cssi/SciSciGPT-SciSciNet huggingface dataset:
Marangoni-driven film climbing on a draining pre-wetted film

Marangoni flow is the motion induced by a surface tension gradient along a fluid–fluid interface. In this study, we report a Marangoni flow generated when a bath of surfactant contacts a pre-wetted film of deionized water on a vertical substrate. The thickness profile of the pre-wetted film is set by gravitational drainage and so varies with the drainage time. The surface tension is lower in the bath due to the surfactant, and thus a liquid film climbs upwards along the vertical substrate due to the surface tension difference. Particle tracking velocimetry is performed to measure the dynamics in the film, where the mean fluid velocity reverses direction as the draining film encounters the front of the climbing film. The effect of the surfactant concentration and the pre-wetted film thickness on the film climbing is then studied. High-speed interferometry is used to measure the front position of the climbing film and the film thickness profile. As a result, higher surfactant concentration induces a faster and thicker climbing film. Also, for high surfactant concentrations, where Marangoni driving dominates, increasing the film thickness increases the rise speed of the climbing front, since viscous resistance is less important. In contrast, for low surfactant concentrations, where Marangoni driving balances gravitational drainage, increasing the film thickness decreases the rise speed of the climbing front while enhancing gravitational drainage. We rationalize these observations by utilizing a dimensionless parameter that compares the magnitudes of the Marangoni stress and gravitational drainage. A model is established to analyse the climbing front, either in the Marangoni-driving-dominated region or in the Marangoni-balanced drainage region. Our work highlights the effects of the gravitational drainage on the Marangoni flow, both by setting the thickness of a pre-wetted film and by resisting the film climbing.
Reducing the pain: A cost-effectiveness analysis of transversus abdominis plane block using liposomal bupivacaine for outpatient laparoscopic ventral hernia repair.

Abstract Background Transversus abdominis plane block with liposomal bupivacaine has been studied as an effective method of reducing the need for postoperative opioids and increasing same-day discharge rates. However, less is known about the cost-effectiveness of this strategy relative to opioids alone for hernia repair. We performed an economic evaluation of these strategies using a computer simulation model. Methods A decision tree was constructed to determine cost-effectiveness as measured by incremental cost-effectiveness ratios per quality-adjusted life-year. Base-case costs, quality-adjusted life-year values, and probabilities were derived from published studies and Medicare fee schedules. For input parameters for which we could not find values in the published literature, we used expert opinion. A 1-month time horizon was selected to focus on the immediate postoperative period. Finally, we performed 1-way, 2-way, and probabilistic sensitivity analyses. Results The liposomal bupivacaine transversus abdominis plane block was a dominant strategy yielding a $456.75 decrease in cost and an 0.1 increase in quality-adjusted life-years relative to opioids alone. In 1-way sensitivity analysis of cost incremental cost-effectiveness ratio, values were most sensitive to variations in the amount saved by same-day discharge and the cost of bupivacaine. In probabilistic sensitivity analyses, transversus abdominis plane strategy was cost-effective at a willingness-to-pay threshold of $50,000/quality-adjusted life-year in 94.5% of iterations and at a willingness-to-pay threshold of $100,000/quality-adjusted life-year in 97.1% of iterations. Conclusion The use of liposomal bupivacaine transversus abdominis plane block resulted in cost savings and improved quality-adjusted life-years in base-case analyses and was cost-effective at conventional willingness-to-pay thresholds in the majority of iterations in probabilistic sensitivity analyses.
Effectiveness and Equity in Social Spending - The Case of Spain

Spain is experiencing sustained economic and social disparities in several areas. Social spending policies have a heightened responsibility to respond but are challenged by high public debt and pressures from an aging society. This study takes stock of the level and effectiveness of public social expenditure from a cross-country and macroeconomic view, complementing recent targeted spending reviews. The results suggest that social protection spending should aim to improve redistribution through better targeting the most vulnerable while more effective education and active labor market policies should aim to create more equal opportunities and income prospects. In some areas more fiscal resources are needed. But social spending alone cannot reduce inequality, and efforts also should be directed toward making the labor market more inclusive.
No evidence for brown carbon formation in ambient particles undergoing atmospherically relevant drying

Recent laboratory studies have reported the formation of light-absorbing organic carbon compounds (brown carbon, BrC) in particles undergoing drying. Atmospheric particles undergo cycles of humidification and drying during vertical transport and through daily variations in temperature and humidity, which implies particle drying could potentially be an important source of BrC globally. In this work, we investigated BrC formation in ambient particles undergoing drying at a site in the eastern United States during summer. Aerosol BrC concentrations were linked to secondary organic aerosol (SOA) formation, consistent with seasonal expectations for this region. Measurements of water-soluble organic aerosol concentrations and light absorption (365 nm) were alternated between an unperturbed channel and a channel that dried particles to 41% or 35% relative humidity (RH), depending on the system configuration. The RH maintained in the dry channels was below most ambient RH levels observed throughout the study. We did not observe BrC formation in particles that were dried to either RH level. The results were consistent across two summers, spanning ∼5 weeks of measurements that included a wide range of RH conditions and organic and inorganic aerosol loadings. This work suggests that mechanisms aside from humidification-drying cycles are more important contributors to ambient particle BrC loadings. The implications of this work on the atmospheric budget of BrC are discussed.
Nonlinear Equivalent Resistance-based Maximum Power Point Tracking (MPPT)

We present a nonlinear equivalent resistance tracking method to optimize the power output for solar arrays. Tracking an equivalent resistance results in nonlinear voltage step sizes in the gradient descent search loop. We introduce a new model for the combined solar module along with a DC-DC converter which results in a highly nonlinear dynamical system due to the inherent non-linearity of the PV cell topology and the switched DC-DC converter system. To guarantee stability over a range of possible operating regimes, we utilize a feedback linearization control approach to exponentially converge to the setpoint. Simulations are presented to illustrate the performance and robustness of the proposed technique.
Social Homelessness on US Campuses

It is a series of selfie (self-portrait photograph) with light and computer algorithm, compared to photography, drawing with light. Initially, it began with an interactive art, Being Ignored Version 1.0, to recognize the dignity and humanity of those who are homeless, being invisibly treated, and ignored, in Porter County, Indiana, which is relatively a wealthy county, compared with neighboring counties in Indiana. Later it has extended to the project, the Social Homelessness on US Campuses. It was inspired by the book "Here I am, Faith Stories of Korean American Clergy-women". It defines that social homelessness has no friends with whom they can associate or count on, they have no place to go. They seem to be very isolated, and alienated [1]. Asian female faculty on US campuses may experience the highest levels of isolation, and marginalization on US campuses [2], compared with other minority faculty groups. Particularly the Asian female faculty from predominantly patriarchal Asian cultures may be deeply challenged to navigate where they belong between two distinguished communities, Asian, and American. For instance, their conventional social role as being a full-time mother would be more supportive, inclusive and welcome in their traditional ethnic groups, but working as Asian female faculty may occasionally face less hospitable and somewhat harsh responses from the groups since their existences may impact negatively their patriarchal hierarchy led by men. Simultaneously the Asian female faculty may invisibly being demanded to be more persistent, dutiful, and non-complaining for extra service activities without compensatory supports on US campuses. Also, they may be subject to harsh teaching environments by student resistances and lower student evaluations on US campuses. It is routinely challenged for the Asian female faculty to find psychological and social comfort zones to rest between two exclusive communities, Asian, and American. The social homelessness on US campuses is a multidisciplinary art research project to raise awareness of the Asian female faculty on US campuses by using a series of self-portrait photograph with the software, Being Ignored 1.0 as a mobile application.
To Update or Not To Update?: Bandwidth-Efficient Intelligent Replacement Policies for DRAM Caches

This paper investigates intelligent replacement policies for improving the hit-rate of gigascale DRAM caches. Cache replacement policies are commonly used to improve the hit-rate of on-chip caches. The most effective replacement policies often require the cache to track and update per-line reuse state to inform their decision. A fundamental challenge on DRAM caches, however, is that stateful policies would require significant bandwidth to maintain per-line DRAM cache state. As such, DRAM cache replacement policies have primarily been stateless policies, such as always-install or probabilistic bypass. Unfortunately, we find that stateless policies are often too coarse-grain and become ineffective at the size and associativity of DRAM caches. Ideally, we want a replacement policy that can obtain the hit-rate benefits of stateful replacement policies, but keep the bandwidth-efficiency of stateless policies. We perform our study on a DRAM cache design similar to the one in Knights Landing, and find that tracking per-line reuse state can enable an effective replacement policy that can mitigate the common thrashing patterns seen in gigascale caches. We propose a stateful replacement/bypass policy called RRIP Age-On-Bypass (RRIP-AOB), that tracks reuse state for high-reuse lines, protects such lines by bypassing other lines, and Ages the state On cache Bypass. Unfortunately, such a stateful technique requires significant bandwidth to update state. To this end, we propose Efficient Tracking of Reuse (ETR). ETR makes state tracking efficient by accurately tracking the state of only one line from a region, and using the state of that line to guide the replacement decisions for other lines in that region. ETR reduces the bandwidth for tracking replacement state by 70%, and makes stateful policies practical for DRAM caches. Our evaluations with a 2GB DRAM cache, show that our RRIP-AOB and ETR techniques provide 18% speedup while needing <1KB SRAM.
Inference of synaptic connectivity and external variability in neural microcircuits

A major goal in neuroscience is to estimate neural connectivity from large scale extracellular recordings of neural activity in vivo. This is challenging in part because any such activity is modulated by the unmeasured external synaptic input to the network, known as the common input problem. Many different measures of functional connectivity have been proposed in the literature, but their direct relationship to synaptic connectivity is often assumed or ignored. For in vivo data, measurements of this relationship would require a knowledge of ground truth connectivity, which is nearly always unavailable. Instead, many studies use in silico simulations as benchmarks for investigation, but such approaches necessarily rely upon a variety of simplifying assumptions about the simulated network and can depend on numerous simulation parameters. We combine neuronal network simulations, mathematical analysis, and calcium imaging data to address the question of when and how functional connectivity, synaptic connectivity, and latent external input variability can be untangled. We show numerically and analytically that, even though the precision matrix of recorded spiking activity does not uniquely determine synaptic connectivity, it is in practice often closely related to synaptic connectivity. This relation becomes more pronounced when the spatial structure of neuronal variability is jointly considered.
The Soft-Landing Problem: Minimizing Energy Loss by a Legged Robot Impacting Yielding Terrain

Enabling robots to walk and run on yielding terrain is vital to endeavors ranging from disaster response to extraterrestrial exploration. While dynamic legged locomotion on rigid ground is challenging enough, yielding terrain presents additional challenges such as ground deformation which dissipates energy. In this paper, we examine the soft-landing problem: given some impact momentum, bring the robot to rest while minimizing foot penetration depth. To gain insight into properties of penetration depth-minimizing control policies, we formulate a constrained optimal control problem and obtain a bang-bang open-loop force profile. Motivated by examples from biology and recent advances in legged robotics, we also examine impedance-control solutions to the soft-landing problem. Through simulations and experiments, we find that optimal impedance reduces penetration depth nearly as much as the open-loop force profile, while remaining robust to model uncertainty. Lastly, we discuss the relevance of this work to minimum-cost-of-transport locomotion for several actuator design choices.
How do U.S. Visa Policies Affect Unauthorized Immigration

Abstract How would increasing the number of visas available to potential migrants affect unauthorized immigration from Mexico to the U.S.? Current U.S. policy bans people who are deported from receiving legal status for a period of time. This policy aims to serve as an additional deterrent to unauthorized immigration, but may be ineffective given that most potential Mexican migrants have an extremely low probability of ever being able to legally move to the U.S. We develop a dynamic discrete location choice model, which we estimate using data from the Mexican Migration Project, and consider various counterfactual policies that vary the intensity of enforcement and access to work visas. We find that legal entry bans for deported individuals are ineffective at current rates of legal immigration, but that increased legalization rates would amplify the deterrent effects of deportation. We also show that a temporary work visa program would yield similar deterrent effects as an increase in permanent legalization without resulting in very large increases in the total stock of migrants residing in the U.S. These findings have important implications for structuring future immigration reforms.
Identification of the Targets of T-cell Receptor Therapeutic Agents and Cells by Use of a High-Throughput Genetic Platform

T-cell receptor (TCR)-based therapeutic cells and agents have emerged as a new class of effective cancer therapies. These therapies work on cells that express intracellular cancer-associated proteins by targeting peptides displayed on MHC receptors. However, cross-reactivities of these agents to off-target cells and tissues have resulted in serious, sometimes fatal, adverse events. We have developed a high-throughput genetic platform (termed "PresentER") that encodes MHC-I peptide minigenes for functional immunologic assays and determines the reactivities of TCR-like therapeutic agents against large libraries of MHC-I ligands. In this article, we demonstrated that PresentER could be used to identify the on-and-off targets of T cells and TCR-mimic (TCRm) antibodies using in vitro coculture assays or binding assays. We found dozens of MHC-I ligands that were cross-reactive with two TCRm antibodies and two native TCRs and that were not easily predictable by other methods.
Optimal Path Planning for Connected and Automated Vehicles at Urban Intersections

In earlier work, a decentralized optimal control framework was established for coordinating online connected and automated vehicles (CAVs) at urban intersections. The policy designating the sequence that each CAV crosses the intersection, however, was based on a first-in-first-out queue, imposing limitations on the optimal solution. Moreover, no lane changing, or left and right turns were considered. In this paper, we formulate an upper-level optimization problem, the solution of which yields, for each CAV, the optimal sequence and lane to cross the intersection. The effectiveness of the proposed approach is illustrated through simulation.
Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach

In this work, we consider the resilience of distributed algorithms based on stochastic gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who could send arbitrary information to the parameter server to disrupt the training process. Toward this end, we propose a new Lipschitz-inspired coordinate-wise median approach (LICM-SGD) to mitigate Byzantine attacks. We show that our LICM-SGD algorithm can resist up to half of the workers being Byzantine attackers, while still converging almost surely to a stationary region in non-convex settings. Also, our LICM- SGD method does not require any information about the number of attackers and the Lipschitz constant, which makes it attractive for practical implementations. Moreover, our LICM- SGD method enjoys the optimal O(md) computational time- complexity in the sense that the time-complexity is the same as that of the standard SGD under no attacks. We conduct extensive experiments to show that our LICM-SGD algorithm consistently outperforms existing methods in training multiclass logistic regression and convolutional neural networks with MNIST and CIFAR-10 datasets. In our experiments, LICM- SGD also achieves a much faster running time thanks to its low computational time-complexity.
Abnormal expression of GABAA receptor sub-units and hypomotility upon loss of gabra1 in zebrafish.

We used whole exome sequencing (WES) to determine the genetic etiology of a patient with a multi-system disorder characterized by a seizure phenotype. WES identified a heterozygous de novo missense mutation in the GABRA1 gene (c.875C>T). GABRA1 encodes the alpha subunit of the Gamma-Aminobutyric Acid receptor A (GABAAR). The GABAAR is a ligand gated ion channel that mediates the fast inhibitory signals of the nervous system and mutations in the sub-units that compose the GABAAR have been previously associated with human disease. To understand the mechanisms by which GABRA1 regulates brain development, we developed a zebrafish model of gabra1 deficiency. gabra1 expression is restricted to the nervous system and behavioral analysis of morpholino injected larvae suggests that the knockdown of gabra1 results in hypoactivity and defects in the expression of other sub-units of the GABAAR. Expression the human GABRA1 protein in morphants partially restored the hypomotility phenotype. In contrast, the expression of the c.875C>T variant did not restore these behavioral deficits. Collectively, these results represent a functional approach to understand the mechanisms by which loss of function alleles cause disease.
Infinite Nuclear Matter on the Light Front: a Modern Approach to Brueckner Theory

Understanding an important class of experiments requires that light-front dynamics and the related light cone variables k+, k⊥ be used. If one uses k+=k0+k3 as a momentum variable the corresponding canonical spatial variable is x-=x0-x3 and the time variable is x0+x3. This is the light front (LF) approach of Dirac. A relativistic light front formulation of nuclear dynamics is developed and applied to treating infinite nuclear matter in a method which includes the correlations of pairs of nucleons. This is light front Brueckner theory.
Thermodynamics of computing with circuits

Digital computers implement computations using circuits, as do many naturally occurring systems (e.g., gene regulatory networks). The topology of any such circuit restricts which variables may be physically coupled during the operation of a system implementing a circuit. We investigate how such restrictions on the physical coupling affects the thermodynamic costs of the circuit. To do this we first calculate the minimal additional entropy production that arises when we run a given gate in a circuit, if the physical process implementing that gate can only be coupled to variables in the rest of the circuit that feed in or out of that gate. We then build on this calculation, to analyze how the thermodynamic costs of implementing a computation with a full circuit, comprising multiple connected gates, depends on the topology of that circuit. This analysis provides a rich new set of optimization problems that must be addressed by any designer of a circuit, if they wish to minimize thermodynamic costs.
StartReact increases the probability of muscle activity and distance in severe/moderate stroke survivors during two-dimensional reaching task

StartReact elicits faster, larger, and more appropriate muscle activation in stroke survivors but has been only cursorily studied to date during multi-jointed reaching. Our objective was to evaluate StartReact on unrestricted, two-dimensional point-to-point reaching tasks post-stroke. Data from 23 individuals with stroke were collected during point-to-point reaching. Voluntary and StartReact trials were compared between mild, severe/moderate, and the unimpaired arm. StartReact showed an increase in probability of muscle activity, larger muscle activity amplitude, and faster muscle activity onset. Despite changes in muscle activity, metrics of movement (distance, final error, linear deviation) were not different between StartReact and Voluntary trials except in severe/moderate stroke who had larger reaching distances during StartReact. While StartReact impacted many metrics of muscle activity, the most profound effect was on the probability of muscle activity increasing 34% compared to voluntary which allowed severe/moderate subjects to increase reaching distance but did not translate to decrease in final error suggesting that the additional movement was not always directed towards the appropriate target. These results indicate that SR has the capacity to activate paralyzed muscle in severe/moderate patients, but future studies are needed to explore the possible use of SR in the rehabilitation.
p-Cycle Design in Survivable WDM Networks with Shared Risk Link Groups (SRLGs)

p-Cycle survivable network design under the single link failure assumption has been studied extensively. Shared risk link group (SRLG) is a concept that better reflects the nature of network failures. An SRLG is a set of links that may fail simultaneously because of a common risk they share. The capability of dealing with SRLG failures is essential to network survivability. In this paper, we extend the p-cycle survivable network design from the single link failure model to the single SRLG failure model. An integer linear programming (ILP) formulation that minimizes spare capacity requirement is provided. To avoid enumerating all cycles of a network, we also provide a polynomial-time algorithm to generate a basic candidate p-cycle set that guarantees 100% restorability in case of any single SRLG failure given enough spare capacities. Moreover, we present the SRLG failure detection problem that prevents fast restoration upon an SRLG failure. To solve this problem, we introduce the concept of SRLG-independent restorability, which enables the restoration of each link in a failed SRLG to start immediately without knowing which SRLG has failed. We present an approach to optimal p-cycle design with SRLG-independent restorability and show that it is NP-hard to generate a candidate p-cycle set such that each link can be SRLG-independently restored by at least one cycle in the set.
The Adjunction Inequality for Weyl-Harmonic Maps

In this paper we study an analog of minimal surfaces called Weyl-minimal surfaces in conformal manifolds with a Weyl connection $(M^4,c,D)$. We show that there is an Eells-Salamon type correspondence between nonvertical $\mathcal{J}$-holomorphic curves in the weightless twistor space and branched Weyl-minimal surfaces. When $(M,c,J)$ is conformally almost-Hermitian, there is a canonical Weyl connection. We show that for the canonical Weyl connection, branched Weyl-minimal surfaces satisfy the adjunction inequality \begin{equation}\label{adj} \chi(T_f\Sigma)+\chi(N_f\Sigma) \le \pm c_1(f^*T^{(1,0)}M). \end{equation} The $\pm J$-holomorphic curves are automatically Weyl-minimal and satisfy the corresponding equality.
The algebra of Schur operators

Abstract We study a representation of the (local) plactic monoid given by Schur operators u i , which act on partitions by adding a box in column i (if possible). In particular, we give a complete list of the relations that hold in the algebra of Schur operators.
Efficient Trainable Front-Ends for Neural Speech Enhancement

Many neural speech enhancement and source separation systems operate in the time-frequency domain. Such models often benefit from making their Short-Time Fourier Transform (STFT) front-ends trainable. In current literature, these are implemented as large Discrete Fourier Transform matrices; which are prohibitively inefficient for low-compute systems. We present an efficient, trainable front-end based on the butterfly mechanism to compute the Fast Fourier Transform, and show its accuracy and efficiency benefits for low-compute neural speech enhancement models. We also explore the effects of making the STFT window trainable.
Initially regular sequences and depths of ideals

Abstract For an arbitrary ideal I in a polynomial ring R we define the notion of initially regular sequences on R / I . These sequences share properties with regular sequences. In particular, the length of an initially regular sequence provides a lower bound for the depth of R / I when I is homogeneous. Using combinatorial information from the initial ideal of I we construct sequences of linear polynomials that form initially regular sequences on R / I . We identify situations where initially regular sequences are also regular sequences, and we show that our results can be combined with polarization to improve known depth bounds for general monomial ideals.
Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis

Human brain development is a complex and dynamic process caused by several factors such as genetics, sex hormones, and environmental changes. A number of recent studies on brain development have examined functional connectivity (FC) defined by the temporal correlation between time series of different brain regions. We propose to add the directional flow of information during brain maturation. To do so, we extract effective connectivity (EC) through Granger causality (GC) for two different groups of subjects, i.e., children and young adults. The motivation is that the inclusion of causal interaction may further discriminate brain connections between two age groups and help to discover new connections between brain regions. The contributions of this study are threefold. First, there has been a lack of attention to EC-based feature extraction in the context of brain development. To this end, we propose a new kernel-based GC (KGC) method to learn nonlinearity of complex brain network, where a reduced Sine hyperbolic polynomial (RSP) neural network was used as our proposed learner. Second, we used causality values as the weight for the directional connectivity between brain regions. Our findings indicated that the strength of connections was significantly higher in young adults relative to children. In addition, our new EC-based feature outperformed FC-based analysis from Philadelphia neurocohort (PNC) study with better discrimination of different age groups. Moreover, the fusion of these two sets of features (FC + EC) improved brain age prediction accuracy by more than 4%, indicating that they should be used together for brain development studies.
Establishment Surveys With Population Survey–Generated Sampling Frames†

When stand-alone sampling frames list all establishments and size measures, the Hansen–Hurwitz (HH) pps estimator is generally used to estimate the volume of transactions between establishments and households. The network sampling (NS) version of the HH estimator depends on a population-survey-generated establishment frame, which lists establishments that have transactions with households in a population sample survey and the number of transactions that establishments have with survey households. The NS estimator is a competitor of the HH estimator whenever stand-alone frames of good quality are unavailable and, as this article indicates, the NS estimator may be competitive with the HH estimator when stand-alone and population-survey-generated frames are flawless. Keywords: establishment surveys; sampling frames; network sampling; Hansen–Hurwitz estimator
Specialization, Firms, and Markets: The Division of Labor Within and Between Law Firms

What is the role of firms and markets in mediating the division of labor? This paper uses confidential microdata from the Census of Services to examine law firms' boundaries. We first examine how the specialization of lawyers and firms increases as lawyers' returns to specializatio n increase. In fields where lawyers increasingly specialize with market size, the relationship between the share of lawyers who work in a field-specialized firm and market size indicates whether firms or markets more efficiently mediate relationships between lawyers in this and other fields. We then examine which pairs of specialists tend to work in the same versus different firms; this provides evidence on the scope of firms that are not field-specialized. We find that whether firms or markets mediate the division of labor varies across fields in a way that corresponds to differences in the value of cross -field referrals, consistent with Garicano and Santos' (2001) proposition that firms facilitate specialization by mediating exchanges of economic opportunities more efficiently than markets.
Salience, Myopia, and Complex Dynamic Incentives: Evidence from Medicare Part D

The standard Medicare Part D drug insurance contract is non-linear—with reduced subsidies in a coverage gap—resulting in a dynamic purchase problem. We consider enrolees who arrived near the gap early in the year and show that they should expect to enter the gap with high probability, implying that, under a benchmark model with neoclassical preferences, the gap should impact them very little. We find that these enrolees have flat spending in a period before the doughnut hole and a large spending drop in the gap, providing evidence against the benchmark model. We structurally estimate behavioural dynamic drug purchase models and find that a price salience model where enrolees do not incorporate future prices into their decision-making at all fits the data best. For a nationally representative sample, full price salience would decrease enrolee spending by 31%. Entirely eliminating the gap would increase insurer spending 27%, compared to 7% for generic-drug-only gap coverage.
A murine model of the exercise pressor reflex

Key points The decerebrate mouse provides a novel working model of the exercise pressor reflex (EPR). The decerebrate mouse model of the EPR is similar to the previously described decerebrate rat model. Studying the EPR in transgenic mouse models can define exact mechanisms of the EPR in health and disease. Abstract The exercise pressor reflex (EPR) is defined by a rise in mean arterial pressure (MAP) and heart rate (HR) in response to exercise and is necessary to match metabolic demand and prevent premature fatigue. While this reflex is readily tested in humans, mechanistic studies are largely infeasible. Here, we have developed a novel murine model of the EPR to allow for mechanistic studies in various mouse models. We observed that ventral root stimulation (VRS) in an anaesthetized mouse causes a depressor response and a reduction in HR. In contrast, the same stimulation in a decerebrate mouse causes a rise in MAP and HR which is abolished by dorsal rhizotomy or by neuromuscular blockade. Moreover, we demonstrate a reduced MAP response to VRS using TRPV1 antagonism or in Trpv1 null mice while the response to passive stretch remains intact. Additionally, we demonstrate that intra-arterial infusion of capsaicin results in a dose-related rise in MAP and HR that is significantly reduced by a selective and potent TRPV1 antagonist or is completely abolished in Trpv1 null mice. These data serve to validate the development of a decerebrate mouse model for the study of cardiovascular responses to exercise and further define the role of the TRPV1 receptor in mediating the EPR. This novel model will allow for extensive study of the EPR in unlimited transgenic and mutant mouse lines, and for an unprecedented exploration of the molecular mechanisms that control cardiovascular responses to exercise in health and disease.
Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods

Researchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate. We compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a protein pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric. Our work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.
Cross-hemispheric gamma synchrony between prefrontal parvalbumin interneurons supports behavioral adaptation during rule shift learning

Organisms must learn new strategies to adapt to changing environments. Activity in different neurons often exhibits synchronization that can dynamically enhance their communication and might create flexible brain states that facilitate changes in behavior. We studied the role of gamma-frequency (~40 Hz) synchrony between prefrontal parvalbumin (PV) interneurons in mice learning multiple new cue-reward associations. Voltage indicators revealed cell-type-specific increases of cross-hemispheric gamma synchrony between PV interneurons when mice received feedback that previously learned associations were no longer valid. Disrupting this synchronization by delivering out-of-phase optogenetic stimulation caused mice to perseverate on outdated associations, an effect not reproduced by in-phase stimulation or out-of-phase stimulation at other frequencies. Gamma synchrony was specifically required when new associations used familiar cues that were previously irrelevant to behavioral outcomes, not when associations involved new cues or for reversing previously learned associations. Thus, gamma synchrony is indispensable for reappraising the behavioral salience of external cues.
An atlas of the catalytically active liver and spleen kinases in chicken identified by chemoproteomics

Abstract Phosphorylation is a post-translational protein modification regulating most known cellular processes. While protein kinases constitute a large family of highly conserved enzymes, identification of active kinases is challenging due to a low abundance of some of these signaling molecules. Although chicken is the first agricultural animal to have a sequenced genome, annotation of the kinome, i.e., a complement of all protein kinases in the genome is limited. We used chemical probes consisting of ATP and ADP derivatives binding to specific lysine (Lys) residues within the ATP-binding pocket of kinases, combined with proteomics, to identify 267 peptides labeled with the ATP and ADP acyl derivatives and 188 corresponding chicken kinases in chicken spleen and liver. Our description of active chicken kinases and ATP binding sites will support future studies focused on identifying the role of this important class of enzymes in chicken health and disease. Significance Advances made in understanding chicken enzymes are critical for the improved knowledge of the regulatory pathways controlling physiological processes in chicken. Since protein phosphorylation controls multiple aspects of cell fate, it is often linked to pathological conditions, and understanding of the kinase expression in chicken is essential for future therapeutic approaches. We coupled proteomics and labeling with active-site probes binding to Lys residues within the ATP-binding pocket of kinases to identify 188 kinases and corresponding 267 peptides labeled with the ATP and ADP acyl derivatives in chicken spleen and liver. Results of the present study describing catalytically active kinases is a starting point for chemoproteomic-based interrogation of kinases in chicken exposed to different conditions. Kinases identified in this study are available through the Chickspress genome browser that has previously published mRNA, miRNA, and shotgun proteomics data.
Towards Backward-Compatible Representation Learning

We propose a way to learn visual features that are compatible with previously computed ones even when they have different dimensions and are learned via different neural network architectures and loss functions. Compatible means that, if such features are used to compare images, then ``new'' features can be compared directly to ``old'' features, so they can be used interchangeably. This enables visual search systems to bypass computing new features for all previously seen images when updating the embedding models, a process known as backfilling. Backward compatibility is critical to quickly deploy new embedding models that leverage ever-growing large-scale training datasets and improvements in deep learning architectures and training methods. We propose a framework to train embedding models, called backward-compatible training (BCT), as a first step towards backward compatible representation learning. In experiments on learning embeddings for face recognition, models trained with BCT successfully achieve backward compatibility without sacrificing accuracy, thus enabling backfill-free model updates of visual embeddings.
In vitro platform establishes antigen-specific CD8 + T cell cytotoxicity to encapsulated cells via indirect antigen recognition

The curative potential of non-autologous cellular therapy is hindered by the requirement of anti-rejection therapy. Cellular encapsulation within nondegradable biomaterials has the potential to inhibit immune rejection, but the efficacy of this approach in robust preclinical and clinical models remains poor. While the responses of innate immune cells to the encapsulating material have been characterized, little attention has been paid to the contributions of adaptive immunity in encapsulated graft destabilization. Avoiding the limitations of animal models, we established an efficient, antigen-specific in vitro platform capable of delineating direct and indirect host T cell recognition to microencapsulated cellular grafts and evaluated their consequential impacts. Using ovalbumin (OVA) as a model antigen, we determined that alginate microencapsulation abrogates direct CD8+ T cell activation by interrupting donor-host interaction; however, indirect T cell activation, mediated by host antigen presenting cells (APCs) primed with shed donor antigens, still occurs. These activated T cells imparted cytotoxicity on the encapsulated cells, likely via diffusion of cytotoxic solutes. Overall, this platform delivers unique mechanistic insight into the impacts of hydrogel encapsulation on host adaptive immune responses, comprehensively addressing a long-standing hypothesis of the field. Furthermore, it provides an efficient benchtop screening tool for the investigation of new encapsulation methods and/or synergistic immunomodulatory agents.
A simple load balancing problem with decentralized information

The following load balancing problem is investigated in discrete time: A service system consists of two service stations and two controllers, one in front of each station. The service stations provide the same service with identical service time distributions and identical waiting costs. Customers requiring service arrive at a controller's site and are routed to one of the two stations by the controller. The processes describing the two arrival streams are identical. Each controller has perfect knowledge of the workload in its own station and receives information about the other station's workload with one unit of delay. The controllers' routing strategies that minimize the customers' total flowtime are determined for a certain range of the parameters that describe the arrival process and the service distribution. Specifically, we prove that optimal routing strategies are characterized by thresholds that are either precisely specified or take one of two possible values.
MKPipe: a compiler framework for optimizing multi-kernel workloads in OpenCL for FPGA

OpenCL for FPGA enables developers to design FPGAs using a programming model similar for processors. Recent works have shown that code optimization at the OpenCL level is important to achieve high computational efficiency. However, existing works either focus primarily on optimizing single kernels or solely depend on channels to design multi-kernel pipelines. In this paper, we propose a source-to-source compiler framework, MKPipe, for optimizing multi-kernel workloads in OpenCL for FPGA. Besides channels, we propose new schemes to enable multi-kernel pipelines. Our optimizing compiler employs a systematic approach to explore the tradeoffs of these optimizations methods. To enable more efficient overlapping between kernel execution, we also propose a novel workitem/workgroup-id remapping technique. Furthermore, we propose new algorithms for throughput balancing and resource balancing to tune the optimizations upon individual kernels in the multi-kernel workloads. Our results show that our compiler-optimized multi-kernels achieve up to 3.6x (1.4x on average) speedup over the baseline, in which the kernels have already been optimized individually.
Evaluation of High Solar Penetration Impact on Bulk System Stability through a Transmission-Distribution Dynamics Co-simulation

Currently, utilities are greatly interested in understanding the interactions between power transmission and distribution, particularly those that impact bulk power stability, as the penetration of distributed energy resources increases. In this paper, we present a transmission and distribution dynamic co-simulation to assess the impacts of increasing distributed photovoltaic (DGPV) penetration on bulk power stability. This method uses a non-iterative coupling algorithm to link the separate dynamic simulators for transmission and distribution systems. We carry out case studies of an IEEE 14-bus transmission system with IEEE 8500-node distribution feeders for disturbances such as loss of generation, faults on the transmission and distribution under different PV penetration levels.
Elastic Multi-resource Network Slicing: Can Protection Lead to Improved Performance?

In order to meet the performance/privacy requirements of future data-intensive mobile applications, e.g., self-driving cars, mobile data analytics, and AR/VR, service providers are expected to draw on shared storage/computation/connectivity resources at the network "edge". To be cost-effective, a key functional requirement for such infrastructure is enabling the sharing of heterogeneous resources amongst tenants/service providers supporting spatially varying and dynamic user demands. This paper proposes a resource allocation criterion, namely, Share Constrained Slicing (SCS), for slices allocated predefined shares of the network's resources, which extends the traditional alpha-fairness criterion, by striking a balance among inter- and intra-slice fairness vs. overall efficiency. We show that SCS has several desirable properties including slice-level protection, envyfreeness, and load driven elasticity. In practice, mobile users' dynamics could make the cost of implementing SCS high, so we discuss the feasibility of using a simpler (dynamically) weighted max-min as a surrogate resource allocation scheme. For a setting with stochastic loads and elastic user requirements, we establish a sufficient condition for the stability of the associated coupled network system. Finally, and perhaps surprisingly, we show via extensive simulations that while SCS (and/or the surrogate weighted max-min allocation) provides inter-slice protection, they can achieve improved job delay and/or perceived throughput, as compared to other weighted max-min based allocation schemes whose intra-slice weight allocation is not share-constrained, e.g., traditional max-min or discriminatory processor sharing.