ bosons below 2.7 TeV, Randall-Sundrum gravitons below 1.8 TeV and in the range 1.9 to 2.5 TeV, and dark matter mediators below 2.6 TeV. The limits on both vector and axial-vector mediators, in a simplified model of interactions between quarks and dark matter particles, are presented as functions of dark matter particle mass and coupling to quarks. Searches are also presented for broad resonances, including for the first time spin-1 resonances with intrinsic widths as large as 30% of the resonance mass. The broad resonance search improves and extends the exclusions of a dark matter mediator to larger values of its mass and coupling to quarks.

ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications

Abstract: Medical expert systems are part of the portable and smart healthcare monitoring devices used in day-to-day life. Arrhythmic beat classification is mainly used in electrocardiogram (ECG) abnormality detection for identifying heart related problems. In this paper, ECG signal preprocessing and support vector machine-based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. In ECG signal preprocessing, a delayed error normalized LMS adaptive filter is used to achieve high speed and low latency design with less computational elements. Since the signal processing technique is developed for remote healthcare systems, white noise removal is mainly focused. Discrete wavelet transform is applied on the preprocessed signal for HRV feature extraction and machine learning techniques are used for performing arrhythmic beat classification. In this paper, SVM classifier and other popular classifiers have been used on noise removed feature extracted signal for beat classification. Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers.

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network.

Abstract: Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets that can be used for learning human activities However, successful learning of human activities from motion data involves the design and use of proper feature representations of IMU sensor data and suitable classifiers Furthermore, the scarcity of labelled data is an impeding factor in the process of understanding the performance capabilities of data-driven learning models To tackle these challenges, two primary contributions are in this article: first; by using raw IMU sensor data, a spectrogram-based feature extraction approach is proposed Second, an ensemble of data augmentations in feature space is proposed to take care of the data scarcity problem Performance tests were conducted on a deep long term short term memory (LSTM) neural network architecture to explore the influence of feature representations and the augmentations on activity recognition accuracy The proposed feature extraction approach combined with the data augmentation ensemble produces state-of-the-art accuracy results in HAR A performance evaluation of each augmentation approach is performed to show the influence on classification accuracy Finally, in addition to using our own dataset, the proposed data augmentation technique is evaluated against the University of California, Irvine (UCI) public online HAR dataset and yields state-of-the-art accuracy results at various learning rates

Combined search for electroweak production of charginos and neutralinos in proton-proton collisions at √s=13 TeV

Abstract: A statistical combination of several searches for the electroweak production of charginos and neutralinos is presented. All searches use proton-proton collision data at $ \sqrt{s}=13 $ TeV, recorded with the CMS detector at the LHC in 2016 and corresponding to an integrated luminosity of 35.9 fb$^{−1}$. In addition to the combination of previous searches, a targeted analysis requiring three or more charged leptons (electrons or muons) is presented, focusing on the challenging scenario in which the difference in mass between the two least massive neutralinos is approximately equal to the mass of the Z boson. The results are interpreted in simplified models of chargino-neutralino or neutralino pair production. For chargino-neutralino production, in the case when the lightest neutralino is massless, the combination yields an observed (expected) limit at the 95% confidence level on the chargino mass of up to 650 (570) GeV, improving upon the individual analysis limits by up to 40 GeV. If the mass difference between the two least massive neutralinos is approximately equal to the mass of the Z boson in the chargino-neutralino model, the targeted search requiring three or more leptons obtains observed and expected exclusion limits of around 225 GeV on the second neutralino mass and 125 GeV on the lightest neutralino mass, improving the observed limit by about 60 GeV in both masses compared to the previous CMS result. In the neutralino pair production model, the combined observed (expected) exclusion limit on the neutralino mass extends up to 650–750 (550–750) GeV, depending on the branching fraction assumed. This extends the observed exclusion achieved in the individual analyses by up to 200 GeV. The combined result additionally excludes some intermediate gaps in the mass coverage of the individual analyses.

What Is There in Seeds? Vertically Transmitted Endophytic Resources for Sustainable Improvement in Plant Growth

Abstract: Phytobeneficial microbes, particularly endophytes, such as fungi and bacteria, are concomitant partners of plants throughout its developmental stages, including seed germination, root and stem growth, and fruiting. Endophytic microbes have been identified in plants that grow in a wide array of habitats; however, seed-borne endophytic microbes have not been fully explored yet. Seed-borne endophytes are of great interest because of their vertical transmission; their potential to produce various phytohormones, enzymes, antimicrobial compounds, and other secondary metabolites; and improve plant biomass and yield under biotic and abiotic stresses. This review addresses the current knowledge on endophytes, their ability to produce metabolites, and their influence on plant growth and stress mitigation.

A New Approach for Loading Anticancer Drugs Into Mesenchymal Stem Cell-Derived Exosome Mimetics for Cancer Therapy.

Abstract: Exosomes derived from mesenchymal stem cells (MSCs) have been evaluated for their potential to be used as drug delivery vehicles. Synthetically personalized exosome mimetics (EMs) could be the alternative vesicles for drug delivery. In this study, we aimed to isolate EMs from human MSCs. Cells were mixed with paclitaxel (PTX) and PTX-loaded EMs (PTX-MSC-EMs) were isolated and evaluated for their anticancer effects against breast cancer. EMs were isolated from human bone marrow-derived MSCs. MSCs (4 × 106 cells/mL) were mixed with or without PTX at different concentrations in phosphate-buffered saline (PBS) and serially extruded through 10-, 5-, and 1-μm polycarbonate membrane filters using a mini-extruder. MSCs were centrifuged to remove debris and the supernatant was filtered through a 0.22-μm filter, followed by ultracentrifugation to isolate EMs and drug-loaded EMs. EMs without encapsulated drug (MSC-EMs) and those with encapsulated PTX (PTX-MSC-EMs) were characterized by western blotting, nanoparticle tracking analysis (NTA), and transmission electron microscopy (TEM). The anticancer effects of MSC-EMs and PTX-MSC-EMs were assessed with breast cancer (MDA-MB-231) cells both in vitro and in vivo using optical imaging. EMs were isolated by the extrusion method and ultracentrifugation. The isolated vesicles were positive for membrane markers (ALIX and CD63) and negative for golgi (GM130) and endoplasmic (calnexin) marker proteins. NTA revealed the size of MSC-EM to be around 149 nm, while TEM confirmed its morphology. PTX-MSC-EMs significantly (p < 0.05) decreased the viability of MDA-MB-231 cells in vitro at increasing concentrations of EM. The in vivo tumor growth was significantly inhibited by PTX-MSC-EMs as compared to control and/or MSC-EMs. Thus, MSC-EMs were successfully isolated using simple procedures and drug-loaded MSC-EMs were shown to be therapeutically efficient for the treatment of breast cancer both in vitro and in vivo. MSC-EMs may be used as drug delivery vehicles for breast cancers.