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Extracellular vesicles (EVs) are nanovesicles released by all eukaryotic cells. This work reports the first nanoscale fluorescent visualization of tumor-originating vesicles bearing an angiogenic microRNA (miR)-126 cargo. In a validated experimental model of lethal murine vascular neoplasm, tumor-originating EV delivered its miR-126 cargo to tumor-associated macrophages (TAMs). Such delivery resulted in an angiogenic (LYVE

FACT (FAcilitates Chromatin Transcription) is a heterodimeric protein complex composed of SUPT16H and SSRP1, and a histone chaperone participating in chromatin remodeling during gene transcription. FACT complex is profoundly regulated, and contributes to both gene activation and suppression. Here we reported that SUPT16H, a subunit of FACT, is acetylated in both epithelial and natural killer (NK) cells. The histone acetyltransferase TIP60 contributes to the acetylation of SUPT16H middle domain (MD) at lysine 674 (K674). Such acetylation of SUPT16H is recognized by bromodomain protein BRD4, which promotes protein stability of SUPT16H in both epithelial and NK cells. We further demonstrated that SUPT16H-BRD4 associates with histone modification enzymes (HDAC1, EZH2), and further regulates their activation status and/or promoter association as well as affects the relevant histone marks (H3ac, H3K9me3 and H3K27me3). BRD4 is known to profoundly regulate interferon (IFN) signaling, while such function of SUPT16H has never been explored. Surprisingly, our results revealed that SUPT16H genetic knockdown via RNAi or pharmacological inhibition by using its inhibitor, curaxin 137 (CBL0137), results in the induction of IFNs and interferon-stimulated genes (ISGs). Through this mechanism, depletion or inhibition of SUPT16H is shown to efficiently inhibit infection of multiple viruses, including Zika, influenza, and SARS-CoV-2. Furthermore, we demonstrated that depletion or inhibition of SUPT16H also causes the remarkable activation of IFN signaling in NK cells, which promotes the NK-mediated killing of virus-infected cells in a co-culture system using human primary NK cells. Overall, our studies unraveled the previously un-appreciated role of FACT complex in coordinating with BRD4 and regulating IFN signaling in both epithelial and NK cells, and also proposed the novel application of the FACT inhibitor CBL0137 to treat viral infections.

Although supercomputers are becoming increasingly powerful, their components have thus far not scaled proportionately. Compute power is growing enormously and is enabling finely resolved simulations that produce never-before-seen features. However, I/O capabilities lag by orders of magnitude, which means only a fraction of the simulation data can be stored for post hoc analysis. Prespecified plans for saving features and quantities of interest do not work for features that have not been seen before. Data-driven intelligent sampling schemes are needed to detect and save important parts of the simulation while it is running. Here, we propose a novel sampling scheme that reduces the size of the data by orders-of-magnitude while still preserving important regions. The approach we develop selects points with unusual data values and high gradients. We demonstrate that our approach outperforms traditional sampling schemes on a number of tasks.

In this work we study a class of recently discovered metrewave solar transients referred to as Weak Impulsive Narrowband Quiet Sun Emission \citep[WINQSEs,][]{mondal2020}. Their strength is a few percent of the quiet Sun background and are characterised by their very impulsive, narrow-band and ubiquitous presence in quiet Sun regions. \citet{mondal2020} hypothesised that these emissions might be the radio counterparts of the nanoflares and their potential significance warrants detailed studies. Here we present an analysis of data from an extremely quiet time and with an improved methodology over the previous work. As before, we detect numerous WINQSEs, which we have used for their further characterisation. Their key properties, namely, their impulsive nature and ubiquitous presence on the quiet Sun are observed in these data as well. Interestingly, we also find some of the observed properties to differ significantly from the earlier work. With this demonstration of routine detection of WINQSEs, we hope to engender interest in the larger community to build a deeper understanding of WINQSEs., Accepted for publication in the Astrophysical Journal

As high-performance computing systems continue to advance, thegap between computing performance and I/O capabilities is widen-ing. This bottleneck limits the storage capabilities of increasinglylarge-scale simulations, which generate data at never-before-seengranularities while only being able to store a small subset of the rawdata. Recently, strategies for data-driven sampling have been pro-posed to intelligently sample the data in a way that achieves high datareduction rates while preserving important regions or features withhigh fidelity. However, a thorough analysis of how such intelligentsamples can be used for data reconstruction is lacking. We proposean AI-driven approach based on training neural networks to recon-struct full-scale datasets based on a simulation’s sampled output.Compared to current state-of-the-art reconstruction approaches suchas Delaunay triangulation, we demonstrate that deep learning-basedreconstruction has several advantages, including reconstruction qual-ity and time-to-reconstruct. We propose and evaluate strategies thatbalance the sampling rates with model training and data reconstruc-tion time to demonstrate how such AI approaches can be tailored forboth speed and quality, and develop a visual analytics interface forcomparing the reconstruction quality of grid-based datasets

For the ubiquitous coherent type-1 feed-forward loop (C1-FFL) motif, the master and co-regulators act as sources of information in decoding the output gene expression state. Using the variance-based definition of information within a Gaussian framework at steady state, we apply the partial information decomposition technique to quantify the redundant (common) and synergistic (complementary) information transfers to the output gene. By enabling the generic C1-FFL motif with complementarily tunable regulatory pathways and fixed gene product abundances, we examine the role of output gene regulation in maintaining the flow of these two multivariate information flavors. We find that the redundant and synergistic information transfers are simultaneously maximized when the direct and indirect output regulatory strengths are nearly balanced. All other manifestations of the generic C1-FFL motif, including the two terminal ones, namely, the two-step cascade and fan-out, transduce lesser amounts of these two types of information. This optimal decoding of the output gene expression state by a nearly balanced C1-FFL motif holds true in an extended repertoire of biologically relevant parametric situations. These realizations involve additional layers of regulation through changing gene product abundances, activation coefficients, and degradation rates. Our analyses underline the regulatory mechanisms through which the C1-FFL motif is able to optimally reduce its output uncertainty concurrently via redundant and synergistic modes of information transfer. We find that these information transfers are guided by fluctuations in the motif. The prevalence of redundancy over synergy in all regulatory implementations is also noteworthy.

The photon-pairs generated through spontaneous parametric down-conversion (SPDC) possess strong correlations in their transverse position and momentum degrees of freedom. For such photon pairs, the transverse position correlation length depends on the crystal thickness and the pump wavelength, whereas the transverse momentum correlation length depends on the beam waist and spatial coherence length of the pump. By controlling these parameters, it is possible to engineer the spatial entanglement. Here, we utilize the circular asymmetry of the pump by using an elliptical-Gaussian beam to change the degree of entanglement in transverse directions, which we call anisotropic entanglement. We show the interrelation between the degree of anisotropic entanglement and the asymmetry in the beam width. In addition, we also show that for a highly asymmetric pump beam, the entanglement along the y-direction completely vanishes, whereas the entanglement in the x-direction remains intact.

The confluence of the data from the Murchison Widefield Array (MWA) and an imaging pipeline tailored for spectroscopic snapshot images of the Sun at low radio frequencies have led to enormous improvements in the imaging quality of the Sun. Among other science advances, these developments have lowered the detection threshold for weak nonthermal emissions by up to two orders of magnitude as compared to earlier studies, and have enabled our discovery of Weak Impulsive Narrowband Quiet Sun Emissions (WINQSEs). Their typical flux densities lie in the range of a few mSFU (1 SFU = 10,000 Jy) and they are found to occur in large numbers all over the quiet Sun regions. In the solar radio images, they appear as compact sources and our estimate of their median duration is limited by the instrumental resolution of 0.5 s. Their spatial distribution and various other properties are consistent with being the radio signatures of coronal nanoflares hypothesized by Parker (1988) to explain coronal heating in the quiet Sun emissions. As steps towards exploring this tantalising possibility of making progress on the coronal heating problem, we have been pursuing multiple projects to improve our ability to detect and characterise WINQSEs. These include attempts to look for WINQSEs in multiple independent datasets; using different independent detection techniques; attempting to characterise their morphologies in radio maps using Artificial Intelligence/Machine Learning based approaches; looking for their counter parts in EUV wavelengths; estimating the energy associated with groups of WINQSEs; and investigation of the spectro-temporal structure of WINQSEs. Here we present the current status of these projects and summarise our learnings from them.

Sensitive radio instruments are optimized for observing faint astronomical sources, and usually need to attenuate the received signal when observing the Sun. There are only a handful of flux density calibrators which can comfortably be observed with the same attenuation setup as the Sun. Additionally, for wide field-of-view (FoV) instruments like the Murchison Widefield Array (MWA) calibrator observations are generally done when the Sun is below the horizon to avoid the contamination from solar emissions. These considerations imply that the usual radio interferometric approach to flux density calibration is not applicable for solar imaging. A novel technique, relying on a good sky model and detailed characterization of the MWA hardware, was developed for solar flux density calibration for MWA. Though successful, this technique is not general enough to be extended to the data from the extended configuration of the MWA Phase II. Here, we present a robust flux density calibration method for solar observations with MWA independent of the array configuration. We use different approaches -- the serendipitous presence of strong sources; detection of numerous background sources using high dynamic range images in the FoV along with the Sun and observations of strong flux density calibrators with and without the additional attenuation used for solar observations; to obtain the flux scaling parameters required for the flux density calibration. Using the present method, we have achieved an absolute flux density uncertainty $\sim10\%$ for solar observations even in the absence of dedicated calibrator observations., 17 pages, 8 figures, 3 tables, Published in the Astrophysical Journal

An eavesdropper may gain information by exploiting device imperfections while implementing quantum key distribution protocols. We find information leakage due to coupling mismatch at receiver’s detectors in terms of mutual information between eaves-dropper and receiver.

Many important aspects of the dynamic thermo-mechanical response of materials occur at the mesoscale, i.e. a physical scale of interactions smaller than what can be adequately described by homogenous behaviors, yet larger than the scale of the atomic lattice. Concurrent advancements in computational power, continuum theory, and experimental diagnostics are enabling unprecedented understanding of such interactions. However, we cannot develop a sufficient level of confidence in such mesoscale capability until the constitutive description of the underlying constituents is reliably representative of their actual physical behavior. Therefore, there is a strong need to combine experimental, modeling, and data-science techniques to validate models of the thermomechanical response of individual single crystals. One experimental diagnostic with high potential impact to shock physics and materials science is in-situ x-ray diffraction. This paper is primarily focused on simulation of x-ray diffraction in shock physics, but with an aim toward quantifying parametric uncertainty of simulation models. We develop and demonstrate a data-science and model-driven approach to constrain the parameterization of continuum models of crystal lattice deformation associated with the shock response of crystalline materials. The framework is built around the connection between continuum hydrodynamic simulations of lattice deformation and a new Bragg diffraction simulation code, BarberShop. The dynamic deformation of a crystal lattice is modeled using the DiscoFlux model within an arbitrary Lagrangian-Eulerian hydrodynamic code, FLAG. These detailed continuum simulations of lattice deformation can be computationally slow, thus a statistical model is used to emulate the evolution of lattice deformation fields in time and across the considered model parameter space. Emulated lattice deformation fields can then be generated rapidly for any combination of physics model parameters. In turn, these fields can be fed into BarberShop to realize a rapid prediction of Bragg diffraction patterns associated with particular values of physics model parameters. The framework enables parameterization of the single crystal model to obtain Bragg diffraction patterns that most closely resemble a corresponding measurement. Furthermore, the framework naturally provides sensitivities of the lattice deformation to the physics parameters. We highlight the utility of this framework through the application to a synthetic closed-loop inverse problem leading to the parameterization of a single crystal material model. As a model problem, we consider the dynamic response of the energetic molecular crystal, cyclotrimethylenetrinitramine (or RDX), under dynamic compression induced by simulated flyer plate impact experiments.

Validating simulations with experimental results is a vital component of modern materials science. Existing workflows require substantial human intervention to interpret their results. We present a statistical approach to identify physically meaningful features, and we construct a visualization system that allows users to interactively and intuitively explore their datasets.

We study the correlation properties of single photons carrying orbital angular momentum (OAM) in a Hanbury Brown and Twiss (HBT) type experiment. We have characterized single photon sources obtaine...

A typical implementation of BB84 protocol for quantum communication uses four laser diodes for transmitting weak coherent pulses, which may not have the same characteristics. We have characterized these lasers for mismatch in various parameters such as spectral width, pulse width, spatial mode, peak wavelength, polarization and their arrival times at the receiver. This information is utilized to calculate possible information leakage through side channel attacks by evaluating mutual information between source and eavesdropper. Based on our experimental observations of cross correlation between parameter values for different laser diodes, we suggest methods to reduce information leakage to Eve.

Polyamines are critical metabolites involved in various cellular processes and often dysregulated in cancers. Kaposi’s sarcoma-associated Herpesvirus (KSHV), a defined human oncogenic virus, leads to profound alterations of host metabolic landscape to favor development of KSHV-associated malignancies. In our studies, we identified that polyamine biosynthesis and eIF5A hypusination are dynamically regulated by KSHV infection through modulation of key enzymes (ODC1 and DHPS) of these pathways. During KSHV latency, ODC1 and DHPS are upregulated along with increase of hypusinated eIF5A (hyp-eIF5A), while hyp-eIF5A is further induced along with reduction of ODC1 and intracellular polyamines during KSHV lytic reactivation. In return these metabolic pathways are required for both KSHV lytic reactivation and de novo infection. Further analysis unraveled that synthesis of critical KSHV latent and lytic proteins (LANA, RTA) depends on hypusinated-eIF5A. We also demonstrated that KSHV infection can be efficiently and specifically suppressed by inhibitors targeting these pathways. Collectively, our results illustrated that the dynamic and profound interaction of a DNA tumor virus (KSHV) with host polyamine biosynthesis and eIF5A hypusination pathways promote viral propagation, thus defining new therapeutic targets to treat KSHV-associated malignancies.

The study of many extreme weather events requires simulations with high spatiotemporal data that can grow in size quickly. Storing all the raw data from such a large-scale simulation for traditional post hoc analyses is soon going to be prohibitive as the data generation speed is outpacing the data storage capability in supercomputers. In situ analysis has emerged as a solution to this problem; data is analyzed when it is being produced, bypassing the slower disk input/output (I/O). In this work, we develop a new in situ analysis pathway for Energy Exascale Earth System Model (E3SM) and propose an algorithm for analyzing the impacts of sudden stratospheric warmings (SSWs), which can cause extreme cold temperature outbreaks at the surface, resulting in hazardous weather and disrupting many socioeconomic sectors. We detect SSWs and model the surface temperature data distributions in situ and show that post hoc analysis using the distribution models can predict the impact of SSWs in the continental United States.

As extreme-scale physics simulation becomes increasingly memory and storage expensive, the ability to access full simulation data for statistical analysis is becoming increasingly limited. The capability to perform in-situ statistical inference of state variables is becoming increasingly important for the comprehensive utilization of the huge amounts of information generated by these simulations. In this work, we report the first results fitting scalable Gaussian process regression to the state information of an expensive simulation in-situ. For this, spatial regression of upper atmosphere temperature data was performed using Julia coupled to the E3SM climate model. The resulting sparse Gaussian process model shows strong predictive performance using a small number of representative observations. These results provide the backbone for more general in-situ spatial inference with Gaussian process models in complex physics simulations.

In a biochemical assay that converts fan-in networks into feed-forward loops (FFLs), we show that the inter-regulator redundant information about the output gene product can be decomposed into finer components, mediated by the constituent pathways. Variance-based information within the linear noise regime facilitates quantifying these submodular redundancies. Contrary to the conventional wisdom on information decomposition, we report that information redundancy depends nontrivially on inter-regulator correlation. For the type-1 coherent (C1) and incoherent (I1) FFLs, the direct regulatory path-mediated redundancy is certainly correlation independent. However, components induced by the indirect regulatory path and interpathway interference are correlation dependent in (non)linear fashion. The trade-off between information redundancy and similarly decomposable extrinsic noise from input to output node has been demonstrated for the pathways and full motifs. Our analyses suggest that the interpathway cross redundancy positively and negatively influences the superposition of elementary redundancies in the C1- and I1-FFLs, respectively. Their corresponding total extrinsic noise is produced by the weighted sum and difference of the pathway-specific components. We find that the I1-FFL is able to manufacture more varied redundancy and extrinsic noise responses compared to the C1-FFL. Underlying the differing characteristics of the composite metrics across FFL variants, there exist uniformly behaving pathway-dependent elements. The decomposition framework has been meticulously explored in biologically rational parametric realizations through analytical estimates and stochastic simulations.

This work presents a 16mm2 heterogeneous RISC-V system-on-a-chip (SoC) composed of a high-performance out-of-order core, energy-efficient in-order core, data-parallel vector accelerator, and systolic array deep neural network (DNN) accelerator in low-power Intel 22FFL for general-purpose compute, DNN, and vector workloads. The heterogeneous RISC-V SoC is composed of fully open-source components, including a second-generation Berkeley Out-of-Order Machine (BOOM) with a non-speculative mode attached to a Hwacha vector accelerator, a Rocket in-order core attached to a Gemmini systolic array DNN accelerator, as well as a IMiB L2 cache and off-chip I/Os. Combined, the variety of heterogeneous compute allows for wide programmability while providing up to a 286x MOPS/W improvement or 282x MOPS improvement over the RISC-V in-order core.

SummarySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection leads to NF-κB activation and induction of pro-inflammatory cytokines, though the underlying mechanism for this activation is not fully understood. Our results reveal that the SARS-CoV-2 Nsp14 protein contributes to the viral activation of NF-κB signaling. Nsp14 caused the nuclear translocation of NF-κB p65. Nsp14 induced the upregulation of IL-6 and IL-8, which also occurred in SARS-CoV-2 infected cells. IL-8 upregulation was further confirmed in lung tissue samples from COVID-19 patients. A previous proteomic screen identified the putative interaction of Nsp14 with host Inosine-5’-monophosphate dehydrogenase 2 (IMPDH2) protein, which is known to regulate NF-κB signaling. We confirmed the Nsp14-IMPDH2 protein interaction and found that IMPDH2 knockdown or chemical inhibition using ribavirin (RIB) and mycophenolic acid (MPA) abolishes Nsp14-mediated NF-κB activation and cytokine induction. Furthermore, IMDPH2 inhibitors (RIB, MPA) efficiently blocked SARS-CoV-2 infection, indicating that IMDPH2, and possibly NF-κB signaling, is beneficial to viral replication. Overall, our results identify a novel role of SARS-CoV-2 Nsp14 in causing the activation of NF-κB.

Summary/AbstractFACT (FAcilitates Chromatin Transcription) is a heterodimeric protein complex composed of SUPT16H and SSRP1, and a histone chaperone participating in chromatin remodeling during gene transcription. FACT complex is profoundly regulated, and contributes to both gene activation and suppression. Here we reported that SUPT16H, a subunit of FACT, is acetylated at lysine 674 (K674) of middle domain (MD), which involves TIP60 histone acetyltransferase. Such acetylation of SUPT16H is recognized by bromodomain protein BRD4, which promotes protein stability of SUPT16H. We further demonstrated that SUPT16H-BRD4 associates with histone modification enzymes (EZH2, HDAC1) and affects histone marks (H3K9me3, H3K27me3 and H3ac). BRD4 is known to profoundly regulate interferon (IFN) signaling, while such function of SUPT16H has never been explored. Surprisingly, our results revealed that SUPT16H genetic knockdown via RNAi or pharmacological inhibition by using its inhibitor, curaxin 137 (CBL0137), results in the induction of IFNs and interferon-stimulated genes (ISGs). Through this mechanism, CBL0137 is shown to efficiently inhibit infection of multiple viruses, including Zika, influenza, and SARS-CoV-2. Furthermore, we demonstrated that CBL0137 also causes the remarkable activation of IFN signaling in natural killer (NK) cells, which promotes the NK-mediated killing of virus-infected cells in a co-culture system using human primary NK cells. Overall, our studies unraveled the previously un-appreciated role of FACT complex in regulating IFN signaling in both epithelial and NK cells, and also proposed the novel application of CBL0137 to treat viral infections.

Neuromorphic computing, inspired by the brain, promises extreme efficiency for certain classes of learning tasks, such as classification and pattern recognition. The performance and power consumption of neuromorphic computing depend heavily on the choice of the neuron architecture. Digital neurons (Dig-N) are conventionally known to be accurate and efficient at high speed while suffering from high leakage currents from a large number of transistors in a large design. On the other hand, analog/mixed-signal neurons (MS-Ns) are prone to noise, variability, and mismatch but can lead to extremely low-power designs. In this paper, we will analyze, compare, and contrast existing neuron architectures with a proposed MS-N in terms of performance, power, and noise, thereby demonstrating the applicability of the proposed MS-N for achieving extreme energy efficiency (femtojoule/multiply and accumulate or less). The proposed MS-N is implemented in 65-nm CMOS technology and exhibits $> 100\times $ better energy efficiency across all frequencies over two traditional Dig-Ns synthesized in the same technology node. We also demonstrate that the inherent error resiliency of a fully connected or even convolutional neural network can handle the noise as well as the manufacturing nonidealities of the MS-N up to certain degrees. Notably, a system-level implementation on CIFAR-10 data set exhibits a worst case increase in classification error by 2.1% when the integrated noise power in the bandwidth is $\sim 0.1 ~\mu $ V2, along with $\pm 3\sigma $ amount of variation and mismatch introduced in the transistor parameters for the proposed neuron with 8-bit precision.

Both Kaposi’s sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV) establish the persistent, life-long infection primarily at the latent status, and associate with certain types of tumors, such as B cell lymphomas, especially in immuno-compromised individuals including people living with HIV (PLWH). Lytic reactivation of these viruses can be employed to kill tumor cells harboring latently infected viral episomes through the viral cytopathic effects and the subsequent antiviral immune responses. In this study, we identified that polo-like kinase 1 (PLK1) is induced by KSHV de novo infection as well as lytic switch from KSHV latency. We further demonstrated that PLK1 depletion or inhibition facilitates KSHV reactivation and promotes cell death of KSHV-infected lymphoma cells. Mechanistically, PLK1 regulates Myc that is critical to both maintenance of KSHV latency and support of cell survival, and preferentially affects the level of H3K27me3 inactive mark both globally and at certain loci of KSHV viral episomes. Furthremore, we recognized that PLK1 inhibition synergizes with STAT3 inhibition to efficiently induce KSHV reactivation. We also confirmed that PLK1 depletion or inhibition yields the similar effect on EBV lytic reactivation and cell death of EBV-infected lymphoma cells. Lastly, we noticed that PLK1 in B cells is elevated in the context of HIV infection and caused by HIV Nef protein to favor KSHV/EBV latency., Author summary KSHV is a large double-stranded DNA virus belonging to human gamma-herpesviruses and capable of establishing the long-term persistent infection, which causes cancers under immunocompromised states, such as acquired immunodeficiency syndrome (AIDS). There are currently still no effective therapies to specifically treat KSHV-associated tumors, nor eliminate its persistent infection. In this study, we illustrated that PLK1 is upregulated by KSHV, which in return suppresses KSHV lytic infection. We further demonstrated that PLK1 is a novel host target that can be inhibited pharmacologically to benefit the viral oncolysis for promotion of KSHV lytic reactivation and elimination of KSHV-positive lymphoma cells, particularly for people living with HIV (PLWH). PLK1 inhibitors also exerted the similar effect on EBV that is the other member of human gamma-herpesvirus.