cssi/SciSciGPT-SciSciNet huggingface dataset:
Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health

Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal disease. 1215 intraoral fluorescent images, from 284 consenting adults aged 18-90, were analyzed using a machine learning classifier that can segment periodontal inflammation. The classifier achieved an AUC of 0.677 with precision and recall of 0.271 and 0.429, respectively, indicating a learned association between disease signatures in collected images. Periodontal diseases were more prevalent among males (p=0.0012) and older subjects (p=0.0224) in the screened population. Physicians independently examined the collected images, assigning localized modified gingival indices (MGIs). MGIs and periodontal disease were then cross-correlated with responses to a medical history questionnaire, blood pressure and body mass index measurements, and optic nerve, tympanic membrane, neurological, and cardiac rhythm imaging examinations. Gingivitis and early periodontal disease were associated with subjects diagnosed with optic nerve abnormalities (p<0.0001) in their retinal scans. We also report significant co-occurrences of periodontal disease in subjects reporting swollen joints (p=0.0422) and a family history of eye disease (p=0.0337). These results indicate cross-correlation of poor periodontal health with systemic health outcomes and stress the importance of oral health screenings at the primary care level. Our screening process and analysis method, using images and machine learning, can be generalized for automated diagnoses and systemic health screenings for other diseases.
On Frequent Batch Auctions for Stocks

I show that frequent batch auctions for stocks have the potential to reduce the severity of stock price crashes when they occur. For a given sequence of orders from a continuous electronic limit order book market, matching orders using one second apart batch auctions results in nearly the same trades and prices. Increasing the time interval between auctions to one minute significantly reduces the severity stock price crashes. In spite of this and other advantages pointed out in the literature, frequent batch auctions have not caught on. There is a need for carefully designed market experiments to understand why, and what aspect of reality academic research may be missing.
Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community

Moderators of online communities often employ comment deletion as a tool. We ask here whether, beyond the positive effects of shielding a community from undesirable content, does comment removal actually cause the behavior of the comment's author to improve? We examine this question in a particularly well-moderated community, the ChangeMyView subreddit. The standard analytic approach of interrupted time-series analysis unfortunately cannot answer this question of causality because it fails to distinguish the effect of having made a non-compliant comment from the effect of being subjected to moderator removal of that comment. We therefore leverage a "delayed feedback" approach based on the observation that some users may remain active between the time when they posted the non-compliant comment and the time when that comment is deleted. Applying this approach to such users, we reveal the causal role of comment deletion in reducing immediate noncompliance rates, although we do not find evidence of it having a causal role in inducing other behavior improvements. Our work thus empirically demonstrates both the promise and some potential limits of content removal as a positive moderation strategy, and points to future directions for identifying causal effects from observational data.
Race and Representation in Campaign Finance

Racial inequality in voter turnout is well-documented, but we know less about racial inequality in campaign contributions. Using new data on the racial identities of over 27 million donors, we find an unrepresentative contributor class. Black and Latino shares of contributions are smaller than their shares of the population, electorate, and elected offices. However, we argue that the presence of ethnoracial minority candidates mobilizes coethnic donors. Results from regression discontinuity and difference-in-difference designs suggest that the presence of ethnoracial minority candidates increases the share of minority contributions in US House elections. We find a reduction in white contributions to black Democrats, and to black and Latino Republicans, but little difference in overall fundraising competitiveness. Although we cannot definitively rule out alternative mechanisms that covary with candidate ethnorace, the results suggest that the nomination of minority candidates can increase the ethnoracial representativeness of campaign finance without costs to fundraising.
Binary Conditional Forecasts

While conditional forecasting has become prevalent both in the academic literature and in practice (e.g., bank stress testing, scenario forecasting), its applications typically focus on continuous ...
Liability for Autonomous Agent Design

Though exciting scientifically, autonomous agent design can result in legal liability. This paper surveys those legal concerns, focusing on issues arising from the unique qualities of agents not found in conventional software. Informed designers can more effectively reduce their liability exposure and influence emerging agent liability law and policies.
Application of the one-dimensional Fourier transform for tracking moving objects in noisy environments

In Riddle and Rajala (1981), an algorithm was presented which operates on an image sequence to identify all sets of pixels having the same velocity. The algorithm operates by performing a transformation in which all pixels with the same two-dimensional velocity map to a peak in a transform space. The transform can be decomposed into applications of the one-dimensional Fourier transform and therefore can gain from the computational advantages of the FFT. The aim of this paper is the concern with the fundamental limitations of that algorithm, particularly as relates to its sensitivity to image-disturbing parameters as noise, jitter, and clutter. A modification to the algorithm is then proposed which increases its robustness in the presence of these disturbances.
Electron Transport Chain Complex II Sustains High Mitochondrial Membrane Potential in Hematopoietic Stem and Progenitor Cells

The role of mitochondria in the fate determination of hematopoietic stem and progenitor cells (HSPCs) is not solely limited to the switch from glycolysis to oxidative phosphorylation, but also involves alterations in mitochondrial features and properties, including mitochondrial membrane potential (ΔΨmt). HSPCs have a high ΔΨmt even when the rates of respiration and phosphorylation are low, and we have previously shown that the minimum proton flow through ATP synthesis (or complex V) enables high ΔΨmt in HSPCs. Here we show that HSPCs sustain a unique equilibrium between electron transport chain (ETC) complexes and ATP production. HSPCs exhibit high expression of ETC complex II, which sustains complex III in proton pumping, although the expression levels of complex I or V are relatively low. Complex II inhibition by TTFA caused a substantial decrease of ΔΨmt, particularly in HSPCs, while the inhibition of complex I by Rotenone mainly affected mature populations. Functionally, pharmacological inhibition of complex II reduced in vitro colony-replating capacity but this was not observed when complex I was inhibited, which supports the distinct roles of complex I and II in HSPCs. Taken together, these data highlight complex II as a key regulator of ΔΨmt in HSPCs and open new and interesting questions regarding the precise mechanisms that regulate mitochondrial control to maintain hematopoietic stem cell self-renewal.
SPECULATIVE FIELDS: Property in the Shadow of Post-Conflict Colombia

In Colombia’s attempts to bring its decades-long conflict to a close, the state engaged in a broad endeavor to bring about a new era: the “post-conflict.” Land restitution, which aims to return and title land to those who lost it in the conflict, was billed as part of the path to peace. In the shadow of the post-conflict, however, restitution has given rise to speculation on uncertain market and legal regimes, as well as regimes of violence, which I explore drawing on ethnographic fieldwork in the region of Uraba. Speculative possession of land calls up historical forms of possession as well as potential futures, defining this shadow as peace is continually deferred. RESUMEN Los intentos por terminar el largo conflicto armado en Colombia incluyen grandes esfuerzos por parte del Estado colombiano orientados a producir una nueva epoca: el “posconflicto”. La restitucion de tierras, una politica que aspira devolver y titular tierras a los que las perdieron por causa del conflicto, fue promovida precisamente como parte de dicho esfuerzo hacia la paz. A partir de trabajo etnografico en la region del Uraba exploro como, en la sombra del posconflicto, la restitucion de tierras ha traido consigo diversas formas de especulacion en torno a regimenes juridicos, economicos y de violencia. En este contexto, la posesion especulativa de tierras simultaneamente invoca formas historicas de posesion asi como futuros posibles, definiendo la sombra del posconflicto mientras la paz continuamente se difiere.
Inhibitory Effects of Probenecid on Pharmacokinetics of Tenofovir Disoproxil Fumarate and Emtricitabine for On-Demand HIV Preexposure Prophylaxis.

In a randomized, crossover pharmacokinetic study in healthy volunteers (N = 14), a single dose of 2 g probenecid (PRO)-boosted 600 mg tenofovir disoproxil fumarate (TDF)/400 mg emtricitabine (FTC) (test (T) +PRO) was compared with the current on-demand HIV preexposure prophylaxis from the IPERGAY study (a 600 mg TDF/400 mg FTC on day 1 and 300 mg TDF/200 mg FTC on days 2 and 3) (control, C IPERGAY). PRO increased mean single-dose area under the plasma concentration-time curve extrapolated to infinity (AUC0-∞,SD ) of tenofovir (TFV) and FTC by 61% and 68%, respectively. The TFV-diphosphate (TFV-DP) concentrations in peripheral blood mononuclear cells were higher (~30%) at 24 hours in T +PRO but then fell significantly lower (~40%) at 72 hours compared with C IPERGAY. The interaction between FTC and PRO was unexpected and novel. Further study is needed to determine if this PRO-boosted TDF/FTC regimen would be clinically effective.
Kinetics of formic acid decomposition in subcritical and supercritical water – a Raman spectroscopic study

Abstract The decomposition of formic acid is studied in a continuous sub- or supercritical water reactor at temperatures between 300 and 430 °C, a pressure of 25 MPa, residence times between 4 and 65 s, and a feedstock concentration of 3.6 wt%. In situ Raman spectroscopy is used to produce real-time data and accurately quantify decomposition product yields of H2, CO2, and CO. Collected spectra are used to determine global decomposition rates and kinetic rates for individual reaction pathways. First-order global Arrhenius parameters are determined as log A (s−1) = 1.6 ± 0.20 and EA = 9.5 ± 0.55 kcal/mol for subcritical decomposition, and log A (s−1) = 12.56 ± 1.96 and EA = 41.90 ± 6.08 kcal/mol for supercritical decomposition. Subcritical and supercritical Arrhenius parameters for individual pathways are proposed. The variance in rate parameters is likely due to changing thermophysical properties of water across the critical point. There is strong evidence for a surface catalyzed free-radical mechanism responsible for rapid decomposition above the critical point, facilitated by low density at supercritical conditions.
Deactivating deformation twinning in medium-entropy CrCoNi with small additions of aluminum and titanium

Abstract High strain-hardening rates in equiatomic CrCoNi and other multi-principal element alloys have been attributed to deformation twinning. This work shows that small additions of Al and Ti to a CrCoNi alloy deactivate deformation twinning with only minor changes to uniform elongation and ultimate tensile strength. The initial microstructure is free of chemically ordered (Al,Ti)-rich precipitates after solutionizing and quenching. Tensile properties for the alloy are reported and compared to equiatomic CrCoNi, and the post-deformation microstructure is assessed. Density functional theory calculations indicate that energetically unfavorable Al-Al bonds may discourage shearing via partial dislocations, which are necessary for twinning to occur.
An Analysis of Relief Bridge Scour

Based on the proposition that the limit of clear-water scour is a boundary shear equal to the critical tractive force, analytical relationships are obtained for the scour in a long contraction, at an abutment, and around a pier. The pier and abutment solutions make use of the assumption that the flow beyond the scour hole can be ignored and that the depth of scour at the pier or abutment is a multiple r of the scour in the equivalent long contraction. An expression for the active phase of scour is obtained using a simplified transport equation. Comparison of predictions with measurements from several laboratories is reasonably satisfactory.
Beyond the Black Box: An Intuitive Approach to Investment Prediction with Machine Learning

The complexity of machine learning models presents a substantial barrier to their adoption for many investors. The algorithms that generate machine learning predictions are sometimes regarded as a black box and demand interpretation. In this article, the authors present a framework for demystifying the behavior of machine learning models. They decompose model predictions into linear, nonlinear, and interaction components and study a model’s predictive efficacy using the same components. Together, this forms a fingerprint to summarize key characteristics, similarities, and differences among different models. The presented framework is demonstrated in a case study applying random forest, gradient boosting machine, and neural network models to the challenge of predicting monthly currency returns. All models reliably identify intuitive effects in the currency market but also find new relationships attributable to nonlinearities and variable interactions. The authors argue that an understanding of these predictive components may help astute investors generate superior risk-adjusted returns. TOPICS:Statistical methods, simulations, big data/machine learning Key Findings • This article presents a framework for the implementation and interpretation of machine learning model predictions applied to investment portfolios. • Model predictions are decomposed into the linear, nonlinear, and interaction components, and their predictive efficacy is evaluated using these components. • Using a currency prediction case study, it is demonstrated that machine learning models reliably identify known effects and find new nonlinear relationships and interactions.
Laboratory Analysis of an Outbreak of Candida auris in New York from 2016 to 2018: Impact and Lessons Learned.

ABSTRACT Candida auris is a multidrug-resistant yeast which has emerged in health care facilities worldwide; however, little is known about identification methods, patient colonization, environmental survival, spread, and drug resistance. Colonization on both biotic (patients) and abiotic (health care objects) surfaces, along with travel, appear to be the major factors for the spread of this pathogen across the globe. In this investigation, we present laboratory findings from an ongoing C. auris outbreak in New York (NY) from August 2016 through 2018. A total of 540 clinical isolates, 11,035 patient surveillance specimens, and 3,672 environmental surveillance samples were analyzed. Laboratory methods included matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) for yeast isolate identification, real-time PCR for rapid surveillance sample screening, culture on selective/nonselective media for recovery of C. auris and other yeasts from surveillance samples, antifungal susceptibility testing to determine the C. auris resistance profile, and Sanger sequencing of the internal transcribed spacer (ITS) and D1/D2 regions of the ribosomal gene for C. auris genotyping. Results included (a) identification and confirmation of C. auris in 413 clinical isolates and 931 patient surveillance isolates as well as identification of 277 clinical cases and 350 colonized cases from 151 health care facilities, including 59 hospitals, 92 nursing homes, 1 long-term acute care hospital (LTACH), and 2 hospices, (b) successful utilization of an in-house developed C. auris real-time PCR assay for the rapid screening of patient and environmental surveillance samples, (c) demonstration of relatively heavier colonization of C. auris in nares than in the axilla/groin, and (d) predominance of the South Asia clade I with intrinsic resistance to fluconazole and elevated MIC to voriconazole (81%), amphotericin B (61%), flucytosine (5FC) (3%), and echinocandins (1%). These findings reflect greater regional prevalence and incidence of C. auris and the deployment of better detection tools in an unprecedented outbreak.
Stochastic Approximate Gradient Descent via the Langevin Algorithm.

We introduce a novel and efficient algorithm called the stochastic approximate gradient descent (SAGD), as an alternative to the stochastic gradient descent for cases where unbiased stochastic gradients cannot be trivially obtained. Traditional methods for such problems rely on general-purpose sampling techniques such as Markov chain Monte Carlo, which typically requires manual intervention for tuning parameters and does not work efficiently in practice. Instead, SAGD makes use of the Langevin algorithm to construct stochastic gradients that are biased in finite steps but accurate asymptotically, enabling us to theoretically establish the convergence guarantee for SAGD. Inspired by our theoretical analysis, we also provide useful guidelines for its practical implementation. Finally, we show that SAGD performs well experimentally in popular statistical and machine learning problems such as the expectation-maximization algorithm and the variational autoencoders.
Story Realization: Expanding Plot Events into Sentences

Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries. Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence). However, typical neural language generation approaches to event-to-sentence can ignore the event details and produce grammatically-correct but semantically-unrelated sentences. We present an ensemble-based model that generates natural language guided by events. We provide results—including a human subjects study—for a full end-to-end automated story generation system showing that our method generates more coherent and plausible stories than baseline approaches 1.
Loneliness Following Widowhood: The Role of the Military and Social Support

OBJECTIVES Increased loneliness is a common consequence of widowhood in later life. However, individuals with high levels of perceived social support from friends tend to cope more effectively following major social losses like widowhood. Military service is associated with cultivation of strong social support structures. This effect may not only influence those who serve, but also their spouses. Roughly half of older women today are married to veterans, which could shape how they cope with widowhood. We tested two hypotheses: (a) widows of veterans will be less lonely following widowhood compared to their nonveteran counterparts, and (b) this effect will be explained by perceived social support from friends. METHODS We used the Health and Retirement Study (HRS) to examine changes in loneliness following widowhood among wives of veterans and nonveterans. We used Ordinary Least Squares regression and mediation tests to address our hypotheses. RESULTS Net of baseline differences, widows of veterans reported statistically lower levels of loneliness (p < .05) following widowhood compared to widows of nonveterans. Widows of veterans retained the same level of perceived social support from friends pre- and postwidowhood, whereas nonveteran wives experienced a loss. Perceived social support from friends mediated the association between veteran status of the deceased spouse and loneliness. DISCUSSION Our findings suggest wives of veterans may have more resilient social support structures than nonveteran spouses, helping them cope at widowhood. Future research should explore whether these effects persist in association with other major stressful events in later life.
Biophysical mechanisms in the mammalian respiratory oscillator re-examined with a new data-driven computational model

An autorhythmic population of excitatory neurons in the brainstem pre-Botzinger complex is a critical component of the mammalian respiratory oscillator. Two intrinsic neuronal biophysical mechanisms-a persistent sodium current ([Formula: see text]) and a calcium-activated non-selective cationic current ([Formula: see text])-were proposed to individually or in combination generate cellular- and circuit-level oscillations, but their roles are debated without resolution. We re-examined these roles in a model of a synaptically connected population of excitatory neurons with [Formula: see text] and [Formula: see text]. This model robustly reproduces experimental data showing that rhythm generation can be independent of [Formula: see text] activation, which determines population activity amplitude. This occurs when [Formula: see text] is primarily activated by neuronal calcium fluxes driven by synaptic mechanisms. Rhythm depends critically on [Formula: see text] in a subpopulation forming the rhythmogenic kernel. The model explains how the rhythm and amplitude of respiratory oscillations involve distinct biophysical mechanisms.
Signed Network Formation Games and Clustering Balance

We propose a signed network formation game, in which pairs of individuals strategically change the signs of the edges in a complete network. These individuals are members of a social network who strategically reduce cognitive dissonances by changing their interpersonal appraisals. We characterize the best-response dynamics for this game and prove that its implementation can dynamically drive the network to a sociologically meaningful sign configuration called clustering balance. In this configuration, agents in the social network form one or more clusters that have positive relationships among their members but negative relationships among members of other clusters. In the past, various researchers in the fields of psycho-sociology, political science, and physics have looked at models that explain the generation of up to two clusters. Our work contributes to these fields by proposing a simple model that generates a broader class of signed networks.
Spatiotemporal control of gene expression boundaries using a feedforward loop

Background A feedforward loop (FFL) is commonly observed in several biological networks. The FFL network motif has been mostly studied with respect to variation of the input signal in time, with only a few studies of FFL activity in a spatially distributed system such as morphogen-mediated tissue patterning. However, most morphogen gradients also evolve in time. Results We studied the spatiotemporal behavior of a coherent FFL in two contexts: (a) a generic, oscillating morphogen gradient and (b) the dorsal-ventral patterning of the early Drosophila embryo by a gradient of the NF-κB homolog dorsal with its early target Twist. In both models, we found features in the dynamics of the intermediate node-phase difference and noise filtering-that were largely independent of the parameterization of the models, and thus were functions of the structure of the FFL itself. In the dorsal gradient model, we also found that proper target gene expression was not possible without including the effect of maternal pioneer factor Zelda. Conclusions An FFL buffers fluctuation to changes in the morphogen signal ensuring stable gene expression boundaries.
Directed Technological Change & Cross Country Income Differences: A Quantitative Analysis

Research aimed at understanding cross-country income differences finds that inputs of human and physical capital play a limited role in explaining those differences. However, most of this work assumes workers with different education levels are perfect substitutes. Does moving away from this assumption affect our conclusions about the causes of long run development? To answer this question we construct measures of skill-specific productivity and barriers to innovation for a large sample of countries over the period 1910-2010. We use a model of endogenous directed technological change together with a new data set on output and labor force composition across countries. We find that rich countries use labor of all skill categories more efficiently, however, in the absence or barriers to entry, poor countries would actually be more efficient at using low-skill labor. Our estimates imply that after 1950 the world technology frontier expanded much faster for college-educated workers than for those with lower skill sets. This technology diffused to many countries, allowing even poorer countries to experience relatively robust growth of high-skill-specific productivity. Their GDP growth failed to reflect that because of their labor composition; they have very few workers in the higher skilled category. Finally, we investigate the relative importance of factor endowments versus barriers to technology in explaining the current disparities of standards of living and find it to depend crucially on the value of the elasticity of substitution between skill-types. Under a lower value of 1.6, our model yields barrier estimates that are lower and relatively less important in explaining cross-country income differences: in this scenario physical and human capital account almost 70% of variance in 2010 GDP per worker in our sample. Using elasticity of 2.6, we find barriers that are higher and explain most of the variation in output. We provide some evidence that the higher value of elasticity is preferred.
DNA unwinding mechanism of a eukaryotic replicative CMG helicase

High-resolution structures have not been reported for replicative helicases at a replication fork at atomic resolution, a prerequisite to understanding the unwinding mechanism. The eukaryotic replicative CMG (Cdc45, Mcm2-7, GINS) helicase contains a Mcm2-7 motor ring, with the N-tier ring in front and the C-tier motor ring behind. The N-tier ring is structurally divided into a zinc finger (ZF) sub-ring followed by the oligosaccharide/oligonucleotide-binding (OB) fold ring. Here we report the cryo-EM structure of CMG on forked DNA at 3.9 A, revealing that parental DNA enters the ZF sub-ring and strand separation occurs at the bottom of the ZF sub-ring, where the lagging strand is blocked and diverted sideways by OB hairpin-loops of Mcm3, Mcm4, Mcm6, and Mcm7. Thus, instead of employing a specific steric exclusion process, or even a separation pin, unwinding is achieved via a “dam-and-diversion tunnel” mechanism that does not require specific protein-DNA interaction. The C-tier motor ring contains spirally configured PS1 and H2I loops of Mcms 2, 3, 5, 6 that translocate on the spirally-configured leading strand, and thereby pull the preceding DNA segment through the diversion tunnel for strand separation. The DNA duplex is known to be split apart in a steric exclusion manner during replication, but the specific mechanism has remained unclear. Here the authors present a cryo-EM structure of a eukaryotic replicative CMG helicase on forked DNA, revealing the mechanism of DNA unwinding.
Poly-Enrich: count-based methods for gene set enrichment testing with genomic regions.

Gene set enrichment (GSE) testing enhances the biological interpretation of ChIP-seq data and other large sets of genomic regions. Our group has previously introduced two GSE methods for genomic regions: ChIP-Enrich for narrow regions and Broad-Enrich for broad regions. Here, we introduce Poly-Enrich, which has wider applicability, additional capabilities and models the number of peaks assigned to a gene using a generalized additive model with a negative binomial family to determine gene set enrichment, while adjusting for gene locus length. As opposed to ChIP-Enrich, Poly-Enrich works well even when nearly all genes have a peak, illustrated by using Poly-Enrich to characterize pathways and types of genic regions enriched with different families of repetitive elements. By comparing Poly-Enrich and ChIP-Enrich results with ENCODE ChIP-seq data, we found that the optimal test depends more on the pathway being regulated than on properties of the transcription factors. Using known transcription factor functions, we discovered clusters of related biological processes consistently better modeled with Poly-Enrich. This suggests that the regulation of certain processes may be modified by multiple binding events, better modeled by a count-based method. Our new hybrid method automatically uses the optimal method for each gene set, with correct FDR-adjustment.
C-Terminal Tail Polyglycylation and Polyglutamylation Alter Microtubule Mechanical Properties

Abstract Microtubules are biopolymers that perform diverse cellular functions. Microtubule behavior regulation occurs in part through post-translational modification of both the α- and β-subunits of tubulin. One class of modifications is the heterogeneous addition of glycine and/or glutamate residues to the disordered C-terminal tails (CTTs) of tubulin. Because of their prevalence in stable, high-stress cellular structures such as cilia, we sought to determine if these modifications alter microtubules’ intrinsic stiffness. Here, we describe the purification and characterization of differentially modified pools of tubulin from Tetrahymena thermophila. We found that post-translational modifications do affect microtubule stiffness but do not affect the number of protofilaments incorporated into microtubules. We measured the spin dynamics of nuclei in the CTT backbone by NMR spectroscopy to explore the mechanism of this change. Our results show that the α-tubulin CTT does not protrude out from the microtubule surface, as is commonly depicted in models, but instead interacts with the dimer’s surface. This suggests that the interactions of the α-tubulin CTT with the tubulin body contributes to the stiffness of the assembled microtubule, thus providing insight into the mechanism by which polyglycylation and polyglutamylation can alter microtubule mechanical properties.
The multi-variable Affine Index Polynomial

Abstract We define a multi-variable version of the Affine Index Polynomial for virtual links. This invariant reduces to the original Affine Index Polynomial in the case of virtual knots, and also generalizes the version for compatible virtual links recently developed by L. Kauffman. We prove that this invariant is a Vassiliev invariant of order one, and study what happens as we shift the coloring of one or more components.
Modeling the seasonality of extreme coastal water levels with mixtures of circular probability density functions

Understanding when floods occur is fundamental to reducing flood risk, yet depictions of flood seasonality rarely address two key issues: multiple seasons and the periodicity of seasonal data. We analyze the timing of extreme water levels at 47 presently operational tide gages located along the US coastlines with periods of record ranging from 29 to 118 years. Because the dates of extreme water levels are naturally multimodal periodic data, we model them with weighted mixtures of circular von Mises distributions. Fitting probability density functions on the circumference of a circle allow periodicity to be handled implicitly, while mixture models provide insight into the timing and importance of the various modes in the overall distribution. To our knowledge, this is the first application of circular statistical modeling to coastal water levels. We find clear spatial patterns in extreme water level seasonality along the US coasts. Gages located on the West Coast experience their main flood season in the winter and a secondary season in the summer, while those on the East and Gulf coasts have more uniform distributions, fewer seasons, and maximum flood likelihood in the late summer. Analysis of water level after removing tides and sea level change reduces the importance of secondary and higher seasons and yields greater uniformity compared with raw water levels. Finally, peaks-over-threshold datasets reveal secondary flood seasons that are obscured in annual maxima series, which may become increasingly impactful as sea levels rise. These findings are pertinent to coastal infrastructure design, risk sharing via financial instruments, and emergency preparedness and response.
Hyperfinite Graph Limits

Gabor Elek introduced the notion of a hyperfinite graph family: a collection of graphs is hypefinite if for every e > 0 there is some finite k such that each graph G in the collection can be broken into connected components of size at most k by removing a set of edges, of size at most e|V(G)|. We presently extend this notion to a certain compactification of finite bounded-degree graphs and show that if a sequence of finite graphs converges to a hyperfinite limit, then the sequence itself is hyperfinite.
Reactive Supervisory Control of Open Discrete Event Systems

The conventional Wonham-Ramadge supervisory control framework of discrete event systems enforces a closed discrete event system to generate correct behaviors under certain environments, which can be captured by an appropriate plant model. Nevertheless, such control methods cannot be directly applied for many practical engineering systems nowadays since they are open systems and their operation heavily depends on nontrivial interactions between the systems and the external environments. These open systems should be controlled in such a way that accomplishment of the control objective can be guaranteed for any possible environment, which may be dynamic, uncertain and sometimes unpredictable. In this paper, we aim at extending the conventional supervisory control theory to open discrete event systems in a reactive manner. Starting from a novel input-output automaton model of an open system, we consider control objectives that characterize the desired input-output behaviors of the system, based on which a game-theoretic approach is carried out to compute a reactive supervisor that steers the system to fulfill the specifications regardless of the environment behaviors. We present a necessary and sufficient conditions for the existence of such a reactive supervisor. Furthermore, illustrative examples are given throughout this paper to demonstrate the key definitions and the effectiveness of the proposed reactive supervisor synthesis framework.
Strategy Synthesis for Surveillance-Evasion Games with Learning-Enabled Visibility Optimization

This paper studies a two-player game with a quantitative surveillance requirement on an adversarial target moving in a discrete state space and a secondary objective to maximize short-term visibility of the environment. We impose the surveillance requirement as a temporal logic constraint. We then use a greedy approach to determine vantage points that optimize a notion of information gain, namely, the number of newly-seen states. By using a convolutional neural network trained on a class of environments, we can efficiently approximate the information gain at each potential vantage point. Subsequent vantage points are chosen such that moving to that location will not jeopardize the surveillance requirement, regardless of any future action chosen by the target. Our method combines guarantees of correctness from formal methods with the scalability of machine learning to provide an efficient approach for surveillance-constrained visibility optimization.
What Does the Transient Absorption Spectrum of CdSe Quantum Dots Measure

The roles of conduction and valence band state filling and stimulated emission in the room temperature transient absorption spectroscopy of CdSe and CdSe/CdS quantum dots are discussed. Photolumine...
Healthcare Quality and Hospital Financial Performance: A Multilevel Framework

Given the recent reports on much-needed healthcare quality improvement in the U.S. we suggest that it is now time to adopt a systemic perspective to measuring quality in U.S. healthcare and trackin...
New Additions to the CRISPR Toolbox: CRISPR-CLONInG and CRISPR-CLIP for Donor Construction in Genome Editing.

CRISPR-Cas has proven to be the most versatile genetic tinkering system of our time, predominantly as a precision genome editing tool. Here, we demonstrate two additions to the repertoire of CRISPR...
Physics-Aware Neural Networks for Distribution System State Estimation

The distribution system state estimation problem seeks to determine the network state from available measurements. Widely used Gauss-Newton approaches are very sensitive to the initialization and often not suitable for real-time estimation. Learning approaches are very promising for real-time estimation, as they shift the computational burden to an offline training stage. Prior machine learning approaches to power system state estimation have been electrical model-agnostic, in the sense that they did not exploit the topology and physical laws governing the power grid to design the architecture of the learning model. In this paper, we propose a novel learning model that utilizes the structure of the power grid. The proposed neural network architecture reduces the number of coefficients needed to parameterize the mapping from the measurements to the network state by exploiting the separability of the estimation problem. This prevents overfitting and reduces the complexity of the training stage. We also propose a greedy algorithm for phasor measuring units placement that aims at minimizing the complexity of the neural network required for realizing the state estimation mapping. Simulation results show superior performance of the proposed method over the Gauss-Newton approach.
Consumer Learning from Own Experience and Social Information: An Experimental Study

We investigate how different types of social information affect the demand characteristics of firms competing through service quality. We first generate behavioral hypotheses around both consumers’...
Rigid Analytic Vectors in Locally Analytic Representations

Let H be a uniform pro-p group. Associated to H are rigid analytic affinoid groups $${\mathbb {H}}_n$$ , and their “wide open” subgroups $${\mathbb {H}}_n^{\circ }$$ . Denote by $$D^\mathrm{la}(H)= C^\mathrm{la}(H)'_b$$ the locally analytic distribution algebra of H and by $$D({\mathbb {H}}_n^{\circ }, H)$$ Emerton’s ring of $${\mathbb {H}}_n^{\circ }$$ -rigid analytic distributions on H. If V is an admissible locally analytic representation of H, and if $$V_{{\mathbb {H}}_n^\circ -\mathrm{an}}$$ denotes the subspace of $${\mathbb {H}}_n^\circ $$ -rigid analytic vectors (with its intrinsic topology), then we show that the continuous dual of $$V_{{\mathbb {H}}_n^\circ -\mathrm{an}}$$ is canonically isomorphic to $$D({\mathbb {H}}_n^{\circ }, H)\otimes _{D^\mathrm{la}(H)} V'$$ . From this we deduce the exactness of the functor $$V \rightsquigarrow V_{{\mathbb {H}}_n^\circ -\mathrm{an}}$$ on the category of admissible locally analytic representations of H.
Far-ultraviolet extinction in σ Scorpii

It was found earlier from OAO-2 data (Bless and Savage, 1972) that considerable variability with direction in space is present in both the shape and level (relative toB-V color excess) of the interstellar extinction curve in the far ultraviolet. The star σ Sco was shown to be a case of extremely low UV extinction, but there was some question of whether this could be due to scattered nebular light entering the large entrance slit of the Wisconsin spectrometer aboard OAO-2. We have obtained UV data on σ Sco usingCopernicus (OAO-3), which has an entrance slit on the order of 103 times smaller in projected area than that of OAO-2, so that the contribution to the signal from scattered nebular light would be correspondingly smaller. We find very good agreement with the extinction curve of Bless and Savage, confirming the low UV extinction in the line of sight to σ Sco. The curve is extended down to 100 A, showing a continued rise towards short wavelengths.
Employment at Will

Employment at will (EAW), a common law doctrine dating back to 1887, states that absent law, contract obligations, or (in the U.S.) union agreements, employers have the right to hire, promote, demote, and fire whomever and whenever they wish “for a good cause, for no cause, or even for causes that are morally wrong, thereby without being guilty of legal wrong”. Effectively, EAW lets employers sidestep due process and grievance procedures. Legal trends in the U.S. indicate expanding EAW protections for employees, particularly where discrimination is an issue, and disallowing practices that curtail employees' rights as citizens, e.g., voting or collecting unemployment. EAW may be justified on the basis of employers' proprietary rights; co-equal rights of employees who voluntarily contract to be hired to quit at will; transparency of the law and practice; avoiding time consumed in due process activities that reduce productivity; and avoiding legal requirements that may undermine employer/employee relationship. Critics of EAW suggest it may be seen as justifying arbitrary management behavior that is at odds with a preference for consistent and rational decision-making. Keywords: common law; employee firing; employee hiring; employment law; equal opportunity; just cause for dismissal; proprietary rights of owners; workplace discrimination
Neuropeptide Y Expression Defines a Novel Class of GABAergic Projection Neuron in the Inferior Colliculus.

Located in the midbrain, the inferior colliculus (IC) integrates information from numerous auditory nuclei and is an important hub for sound processing. Despite its importance, little is known about the molecular identity and functional roles of defined neuron types in the IC. Using a multifaceted approach in mice of both sexes, we found that neuropeptide Y (NPY) expression identifies a major class of inhibitory neurons, accounting for approximately one-third of GABAergic neurons in the IC. Retrograde tracing showed that NPY neurons are principal neurons that can project to the medial geniculate nucleus. In brain slice recordings, many NPY neurons fired spontaneously, suggesting that NPY neurons may drive tonic inhibition onto postsynaptic targets. Morphologic reconstructions showed that NPY neurons are stellate cells, and the dendrites of NPY neurons in the tonotopically organized central nucleus of the IC cross isofrequency laminae. Immunostaining confirmed that NPY neurons express NPY, and we therefore hypothesized that NPY signaling regulates activity in the IC. In crosses between Npy1rcre and Ai14 Cre-reporter mice, we found that NPY Y1 receptor (Y1R)-expressing neurons are glutamatergic and were broadly distributed throughout the rostrocaudal extent of the IC. In whole-cell recordings, application of a high-affinity Y1R agonist led to hyperpolarization in most Y1R-expressing IC neurons. Thus, NPY neurons represent a novel class of inhibitory principal neurons that are well poised to use GABAergic and NPY signaling to regulate the excitability of circuits in the IC and auditory thalamus.SIGNIFICANCE STATEMENT The identification of neuron types is a fundamental question in neuroscience. In the inferior colliculus (IC), the hub of the central auditory pathway, molecular markers for distinct classes of inhibitory neurons have remained unknown. We found that neuropeptide Y (NPY) expression identifies a class of GABAergic principal neurons that constitute one-third of the inhibitory neurons in the IC. NPY neurons fire spontaneously, have a stellate morphology, and project to the auditory thalamus. Additionally, we found that NPY signaling hyperpolarized the membrane potential of a subset of excitatory IC neurons that express the NPY Y1 receptor. Thus, NPY neurons are a novel class of inhibitory neurons that use GABA and NPY signaling to regulate activity in the IC and auditory thalamus.
Which Social Cost of Carbon? A Theoretical Perspective

AbstractThis paper develops a theoretical foundation for the social cost of carbon (SCC). The model highlights the source of debate over whether countries should use the global or domestic SCC for ...
TDEFSI: Theory-guided Deep Learning-based Epidemic Forecasting with Synthetic Information

Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data are updated weekly and provided at a spatially coarse resolution. Producing timely and reliable high-resolution spatiotemporal forecasts for ILI is crucial for local preparedness and optimal interventions. We present Theory-guided Deep Learning-based Epidemic Forecasting with Synthetic Information (TDEFSI),1 an epidemic forecasting framework that integrates the strengths of deep neural networks and high-resolution simulations of epidemic processes over networks. TDEFSI yields accurate high-resolution spatiotemporal forecasts using low-resolution time-series data.During the training phase, TDEFSI uses high-resolution simulations of epidemics that explicitly model spatial and social heterogeneity inherent in urban regions as one component of training data. We train a two-branch recurrent neural network model to take both within-season and between-season low-resolution observations as features and output high-resolution detailed forecasts. The resulting forecasts are not just driven by observed data but also capture the intricate social, demographic, and geographic attributes of specific urban regions and mathematical theories of disease propagation over networks.We focus on forecasting the incidence of ILI and evaluate TDEFSI’s performance using synthetic and real-world testing datasets at the state and county levels in the USA. The results show that, at the state level, our method achieves comparable/better performance than several state-of-the-art methods. At the county level, TDEFSI outperforms the other methods. The proposed method can be applied to other infectious diseases as well.