cssi/SciSciGPT-SciSciNet huggingface dataset:
Cover crops improve early season natural enemy recruitment and pest management in cotton production

Abstract A shift to more ecologically based farming practices could improve the sustainability and economic stability of agricultural systems. Habitat management in and around agricultural fields can provide stable environments that aid in the proliferation of natural enemy communities that moderate pest populations and injury. Winter cover crops offer a potentially cost-effective approach to improving habitat that supports natural enemy communities early in the growing season. We investigated the effects of winter cover crops including cereal rye (Secale cereal L.) and crimson clover (Trifolium incarnatum L.) on the abundance and diversity of natural enemies, key pest populations, biological control services, and cotton yield. Winter cover crops were established on 0.4 ha replicated field plots in the fall of 2017 and 2018. Suction sampling during each cotton development stage demonstrated that a rye cover crop promoted greater abundance and diversity of natural enemy communities in early cotton stages. Extensive leaf sampling of seedling cotton showed that cover crops significantly reduced thrips infestations. Furthermore, stink bug boll injury decreased on plots prepared with a rye cover compared to cotton lacking this additional habitat. Combining end of season yield results and management practices with an economic analysis of the costs of production, the value of cotton grown into a cover crop was cost competitive with conventional (no cover) cotton production. These results suggest that conventional growers utilizing cover crops could reduce insecticide inputs through natural reductions in pest pressure, and overall do not incur additional production costs.
Adaptive multi‐index collocation for uncertainty quantification and sensitivity analysis

In this paper, we present an adaptive algorithm to construct response surface approximations of high-fidelity models using a hierarchy of lower fidelity models. Our algorithm is based on multi-index stochastic collocation and automatically balances physical discretization error and response surface error to construct an approximation of model outputs. This surrogate can be used for uncertainty quantification (UQ) and sensitivity analysis (SA) at a fraction of the cost of a purely high-fidelity approach. We demonstrate the effectiveness of our algorithm on a canonical test problem from the UQ literature and a complex multi-physics model that simulates the performance of an integrated nozzle for an unmanned aerospace vehicle. We find that, when the input-output response is sufficiently smooth, our algorithm produces approximations that can be over two orders of magnitude more accurate than single fidelity approximations for a fixed computational budget.
Geometrical Dynamics in a Transitioning Superconducting Sphere

Recent theoretical works have concentrated on calculating the Casimir effect in curved spacetime. In this paper we outline the forward problem of metrical variation due to the Casimir effect for spherical geometries. We consider a scalar quantum field inside a hollow superconducting sphere. Metric equations are developed describing the evolution of the scalar curvature after the sphere transitions to the normal state.
Extension of Two-Dimensional Mean Curvature Flow with Free Boundary

Given a mean curvature flow of compact, embedded $$C^{2}$$ surfaces satisfying Neumann free boundary condition on a mean convex, smooth support surface in 3-dimensional Euclidean space, we show that it can be extended as long as its mean curvature and perimeter stay uniformly bounded along the flow.
Quantum chemistry as a benchmark for near-term quantum computers

We present a quantum chemistry benchmark for noisy intermediate-scale quantum computers that leverages the variational quantum eigensolver, active-space reduction, a reduced unitary coupled cluster ansatz, and reduced density purification as error mitigation. We demonstrate this benchmark using 4 of the available qubits on the 20-qubit IBM Tokyo and 16-qubit Rigetti Aspen processors via the simulation of alkali metal hydrides (NaH, KH, RbH), with accuracy of the computed ground state energy serving as the primary benchmark metric. We further parameterize this benchmark suite on the trial circuit type, the level of symmetry reduction, and error mitigation strategies. Our results demonstrate the characteristically high noise level present in near-term superconducting hardware, but provide a relevant baseline for future improvement of the underlying hardware, and a means for comparison across near-term hardware types. We also demonstrate how to reduce the noise in post processing with specific error mitigation techniques. Particularly, the adaptation of McWeeny purification of noisy density matrices dramatically improves accuracy of quantum computations, which, along with adjustable active space, significantly extends the range of accessible molecular systems. We demonstrate that for specific benchmark settings and a selected range of problems, the accuracy metric can reach chemical accuracy when computing over the cloud on certain quantum computers.
Response Prediction for Low-Regret Agents

Companies like Google and Microsoft run billions of auctions every day to sell advertising opportunities. Any change to the rules of these auctions can have a tremendous effect on the revenue of the company and the welfare of the advertisers and the users. Therefore, any change requires careful evaluation of its potential impacts. Currently, such impacts are often evaluated by running simulations or small controlled experiments. This, however, misses the important factor that the advertisers respond to changes. Our goal is to build a theoretical framework for predicting the actions of an agent (the advertiser) that is optimizing her actions in an uncertain environment. We model this problem using a variant of the multi-armed bandit setting where playing an arm is costly. The cost of each arm changes over time and is publicly observable. The value of playing an arm is drawn stochastically from a static distribution and is observed by the agent and not by us. We, however, observe the actions of the agent. Our main result is that assuming the agent is playing a strategy with a regret of at most f(T) within the first T rounds, we can learn to play the multi-armed bandits game (without observing the rewards) in such a way that the regret of our selected actions is at most \(O(k^4(f(T)+1)\log (T))\), where k is the number of arms.
PSI-Sigma: a comprehensive splicing-detection method for short-read and long-read RNA-seq analysis

MOTIVATION: Percent Spliced-In (PSI) values are commonly used to report alternative pre-mRNA splicing (AS) changes. Previous PSI-detection tools were limited to specific AS events and were evaluated by in silico RNA-seq data. We developed PSI-Sigma, which uses a new PSI index, and we employed actual (non-simulated) RNA-seq data from spliced synthetic genes (RNA Sequins) to benchmark its performance (i.e., precision, recall, false positive rate, and correlation) in comparison with three leading tools (rMATS, SUPPA2, and Whippet). RESULTS: PSI-Sigma outperformed these tools, especially in the case of AS events with multiple alternative exons and intron-retention events. We also briefly evaluated its performance in long-read RNA-seq analysis, by sequencing a mixture of human RNAs and RNA Sequins with nanopore long-read sequencers. AVAILABILITY: PSI-Sigma is implemented is available at https://github.com/wososa/PSI-Sigma.
Opacity, obscurity, and the geometry of question-asking.

Abstract Asking questions is a pervasive human activity, but little is understood about what makes them difficult to answer. An analysis of a pair of large databases, New York Times crosswords and questions from the quiz-show Jeopardy, establishes two orthogonal dimensions of question difficulty: obscurity (the rarity of the answer) and opacity (the indirectness of question cues, operationalized with word2vec). The importance of opacity, and the role of synergistic information in resolving it, suggests that accounts of difficulty in terms of prior expectations captures only a part of the question-asking process. A further regression analysis shows the presence of additional dimensions to question-asking: question complexity, the answer's local network density, cue intersection, and the presence of signal words. Our work shows how question-askers can help their interlocutors by using contextual cues, or, conversely, how a particular kind of unfamiliarity with the domain in question can make it harder for individuals to learn from others. Taken together, these results suggest how Bayesian models of question difficulty can be supplemented by process models and accounts of the heuristics individuals use to navigate conceptual spaces.
Empirical Priors and Coverage of Posterior Credible Sets in a Sparse Normal Mean Model

Bayesian methods provide a natural means for uncertainty quantification, that is, credible sets can be easily obtained from the posterior distribution. But is this uncertainty quantification valid in the sense that the posterior credible sets attain the nominal frequentist coverage probability? This paper investigates the frequentist validity of posterior uncertainty quantification based on a class of empirical priors in the sparse normal mean model. In particular, we show that our marginal posterior credible intervals achieve the nominal frequentist coverage probability under conditions slightly weaker than needed for selection consistency and a Bernstein–von Mises theorem for the full posterior, and numerical investigations suggest that our empirical Bayes method has superior frequentist coverage probability properties compared to other fully Bayes methods.
Between hard and soft thresholding: optimal iterative thresholding algorithms

Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective, and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator, and asks whether it is possible to achieve stronger guarantees than what is possible with hard thresholding. We develop the notion of relative concavity of a thresholding operator, a quantity that characterizes the worst-case convergence performance of any thresholding operator on the target optimization problem. Surprisingly, we find that commonly used thresholding operators, such as hard thresholding and soft thresholding, are suboptimal in terms of worst-case convergence guarantees. Instead, a general class of thresholding operators, lying between hard thresholding and soft thresholding, is shown to be optimal with the strongest possible convergence guarantee among all thresholding operators. Examples of this general class includes $\ell_q$ thresholding with appropriate choices of $q$, and a newly defined {\em reciprocal thresholding} operator. We also investigate the implications of the improved optimization guarantee in the statistical setting of sparse linear regression, and show that this new class of thresholding operators attain the optimal rate for computationally efficient estimators, matching the Lasso.
Differentiable Grammars for Videos

This paper proposes a novel algorithm which learns a formal regular grammar from real-world continuous data, such as videos. Learning latent terminals, non-terminals, and production rules directly from continuous data allows the construction of a generative model capturing sequential structures with multiple possibilities. Our model is fully differentiable, and provides easily interpretable results which are important in order to understand the learned structures. It outperforms the state-of-the-art on several challenging datasets and is more accurate for forecasting future activities in videos. We plan to open-source the code.1
Many-body collision contributions to electron momentum damping rates in a plasma influenced by electron strong coupling

Experimental studies of electron-ion collision rates in an ultracold neutral plasma can be conducted through measuring the rate of electron plasma oscillation damping. For sufficiently cold and dense conditions where strong coupling influences are important, the measured damping rate was faster by over 30% as compared to theoretical expectations [Chen et al., Phys. Rev. E 96, 013203 (2017)]. We have conducted a series of numerical simulations to isolate the primary source of this difference. By analyzing the distribution of electron velocity changes due to collisions in a molecular dynamics simulation, examining the trajectory of electrons with a high deflection angle in such simulations, and examining the oscillation damping rate while varying the ratio of two-body to three-body electron-ion collision rates, we have found that the difference is consistent with the effect due to many-body collisions that lead to bound electrons. This has implications for other electron-ion collision related transport properties in addition to electron oscillation damping.
Privacy Risks of Interoperable Electronic Health Records: Segmentation of Sensitive Information Will Help

The proposed interoperability rules for electronic health records (EHRs) issued on March 4, 2019, by the Centers for Medicare and Medicaid Services address the last major element of the nation’s system of EHRs. The ability of health care providers and other users to supplement, exchange, and access comprehensive EHRs is essential to achieve efficient and high quality medical care. Interoperability, however, also increases the privacy risks of EHRs by enabling the unnecessary disclosure of sensitive health information to individuals and entities without a need to access them. This article reviews some leading proposals to protect health privacy within an interoperable system, highlighting the proposal from the National Committee on Vital and Health Statistics to permit patients to segment certain fields of sensitive information from the main body of their EHRs.
Rethinking Modulation and Detection for High Doppler Channels

We present two modulation and detection techniques that are designed to allow for efficient equalization for channels that exhibit an arbitrary Doppler spread but no delay spread. These techniques are based on principles similar to techniques designed for time-invariant delay spread channels (e.g., Orthogonal Frequency Division Multiplexing or OFDM) and have the same computational complexity. Through numerical simulations, we show that effective equalization is possible for channels that exhibit a high Doppler spread and even a modest delay spread, whereas equalized OFDM exhibits a strictly worse performance in these environments. Our results indicate that, in rapidly time-varying channels, such as those found in high-mobility or mmWave deployments, new modulation coupled with appropriate channel estimation and equalization techniques may significantly outperform modulation and detection schemes that are designed for static or slowly time varying multipath channels.
Enhanced Diffusion and Chemotaxis of Enzymes.

Many enzymes appear to diffuse faster in the presence of substrate and to drift either up or down a concentration gradient of their substrate. Observations of these phenomena, termed enhanced enzym...
Hormonal and neural correlates of care in active versus observing poison frog parents.

Abstract The occasional reversal of sex-typical behavior suggests that many of the neural circuits underlying behavior are conserved between males and females and can be activated in response to the appropriate social condition or stimulus. Most poison frog species (Family Dendrobatidae) exhibit male uniparental care, but flexible compensation has been observed in some species, where females will take over parental care duties when males disappear. We investigated hormonal and neural correlates of sex-typical and sex-reversed parental care in a typically male uniparental species, the Dyeing Poison Frog (Dendrobates tinctorius). We first characterized hormone levels and whole brain gene expression across parental care stages during sex-typical care. Surprisingly, hormonal changes and brain gene expression differences associated with active parental behavior in males were mirrored in their non-caregiving female partners. To further explore the disconnect between neuroendocrine patterns and behavior, we characterized hormone levels and neural activity patterns in females performing sex-reversed parental care. In contrast to hormone and gene expression patterns, we found that patterns of neural activity were linked to the active performance of parental behavior, with sex-reversed tadpole transporting females exhibiting neural activity patterns more similar to those of transporting males than non-caregiving females. We suggest that parallels in hormones and brain gene expression in active and observing parents are related to females' ability to flexibly take over parental care in the absence of their male partners.
Room-temperature single photon emitters in cubic boron nitride nanocrystals

Color centers in wide bandgap semiconductors are attracting broad attention for use as platforms for quantum technologies relying on room-temperature single-photon emission (SPE), and for nanoscale metrology applications building on the centers’ response to electric and magnetic fields. Here, we demonstrate room-temperature SPE from defects in cubic boron nitride (cBN) nanocrystals, which we unambiguously assign to the cubic phase using spectrally resolved Raman imaging. These isolated spots show photoluminescence (PL) spectra with zero-phonon lines (ZPLs) within the visible region (496–700 nm) when subject to sub-bandgap laser excitation. Second-order autocorrelation of the emitted photons reveals antibunching with g2(0) ∼ 0.2, and a decay constant of 2.75 ns that is further confirmed through fluorescence lifetime measurements. The results presented herein prove the existence of optically addressable isolated quantum emitters originating from defects in cBN, making this material an interesting platform for opto-electronic devices and quantum applications.
Deep Visual Template-Free Form Parsing

Automatic, template-free extraction of information from form images is challenging due to the variety of form layouts. This is even more challenging for historical forms due to noise and degradation. A crucial part of the extraction process is associating input text with pre-printed labels. We present a learned, template-free solution to detecting pre-printed text and input text/handwriting and predicting pair-wise relationships between them. While previous approaches to this problem have been focused on clean images and clear layouts, we show our approach is effective in the domain of noisy, degraded, and varied form images. We introduce a new dataset of historical form images (late 1800s, early 1900s) for training and validating our approach. Our method uses a convolutional network to detect pre-printed text and input text lines. We pool features from the detection network to classify possible relationships in a language-agnostic way. We show that our proposed pairing method outperforms heuristic rules and that visual features are critical to obtaining high accuracy.
An inverse problem formulation of the immersed‐boundary method

We formulate the immersed-boundary method (IBM) as an inverse problem. A control variable is introduced on the boundary of a larger domain that encompasses the target domain. The optimal control is the one that minimizes the mismatch between the state and the desired boundary value along the immersed target-domain boundary. We begin by investigating a naive problem formulation that we show is ill-posed: in the case of the Laplace equation, we prove that the solution is unique but it fails to depend continuously on the data; for the linear advection equation, even solution uniqueness fails to hold. These issues are addressed by two complimentary strategies. The first strategy is to ensure that the enclosing domain tends to the true domain as the mesh is refined. The second strategy is to include a specialized parameter-free regularization that is based on penalizing the difference between the control and the state on the boundary. The proposed inverse IBM is applied to the diffusion, advection, and advection-diffusion equations using a high-order discontinuous Galerkin discretization. The numerical experiments demonstrate that the regularized scheme achieves optimal rates of convergence and that the reduced Hessian of the optimization problem has a bounded condition number as the mesh is refined.
Brain microRNAs associated with late-life depressive symptoms are also associated with cognitive trajectory and dementia

Late-life depression is associated with an increased risk for dementia but we have limited knowledge of the molecular mechanisms underlying this association. Here we investigated whether brain microRNAs, important posttranscriptional regulators of gene expression, contribute to this association. Late-life depressive symptoms were assessed annually in 300 participants of the Religious Orders Study and Rush Memory and Aging Project for a mean of 7 years. Participants underwent annual cognitive testing, clinical assessment of cognitive status, and uniform neuropathologic examination after death. microRNAs were profiled from the prefrontal cortex using NanoString platform in the discovery cohort and small RNA sequencing in the replication cohort. A global microRNA association study of late-life depressive symptoms was performed using linear mixed model adjusting for the potential confounding factors. Four brain microRNAs were associated with late-life depressive symptoms at adjusted p < 0.05: miR-484, miR-26b-5p, miR-30d-5p, and miR-197-3p. Lower expression levels of these miRNAs were associated having greater depressive symptoms. Furthermore, lower levels of miR-484 and miR-197-3p were associated with faster decline of cognition over time. Moreover, lower miR-484 level was associated with higher probability of having Alzheimer’s dementia. Importantly, the associations between miR-484 and depressive symptoms and Alzheimer’s dementia, respectively, were replicated in an independent cohort. Lastly, the predicted targets of miR-484 were enriched in a brain protein co-expression module involving synaptic transmission and regulation of synaptic plasticity. This study identified four brain microRNAs associated with late-life depressive symptoms assessed longitudinally. In addition, we found a molecular connection between late-life depression and dementia through miR-484.
Matrix-Matched Calibration Curves for Assessing Analytical Figures of Merit in Quantitative Proteomics

Mass spectrometry is a powerful tool for quantifying protein abundance in complex samples. Advances in sample preparation and the development of data-independent acquisition (DIA) mass spectrometry approaches have increased the number of peptides and proteins measured per sample. Here, we present a series of experiments demonstrating how to assess whether a peptide measurement is quantitative by mass spectrometry. Our results demonstrate that increasing the number of detected peptides in a proteomics experiment does not necessarily result in increased numbers of peptides that can be measured quantitatively.
Finite quotients of braid groups

We derive a lower bound on the size of finite non-cyclic quotients of the braid group that is superexponential in the number of strands. We also derive a similar lower bound for nontrivial finite quotients of the commutator subgroup of the braid group.
Non-Stationary Polar Codes for Resistive Memories

Resistive memories are considered a promising memory technology enabling high storage densities. However, the readout reliability of resistive memories is impaired due to the inevitable existence of wire resistance, resulting in the sneak path problem. Motivated by this problem, we study polar coding over channels with different reliability levels, termed non-stationary polar codes, and we propose a technique improving the bit error rate (BER) performance. We then apply the framework of non-stationary polar codes to the crossbar array and evaluate its BER performance under two modeling approaches, namely binary symmetric channels and binary asymmetric channels. Finally, we propose a technique for biasing the proportion of high-resistance states in the crossbar array and show its advantage in reducing further the BER. Several simulations are carried out using a SPICE-like simulator, exhibiting significant reduction in BER.
Chinese liquidity effects on the Australian macroeconomy, 2002–2017

ABSTRACTChina’s growing importance to the Australian economy has been well recognized in policy circles but remained relatively untested in formal empirical analysis. This paper examines the reacti...
Disease state prediction from single-cell data using graph attention networks

Single-cell RNA sequencing (scRNA-seq) has revolutionized bio-logical discovery, providing an unbiased picture of cellular heterogeneity in tissues. While scRNA-seq has been used extensively to provide insight into health and disease, it has not been used for disease prediction or diagnostics. Graph Attention Networks have proven to be versatile for a wide range of tasks by learning from both original features and graph structures. Here we present a graph attention model for predicting disease state from single-cell data on a large dataset of Multiple Sclerosis (MS) patients. MS is a disease of the central nervous system that is difficult to diagnose. We train our model on single-cell data obtained from blood and cerebrospinal fluid (CSF) for a cohort of seven MS patients and six healthy adults (HA), resulting in 66,667 individual cells. We achieve 92% accuracy in predicting MS, outperforming other state-of-the-art methods such as a graph convolutional network, random forest, and multi-layer perceptron. Further, we use the learned graph attention model to get insight into the features (cell types and genes) that are important for this prediction. The graph attention model also allow us to infer a new feature space for the cells that emphasizes the difference between the two conditions. Finally we use the attention weights to learn a new low-dimensional embedding which we visualize with PHATE and UMAP. To the best of our knowledge, this is the first effort to use graph attention, and deep learning in general, to predict disease state from single-cell data. We envision applying this method to single-cell data for other diseases.
Cooperative Audio Source Separation and Enhancement Using Distributed Microphone Arrays and Wearable Devices

Augmented listening devices such as hearing aids often perform poorly in noisy and reverberant environments with many competing sound sources. Large distributed microphone arrays can improve performance, but data from remote microphones often cannot be used for delay-constrained real-time processing. We present a cooperative audio source separation and enhancement system that leverages wearable listening devices and other microphone arrays spread around a room. The full distributed array is used to separate sound sources and estimate their statistics. Each listening device uses these statistics to design real-time binaural audio enhancement filters using its own local microphones. The system is demonstrated experimentally using 10 speech sources and 160 microphones in a large, reverberant room.
Matrix algebras over algebras of unbounded operators

Let $${{\mathscr {M}}}$$ be a $$II_1$$ factor acting on the Hilbert space $${{\mathscr {H}}}$$ , and $${\mathscr {M}} _{\text {aff}}$$ be the Murray–von Neumann algebra of closed densely-defined operators affiliated with $${{\mathscr {M}}}$$ . Let $$\tau $$ denote the unique faithful normal tracial state on $$\mathscr {M}$$ . By virtue of Nelson’s theory of non-commutative integration, $${\mathscr {M}} _{\text {aff}}$$ may be identified with the completion of $${{\mathscr {M}}}$$ in the measure topology. In this article, we show that $$M_n({\mathscr {M}} _{\text {aff}}) \cong M_n({{\mathscr {M}}})_{\text {aff}}$$ as unital ordered complex topological $$*$$ -algebras with the isomorphism extending the identity mapping of $$M_n({{\mathscr {M}}}) \rightarrow M_n({{\mathscr {M}}})$$ . Consequently, the algebraic machinery of rank identities and determinant identities are applicable in this setting. As a step further in the Heisenberg–von Neumann puzzle discussed by Kadison–Liu (SIGMA Symmetry Integrability Geom. Methods Appl., 10:Paper 009, 40, 2014), it follows that if there exist operators P, Q in $${\mathscr {M}} _{\text {aff}}$$ satisfying the commutation relation $$Q \; {{\hat{\cdot }}} \;P \; {\hat{-}} \;P \; {{\hat{\cdot }}} \;Q = {i\,}I$$ , then at least one of them does not belong to $$L^p({{\mathscr {M}}}, \tau )$$ for any $$0 < p \le \infty $$ . Furthermore, the respective point spectrums of P and Q must be empty. Hence the puzzle may be recasted in the following equivalent manner - Are there invertible operators P, A in $${{\mathscr {M}}}_{\text {aff}}$$ such that $$P^{-1} \; {{\hat{\cdot }}} \;A \; {{\hat{\cdot }}} \;P = I \; {\hat{+}} \;A$$ ? This suggests that any strategy towards its resolution must involve the study of conjugacy invariants of operators in $${{\mathscr {M}}}_{\text {aff}}$$ in an essential way.
NONU-1 Encodes a Conserved Endonuclease Required for mRNA Translation Surveillance

Summary Cellular translation surveillance rescues ribosomes that stall on problematic mRNAs. During translation surveillance, endonucleolytic cleavage of the problematic mRNA is a critical step in rescuing stalled ribosomes. Here we identify NONU-1 as a factor required for translation surveillance pathways including no-go and nonstop mRNA decay. We show that (1) NONU-1 reduces nonstop and no-go mRNA levels; (2) NONU-1 contains an Smr RNase domain required for mRNA decay; (3) the domain architecture and catalytic residues of NONU-1 are conserved throughout metazoans and eukaryotes, respectively; and (4) NONU-1 is required for the formation of mRNA cleavage fragments in the vicinity of stalled ribosomes. We extend our results in C. elegans to homologous factors in S. cerevisiae, showing the evolutionarily conserved function of NONU-1. Our work establishes the identity of a factor critical to translation surveillance and will inform mechanistic studies at the intersection of translation and mRNA decay.
A Challenging Combination: Anomalous Left Anterior Descending Coronary Artery, Myocardial Bridging, and Endothelial Dysfunction

50 years old female patient with a medical history of hypertension presented to the clinic with chest pain, palpitations, and dyspnea on exertion of 2 years duration. Extensive workup in search of the culprit etiology of her chest pain revealed a challenging combination of an anomalous left anterior descending artery with myocardial bridging and endothelial dysfunction. She was treated medically with long acting nitrates, L-arginine and calcium channel blockers, and remains asymptomatic after 12 months of follow up.
Lateralized Expression of Cortical Perineuronal Nets during Maternal Experience is Dependent on MECP2

Cortical neuronal circuits along the sensorimotor pathways are shaped by experience during critical periods of heightened plasticity in early postnatal development. After closure of critical periods, measured histologically by the formation and maintenance of extracellular matrix structures called perineuronal nets (PNNs), the adult mouse brain exhibits restricted plasticity and maturity. Mature PNNs are typically considered to be stable structures that restrict synaptic plasticity on cortical parvalbumin+ (PV+) GABAergic neurons. Changes in environment (i.e., novel behavioral training) or social contexts (i.e., motherhood) are known to elicit synaptic plasticity in relevant neural circuitry. However, little is known about concomitant changes in the PNNs surrounding the cortical PV+ GABAergic neurons. Here, we show novel changes in PNN density in the primary somatosensory cortex (SS1) of adult female mice after maternal experience [called surrogate (Sur)], using systematic microscopy analysis of a whole brain region. On average, PNNs were increased in the right barrel field and decreased in the left forelimb regions. Individual mice had left hemisphere dominance in PNN density. Using adult female mice deficient in methyl-CpG-binding protein 2 (MECP2), an epigenetic regulator involved in regulating experience-dependent plasticity, we found that MECP2 is critical for this precise and dynamic expression of PNN. Adult naive Mecp2-heterozygous (Het) females had increased PNN density in specific subregions in both hemispheres before maternal experience, compared with wild-type (WT) littermate controls. The laterality in PNN expression seen in naive Het (NH) was lost after maternal experience in Sur Het (SH) mice, suggesting possible intact mechanisms for plasticity. Together, our results identify subregion and hemisphere-specific alterations in PNN expression in adult females, suggesting extracellular matrix plasticity as a possible neurobiological mechanism for adult behaviors in rodents.
Estimating Global Damages from Sea Level Rise with the Coastal Impact and Adaptation Model (CIAM)

Coastal sector impacts from sea level rise (SLR) are a key component of the projected economic damages of climate change, a major input to decision-making and design of climate policy. Moreover, the ultimate global costs to coastal resources will depend strongly on adaptation, society’s response to cope with the local impacts. This paper presents a new open-source optimization model to assess global coastal impacts from SLR from the perspective of economic efficiency. The Coastal Impact and Adaptation Model (CIAM) determines the optimal strategy for adaptation at the local level, evaluating over 12,000 coastal segments, as described in the DIVA database (Vafeidis et al. 2006), based on their socioeconomic characteristics and the potential impacts of relative sea level rise and uncertain sea level extremes. A deterministic application of CIAM demonstrates the model’s ability to assess local impacts and direct costs, choose the least-cost adaptation, and estimate global net damages for several climate scenarios that account for both global and local components of SLR (Kopp et al. 2014). CIAM finds that there is large potential for coastal adaptation to reduce the expected impacts of SLR compared to the alternative of no adaptation, lowering global net present costs through 2100 by a factor of seven to less than $1.7 trillion, although this does not include initial transition costs to overcome an under-adapted current state. In addition to producing aggregate estimates, CIAM results can also be interpreted at the local level, where retreat (e.g., relocate inland) is often a more cost-effective adaptation strategy than protect (e.g., construct physical defenses).
Chapter 13 – Membrane Reconstitution

Membranes are unimaginably complex. They consist of hundreds of different proteins floating in a bewildering and crowded sea of a 1000 or more different lipids and this mixture is in constant flux. A membrane is not homogeneous but exists in fleeting patches that exhibit both lateral and transmembrane asymmetry. Also, countless reactions occur simultaneously in and around the membrane. Can the membrane problem be simplified? In this chapter we will examine how one membrane protein can be isolated away from the others and reconstituted into a model lipid bilayer membrane in order to determine how the protein functions. Finally, we will investigate the isolation and properties of an important membrane domain involved in cell signaling, the lipid raft.
Meta-analysis of Diets Used in Drosophila Microbiome Research and Introduction of the Drosophila Dietary Composition Calculator (DDCC)

Nutrition is a major factor influencing many aspects of Drosophila melanogaster physiology. However, a wide range of diets, many of which are termed "standard" in the literature, are utilized for D. melanogaster research, leading to inconsistencies in reporting of nutrition-dependent phenotypes across the field. This is especially evident in microbiome studies, as diet has a pivotal role in microbiome composition and resulting host-microbe interactions. Here, we performed a meta-analysis of diets used in fly microbiome research and provide a web-based tool for researchers to determine the nutritional content of diets of interest. While our meta-analysis primarily focuses on microbiome studies, our goal in developing these resources is to aid the broader community in contextualizing past and future studies across the scope of D. melanogaster research to better understand how individual lab diets can contribute to observed phenotypes.
Chapter 13 – Selected Pharmacological Methods

Detailed methods are described utilizing real data to illustrate saturation and displacement binding, functional comparison of agonists with respect to affinity and efficacy, the calculation of equilibrium dissociation constants of antagonists-receptor complexes for competitive and non competitiveorthosteric antagonists and also the effects of allosteric modulators on agonist affinity and efficacy.
Identifying sequence perturbations to an intrinsically disordered protein that determine its phase-separation behavior

Phase separation of intrinsically disordered proteins (IDPs) commonly underlies the formation of membraneless organelles, which compartmentalize molecules intracellularly in the absence of a lipid membrane. Identifying the protein sequence features responsible for IDP phase separation is critical for understanding physiological roles and pathological consequences of biomolecular condensation, as well as for harnessing phase separation for applications in bioinspired materials design. To expand our knowledge of sequence determinants of IDP phase separation, we characterized variants of the intrinsically disordered RGG domain from LAF-1, a model protein involved in phase separation and a key component of P granules. Based on a predictive coarse-grained IDP model, we identified a region of the RGG domain that has high contact probability and is highly conserved between species; deletion of this region significantly disrupts phase separation in vitro and in vivo. We determined the effects of charge patterning on phase behavior through sequence shuffling. We designed sequences with significantly increased phase separation propensity by shuffling the wild-type sequence, which contains well-mixed charged residues, to increase charge segregation. This result indicates the natural sequence is under negative selection to moderate this mode of interaction. We measured the contributions of tyrosine and arginine residues to phase separation experimentally through mutagenesis studies and computationally through direct interrogation of different modes of interaction using all-atom simulations. Finally, we show that despite these sequence perturbations, the RGG-derived condensates remain liquid-like. Together, these studies advance our fundamental understanding of key biophysical principles and sequence features important to phase separation.
The Zachman Framework: An Information System Architecture for DMS

A Disease Management System (DMS) promises patient centered care delivered throughout the course of a disease in an integrated healthcare delivery system. The DMS is a radical departure from traditional modes of care that primarily focus on the caregiver—caretaker relationship. The DMS is not meant to undermine this relationship, but add continuity and intelligence to the ongoing care of patients. Compared to the traditional relationship, a DMS requires more thought, consideration, and planning. An extensive data, information, and knowledge infrastructure is needed that facilitates the operation of the DMS. Yet, such an infrastructure requires a new paradigm for its creation, implementation, and maintenance. We propose the need for an information system architecture in this setting. Others have used the Zachman Framework for Information Systems Architecture (ZFISA) in different industries for this purpose. Whether the ZFISA is chosen is less significant than the need for comprehensive information system m...
Tax competition with parasitic tax havens

We develop a tax competition framework in which some jurisdictions, called tax havens, are parasitic on the revenues of other countries. The havens use real resources to help companies camouflage their home-country tax avoidance, and countries use resources in an attempt to limit the transfer of tax revenues to the havens. The equilibrium price for this service depends on the demand and supply for such protection. Recognizing that taxes on wage income are also evaded, we solve for the equilibrium tax rates on mobile capital and immobile labor, and we demonstrate that the full or partial elimination of tax havens would improve welfare in non-haven countries, in part because countries would be induced to increase their tax rates, which they have set at inefficiently low levels in an attempt to attract mobile capital. We also demonstrate that the smaller countries choose to become tax havens, and we show that the abolishment of a sufficiently small number of the relatively large havens leaves all countries better off, including the remaining havens.
POLITICAL CAREERS OR CAREER POLITICIANS

Two main career paths are prevalent among politicians in modern democracies: there are career politicians (i.e., politicians who work in the political sector until retirement), and political careers (i.e., there are politicians who leave politics before retirement and work in the private sector). In this paper, we propose a dynamic equilibrium model of the careers of politicians in a political economy with a private sector and a political sector, where individuals are heterogeneous with respect to their market ability as well as their political skills. We characterize the conditions under which the two career paths emerge in equilibrium, and establish a number of comparative statics results with respect to the quality of politicians and their careers.
GETTING OUT THE WELL-FIXED KNEE: ALL HANDS ON DECK

Expedient removal of a well fixed total knee replacement relates to obtaining access to the fixation surfaces so as to disrupt the bone implant or bone cement interfaces. The first point is wide exposure, most commonly with a modification of a quads snip procedure. Fixation surface disruption is achieved with a combination of saws, high speed burrs and osteotomes. More extensive metaphyseal fixation may require osteotomy to achieve access. Tubercle osteotomy is one option. We more commonly utilise a medial tibial cortical osteotomy, which preserves the extensor function. Video techniques will be presented.
Investment in new activities and the welfare cost of uncertainty

Abstract Recent literature has shown that trade distortions are associated with first-order costs stemming from the induced drop in the formation of new activities. We demonstrate that uncertainty may induce similar welfare costs, and that these costs apply also to a closed economy. This argument is illustrated in Romer's model of a dependent economy, where foreign direct investment is needed to enable the importation of capital goods and intermediate products used in domestic production. We show that Knightian uncertainty inhibits the formation of new activities, leading to first-order losses, whereas risk aversion alone leads to second-order losses.
Macroeconomic Implications of Modeling the Internal Revenue Code in a Heterogeneous-Agent Framework

Abstract Tax policy analysis in heterogeneous-agent models typically involves the use of smooth tax functions to approximate complex present tax law and proposed reforms. In this paper, we explore the extent to which the tax detail omitted under this conventional approach has macroeconomic implications relevant for policy analysis. To do this, we develop an alternative approach by embedding an internal tax calculator into a large-scale overlapping generations model that, while conditioning on idiosyncratic household characteristics, explicitly models key provisions in the Internal Revenue Code applied to labor income. We find that for a debt-constant steady state analysis of a given tax policy change, both approaches generate similar policy-induced patterns of macroeconomic activity despite variation in the underlying patterns of household tax-preferred consumption and labor supply behavior. However, this variation in underlying behavior is associated with significant quantitative and qualitative differences in macroeconomic aggregates along a debt-financed transition path immediately following a policy change. Consequentially, although the use of unconditional smooth tax functions may be a reasonable modeling simplification for steady state analysis of tax policy, caution should be taken for their use in transition path analysis within heterogeneous-agent models.
Secure Two-Party k-Means Clustering.

The k-Means Clustering problem is one of the most-explored problems in data mining to date. With the advent of protocols that have proven to be successful in performing single database clustering, the focus has changed in recent years to the question of how to extend the single database protocols to a multiple database setting. To date there have been numerous attempts to create specific multiparty kmeans clustering protocols that protect the privacy of each database, but according to the standard cryptographic definitions of “privacyprotection,” so far all such attempts have fallen short of providing adequate privacy. In this paper we describe a Two-Party k-Means Clustering Protocol that guarantees privacy, and is more efficient than utilizing a general multiparty “compiler” to achieve the same task. In particular, a main contribution of our result is a way to compute efficiently multiple iterations of k-means clustering without revealing the intermediate values. To achieve this, we use novel techniques to perform two-party division and sample uniformly at random from an unknown domain size. Our techniques are quite general and can be realized based on the existence of any semantically secure homomorphic encryption scheme. For concreteness, we describe our protocol based on Paillier Homomorphic Encryption scheme (see [23]). We will also demonstrate that our protocol is efficient in terms of communication, remaining competitive with existing protocols (such as [15]) that fail to protect privacy.
Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy

Refractory multi-principal element alloys (MPEAs) have exceptional mechanical properties, including high strength-to-weight ratio and fracture toughness, at high temperatures. Here we elucidate the complex interplay between segregation, short-range order, and strengthening in the NbMoTaW MPEA through atomistic simulations with a highly accurate machine learning interatomic potential. In the single crystal MPEA, we find greatly reduced anisotropy in the critically resolved shear stress between screw and edge dislocations compared to the elemental metals. In the polycrystalline MPEA, we demonstrate that thermodynamically driven Nb segregation to the grain boundaries (GBs) and W enrichment within the grains intensifies the observed short-range order (SRO). The increased GB stability due to Nb enrichment reduces the von Mises strain, resulting in higher strength than a random solid solution MPEA. These results highlight the need to simultaneously tune GB composition and bulk SRO to tailor the mechanical properties of MPEAs.