cssi/SciSciGPT-SciSciNet huggingface dataset:
Novel Probiotic Mechanisms of the Oral Bacterium Streptococcus sp. A12 as Explored with Functional Genomics

Health-associated biofilms in the oral cavity are composed of a diverse group of microbial species that can foster an environment that is less favorable for the outgrowth of dental caries pathogens, like Streptococcus mutans. A novel oral bacterium, designated Streptococcus A12, was previously isolated from supragingival dental plaque of a caries-free individual, and was shown to interfere potently with the growth and virulence properties of S. mutans. Here, we apply functional genomics to begin to identify molecular mechanisms used by A12 to antagonize, and to resist the antagonistic factors of, S. mutans. Using bioinformatics, genes that could encode factors that enhance the ability of A12 to compete with S. mutans were identified. Selected genes, designated as potential competitive factors (pcf), were deleted. Certain mutant derivatives showed a reduced capacity to compete with S. mutans compared to the parental strain. The A12 pcfO mutant lost the ability to inhibit comX-inducing peptide (XIP) signaling by S. mutans, while mutants in the pcfFEG locus were impaired in sensing of, and were more sensitive to, the lantibiotic nisin. Loss of PcfV, annotated as a colicin V biosynthetic protein, resulted in diminished antagonism of S. mutans. Collectively, the data provide new insights into the complexities and variety of factors that affect biofilm ecology and virulence. Continued exploration of the genomic and physiologic factors that distinguish commensals from truly beneficial members of the oral microbiota will lead to a better understanding of the microbiome and new approaches to promote oral health. Importance Advances in defining the composition of health-associated biofilms have highlighted the important role for beneficial species in maintaining health. Comparatively little, however, has been done to address the genomic and physiological basis underlying the probiotic mechanisms of beneficial commensals. In this study, we explored the ability of a novel oral bacterial isolate, Streptococcus A12, to compete with the dental pathogen Streptococcus mutans using various gene products with diverse functions. A12 displayed enhanced competitiveness by: i) disrupting intercellular communication pathways of S. mutans, ii) sensing and resisting antimicrobial peptides, and iii) producing factors involved in the production of a putative antimicrobial compound. Research on the probiotic mechanisms employed by Streptococcus A12 is providing essential insights into how beneficial bacteria may help maintain oral health, which will aid in the development of biomarkers and therapeutics that can improve the practice of clinical dentistry.
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning

Ensemble learning is a standard approach to building machine learning systems that capture complex phenomena in real-world data. An important aspect of these systems is the complete and valid quantification of model uncertainty. We introduce a Bayesian nonparametric ensemble (BNE) approach that augments an existing ensemble model to account for different sources of model uncertainty. BNE augments a model’s prediction and distribution functions using Bayesian nonparametric machinery. It has a theoretical guarantee in that it robustly estimates the uncertainty patterns in the data distribution, and can decompose its overall predictive uncertainty into distinct components that are due to different sources of noise and error. We show that our method achieves accurate uncertainty estimates under complex observational noise, and illustrate its real-world utility in terms of uncertainty decomposition and model bias detection for an ensemble in predict air pollution exposures in Eastern Massachusetts, USA.
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees

Gibbs sampling is a Markov chain Monte Carlo method that is often used for learning and inference on graphical models. Minibatching, in which a small random subset of the graph is used at each iteration, can help make Gibbs sampling scale to large graphical models by reducing its computational cost. In this paper, we propose a new auxiliary-variable minibatched Gibbs sampling method, {\it Poisson-minibatching Gibbs}, which both produces unbiased samples and has a theoretical guarantee on its convergence rate. In comparison to previous minibatched Gibbs algorithms, Poisson-minibatching Gibbs supports fast sampling from continuous state spaces and avoids the need for a Metropolis-Hastings correction on discrete state spaces. We demonstrate the effectiveness of our method on multiple applications and in comparison with both plain Gibbs and previous minibatched methods.
The influence of loan officers on loan contract design and performance

Abstract We investigate the extent to which loan officers generate independent, individual effects on the design and performance of syndicated loans. We construct a large database containing the identities of loan officers involved in structuring syndicated loan deals, allowing us to systematically disentangle borrower, bank, and loan officer fixed effects. We find that loan officers have significant influence on interest spreads, loan covenant design, and loan performance. Inclusion of borrower fixed effects increases our power to rule out the alternative that loan officer fixed effects reflect the matching of officerds to borrowers based on time-invariant borrower characteristics. We document heterogeneity in loan officers’ influence across loan contract terms, with loan officers exerting stronger influence over covenant package design than over interest spreads, but marginal influence on loan maturity. Lead officers have greater influence than participant officers over covenant package design and loan performance, but less robust differential influence on interest spreads.
Potential Tools for Eradicating HIV Reservoirs in the Brain: Development of Trojan Horse Prodrugs for the Inhibition of P-Glycoprotein with Anti-HIV-1 Activity.

Combination antiretroviral therapy is the mainstay of HIV treatment, lowering plasma viral levels below detection. However, eradication of HIV is a major challenge due to cellular and anatomical vi...
An omnidirectional visualization model of personalized gene regulatory networks

Gene regulatory networks (GRNs) have been widely used as a fundamental tool to reveal the genomic mechanisms that underlie the individual’s response to environmental and developmental cues. Standard approaches infer GRNs as holistic graphs of gene co-expression, but such graphs cannot quantify how gene–gene interactions vary among individuals and how they alter structurally across spatiotemporal gradients. Here, we develop a general framework for inferring informative, dynamic, omnidirectional, and personalized networks (idopNetworks) from routine transcriptional experiments. This framework is constructed by a system of quasi-dynamic ordinary differential equations (qdODEs) derived from the combination of ecological and evolutionary theories. We reconstruct idopNetworks using genomic data from a surgical experiment and illustrate how network structure is associated with surgical response to infrainguinal vein bypass grafting and the outcome of grafting. idopNetworks may shed light on genotype–phenotype relationships and provide valuable information for personalized medicine. Networks are central to the functionality of biological systems. Understanding how the underlying components of a system are interconnected through biological principles to determine the system’s behavior and dynamics presents an interesting but challenging computational topic of research in modern medicine. A team led by Rongling Wu at Pennsylvania State University, in collaboration with researchers from multiple universities in US and abroad, developed an innovative tool to recover and infer dynamic gene regulatory networks from static omics data. This tool overcomes the significant limitation of existing approaches that can only infer a single structurally simple, aggregate network from a large number of samples. The new tool reconstructs informative, dynamic, omnidirectional, and personalized networks (idopNetworks), which can track and monitor how regulatory networks drive genotype–phenotype relationships and facilitate the use of network tools to precision medicine.
ObliDB: oblivious query processing for secure databases

Hardware enclaves such as Intel SGX are a promising technology for improving the security of databases outsourced to the cloud. These enclaves provide an execution environment isolated from the hyper-visor/OS, and encrypt data in RAM. However, for applications that use large amounts of memory, including most databases, enclaves do not protect against access pattern leaks, which let attackers gain a large amount of information about the data. Moreover, the naive way to address this issue, using Oblivious RAM (ORAM) primitives from the security literature, adds substantial overhead.A number of recent works explore trusted hardware enclaves as a path toward secure, access-pattern oblivious outsourcing of data storage and analysis. While these works efficiently solve specific subproblems (e.g. building secure indexes or running analytics queries that always scan entire tables), no prior work has supported oblivious query processing for general query workloads on a DBMS engine with multiple access methods. Moreover, applying these techniques individually does not guarantee that an end-to-end workload, such as a complex SQL query over multiple tables, will be oblivious. In this paper, we introduce ObliDB, an oblivious database engine design that is the first system to provide obliviousness for general database read workloads over multiple access methods.ObliDB introduces a diverse array of new oblivious physical operators to accelerate oblivious SQL queries, giving speedups of up to an order of magnitude over naive ORAM. It supports a broad range of queries, including aggregation, joins, insertions, deletions and point queries. We implement ObliDB and show that, on analytics workloads, ObliDB ranges from 1.1--19x faster than Opaque, a previous oblivious, enclave-based system designed only for analytics, and comes within 2.6 x of Spark SQL, which provides no security guarantees. In addition, ObliDB supports point queries with 3--10ms latency, which is comparable to index-only trusted hardware systems, and runs over 7x faster than HIRB, a previous encryption-based oblivious index system that supports point queries.
2.5D Visual Sound

Binaural audio provides a listener with 3D sound sensation, allowing a rich perceptual experience of the scene. However, binaural recordings are scarcely available and require nontrivial expertise and equipment to obtain. We propose to convert common monaural audio into binaural audio by leveraging video. The key idea is that visual frames reveal significant spatial cues that, while explicitly lacking in the accompanying single-channel audio, are strongly linked to it. Our multi-modal approach recovers this link from unlabeled video. We devise a deep convolutional neural network that learns to decode the monaural (single-channel) soundtrack into its binaural counterpart by injecting visual information about object and scene configurations. We call the resulting output 2.5D visual sound---the visual stream helps "lift" the flat single channel audio into spatialized sound. In addition to sound generation, we show the self-supervised representation learned by our network benefits audio-visual source separation. Our video results: http://vision.cs.utexas.edu/projects/2.5D_visual_sound/
High performance Monte Carlo simulation of ising model on TPU clusters

Large-scale deep learning benefits from an emerging class of AI accelerators. Some of these accelerators' designs are general enough for compute-intensive applications beyond AI and Cloud TPU is one such example. In this paper, we demonstrate a novel approach using TensorFlow on Cloud TPU to simulate the two-dimensional Ising Model. TensorFlow and Cloud TPU framework enable the simple and readable code to express the complicated distributed algorithm without compromising the performance. Our code implementation fits into a small Jupyter Notebook and fully utilizes Cloud TPU's efficient matrix operation and dedicated high speed inter-chip connection. The performance is highly competitive: it outperforms the best published benchmarks to our knowledge by 60% in single-core and 250% in multi-core with good linear scaling. When compared to Tesla V100 GPU, the single-core performance maintains a ~10% gain. We also demonstrate that using low precision arithmetic---bfloat16---does not compromise the correctness of the simulation results.
Electron Field Emission From Diamond Grown By Laser QQC Process

A new diamond deposition process utilizing a plasma and a variety of interactions from a multiple laser system has been demonstrated, with WCKo substrates. The process is conducted in open air and does not involve hydrogen. Structural characterization of the diamond coatings, which have exceptional adhesion to cutting tool inserts, indicates a cubic diamond structure. Tungsten and cobalt atoms are incorporated into the film and a layer depleted in cobalt exists at the diamond- WCKo interface. Electron field emission current densities reached 6mA/cm2 at an applied voltage of 3000V for a film-anode distance of 20pm. 1. QQC PROCESS
ADMM for multiaffine constrained optimization

We expand the scope of the alternating direction method of multipliers (ADMM). Specifically, we show that ADMM, when employed to solve problems with multiaffine constraints that satisfy certain ver...
The Frontier of Reionization: Theory and Forthcoming Observations

The cosmic microwave background provides an image of the Universe 0.4 million years after the Big Bang, when atomic hydrogen formed out of free electrons and protons. One of the primary goals of observational cosmology is to obtain follow-up images of the Universe during the epoch of reionization, hundreds of millions of years later, when cosmic hydrogen was ionized once again by the UV photons emitted from the first galaxies. To achieve this goal, new observatories are being constructed, including low-frequency radio arrays capable of mapping cosmic hydrogen through its redshifted 21 cm emission, as well as imagers of the first galaxies such as the James Webb Space Telescope (JWST) and large aperture ground-based telescopes. The construction of these observatories is being motivated by a rapidly growing body of theoretical work. Numerical simulations of reionization are starting to achieve the dynamical range required to resolve galactic sources across the scale of hundreds of comoving Mpc, larger than the biggest ionized regions.
Hierarchical Bayesian Model for Probabilistic Analysis of Electric Vehicle Battery Degradation

This article proposes a hierarchical Bayesian model for probabilistic estimation of the electric vehicle (EV) battery capacity fade. Deterministic estimation of the EV battery degradation is not reliable, as its aging factors are mostly external and varies by time, location, and the driver’s usage pattern, while probabilistic models can address the observations uncertainties and provide more accurate estimations. We have developed a comprehensive Bayesian network model that incorporates two main aspects of the EV battery aging: hierarchy and variety of external effectual factors. Multiple levels of hierarchical relationship with intermediate hidden variables connect the external factors, such as driving behavior and habits, charging options, and grid services to the battery capacity fade. The mathematical expression of the model is extracted based on Bayes’ theorem, the probability distributions for all variables are carefully chosen, and the Metropolis–Hastings Markov chain Monte Carlo sampling method is applied to generate the posterior distributions. The model is trained with a subset of experimental data (85%) and tested with the other 15% of data to prove its accuracy. Also, three case studies for different drivers, different grid services’ repetitions, and different climates are explored to show model’s flexibility with different input data.
Sub-Optimal Tracking in Switched Systems With Controlled Subsystems and Fixed-Mode Sequence Using Approximate Dynamic Programming

Optimal tracking in switched systems with controlled subsystem and Discrete-time (DT) dynamics is investigated. A feedback control policy is generated such that a) the system tracks the desired reference signal, and b) the optimal switching time instants are sought. For finding the optimal solution, approximate dynamic programming is used. Simulation results are provided to illustrate the effectiveness of the solution.
Signatures of representations of Hecke algebras and rational Cherednik algebras

Abstract Determining whether an irreducible representation of a group (or ⁎-algebra) admits a non-degenerate invariant, positive-definite Hermitian form is an important problem in representation theory. In this paper, we study the related notion of signatures. Let q ∈ C with | q | = 1 , κ = − 1 / c with c ∈ Q 0 with certain additional (mild) restrictions, and n ∈ Z ≥ 2 . We study representations S λ ( q ) of H n ( q ) , the Hecke algebra of type A n − 1 , and representations M κ ( λ ) of H κ , the rational Cherednik algebra of type A n − 1 , which have unique (up to scaling) invariant Hermitian forms (here λ is a partition of n). The signature is the number of elements with positive norm minus the number of elements with negative norm, and we analogously define the signature character in the case that there is a natural grading on the module. We provide formulas for (1) signatures of modules over H n ( q ) and (2) signature characters of modules over H κ . We study the limit c → − ∞ , in which case the signature character has a simpler form in terms of inversions and descents of permutations in S n . We provide examples corresponding to some special shapes, and small values of n. Finally, when q = e 2 π i c , we show that the asymptotic signature character of the H κ -module M κ ( τ ) is the signature of the H n ( q ) -module S τ ( q ) .
Optimal Streaming and Tracking Distinct Elements with High Probability

The distinct elements problem is one of the fundamental problems in streaming algorithms—given a stream of integers in the range l 1,… ,nr, we wish to provide a (1+e) approximation to the number of distinct elements in the input. After a long line of research an optimal solution for this problem with constant probability of success, using O(1/e2+lg n) bits of space, was given by Kane, Nelson, and Woodruff in 2010.The standard approach used to achieve low failure probability δ is to take the median of lg δ−1 parallel repetitions of the original algorithm. We show that such a multiplicative space blow-up is unnecessary: We provide an optimal algorithm using O(lg δ−1/e2 + lg n) bits of space—matching known lower bounds for this problem. That is, the lg δ−1; factor does not multiply the lg n term. This settles completely the space complexity of the distinct elements problem with respect to all standard parameters.We consider also the strong tracking (or continuous monitoring) variant of the distinct elements problem, where we want an algorithm that provides an approximation of the number of distinct elements seen so far, at all times of the stream. We show that this variant can be solved using O(lg lg n + lg δ −1/e2 + lg n) bits of space, which we show to be optimal.
Everyday Discrimination and Kidney Function Among Older Adults: Evidence From the Health and Retirement Study.

BACKGROUND With advancing age, there is an increase in the time of and number of experiences with psychosocial stressors that may lead to the initiation and/or progression of chronic kidney disease (CKD). Our study tests whether one type of experience, everyday discrimination, predicts kidney function among middle and older adults. METHODS The data were from 10 973 respondents (ages 52-100) in the 2006/2008 Health and Retirement Study, an ongoing biennial nationally representative survey of older adults in the United States. Estimated glomerular filtration rate (eGFR) derives from the Chronic Kidney Disease Epidemiology Collaboration equation. Our indicator of everyday discrimination is drawn from self-reports from respondents. Ordinary Least Squared regression (OLS) models with robust standard errors are applied to test hypotheses regarding the link between everyday discrimination and kidney function. RESULTS Everyday discrimination was associated with poorer kidney function among respondents in our study. Respondents with higher everyday discrimination scores had lower eGFR after adjusting for demographic characteristics (B = -1.35, p < .05), and while attenuated, remained significant (B = -0.79, p < .05) after further adjustments for clinical, health behavior, and socioeconomic covariates. CONCLUSIONS Our study suggests everyday discrimination is independently associated with lower eGFR. These findings highlight the importance of psychosocial factors in predicting insufficiency in kidney function among middle-aged and older adults.
Transcriptomic profile analysis of brain inferior colliculus following acute hydrogen sulfide exposure.

Hydrogen sulfide (H2S) is a gaseous molecule found naturally in the environment, and as an industrial byproduct, and is known to cause acute death and induces long-term neurological disorders following acute high dose exposures. Currently, there is no drug approved for treatment of acute H2S-induced neurotoxicity and/or neurological sequelae. Lack of a deep understanding of pathogenesis of H2S-induced neurotoxicity has delayed the development of appropriate therapeutic drugs that target H2S-induced neuropathology. RNA sequencing analysis was performed to elucidate the cellular and molecular mechanisms of H2S-induced neurodegeneration, and to identify key molecular elements and pathways that contribute to H2S-induced neurotoxicity. C57BL/6J mice were exposed by whole body inhalation to 700 ppm of H2S for either one day, two consecutive days or 4 consecutive days. Magnetic resonance imaging (MRI) scan analyses showed H2S exposure induced lesions in the inferior colliculus (IC) and thalamus (TH). This mechanistic study focused on the IC. RNA Sequencing analysis revealed that mice exposed once, twice, or 4 times had 283, 193 and 296 differentially expressed genes (DEG), respectively (q-value 1.5). Hydrogen sulfide exposure modulated multiple biological pathways including unfolded protein response, neurotransmitters, oxidative stress, hypoxia, calcium signaling, and inflammatory response in the IC. Hydrogen sulfide exposure activated PI3K/Akt and MAPK signaling pathways. Pro-inflammatory cytokines were shown to be potential initiators of the modulated signaling pathways following H2S exposure. Furthermore, microglia were shown to release IL-18 and astrocytes released both IL-1β and IL-18 in response to H2S. This transcriptomic analysis data revealed complex signaling pathways involved in H2S-induced neurotoxicity and may provide important associated mechanistic insights.
Influenza in Children

Influenza poses a significant disease burden on children worldwide, with high rates of hospitalization and substantial morbidity and mortality. Although the clinical presentation of influenza in children has similarities to that seen in adults, there are unique aspects to how children present with infection that are important to recognize. In addition, children play a significant role in viral transmission within communities. Growing evidence supports the idea that early influenza infection can uniquely establish lasting immunologic memory, making an understanding of how viral immunity develops in this population critical to better protect children from infection and to facilitate efforts to develop a more universally protective influenza vaccine.
VEF: a variant filtering tool based on ensemble methods.

MOTIVATION Variants identified by current genomic analysis pipelines contain many incorrectly called variants. These can be potentially eliminated by applying state-of-the-art filtering tools, such as Variant Quality Score Recalibration (VQSR) or Hard Filtering (HF). However, these methods are very user-dependent and fail to run in some cases. We propose VEF, a variant filtering tool based on decision tree ensemble methods that overcomes the main drawbacks of VQSR and HF. Contrary to these methods, we treat filtering as a supervised learning problem, using variant call data with known 'true' variants, i.e. gold standard, for training. Once trained, VEF can be directly applied to filter the variants contained in a given Variants Call Format (VCF) file (we consider training and testing VCF files generated with the same tools, as we assume they will share feature characteristics). RESULTS For the analysis, we used whole genome sequencing (WGS) Human datasets for which the gold standards are available. We show on these data that the proposed filtering tool VEF consistently outperforms VQSR and HF. In addition, we show that VEF generalizes well even when some features have missing values, when the training and testing datasets differ in coverage, and when sequencing pipelines other than GATK are used. Finally, since the training needs to be performed only once, there is a significant saving in running time when compared with VQSR (4 versus 50 min approximately for filtering the single nucleotide polymorphisms of a WGS Human sample). AVAILABILITY AND IMPLEMENTATION Code and scripts available at: github.com/ChuanyiZ/vef. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Rapid Trajectory optimization Using C-FROST with Illustration on a Cassie-Series Dynamic Walking Biped

One of the big attractions of low-dimensional models for gait design has been the ability to compute solutions rapidly, whereas one of their drawbacks has been the difficulty in mapping the solutions back to the target robot. This paper presents a set of tools for rapidly determining solutions for “humanoids” without removing or lumping degrees of freedom. The main tools are: (1) C-FROST, an open-source C++ interface for FROST, a direct collocation optimization tool; and (2) multi-threading. The results will be illustrated on a 20-DoF floating-base model for a Cassie-series bipedal robot through numerical optimization and physical experiments.
Threshold factor models for high-dimensional time series

Abstract We consider a threshold factor model for high-dimensional time series in which the dynamics of the time series is assumed to switch between different regimes according to the value of a threshold variable. This is an extension of threshold modeling to a high-dimensional time series setting under a factor structure. Specifically, within each threshold regime, the time series is assumed to follow a factor model. The regime switching mechanism creates structural changes in the factor loading matrices. It provides flexibility in dealing with situations that the underlying states may be changing over time, as often observed in economic time series and other applications. We develop the procedures for the estimation of the loading spaces, the number of factors and the threshold value, as well as the identification of the threshold variable, which governs the regime change mechanism. The theoretical properties are investigated. Simulated and real data examples are presented to illustrate the performance of the proposed method.
The future of artificial satellite theories Hybrid ephemeris compression model

Since the time of Newton, astrodynamics has focused on the analytical solution of orbital problems. This was driven by the desire to obtain a theoretical understanding of the motion and the practical desire to be able to produce a computational result, Only with the advent of the computer did numerical integration become a practical consideration for solving dynamical problems. Although computer technology is not yet to the point of being able to provide numerical integration support for all satellite orbits, we are in a transition period which is being driven by the unprecedented increase in computational power, This transition will affect the future of analytical, semi-analytical and numerical artificial satellite theories in a dramatic way, In fact, the role for semi-analytical theories may disappear. During the time of transition, a central site may have the capacity to maintain the orbits using numerical integration, but the user may not have such a capacity or may need results in a more timely manner, One way to provide for this transition need is through the use of some type of satellite ephemeris compression. Through the combined use of a power series and a Fourier series, good quality ephemeris compression has been achieved for 7 day periods, The ephemeris compression requires less than 40 terms and is valid for all eccentricities and inclinations.
Hyperuniform Structures Formed by Shearing Colloidal Suspensions.

In periodically sheared suspensions there is a dynamical phase transition, characterized by a critical strain amplitude ${\ensuremath{\gamma}}_{c}$, between an absorbing state where particle trajectories are reversible and an active state where trajectories are chaotic and diffusive. Repulsive nonhydrodynamic interactions between ``colliding'' particles' surfaces have been proposed as a source of this broken time reversal symmetry. A simple toy model called random organization qualitatively reproduces the dynamical features of this transition. Random organization and other absorbing state models exhibit hyperuniformity, a strong suppression of density fluctuations on long length scales quantified by a structure factor $S(q\ensuremath{\rightarrow}0)\ensuremath{\sim}{q}^{\ensuremath{\alpha}}$ with $\ensuremath{\alpha}g0$, at criticality. Here we show experimentally that the particles in periodically sheared suspensions organize into structures with anisotropic short-range order but isotropic, long-range hyperuniform order when oscillatory shear amplitudes approach ${\ensuremath{\gamma}}_{c}$.
The science and engineering of stem cell-derived organoids-examples from hepatic, biliary, and pancreatic tissues

The field of organoid engineering promises to revolutionize medicine with wide-ranging applications of scientific, engineering, and clinical interest, including precision and personalized medicine, gene editing, drug development, disease modelling, cellular therapy, and human development. Organoids are a three-dimensional (3D) miniature representation of a target organ, are initiated with stem/progenitor cells, and are extremely promising tools with which to model organ function. The biological basis for organoids is that they foster stem cell self-renewal, differentiation, and self-organization, recapitulating 3D tissue structure or function better than two-dimensional (2D) systems. In this review, we first discuss the importance of epithelial organs and the general properties of epithelial cells to provide a context and rationale for organoids of the liver, pancreas, and gall bladder. Next, we develop a general framework to understand self-organization, tissue hierarchy, and organoid cultivation. For each of these areas, we provide a historical context, and review a wide range of both biological and mathematical perspectives that enhance understanding of organoids. Next, we review existing techniques and progress in hepatobiliary and pancreatic organoid engineering. To do this, we review organoids from primary tissues, cell lines, and stem cells, and introduce engineering studies when applicable. We discuss non-invasive assessment of organoids, which can reveal the underlying biological mechanisms and enable improved assays for growth, metabolism, and function. Applications of organoids in cell therapy are also discussed. Taken together, we establish a broad scientific foundation for organoids and provide an in-depth review of hepatic, biliary and pancreatic organoids.
Boundary Integral Formula for Harmonic Functions on Riemann Surfaces

We construct a boundary integral formula for harmonic functions on smoothly-bordered subdomains of Riemann surfaces embeddable into $${\mathbb {C}}{\mathbb {P}}^2$$ . The formula may be considered as an analogue of the Green’s formula for domains in $${\mathbb {C}}$$ .
Uncertainty Shocks and Business Cycle Research

Abstract We review the literature on uncertainty shocks and business cycle research. First, we motivate the study of uncertainty shocks by documenting the presence of time-variation in the volatility of macroeconomic time series. Second, we enumerate the mechanisms that researchers have postulated to link uncertainty shocks and business cycles. Third, we outline how we can specify uncertainty shocks. Fourth, we postulate a real business cycle model augmented with financial frictions and uncertainty shocks. Fifth, we use the model to illustrate our previous discussions and to show how uncertainty shocks can be expansionary, a useful finding in several contexts.
Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development

The classification of streets on road networks has been focused on the vehicular transportational features of streets such as arterials, major roads, minor roads and so forth based on their transportational use. City authorities on the other hand have been shifting to more urban inclusive planning of streets, encompassing the side use of a street combined with the transportational features of a street. In such classification schemes, streets are labeled for example as commercial throughway, residential neighborhood, park etc. This modern approach to urban planning has been adopted by major cities such as the city of San Francisco, the states of Florida and Pennsylvania among many others. Currently, the process of labeling streets according to their contexts is manual and hence is tedious and time consuming. In this paper, we propose an approach to collect and label imagery data then deploy advancements in computer vision towards modern urban planning. We collect and label street imagery then train deep convolutional neural networks (CNN) to perform the classification of street context. We show that CNN models can perform well achieving accuracies in the 81% to 87%, we then visualize samples from the embedding space of streets using the t-SNE method and apply class activation mapping methods to interpret the features in street imagery contributing to output classification from a model.
Investigating Real-Time Entropy Features of DDoS Attack Based on Categorized Partial-Flows

With the advent of IoT devices and exponential growth of nodes on the internet, computer networks are facing new challenges, with one of the more important ones being DDoS attacks. In this paper, new features to detect initiation and termination of DDoS attacks are investigated. The method to extract these features is devised with respect to some openflowbased switch capabilities. These features provide us with a higher resolution to view and process packet count entropies, thus improving DDoS attack detection capabilities. Although some of the technical assumptions are based on SDN technology and openflow protocol, the methodology can be applied in other networking paradigms as well.
Monetary Policy Spillovers under Intermediate Exchange Rate Regimes

I investigate monetary policy transmission under the Trilemma across Advanced and Emerging Market Economies, paying particular attention on the extent of spillovers under intermediate exchange rate regimes (i.e. managed floats). The extent of monetary pass-through: 1) is broadly significant, but more incomplete in Emerging Markets than Advanced Economies, 2) varies within intermediate exchange rate regimes, 3) appears to be diversifiable under a basket peg, and 4) is non-linear in exchange rate flexibility, especially across Emerging Markets. The latter three points suggest that near-corner exchange rate policies can carry starkly different implications from corner policies themselves: Countries can face almost the same monetary autonomy as under a float without resorting to a pure float. Countries under a fixed regime appear to gain disproportionate monetary independence by giving up relatively little exchange rate stability. The use of international reserves as an additional policy instrument appears to play a role in explaining these non-linearities, particularly for Emerging Markets. Such gains in monetary autonomy are allocated towards domestic objectives differently across Advanced Economies and Emerging Markets. Advanced Economies tend to put greater emphasis on output stabilization while Emerging Markets focus on inflation.
On general rogue waves in the parity-time-symmetric nonlinear Schrödinger equation

Abstract This article addresses the question of general rogue-wave solutions in the nonlocal parity-time-symmetric nonlinear Schrodinger equation. By generalizing the previous bilinear method, large classes of rogue waves are derived as Gram determinants with Schur polynomial elements. It is shown that these rogue waves contain previously reported ones as special cases. More importantly, they contain many new rogue wave families. It is conjectured that the rogue waves derived in this article are all rogue-wave solutions in the parity-time-symmetric nonlinear Schrodinger equation.
Mach Wave Emission from Supersonic Jets

An experimental investigation has been conducted on supersonic jets at a Mach number of 1.43 over a temperature range from about 420 K to 1370 K (300 F to 2000 F) in which it was found that the noise in the far field was dominated by eddy Mach waves. It is shown that the strength of the Mach waves is determined by the product of the mean shear and the density fluctuations of the jet. Thus, the source of sound arises from the mixing of hot and cold streams as well as from those compressions and expansions that are intuitively associated with sound generation. For the temperature range investigated, the Mach waves were emitted at angles between 37 deg and 59 deg with respect to the jet axis. These values represent those in the region of the jet where the Mach angle was constant.
Locating the Manhattan housing market: GIS evidence for 1880-1910

There is a dearth of systematic information about the historical New York City housing market. We present a new sample containing rental price and characteristic data for 10,715 Manhattan units whi...
Embedded cavity drag in steady and unsteady flows

The numerical solution of the laminar boundary-layer flow over an embedded cavity is studied. The purpose of the study is to examine the relevant drag characteristics of laminar cavity flow. The solution field is obtained in terms of velocity and vorticity variables, with the stream function and pressure derivable from the directly computed variables. An analysis and comparison is made among four square cavities, ranging in size from 0.25 to 1.00 boundary-layer thicknesses deep. The dominant flow features are examined in the vicinity of the cavity by means of the stream function and iso-vorticity contours. The dominant physics in the overall drag characteristics of the flow is examined by an analysis of the pressure and wall shear stress distributions in the cavity, and upstream and downstream of the cavity. Pressure forces and frictional forces in, and in the vicinity of, the cavity are determined. Stress relaxation distances, both upstream and downstream of the cavity, are calculated and analyzed. The flow dynamics of the boundary-layer flow over an embedded cavity is summarized. Finally, the relevance of the present results to the control of flow separation in such flows is discussed.
Efficient table-top dual-wavelength beamline for ultrafast transient absorption spectroscopy in the soft X-ray region.

We present a table-top beamline providing a soft X-ray supercontinuum extending up to 370 eV from high-order harmonic generation with sub-13 fs 1300 nm driving pulses and simultaneous production of sub-5 fs pulses centered at 800 nm. Optimization of high harmonic generation in a long and dense gas medium yields a photon flux of ~ 1.4 × 106 photons/s/1% bandwidth at 300 eV. The temporal resolution of X-ray transient absorption experiments with this beamline is measured to be 11 fs for 800 nm excitation. This dual-wavelength approach, combined with high flux and high spectral and temporal resolution soft X-ray absorption spectroscopy, is a new route to the study of ultrafast electronic dynamics in carbon-containing molecules and materials at the carbon K-edge.
Building Representative Matched Samples with Multi-valued Treatments in Large Observational Studies*

In this article, we present a new way of matching in observational studies that overcomes three limitations of existing matching approaches. First, it directly balances covariates with multi-valued...
Hyperplane integrability conditions and smoothing for Radon transforms

This paper may be viewed as a companion paper to Greenblatt ( arXiv:1910.04547 ). In that paper, $$L^2$$ Sobolev estimates derived from a Newton polyhedron-based resolution of singularities method are combined with interpolation arguments to prove $$L^p$$ to $$L^q_s$$ estimates, some sharp up to endpoints, for translation invariant Radon transforms over hypersurfaces and related operators. Here $$q \ge p$$ and s can be positive, negative, or zero. In this paper, we instead use $$L^2$$ Sobolev estimates derived from the resolution of singularities methods of Greenblatt ( arXiv:1810.10507 ) and combine with analogous interpolation arguments, again resulting in $$L^p$$ to $$L^q_s$$ estimates for translation invariant Radon transforms for $$q \ge p$$ which can be sharp up to endpoints. It will turn out that sometimes the results of this paper are stronger, and sometimes the results of Greenblatt ( arXiv:1910.04547 ) are stronger. As in Greenblatt (submitted), some of the sharp estimates of this paper occur when $$s = 0$$ , thereby giving some new sharp $$L^p$$ to $$L^q$$ estimates for such operators, again up to endpoints. Our results lead to natural global analogues whose statements can be recast in terms of a hyperplane integrability condition analogous to that of Iosevich and Sawyer in their work (Adv Math 132(1):46–119, 1997) on the $$L^p$$ boundedness of maximal averages over hypersurfaces.
Microstructural inelastic fingerprints and data-rich predictions of plasticity and damage in solids

Inelastic mechanical responses in solids, such as plasticity, damage and crack initiation, are typically modeled in constitutive ways that display microstructural and loading dependence. Nevertheless, linear elasticity at infinitesimal deformations is used for microstructural properties. We demonstrate a framework that builds on sequences of microstructural images to develop fingerprints of inelastic tendencies, and then use them for data-rich predictions of mechanical responses up to failure. In analogy to common fingerprints, we show that these two-dimensional instability-precursor signatures may be used to reconstruct the full mechanical response of unknown sample microstructures; this feat is achieved by reconstructing appropriate average behaviors with the assistance of a deep convolutional neural network that is fine-tuned for image recognition. We demonstrate basic aspects of microstructural fingerprinting in a toy model of dislocation plasticity and then, we illustrate the method’s scalability and robustness in phase field simulations of model binary alloys under mode-I fracture loading.
Pangolin: an efficient and flexible graph mining system on CPU and GPU

There is growing interest in graph pattern mining (GPM) problems such as motif counting. GPM systems have been developed to provide unified interfaces for programming algorithms for these problems and for running them on parallel systems. However, existing systems may take hours to mine even simple patterns in moderate-sized graphs, which significantly limits their real-world usability.We present Pangolin, an efficient and flexible in-memory GPM framework targeting shared-memory CPUs and GPUs. Pangolin is the first GPM system that provides high-level abstractions for GPU processing. It provides a simple programming interface based on the extend-reduce-filter model, which allows users to specify application specific knowledge for search space pruning and isomorphism test elimination. We describe novel optimizations that exploit locality, reduce memory consumption, and mitigate the overheads of dynamic memory allocation and synchronization.Evaluation on a 28-core CPU demonstrates that Pangolin outperforms existing GPM frameworks Arabesque, RStream, and Fractal by 49×, 88×, and 80× on average, respectively. Acceleration on a V100 GPU further improves performance of Pangolin by 15× on average. Compared to state-of-the-art hand-optimized GPM applications, Pangolin provides competitive performance with less programming effort.
How do global banks scramble for liquidity? Evidence from the asset-backed commercial paper freeze of 2007 ☆

Abstract We investigate how banks scrambled for liquidity following the asset-backed commercial paper (ABCP) market freeze of August 2007 and its implications for corporate borrowing. Commercial banks in the United States raised dollar deposits and took advances from Federal Home Loan Banks (FHLBs), while foreign banks had limited access to such alternative dollar funding. Relative to before the ABCP freeze and relative to their non-dollar lending, foreign banks with ABCP exposure charged higher interest rates to corporations for dollar-denominated syndicated loans. The results point to a funding risk manifesting as currency shortages for banks engaged in maturity transformation in foreign countries.
17 – Soil Stress

Publisher Summary The most important environmental factor that affects the health of a tree is the soil conditions in which it grows. Soil conditions such as moisture have a selective effect on plant growth. The knowledge of the soil conditions that favor each tree and shrub is necessary in assessing soil stress. Soil is classified by its chemical and physical composition. These two properties of soil are important to plant health. Soil must contain the necessary balance of essential minerals for normal root growth. The chemical stress in soil can be because of unfavorable pH and/or imbalances in nutrients, but it can also be caused by a toxic material in the soil such as that produced by the roots of black walnut and butternut. The physical stress in soil results from factors that restrict root growth. Root girdling is a common result of impervious layers or barriers to root growth. One important measure of soil condition is the degree of acidity or alkalinity. Most trees grow best in soil pH 5.5 to 6.5, that is, slightly acidic.