cssi/SciSciGPT-SciSciNet huggingface dataset:
Molecular simulations for the spectroscopic detection of atmospheric gases

Unambiguously identifying molecules in spectra is of fundamental importance for a variety of scientific and industrial uses. Interpreting atmospheric spectra for the remote detection of volatile compounds requires information about the spectrum of each relevant molecule. However, spectral data currently exist for a few hundred molecules and only a fraction of those have complete spectra (e.g. H2O, NH3). Consequently, molecular detections in atmospheric spectra remain vulnerable to false positives, false negatives, and missassignments. There is a key need for spectral data for a broad range of molecules. Given how challenging it is to obtain high-resolution molecular spectra, there is great value in creating intermediate approximate spectra that can provide a starting point for the analysis of atmospheric spectra. Using a combination of experimental measurements, organic chemistry, and quantum mechanics, RASCALL (Rapid Approximate Spectral Calculations for ALL) is a computational approach that provides approximate spectral data for any given molecule, including thousands of potential atmospheric gases. RASCALL is a new theoretical chemistry method for the simulation of spectral data. RASCALL 1.0, presented here, is capable of simulating molecular spectral data, in a few seconds, by interpreting functional group data from experimental and theoretical sources to estimate the position and strength of molecular bands. The RASCALL 1.0 spectra consist of approximate band centers and qualitative intensities. RASCALL 1.0 is also able to assess hundreds of molecules simultaneously, which will inform prioritization protocols for future, computationally and experimentally costly, high-accuracy physical chemistry studies. Finally, RASCALL can be used to study spectral patterns between molecules, highlighting ambiguities in molecular detections and also directing observations towards spectral regions that reduce the degeneracy in molecular identification. The RASCALL catalogue, and its preliminary version RASCALL 1.0, contains spectral data for more molecules than any other publicly available database, with applications in all fields interested in the detection of molecules in the gas phase (e.g., medical imaging, petroleum industry, pollution monitoring, astrochemistry). The preliminary catalogue of molecular data and associated documentation are freely available online and will be routinely updated.
The rhg1‐a (Rhg1 low‐copy) nematode resistance source harbors a copia‐family retrotransposon within the Rhg1‐encoded α‐SNAP gene

Soybean growers widely use the Resistance to Heterodera glycines 1 (Rhg1) locus to reduce yield losses caused by soybean cyst nematode (SCN). Rhg1 is a tandemly repeated four gene block. Two classes of SCN resistance-conferring Rhg1 haplotypes are recognized: rhg1-a ("Peking-type," low-copy number, three or fewer Rhg1 repeats) and rhg1-b ("PI 88788-type," high-copy number, four or more Rhg1 repeats). The rhg1-a and rhg1-b haplotypes encode α-SNAP (alpha-Soluble NSF Attachment Protein) variants α-SNAP Rhg1 LC and α-SNAP Rhg1 HC, respectively, with differing atypical C-terminal domains, that contribute to SCN resistance. Here we report that rhg1-a soybean accessions harbor a copia retrotransposon within their Rhg1 Glyma.18G022500 (α-SNAP-encoding) gene. We termed this retrotransposon "RAC," for Rhg1 alpha-SNAP copia. Soybean carries multiple RAC-like retrotransposon sequences. The Rhg1 RAC insertion is in the Glyma.18G022500 genes of all true rhg1-a haplotypes we tested and was not detected in any examined rhg1-b or Rhg1WT (single-copy) soybeans. RAC is an intact element residing within intron 1, anti-sense to the rhg1-a α-SNAP open reading frame. RAC has intrinsic promoter activities, but overt impacts of RAC on transgenic α-SNAP Rhg1 LC mRNA and protein abundance were not detected. From the native rhg1-a RAC+ genomic context, elevated α-SNAP Rhg1 LC protein abundance was observed in syncytium cells, as was previously observed for α-SNAP Rhg1 HC (whose rhg1-b does not carry RAC). Using a SoySNP50K SNP corresponding with RAC presence, just ~42% of USDA accessions bearing previously identified rhg1-a SoySNP50K SNP signatures harbor the RAC insertion. Subsequent analysis of several of these putative rhg1-a accessions lacking RAC revealed that none encoded α-SNAPRhg1LC, and thus, they are not rhg1-a. rhg1-a haplotypes are of rising interest, with Rhg4, for combating SCN populations that exhibit increased virulence against the widely used rhg1-b resistance. The present study reveals another unexpected structural feature of many Rhg1 loci, and a selectable feature that is predictive of rhg1-a haplotypes.
Same-Cluster Querying for Overlapping Clusters

Overlapping clusters are common in models of many practical data-segmentation applications. Suppose we are given $n$ elements to be clustered into $k$ possibly overlapping clusters, and an oracle that can interactively answer queries of the form ``do elements $u$ and $v$ belong to the same cluster?'' The goal is to recover the clusters with minimum number of such queries. This problem has been of recent interest for the case of disjoint clusters. In this paper, we look at the more practical scenario of overlapping clusters, and provide upper bounds (with algorithms) on the sufficient number of queries. We provide algorithmic results under both arbitrary (worst-case) and statistical modeling assumptions. Our algorithms are parameter free, efficient, and work in the presence of random noise. We also derive information-theoretic lower bounds on the number of queries needed, proving that our algorithms are order optimal. Finally, we test our algorithms over both synthetic and real-world data, showing their practicality and effectiveness.
On mean-field theories of dynamics in supercooled liquids

We develop a hybrid numerical approach to extract the exact memory function K(t) of a tagged particle in three-dimensional glass-forming liquids. We compare the behavior of the exact memory function to two mean-field approaches, namely, the standard mode-coupling theory and a recently proposed ansatz for the memory function that forms the basis of a new derivation of the exact form of K(t) for a fluid with short-ranged interactions in infinite dimensions. Each of the mean-field functions qualitatively and quantitatively share traits with the exact K(t), although several important quantitative differences are manifest.
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms

We resolve the fundamental problem of online decoding with general nth order ergodic Markov chain models. Specifically, we provide deterministic and randomized algorithms whose performance is close to that of the optimal offline algorithm even when latency is small. Our algorithms admit efficient implementation via dynamic programs, and readily extend to (adversarial) non-stationary or time-varying settings. We also establish lower bounds for online methods under latency constraints in both deterministic and randomized settings, and show that no online algorithm can perform significantly better than our algorithms. To our knowledge, our work is the first to analyze general Markov chain decoding under hard constraints on latency. We provide strong empirical evidence to illustrate the potential impact of our work in applications such as gene sequencing.
A theoretical study of process dependence for critical statistics in standard serial models and standard parallel models

Abstract Long before the mathematical developments that led inexorably to the development of systems factorial technology, the somewhat arduous, but arguably requisite labors which precisely defined parallel and serial architectures had begun (e.g., Townsend, 1969, 1972). Both then and now, what are now referred to as standard serial models and standard parallel models not only play an important role in psychological science, they are often what non-mathematical psychologists are (sometimes in an unschooled fashion) referring to when they bring up architectural concepts. Interestingly, two strategic and critical properties, and therefore implicit predictions of the canonical serial and parallel models have witnessed little analysis. In this article, we address three issues: (1) Standard parallel models predict stochastically independent processing times and therefore total completion times. There is a partially valid intuition that standard serial models will predict positive dependence among totally completion times and standard serial models based on exponential processing time do predict this (Townsend and Ashby, 1983, p. 73). This also holds if only one order of processing is possible. However, if there is a mixture of processing orders, certain distributions can predict negative dependencies on this statistic. (2) Analogously, standard serial models predict independent and identically distributed processing times and therefore intercompletion times. Certain examples of standard parallel models (again, based on exponential processing times) suggest that the intercompletion times tend to increase over time and stages although there has been no information on their interdependence. Interestingly, we again find that there is an inclination for standard parallel models to emulate this behavior but that this tendency can be over-ridden by dramatically increasing parallel-channel hazard functions. (3) Are there psychological questions that pertain to these distinctions and are there any experiments that have emerged to assess the latter? With regard to experimental data on these issues, it turns out that there exists some data from categorical free recall that bear on some of the behavior of intercompletion times, which we discuss. To date, we know of no investigations that explore correlations of total completion times, although such analyses appear relatively straightforward.
Finite-Time Distributed State Estimation over Time-Varying Graphs: Exploiting the Age-of-Information

We study the problem of collaboratively estimating the state of a discrete-time LTI process by a network of sensor nodes interacting over a time-varying directed communication graph. Existing approaches to this problem either (i) make restrictive assumptions on the dynamical model, or (ii) make restrictive assumptions on the sequence of communication graphs, or (iii) require multiple consensus iterations between consecutive time-steps of the dynamics, or (iv) require higher-dimensional observers. In this paper, we develop a distributed observer that operates on a single time-scale, is of the same dimension as that of the state, and works under mild assumptions of joint observability of the sensing model, and joint strong-connectivity of the sequence of communication graphs. Our approach is based on the notion of a novel “freshness-index” that keeps track of the age-of-information being diffused across the network. In particular, such indices enable nodes to reject stale information regarding the state of the system, and in turn, help achieve stability of the estimation error dynamics. Based on the proposed approach, the estimate of each node can be made to converge to the true state exponentially fast, at any desired convergence rate. In fact, we argue that finite-time convergence can also be achieved through a suitable selection of the observer gains. Our proof of convergence is self-contained, and employs simple arguments from linear system theory and graph theory.
Quantitative Methods for the Comparative Analysis of Cities in History

Comparative studies of cities throughout history are one of the greatest sources of insight into the nature of change in human societies. This paper discusses strategies to anchor these comparisons on well-defined, quantitative and empirical characteristics of cities, derived from theory and observable in the archeological and historical records. We show how quantitative comparisons based on a few simple variables across settlements allow us to analyze how different places and peoples dealt with general problems of any society. These include demographic change, the organization of built spaces, the intensity and size of socioeconomic networks and the processes underlying technological change and economic growth. Because the historical record contains a much more varied and more independent set of experiences than contemporary urbanization, it has a unique power of illuminating present puzzles of human development and testing emergent urban theory.
Higher-order Rewriting for Executable Compiler Specifications

In this paper we outline how a simple compiler can be completely specified using higher order rewriting in all stages: parsing, analysis/optimization, and cod e emission, specifically using the crsx.sf.net system for a small declarative language called “X” inspired by XQuery (for which we are building a production quality compiler in the same way).
Mexican Patent Litigation, International Intellectual Property Treaties and the Extension of Patent Term of Protection

This article analyzes how the provisions on patent term adjustments contained in the new United States-Mexico-Canada Trade Agreement (USMCA) will be problematic for Mexico. To illustrate this point, the article revisits a patent litigation in Mexico. The plaintiff was a Japanese company who was suing over the infringement of its rights by a Mexican Lab. In this case, Transitional Article Twelve of the Mexican Industrial Property Act was applied to recognize a priority filing date seven years later than its original filing in Japan. The litigation involved several issues such as the extension of the term of protection and the alleged infringement by the defendant after the authorities had extended the term of the patent. There are several principles and concerns discussed, including the principle of patent independence, national treatment, and minimum term of protection, all of which are contained in the Paris Convention, the TRIPS Agreement, and the Patent Cooperation Treaty.
Centralized Versus Decentralized Detection of Attacks in Stochastic Interconnected Systems

We consider a security problem for interconnected systems with linear, discrete, time-invariant, stochastic dynamics, where the objective is to detect exogenous attacks by processing measurements at different locations. We consider centralized and decentralized detectors, which differ primarily in their knowledge of the system model. In particular, a decentralized detector has a model of the dynamics of the isolated subsystems, but is unaware of the interconnection signals that are exchanged among subsystems. Instead, a centralized detector has a model of the entire dynamical system. We characterize the performance of the two detectors and show that, depending on the system and attack parameters, each of the detectors can outperform the other. Hence, it may be possible for the decentralized detector to outperform its centralized counterpart, despite having less information about the system dynamics, and this property is due to the nature of the considered attack detection problem (that is, a simple vs. composite hypothesis testing problem). Finally, we numerically validate our findings on a power system model.
Learn to Explain Efficiently via Neural Logic Inductive Learning

The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to explain the problem in the scope of inductive logic programming (ILP). We propose Neural Logic Inductive Learning (NLIL), an efficient differentiable ILP framework that learns first-order logic rules that can explain the patterns in the data. In experiments, compared with the state-of-the-art models, we find NLIL is able to search for rules that are x10 times longer while remaining x3 times faster. We also show that NLIL can scale to large image datasets, i.e. Visual Genome, with 1M entities.
Larval mannitol diets increase mortality, prolong development and decrease adult body sizes in fruit flies (Drosophila melanogaster).

ABSTRACT The ability of polyols to disrupt holometabolous insect development has not been studied and identifying compounds in food that affect insect development can further our understanding of the pathways that connect growth rate, developmental timing and body size in insects. High-sugar diets prolong development and generate smaller adult body sizes in Drosophila melanogaster. We tested for concentration-dependent effects on development when D. melanogaster larvae are fed mannitol, a polyalcohol sweetener. We also tested for amelioration of developmental effects if introduction to mannitol media is delayed past the third instar, as expected if there is a developmental sensitive-period for mannitol effects. Both male and female larvae had prolonged development and smaller adult body sizes when fed increasing concentrations of mannitol. Mannitol-induced increases in mortality were concentration dependent in 0 M to 0.8 M treatments with mortality effects beginning as early as 48 h post-hatching. Larval survival, pupariation and eclosion times were unaffected in 0.4 M mannitol treatments when larvae were first introduced to mannitol 72 h post-hatching (the beginning of the third instar); 72 h delay of 0.8 M mannitol introduction reduced the adverse mannitol effects. The developmental effects of a larval mannitol diet closely resemble those of high-sugar larval diets. This article has an associated First Person interview with the first author of the paper.
Uncertainty quantification of DFT-predicted finite temperature thermodynamic properties within the Debye model

Density functional theory (DFT) calculations are routinely used to screen for functional materials for a variety of applications. This screening is often carried out with a few descriptors, which use ground-state properties that typically ignore finite temperature effects. Finite-temperature effects can be included by calculating the vibration properties, and this can greatly improve the fidelity of computational screening. An important challenge for DFT-based screening is the sensitivity of the predictions to the choice of the exchange correlation function. In this work, we rigorously explore the sensitivity of finite temperature thermodynamic properties to the choice of the exchange correlation functional using the built-in error estimation capabilities within the Bayesian Error Estimation Functional-van der Waals (BEEF-vdW). The vibrational properties are estimated using the Debye model, and we quantify the uncertainty associated with finite-temperature properties for a diverse collection of materials. We find good agreement with experiment and small spread in predictions over different exchange correlation functionals for Mg, Al2O3, Al, Ca, and GaAs. In the case of Li, Li2O, and NiO, however, we find a large spread in predictions as well as disagreement between experiment and functionals due to complex bonding environments. While the energetics generated by the BEEF-vdW ensemble is typically normal, the complex mapping through the Debye model leads to the derived finite temperature properties having non-Gaussian behavior. We test a wide variety of probability distributions that best represent the finite temperature distribution and find that properties such as specific heat, Gibbs free energy, entropy, and thermal expansion coefficient are well described by normal or transformed normal distributions, while the prediction spread of volume at a given temperature does not appear to be drawn from a single distribution. Given the computational efficiency of the approach, we believe that uncertainty quantification should be routinely incorporated into finite-temperature predictions. In order to facilitate this, we have open-sourced the code base under the name dePye.
Free-Boolean independence with amalgamation

In this paper, we develop the notion of free-Boolean independence in an amalgamated setting. We construct free-Boolean cumulants and show that the vanishing of mixed free-Boolean cumulants is equiv...
What BERT Is Not: Lessons from a New Suite of Psycholinguistic Diagnostics for Language Models

Pre-training by language modeling has become a popular and successful approach to NLP tasks, but we have yet to understand exactly what linguistic capacities these pre-training processes confer upon models. In this paper we introduce a suite of diagnostics drawn from human language experiments, which allow us to ask targeted questions about information used by language models for generating predictions in context. As a case study, we apply these diagnostics to the popular BERT model, finding that it can generally distinguish good from bad completions involving shared category or role reversal, albeit with less sensitivity than humans, and it robustly retrieves noun hypernyms, but it struggles with challenging inference and role-based event prediction -- and in particular, it shows clear insensitivity to the contextual impacts of negation.
Sharing is Caring: Socially-Compliant Autonomous Intersection Negotiation

Current methods for autonomous management use strict first-come, first-serve (FCFS) ordering to manage incoming autonomous vehicles at an intersection. In this work, we present a coordination policy that swaps agent ordering to increase the system-wide performance while ensuring that the swaps are socially compliant. By considering an agent’s Social Value Orientation (SVO), a social psychology metric for their willingness to help another vehicle, the central coordinator can reduce system delays while ensuring each individual vehicle increases their own utility. The FCFS-SVO algorithm is both computationally tractable and accounts for a variety of real-world agent types, such as human drivers and a variety of social orientations. Simulation results show that average vehicle delays decrease with swapping by enabling cooperation between agents. In addition, we show that the proportion of human drivers, as well as, the distribution of prosocial and egoistic vehicles in the system can have a prominent effect on the performance of the system.
Cure Dataset: Ladder Networks for Audio Event Classification

Audio event classification is an important task for several applications such as surveillance, audio, video and multimedia retrieval etc. There are approximately 340 million people with hearing loss who can’t perceive events happening around them. This paper establishes the CURE dataset which contains curated set of specific audio events most relevant for people with hearing loss. It is formatted as 5 sec sound recordings derived from the Freesound project. We propose a ladder network based audio event classifier. We adopted the state-of-the-art convolutional neural network (CNN) embeddings as audio features for this task. We start with signal and feature normalization that aims to reduce the mismatch between different recordings scenarios. Initially, a CNN is trained on weakly labeled Audioset data. Next, the pre-trained model is adopted as feature extractor for proposed CURE corpus. We also explore the performance of extreme learning machine (ELM) and use support vector machine (SVM) as baseline classifier. As a second evaluation set we incorporate ESC-50. Results and discussions validate the superiority of Ladder network over ELM and SVM classifier in terms of robustness and increased classification accuracy.
Enantio- and Diastereoselective Synthesis of Homoallylic α-Trifluoromethyl Amines by Catalytic Hydroalkylation of Dienes.

We describe a strategy for the enantio- and diastereoselective synthesis of homoallylic α-trifluoromethyl amines by the catalytic hydroalkylation of terminal dienes. Trifluoromethyl-substituted isa...
Linearized Stability of Bardeen de-Sitter Thin-Shell Wormholes

A thin-shell wormhole is crafted by the cut-and-paste method of two Bardeen-de Sitter black holes using the Darmois-Israel formalism. Energy conditions are considered for different values of magnetic charge while both mass and cosmological constant are fixed. The attractive and repulsive characteristics of the throat of the thin-shell wormhole are also examined through the radial acceleration. Dynamics and stability of the wormhole are studied around the static solutions of the linearized radial perturbations at the throat of the wormhole. The regions of stability are determined by checking out the condition of concavity of the potential as a function in the throat radius for different values of magnetic charges.
Formate dehydrogenase, ubiquinone, and cytochrome bd -I are required for peptidoglycan recognition protein-induced oxidative stress and killing in Escherichia coli

Mammalian Peptidoglycan Recognition Proteins (PGRPs) kill bacteria through induction of synergistic oxidative, thiol, and metal stress. PGRPs induce oxidative stress in bacteria through a block in the respiratory chain, which results in decreased respiration and incomplete reduction of oxygen (O2) to hydrogen peroxide (H2O2). In this study we identify the site of PGRP-induced generation of H2O2 in Escherichia coli. Tn-seq screening of E. coli Tn10 insertion library revealed that mutants in formate dehydrogenase (FDH) genes had the highest survival following PGRP treatment. Mutants lacking functional FDH-O had abolished PGRP-induced H2O2 production and the highest resistance to PGRP-induced killing, and formate enhanced PGRP-induced killing and H2O2 production in an FDH-dependent manner. Mutants in ubiquinone synthesis (but not menaquinone and demethylmenaquinone) and cytochrome bd-I (but not cytochromes bo3 and bd-II) also had completely abolished PGRP-induced H2O2 production and high resistance to PGRP-induced killing. Because electrons in the respiratory chain flow from dehydrogenases' substrates through quinones and then cytochromes to O2, these results imply that the site of PGRP-induced incomplete reduction of O2 to H2O2 is downstream from dehydrogenases and ubiquinone at the level of cytochrome bd-I, which results in oxidative stress. These results reveal several essential steps in PGRP-induced bacterial killing.
Sex differences in the effects of gonadal hormones on white matter microstructure development in adolescence.

Abstract Adolescence is characterized by rapid brain development in white matter (WM) that is attributed in part to surges in gonadal hormones. To date, however, there have been few longitudinal investigations relating changes in gonadal hormones and WM development in adolescents. We acquired diffusion-weighted MRI to estimate mean fractional anisotropy (FA) from 10 WM tracts and salivary testosterone from 51 females and 29 males (ages 9–14 years) who were matched on pubertal stage and followed, on average, for 2 years. We tested whether interactions between sex and changes in testosterone levels significantly explained changes in FA. We found positive associations between changes in testosterone and changes in FA within the corpus callosum, cingulum cingulate, and corticospinal tract in females (all ps
Monopsony and two‐part tariffs

In his classic article, Walter Oi (1971) analyzed two-part tariffs by a monopolist. We adapt his analysis to the case of monopsony. We show that the resulting offer is that the seller pays its producer surplus as an access fee in exchange for the buyers promise to buy everything that the seller wants to sell when price equals marginal cost. In addition, we show that this is equivalent to the surplus that the buyer captures with first-degree price discrimination as well as an all-or-nothing offer. We also extend this analysis to the case of uncertainty for a risk-averse monopsonist.
Overview of US Maternal Mortality Policy

The United States maternal mortality rate has been rising for many years putting the US out of step with peer countries. There are many complex reasons for the rise in maternal deaths and recent data has demonstrated that there is a disproportionate risk for women of color. This article provides an overview of current policy and policy issues aimed at improving the maternal mortality rate in the United States.
Curation Algorithms and Filter Bubbles in Social Networks

Do curation and personalization algorithms on social media create filter bubbles and increase content polarization?
Phylogenetics Identifies Two Eumetazoan TRPM Clades and an Eighth TRP Family, TRP Soromelastatin (TRPS)

Transient receptor potential melastatins (TRPMs) are most well known as cold and menthol sensors, but are in fact broadly critical for life, from ion homeostasis to reproduction. Yet, the evolutionary relationship between TRPM channels remains largely unresolved, particularly with respect to the placement of several highly divergent members. To characterize the evolution of TRPM and like channels, we performed a large-scale phylogenetic analysis of >1,300 TRPM-like sequences from 14 phyla (Annelida, Arthropoda, Brachiopoda, Chordata, Cnidaria, Echinodermata, Hemichordata, Mollusca, Nematoda, Nemertea, Phoronida, Priapulida, Tardigrada, and Xenacoelomorpha), including sequences from a variety of recently sequenced genomes that fill what would otherwise be substantial taxonomic gaps. These findings suggest: 1) the previously recognized TRPM family is in fact two distinct families, including canonical TRPM channels and an eighth major previously undescribed family of animal TRP channel, TRP soromelastatin; 2) two TRPM clades predate the last bilaterian-cnidarian ancestor; and 3) the vertebrate-centric trend of categorizing TRPM channels as 1-8 is inappropriate for most phyla, including other chordates.
Real-time Ecological Velocity Planning for Plug-in Hybrid Vehicles with Partial Communication to Traffic Lights

This paper presents the design of an ecological adaptive cruise controller (ECO-ACC) for a plug-in hybrid vehicle (PHEV) which exploits automated driving and connectivity. Most existing papers for ECO-ACC focus on a shortsighted control scheme. A two-level control framework for longsighted ECO-ACC was only recently introduced [1]. However, that work is based on a deterministic traffic signal phase and timing (SPaT) over the entire route. In practice, connectivity with traffic lights may be limited by communication range, e.g. just one upcoming traffic light. We propose a two-level receding-horizon control framework for long-sighted ECO-ACC that exploits deterministic SPaT for the upcoming traffic light, and utilizes historical SPaT for other traffic lights within a receding control horizon. We also incorporate a powertrain control mechanism to enhance PHEV energy prediction accuracy. Hardware-in-the-loop simulation results validate the energy savings of the receding-horizon control framework in various traffic scenarios.
State Estimation in Water Distribution Networks through a New Successive Linear Approximation

State estimation (SE) of water distribution networks (WDNs) is difficult to solve due to nonlinearity/nonconvexity of water flow models, uncertainties from parameters and demands, lack of redundancy of measurements, and inaccurate flow and pressure measurements. This paper proposes a new, scalable successive linear approximation to solve the SE problem in WDNs. The approach amounts to solving either a sequence of linear or quadratic programs—depending on the operators’ objectives. The proposed successive linear approximation offers a seamless way of dealing with valve/pump model nonconvexities, is different than a first order Taylor series linearization, and can be incorporated into with robust uncertainty modeling. Two simple test-cases are adopted to illustrate the effectiveness of proposed approach using head measurements at select nodes.
From Quantum Systems to L-Functions: Pair Correlation Statistics and Beyond

The discovery of connections between the distribution of energy levels of heavy nuclei and spacings between prime numbers has been one of the most surprising and fruitful observations in the twentieth century. The connection between the two areas was first observed through Montgomery’s work on the pair correlation of zeros of the Riemann zeta function. As its generalizations and consequences have motivated much of the following work, and to this day remains one of the most important outstanding conjectures in the field, it occupies a central role in our discussion below. We describe some of the many techniques and results from the past sixty years, especially the important roles played by numerical and experimental investigations, that led to the discovery of the connections and progress towards understanding the behaviors. In our survey of these two areas, we describe the common mathematics that explains the remarkable universality. We conclude with some thoughts on what might lie ahead in the pair correlation of zeros of the zeta function, and other similar quantities.
Efficient computation of Kubo conductivity for incommensurate 2D heterostructures

We introduce a numerical method for computing conductivity via the Kubo formula for incommensurate 2D bilayer heterostructures using a tight-binding framework. We begin by deriving the momentum space formulation and Kubo formula from the real space tight-binding model using the appropriate Bloch transformation operator. We further discuss the resulting algorithm along with its convergence rate and computational cost in terms of model parameters such as relaxation time and temperature. In particular, we show that for low frequencies, low temperature, and long relaxation times conductivity can be computed very efficiently using the momentum space algorithm for a wide class of materials. We then showcase our method by computing conductivity for twisted bilayer graphene (tBLG) for small twist angles.
Chapter 4 – Enzymes

Chapter 4 introduces enzymes, which collectively represent a highly influential special class of receptors, and which comprise several major classes of protein drug targets. Mechanisms that account for the catalytic action of enzymes are extensively covered, together with the detailed mechanisms of action of key coenzymes, including pyridoxal 5′-phosphate, tetrahydrofolate, pyridine nucleotides, flavin, heme, and ATP. Enzymes used in drug synthesis and those that can serve as therapeutic agents, rather than as drug targets, also are discussed.
Mobility Shift DNA-Binding Assay Using Gel Electrophoresis

DNA-binding assay using nondenaturing polyacrylamide gel electrophoresis (PAGE) provides a simple, rapid, and extremely sensitive method for detecting sequence-specific DNA-binding proteins. Proteins that bind specifically to an end-labeled DNA fragment retard the mobility of the fragment during electrophoresis, resulting in discrete bands corresponding to the individual protein-DNA complexes. The assay described in this unit can be used to test binding of purified proteins or of uncharacterized factors found in crude extracts. This assay also permits quantitative determination of the affinity, abundance, association rate constants, dissociation rate constants, and binding specificity of DNA-binding proteins. Three additional protocols describe a competition assay using unlabeled competitor DNA, an antibody supershift assay, and multicomponent gel shift assays.
The Decline, Rebound, and Further Rise in SNAP Enrollment: Disentangling Business Cycle Fluctuations and Policy Changes

One-in-seven Americans received benefits from the Supplemental Nutrition Assistance Program in 2011, an all-time high. We analyze changes in program enrollment over the past two decades, quantifying the contributions of unemployment and state policy changes. Using instrumental variables to address measurement error, we estimate that a one percentage point increase in unemployment raises enrollment by 15 percent. Unemployment explains most of the decrease in enrollment in the late 1990s, state policy changes explain more of the increase in enrollment in the early 2000s, and unemployment explains most of the increase in enrollment in the aftermath of the Great Recession.
Multinational Firms and International Business Cycle Transmission

We investigate how multinational firms contribute to the transmission of shocks across countries using a large multicountry firm-level data set that contains cross-border ownership information. We use these data to document two novel empirical patterns. First, foreign affiliate and headquarter sales exhibit strong positive comovement: a 10% growth in the sales of the headquarter is associated with a 2% growth in the sales of the affiliate. Second, shocks to the source country account for a significant fraction of the variation in sales growth at the source-destination level. We propose a parsimonious quantitative model to interpret these findings and to evaluate the role of multinational firms for international business cycle transmission. For the typical country, the impact of foreign shocks transmitted by all foreign multinationals combined is non-negligible, accounting for about 10% of aggregate productivity shocks. On the other hand, since bilateral multinational production shares are small, interdependence between most individual country pairs is minimal. Our results do reveal substantial heterogeneity in the strength of this mechanism, with the most integrated countries significantly more affected by foreign shocks.
The Maniac II System

The operating system to be described in this paper serves the Maniac II computer and about 40 users. Maniac II is a research computer and undergoes more or less continual modification and improvement. In its eleven-year life, it has grown in speed from 10,000 instructions per second to 100,000, in vocabulary from 80 instructions to about 300, and in memory size from 12,000 words to 80,000 (changing from half- to full-word instructions in the process). It runs in an open shop. It is in operation about 130 hours a week with only about 50 hours being handled by professional machine operators. At least 8 hours a week are devoted to new construction. On-line input/output equipment consists of console typewriter, scope display, paper tape reader and punch, card reader, line printer, and magnetic tapes. Off-line there are two magnetic tape driven Cal-Comp plotters, an IBM 047 keypunch, and the modified Flexowriters referred to below.
High-resolution epitope mapping of anti-Hu and anti-Yo autoimmunity by programmable phage display.

Paraneoplastic neurological disorders are immune-mediated diseases understood to manifest as part of a misdirected anti-tumor immune response. Paraneoplastic neurological disorder-associated autoantibodies can assist with diagnosis and enhance our understanding of tumor-associated immune processes. We designed a comprehensive library of 49-amino-acid overlapping peptides spanning the entire human proteome, including all splicing isoforms and computationally predicted coding regions. Using this library, we optimized a phage immunoprecipitation and sequencing protocol with multiple rounds of enrichment to create high-resolution epitope profiles in serum and cerebrospinal fluid (CSF) samples from patients suffering from two common paraneoplastic neurological disorders, the anti-Yo (n = 36 patients) and anti-Hu (n = 44 patients) syndromes. All (100%) anti-Yo patient samples yielded enrichment of peptides from the canonical anti-Yo (CDR2 and CDR2L) antigens, while 38% of anti-Hu patients enriched peptides deriving from the nELAVL (neuronal embryonic lethal abnormal vision like) family of proteins, the anti-Hu autoantigenic target. Among the anti-Hu patient samples that were positive for nELAVL, we noted a restricted region of immunoreactivity. To achieve single amino acid resolution, we designed a novel deep mutational scanning phage library encoding all possible single-point mutants targeting the reactive nELAVL region. This analysis revealed a distinct preference for the degenerate motif, RLDxLL, shared by ELAVL2, 3 and 4. Lastly, phage immunoprecipitation sequencing identified several known autoantigens in these same patient samples, including peptides deriving from the cancer-associated antigens ZIC and SOX families of transcription factors. Overall, this optimized phage immunoprecipitation sequencing library and protocol yielded the high-resolution epitope mapping of the autoantigens targeted in anti-Yo and anti-Hu encephalitis patients to date. The results presented here further demonstrate the utility and high-resolution capability of phage immunoprecipitation sequencing for both basic science and clinical applications and for better understanding the antigenic targets and triggers of paraneoplastic neurological disorders.
Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platforms

Mental illness is a global health problem, but access to mental healthcare resources remain poor worldwide. Online peer-to-peer support platforms attempt to alleviate this fundamental gap by enabling those who struggle with mental illness to provide and receive social support from their peers. However, successful social support requires users to engage with each other and failures may have serious consequences for users in need. Our understanding of engagement patterns on mental health platforms is limited but critical to inform the role, limitations, and design of these platforms. Here, we present a large-scale analysis of engagement patterns of 35 million posts on two popular online mental health platforms, TalkLife and Reddit. Leveraging communication models in human-computer interaction and communication theory, we operationalize a set of four engagement indicators based on attention and interaction. We then propose a generative model to jointly model these indicators of engagement, the output of which is synthesized into a novel set of eleven distinct, interpretable patterns. We demonstrate that this framework of engagement patterns enables informative evaluations and analysis of online support platforms. Specifically, we find that mutual back-and-forth interactions are associated with significantly higher user retention rates on TalkLife. Such back-and-forth interactions, in turn, are associated with early response times and the sentiment of posts.
The Dual Epidemics of COVID-19 and Influenza: Vaccine Acceptance, Coverage, and Mandates.

The confluence of coronavirus disease 2019 (COVID-19) and seasonal influenza this fall and winter will result in considerable morbidity and mortality, stressing the health system. With more than 100 000 COVID-19–related deaths already, the US could see a second wave of disease later this year. In 2018-2019 (a “moderate” year for influenza), the US experienced 35.5 million influenza cases, with 490 600 hospitalizations and 34 200 deaths related to influenza.1 An effective COVID-19 vaccine is unlikely until 2021. Even though seasonal influenza vaccines have variable year-to-year effectiveness, they can significantly reduce morbidity and mortality, especially with high coverage. The health system, and wider society, must prepare for the likelihood of co-epidemics of COVID-19 and influenza. What are the most effective strategies for increasing influenza vaccine coverage across the population and particularly in schools, businesses, and hospitals? Should states or businesses require vaccinations? Influenza vaccination, moreover, could offer valuable lessons for ensuring vaccine acceptance and uptake when COVID-19 vaccines become available.
Modeling Phylogenetic Biome Shifts on a Planet with a Past

The spatial distribution of biomes has changed considerably over deep time, so the geographical opportunity for an evolutionary lineage to shift into a new biome may depend on how the availability and connectivity of biomes has varied temporally. To better understand how lineages shift between biomes in space and time, we developed a phylogenetic biome shift model in which each lineage shifts between biomes and disperses between regions at rates that depend on the lineage's biome affinity and location relative to the spatial distribution of biomes at any given time. To study the behavior of the biome shift model in an empirical setting, we developed a literature-based representation of paleobiome structure for three mesic forest biomes, six regions, and eight time strata, ranging from the Late Cretaceous (100 Ma) through the present. We then fitted the model to a time-calibrated phylogeny of 119 Viburnum species to compare how the results responded to various realistic or unrealistic assumptions about paleobiome structure. Ancestral biome estimates that account for paleobiome dynamics reconstructed a warm temperate (or tropical) origin of Viburnum, which is consistent with previous fossil-based estimates of ancestral biomes. Imposing unrealistic paleobiome distributions led to ancestral biome estimates that eliminated support for tropical origins, and instead inflated support for cold temperate ancestry throughout the warmer Paleocene and Eocene. The biome shift model we describe is applicable to the study of evolutionary systems beyond Viburnum, and the core mechanisms of our model are extensible to the design of richer phylogenetic models of historical biogeography and/or lineage diversification. We conclude that biome shift models that account for dynamic geographical opportunities are important for inferring ancestral biomes that are compatible with our understanding of Earth history.[Ancestral states; biome shifts; historical biogeography; niche conservatism; phylogenetics].
Lineage Reversion Drives WNT Independence in Intestinal Cancer

The WNT pathway is a fundamental regulator of intestinal homeostasis, and hyperactivation of WNT signaling is the major oncogenic driver in colorectal cancer. To date, there are no described mechanisms that bypass WNT dependence in intestinal tumors. Here, we show that although WNT suppression blocks tumor growth in most organoid and in vivo colorectal cancer models, the accumulation of colorectal cancer-associated genetic alterations enables drug resistance and WNT-independent growth. In intestinal epithelial cells harboring mutations in KRAS or BRAF, together with disruption of TP53 and SMAD4, transient TGFβ exposure drives YAP/TAZ-dependent transcriptional reprogramming and lineage reversion. Acquisition of embryonic intestinal identity is accompanied by a permanent loss of adult intestinal lineages, and long-term WNT-independent growth. This work identifies genetic and microenvironmental factors that drive WNT inhibitor resistance, defines a new mechanism for WNT-independent colorectal cancer growth, and reveals how integration of associated genetic alterations and extracellular signals can overcome lineage-dependent oncogenic programs. SIGNIFICANCE: Colorectal and intestinal cancers are driven by mutations in the WNT pathway, and drugs aimed at suppressing WNT signaling are in active clinical development. Our study identifies a mechanism of acquired resistance to WNT inhibition and highlights a potential strategy to target those drug-resistant cells.This article is highlighted in the In This Issue feature, p. 1426.
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM

System identification is a key step for model-based control, estimator design, and output prediction. This work considers the offline identification of partially observed nonlinear systems. We empirically show that the certainty-equivalent approximation to expectation-maximization can be a reliable and scalable approach for high-dimensional deterministic systems, which are common in robotics. We formulate certainty-equivalent expectation-maximization as block coordinate-ascent, and provide an efficient implementation. The algorithm is tested on a simulated system of coupled Lorenz attractors, demonstrating its ability to identify high-dimensional systems that can be intractable for particle-based approaches. Our approach is also used to identify the dynamics of an aerobatic helicopter. By augmenting the state with unobserved fluid states, a model is learned that predicts the acceleration of the helicopter better than state-of-the-art approaches. The codebase for this work is available at this https URL.