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This paper investigates the key factors in determining disaggregated portfolio investment flows to Korea. I categorize total portfolio investment flows by investor type, such as global banks, investment funds, securities firms, and pension companies. From the structural vector autoregression model with dummy variables, this paper finds that the properties of each institution's capital inflows are quite different. For example, investment funds and securities firm flows are more responsive to stock market index, whereas pension companies are more sensitive to domestic output growth. This implies that the impact of any economic shock on the total foreign capital flows cannot be generalized as the impact on each investment group's capital flow.

In two-dimensional (2D) solids, point defects, i.e., vacancies and interstitials, are bound states of topological defects of edge dislocations and disclinations. They are expected to play an important role in the thermodynamics of the system. Yet very little is known about the detailed dynamical processes of these defects. Two-dimensional colloidal crystals of submicrometer microspheres provide a convenient model solid system in which the microscopic dynamics of these defects can be studied in real time using video microscopy. Here we report a study of the dynamical processes of interstitials in a 2D colloidal crystal. The diffusion constants of both mono- and diinterstitials are measured and found to be significantly larger than those of vacancies. Diinterstitials are clearly slower than monointerstitials. We found that, by plotting the accumulative positions of five- and sevenfold disclinations relative to the center-of-mass position of the defect, a sixfold symmetric pattern emerges for monointerstitials. This is indicative of an equilibrium behavior that satisfies local detailed balance that the lattice remains elastic and can be thermally excited between lattice configurations reversibly. However, for diinterstitials the sixfold symmetry is not observed in the same time window, and the local lattice distortions are too severe to recover quickly. This observation suggests a possible route to creating local melting of a lattice (similarly one can create local melting by creating divacancies). This work opens up an avenue for microscopic studies of the dynamics of melting in colloidal model systems.

Significance While it would be desirable that the output of binary classification algorithms be the probability that the classification is correct, most algorithms do not provide a method to calculate such a probability. We propose a probabilistic output for binary classifiers based on an unexpected mapping of the probability of correct classification to the probability of occupation of a fermion in a quantum system, known as the Fermi–Dirac distribution. This mapping allows us to compute the optimal threshold to separate predicted classes and to calculate statistical parameters necessary to estimate confidence intervals of performance metrics. Using this mapping we propose an ensemble learning algorithm. In short, the Fermi–Dirac distribution provides a calibrated probabilistic output for binary classification., Binary classification is one of the central problems in machine-learning research and, as such, investigations of its general statistical properties are of interest. We studied the ranking statistics of items in binary classification problems and observed that there is a formal and surprising relationship between the probability of a sample belonging to one of the two classes and the Fermi–Dirac distribution determining the probability that a fermion occupies a given single-particle quantum state in a physical system of noninteracting fermions. Using this equivalence, it is possible to compute a calibrated probabilistic output for binary classifiers. We show that the area under the receiver operating characteristics curve (AUC) in a classification problem is related to the temperature of an equivalent physical system. In a similar manner, the optimal decision threshold between the two classes is associated with the chemical potential of an equivalent physical system. Using our framework, we also derive a closed-form expression to calculate the variance for the AUC of a classifier. Finally, we introduce FiDEL (Fermi–Dirac-based ensemble learning), an ensemble learning algorithm that uses the calibrated nature of the classifier’s output probability to combine possibly very different classifiers.

ObjectiveSurveillance tools for early cancer detection are suboptimal, including hepatocellular carcinoma (HCC), and biomarkers are urgently needed. Extracellular vesicles (EVs) have gained increasing scientific interest due to their involvement in tumour initiation and metastasis; however, most extracellular RNA (exRNA) blood-based biomarker studies are limited to annotated genomic regions.DesignEVs were isolated with differential ultracentrifugation and integrated nanoscale deterministic lateral displacement arrays (nanoDLD) and quality assessed by electron microscopy, immunoblotting, nanoparticle tracking and deconvolution analysis. Genome-wide sequencing of the largely unexplored small exRNA landscape, including unannotated transcripts, identified and reproducibly quantified small RNA clusters (smRCs). Their key genomic features were delineated across biospecimens and EV isolation techniques in prostate cancer and HCC. Three independent exRNA cancer datasets with a total of 479 samples from 375 patients, including longitudinal samples, were used for this study.ResultsExRNA smRCs were dominated by uncharacterised, unannotated small RNA with a consensus sequence of 20 nt. An unannotated 3-smRC signature was significantly overexpressed in plasma exRNA of patients with HCC (pConclusionThese findings directly lead to the prospect of a minimally invasive, blood-only, operator-independent clinical tool for HCC surveillance, thus highlighting the potential of unannotated smRCs for biomarker research in cancer.

Identifying and characterizing the effect of combination cancer therapies is of paramount importance in cancer research. The benefit of a combination can either be due to inherent heterogeneity in patient populations or because of molecular synergy between the compounds given in combination, usually studied in cell culture, or both. To shed light and help characterized combinations and their enhanced benefits over single therapies, we introduce Correlated Drug Action (CDA) as a baseline additivity model. We formulate the CDA model as a closed-form expression, which lends itself to be scalable and interpretable, both in the temporal domain (tCDA) to explain survival curves, and in the dose domain (dCDA), to explain dose-response curves. CDA can be used in clinical trials and cell culture experiments. At the level of clinical trials, we demonstrate tCDA’s utility in explaining the benefit of clinical combinations, identifying non-additive combinations, and cases where biomarkers may be able to decouple the combination into monotherapies. At the level of cells in culture, dCDA naturally embodies null models such as Bliss additivity and the Highest Single Agent model as special cases, and can be extended to be sham combination compliant. We demonstrate the applicability of dCDA in assessing non-additive combinations and doses. Additionally, we introduce a new synergy metric, Excess over CDA (EOCDA), that incorporates elements of Bliss additivity and dose equivalence concepts in the same measure. CDA is a novel general framework for additivity at the cell line and patient population levels and provides a method to characterize and quantify the action of drug combinations.

AMI is a technology that provides electricity usage and billing information in real time by two-way communication between consumers and a supplier which is a power company. The two-way communication environment can be used as a variety of security threat paths and security vulnerabilities may occur due to the increased adoption of multi-devices by multiple manufacturers. Security threats to AMI can be a major threat to cybersecurity across the country, so countermeasures are needed. In order to solve these problems, an electronic certificate-based AMI device authentication system was established, but a system for managing and injecting keys and certificates for a large number of devices is required. Management technology for security authentication information such as keys and certificates of AMI devices is an important factor in ensuring AMI security safety. Therefore, in this paper, we propose a method to inject mass security authentication information for AMI devices to systematically, effectively manage and safely inject authentication information.

Circulating extracellular vesicles (EVs) present in the bodily fluids of patients with cancer may provide non-invasive access to the tumor tissue. Yet, the transcriptomic lineage of tumor-derived EVs before and after tumor-resection remains poorly understood. Here, we established 60 total small RNA-sequencing profiles from 17 aggressive prostate cancer (PCa) patient’s tumor and adjacent normal tissue, and EVs isolated from urine, serum, and cancer cell culture media. We interrogated the key satellite alteration in tumor-derived EVs and found that resection of tumor prostate tissue leads to differential expression of reactive oxygen species (ROS), P53 pathways, inflammatory/cytokines, oncogenes, and tumor suppressor genes in the EV nanosatellites. Furthermore, we provide a set of novel EV-specific RNA signature, which are present in cancer but are nonexistent in post-resection patients with undetectable cancer. Finally, using a de novo RNAseq assembly followed by characterization of the small RNA landscape, we found novel small RNA clusters (smRCs) in the EVs, which reside in the unannotated regions. Novel smRCs were orthogonally validated for their differential expression in the ‘biomarker discovery’ cohort using RT-qPCR. We demonstrate that circulating tumor EVs provide a glimpse of the tumor tissue biology, resolving a major bottleneck in the current liquid biopsy efforts. Secretory vesicles appear to be playing a key role in non-canonical Wnt signaling and miRNA pathways, similar to the circulating tumor cells (CTCs), hence, we propose that such vesicles be called circulating tumor extracellular vesicles (CTEVs).

BackgroundHepatocellular carcinoma (HCC) is among the deadliest malignancies and surveillance tools for early detection are suboptimal. Extracellular vesicles (EVs) have gained increasing scientific interest due to their involvement in tumor initiation and metastasis, however, most extracellular RNA (exRNA) biomarker studies are limited to annotated genomic regions.MethodsEVs were isolated with ultracentrifugation and nanoDLD and quality assessed by electron microscopy, immunoblotting, nanoparticle tracking, and deconvolution analysis. We performed genome-wide small exRNA sequencing, including unannotated transcripts. We identified small RNA clusters (smRCs) and delineated their key genomic features across biospecimens (blood, urine, tissue) and EV isolation techniques. A 3-smRC signature for early HCC detection was trained and validated in two independent cohorts.ResultsEV-derived smRCs were dominated by uncharacterized, unannotated small RNA and uniformly tiled across the genome with a consensus sequence of 20bp. A 3-smRC signature was significantly overexpressed in circulating EVs of HCC patients compared to controls at risk or patients with non-HCC malignancies (pConclusionAn exRNA-based 3-smRC signature from plasma detects early stage HCC, which directly leads to the prospect of a minimally-invasive, blood-only, operator-independent surveillance biomarker.One sentence summaryWe employ a novel, data-driven approach to identify and characterize small RNA clusters from unannotated loci in extracellular vesicle-derived RNA across different cancer types, isolation techniques, and biofluids, facilitating discovery of a robust biomarker for detection of early stage liver cancer.

The advent of microfluidics in the 1990s promised a revolution in multiple industries from healthcare to chemical processing. Deterministic lateral displacement (DLD) is a continuous-flow microfluidic particle separation method discovered in 2004 that has been applied successfully and widely to the separation of blood cells, yeast, spores, bacteria, viruses, DNA, droplets, and more. Deterministic lateral displacement is conceptually simple and can deliver consistent performance over a wide range of flow rates and particle concentrations. Despite wide use and in-depth study, DLD has not yet been fully elucidated or optimized, with different approaches to the same problem yielding varying results. We endeavor here to provide up-to-date expert opinion on the state-of-art and current fundamental, practical, and commercial challenges with DLD as well as describe experimental and modeling opportunities. Because these challenges and opportunities arise from constraints on hydrodynamics, fabrication, and operation at the micro- and nanoscale, we expect this Perspective to serve as a guide for the broader micro- and nanofluidic community to identify and to address open questions in the field.

We studied the trajectories of polymers being advected while diffusing in a pressure driven flow along a periodic pillar nanostructure known as nanoscale deterministic lateral displacement (nanoDLD) array. We found that polymers follow different trajectories depending on their length, flow velocity and pillar array geometry, demonstrating that nanoDLD devices can be used as a continuous polymer fractionation tool. As a model system, we used double-stranded DNA (dsDNA) with various contour lengths and demonstrated that dsDNA in the range of 100–10 000 base pairs (bp) can be separated with a size-selective resolution of 200 bp. In contrast to spherical colloids, a polymer elongates by shear flow and the angle of polymer trajectories with respect to the mean flow direction decreases as the mean flow velocity increases. We developed a phenomenological model that explains the qualitative dependence of the polymer trajectories on the gap size and on the flow velocity. Using this model, we found the optimal separation conditions for dsDNA of different sizes and demonstrated the separation and extraction of dsDNA fragments with over 75% recovery and 3-fold concentration. Importantly, this velocity dependence provides a means of fine-tuning the separation efficiency and resolution, independent of the nanoDLD pillar geometry.

Significance Deterministic lateral displacement (DLD) is a technique for size fractionation of particles in continuous flow that has shown great potential for biological and clinical applications. Several theoretical models have been proposed to explain the trajectories of different-sized particles in relation to the geometry of the pillar array, but experimental evidence has demonstrated that a rich class of intermediate migration behavior exists, which is not predicted by models. In this work, we present a unified theoretical framework to infer the trajectory of particles in the whole array on the basis of trajectories in the unit cell. This framework explains many of the unexpected particle trajectories reported in literature and can be used to design arrays for the fractionation of particles, even at the smallest scales reaching the molecular realm. We also performed experiments that verified our predictions, even at the nanoscales. Using our model as a set of design rules, we developed a condenser structure that achieves full particle separation with a single fluidic input.

Efficient isolation of circulating biomarkers is key for enabling liquid biopsies for cancer diagnosis and prognosis. Circulating biomarkers such as exosomes and cell-free DNA hold promise for non-invasive early cancer diagnosis, determining cancer stage and prognosis, as well as monitoring treatment progression and the development of drug resistance. However, isolation of these biomarkers has proven challenging in the clinic. Exosomes range in size from approximately 30 to 150 nm and the current gold standard isolation method includes large-scale ultracentrifugation, which is time-consuming and lacks reproducibility. Circulating cell-free DNA is found in a variety of sizes and states and while clinical applications for prenatal testing have emerged, applications in oncology remain elusive in part due to high background and challenges in isolating relevant molecules. We have developed a lab-on-a-chip technology based on deterministic lateral displacement at the nanoscale (nanoDLD) which separates and concentrates particles as small as 20 nm from smaller particles in continuous flow. Analysis of nanoDLD isolation of exosomes and DNA show improved resolution, recovery, and concentration compared to standard techniques. Current efforts to scale nanoDLD technology to clinically relevant volumes will provide a reproducible and automated tool for isolation, study, and clinical use of these important non-invasive biomarkers. Citation Format: Stacey M. Gifford, Benjamin H. Wunsch, Joshua T. Smith, Navneet Dogra, Sungcheol Kim, Gustavo Stolovitzky. Separation of circulating biomarkers by nanoDLD lab-on-a-chip technology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1580.

Employing integrated nano- and microfluidic circuits for detecting and characterizing biological compounds through resistive pulse sensing technology is a vibrant area of research at the interface of biotechnology and nanotechnology. Resistive pulse sensing platforms can be customized to study virtually any particle of choice which can be threaded through a fluidic channel and enable label-free single-particle interrogation with the primary read-out signal being an electric current fingerprint. The ability to perform label-free molecular screening with single-molecule and even single binding site resolution makes resistive pulse sensing technology a powerful tool for analyzing the smallest units of biological systems and how they interact with each other on a molecular level. This task is at the core of experimental systems biology and in particular 'omics research which in combination with next-generation DNA-sequencing and next-generation drug discovery and design forms the foundation of a novel disruptive medical paradigm commonly referred to as personalized medicine or precision medicine. DNA-sequencing has approached the 1000-Dollar-Genome milestone allowing for decoding a complete human genome with unmatched speed and at low cost. Increased sequencing efficiency yields massive amounts of genomic data. Analyzing this data in combination with medical and biometric health data eventually enables understanding the pathways from individual genes to physiological functions. Access to this information triggers fundamental questions for doctors and patients alike: what are the chances of an outbreak for a specific disease? Can individual risks be managed and if so how? Which drugs are available and how should they be applied? Could a new drug be tailored to an individual's genetic predisposition fast and in an affordable way? In order to provide answers and real-life value to patients, the rapid evolvement of novel computing approaches for analyzing big data in systems genomics has to be accompanied by an equally strong effort to develop next-generation DNA-sequencing and next-generation drug screening and design platforms. In that context lab-on-a-chip devices utilizing nanopore- and nanochannel based resistive pulse-sensing technology for DNA-sequencing and protein screening applications occupy a key role. This paper describes the status quo of resistive pulse sensing technology for these two application areas with a special focus on current technology trends and challenges ahead.

The recently discovered bactericidal properties of nanostructures on wings of insects such as cicadas and dragonflies have inspired the development of similar nanostructured surfaces for antibacterial applications. Since most antibacterial applications require nanostructures covering a considerable amount of area, a practical fabrication method needs to be cost-effective and scalable. However, most reported nanofabrication methods require either expensive equipment or a high temperature process, limiting cost efficiency and scalability. Here, we report a simple, fast, low-cost, and scalable antibacterial surface nanofabrication methodology. Our method is based on metal-assisted chemical etching that only requires etching a single crystal silicon substrate in a mixture of silver nitrate and hydrofluoric acid for several minutes. We experimentally studied the effects of etching time on the morphology of the silicon nanospikes and the bactericidal properties of the resulting surface. We discovered that 6 min...

In contrast to a standard biopsy, the so-called ‘liquid biopsy’offers a rapid, non-invasive, and cost effective alternative for cancer diagnosis. Exosomes, which are secreted by most eukaryotic cells and range in size from 30-150nm, are the enabling biocolloid in this technique as they carry a diverse variety of genetically rich cargo, including proteins, RNA, and DNA. Hence, studying exosomes could potentially provide vital information about undesirable genetic deviations occurring in their cell of origin. Rapid isolation of exosomes from blood, urine or other body fluids remains a key challenge in this growing field.Deterministic lateral displacement (DLD) pillar arrays have proven an effective means to sort, segregate, and enrich micron-size particles, such as parasites and blood cells. Here, we have developed a nanoscale DLD devices, containing gap sizes as small as ∼25nm, with nanoscale sorting resolution of biological particles. This development in nanofluidics and engineering has enabled us to sort colloidal particles at the tens of nanometers scale. Additionally, we have developed predictive computational models to provide key insights into the behavior of particles in these systems. Furthermore, we have successfully demonstrated on-chip, size-based separation of exosomes, indicating the potential of this technology for sorting plasma, urine, serum or circulating tumor-derived exosomes.

Solid-state nanopores have been shown to be suitable for single molecule detection. While numerous modeling investigations exist for DNA within nanopores, there are few simulations of protein translocations. In this paper, we use atomistic molecular dynamics to investigate the translocation of proteins through a silicon nitride nanopore. The nanopore dimensions and profile are representative of experimental systems. We are able to calculate the change in blockade current and friction coefficient for different positions of the protein within the pore. The change in ionic current is found to be negligible until the protein is fully within the pore and the current is lowest when the protein is in the pore center. Using a simple theory that gives good quantitative agreement with the simulation results we are able to show that the variation in current with position is a function of the pore shape. In simulations that guide the protein through the nanopore we identify the effect that confinement has on the friction coefficient of the protein. This integrated view of translocation at the nanoscale provides useful insights that can be used to guide the design of future devices.

One promising path for future drug screening technologies is to examine the binding of ligands to target proteinsat the single molecule level by passing them through nanometer sized pores and measuring the change in porecurrent during translocation. With the aim of evaluating such technologies we perform virtual experiments onthe translocation of proteins through silicon nitride nanopores. These simulations consist of large scale, fullyatomistic models of the translocation process that involve steering a test protein through the nanopore on atimescale of tens of nanoseconds. We make a comparison between theoretically expected and simulated values ofthe current drop that is seen when the protein occupies the pore. Details of the stability of the protein and thepreservation of its function as measured by its secondary and tertiary structure will be presented to validate boththe simulation results and the fundamental design of the proposed device. Finally, the results will be placed inthe context of experimental work that combines nanofabrication and microuidics to create a high throughput,low cost, drug screening device.Keywords: nanosensors, single molecule sensing, molecular dynamics, molecular simulation, drug screening,nanotechnology, microuidics

This paper reports on some new results from the analyses of the video microscopy data obtained in a prior experiment on two-dimensional (2D) colloidal crystals. It was reported previously that optical tweezers can be used to create mono- and di-vacancies in a 2D colloidal crystal. Here we report the results on the creation of a vacancy-interstitial pair, as well as tri-vacancies. It is found that the vacancy-interstitial pair can be long-lived, but they do annihilate each other. The behavior of tri-vacancies is most intriguing, as it fluctuates between a configuration of bound pairs of dislocations and that of a locally amorphous state. The relevance of this observation to the issue of the nature of 2D melting is discussed., Comment: 6 pages, 4 figures

We carried out solid-state nanopore experiments on designed single-stranded DNA molecule complex with double-stranded segments. We have designed short oligomers of single-stranded DNA of about 130 bases long each with 12-bases long sticky ends that are complimentary to those on one end of other oligomers to form ds-DNA regions by Watson-Crick base-pairing in these regions. Such a design facilitates the formation of a chain of single strands DNA with ds-DNA regions interspersed. In order to slow down the translocation speed of these complexes through solid-state nanopores that could enable one to identify the ds-DNA region markers in the blockage current signal during translocation, we have attached these ss-DNA complexes with a polystyrene bead on one end. We present the results of our preliminary studies that show that the signature of these ds-DNA region markers could be identified.