cssi/SciSciGPT-SciSciNet huggingface dataset:
The Broad Optimality of Profile Maximum Likelihood

We study three fundamental statistical-learning problems: distribution estimation, property estimation, and property testing. We establish the profile maximum likelihood (PML) estimator as the first unified sample-optimal approach to a wide range of learning tasks. In particular, for every alphabet size $k$ and desired accuracy $\varepsilon$: \textbf{Distribution estimation} Under $\ell_1$ distance, PML yields optimal $\Theta(k/(\varepsilon^2\log k))$ sample complexity for sorted-distribution estimation, and a PML-based estimator empirically outperforms the Good-Turing estimator on the actual distribution; \textbf{Additive property estimation} For a broad class of additive properties, the PML plug-in estimator uses just four times the sample size required by the best estimator to achieve roughly twice its error, with exponentially higher confidence; \textbf{$\alpha$-R\'enyi entropy estimation} For an integer $\alpha>1$, the PML plug-in estimator has optimal $k^{1-1/\alpha}$ sample complexity; for non-integer $\alpha>3/4$, the PML plug-in estimator has sample complexity lower than the state of the art; \textbf{Identity testing} In testing whether an unknown distribution is equal to or at least $\varepsilon$ far from a given distribution in $\ell_1$ distance, a PML-based tester achieves the optimal sample complexity up to logarithmic factors of $k$. With minor modifications, most of these results also hold for a near-linear-time computable variant of PML.
Classification of Thurston relation subfactor planar algebras

Bisch and Jones suggested the skein theoretic classification of planar algebras and investigated the ones generated by 2-boxes with the second author. In this paper, we consider 3-box generators and classify subfactor planar algebras generated by a non-trivial 3-box satisfying a relation proposed by Thurston. The subfactor planar algebras in the classification are either $E^6$ or the ones from representations of quantum $SU(N)$. We introduce a new method to determine positivity of planar algebras and new techniques to reduce the complexity of computations.
Adding structural characteristics to distribution-based accelerometer representations for activity recognition using wearables.

Feature extraction is a critical step in sliding-window based standard activity recognition chains. Recently, distribution based features have been introduced that showed excellent generalization capabilities across a wide range of application domains in human activity recognition scenarios based on body-worn sensors. These features capture the data distribution of individual analysis frames, yet they ignore temporal structure inherent to the signal of a frame. We explore four variants of adding temporal structure to distribution based features and demonstrate their potential for statistically significant improvements of activity recognition in general. The addition of temporal structure comes with a moderate increase in computational complexity rendering the proposed methods applicable to mobile and embedded scenarios.
Maximum likelihood estimation of low rank signals for multiepoch MEG/EEG data

An algorithm for reducing spatially colored noise in evoked response magneto- and electro-encephalography data is presented. The algorithm models the repeatable component of the data, or signal of interest, as the mean, while the noise is modeled as Gaussian with unknown covariance structure. The mean matrix has a low rank structure due to the temporal and spatial structure of the data. Maximum likelihood estimates of the components of the low-rank signal structure are derived in order to estimate the signal component. The effectiveness of this approach is demonstrated using simulated and real MEG data.
Distance-Distributed Design for Gaussian Process Surrogates

A common challenge in computer experiments and related fields is to efficiently explore the input space using a small number of samples, that is, the experimental design problem. Much of the recent...
The pediatric submersion score predicts children at low risk for injury following submersions

Introduction: Drowning is an important cause of unintentional childhood death. Pediatric submersion victims are often admitted to hospital for observation. We created a scoring tool to identify pediatric submersion victims at low risk for injury who do not require hospital admission after 8 hours of observation in the emergency department (ED). Methods: This was a single-center derivation and validation cross-sectional study of children aged 0-18 years who presented to a large, urban, tertiary-care, children’s hospital ED following a submersion event between the years 2008 to 2015. Pre-hospital, submersion characteristics …
3DFPN-HS\(^2\): 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection

Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limited the automatic diagnosis in routine clinical practice. In this paper, we propose a novel pulmonary nodule detection framework based on a 3D Feature Pyramid Network (3DFPN) to improve the sensitivity of nodule detection by employing multi-scale features to increase the resolution of nodules, as well as a parallel top-down path to transit the high-level semantic features to complement low-level general features. Furthermore, a High Sensitivity and Specificity (HS\(^2\)) network is introduced to eliminate the falsely detected nodule candidates by tracking the appearance changes in continuous CT slices of each nodule candidate. The proposed framework is evaluated on the public Lung Nodule Analysis (LUNA16) challenge dataset. Our method is able to accurately detect lung nodules at high sensitivity and specificity and achieves \(90.4\%\) sensitivity with 1/8 false positive per scan which outperforms the state-of-the-art results \(15.6\%\).
Regulation of nuclear-cytoplasmic partitioning by the lin-28-lin-46 pathway reinforces microRNA repression of HBL-1 to confer robust cell-fate progression in C. elegans

MicroRNAs target complementary mRNAs for degradation or translational repression, reducing or preventing protein synthesis. In Caenorhabditis elegans, the transcription factor HBL-1 (Hunchback-like 1) promotes early larval (L2)-stage cell fates, and the let-7 family microRNAs temporally downregulate HBL-1 to enable the L2-to-L3 cell-fate progression. In parallel to let-7-family microRNAs, the conserved RNA-binding protein LIN-28 and its downstream gene lin-46 also act upstream of HBL-1 in regulating the L2-to-L3 cell-fate progression. The molecular function of LIN-46, and how the lin-28-lin-46 pathway regulates HBL-1, are not understood. Here, we report that the regulation of HBL-1 by the lin-28-lin-46 pathway is independent of the let-7/lin-4 microRNA complementary sites (LCSs) in the hbl-1 3'UTR, and involves stage-specific post-translational regulation of HBL-1 nuclear accumulation. We find that LIN-46 is necessary and sufficient to prevent nuclear accumulation of HBL-1. Our results illuminate that robust progression from L2 to L3 cell fates depends on the combination of two distinct modes of HBL-1 downregulation: decreased synthesis of HBL-1 via let-7-family microRNA activity, and decreased nuclear accumulation of HBL-1 via action of the lin-28-lin-46 pathway.
Supplier Competition and Cost Reduction with Endogenous Information Asymmetry

Problem definition: We consider a buyer sourcing from multiple competing suppliers who exert cost-reduction efforts before procurement contracts are awarded. Academic/practical relevance: The suppl...
Floorplan: spatial layout in memory management systems

In modern runtime systems, memory layout calculations are hand-coded in systems languages. Primitives in these languages are not powerful enough to describe a rich set of layouts, leading to reliance on ad-hoc macros, numerous interrelated static constants, and other boilerplate code. Memory management policies must also carefully orchestrate their application of address calculations in order to modify memory cooperatively, a task ill-suited to low-level systems languages at hand which lack proper safety mechanisms. In this paper we introduce Floorplan, a declarative language for specifying high level memory layouts. Constraints formerly implemented by describing how to compute locations are, in Floorplan, defined declaratively using explicit layout constructs. The challenge here was to discover constructs capable of sufficiently enabling the automatic generation of address calculations. Floorplan is implemented as a compiler for generating a Rust library. In a case study of an existing implementation of the immix garbage collection algorithm, Floorplan eliminates 55 out of the 63 unsafe lines of code: 100% of unsafe lines pertaining to memory safety.
Effect of Tethered, Axial Thioether Coordination on Rhodium(II)-Catalyzed Silyl-Hydrogen Insertion

The effect of tethering a thioether to a rhodium paddlewheel complex are evaluated in diazo-mediated Si–H insertion reactions. This study provides further evidence for the benefits of incorporating thioethers into the rhodium paddlewheel motif. When compared to a non-tethered complex, the in situ carbenoid formation and subsequent Si–H insertion are not impeded by the thioether tether, improves yields by up to 12%, and is more selective for diazo compounds when compared to non-tethered catalysts. Computational modeling of the complexes at the M06-2X/def2-TZVPP level of theory is also presented in order to rationalize the results.
Learning How to Autonomously Race a Car: A Predictive Control Approach

We present a learning model predictive controller (LMPC) for autonomous racing. We model the autonomous racing problem as a minimum time iterative control task, where an iteration corresponds to a lap. The system trajectory and input sequence of each lap are stored and used to systematically update the controller for the next lap. In the proposed approach, the race time does not increase at each iteration. The first contribution is to propose a local LMPC which reduces the computational burden associated with existing LMPC strategies. In particular, we show how to construct a local safe set and approximation to the value function, using a subset of the stored data. The second contribution is to present a system identification strategy for the autonomous racing iterative control task. We use data from previous iterations and the vehicle’s kinematic equations of motion to build an affine time-varying prediction model. The effectiveness of the proposed strategy is demonstrated by experimental results on the Berkeley Autonomous Race Car (BARC) platform.
Improved metagenomic analysis with Kraken 2.

Although Kraken’s k-mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.
Classifying spaces and Bredon (co)homology for transitive groupoids

We define the orbit category for transitive topological groupoids and their equivariant CW-complexes. By using these constructions we define equivariant Bredon homology and cohomology for actions of transitive topological groupoids. We show how these theories can be obtained by looking at the action of a single isotropy group on a fiber of the anchor map, extending equivariant results for compact group actions. We also show how this extension from a single isotropy group to the entire groupoid action can be applied to the structure of principal bundles and classifying spaces.
On the Thermal Conductivities of Crystals and Other Bad Conductors

Our knowledge of the Thermal Conductivities of Crystals is derived mainly from the experiments of de Senarmont, von Lang, and Jannetaz, who, using the wax melting or analogous methods, have determined the ratios of what may be called the “principal conductivities” and the positions of the axes of conductivity within a number of crystals belonging to the simple systems. According to their experi­ments the isothermal surfaces about a heated point in a crystal are, in general, ellipsoids, having their axes parallel to the optical axes. In the case of a uniaxal crystal, this ellipsoid becomes a spheroid of revolution about the axis, and is, as a rule, oblate or prolate according as the wave-surface for the extraordinary ray is oblate or prolate. Although this rule has a number of exceptions, it is sufficiently general to render it probable that there may be some relation between the passage of light and of heat through a crystal. The recent determinations of the refractive indices of metals by Kundt have shown that they stand in the same order as con­ductors of heat, and as to the velocity of propagation of light through them, and this fact brings again into prominence the old determinations with respect to crystals. That the comparison which Kundt has made for the metals cannot be carried to other bodies is at once seen from the fact that the index of refraction of iron differs little from those of glass and several commoner crystals, the conductivities of which are shown to be very small compared to that of iron. A comparison may, however, be possible among transparent bodies themselves, and the following experiments were made with the object of furnishing data for this comparison, the results given by previous observers differing greatly from each other. They have, however, been extended to embrace non-transparent bodies commonly in use in a physical laboratory, and about the conductivity of which we have had a very meagre or absolutely no knowledge. The most important consideration in determining the method to be used is the fact that it is difficult to get large pieces of the crystals to be experimented on. This excludes methods requiring large plates, such as that of Weber and Tuschmid, or large spheres or cubes, such as that of Kirchhoff or Thomson. A method which seemed to present several advantages was the one first suggested by Lodge, and which may be called the “divided bar” method, and after some preliminary experi­ments had been made to determine its suitability, it was finally adopted. It consists in observing the temperature along a bar heated at one end and cooled at the other, and divided halfway between the two ends by a plane perpendicular to its axis, when (1) the divided ends are together, (2) a disc of the crystal or other body is between.
Infinite order corks via handle diagrams

The author recently proved the existence of an infinite order cork: a compact, contractible submanifold C of a 4–manifold and an infinite order diffeomorphism f of ∂C such that cutting out C and regluing it by distinct powers of f yields pairwise nondiffeomorphic manifolds. The present paper exhibits the first handle diagrams of this phenomenon, by translating the earlier proof into this language (for each of the infinitely many corks arising in the first paper). The cork twists in these papers are twists on incompressible tori. We give conditions guaranteeing that such twists do not change the diffeomorphism type of a 4–manifold, partially answering a question from the original paper. We also show that the “δ–moves” recently introduced by Akbulut are essentially equivalent to torus twists.
Robust Hierarchical-Optimization RLS Against Sparse Outliers

This letter fortifies the recently introduced hierarchical-optimization recursive least squares (HO-RLS) against outliers which contaminate infrequently linear-regression models. Outliers are modeled as nuisance variables and are estimated together with the linear filter/system variables via a sparsity-inducing (non-)convexly regularized least-squares task. The proposed outlier-robust HO-RLS builds on steepest-descent directions with a constant step size (learning rate), needs no matrix inversion (lemma), accommodates colored nominal noise of known correlation matrix, exhibits small computational footprint, and offers theoretical guarantees, in a probabilistic sense, for the convergence of the system estimates to the solutions of a hierarchical-optimization problem: Minimize a convex loss, which models a-priori knowledge about the unknown system, over the minimizers of the classical ensemble LS loss. Extensive numerical tests on synthetically generated data in both stationary and non-stationary scenarios showcase notable improvements of the proposed scheme over state-of-the-art techniques.
Expressing Objects Just Like Words: Recurrent Visual Embedding for Image-Text Matching.

Existing image-text matching approaches typically infer the similarity of an image-text pair by capturing and aggregating the affinities between the text and each independent object of the image. However, they ignore the connections between the objects that are semantically related. These objects may collectively determine whether the image corresponds to a text or not. To address this problem, we propose a Dual Path Recurrent Neural Network (DP-RNN) which processes images and sentences symmetrically by recurrent neural networks (RNN). In particular, given an input image-text pair, our model reorders the image objects based on the positions of their most related words in the text. In the same way as extracting the hidden features from word embeddings, the model leverages RNN to extract high-level object features from the reordered object inputs. We validate that the high-level object features contain useful joint information of semantically related objects, which benefit the retrieval task. To compute the image-text similarity, we incorporate a Multi-attention Cross Matching Model into DP-RNN. It aggregates the affinity between objects and words with cross-modality guided attention and self-attention. Our model achieves the state-of-the-art performance on Flickr30K dataset and competitive performance on MS-COCO dataset. Extensive experiments demonstrate the effectiveness of our model.
End-to-End Game-Focused Learning of Adversary Behavior in Security Games

Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary. Motivated by green security, where the defender may only observe an adversary's response to defense on a limited set of targets, we study the problem of learning a defense that generalizes well to a new set of targets with novel feature values and combinations. Traditionally, this problem has been addressed via a two-stage approach where an adversary model is trained to maximize predictive accuracy without considering the defender's optimization problem. We develop an end-to-end game-focused approach, where the adversary model is trained to maximize a surrogate for the defender's expected utility. We show both in theory and experimental results that our game-focused approach achieves higher defender expected utility than the two-stage alternative when there is limited data.
Cognitive Rehabilitation for Cognitive Dysfunction after Cancer and Cancer Treatment: Implications for Nursing Practice

ABSTRACT Objective To provide an overview of cognitive rehabilitation approaches for cognitive dysfunction after cancer and cancer treatment. Data Sources Review and synthesis of empirical articles. Conclusion Cognitive rehabilitation approaches, including cognitive behavioral therapy and cognitive training, for cognitive dysfunction appear feasible to deliver, satisfactory to participants, and have shown promising results in cancer survivors. Future research is needed to address optimal dose, delivery method, access, cost, and the vulnerable aging cancer survivor population. Implications for Nursing Practice Oncology nurses must understand the available evidence and be able to provide information and options to cancer survivors to address cognitive changes after cancer.
Diplomacy and the Settlement of International Trade Disputes

How do countries settle disputes in the shadow of the law? Even in the presence of legalized dispute settlement, countries still rely on diplomatic channels to resolve conflicts. But it can be diff...
An intuitive approach to the Martin boundary

Abstract An intuitive probabilistic alternative for the construction of the Martin boundary is presented along with a construction of maximal representing measures for positive harmonic functions.
Cotangent bundles for “matrix algebras converge to the sphere”

Abstract In the high-energy quantum-physics literature one finds statements such as “matrix algebras converge to the sphere”. Earlier I provided a general setting for understanding such statements, in which the matrix algebras are viewed as compact quantum metric spaces, and convergence is with respect to a quantum Gromov-Hausdorff-type distance. More recently I have dealt with corresponding statements in the literature about vector bundles on spheres and matrix algebras. But physicists want, even more, to treat structures on spheres (and other spaces) such as Dirac operators, Yang–Mills functionals, etc., and they want to approximate these by corresponding structures on matrix algebras. In preparation for understanding what the Dirac operators should be, we determine here what the corresponding “cotangent bundles” should be for the matrix algebras, since it is on them that a “Riemannian metric” must be defined, which is then the information needed to determine a Dirac operator. (In the physics literature there are at least 3 inequivalent suggestions for the Dirac operators.)
Feeding-dependent VIP neuron–ILC3 circuit regulates the intestinal barrier

The intestinal mucosa serves both as a conduit for the uptake of food-derived nutrients and microbiome-derived metabolites, and as a barrier that prevents tissue invasion by microorganisms and tempers inflammatory responses to the myriad contents of the lumen. How the intestine coordinates physiological and immune responses to food consumption to optimize nutrient uptake while maintaining barrier functions remains unclear. Here we show in mice how a gut neuronal signal triggered by food intake is integrated with intestinal antimicrobial and metabolic responses that are controlled by type-3 innate lymphoid cells (ILC3)1–3. Food consumption rapidly activates a population of enteric neurons that express vasoactive intestinal peptide (VIP)4. Projections of VIP-producing neurons (VIPergic neurons) in the lamina propria are in close proximity to clusters of ILC3 that selectively express VIP receptor type 2 (VIPR2; also known as VPAC2). Production of interleukin (IL)-22 by ILC3, which is upregulated by the presence of commensal microorganisms such as segmented filamentous bacteria5–7, is inhibited upon engagement of VIPR2. As a consequence, levels of antimicrobial peptide derived from epithelial cells are reduced but the expression of lipid-binding proteins and transporters is increased8. During food consumption, the activation of VIPergic neurons thus enhances the growth of segmented filamentous bacteria associated with the epithelium, and increases lipid absorption. Our results reveal a feeding- and circadian-regulated dynamic neuroimmune circuit in the intestine that promotes a trade-off between innate immune protection mediated by IL-22 and the efficiency of nutrient absorption. Modulation of this pathway may therefore be effective for enhancing resistance to enteropathogens2,3,9 and for the treatment of metabolic diseases. Feeding controls a neuroimmune circuit comprising VIP-producing neurons and type-3 innate lymphoid cells that helps to regulate the efficiency of nutrient uptake and IL-22-mediated immune protection in the intestine.
Readthrough errors purge deleterious cryptic sequences, facilitating the birth of coding sequences.

De novo protein-coding innovations sometimes emerge from ancestrally noncoding DNA, despite the expectation that translating random sequences is overwhelmingly likely to be deleterious. The "preadapting selection" hypothesis claims that emergence is facilitated by prior, low-level translation of noncoding sequences via molecular errors. It predicts that selection on polypeptides translated only in error is strong enough to matter and is strongest when erroneous expression is high. To test this hypothesis, we examined noncoding sequences located downstream of stop codons (i.e., those potentially translated by readthrough errors) in Saccharomyces cerevisiae genes. We identified a class of "fragile" proteins under strong selection to reduce readthrough, which are unlikely substrates for co-option. Among the remainder, sequences showing evidence of readthrough translation, as assessed by ribosome profiling, encoded C-terminal extensions with higher intrinsic structural disorder, supporting the preadapting selection hypothesis. The cryptic sequences beyond the stop codon, rather than spillover effects from the regular C-termini, are primarily responsible for the higher disorder. Results are robust to controlling for the fact that stronger selection also reduces the length of C-terminal extensions. These findings indicate that selection acts on 3' UTRs in Saccharomyces cerevisiae to purge potentially deleterious variants of cryptic polypeptides, acting more strongly in genes that experience more readthrough errors.
Acetate Production from Cafeteria Wastes and Corn Stover Using a Thermophilic Anaerobic Consortium: A Prelude Study for the Use of Acetate for the Production of Value-Added Products.

Efficient and sustainable biochemical production using low-cost waste assumes considerable industrial and ecological importance. Solid organic wastes (SOWs) are inexpensive, abundantly available resources and their bioconversion to volatile fatty acids, especially acetate, aids in relieving the requirements of pure sugars for microbial biochemical productions in industries. Acetate production from SOW that utilizes the organic carbon of these wastes is used as an efficient solid waste reduction strategy if the environmental factors are optimized. This study screens and optimizes influential factors (physical and chemical) for acetate production by a thermophilic acetogenic consortium using two SOWs—cafeteria wastes and corn stover. The screening experiment revealed significant effects of temperature, bromoethane sulfonate, and shaking on acetate production. Temperature, medium pH, and C:N ratio were further optimized using statistical optimization with response surface methodology. The maximum acetate concentration of 8061 mg L−1 (>200% improvement) was achieved at temperature, pH, and C:N ratio of 60 °C, 6, 25, respectively, and acetate accounted for more than 85% of metabolites. This study also demonstrated the feasibility of using acetate-rich fermentate (obtained from SOWs) as a substrate for the growth of industrially relevant yeast Yarrowia lipolytica, which can convert acetate into higher-value biochemicals.
Beyond Activity Space: Detecting Communities in Ecological Networks

Emerging research suggests that the extent to which activity spaces - the collection of an individual's routine activity locations - overlap provides important information about the functioning of a city and its neighborhoods. To study patterns of overlapping activity spaces, we draw on the notion of an ecological network, a type of two-mode network with the two modes being individuals and the geographic locations where individuals perform routine activities. We describe a method for detecting "ecological communities" within these networks based on shared activity locations among individuals. Specifically, we identify latent activity pattern profiles, which, for each community, summarize its members' probability distribution of going to each location, and community assignment vectors, which, for each individual, summarize his/her probability distribution of belonging to each community. Using data from the Adolescent Health and Development in Context (AHDC) Study, we employ latent Dirichlet allocation (LDA) to identify activity pattern profiles and communities. We then explore differences across neighborhoods in the strength, and within-neighborhood consistency of community assignment. We hypothesize that these aspects of the neighborhood structure of ecological community membership capture meaningful dimensions of neighborhood functioning likely to co-vary with economic and racial composition. We discuss the implications of a focus on ecological communities for the conduct of "neighborhood effects" research more broadly.
Latent periodic process inference from single-cell RNA-seq data

The development of a phenotype in a multicellular organism often involves multiple, simultaneously occurring biological processes. Advances in single-cell RNA-sequencing make it possible to infer latent developmental processes from the transcriptomic profiles of cells at various developmental stages. Accurate characterization is challenging however, particularly for periodic processes such as cell cycle. To address this, we develop Cyclum, an autoencoder approach identifying circular trajectories in the gene expression space. Cyclum substantially improves the accuracy and robustness of cell-cycle characterization beyond existing approaches. Applying Cyclum to removing cell-cycle effects substantially improves delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. Traditional methods for determining cell type composition lack scalability, while single-cell technologies remain costly and noisy compared to bulk RNA-seq. Here, the authors present a highly efficient tool to measure cellular heterogeneity in bulk expression through robust integration of single-cell information.
Long-Term Assessment of the Cecal Intubation Rates in High-Performing Colonoscopists: Time for Review.

Objectives The cecal intubation rate (CIR) is one of the 3 priority indicators for quality in colonoscopy. Whether continuous measurement of CIR is useful in high performers is uncertain. Methods At an academic center, we identified 16 physicians who performed at least 50 procedures over 6 consecutive years. We analyzed all colonoscopy procedures excluding those with poor/inadequate preparation or severe colitis for CIR trend over the years. We calculated the numbers needed to establish CIR over minimum threshold levels with 95% confidence. Results The overall CIR was 99.4%. None of the 16 physicians had a CIR 95%. Discussion Continuous measurement of CIR, at least in high performers, appears to be of limited value. Very high performers need to evaluate small number of cases to demonstrate that CIR is above the recommended thresholds.
CIRFESS: An Interactive Resource for Querying the Set of Theoretically Detectable Peptides for Cell Surface and Extracellular Enrichment Proteomic Studies

Cell surface transmembrane, extracellular, and secreted proteins are high value targets for immunophenotyping, drug development, and studies related to intercellular communication in health and disease. As the number of specific and validated affinity reagents that target this subproteome are limited, mass spectrometry (MS)-based approaches will continue to play a critical role in enabling discovery and quantitation of these molecules. Given the technical considerations that make MS-based cell surface proteome studies uniquely challenging, it can be difficult to select an appropriate experimental approach. To this end, we have integrated multiple prediction strategies and annotations into a single online resource, Compiled Interactive Resource for Extracellular and Surface Studies (CIRFESS). CIRFESS enables rapid interrogation of the human proteome to reveal the cell surface proteome theoretically detectable by current approaches and highlights where current prediction strategies provide concordant and discordant information. We applied CIRFESS to identify the percentage of various subsets of the proteome which are expected to be captured by targeted enrichment strategies, including two established methods and one that is possible but not yet demonstrated. These results will inform the selection of available proteomic strategies and development of new strategies to enhance coverage of the cell surface and extracellular proteome. CIRFESS is available at www.cellsurfer.net/cirfess.
DNA Helicase-SSB Interactions Critical to the Regression and Restart of Stalled DNA Replication Forks in Escherichia coli

In Escherichia coli, DNA replication forks stall on average once per cell cycle. When this occurs, replisome components disengage from the DNA, exposing an intact, or nearly intact fork. Consequently, the fork structure must be regressed away from the initial impediment so that repair can occur. Regression is catalyzed by the powerful, monomeric DNA helicase, RecG. During this reaction, the enzyme couples unwinding of fork arms to rewinding of duplex DNA resulting in the formation of a Holliday junction. RecG works against large opposing forces enabling it to clear the fork of bound proteins. Following subsequent processing of the extruded junction, the PriA helicase mediates reloading of the replicative helicase DnaB leading to the resumption of DNA replication. The single-strand binding protein (SSB) plays a key role in mediating PriA and RecG functions at forks. It binds to each enzyme via linker/OB-fold interactions and controls helicase-fork loading sites in a substrate-dependent manner that involves helicase remodeling. Finally, it is displaced by RecG during fork regression. The intimate and dynamic SSB-helicase interactions play key roles in ensuring fork regression and DNA replication restart.
Physiological differences between advanced CrossFit athletes, recreational CrossFit participants, and physically-active adults.

This investigation examined anthropometric, hormonal, and physiological differences between advanced (ADV; n = 8, 27.8 ± 4.2 years, 170 ± 11 cm, 79.8 ± 13.3 kg) and recreational (REC; n = 8, 33.5 ± 8.1 years, 172 ± 14 cm, 76.3 ± 19.5 kg) CrossFit (CF) trained participants in comparison to physically-active controls (CON; n = 7, 27.5 ± 6.7 years, 171 ± 14 cm, 74.5 ± 14.3 kg). ADV and REC were distinguished by their past competitive success. REC and CON were resistance-trained (>2 years) and exercised on 3–5 days·wk-1 for the past year, but CON utilized traditional resistance and cardiovascular exercise. All participants provided a fasted, resting blood sample and completed assessments of resting metabolic rate, body composition, muscle morphology, isometric mid-thigh pull strength, peak aerobic capacity, and a 3-minute maximal cycle ergometer sprint across two separate occasions (separated by 3–7 days). Blood samples were analyzed for testosterone, cortisol, and insulin-like growth factor-1. Compared to both REC and CON, one-way analysis of variance revealed ADV to possess lower body fat percentage (6.7–8.3%, p = 0.007), greater bone and non-bone lean mass (12.5–26.8%, p ≤ 0.028), muscle morphology characteristics (14.2–59.9%, p < 0.05), isometric strength characteristics (15.4–41.8%, p < 0.05), peak aerobic capacity (18.8–19.1%, p = 0.002), and 3-minute cycling performance (15.4–51.1%, p ≤ 0.023). No differences were seen between REC and CON, or between all groups for resting metabolic rate or hormone concentrations. These data suggest ADV possess several physiological advantages over REC and CON, whereas similar physiological characteristics were present in individuals who have been regularly participating in either CF or resistance and cardiovascular training for the past year.
On odd torsion in even Khovanov homology

This short note resolves the most important part of the PS braid conjecture while introducing the first known examples of knots and links with odd torsion of order 9, 27, 81, and 25 in their even K...
Predicting Word Error Rate for Reverberant Speech

Reverberation negatively impacts the performance of automatic speech recognition (ASR). Prior work on quantifying the effect of reverberation has shown that clarity (C50), a parameter that can be estimated from the acoustic impulse response, is correlated with ASR performance. In this paper we propose predicting ASR performance in terms of the word error rate (WER) directly from acoustic parameters via a polynomial, sigmoidal, or neural network fit, as well as blindly from reverberant speech samples using a convolutional neural network (CNN). We carry out experiments on two state-of-the-art ASR models and a large set of acoustic impulse responses (AIRs). The results confirm C50 and C80 to be highly correlated with WER, allowing WER to be predicted with the proposed fitting approaches. The proposed non-intrusive CNN model outperforms C50-based WER prediction, indicating that WER can be estimated blindly, i.e., directly from the reverberant speech samples without knowledge of the acoustic parameters.
Estimating and forecasting the smoking-attributable mortality fraction for both genders jointly in over 60 countries

Smoking is one of the leading preventable threats to human health and a major risk factor for lung cancer, upper aerodigestive cancer and chronic obstructive pulmonary disease. Estimating and forecasting the smoking attributable fraction (SAF) of mortality can yield insights into smoking epidemics and also provide a basis for more accurate mortality and life expectancy projection. Peto et al. (Lancet 339 (1992) 1268–1278) proposed a method to estimate the SAF using the lung cancer mortality rate as an indicator of exposure to smoking in the population of interest. Here, we use the same method to estimate the all-age SAF (ASAF) for both genders for over 60 countries. We document a strong and cross-nationally consistent pattern of the evolution of the SAF over time. We use this as the basis for a new Bayesian hierarchical model to project future male and female ASAF from over 60 countries simultaneously. This gives forecasts as well as predictive distributions that can be used to find uncertainty intervals for any quantity of interest. We assess the model using out-of-sample predictive validation and find that it provides good forecasts and well-calibrated forecast intervals, comparing favorably with other methods.
Chapter 14 – Cognitive Radio Architecture

Publisher Summary This chapter develops five complementary perspectives of cognitive radio architecture (CRA), called CRA-I through CRA-V, each building on the previous in capability. Architecture is driven top-down by market needs and bottom-up by available, affordable technologies. Taking the top-down perspective requires some attention to the use cases that the functions are intended to realize. This chapter therefore reviews the substantial changes in use cases that drive cognitive wireless architecture. Often technical architectures of the kind accelerate the state of practice by catalyzing work across the industry on plug-and-play, teaming, and collaboration. The thought is that to propel wireless technology from limited spectrum awareness toward valuable user awareness, an architecture is needed. The CRA articulates the functions, components, and design rules of next-generation stand-alone and embedded wireless devices and networks.
The wealth of subnations: Geography, institutions, and within-country development☆

Abstract I study determinants of economic development in a new dataset covering 1867 subnational regions from 101 countries, focusing on within-country effects of geography and institutions. Several geographic factors have significant explanatory power for within-country differences in per-capita GDP, including terrain ruggedness, tropical climate, ocean access, temperature range, storm risk, and natural resources such as oil, diamonds, or iron. Institutions have a significant positive effect on income among subnational regions with greater autonomy, suggesting that strong subnational institutions enhance development when not dominated by national institutions.
Estimation of COVID-19 spread curves integrating global data and borrowing information

Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19.
SUPPLY SHOCKS AND OPTIMAL MONETARY POLICY

This paper demonstrates that if current shocks are observed instantaneously, output can be stabilized perfectly for completely general supply disturbances, using simple monetary rules based only on: (1) the current shock; (2) the previous forecast of the current shock; and (3) the forecast for just one period ahead. The optimal rule can be expressed in an infinite number of ways and various alternatives are considered. With optimal wage indexation, the monetary rule is even simpler. If current shocks are not observed instantaneously, but are inferred from other signals, the optimal rules are of the same form, with the current perceived disturbance replacing the actual. Copyright 1987 by Royal Economic Society.
Existence of hypersurfaces with prescribed mean curvature I – generic min-max

We prove that, for a generic set of smooth prescription functions $h$ on a closed ambient manifold, there always exists a nontrivial, smooth, closed hypersurface of prescribed mean curvature $h$. The solution is either an embedded minimal hypersurface with integer multiplicity, or a non-minimal almost embedded hypersurface of multiplicity one. More precisely, we show that our previous min-max theory, developed for constant mean curvature hypersurfaces, can be extended to construct min-max prescribed mean curvature hypersurfaces for certain classes of prescription function, including smooth Morse functions and nonzero analytic functions. In particular we do not need to assume that $h$ has a sign.
On the robustness of MEOP design versus asymptotic LQG synthesis

An analysis is presented of generalized LQG (linear-quadratic Gaussian)-type algorithms with regard to robustness characteristics. Comparisons are made of the maximum entropy/optimal projection (MEOP) methodology versus the linear quadratic Gaussian with loop transfer recovery (LQG/LTR) technique and the parametric robust linear quadratic Gaussian synthesis procedure. The performance of a reduced-order direct controller design is compared with the full-order designs on a example representative of typical flexible-structure dynamics. >
Learning and Asset-Price Jumps

W e develop a general equilibrium model in which income and dividends are smooth but asset prices contain large moves (jumps). These large price jumps are triggered by optimal decisions of investors to learn the unobserved state. We show that learning choice is determined by preference parameters and the conditional volatility of income process. An important model prediction is that income volatility predicts future jump periods, while income growth does not. Consistent with the model, large moves in returns in the data are predicted by consumption volatility but not by consumption growth. The model quantitatively captures these novel features of the data. ( JEL G00, G12, G14, D83) A prominent feature of financial markets is infrequent but large price movements (jumps). 1 In this article, we develop a model in which income and dividends have smooth Gaussian dynamics; however, asset prices are subject to large infrequent jumps. In our model, large moves in asset prices obtain from the actions of the representative agent to acquire more information about the unobserved state of the economy for a cost. We show that the optimal decision to incur a cost and learn the true economic state is directly related to the level of uncertainty in the economy. This implies that aggregate economic volatility, as well as market volatility, should predict jumps in returns. We show that indeed in the data, consistent with the model, return jumps are predicted by consumption volatility (market volatility). Further, the implied asset-price implications from our model are consistent with the key findings from parametric models about frequency and predictability of jumps as well as nonparametric jump-detection analysis of Barndorff-Nielsen and Shephard (2006). Based on
Hydraulic Flow Through Saturated Clays

The factors. (1) possible violations of Darcy’s law. (2) electrokinetic coupling. (3) high viscosity. (4) tortuous flow paths, an. (5) unequal pore sizes have been suggested as possible explanations for the differences between hydraulic flow rates in liquid-saturated clays and sands. The effects of these factors on hydraulic flow rates through saturated clays were investigated. Hydraulic flow rates, electrical conductivities, and streaming potentials were measured on natural, sodium, and calcium samples of kaolinite, illite and Boston blue clay. Data were taken after increments of one-dimensional consolidation and rebound over the pressure range from one-sixteenth to 256 atm. The influences of electrokinetic coupling on the hydraulic flow rates were calculated from irreversible thermodynamic relationships together with the hydraulic and electrical data. The other factors were studied by examining the extent to which each factor explains the discrepancies between measured flow rates and those predicted from Darcy’s law and the Kozeny-Carman equation. The results show that. (1) the possible violations of Darcy’s law and electrokinetic coupling are insignificant. (2) high viscosity and/or tortuous flow paths fail completely to account for the discrepancies between measured and predicted flow rates in clays, an. (3) unequal pore sizes can explain all the discrepancies.
THE BEHAVIORALIST AS NUTRITIONIST: LEVERAGING BEHAVIORAL ECONOMICS TO IMPROVE CHILD FOOD CHOICE AND CONSUMPTION

We leverage behavioral economics to explore new approaches to tackling child food choice and consumption. Using a field experiment with >1500 children, we report several key insights. We find that incentives have large influences: in the control, 17% of children prefer the healthy snack, whereas introduction of small incentives increases take-up of the healthy snack to ∼75%. There is some evidence that the effects continue post-treatment, consistent with a model of habit formation. We find little evidence that the framing of incentives (loss vs. gain) matters. Educational messaging alone has little effect, but we observe a combined effect of messaging and incentives: together they provide an important influence on food choice.