cssi/SciSciGPT-SciSciNet huggingface dataset:
Coronavirus S protein-induced fusion is blocked prior to hemifusion by Abl kinase inhibitors

Enveloped viruses gain entry into host cells by fusing with cellular membranes, a step that is required for virus replication. Coronaviruses, including the severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV) and infectious bronchitis virus (IBV), fuse at the plasma membrane or use receptor-mediated endocytosis and fuse with endosomes, depending on the cell or tissue type. The virus spike (S) protein mediates fusion with the host cell membrane. We have shown previously that an Abelson (Abl) kinase inhibitor, imatinib, significantly reduces SARS-CoV and MERS-CoV viral titres and prevents endosomal entry by HIV SARS S and MERS S pseudotyped virions. SARS-CoV and MERS-CoV are classified as BSL-3 viruses, which makes experimentation into the cellular mechanisms involved in infection more challenging. Here, we use IBV, a BSL-2 virus, as a model for studying the role of Abl kinase activity during coronavirus infection. We found that imatinib and two specific Abl kinase inhibitors, GNF2 and GNF5, reduce IBV titres by blocking the first round of virus infection. Additionally, all three drugs prevented IBV S-induced syncytia formation prior to the hemifusion step. Our results indicate that membrane fusion (both virus–cell and cell–cell) is blocked in the presence of Abl kinase inhibitors. Studying the effects of Abl kinase inhibitors on IBV will be useful in identifying the host cell pathways required for coronavirus infection. This will provide an insight into possible therapeutic targets to treat infections by current as well as newly emerging coronaviruses.
Reasons and Religion

The Journal of the American Academy of Religion has solicited eight thoughtful reviews of my book, Religious Freedom in an Egalitarian Age. This is my reply, which will appear together alongside the reviews and an introduction by the editor. In the reply, I isolate two themes that run through the reviews: curiosity about the role of reasons in conflicts between religious freedom and equality law, and concern about the concept of religion that the book tacitly employs. I first clarify that the book's methodological argument is just that reasons can be given to support outcomes in this field, contrary to the contentions of certain skeptics. Nothing about the book's method is meant to eliminate conflict. On the contrary, reasonable disagreement is a fixed feature of any democratic society. Second, I embrace the observations of critical theorists of religion, and I explain why they enrich rather than undermine the main arguments of the book. Finally, I observe that these two themes interact in interesting ways. By creatively deploying the coherence method I propose, critical theorists of religion can promote new possibilities for free and equal citizenship. At least one of the reviews demonstrates that possibility.
How the Reformulation of OxyContin Ignited the Heroin Epidemic

Abstract We attribute the recent quadrupling of heroin death rates to the August 2010 reformulation of an oft-abused prescription opioid, OxyContin. The new abuse-deterrent formulation led many con...
Excess audit committee compensation and audit pricing

In this study, we explore audit pricing decisions in the presence of excess audit committee compensation. Specifically, we examine whether excess compensation of audit committee members is related to audit fees. We find that excess compensation is positively associated with audit fees. We further examine whether the excess compensation affects auditors' pricing of earnings manipulation, and find evidence suggesting that auditors perceive earnings manipulation risk relatively low when the audit committee members are paid excess compensation. Overall, our results support the notion that audit committee excess compensation represents greater monitoring over a firm's financial reporting process, which affects auditor's risk assessment.
Neural mechanisms of speed-accuracy tradeoff of visual search: saccade vigor, the origin of targeting errors, and comparison of the superior colliculus and frontal eye field.

Neurophysiological mechanisms of speed-accuracy tradeoff (SAT) have only recently been investigated. This article reports the first replication of SAT performance in nonhuman primates, the first re...
Tree shift topological entropy

Abstract We give a definition of topological entropy for tree shifts, prove that the limit in the definition exists, and show that it dominates the topological entropy of the associated one-dimensional shift of finite type when the labeling of the tree shares the same restrictions.
Nonlinear dimension reduction via outer Bi-Lipschitz extensions

We introduce and study the notion of *an outer bi-Lipschitz extension* of a map between Euclidean spaces. The notion is a natural analogue of the notion of *a Lipschitz extension* of a Lipschitz map. We show that for every map f there exists an outer bi-Lipschitz extension f′ whose distortion is greater than that of f by at most a constant factor. This result can be seen as a counterpart of the classic Kirszbraun theorem for outer bi-Lipschitz extensions. We also study outer bi-Lipschitz extensions of near-isometric maps and show upper and lower bounds for them. Then, we present applications of our results to prioritized and terminal dimension reduction problems, described next. We prove a *prioritized* variant of the Johnson–Lindenstrauss lemma: given a set of points X⊂ ℝd of size N and a permutation (”priority ranking”) of X, there exists an embedding f of X into ℝO(logN) with distortion O(loglogN) such that the point of rank j has only O(log3 + e j) non-zero coordinates – more specifically, all but the first O(log3+e j) coordinates are equal to 0; the distortion of f restricted to the first j points (according to the ranking) is at most O(loglogj). The result makes a progress towards answering an open question by Elkin, Filtser, and Neiman about prioritized dimension reductions. We prove that given a set X of N points in ℜd, there exists a *terminal* dimension reduction embedding of ℝd into ℝd′, where d′ = O(logN/e4), which preserves distances ||x−y|| between points x∈ X and y ∈ ℝd, up to a multiplicative factor of 1 ± e. This improves a recent result by Elkin, Filtser, and Neiman. The dimension reductions that we obtain are nonlinear, and this nonlinearity is necessary.
Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation

In this paper, we develop a two-stage neural network solution for the challenging task of white-matter lesion segmentation. To cope with the vast variability in lesion sizes, we sample brain MR scans with patches at three different dimensions and feed them into separate fully convolutional neural networks (FCNs). In the second stage, we process large and small lesion separately, and use ensemble-nets to combine the segmentation results generated from the FCNs. A novel activation function is adopted in the ensemble-nets to improve the segmentation accuracy measured by Dice Similarity Coefficient. Experiments on MICCAI 2017 White Matter Hyperintensities (WMH) Segmentation Challenge data demonstrate that our two-stage-multi-sized FCN approach, as well as the new activation function, are effective in capturing white-matter lesions in MR images.
Statistical Model Compression for Small-Footprint Natural Language Understanding.

In this paper we investigate statistical model compression applied to natural language understanding (NLU) models. Small-footprint NLU models are important for enabling offline systems on hardware restricted devices, and for decreasing on-demand model loading latency in cloud-based systems. To compress NLU models, we present two main techniques, parameter quantization and perfect feature hashing. These techniques are complementary to existing model pruning strategies such as L1 regularization. We performed experiments on a large scale NLU system. The results show that our approach achieves 14-fold reduction in memory usage compared to the original models with minimal predictive performance impact.
Bayesian bivariate subgroup analysis for risk–benefit evaluation

Subgroup analysis is a frequently used tool for evaluating heterogeneity of treatment effect and heterogeneity in treatment harm across observed baseline patient characteristics. While treatment efficacy and adverse event measures are often reported separately for each subgroup, analyzing their within-subgroup joint distribution is critical for better informed patient decision-making. In this paper, we describe Bayesian models for performing a subgroup analysis to compare the joint occurrence of a primary endpoint and an adverse event between two treatment arms. Our approach emphasizes estimation of heterogeneity in this joint distribution across subgroups, and our approach directly accommodates subgroups with small numbers of observed primary and adverse event combinations. In addition, we describe several ways in which our models may be used to generate interpretable summary measures of benefit-risk tradeoffs for each subgroup. The methods described here are illustrated throughout using a large cardiovascular trial ( $$N = 9361$$ ) investigating the efficacy of an intervention for reducing systolic blood pressure to a lower-than-usual target.
Equity market momentum: A synthesis of the literature and suggestions for future work

Abstract I review the literature on equity market momentum, a seminal and intriguing finding in finance. This phenomenon is the ability of returns over the past one to four quarters to predict future returns over the same period in the cross-section of equities. I am able to document about ten different theories for momentum, and a large volume of empirical work on the topic. I find, however, that after a quarter century following the discovery of momentum by Jegadeesh and Titman (1993), we are still no closer to finding a discernible cause for this phenomenon, in spite of the extensive work on the topic. More needs to be done to develop tests that are focused not so much on testing one specific theory, but on ruling out alternative explanations.
Chromosomal microarray analysis on uncultured chorionic villus sampling can be complicated by confined placental mosaicism for aneuploidy and microdeletions.

Objective This study aims to establish the incidence and implications of confined placental mosaicism (CPM) in the context of prenatal chromosomal microarray analysis (CMA). Methods We retrospectively reviewed prenatal array data on 1382 consecutive chorionic villus sampling (CVS) specimens spanning the past 6 years, focusing on those for which whole CVS biopsy (both cytotrophoblast and mesenchymal cells) was used for CMA and cultured cells (primarily mesenchyme) was also analyzed or amniotic fluid (AF)/newborn blood was used for confirmation, to determine the frequency of mosaic abnormal findings that were the result of CPM. Results Out of a total of 1382 consecutive CVS cases, we identified 42 (42/1382 = 3.0%) cases with abnormal array findings suggestive of mosaicism. Among them, 10 cases were unequivocally interpreted as CPM based on a normal AF/newborn blood confirmatory result. In addition, another 10 cases were interpreted as provisional CPM based on normal results on cultured cells. Notably, 40% (8/20) of the cases revealed complex findings, including multiple mosaic aneuploidies, mosaic submicroscopic copy number variation (CNV), and mosaic aneuploidy plus mosaic CNV. Conclusion Abnormal CMA results from CVS specimens should be interpreted with caution when mosaicism is evident or suspected. Furthermore, confirmatory testing on amniotic fluid, which contains cells derived from the fetus, is recommended in these cases.
The Itchy Patient: A Practical Approach

Pruritus can occur as a primary condition or as a secondary manifestation of a primary cutaneous disease such as atopic dermatitis, contact dermatitis, miliaria, scabies, urticaria, or other conditions. This article deals mainly with pruritus as a primary condition. Pruritus ani, pruritus vulvae, and pruritus of the scalp are presented as localized disease. Generalized pruritus as a secondary symptom of an underlying systemic disease is also reviewed.
Regional Crop Diversity and Weather Shocks in India

Agriculture in both the developing and developed country context is highly sensitive to weather shocks. The intensity of these shocks is likely to increase under climate change, leading to an ongoi...
Distinct molecular pathways in ovarian endometrioid adenocarcinoma with concurrent endometriosis.

Women with endometriosis, a benign growth of endometrial tissue outside the uterine cavity, are at increased risk of specific histotypes of epithelial ovarian cancer, such as ovarian endometrioid adenocarcinoma (OEA). Women with OEA who have endometriosis at time of surgical staging demonstrate improved clinical prognosis compared to women with OEA without evidence of endometriosis. However, the molecular contributions of the endometriotic tumor microenvironment to these ovarian cancers remain poorly understood. As a starting point, we used a platform for genome-wide transcriptomic profiling to compare specimens of OEA from women with and without concurrent endometriosis and benign reproductive tract tissues, including proliferative endometrium and typical and atypical endometrioma samples (n = 20). Principle component analysis revealed distinct clustering between benign and malignant samples as well as malignant samples with and without concurrent endometriosis. Examination of gene signatures revealed that OEA with concurrent endometriosis contained a unique molecular signature compared to OEA without concurrent endometriosis, distinguished by 682 unique genes differentially expressed (fold change 1.5, p < 0.01). Bioinformatic analysis of these differentially expressed gene products using ingenuity pathway analysis revealed activation of NFkB signaling, an inflammatory signaling pathway constitutively active in endometriosis. DAVID functional annotation clustering further revealed enrichment in RAS signaling as both cytoskeleton organization and GTPase regulator activity relied heavily on RAS protein signal transduction. Gene set enrichment analysis highlighted immune and inflammatory nodes involved in OEA with concurrent endometriosis. These observations provide novel resources for understanding molecular subtleties potentially involved in OEA within the context of the endometriotic tumor microenvironment.
Enhanced diffusion by binding to the crosslinks of a polymer gel

Creating a selective gel that filters particles based on their interactions is a major goal of nanotechnology, with far-reaching implications from drug delivery to controlling assembly pathways. However, this is particularly difficult when the particles are larger than the gel’s characteristic mesh size because such particles cannot passively pass through the gel. Thus, filtering requires the interacting particles to transiently reorganize the gel’s internal structure. While significant advances, e.g., in DNA engineering, have enabled the design of nano-materials with programmable interactions, it is not clear what physical principles such a designer gel could exploit to achieve selective permeability. We present an equilibrium mechanism where crosslink binding dynamics are affected by interacting particles such that particle diffusion is enhanced. In addition to revealing specific design rules for manufacturing selective gels, our results have the potential to explain the origin of selective permeability in certain biological materials, including the nuclear pore complex.
Practical shape analysis and segmentation methods for point cloud models

Abstract Current point cloud processing algorithms do not have the capability to automatically extract semantic information from the observed scenes, except in very specialized cases. Furthermore, existing mesh analysis paradigms cannot be directly employed to automatically perform typical shape analysis tasks directly on point cloud models. We present a potent framework for shape analysis, similarity and segmentation of noisy point cloud models for real objects of engineering interest, models that may be incomplete. The proposed framework relies on spectral methods and the heat diffusion kernel to construct compact shape signatures, and we show that the framework supports a variety of clustering techniques that have traditionally been applied only on mesh models. We developed and implemented one practical and convergent estimate of the Laplace–Beltrami operator for point clouds as well as a number of clustering techniques adapted to work directly on point clouds to produce geometric features of engineering interest. The key advantage of this framework is that it supports practical shape analysis capabilities that operate directly on point cloud models of objects without requiring surface reconstruction or global meshing. We show that the proposed technique is robust against typical noise present in possibly incomplete point clouds, and segment point clouds scanned by depth cameras (e.g. Kinect) into semantically-meaningful sub-shapes.
Linearizable boundary value problems for the nonlinear Schrödinger equation in laboratory coordinates

Abstract Boundary value problems for the nonlinear Schrodinger equation on the half line in laboratory coordinates are considered. A class of boundary conditions that lead to linearizable problems is identified by introducing appropriate extensions to initial-value problems on the infinite line, either explicitly or by constructing a suitable Backlund transformation. Various soliton solutions are explicitly constructed and studied.
Reconstruction-Aware Imaging System Ranking by Use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference

It is widely accepted that optimization of imaging system performance should be guided by task-based measures of image quality. It has been advocated that imaging hardware or data-acquisition designs should be optimized by use of an ideal observer that exploits full statistical knowledge of the measurement noise and class of objects to be imaged, without consideration of the reconstruction method. In practice, accurate and tractable models of the complete object statistics are often difficult to determine. Moreover, in imaging systems that employ compressive sensing concepts, imaging hardware and sparse image reconstruction are innately coupled technologies. In this paper, a sparsity-driven observer (SDO) that can be employed to optimize hardware by use of a stochastic object model describing object sparsity is described and investigated. The SDO and sparse reconstruction method can, therefore, be “matched” in the sense that they both utilize the same statistical information regarding the class of objects to be imaged. To efficiently compute the SDO test statistic, computational tools developed recently for variational Bayesian inference with sparse linear models are adopted. The use of the SDO to rank data-acquisition designs in a stylized example as motivated by magnetic resonance imaging is demonstrated. This paper reveals that the SDO can produce rankings that are consistent with visual assessments of the reconstructed images but different from those produced by use of the traditionally employed Hotelling observer.
Microparticle-associated tissue factor activity: A link between cancer and thrombosis?

3637 Background: Cancer, in particular mucinous adenocarcinomas, is associated with an increased risk of venous thromboembolism (VTE). Tissue factor (TF), a transmembrane-receptor protein and initiator of coagulation, plays a central role in the paradigm that clotting and tumor growth form a vicious circle, in which hypercoagulability facilitates the aggressive biology of cancer and vice versa. Expression of TF is associated with poor differentiation of tumors and poor prognosis. We initiated a study in cancer patients, to find out whether microparticles (MP) derived from malignant epithelial cells, may directly initiate blood coagulation locally or at distant sites via TF expressed on these MP and play a critical role in the development of the hypercoagulability Patients & Methods: MP were isolated from peripheral blood samples obtained from healthy subjects, and unselected cancer patients, i.e. patients with primary breast carcinoma (before and after surgery), with metastatic breast carcinoma and with pancreatic carcinoma. Tissue factor procoagulant activity associated with isolated MP was studied by factor VIIa dependent generation of factor Xa. MP were examined by flow cytrometric phenotyping, and electron and confocal immunofluorescence laser scanning microscopical examination. Results: Compared to 37 healthy sujects, significantly increased levels of MP-associated TF activity were found in patients with disseminated breast and pancreatic cancer. Pancreatic (n=5) and breast cancer patients (n=2) who presented with clinically manifest venous thrombosis had a 18-fold increase in MP-associated TF activity as compared to healthy subjects or 6 subjects with idiopathic VTE (P
Revealing Quantum Mechanical Effects in Enzyme Catalysis With Large-Scale Electronic Structure Simulation

Enzymes have evolved to facilitate challenging reactions at ambient conditions with specificity seldom matched by other catalysts. Computational modeling provides valuable insight into catalytic mechanism, and the large size of enzymes mandates multi-scale, quantum mechanical-molecular mechanical (QM/MM) simulations. Although QM/MM plays an essential role in balancing simulation cost to enable sampling with full QM treatment needed to understand electronic structure in enzyme active sites, the relative importance of these two strategies for understanding enzyme mechanism is not well known. We explore challenges in QM/MM for studying the reactivity and stability of three diverse enzymes: i) Mg2+-dependent catechol O-methyltransferase (COMT), ii) radical enzyme choline trimethylamine lyase (CutC), and iii) DNA methyltransferase (DNMT1), which has structural Zn2+ binding sites. In COMT, strong non-covalent interactions lead to long range coupling of electronic structure properties across the active site, but the more isolated nature of the metallocofactor in DNMT1 leads to faster convergence of some properties. We quantify these effects in COMT by computing covariance matrices of by-residue electronic structure properties during dynamics and along the reaction coordinate. In CutC, we observe spontaneous bond cleavage following initiation events, highlighting the importance of sampling and dynamics. We use electronic structure analysis to quantify the relative importance of CHO and OHO non-covalent interactions in imparting reactivity. These three diverse cases enable us to provide some general recommendations regarding QM/MM simulation of enzymes.
Implications of smartphone user privacy leakage from the advertiser's perspective

Abstract Many smartphone apps routinely gather various private user data and send them to advertisers. Despite recent study on protection mechanisms and analysis on apps’ behavior, the understanding of the consequences of such privacy losses remains limited. In this paper, we investigate how much an advertiser can infer about users’ social and community relationships. After one month’s user study involving about 190 most popular Android apps, we find that an advertiser can infer 90% of the social relationships. We further propose a privacy leakage inference framework and use real mobility traces and Foursquare data to quantify the consequences of privacy leakage. We find that achieving 90% inference accuracy of the social and community relationships requires merely 3 weeks’ user data. Finally, we present a real-time privacy leakage visualization tool that captures and displays the spatial–temporal characteristics of the leakages. The discoveries underscore the importance of early adoption of privacy protection mechanisms.
Teaching economics using NBC’s Parks and Recreation

Parks and Recreation was a television sitcom based in the fictional town of Pawnee, Indiana, which was described as more exciting than New York, more glamorous than Hollywood, and roughly the same ...
Portable and Low Power Efficient Pre-Fall Detection Methodology

Fall in recent years have become a potential threat to elder generation. It occurs because of side effects of medication, lack of physical activities, limited vision, and poor mobility. Looking at the problems faced by people and cost of treatment after falling, it is of high importance to develop a system that will help in detecting the fall before it occurs. Over the years, this has influenced researchers to pursue the development to automatic fall detection system. However, much of existing work achieved a hardware system to detect pre and post fall patterns, the existing systems deficient in achieving low power consumption, user-friendly hardware implementation and high precision on a single portable system. This research points towards the development of dependable and low power embedded system device with easy to wear capabilities and optimal sensor structure. The designed system is triggered on interrupts from motion sensor to monitor users balanced, and unbalanced states. The fall decision parameters; pitch, roll, Signal Vector Magnitude (SVM), and Signal Magnitude Area (SMA) are layered to classify subject’s different body posture. When the fall flag is set, the device sends important information like GPS location and fall type to caretaker. Early fall detection gives milliseconds of time to initiates the preventive measures. Near 100% sensitivity, 96% accuracy, and 95% specificity for fall detection were measured. The system can detect Front, Back, Side and Stair fall with consumption of 100uA (650uA with BLE consumption) in deep sleep mode, 6.5mA in active mode with no fall, and 14.5mA, of which 8.5 mA is consumed via the BLE when fall is declared in active mode.
Taking my talents to South Beach (and back): evidence from a superstar athlete

We study the local economic spillovers generated by LeBron James’ presence on a team in the National Basketball Association. We find that Mr. James has a statistically and economically significant ...
Kinetics of hydrogen storage on catalytically-modified porous silicon

Abstract Porous silicon has been demonstrated as a hydrogen storage media with surface-bound hydrogen content as high as 6.6% by weight. Hydrogenated porous silicon is readily synthesized by electrochemical etching in a solution of hydrofluoric acid . Hydrogen gas can be released thermally at temperatures starting at 280 °C. It has been proposed that a suitable catalyst at the pore mouth can both reduce the desorption temperature and facilitate gaseous recharge of the silicon matrix. This work presents a detailed kinetic study using density functional theory (DFT) of a reversible hydrogen storage system based on porous silicon via the mechanisms of dissociation, spillover, and bond-hopping of hydrogen atoms. For each of these steps, activation energy values and vibrational frequency has been determined. Using these activation energies along with vibrational frequency values evaluated from the micro level DFT study, the kinetic performance of catalytically-modified porous silicon as a potential hydrogen storage material has been completed for the first time. The energy difference between full and empty charge is computed at the atomic scale and compared to macroscopic calculations, showing close agreement. These results show the potential for rapid recharge at 8 bar at temperatures commensurate with waste heat from a proton-exchange membrane fuel cell.
Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi-Supervised Learning Strategy

The research of metamaterials has achieved enormous success in the manipulation of light in a prescribed manner using delicately designed subwavelength structures, so-called meta-atoms. Even though modern numerical methods allow for the accurate calculation of the optical response of complex structures, the inverse design of metamaterials, which aims to retrieve the optimal structure according to given requirements, is still a challenging task owing to the nonintuitive and nonunique relationship between physical structures and optical responses. To better unveil this implicit relationship and thus facilitate metamaterial designs, it is proposed to represent metamaterials and model the inverse design problem in a probabilistically generative manner, enabling to elegantly investigate the complex structure-performance relationship in an interpretable way, and solve the one-to-many mapping issue that is intractable in a deterministic model. Moreover, to alleviate the burden of numerical calculations when collecting data, a semisupervised learning strategy is developed that allows the model to utilize unlabeled data in addition to labeled data in an end-to-end training. On a data-driven basis, the proposed deep generative model can serve as a comprehensive and efficient tool that accelerates the design, characterization, and even new discovery in the research domain of metamaterials, and photonics in general.
“Constitution (Written or Unwritten)”: Legitimacy and Legality in the Thought of John Rawls

John Rawls proposed, as what he called “the liberal principle of legitimacy,” that coercive exercises of political power can be justified to free and equal dissenters when “in accordance with a constitution (written or unwritten) the essentials of which all citizens, as reasonable and rational, can endorse.” Does “unwritten constitution” there refer to norms of constitutional import, but that subsist only as custom, not as law? To norms that subsist as common law but not as code law? To empirical regularities of political practice, as opposed to normative rules and standards? Which interpretation is best?
Pentacarbonylmethylmanganese(I) as a synthon for Mn(I) pincer catalysts

Mn(I) complexes that enable metal–ligand cooperative substrate activation catalyze a range of transformations. Use of MeMn(CO)5 as a synthon in place of typical Mn(CO)5Br was explored and found to be quite versatile, generating catalytically active species in situ by activation of O–H, N–H, and even C–H bonds.
AQuRate: MRAM-based Stochastic Oscillator for Adaptive Quantization Rate Sampling of Sparse Signals

Recently, the promising aspects of compressive sensing have inspired new circuit-level approaches for their efficient realization within the literature. However, most of these recent advances involving novel sampling techniques have been proposed without considering hardware and signal constraints. Additionally, traditional hardware designs for generating non-uniform sampling clock incur large area overhead and power dissipation. Herein, we propose a novel non-uniform clock generator called Adaptive Quantization Rate (AQR) generator using Magnetic Random Access Memory (MRAM)-based stochastic oscillator devices. Our proposed AQR generator provides ~25-fold reduction in area, on average, while offering ~6-fold reduced power dissipation, on average, compared to the state-of-the-art non-uniform clock generators.
Enforcing Competition and Firm Productivity : Evidence from 1,800 Peruvian Municipalities

This paper uses a unique data set that captures the elimination of subnational regulatory barriers to firm entry and competition across 1,800 municipalities and matches it with establishment census panel data to estimate the impact on establishment productivity and markups. The elimination of local barriers that were inconsistent with national legislation was the result of legal reforms that strengthened the mandate of Peru's competition authority. Legislative changes in 2013/14 empowered the competition authority to enforce the elimination of illegal, sector-specific subnational regulatory barriers to firm entry and competition, conditional on the existence of a precedence. The changes provide a unique quasi-experimental setting to identify the impact of enforcing competition within the controlled institutional environment of a single country. The paper finds that the elimination of subnational barriers to entry boosted the (revenue) productivity of establishments operating in reform municipalities and sectors relative to establishments in nonreform municipalities/sectors. But it did not raise the establishments' markups, which, if anything, declined, suggesting that physical productivity improved. The paper provides a wide range of evidence supporting a causal interpretation of this finding. The results suggest that strengthening the mandate of institutions enforcing competition is critical to raise productivity.
Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes

Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians' decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of Unremarkable Computing, that by augmenting the users' routines technology/AI can have significant importance for the users yet remain unobtrusive. Our field evaluation suggests clinicians are more likely to encounter and embrace such a DST. Drawing on their responses, we discuss the importance and intricacies of finding the right level of unremarkableness in DST design, and share lessons learned in prototyping critical AI systems as a situated experience.
Why Do Auditors Fail? What Might Work? What Won't?

Auditing failures and scandals have become commonplace. In response, reformers (including the Kingman Review in the U.K. and a recent report of the U.K.’s Competition and Market Authority) have pro...
The evolving X-ray spectrum of active galactic nuclei: Evidence for an increasing reflection fraction with redshift

The cosmic X-ray background (XRB) spectrum and active galaxy number counts encode essential information about the spectral evolution of active galactic nuclei (AGNs) and have been successfully modeled by XRB synthesis models for many years. Recent measurements of the $8$--$24$ keV AGN number counts by NuSTAR and Swift-BAT are unable to be simultaneously described by existing XRB synthesis models, indicating a fundamental breakdown in our understanding of AGN evolution. Here we show that the $8$--$24$ keV AGN number counts can be successfully modeled with an XRB synthesis model in which the reflection strength ($R$) in the spectral model increases with redshift. We show that an increase in reflection strength with redshift is a natural outcome of (1) connecting $R$ to the incidence of high column density gas around AGNs, and (2) the growth in the AGN obscured fraction to higher redshifts. In addition to the redshift evolution of $R$, we also find tentative evidence that an increasing Compton-thick fraction with $z$ is necessary to describe the $8$--$24$ keV AGN number counts determined by NuSTAR. These results show that, in contrast to the traditional orientation-based AGN unification model, the nature and covering factor of the absorbing gas and dust around AGNs evolve over time and must be connected to physical processes in the AGN environment. Future XRB synthesis models must allow for the redshift evolution of AGN spectral parameters. This approach may reveal fundamental connections between AGNs and their host galaxies.
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks

The growing demand on high-quality and low-latency multimedia services has led to much interest in edge caching techniques. Motivated by this, we in this paper consider edge caching at the base stations with unknown content popularity distributions. To solve the dynamic control problem of making caching decisions, we propose a deep actor-critic reinforcement learning based multi-agent framework with the aim to minimize the overall average transmission delay. To evaluate the proposed framework, we compare the learning-based performance with three other caching policies, namely least recently used (LRU), least frequently used (LFU), and first-in-first-out (FIFO) policies. Through simulation results, performance improvements of the proposed framework over these three caching algorithms have been identified and its superior ability to adapt to varying environments is demonstrated.
STEREOSPECIFIC SYNTHESIS OF TETRASUBSTITUTED Z-ENOL SILYL ETHERS BY A THREE COMPONENT COUPLING PROCESS

Abstract The coupling of an acylsilane, 2-propenyllithium and an alkyl halide produces tetrasubstituted Z-enol silyl ether in good yields, and provides the first route to these isomerically pure compounds.
Applications of reporter genes.

Reporter proteins can be used in bioanalytical methods to produce signals indicating the presence of a target analyte.
On the Preparation of Carbamoylsilanes

Abstract Various N,N-disubstituted carbamoyltrimethylsilanes can be prepared by the addition of LDA to a mixture of TMSCl and the appropriate formamide in THF at −78°C. Slow (syringe pump) addition of LDA is crucial to obtaining good yields.
Bulk photovoltaic effects in the presence of a static electric field

This paper presents a study of dc photocurrents in biased insulators to the third order in the electric field. We find three photocurrents which are characterized by physical divergences of the third-order free-electron polarization susceptibility. In the absence of momentum relaxation and saturation effects, these dc photocurrents grow as ${t}^{n}\phantom{\rule{0.16em}{0ex}}(n=2,1,0)$ with illumination time. The photocurrents are dubbed jerk, third-order injection, and third-order shift current, respectively, and are generalizations of the second-order injection and shift currents of the bulk photovoltaic effect (BPVE). We also revisit the theory of the second-order bulk photovoltaic effect and include Fermi surface contributions which are important in metals. Finally, we show that injection, shift, and jerk currents admit simple physical interpretations in terms of semiclassical wave packet dynamics in electric fields. Experimental signatures and extensions to higher-order susceptibilities are also discussed.
Highly efficient terpenoid pheromone syntheses via regio and stereocontrolled processing of allyllithiums generated by reductive lithiation of allyl phenyl thioethers

Abstract Previous work had shown that reducuve lithiation of allyl phenyl thioethers, followed by transmetallation, produces allylmetallics which react selectively with carbonyl compounds at the most or least substituted terminus; the latter results in mainly cis olefin. This technology allows extremely efficient syntheses of racemic versions of lavandulol (two-pot, 70% yield), the Comstock mealy bug pheromone (one-pot, 45% yield) and the California red scale pheromone (4-steps, 23% yield).
The fractured landscape of RNA-seq alignment: the default in our STARs

Many tools are available for RNA-seq alignment and expression quantification, with comparative value being hard to establish. Benchmarking assessments often highlight methods' good performance, but are focused on either model data or fail to explain variation in performance. This leaves us to ask, what is the most meaningful way to assess different alignment choices? And importantly, where is there room for progress? In this work, we explore the answers to these two questions by performing an exhaustive assessment of the STAR aligner. We assess STAR's performance across a range of alignment parameters using common metrics, and then on biologically focused tasks. We find technical metrics such as fraction mapping or expression profile correlation to be uninformative, capturing properties unlikely to have any role in biological discovery. Surprisingly, we find that changes in alignment parameters within a wide range have little impact on both technical and biological performance. Yet, when performance finally does break, it happens in difficult regions, such as X-Y paralogs and MHC genes. We believe improved reporting by developers will help establish where results are likely to be robust or fragile, providing a better baseline to establish where methodological progress can still occur.
Palladium-Catalyzed Regio- and Enantioselective Allylic Alkylation of Bis Allylic Carbonates Derived from Morita−Baylis−Hillman Adducts

Morita−Baylis−Hillman diene adducts are used as substrates in the palladium-catalyzed asymmetric allylic alkylation reaction with oxygen and carbon nucleophiles in good regio- and enantioselectivity.
Analysis of the gut and gill microbiome of resistant and susceptible lines of rainbow trout (Oncorhynchus mykiss)

Commensal microorganisms present at mucosal surfaces play a vital role in protecting the host organism from bacterial infection. There are multiple factors that contribute to selecting for the microbiome, including host genetics. Flavobacterium psychrophilum, the causative agent of Bacterial Cold Water Disease in salmonids, accounts for acute losses in wild and farmed rainbow trout (Oncorhynchus mykiss). The U.S. National Center for Cool and Cold Water Aquaculture has used family-based selective breeding to generate a line of rainbow trout with enhanced resistance to F. psychrophilum. The goal of this study is to determine whether selective breeding impacts the gut and gill microbiome of the F. psychrophilum-resistant as compared to a background matched susceptible trout line. Mid-gut and gill samples were collected from juvenile fish maintained at high or low stocking densities and microbial diversity assessed by 16S rDNA amplicon sequencing. Results indicate that alpha diversity was significantly higher in the mid-gut of the susceptible line compared to the resistant line, while no significant differences in alpha diversity were observed in the gills. Mycoplasma sp. was the dominant taxon in the mid-gut of both groups, although it was present at a decreased abundance in the susceptible line. We also observed an increased abundance of the potential opportunistic pathogen Brevinema andersonii in the susceptible line. Within the gills, both lines exhibited similar microbial profiles, with Candidatus Branchiomonas being the dominant taxon. Together, these results suggest that selectively bred F. psychrophilum-resistant trout may harness a more resilient gut microbiome, attributing to the disease resistant phenotype. Importantly, interactions between host genetics and environmental factors such as stocking density have a significant impact in shaping trout microbial communities.
Spatially Constrained Growth Enhances Conversional Meltdown

Cells that mutate or commit to a specialized function (differentiate) often undergo conversions that are effectively irreversible. Slowed growth of converted cells can act as a form of selection, balancing unidirectional conversion to maintain both cell types at a steady-state ratio. However, when one-way conversion is insufficiently counterbalanced by selection, the original cell type will ultimately be lost, often with negative impacts on the population's overall fitness. The critical balance between selection and conversion needed for preservation of unconverted cells and the steady-state ratio between cell types depends on the spatial circumstances under which cells proliferate. We present experimental data on a yeast strain engineered to undergo irreversible conversion: this synthetic system permits cell-type-specific fluorescent labeling and exogenous variation of the relative growth and conversion rates. We find that populations confined to grow on a flat agar surface are more susceptible than their well-mixed counterparts to fitness loss via a conversion-induced "meltdown." We then present analytical predictions for growth in several biologically relevant geometries-well-mixed liquid media, radially expanding two-dimensional colonies, and linear fronts in two dimensions-by employing analogies to the directed-percolation transition from nonequilibrium statistical physics. These simplified theories are consistent with the experimental results.
The role of telomere shortening in carcinogenesis: A hybrid stochastic-deterministic approach

Genome instability is a characteristic of most cancers, contributing to the acquisition of genetic alterations that drive tumor progression. One important source of genome instability is linked to telomere dysfunction in cells with critically short telomeres that lack p53-mediated surveillance of genomic integrity. Here we research the probability that cancer emerges through an evolutionary pathway that includes a telomere-induced phase of genome instability. To implement our models we use a hybrid stochastic-deterministic approach, which allows us to perform large numbers of simulations using biologically realistic population sizes and mutation rates, circumventing the traditional limitations of fully stochastic algorithms. The hybrid methodology should be easily adaptable to a wide range of evolutionary problems. In particular, we model telomere shortening and the acquisition of two mutations: Telomerase activation and p53 inactivation. We find that the death rate of unstable cells, and the number of cell divisions that p53 mutants can sustain beyond the normal senescence setpoint determine the likelihood that the first double mutant originates in a cell with telomere-induced instability. The model has applications to an influential telomerase-null mouse model and p16 silenced human cells. We end by discussing algorithmic performance and a measure for the accuracy of the hybrid approximation.
Synthesis of (±)‐(E,E)‐2,7‐Dimethylocta‐2,4‐dienedioic Acid

(±-(2E,4E)-2,7-Dimethylocta-2,4-dienedioic acid (1) was synthesized efficiently from furan, employing a rhodium-carbenoid-induced furan-ring-unravelling reaction followed by an unusual NaBH4/CeCl3⋅7 H2O reduction, Wittig-Horner olefination, and diester hydrolysis.
Cross-metathesis of α-methylene-β-lactams: the first tetrasubstituted alkenes by CM

α-Alkylidene-β-lactams have been prepared in good to excellent yields by olefin cross-metathesis. Electron-poor α-methylene-β-lactams undergo cross-metathesis more rapidly and efficiently than more electron-rich analogs. Significantly, tetrasubstituted alkenes have for the first time been accessed by CM reactions.
A convenient synthesis of protected and free homoallylic alcohols: Catalytic use of dibutyltin dichloride in the allylation of aldehydes with allyltributyltin

Abstract A new procedure for the allylation of aldehydes with allyltributyltin in the presence of 10 mol% dibutyltin dichloride and either an acid chloride or trimethylsilyl chloride has been developed. Homoallylic esters, carbonates, trimethylsilyl ethers, and alcohols were obtained in up to 99% yield.