cssi/SciSciGPT-SciSciNet huggingface dataset:
Pseudomonas aeruginosa Ethanol Oxidation by AdhA in Low-Oxygen Environments.

ABSTRACT Pseudomonas aeruginosa has a broad metabolic repertoire that facilitates its coexistence with different microbes. Many microbes secrete products that P. aeruginosa can then catabolize, including ethanol, a common fermentation product. Here, we show that under oxygen-limiting conditions P. aeruginosa utilizes AdhA, an NAD-linked alcohol dehydrogenase, as a previously undescribed means for ethanol catabolism. In a rich medium containing ethanol, AdhA, but not the previously described PQQ-linked alcohol dehydrogenase, ExaA, oxidizes ethanol and leads to the accumulation of acetate in culture supernatants. AdhA-dependent acetate accumulation and the accompanying decrease in pH promote P. aeruginosa survival in LB-grown stationary-phase cultures. The transcription of adhA is elevated by hypoxia and under anoxic conditions, and we show that it is regulated by the Anr transcription factor. We have shown that lasR mutants, which lack an important quorum sensing regulator, have higher levels of Anr-regulated transcripts under low-oxygen conditions than their wild-type counterparts. Here, we show that a lasR mutant, when grown with ethanol, has an even larger decrease in pH than the wild type (WT) that is dependent on both anr and adhA. The large increase in AdhA activity is similar to that of a strain expressing a hyperactive Anr-D149A variant. Ethanol catabolism in P. aeruginosa by AdhA supports growth on ethanol as a sole carbon source and electron donor in oxygen-limited settings and in cells growing by denitrification under anoxic conditions. This is the first demonstration of a physiological role for AdhA in ethanol oxidation in P. aeruginosa. IMPORTANCE Ethanol is a common product of microbial fermentation, and the Pseudomonas aeruginosa response to and utilization of ethanol are relevant to our understanding of its role in microbial communities. Here, we report that the putative alcohol dehydrogenase AdhA is responsible for ethanol catabolism and acetate accumulation under low-oxygen conditions and that it is regulated by Anr.
Static Analyzers and Potential Future Research Directions for Scala: An Overview

Static analyzers are tool-sets which are proving to be indispensable to modern programmers. These enable the programmers to detect possible errors and security defects present in the current code base within the implementation phase of the development cycle; rather than relying on a standalone testing phase. Static analyzers typically highlight possible defects within the ’static’ source code and thus does not require the source code to be compiled or executed. The Scala programming language has been gaining wider adoption across various industries in recent years. With such a wide adoption of tools of this nature, this paper presents an overview on the static analysis tools available, both commercial and open-source, for the Scala programming language. This paper discusses in detail about the types of defects that each of these tools can detect, limitations of these tools and also provide potential research direction that can improve the current state of static analyzers for the Scala programming language.
Reconstructing the Transcriptional Ontogeny of Maize and Sorghum Supports an Inverse Hourglass Model of Inflorescence Development.

Summary Assembling meaningful comparisons between species is a major limitation in studying the evolution of organismal form. To understand development in maize and sorghum, closely related species with architecturally distinct inflorescences, we collected RNA-seq profiles encompassing inflorescence body-plan specification in both species. We reconstructed molecular ontogenies from 40 B73 maize tassels and 47 BTx623 sorghum panicles and separated them into transcriptional stages. To discover new markers of inflorescence development, we used random forest machine learning to determine stage by RNA-seq. We used two descriptions of transcriptional conservation to identify hourglass-like stages during inflorescence development. Despite a relatively short 12 million years since their last common ancestor, we found maize and sorghum inflorescences are most different during their hourglass-like stages of development, following an inverse-hourglass model of development. We discuss whether agricultural selection may account for the rapid divergence signatures in these species and the observed separation of evolutionary pressure and developmental reprogramming.
Adding Anisotropy to the Standard Quasi-Harmonic Approximation Still Fails in Several Ways to Capture Organic Crystal Thermodynamics

We evaluate the accuracy of varying thermal expansion models for the quasi-harmonic approximation (QHA) relative to molecular dynamics (MD) for 10 sets of enantiotropic organic polymorphs. Relative...
Gauss–Newton Optimization for Phase Recovery From the Bispectrum

Phase recovery from the bispectrum is a central problem in speckle interferometry which can be posed as an optimization problem minimizing a weighted nonlinear least-squares objective function. We look at two different formulations of the phase recovery problem from the literature, both of which can be minimized with respect to either the recovered phase or the recovered image. Previously, strategies for solving these formulations have been limited to gradient descent or quasi-Newton methods. This article explores Gauss–Newton optimization schemes for the problem of phase recovery from the bispectrum. We implement efficient Gauss–Newton optimization schemes for all the formulations. For the two of these formulations which optimize with respect to the recovered image, we also extend to projected Gauss–Newton to enforce element-wise lower and upper bounds on the pixel intensities of the recovered image. We show that our efficient Gauss–Newton schemes result in better image reconstructions with no or limited additional computational cost compared to previously implemented first-order optimization schemes for phase recovery from the bispectrum. MATLAB implementations of all methods and simulations are made publicly available in the BiBox repository on Github.
Mixture Probabilistic Principal Geodesic Analysis

Dimensionality reduction on Riemannian manifolds is challenging due to the complex nonlinear data structures. While probabilistic principal geodesic analysis (PPGA) has been proposed to generalize conventional principal component analysis (PCA) onto manifolds, its effectiveness is limited to data with a single modality. In this paper, we present a novel Gaussian latent variable model that provides a unique way to integrate multiple PGA models into a maximum-likelihood framework. This leads to a well-defined mixture model of probabilistic principal geodesic analysis (MPPGA) on sub-populations, where parameters of the principal subspaces are automatically estimated by employing an Expectation Maximization algorithm. We further develop a mixture Bayesian PGA (MBPGA) model that automatically reduces data dimensionality by suppressing irrelevant principal geodesics. We demonstrate the advantages of our model in the contexts of clustering and statistical shape analysis, using synthetic sphere data, real corpus callosum, and mandible data from human brain magnetic resonance (MR) and CT images.
Evolutionary Analysis of Operation System-of-Systems (SoS) Network Based on Simulated Data.

By means of complex network methods, we construct the Operation SoS network based on simulated data generated by games in large-scale joint operations and analyze the basic statistical characteristics and evolution. Through analyzing, there are three findings: the first one is that the Operation SoS network have the basic characteristics of complex networks, the second one is Operation SoS network's structure determines its real effects, the third one is that it helps to find the problems and recognize the trend of flattening existing in command system, and to predict the future of warfare by means of complex network methods.
Local Voting Games for Misbehavior Detection in VANETs in Presence of Uncertainty

Cooperation between neighboring vehicles is an effective solution to the problem of malicious node identification in vehicular ad hoc networks (VANETs). However, the outcome is subject to nodes’ beliefs and reactions in the collaboration. In this paper, a plain game-theoretic approach that captures the uncertainty of nodes about their monitoring systems, the type of their neighboring nodes, and the outcome of the cooperation is proposed. In particular, one stage of a local voting-based scheme (game) for identifying a target node is developed using a Bayesian game. In this context, incentives are offered in expected utilities of nodes in order to promote cooperation in the network. The proposed model is then analyzed to obtain equilibrium points, ensuring that no node can improve its utility by changing its strategy. Finally, the behavior of malicious and benign nodes is studied by extensive simulation results. Specifically, it is shown how the existing uncertainties and the designed incentives impact the strategies of the players and, consequently, the correct target-node identification.
Infinite Matrix Representations of Isotropic Pseudodifferential Operators

We characterize the action of isotropic pseudodifferential operators on functions in terms of their action on Hermite functions. We show that an operator $A : S(\mathbb{R}) \to S(\mathbb{R})$ is an isotropic pseudodifferential operator of order r if and only if its "matrix" $(K(A))_{m,n} := _{L^2(\mathbb{R})}$ is rapidly decreasing away from the diagonal $\{m = n\}$, order $\frac {r}{2}$ in $m + n$, and where applying the discrete difference operator along the diagonal decreases the order by one. Additionally, we use this result to prove an analogue of Beal's theorem for isotropic pseudodifferential operators.
A Probabilistic Proof of a Lemma That Is Not Burnside’s

If the group G acts on the set X, we define the orbit of the element x∈X as orb(x)={gx:g∈G}. The orbits partition X, and the orbit-counting lemma (see Neumann [2] for the history of its name) shows...
Stability dependence of local structural heterogeneities of stable amorphous solids

The universal anomalous vibrational and thermal properties of amorphous solids are believed to be related to the local variations of the elasticity. Recently it has been shown that the vibrational properties are sensitive to the glass's stability. Here we study the stability dependence of the local elastic constants of a simulated glass former over a broad range of stabilities, from a poorly annealed glass to a glass whose stability is comparable to laboratory exceptionally stable vapor deposited glasses. We show that with increasing stability the glass becomes more uniform as evidenced by a smaller variance of local elastic constants. We find that, according to the definition of local elastic moduli used in this work, the local elastic moduli are not spatially correlated.
Efficient Uncertainty Modeling for System Design via Mixed Integer Programming

The post-Moore era casts a shadow of uncertainty on many aspects of computer system design. Managing that uncertainty requires new algorithmic tools to make quantitative assessments. While prior uncertainty quantification methods, such as generalized polynomial chaos (gPC), show how to work precisely under the uncertainty inherent to physical devices, these approaches focus solely on variables from a continuous domain. However, as one moves up the system stack to the architecture level many parameters are constrained to a discrete (integer) domain. This paper proposes an efficient and accurate uncertainty modeling technique, named mixed generalized polynomial chaos (M-gPC), for architectural uncertainty analysis. The M-gPC technique extends the generalized polynomial chaos (gPC) theory originally developed in the uncertainty quantification community, such that it can efficiently handle the mixed-type (i.e., both continuous and discrete) uncertainties in computer architecture design. Specifically, we employ some stochastic basis functions to capture the architecture-level impact caused by uncertain parameters in a simulator. We also develop a novel mixed-integer programming method to select a small number of uncertain parameter samples for detailed simulations. With a few highly informative simulation samples, an accurate surrogate model is constructed in place of cycle-level simulators for various architectural uncertainty analysis. In the chip-multiprocessor (CMP) model, we are able to estimate the propagated uncertainties with only 95 samples whereas Monte Carlo requires $5\times 10^{4}$ samples to achieve the similar accuracy. We also demonstrate the efficiency and effectiveness of our method on a detailed DRAM subsystem.
Learning an Unknown Network State in Routing Games

Abstract We study learning dynamics induced by myopic travelers who repeatedly play a routing game on a transportation network with an unknown state. The state impacts cost functions of one or more edges of the network. In each stage, travelers choose their routes according to Wardrop equilibrium based on public belief of the state. This belief is broadcasted by an information system that observes the edge loads and realized costs on the used edges, and performs a Bayesian update to the prior stage’s belief. We show that the sequence of public beliefs and edge load vectors generated by the repeated play converge almost surely. In any rest point, travelers have no incentive to deviate from the chosen routes and accurately learn the true costs on the used edges. However, the costs on edges that are not used may not be accurately learned. Thus, learning can be incomplete in that the edge load vector at rest point and complete information equilibrium can be different. We present some conditions for complete learning and illustrate situations when such an outcome is not guaranteed.
Haptic Teleoperation of UAVs Through Control Barrier Functions

This article presents a novel approach to haptic teleoperation. Specifically, we use control barrier functions (CBFs) to generate force feedback to help human operators safely fly quadrotor UAVs. CBFs take a control signal as input and output a control signal that is as close as possible to the initial control signal, while also meeting specified safety constraints. In the proposed method, we generate haptic force feedback based on the difference between a command issued by the human operator and the safe command returned by a CBF. In this way, if the user issues an unsafe control command, the haptic feedback will help guide the user towards the safe input command that is closest to their current command. We conducted a within-subject user study, in which 12 participants flew a simulated UAV in a virtual hallway environment. Participants completed the task with our proposed CBF-based haptic feedback, no haptic feedback, and haptic feedback generated via parametric risk fields, which is a state-of-the-art method described in the literature. The results of this study show that CBF-based haptic feedback can improve a human operator‘s ability to safely fly a UAV and reduce the operator's perceived workload, without sacrificing task efficiency.
Statistical emulators of irrigated crop yields and irrigation water requirements

Abstract This study provides statistical emulators of global by gridded crop models included in the Inter-Sectoral Impact Model Intercomparison Project Fast Track project to estimate irrigated crop yields and associated irrigation water withdrawals simulated at the grid cell level. An ensemble of crop model simulations is used to build a panel of monthly summer weather variables and corresponding annual yields and irrigation water withdrawals from five gridded crop models. This dataset is then used to estimate crop-specific response functions for each crop model. The average normalized root mean square errors for the response functions range from 3% to 6% for irrigated yields and 2% to 8% for irrigated water withdrawal. Further in- and out-of-sample validation exercises confirm that the statistical emulators are able to replicate the crop models’ spatial patterns of irrigated crop yields and irrigation water withdrawals, both in levels and in terms of changes over time, although accuracy varies by model and by region. The emulators estimated in this study therefore provide a reliable and computationally efficient alternative to global gridded crop yield models.
Promotion of Hyperthermic-Induced rDNA Hypercondensation in Saccharomyces cerevisiae

Ribosome biogenesis is tightly regulated through stress-sensing pathways that impact genome stability, aging and senescence. In Saccharomyces cerevisiae, ribosomal RNAs are transcribed from rDNA located on the right arm of chromosome XII. Numerous studies reveal that rDNA decondenses into a puff-like structure during interphase, and condenses into a tight loop-like structure during mitosis. Intriguingly, a novel and additional mechanism of increased mitotic rDNA compaction (termed hypercondensation) was recently discovered that occurs in response to temperature stress (hyperthermic-induced) and is rapidly reversible. Here, we report that neither changes in condensin binding or release of DNA during mitosis, nor mutation of factors that regulate cohesin binding and release, appear to play a critical role in hyperthermic-induced rDNA hypercondensation. A candidate genetic approach revealed that deletion of either HSP82 or HSC82 (Hsp90 encoding heat shock paralogs) result in significantly reduced hyperthermic-induced rDNA hypercondensation. Intriguingly, Hsp inhibitors do not impact rDNA hypercondensation. In combination, these findings suggest that Hsp90 either stabilizes client proteins, which are sensitive to very transient thermic challenges, or directly promotes rDNA hypercondensation during preanaphase. Our findings further reveal that the high mobility group protein Hmo1 is a negative regulator of mitotic rDNA condensation, distinct from its role in promoting premature condensation of rDNA during interphase upon nutrient starvation.
Identification and Validation of Virtual Battery Model for Heterogeneous Devices

The potential of distributed energy resources in providing grid services can be maximized with the recent advancements in demand side control. Effective utilization of this control strategy requires the knowledge of aggregate flexibility of the distributed energy resources (DERs). Recent works have shown that the aggregate flexibility of DERs can be modeled as a virtual battery (VB) whose state evolution is governed by a first order system including self dissipation. The VB parameters (self dissipation rate, energy capacity) are obtained by solving an optimization problem which minimizes the tracking performance of the ensemble and the proposed first order model. For the identified first order model, time varying power limits are calculated using binary search algorithms. Finally, this proposed framework is demonstrated for different homogeneous and heterogeneous ensembles consisting of air conditioners (ACs) and electric water heaters (EWHs).
Incentive Compatible Active Learning.

We consider active learning under incentive compatibility constraints. The main application of our results is to economic experiments, in which a learner seeks to infer the parameters of a subject’s preferences: for example their attitudes towards risk, or their beliefs over uncertain events. By cleverly adapting the experimental design, one can save on the time spent by subjects in the laboratory, or maximize the information obtained from each subject in a given laboratory session; but the resulting adaptive design raises complications due to incentive compatibility. A subject in the lab may answer questions strategically, and not truthfully, so as to steer subsequent questions in a profitable direction. We analyze two standard economic problems: inference of preferences over risk from multiple price lists, and belief elicitation in experiments on choice over uncertainty. In the first setting, we tune a simple and fast learning algorithm to retain certain incentive compatibility properties. In the second setting, we provide an incentive compatible learning algorithm based on scoring rules with query complexity that differs from obvious methods of achieving fast learning rates only by subpolynomial factors. Thus, for these areas of application, incentive compatibility may be achieved without paying a large sample complexity price.
Bone material analogues for PET/MRI phantoms.

Purpose To develop bone material analogues that can be used in construction of phantoms for simultaneous PET/MRI systems. Methods Plaster was used as the basis for the bone material analogues tested in this study. It was mixed with varying concentrations of an iodinated CT contrast, a gadolinium-based MR contrast agent, and copper sulfate to modulate the attenuation properties and MRI properties (T1 and T2*). Attenuation was measured with CT and 68 Ge transmission scans, and MRI properties were measured with quantitative ultrashort echo time pulse sequences. A proof-of-concept skull was created by plaster casting. Results Undoped plaster has a 511 keV attenuation coefficient (~0.14 cm-1 ) similar to cortical bone (0.10-0.15 cm-1 ), but slightly longer T1 (~500 ms) and T2* (~1.2 ms) MR parameters compared to bone (T1 ~ 300 ms, T2* ~ 0.4 ms). Doping with the iodinated agent resulted in increased attenuation with minimal perturbation to the MR parameters. Doping with a gadolinium chelate greatly reduced T1 and T2*, resulting in extremely short T1 values when the target T2* values were reached, while the attenuation coefficient was unchanged. Doping with copper sulfate was more selective for T2* shortening and achieved comparable T1 and T2* values to bone (after 1 week of drying), while the attenuation coefficient was unchanged. Conclusions Plaster doped with copper sulfate is a promising bone material analogue for a PET/MRI phantom, mimicking the MR properties (T1 and T2*) and 511 keV attenuation coefficient of human cortical bone.
Are Large Deficits and Debt Dangerous

The Traditional View (TV) of large deficits and debt is they have large economic costs, save in a recession and early recovery, because they crowd out investment and lower future income, and taken to extremes, can cause inflation and even a financial crisis. The TV has been challenged, most fundamentally in Olivier Blanchard’s 2019 AEA Presidential Address, an elegant extension of Peter Diamond’s OLG model to account for risk in an expected utility framework. He concludes they may have no fiscal cost and increase welfare. I present evidence of looming large deficit and debt/GDP increases and their effects on recovery from recession, interest rates and long-run growth. I discuss several substantive issues with the “no fiscal cost” view that limit its applicability, including accounting neither for the effect of increasing debt on interest rates and growth nor the pre-existing primary deficit, debt and their projected evolution; disputable readings of the data; strong assumptions and parameter values driving the results; and a political economy of deficits and debt likely to lead to even larger debt ratios. Acknowledging uncertainties, the evidence still suggests that large increases in the debt ratio could lead to much higher taxes, lower future incomes and intergenerational inequity.
Dynamics Concentration of Large-Scale Tightly-Connected Networks

The ability to achieve coordinated behavior – engineered or emergent– on networked systems has attracted widespread interest over several fields. This has led to remarkable advances on the development of a theoretical understanding of the conditions under which agents within a network can reach agreement (consensus) or develop coordinated behaviors such as synchronization. However, fewer advances have been made toward explaining another commonly observed phenomena in tightly-connected networks systems: output responses of nodes in the networks are almost identical to each other despite heterogeneity in their individual dynamics. In this paper, we leverage tools from high-dimensional probability to provide an initial answer to this phenomena. More precisely, we show that for linear networks of nodal random transfer functions, as the network size and connectivity grows, every node in the network follows the same response to an input or disturbance – irrespectively of the source of this input. We term this behavior as dynamics concentration since it stems from the fact that the network transfer matrix uniformly converges in probability, i.e., it concentrates, to a unique dynamic response determined by the distribution of the random transfer function of each node. We further discuss the implications of our analysis in the context of model reduction and robustness, and provide numerical evidence that similar phenomena occur in small deterministic networks over a properly defined frequency band.
“They Told Me What I Was Before I Could Tell Them What I Was”: Black Girls’ Ethnic-Racial Identity Development Within Multiple Worlds:

Current research on ethnic-racial identity (ERI) development among Black youth derives primarily from studies that focus on the impact of parental racial socialization from a racial/monoidentity pe...
Evolutionarily ancient BAH–PHD protein mediates Polycomb silencing

Methylation of histone H3 lysine 27 (H3K27) is widely recognized as a transcriptionally repressive chromatin modification but the mechanism of repression remains unclear. We devised and implemented a forward genetic scheme to identify factors required for H3K27 methylation-mediated silencing in the filamentous fungus Neurospora crassa and identified a bromo-adjacent homology (BAH)-plant homeodomain (PHD)-containing protein, EPR-1 (effector of polycomb repression 1; NCU07505). EPR-1 associates with H3K27-methylated chromatin, and loss of EPR-1 de-represses H3K27-methylated genes without loss of H3K27 methylation. EPR-1 is not fungal-specific; orthologs of EPR-1 are present in a diverse array of eukaryotic lineages, suggesting an ancestral EPR-1 was a component of a primitive Polycomb repression pathway.
The Equitable Distribution of COVID-19 Therapeutics and Vaccines.

Scientists from across the globe are racing to develop effective vaccines and therapeutics for coronavirus disease 2019 (COVID-19). Plans are beginning to emerge for ensuring the equitable worldwide distribution of vaccines and therapeutics resulting from biomedical innovations. Absent broad agreement and buy-in on those plans, governments may prioritize their own populations, resulting in inequitable distribution of medical products both within and among countries. During the 2009 influenza A(H1N1) pandemic, wealthy nations bought virtually all vaccine supplies. Even after the WHO appealed for donations, supplies for low- and middle-income countries (LMICs) were limited. The White House may have already sought exclusive access to a COVID-19 vaccine candidate. European and Asian countries have imposed export controls on personal protective equipment and ventilators, with similar export controls likely to extend to COVID-19 vaccine and therapeutic stocks. The development and widespread distribution of COVID-19 medical treatments are a common global interest. Here we offer a proposal for global cooperation to ensure equitable distribution of vaccines and therapies for COViD-19.
4 – Uniqueness Constraints

Publisher Summary Uniqueness constraints play a pivotal role when the conceptual schema is later mapped onto a relational schema. Once uniqueness constraints have been added to a fact type, some further checks are made to see whether the fact type is of the right arity or length. In particular, there is a simple check based on uniqueness that shows that certain kinds of fact types are not elementary, and hence should be split. Constraints apply to the database and are either static or dynamic. Static constraints apply to each individual state of the database and may usually be specified on a conceptual schema diagram. Dynamic constraints restrict the possible transitions between states and are often expressed in other ways. An information system can only enforce constraints on its formal model of the application. The constraint schema for the “real world” need not be the same as for the “recorded world.” Uniqueness constraints apply to the recorded information and must be at least as strong as those in the real world.
Phylogeography of Equine Infectious Anemia Virus

Equine Infectious Anemia virus (EIAV) is the causative agent of one of the most impacting infectious diseases affecting equids. EIAV is genetically diverse with several lineages circulating globally. To elucidate EIAV global spread patterns, we studied its spatiotemporal dynamics applying Bayesian phylodynamic analyses, using a worldwide compiled dataset composed of unique sequences of the gag gene. In addition, we performed a scoping review of 1.470 publications on EIAV to characterize the spatiotemporal trends in EAIV research. Phylogeographic reconstruction suggested Hungary as the most likely country of origin for current EIAV circulation (root state posterior probability = 0.21), and one of the most important centers of diversification for the disease. Historical EIAV spread was predominantly characterized by long-distance spread across continents. As a result, we found that the American and Asian circulating EIAV (i.e., Mongolia) are more related to European lineages than to other Asian countries, being Europe the continent with the highest EIAV phylogenetic diversity. Our bibliometric analysis showed a continuous increment in the number of publications per year, where the United States and China appeared as the countries with highest EIAV-related scientific production. This study provides a historical geographic mapping of the EIAV linages’ spread patterns. In addition, we identified important asymmetry between the current research effort and the availability of genetic data which restricted our abilities to quantify for example the time frame of virus dispersal.
Impact of Augmented Prenatal Care on Birth Outcomes of Medicaid Recipients in New York City

Abstract I examine whether New York State's Prenatal Care Assistance Program (PCAP) is associated with greater use of prenatal services and improved birth outcomes. PCAP is New York State's augmented prenatal care initiative that became a part of the Medicaid program after expansion in income eligibility thresholds in January, 1990. Data are from the linkage of Medicaid administrative files with New York City birth certificates ( N =23 249). For women on cash assistance, I find PCAP is associated with a 20% increase in the likelihood of enrollment in WIC, an increase in mean birth weight of 35 g and a 1.3 percentage point drop in the rate of low birth weight. Associations between PCAP and improved birth outcomes for women on medical assistance are similar, but appear contaminated by selection bias. Reductions in newborn costs associated with PCAP participation are modest, between US$100–300 per recipient, and are insufficient to offset program expenditures.
Food for thought: the effects of school accountability plans on school nutrition

Abstract With the passage of the No Child Left Behind Act of 2001, school accountability is now federal law, continuing the recent trend of states implementing significant accountability systems for schools. This paper studies an unusual yet powerful school response to accountability systems. Using disaggregated school lunch menus from a random sample of school districts in Virginia, we find that schools threatened with accountability sanctions increase the caloric content of their lunches on testing days in an apparent attempt to boost short-term student cognitive performance. Moreover, we find that the schools that responded the most along these dimensions experienced the greatest improvements in standardized test scores.
Variational nonlinear WKB in the Eulerian frame

Nonlinear WKB is a multiscale technique for studying locally plane-wave solutions of nonlinear partial differential equations (PDEs). Its application comprises two steps: (1) replacement of the original PDE with an extended system separating the large scales from the small and (2) reduction of the extended system to its slow manifold. In the context of variational fluid theories with particle relabeling symmetry, nonlinear WKB in the mean Eulerian frame is known to possess a variational structure. This much has been demonstrated using, for instance, the theoretical apparatus known as the generalized Lagrangian mean. On the other hand, the variational structure of nonlinear WKB in the conventional Eulerian frame remains mysterious. By exhibiting a variational principle for the extended equations from step (1) above, we demonstrate that nonlinear WKB in the Eulerian frame is in fact variational. Remarkably, the variational principle for the extended system admits loops of relabeling transformations as a symmetry group. Noether’s theorem therefore implies that the extended Eulerian equations possess a family of circulation invariants parameterized by S1. As an illustrative example, we use our results to systematically deduce a variational model of high-frequency acoustic waves interacting with a larger-scale compressible isothermal flow.
Two-time energy spectrum of weak magnetohydrodynamic turbulence

This work proposes a perturbative closure of the two-time energy spectrum in the regime of weak magnetohydrodynamic turbulence by generalizing the corresponding weak turbulence closure of the energy spectrum. The results can be extended to other turbulent systems consisting of weakly interacting random waves.
Validation of the wearable acceptability range scale for smart apparel

The wearable acceptability range (WEAR) Scale has been a useful tool used in consumer behaviour and product development research to better identify the social acceptance of wearable devices. In this study, we aimed to introduce an extended WEAR Scale to the apparel field by (a) refining existing measurement items and (b) validating this scale for use with smart apparel. Using a quantitative research method, an online survey was conducted with a convenience sample of US consumers aged 18 years old and over. Total 663 usable data were obtained and used for the data analysis. NVivo, SPSS, and AMOS programs were used to perform (a) a content analysis of the open-ended questions to identify recurring phrases, (b) basic descriptive statistics, and (c) a structural equation model testing, here validating the WEAR Scale, respectively. A 4-factor, 15-item WEAR Scale validated for examining consumers’ acceptance of smart apparel. The four WEAR dimensions are smart apparel in relation to: (a) design and aesthetics (four items), (b) self-expression (four items), (c) consequences (three items), and (d) reflection (four items). The findings of this study show the importance of these dimensions for the social acceptance of smart apparel. Therefore, this scale will be useful for the practical application in the apparel industry by providing better understanding of various aspects that smart apparel should consider during the product design and development stage. The scale can also be integrated with other consumer behavioural concepts to develop an emerging theoretical framework for the studies on smart apparel. Limitations and implications for future research were also discussed.
Approximation rates for neural networks with general activation functions.

Abstract We prove some new results concerning the approximation rate of neural networks with general activation functions. Our first result concerns the rate of approximation of a two layer neural network with a polynomially-decaying non-sigmoidal activation function. We extend the dimension independent approximation rates previously obtained to this new class of activation functions. Our second result gives a weaker, but still dimension independent, approximation rate for a larger class of activation functions, removing the polynomial decay assumption. This result applies to any bounded, integrable activation function. Finally, we show that a stratified sampling approach can be used to improve the approximation rate for polynomially decaying activation functions under mild additional assumptions.
Winding numbers and full extendibility in holomorphic motions

We construct an example of a holomorphic motion of a five-point subset of the Riemann sphere over an annulus such that it satisfies the zero winding number condition but is not fully extendable.
Price discrimination with consumer misperception

The rise of big data and sophisticated, machine learning algorithms is increasing the prevalence of price discrimination and even personalized pricing. In traditional models, where consumers’ willi...
A dopamine-induced gene expression signature regulates neuronal function and cocaine response

Drugs of abuse elevate dopamine levels in the nucleus accumbens (NAc) and alter transcriptional programs believed to promote long-lasting synaptic and behavioral adaptations. Here, we leveraged single-nucleus RNA-sequencing to generate a comprehensive molecular atlas of cell subtypes in the NAc, defining both sex-specific and cell type–specific responses to acute cocaine experience in a rat model system. Using this transcriptional map, we identified an immediate early gene expression program that is up-regulated following cocaine experience in vivo and dopamine receptor activation in vitro. Multiplexed induction of this gene program with a large-scale CRISPR-dCas9 activation strategy initiated a secondary synapse-centric transcriptional profile, altered striatal physiology in vitro, and enhanced cocaine sensitization in vivo. Together, these results define the transcriptional response to cocaine with cellular precision and demonstrate that drug-responsive gene programs can potentiate both physiological and behavioral adaptations to drugs of abuse.
Virtual-Voltage Partition-Based Approach to Optimal Transmission Switching

This article deals with optimal transmission switching (OTS) problems involving binary decisions about network topology and nonconvex power flow constraints. We adopt a semidefinite programming formulation for the optimal power flow (OPF) problem that, however, remains nonconvex due to the presence of discrete variables and bilinear products between the decision variables. To tackle the latter, we introduce a physically inspired, virtual-voltage approximation that leads to provable lower and upper bounds on the solution of the original problem. To deal with the exponential complexity caused by the discrete variables, we introduce a graph partition-based algorithm that breaks the problem into several parallel mixed-integer subproblems of smaller size. Simulations on the IEEE bus test cases demonstrate the high degree of accuracy and affordable computational requirements of our approach.
Price dispersion in bitcoin exchanges

Abstract Bitcoin is traded in a number of exchanges, and there is a large and time-varying price dispersion among them. We identify the sources of price dispersion using a standard time-varying vector autoregression model with stochastic volatility, and we find that shocks to transaction fees and bitcoin price growth explain on average 20%, and sometimes more than 60%, of the variation of price dispersion.
Sharp bounds for finitely many embedded eigenvalues of perturbed Stark type operators

For perturbed Stark operators $Hu=-u^{\prime\prime}-xu+qu$, the author has proved that $\limsup_{x\to \infty}{x}^{\frac{1}{2}}|q(x)|$ must be larger than $\frac{1}{\sqrt{2}}N^{\frac{1}{2}}$ in order to create $N$ linearly independent eigensolutions in $L^2(\mathbb{R}^+)$. In this paper, we apply generalized Wigner-von Neumann type functions to construct embedded eigenvalues for a class of Schr\"odinger operators, including a proof that the bound $\frac{1}{\sqrt{2}}N^{\frac{1}{2}}$ is sharp.
The Integration of Lidar and Legacy Datasets Provides Improved Explanations for the Spatial Patterning of Shell Rings in the American Southeast

Archaeologists have struggled to combine remotely sensed datasets with preexisting information for landscape-level analyses. In the American Southeast, for example, analyses of lidar data using automated feature extraction algorithms have led to the identification of over 40 potential new pre-European-contact Native American shell ring deposits in Beaufort County, South Carolina. Such datasets are vital for understanding settlement distributions, yet a comprehensive assessment requires remotely sensed and previously surveyed archaeological data. Here, we use legacy data and airborne lidar-derived information to conduct a series of point pattern analyses using spatial models that we designed to assess the factors that best explain the location of shell rings. The results reveal that ring deposit locations are highly clustered and best explained through a combination of environmental conditions such as distance to water and elevation as well as social factors.
Higgs assisted razor search for Higgsinos at a 100 TeV $pp$ collider

A 100 TeV proton-proton collider will be an extremely effective way to probe the electroweak sector of the minimal supersymmetric standard model (MSSM). In this paper, we describe a search strategy for discovering pair-produced Higgsino-like next-to-lightest supersymmetric particles (NLSPs) at a 100 TeV hadron collider that decay to Bino-like lightest supersymmetric particles (LSPs) via intermediate Z and SM Higgs bosons that in turn decay to a pair of leptons and a pair of b-quarks respectively: $$\widetilde\chi _2^0\widetilde\chi _3^0 \to ({\rm{Z}}\widetilde\chi _1^0)(h\widetilde\chi _1^0) \to bb\;\ell \ell + \widetilde\chi _1^0\widetilde\chi _1^0$$ In addition, we examine the potential for machine learning techniques to boost the power of our searches. Using this analysis, Higgsinos up to 1.4 TeV can be discovered at the 5σ level for Binos with mass of about 0.9 TeV using 3000 fb−1 of data. Additionally, Higgsinos up to 1.8 TeV can be excluded at 95% C.L. for Binos with mass of about 1.4 TeV. This search channel extends the multi-lepton search limits, especially in the region where the mass difference between the Higgsino NLSPs and the Bino LSP is small.
Beef cattle that respond differently to fescue toxicosis have distinct gastrointestinal tract microbiota.

Tall fescue (Lolium arundinaceum) is a widely used forage grass which shares a symbiosis with the endophytic fungus Epichloe coenophiala. The endophyte produces an alkaloid toxin that provides herbivory, heat and drought resistance to the grass, but can cause fescue toxicosis in grazing livestock. Fescue toxicosis can lead to reduced weight gain and milk yields resulting in significant losses to the livestock industry. The objective of this study was to identify bacterial and fungal communities associated with fescue toxicosis tolerance. In this trial, 149 Angus cows across two farms were continuously exposed to toxic, endophyte-infected, fescue for a total of 13 weeks. Of those 149 cows, 40 were classified into either high (HT) or low (LT) tolerance groups according to their growth performance (weight gain). 20 HT and 20 LT cattle balanced by farm were selected for amplicon sequencing to compare the fecal microbiota of the two tolerance groups. This study reveals significantly (q<0.05) different bacterial and fungal microbiota between HT and LT cattle, and indicates that fungal phylotypes may be important for an animal's response to fescue toxicosis: We found that fungal phylotypes affiliating to the Neocallimastigaceae, which are known to be important fiber-degrading fungi, were consistently more abundant in the HT cattle. Whereas fungal phylotypes related to the genus Thelebolus were more abundant in the LT cattle. This study also found more pronounced shifts in the microbiota in animals receiving higher amounts of the toxin. We identified fungal phylotypes which were consistently more abundant either in HT or LT cattle and may thus be associated with the respective animal's response to fescue toxicosis. Our results thus suggest that some fungal phylotypes might be involved in mitigating fescue toxicosis.
On random multi-dimensional assignment problems

Abstract We study random multidimensional assignment problems where the costs decompose into the sum of independent random variables. In particular, in three dimensions, we assume that the costs W i , j , k satisfy W i , j , k = a i , j + b i , k + c j , k where the a i , j , b i , k , c j , k are independent uniform [ 0 , 1 ] random variables. Our objective is to minimise the total cost and we show that w.h.p. a simple greedy algorithm is a ( 3 + o ( 1 ) ) -approximation. This is in contrast to the case where the W i , j , k are independent exponential rate 1 random variables. Here all that is known is an n o ( 1 ) -approximation, due to Frieze and Sorkin.
Sleeping is Efficient: MIS in O(1)-rounds Node-averaged Awake Complexity

Maximal Independent Set (MIS) is one of the fundamental problems in distributed computing. The round (time) complexity of distributed MIS has traditionally focused on the worst-case time for all nodes to finish. The best-known (randomized) MIS algorithms take O(log n) worst-case rounds on general graphs (where n is the number of nodes). Breaking the O(log n) worst-case bound has been a longstanding open problem, while currently the best-known lower bound is [EQUATION] rounds. Motivated by the goal to reduce total energy consumption in energy-constrained networks such as sensor and ad hoc wireless networks, we take an alternative approach to measuring performance. We focus on minimizing the total (or equivalently, the average) time for all nodes to finish. It is not clear whether the currently best-known algorithms yield constant-round (or even o(log n)) node-averaged round complexity for MIS in general graphs. We posit the sleeping model, a generalization of the traditional model, that allows nodes to enter either "sleep" or "waking" states at any round. While waking state corresponds to the default state in the traditional model, in sleeping state a node is "offline", i.e., it does not send or receive messages (and messages sent to it are dropped as well) and does not incur any time, communication, or local computation cost. Hence, in this model, only rounds in which a node is awake are counted and we are interested in minimizing the average as well as the worst-case number of rounds a node spends in the awake state, besides the traditional worst-case round complexity (i.e., the rounds for all nodes to finish including both the awake and sleeping rounds). Our main result is that we show that MIS can be solved in (expected) O(1) rounds under node-averaged awake complexity measure in the sleeping model. In particular, we present a randomized distributed algorithm for MIS that has expected O(1)-rounds node-averaged awake complexity and, with high probability1 has O(log n)-rounds worst-case awake complexity and O(log3.41 n)-rounds worst-case complexity. Our work is a step towards understanding the node-averaged complexity of MIS both in the traditional and sleeping models, as well as designing energy-efficient distributed algorithms for energy-constrained networks.
Emotional state and Market Behavior

Abstract: We consider the relationship between the emotional state of traders and market prices. We create asset markets with the structure first studied by Smith, Suchanek and Williams (1988), which is known to generate price bubbles and crashes. We analyze participants' facial expressions with facereading software before and while the market is operating. We find that greater positive emotion in facial expressions before the market opens predicts higher prices and larger bubbles. Greater fear predicts lower prices and smaller bubbles. Those traders who remain the most neutral during periods of market volatility achieve the highest earnings. Loss aversion in decision making is correlated with fear, not with other emotions.
Fast and Accurate Langevin Simulations of Stochastic Hodgkin-Huxley Dynamics.

Fox and Lu introduced a Langevin framework for discrete-time stochastic models of randomly gated ion channels such as the Hodgkin-Huxley (HH) system. They derived a Fokker-Planck equation with stat...