cssi/SciSciGPT-SciSciNet huggingface dataset:
Opportunistic Channel Estimation for Implicit 802.11af MU-MIMO.

Multi-User MIMO (MU-MIMO) linear channel coding can greatly increase wireless system capacity when Stations (STAs) have fewer antennas than the Access Point (AP), but it comes at the cost of significant Channel State Information (CSI) estimation overhead. Previous work has suggested that 802.11af MU-MIMO systems might benefit from long channel coherence time, extending the useful duration of CSI. In this paper, we propose and analyze an opportunistic channel sounding policy that avoids sounding overhead in wireless channels by gathering implicit CSI opportunistically. This policy not only avoids CSI overhead, but also has the potential to enable efficient interoperability of multi-user APs with legacy single-stream STAs. To investigate the performance of this new policy, we implement a new mobile channel sounding framework on a custom 802.11af Software-Defined Radio (SDR) system designed for UHF-band experimentation and evaluate channel sounding performance in indoor and outdoor environments under various mobility modes. Additional protocol analysis shows that in UHF channels with sufficient channel coherence time, an opportunistic channel sounding policy offers significant protocol optimization while improving the scalability of next-generation MU-MIMO systems.
Swim exercise in Caenorhabditis elegans extends neuromuscular and gut healthspan, enhances learning ability, and protects against neurodegeneration.

Regular physical exercise is the most efficient and accessible intervention known to promote healthy aging in humans. The molecular and cellular mechanisms that mediate system-wide exercise benefits, however, remain poorly understood, especially as applies to tissues that do not participate directly in training activity. The establishment of exercise protocols for short-lived genetic models will be critical for deciphering fundamental mechanisms of transtissue exercise benefits to healthy aging. Here we document optimization of a long-term swim exercise protocol for Caenorhabditis elegans and we demonstrate its benefits to diverse aging tissues, even if exercise occurs only during a restricted phase of adulthood. We found that multiple daily swim sessions are essential for exercise adaptation, leading to body wall muscle improvements in structural gene expression, locomotory performance, and mitochondrial morphology. Swim exercise training enhances whole-animal health parameters, such as mitochondrial respiration and midlife survival, increases functional healthspan of the pharynx and intestine, and enhances nervous system health by increasing learning ability and protecting against neurodegeneration in models of tauopathy, Alzheimer’s disease, and Huntington’s disease. Remarkably, swim training only during early adulthood induces long-lasting systemic benefits that in several cases are still detectable well into midlife. Our data reveal the broad impact of swim exercise in promoting extended healthspan of multiple C. elegans tissues, underscore the potency of early exercise experience to influence long-term health, and establish the foundation for exploiting the powerful advantages of this genetic model for the dissection of the exercise-dependent molecular circuitry that confers system-wide health benefits to aging adults.
Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs

Modern empirical work in Regression Discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This bandwidth yields an MSE-optimal RD treatment effect estimator, but is by construction invalid for inference. Robust bias corrected (RBC) inference methods are valid when using the MSE-optimal bandwidth, but we show they yield suboptimal confidence intervals in terms of coverage error. We establish valid coverage error expansions for RBC confidence interval estimators and use these results to propose new inference-optimal bandwidth choices for forming these intervals. We find that the standard MSE-optimal bandwidth for the RD point estimator is too large when the goal is to construct RBC confidence intervals with the smallest coverage error. We further optimize the constant terms behind the coverage error to derive new optimal choices for the auxiliary bandwidth required for RBC inference. Our expansions also establish that RBC inference yields higher-order refinements (relative to traditional undersmoothing) in the context of RD designs. Our main results cover sharp and sharp kink RD designs under conditional heteroskedasticity, and we discuss extensions to fuzzy and other RD designs, clustered sampling, and pre-intervention covariates adjustments. The theoretical findings are illustrated with a Monte Carlo experiment and an empirical application, and the main methodological results are available in \texttt{R} and \texttt{Stata} packages.
Using Private and Public Assessments in Security Information Sharing Agreements

Information sharing among organizations has been gaining attention as a method for improving cybersecurity. However, the associated disclosure costs act as deterrents for firms’ voluntary cooperation. In this work, we take a game-theoretic approach to understanding firms’ incentives in these agreements. We propose the design of inter-temporal incentives (i.e. conditioning future cooperation on past interactions). Specifically, we show that incentives for full cooperation can be designed if firms share their private assessments of other firms’ disclosure decisions through a common communication platform. We further show that similar incentives can be designed based on outcomes of a public rating/assessment system.
The orientation of a membrane probe from structural analysis by enhanced Raman scattering.

Abstract Small fluorescent molecules are widely used as probes of biomembranes. Different probes optically indicate membrane properties such as the lipid phase, thickness, viscosity, and electrical potential. The detailed molecular mechanisms behind probe signals are not well understood, in part due to the lack of tools to determine probe position and orientation in the membrane. Optical measurements on aligned biomembranes and lipid bilayers provide some degree of orientational information based on anisotropy in absorption, fluorescence, or nonlinear optical properties. These methods typically find the polar tilt angle between the membrane normal and the long axis of the molecule. Here we show that solution-phase surface enhanced Raman scattering (SERS) spectra of lipid membranes on gold nanorods can be used to determine molecular orientation of molecules within the membrane. The voltage sensitive dye 4-(2-(6-(dibutylamino)-2-naphthalenyl)ethenyl)-1-(3-sulfopropyl)-hydroxide, known as di-4-ANEPPS, is studied. Through the analysis of several peaks in the SERS spectrum, the polar angle from the membrane normal is found to be 66°, and the roll angle around the long axis of the molecule to be 305° from the original orientation. This structural analysis method could help elucidate the meaning of fluorescent membrane probe signals, and how they are affected by different lipid compositions.
Metatranscriptomic reconstruction reveals RNA viruses with the potential to shape carbon cycling in soil

Viruses impact nearly all organisms on Earth, with ripples of influence in agriculture, health, and biogeochemical processes. However, very little is known about RNA viruses in an environmental context, and even less is known about their diversity and ecology in soil, 1 of the most complex microbial systems. Here, we assembled 48 individual metatranscriptomes from 4 habitats within a planted soil sampled over a 22-d time series: Rhizosphere alone, detritosphere alone, rhizosphere with added root detritus, and unamended soil (4 time points and 3 biological replicates). We resolved the RNA viral community, uncovering a high diversity of viral sequences. We also investigated possible host organisms by analyzing metatranscriptome marker genes. Based on viral phylogeny, much of the diversity was Narnaviridae that may parasitize fungi or Leviviridae, which may infect Proteobacteria. Both host and viral communities appear to be highly dynamic, and rapidly diverged depending on experimental conditions. The viral and host communities were structured based on the presence of root litter. Clear temporal dynamics by Leviviridae and their hosts indicated that viruses were replicating. With this time-resolved analysis, we show that RNA viruses are diverse, abundant, and active in soil. When viral infection causes host cell death, it may mobilize cell carbon in a process that may represent an overlooked component of soil carbon cycling.
Computing the statistical significance of optimized communities in networks

In scientific problems involving systems that can be modeled as a network (or “graph”), it is often of interest to find network communities - strongly connected node subsets - for unsupervised learning, feature discovery, anomaly detection, or scientific study. The vast majority of community detection methods proceed via optimization of a quality function, which is possible even on random networks without communities. Therefore there is usually not an easy way to tell if a community is “significant”, in this context meaning more internally connected than would be expected under a random graph model without communities. This paper generalizes existing null models and statistical tests for this purpose to bipartite graphs, and introduces a new significance scoring algorithm called Fast Optimized Community Significance (FOCS) that is highly scalable and agnostic to the type of graph. Compared with existing methods on unipartite graphs, FOCS is more numerically stable and better balances the trade-off between detection power and false positives. On a large-scale bipartite graph derived from the Internet Movie Database (IMDB), the significance scores provided by FOCS correlate strongly with meaningful actor/director collaborations on serial cinematic projects.
If wages fell during a recession

Abstract Many economies exhibit downward wage rigidity. Surveys of managers indicate that employers hold wages rigid because they believe morale will suffer after a wage cut. Otherwise, there is little evidence for how employers’ beliefs contribute to wage rigidity and whether those beliefs are accurate. Using an experiment, we demonstrate that effort falls after workers experience a wage cut. Despite this partial confirmation of the Bewley (1999) morale theory, half of the employers in our experiment cut wages and lose money as a result. Under nominal inflation, real wage cuts do not have a significant effect on effort.
Sub-Optimal Control of Autonomous Wheel Loader With Approximate Dynamic Programming

Optimal control of wheel loaders in short loading cycles is studied in this paper. For modeling the wheel loader, the data from a validated diesel engine model is used to find a control oriented mean value engine model. The driveline is modeled as a switched system with three constant gear ratios (modes) of $-60$ for backwarding, $60$ for forwarding, and zero for stopping. With these three modes, the sequence of active modes in a short loading cycle is fixed as backwarding, stopping, forwarding, and stopping. For the control part, it is assumed that the optimal path is known a priori. Given the mode sequence, the control objective is finding the optimal switching time instants between the modes while the wheel loader tracks the optimal path. To solve the optimal control problem, approximate dynamic programming is used. Simulation results are provided to show the effectiveness of the solution.
Opioids modulate an emergent rhythmogenic process to depress breathing

How mammalian neural circuits generate rhythmic activity in motor behaviors, such as breathing, walking, and chewing, remains elusive. For breathing, rhythm generation is localized to a brainstem nucleus, the preBotzinger Complex (preBotC). Rhythmic preBotC population activity consists of strong inspiratory bursts, which drive motoneuronal activity, and weaker burstlets, which we hypothesize reflect an emergent rhythmogenic process. If burstlets underlie inspiratory rhythmogenesis, respiratory depressants, such as opioids, should reduce burstlet frequency. Indeed, in medullary slices from neonatal mice, the μ-opioid receptor (μOR) agonist DAMGO slowed burstlet generation. Genetic deletion of μORs in a glutamatergic preBotC subpopulation abolished opioid-mediated depression, and the neuropeptide Substance P, but not blockade of inhibitory synaptic transmission, reduced opioidergic effects. We conclude that inspiratory rhythmogenesis is an emergent process, modulated by opioids, that does not rely on strong bursts of activity associated with motor output. These findings also point to strategies for ameliorating opioid-induced depression of breathing.
Neural Habituation Enhances Novelty Detection: an EEG Study of Rapidly Presented Words

Huber and O'Reilly (Cognitive Science, 27(3), 403–430, 2003) proposed that neural habituation aids perceptual processing, separating neural responses to currently viewed objects from recently viewed objects. However, synaptic depression has costs, producing repetition deficits. Prior work confirmed the transition from repetition benefits to deficits with increasing duration of a prime object, but the prediction of enhanced novelty detection was not tested. The current study examined this prediction with a same/different word priming task, using support vector machine (SVM) classification of EEG data, event-related potential (ERP) analyses focused on the N400, and dynamic neural network simulations fit to behavioral data to provide a priori predictions of the ERP effects. Subjects made same/different judgements to a response word in relation to an immediately preceding brief target word; prime durations were short (50 ms) or long (400 ms), and long durations decreased P100/N170 potentials to the response word, suggesting that this manipulation increased habituation. Following long duration primes, correct “different” judgments of primed response words increased, evidencing enhanced novelty detection. An SVM classifier predicted trial-by-trial behavior with 66.34% accuracy on held-out data, with greatest predictive power at a time pattern consistent with the N400. The habituation model was augmented with a maintained semantics layer (i.e., working memory) to generate behavior and N400 predictions. A second experiment used response-locked ERPs, confirming the model’s assumption that residual activation in working memory is the basis of novelty decisions. These results support the theory that neural habituation enhances novelty detection, and the model assumption that the N400 reflects updating of semantic information in working memory.
The statistical atom

This review is the updated and enlarged version of a talk delivered by J. S. on the occasion of the 1982 meeting of Nobel laureates at Lindau, and of talks given by B.-G. E. at several West German universities and Max Planck institutes in 1984.
Adaptive controls of FWER and FDR under block dependence

Abstract Often in multiple testing, the hypotheses appear in non-overlapping blocks with the associated p -values exhibiting dependence within but not between blocks. We consider adapting the Benjamini–Hochberg method for controlling the false discovery rate (FDR) and the Bonferroni method for controlling the familywise error rate (FWER) to such dependence structure without losing their ultimate controls over the FDR and FWER, respectively, in a non-asymptotic setting. We present variants of conventional adaptive Benjamini–Hochberg and Bonferroni methods with proofs of their respective controls over the FDR and FWER. Numerical evidence is presented to show that these new adaptive methods can capture the present dependence structure more effectively than the corresponding conventional adaptive methods. This paper offers a solution to the open problem of constructing adaptive FDR and FWER controlling methods under dependence in a non-asymptotic setting and providing real improvements over the corresponding non-adaptive ones.
The basis problem revisited

We explicitly describe the Jacquet-Langlands correspondence at the level of modular forms. This gives a simpler and more flexible solution to Eichler's basis problem for general level than earlier work of Hijikata-Pizer-Shemanske for elliptic modular forms, and solves the basis problem for Hilbert modular forms. The approach is representation theoretic rather than the classical approach of Eichler and Hijikata-Pizer-Shemanske, and involves both a local and global theory of quaternionic newforms.
Bounds on the dimension of Ext for finite groups of Lie type

Abstract Let G be a finite group of Lie type defined in characteristic p, and let k be an algebraically closed field of characteristic r > 0 . We will assume that r ≠ p (so, we are in the non-defining characteristic case). Let V be a finite-dimensional irreducible left kG-module. In 2011, Guralnick and Tiep found bounds on the dimension of H 1 ( G , V ) in non-defining characteristic, which are independent of V. The aim of this paper is to generalize the work of Gurlanick and Tiep. We assume that G is split and use methods of modular Harish-Chandra theory to find bounds on the dimension of Ext k G 1 ( Y , V ) , where Y and V are irreducible kG-modules. We then use Dipper and Du's algorithms to illustrate our bounds in a series of examples.
Optimal Staged Self-Assembly of General Shapes

We analyze the number of tile types t, bins b, and stages necessary to assemble $$n \times n$$ squares and scaled shapes in the staged tile assembly model. For $$n \times n$$ squares, we prove $$\mathcal {O}\left( \frac{\log {n} - tb - t\log t}{b^2} + \frac{\log \log b}{\log t}\right) $$ stages suffice and $$\varOmega \left( \frac{\log {n} - tb - t\log t}{b^2}\right) $$ are necessary for almost all n. For shapes S with Kolmogorov complexity K(S), we prove $$\mathcal {O}\left( \frac{K(S) - tb - t\log t}{b^2} + \frac{\log \log b}{\log t}\right) $$ stages suffice and $$\varOmega \left( \frac{K(S) - tb - t\log t}{b^2}\right) $$ are necessary to assemble a scaled version of S, for almost all S. We obtain similarly tight bounds when the more powerful flexible glues are permitted.
A Canstatin-Derived Peptide Provides Insight into the Role of Capillary Morphogenesis Gene 2 in Angiogenic Regulation and Matrix Uptake

Capillary Morphogenesis Gene 2 protein (CMG2) is a transmembrane, integrin-like receptor and the primary receptor for the anthrax toxin. CMG2 also plays a role in angiogenic processes. However, the molecular mechanism that mediates the observed CMG2-related angiogenic effects is not fully elucidated. Previous studies have reported that CMG2 binds type IV collagen (Col-IV), a vital component of the vascular basement membrane, as well as other ECM proteins. Here, we further characterize the interaction between CMG2 and individual peptides from Col-IV and explore the effects of this interaction on angiogenesis. Using a peptide array, we observed that CMG2 preferentially binds peptide fragments of the NC1 (noncollagenous domain 1) domains of Col-IV. These domains are also known as the fragments arresten (from the α1 chain) and canstatin (from the α2 chain) and have documented antiangiogenic properties. A second peptide array was probed to map a putative peptide-binding epitope onto the Col-IV structure. A top hit from the initial array, a canstatin-derived peptide, binds to the CMG2 ligand-binding von Willebrand factor A (vWA) domain with a submicromolar affinity (peptide S16, Kd = 400 ± 200 nM). This peptide competes with anthrax protective antigen (PA) for CMG2 binding and does not bind CMG2 in the presence of EDTA. Together these data suggest that, like PA, S16 interacts with CMG2 at the metal-ion dependent adhesion site (MIDAS) of its vWA domain. CMG2 specifically mediates endocytic uptake of S16; both CMG2-/- endothelial cells and WT cells treated with PA show markedly reduced S16 uptake. Furthermore, S16 dramatically reduces directional endothelial cell migration with no impact on cell proliferation. These data demonstrate that this canstatin-derived peptide acts via CMG2 to elicit a marked effect on a critical process required for angiogenesis.
Depressive symptom severity as a predictor of attendance in the HOME behavioral weight loss trial.

Abstract Objective We examined whether total depressive symptoms and symptom clusters predicted behavioral weight loss attendance among economically disadvantaged adults in a randomized controlled trial. Methods 150 adults with obesity were randomized to 12 months of in-person, video conference, or enhanced usual care weight loss groups. We categorized percent session attendance in the intervention arms into three levels: no attendance, poorer attendance, and better attendance. Results Higher baseline Patient Health Questionnaire-8 (PHQ-8) score was associated with a greater odds of being in the poorer versus better attendance group (OR = 1.94, 95% CI: 1.02–3.69, p = .04). A similar relationship between PHQ-8 score and odds of being in the no attendance versus better attendance group was observed but was not statistically significant (OR = 1.63, 95% CI: 0.94–2.81, p = .08). Both cognitive/affective and somatic clusters contributed to the depressive symptoms-attendance relationships. Conclusion Greater depressive symptoms at the start of a behavioral weight loss program may predict poorer subsequent session attendance. Screening for and addressing depression may improve intervention uptake. ClinicalTrials.gov Identifier: NCT02057952
Tyrosinase-Mediated Oxidative Coupling of Tyrosine Tags on Peptides and Proteins

Oxidative coupling (OC) through o-quinone intermediates has been established as an efficient and site-selective way to modify protein N-termini and the unnatural amino acid p-aminophenylalanine (paF). Recently, we reported that the tyrosinase-mediated oxidation of phenol-tagged cargo molecules is a particularly convenient method of generating o-quinones in situ. The coupling partners can be easily prepared and stored, the reaction takes place under mild conditions (phosphate buffer, pH 6.5, 4 to 23 °C), and dissolved oxygen is the only oxidant required. Here, we show an important extension of this chemistry for the activation of tyrosine residues that project into solution from the N or C-termini of peptide and protein substrates. Generating the o-quinone electrophiles from tyrosine allows greater flexibility in choosing the nucleophilic coupling partner and expands the scope of the reaction to include C-terminal positions. We also introduce a new bacterial tyrosinase enzyme that shows improved activation for some tyrosine substrates. The efficacy of several secondary amines and aniline derivatives was evaluated in the coupling reactions, providing important information for coupling partner design. This strategy was used to modify the C-termini of an antibody scFv construct and of Protein L, a human IgG kappa light chain binding protein. The use of the modified proteins as immunolabeling agents was also demonstrated.
Robust temporal pumping in a magneto-mechanical topological insulator

The transport of energy through 1-dimensional (1D) waveguiding channels can be affected by sub-wavelength disorder, resulting in undesirable localization and backscattering phenomena. However, quantized disorder-resilient transport is observable in the edge currents of 2-dimensional (2D) topological band insulators with broken time-reversal symmetry. Topological pumps are able to reduce this higher-dimensional topological insulator phenomena to lower dimensionality by utilizing a pumping parameter (either space or time) as an artificial dimension. Here we demonstrate a temporal topological pump that produces on-demand, robust transport of mechanical energy using a 1D magneto-mechanical metamaterial. We experimentally demonstrate that the system is uniquely resilient to defects occurring in both space and time. Our findings open a path towards exploration of higher-dimensional topological physics with time as a synthetic dimension.
Post model‐fitting exploration via a “Next‐Door” analysis

We propose a simple method for evaluating the model that has been chosen by an adaptive regression procedure, our main focus being the lasso. This procedure deletes each chosen predictor and refits the lasso to get a set of models that are "close" to the one chosen, referred to as "base model". If the deletion of a predictor leads to significant deterioration in the model's predictive power, the predictor is called indispensable; otherwise, the nearby model is called acceptable and can serve as a good alternative to the base model. This provides both an assessment of the predictive contribution of each variable and a set of alternative models that may be used in place of the chosen model. In this paper, we will focus on the cross-validation (CV) setting and a model's predictive power is measured by its CV error, with base model tuned by cross-validation. We propose a method for comparing the error rates of the base model with that of nearby models, and a p-value for testing whether a predictor is dispensable. We also propose a new quantity called model score which works similarly as the p-value for the control of type I error. Our proposal is closely related to the LOCO (leave-one-covarate-out) methods of ([Rinaldo 2016 Bootstrapping]) and less so, to Stability Selection ([Meinshausen 2010 stability]). We call this procedure "Next-Door analysis" since it examines models close to the base model. It can be applied to Gaussian regression data, generalized linear models, and other supervised learning problems with $\ell_1$ penalization. It could also be applied to best subset and stepwise regression procedures. We have implemented it in the R language as a library to accompany the well-known {\tt glmnet} library.
An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments

Real-world autonomous vehicles often operate in a priori unknown environments. Since most of these systems are safety-critical, it is important to ensure they operate safely even when faced with environmental uncertainty. Current safety analysis tools enable autonomous systems to reason about safety given full information about the state of the environment a priori. However, these tools do not scale well to scenarios where the environment is being sensed in real time, such as during navigation tasks. In this work, we propose a novel, real-time safety analysis method based on Hamilton-Jacobi reachability that provides strong safety guarantees despite the unknown parts of the environment. Our safety method is planner-agnostic and provides guarantees for a variety of mapping sensors. We demonstrate our approach in simulation and in hardware to provide safety guarantees around a state-of-the-art vision-based, learning-based planner. Videos of our approach and experiments are available on the project website1.
Community‐Based Approaches to Sex Offender Management

In modern-day America, convicted sex offenders inspire unique fear and disdain, prompting an array of harsh correctional strategies. In addition to imposing lengthy prison terms, jurisdictions have resorted to a variety of non brick-and-mortar options targeting the vastly larger population of individuals who have reentered society. This book chapter examines research findings concerning several of the most noteworthy of these community-based interventions: (1) sex offender registration and community notification (a.k.a. “Megan’s Laws”); (2) residence restrictions; (3) involuntary civil commitment; and (4) electronic monitoring (including global positioning satellite technology).
The Impact of Green Labels on U.S. Hotel Net Operating Income: Operating Statements Analyses

This paper shows Leadership in Energy and Environmental Design (LEED) certified hospitality properties exhibit increased expenses and earn lower net operating income (NOI) than non-certified buildi...
Quickest Detection of Growing Dynamic Anomalies in Networks

The problem of quickest growing dynamic anomaly detection in sensor networks is studied. Initially, the observations at the sensors, which are sampled sequentially by the decision maker, are generated according to a pre-change distribution. At some unknown but deterministic time instant, a dynamic anomaly emerges in the network, affecting different sets of sensors as time progresses. The observations of the affected sensors are generated from a post-change distribution. It is assumed that the number of affected sensors increases with time, and that only the initial and the final size of the anomaly are known to the decision maker. The goal is to detect the emergence of the anomaly as quickly as possible while guaranteeing a sufficiently low frequency of false alarm (FA) events. This detection problem is posed as a stochastic optimization problem by using a delay metric that is based on the worst possible path of the anomaly. A detection rule is proposed that is asymptotically optimal as the mean time to false alarm goes to infinity. Finally, numerical results are provided to validate our theoretical analysis.
Sparse Beamspace Equalization for Massive MU-MIMO MMWave Systems

We propose equalization-based data detection algorithms for all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-out (MU-MIMO) systems that exploit sparsity in the beamspace domain to reduce complexity. We provide a condition on the number of users, basestation antennas, and channel sparsity for which beamspace equalization can be less complex than conventional antenna-domain processing. We evaluate the performance-complexity trade-offs of existing and new beamspace equalization algorithms using simulations with realistic mmWave channel models. Our results reveal that one of our proposed beamspace equalization algorithms achieves up to 8× complexity reduction under line-of-sight conditions, assuming a sufficiently large number of transmissions within the channel coherence interval.
Retrospective ensemble docking of allosteric modulators in an adenosine G-protein-coupled receptor.

Abstract Background Ensemble docking has proven useful in drug discovery and development. It increases the hit rate by incorporating receptor flexibility into molecular docking as demonstrated on important drug targets including G-protein-coupled receptors (GPCRs). Adenosine A1 receptor (A1AR) is a key GPCR that has been targeted for treating cardiac ischemia-reperfusion injuries, neuropathic pain and renal diseases. Development of allosteric modulators, compounds binding to distinct and less conserved GPCR target sites compared with agonists and antagonists, has attracted increasing interest for designing selective drugs of the A1AR. Despite significant advances, more effective approaches are needed to discover potent and selective allosteric modulators of the A1AR. Methods Ensemble docking that integrates Gaussian accelerated molecular dynamic (GaMD) simulations and molecular docking using Autodock has been implemented for retrospective docking of known positive allosteric modulators (PAMs) in the A1AR. Results Ensemble docking outperforms docking of the receptor cryo-EM structure. The calculated docking enrichment factors (EFs) and the area under the receiver operating characteristic curves (AUC) are significantly increased. Conclusions Receptor ensembles generated from GaMD simulations are able to increase the success rate of discovering PAMs of A1AR. It is important to account for receptor flexibility through GaMD simulations and flexible docking. General significance Ensemble docking is a promising approach for drug discovery targeting flexible receptors.
A reporter mouse for in vivo detection of DNA damage in embryonic germ cells

Maintaining genome integrity in the germline is essential for survival and propagation of a species. In both mouse and human, germ cells originate during fetal development and are hypersensitive to both endogenous and exogenous DNA damaging agents. Currently, mechanistic understanding of how primordial germ cells respond to DNA damage is limited in part by the tools available to study these cells. We developed a mouse transgenic reporter strain expressing a 53BP1-mCherry fusion protein under the control of the Oct4ΔPE embryonic germ cell-specific promoter. This reporter binds sites of DNA double strand breaks (DSBs) on chromatin, forming foci. Using ionizing radiation as a DNA DSB-inducing agent, we show that the transgenic reporter expresses specifically in the embryonic germ cells of both sexes and forms DNA damage induced foci in both a dose- and time-dependent manner. The dynamic time-sensitive and dose-sensitive DNA damage detection ability of this transgenic reporter, in combination with its specific expression in embryonic germ cells, makes it a versatile and valuable tool for increasing our understanding of DNA damage responses in these unique cells.
Reconstructing embedded graphs from persistence diagrams

Abstract The persistence diagram (PD) is an increasingly popular topological descriptor. By encoding the size and prominence of topological features at varying scales, the PD provides important geometric and topological information about a space. Recent work has shown that well-chosen (finite) sets of PDs can differentiate between geometric simplicial complexes, providing a method for representing complex shapes using a finite set of descriptors. A related inverse problem is the following: given a set of PDs (or an oracle we can query for persistence diagrams), what is underlying geometric simplicial complex? In this paper, we present an algorithm for reconstructing embedded graphs in R d (plane graphs in R 2 ) with n vertices from n 2 − n + d + 1 directional (augmented) PDs. Additionally, we empirically validate the correctness and time-complexity of our algorithm in R 2 on randomly generated plane graphs using our implementation, and explain the numerical limitations of implementing our algorithm.
Chapter 9 – Plants and Fluxes

Publisher Summary This chapter discusses concentrations and fluxes within a plant community. When water vapor and CO2 fluxes from the soil up to the top of plants are analyzed, the great structural diversity among different types of vegetation is confronted. Each plant community has its own unique spatial patterns for water vapor and CO2 concentration. The possibility of many layers of leaves and the constantly changing illumination also greatly complicate the analysis. Even approximate descriptions of the gas fluxes within carefully selected plant communities involve complex calculations based on models incorporating numerous simplifying assumptions. The chapter considers a cornfield as a specific example. Generally, 70 to 75% of the water vaporized on land is transpired by plants. This water comes from the soil. Therefore, after gas fluxes within a plant community are considered, the chapter examines some of the hydraulic properties of soil. For instance, water in the soil is removed from larger pores before from smaller ones. This removal decreases the soil conductivity for subsequent water movement, and a greater drop in water potential from the bulk soil up to a root is therefore necessary for a particular water flux density.
Chapter 3 – Behavioral Genetics

Publisher Summary Behavioral genetics forms a critical component of nearly all behavioral studies. Understanding genetics establishes a thread that runs from the ultimate evolutionary causes of behavior to the behavior's proximate underpinnings and knowing the genetic underpinnings of behavior is essential to understanding how behavior evolves. To understand the evolutionary roots of behavior (ultimate causes), behavioral geneticists often employ phylogeny. Genetics brings a broad range of tools to behavioral investigations. These tools are used to establish the phylogeny of animal groups, which can then be used to understand patterns of evolution for specific behaviors, such as nest construction by birds or colony defense by bees. Peeling away the layers of genetic and physiological regulation of a behavior using behavioral genetics—starting with differences among species and moving down to the regulation of gene expression—holds great promise for solving many of the mysteries of animal behavior. Single-gene effects on behavior are easily documented, but single-gene effects are relatively rare and may produce the erroneous notion that complex behavior is “controlled” by those genes. In fact, behavior is most often the result of a large number of genes acting together in a regulatory system. Quantitative genetics can be used to provide better explanations than single-gene models for most animal behavior traits. Studying heritability helps to unravel genetic and environmental influences on behavior. Molecular approaches to behavior genetics provide useful techniques in exploring the regulation of behavior.
Political Institutions and Judicial Role in Comparative Constitutional Law

Comparative constitutional law scholarship has largely ignored political institutions. It has therefore failed to realize that radical differences in the configuration of political institutions should bear upon the way courts do their jobs. This Article develops a comparative theory of judicial role that focuses on broad differences in political context, and particularly in party systems, across countries. I use the jurisprudence of the Colombian Constitutional Court (supplemented by briefer studies of the Hungarian and South African Constitutional Courts) to demonstrate how differences in political institutions ought to impact judicial role. Because Colombian parties are unstable and poorly tied to civil society, the Colombian Congress has difficulty initiating policy, monitoring the enforcement of policy, and checking presidential power. The Court has responded by taking many of these functions into its own hands. I argue that the Court’s actions are sensible given Colombia’s institutional context, even though virtually all existing theories of judicial role in American and comparative public law would find this kind of legislativesubstitution inappropriate. Existing theories rest upon assumptions about political institutions that do not hold true in much of the developing world. The American focus on the anti-democratic nature of judicial action assumes a robust constitutional culture outside the courts and a legislature which does a decent job representing popular will—both assumptions tend to be false in newer democracies. The case studies demonstrate that comparative public law scholars must be attentive to political context in order to build tools suitable for evaluating the work of courts outside the United States.
Pathway and Mechanism of Drug Binding to G-Protein-Coupled Receptors

How drugs bind to their receptors—from initial association, through entry into the binding pocket, to adoption of the final bound pose—has remained unknown, even for G-protein-coupled receptor modulators, which constitute one-third of all drugs. We captured this pharmaceutically critical process in atomic detail using the first unbiased molecular dynamics simulations in which drug molecules spontaneously associate with G-protein-coupled receptors to achieve final poses matching those determined crystallographically (PNAS 108:13118 (2011)). We found that several beta blockers and a beta agonist all traverse the same dominant pathway as they bind to the β1- and β2-adrenergic receptors, initially associating with a vestibule on each receptor's extracellular surface. Surprisingly, this association, at a distance of 15 A from the binding pocket, often presents the largest energetic barrier to binding, despite the fact that subsequent entry into the pocket requires the receptor to deform and the drug to squeeze through a narrow passage. The early barrier may reflect the substantial dehydration that occurs as the drug associates with the vestibule. Our atomic-level description of the binding process suggests opportunities for allosteric modulation and provides a structural foundation for future optimization of binding and unbinding rates.View Large Image | View Hi-Res Image | Download PowerPoint Slide
Thermodynamic ground states of platinum metal nitrides.

The thermodynamic stabilities of various phases of the nitrides of the platinum-metal elements are systematically studied using density functional theory. It is shown that for the nitrides of Rh, Pd, Ir, and Pt two new crystal structures, in which the metal ions occupy simple tetragonal lattice sites, have lower formation enthalpies at ambient conditions than any previously proposed structures. The region of stability with respect to those structures extends to 17 GPa for ${\mathrm{PtN}}_{2}$. Calculations show that the ${\mathrm{PtN}}_{2}$ simple tetragonal structures at this pressure are thermodynamically stable also with respect to phase separation. The fact that the local density and generalized gradient approximations predict different values of the absolute formation enthalpies as well different relative stabilities between simple tetragonal and the pyrite or marcasite structures are further discussed.
Survival of Primordial Planetary Atmospheres: Photodissociation-driven Mass Loss

The most widely-studied mechanism of mass loss from extrasolar planets is photoevaporation via XUV ionization, primarily in the context of highly irradiated planets. However, the EUV dissociation of hydrogen molecules can also theoretically drive atmospheric evaporation on low-mass planets. For temperate planets such as the early Earth, impact erosion is expected to dominate in the traditional planetesimal accretion model, but it would be greatly reduced in pebble accretion scenarios, allowing other mass loss processes to be major contributors. We apply the same prescription for photoionization to this photodissociation mechanism and compare it to an analysis of other possible sources of mass loss in pebble accretion scenarios. We find that there is not a clear path to evaporating the primordial atmosphere accreted by an early Earth analog in a pebble accretion scenario. Impact erosion could remove ~2,300 bars of hydrogen if 1% of the planet's mass is accreted as planetesimals, while the combined photoevaporation processes could evaporate ~750 bars of hydrogen. Photodissociation is likely a subdominant, but significant component of mass loss. Similar results apply to super-Earths and mini-Neptunes. This mechanism could also preferentially remove hydrogen from a planet's primordial atmosphere, thereby leaving a larger abundance of primordial water compared to standard dry formation models. We discuss the implications of these results for models of rocky planet formation including Earth's formation and the possible application of this analysis to mass loss from observed exoplanets.
Weeping and Gnashing of Teeth: Teaching Deep Learning in Image and Video Processing Classes

In this rather informal paper and talk I will discuss my own experiences, feelings and evolution as an Image Processing and Digital Video educator trying to navigate the Deep Learning revolution. I will discuss my own ups and downs of trying to deal with extremely rapid technological changes, and how I reacted to, and dealt with consequent dramatic changes in the relevance of the topics I’ve taught for three decades. I arranged the discussion in terms of the stages, over time, of my progression dealing with these sea changes.
Causality-Guided Adaptive Interventional Debugging

Runtime nondeterminism is a fact of life in modern database applications. Previous research has shown that nondeterminism can cause applications to intermittently crash, become unresponsive, or experience data corruption. We propose Adaptive Interventional Debugging (AID) for debugging such intermittent failures. AID combines existing statistical debugging, causal analysis, fault injection, and group testing techniques in a novel way to (1) pinpoint the root cause of an application's intermittent failure and (2) generate an explanation of how the root cause triggers the failure. AID works by first identifying a set of runtime behaviors (called predicates) that are strongly correlated to the failure. It then utilizes temporal properties of the predicates to (over)-approximate their causal relationships. Finally, it uses fault injection to execute a sequence of interventions on the predicates and discover their true causal relationships. This enables AID to identify the true root cause and its causal relationship to the failure. We theoretically analyze how fast AID can converge to the identification. We evaluate AID with six real-world applications that intermittently fail under specific inputs. In each case, AID was able to identify the root cause and explain how the root cause triggered the failure, much faster than group testing and more precisely than statistical debugging. We also evaluate AID with many synthetically generated applications with known root causes and confirm that the benefits also hold for them.
SCAF: a speculation-aware collaborative dependence analysis framework

Program analysis determines the potential dataflow and control flow relationships among instructions so that compiler optimizations can respect these relationships to transform code correctly. Since many of these relationships rarely or never occur, speculative optimizations assert they do not exist while optimizing the code. To preserve correctness, speculative optimizations add validation checks to activate recovery code when these assertions prove untrue. This approach results in many missed opportunities because program analysis and thus other optimizations remain unaware of the full impact of these dynamically-enforced speculative assertions. To address this problem, this paper presents SCAF, a Speculation-aware Collaborative dependence Analysis Framework. SCAF learns of available speculative assertions via profiling, computes their full impact on memory dependence analysis, and makes this resulting information available for all code optimizations. SCAF is modular (adding new analysis modules is easy) and collaborative (modules cooperate to produce a result more precise than the confluence of all individual results). Relative to the best prior speculation-aware dependence analysis technique, by computing the full impact of speculation on memory dependence analysis, SCAF dramatically reduces the need for expensive-to-validate memory speculation in the hot loops of all 16 evaluated C/C++ SPEC benchmarks.
Using financial tips to guide debt repayment: Experimental evidence from low- and moderate-income tax filers

Abstract Tax filing in the U.S. often has substantial implications for the debt burdens of low- and moderate-income (LMI) households. The tax refund—often the largest payment LMI households receive in a year—provides these households with an opportunity to reduce their debt, while tax liabilities can lead them to take on additional debt. Though paying down debt is among the most common uses of the tax refund, households often do not optimally allocate their debt payments. We assess whether offering simple financial guidance can help households meet their debt obligations by administering a field experiment that provided debt management tips to LMI tax filers after tax filing. Four treatment groups saw one of four tips, while a fifth treatment group could select which of the four tips was best suited to their financial situation. Results from a follow-up survey found no effects of the intervention on reported rates of following the tips or on the amount of debt held. However, we do find that the tips reduced the incidence of holding unsecured debt by 5 percentage points (p
A triple integral analog of a multiple zeta value

We establish the triple integral evaluation ∫1∞∫01∫01 dzdydx x(x + y)(x + y + z) = 5 24ζ(3), as well as the equivalent polylogarithmic double sum ∑k=1∞∑ j=k∞(−1)k−1 k2 1 j2j = 13 24ζ(3). This doubl...