cssi/SciSciGPT-SciSciNet huggingface dataset:
Learning the pattern of epistasis linking genotype and phenotype in a protein.

Understanding the pattern of epistasis—the non-independence of mutations—is critical for relating genotype and phenotype. However, the combinatorial complexity of potential epistatic interactions has severely limited the analysis of this problem. Using new mutational approaches, we report a comprehensive experimental study of all 213 mutants that link two phenotypically distinct variants of the Entacmaea quadricolor fluorescent protein—an opportunity to examine epistasis up to the 13th order. The data show the existence of many high-order epistatic interactions between mutations, but also reveal extraordinary sparsity, enabling novel experimental and computational strategies for learning the relevant epistasis. We demonstrate that such information, in turn, can be used to accurately predict phenotypes in practical situations where the number of measurements is limited. Finally, we show how the observed epistasis shapes the solution space of single-mutation trajectories between the parental fluorescent proteins, informative about the protein’s evolutionary potential. This work provides conceptual and experimental strategies to profoundly characterize epistasis in a protein, relevant to both natural and laboratory evolution. Epistasis underlies the complexity of genotype-phenotype maps. Here, the authors analyze 8,192 mutants that link two phenotypically distinct variants of the Entacmaea quadricolor fluorescent protein, and show the existence, but also the sparsity, of high-order epistatic interactions.
Two-dimensional infrared spectroscopy from the gas to liquid phase: density dependent J-scrambling, vibrational relaxation, and the onset of liquid character

Ultrafast 2DIR spectra and pump–probe responses of the N2O ν3 asymmetric stretch in SF6 as a function of density from the gas to supercritical phase and liquid are reported. 2DIR spectra unequivocally reveal free rotor character at all densities studied in the gas and supercritical region. Analysis of the 2DIR spectra determines that J-scrambling or rotational relaxation in N2O is highly efficient, occurring in ∼1.5 to ∼2 collisions with SF6 at all non-liquid densities. In contrast, N2O ν3 vibrational energy relaxation requires ∼15 collisions, and complete vibrational equilibrium occurs on the ∼ns scale at all densities. An independent binary collision model is sufficient to describe these supercritical state point dynamics. The N2O ν3 in liquid SF6 2DIR spectrum shows no evidence of free rotor character or spectral diffusion. Using these 2DIR results, hindered rotor or liquid-like character is found in gas and all supercritical solutions for SF6 densities ≥ρ* = 0.3, and increases with SF6 density. 2DIR spectral analysis offers direct time domain evidence of critical slowing for SF6 solutions closest to the critical point density. Applications of 2DIR to other high density and supercritical solution dynamics and descriptions are discussed.
Robust Quadratic Programming with Mixed-Integer Uncertainty

We study robust convex quadratic programs where the uncertain problem parameters can contain both continuous and integer components. Under the natural boundedness assumption on the uncertainty set,...
The sensitivity of electric power infrastructure resilience to the spatial distribution of disaster impacts

Abstract Credibly assessing the resilience of energy infrastructure in the face of natural disasters is a salient concern facing researchers, government officials, and community members. Here, we explore the influence of the spatial distribution of disruptions due to hurricanes and other natural hazards on the resilience of power distribution systems. We find that incorporating information about the spatial distribution of disaster impacts has significant implications for estimating infrastructure resilience. Specifically, the uncertainty associated with estimated infrastructure resilience metrics to spatially distributed disaster-induced disruptions is much higher than determined by previous methods. We present a case study of an electric power distribution grid impacted by a major landfalling hurricane. We show that improved characterizations of disaster disruption drastically change the way in which the grid recovers, including changes in emergent system properties such as antifragility. Our work demonstrates that previous methods for estimating critical infrastructure resilience may be overstating the confidence associated with estimated network recoveries due to the lack of consideration of the spatial structure of disruptions.
The Difficulties of Addressing Interdisciplinary Challenges at the Foundations of Data Science

The Transdisciplinary Research in Principles of Data Science (TRIPODS) program is an e ort funded by the National Science Foundation (NSF) to establish the foundations of data science. It aims to unite the statistics, mathematics, and theoretical computer science research communities (three areas central to the foundations of data, hence the \TRI" in the name) to build the theoretical foundations that will enable continued data-driven discovery and breakthroughs in the natural and social sciences, engineering, and beyond.
Source localization by enforcing sparsity through a Laplacian prior: an SVD-based approach

We present a source localization method based upon a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing an /spl lscr//sub 1/-norm penalty; this can also be viewed as an estimation problem with a Laplacian prior. Explicitly enforcing the sparsity of the representation is motivated by a desire to obtain a sharp estimate of the spatial spectrum which exhibits superresolution. To summarize multiple time samples we use the singular value decomposition (SVD) of the data matrix. Our formulation leads to an optimization problem, which we solve efficiently in a second-order cone (SOC) programming framework by an interior point implementation. We demonstrate the effectiveness of the method on simulated data by plots of spatial spectra and by comparing the estimator variance to the Cramer-Rao bound (CRB). We observe that our approach has advantages over other source localization techniques including increased resolution; improved robustness to noise, limitations in data quantity, and correlation of the sources; as well as not requiring an accurate initialization.
A Search to Capture and Report on Feasibility of Implementation

This brief report describes the conception, development, and use of a rubric in evaluating the feasibility of a new program. The evaluators searched for a meta-analytic tool to help organize ideas ...
Iron, Ferritin, Hereditary Ferritinopathy, and Neurodegeneration

Cellular growth, function, and protection require proper iron management, and ferritin plays a crucial role as the major iron sequestration and storage protein. Ferritin is a 24 subunit spherical shell protein composed of both light (FTL) and heavy chain (FTH1) subunits, possessing complimentary iron-handling functions and forming three-fold and four-fold pores. Iron uptake through the three-fold pores is well-defined, but the unloading process somewhat less and generally focuses on lysosomal ferritin degradation although it may have an additional, energetically efficient pore mechanism. Hereditary Ferritinopathy (HF) or neuroferritinopathy is an autosomal dominant neurodegenerative disease caused by mutations in the FTL C-terminal sequence, which in turn cause disorder and unraveling at the four-fold pores allowing iron leakage and enhanced formation of toxic, improperly coordinated iron (ICI). Histopathologically, HF is characterized by iron deposition and formation of ferritin inclusion bodies (IBs) as the cells overexpress ferritin in an attempt to address iron accumulation while lacking the ability to clear ferritin and its aggregates. Overexpression and IB formation tax cells materially and energetically, i.e., their synthesis and disposal systems, and may hinder cellular transport and other spatially dependent functions. ICI causes cellular damage to proteins and lipids through reactive oxygen species (ROS) formation because of high levels of brain oxygen, reductants and metabolism, taxing cellular repair. Iron can cause protein aggregation both indirectly by ROS-induced protein modification and destabilization, and directly as with mutant ferritin through C-terminal bridging. Iron release and ferritin degradation are also linked to cellular misfunction through ferritinophagy, which can release sufficient iron to initiate the unique programmed cell death process ferroptosis causing ROS formation and lipid peroxidation. But IB buildup suggests suppressed ferritinophagy, with elevated iron from four-fold pore leakage together with ROS damage and stress leading to a long-term ferroptotic-like state in HF. Several of these processes have parallels in cell line and mouse models. This review addresses the roles of ferritin structure and function within the above-mentioned framework, as they relate to HF and associated disorders characterized by abnormal iron accumulation, protein aggregation, oxidative damage, and the resulting contributions to cumulative cellular stress and death.
An Insect-Scale Self-Sufficient Rolling Microrobot

We design an insect-sized rolling microrobot driven by continuously rotating wheels. It measures 18 mm × 8 mm × 8 mm. There are 2 versions of the robot - a 96 mg laser-powered one and a 130 mg supercapacitor powered one. The robot can move at 27 mm/s (1.5 body lengths per second) with wheels rotating at 300 $^\circ$ /s, while consuming an average power of 2.5 mW. Neither version has any electrical wires coming out of it, with the supercapacitor powered robot also being self-sufficient and is able to roll freely for 8 seconds after a single charge. Low-voltage electromagnetic actuators (1V-3 V) along with a novel double-ratcheting mechanism enable the operation of this device. It is, to the best of our knowledge, the lightest and fastest self-sufficient rolling microrobot reported yet.
Do Massively Pretrained Language Models Make Better Storytellers

Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks. However, the strength of these models as Natural Language Generators is less clear. Though anecdotal evidence suggests that these models generate better quality text, there has been no detailed study characterizing their generation abilities. In this work, we compare the performance of an extensively pretrained model, OpenAI GPT2-117 (Radford et al., 2019), to a state-of-the-art neural story generation model (Fan et al., 2018). By evaluating the generated text across a wide variety of automatic metrics, we characterize the ways in which pretrained models do, and do not, make better storytellers. We find that although GPT2-117 conditions more strongly on context, is more sensitive to ordering of events, and uses more unusual words, it is just as likely to produce repetitive and under-diverse text when using likelihood-maximizing decoding algorithms.
micrIO: an open-source autosampler and fraction collector for automated microfluidic input–output

Microfluidic devices are an enabling technology for many labs, facilitating a wide range of applications spanning high-throughput encapsulation, molecular separations, and long-term cell culture. In many cases, however, their utility is limited by a 'world-to-chip' barrier that makes it difficult to serially interface samples with these devices. As a result, many researchers are forced to rely on low-throughput, manual approaches for managing device input and output (IO) of samples, reagents, and effluent. Here, we present a hardware-software platform for automated microfluidic IO (micrIO). The platform, which is uniquely compatible with positive-pressure microfluidics, comprises an 'AutoSipper' for input and a 'Fraction Collector' for output. To facilitate widespread adoption, both are open-source builds constructed from components that are readily purchased online or fabricated from included design files. The software control library, written in Python, allows the platform to be integrated with existing experimental setups and to coordinate IO with other functions such as valve actuation and assay imaging. We demonstrate these capabilities by coupling both the AutoSipper and Fraction Collector to two microfluidic devices: a simple, valved inlet manifold and a microfluidic droplet generator that produces beads with distinct spectral codes. Analysis of the collected materials in each case establishes the ability of the platform to draw from and output to specific wells of multiwell plates with negligible cross-contamination between samples.
A panel on infrastructures for enabling sustainable energy technologies

This is a description of a panel on the subject of infrastructures in electric power engineering to enable a sustainable future in this area. Experts shall be brought to the panel from world wide venues, and experience in the area of enabling infrastructures shall be described. The main objective is to familiarize attendees with the most recent technologies, worldwide, on power generation infrastructures that address the issues of sustainability.
Tailoring Tryptophan Synthase TrpB for Selective Quaternary Carbon Bond Formation.

We previously engineered the β-subunit of tryptophan synthase (TrpB), which catalyzes the condensation of l-serine and indole to l-tryptophan, to synthesize a range of noncanonical amino acids from...
Local Cliques in ER-Perturbed Random Geometric Graphs.

Random graphs are mathematical models that have applications in a wide range of domains. We study the following model where one adds Erdős--Renyi (ER) type perturbation to a random geometric graph. More precisely, assume $G_\mathcal{X}^{*}$ is a random geometric graph sampled from a nice measure on a metric space $\mathcal{X} = (X,d)$. The input observed graph $\widehat{G}(p,q)$ is generated by removing each existing edge from $G_\mathcal{X}^*$ with probability $p$, while inserting each non-existent edge to $G_\mathcal{X}^{*}$ with probability $q$. We refer to such random $p$-deletion and $q$-insertion as ER-perturbation. Although these graphs are related to the objects in the continuum percolation theory, our understanding of them is still rather limited. In this paper we consider a localized version of the classical notion of clique number for the aforementioned ER-perturbed random geometric graphs: Specifically, we study the edge clique number for each edge in a graph, defined as the size of the largest clique(s) in the graph containing that edge. The clique number of the graph is simply the largest edge clique number. Interestingly, given a ER-perturbed random geometric graph, we show that the edge clique number presents two fundamentally different types of behaviors, depending on which "type" of randomness it is generated from. As an application of the above results, we show that by using a filtering process based on the edge clique number, we can recover the shortest-path metric of the random geometric graph $G_\mathcal{X}^*$ within a multiplicative factor of $3$, from an ER-perturbed observed graph $\widehat{G}(p,q)$, for a significantly wider range of insertion probability $q$ than in previous work.
Sequential Graph Dependency Parser.

We propose a method for non-projective dependency parsing by incrementally predicting a set of edges. Since the edges do not have a pre-specified order, we propose a set-based learning method. Our method blends graph, transition, and easy-first parsing, including a prior state of the parser as a special case. The proposed transition-based method successfully parses near the state of the art on both projective and non-projective languages, without assuming a certain parsing order.
Flatly foliated relativity

Flatly Foliated Relativity (FFR) is a new theory which conceptually lies between Special Relativity (SR) and General Relativity (GR), in which spacetime is foliated by flat Euclidean spaces. While GR is based on the idea that "matter curves spacetime", FFR is based on the idea that "matter curves spacetime, but not space". This idea, inspired by the observed spatial flatness of our local universe, is realized by considering the same action as used in GR, but restricting it only to metrics which are foliated by flat spatial slices. FFR can be thought of as describing gravity without gravitational waves. In FFR, a positive cosmological constant implies several interesting properties which do not follow in GR: the metric equations are elliptic on each euclidean slice, there exists a unique vacuum solution among those spherically symmetric at infinity, and there exists a geometric way to define the arrow of time. Furthermore, as gravitational waves do not exist in FFR, there are simple analogs to the positive mass theorem and Penrose-type inequalities. Importantly, given that gravitational waves have a negligible effect on the curvature of spacetime, and that the universe appears to be locally flat, FFR may be a good approximation of GR. Moreover, FFR still admits many notable features of GR including the big bang, an accelerating expansion of the universe, and the Schwarzschild spacetime. Lastly, FFR is already known to have an existence theory for some simplified cases, which provokes an interesting discussion regarding the possibility of a more general existence theory, which may be relevant to understanding existence of solutions to GR.
Design and Construction of Navy’s Largest Drydock

Drydock at Puget Sound Naval Shipyard, Bremerton, Wash; structure will be 1180 ft long from head end to outboard face, 180 ft wide at coping, and 61 ft deep; sidewalls were designed as cantilevers supported by 7 ft thick floor slab; dock will be built entirely in dry, within cofferdam structure, comprising dams and lines of steel sheet piles; description of special pressure relief system and dewatering pump system.
Influence of Load Models on Equilibria, Stability and Algebraic Manifolds of Power System Differential-Algebraic System

Load models have a great impact on voltage behaviors as well as power system transient dynamics. Extensive work has been done on this topic, proposing appropriate load models and capturing better load behaviors during transient. This paper presents a comprehensive study to investigate the geometric and topological changes induced by different load models for the traditional power system differential-algebraic equations. Specifically, we attempt to reveal the deformation of equilibria, stability regions, and algebraic manifolds during a continuous evolution of load model. Several findings are presented in the paper, some of which countering traditional recognitions and intuitions. A major discovery is that the load model with a large proportion of constant impedance and a small proportion of constant power exhibits much more complex features than the load model with the reversed proportions of impedance and power. The increase of complexity is thoroughly recorded and investigated by the changes of geometric properties and mutations of topological invariants in the sense of equilibria, stability regions, and algebraic manifolds for the DAE system. However, most of the changes seem to occur on unstable components of algebraic manifolds or near the singular boundary surfaces, suggesting a limited variation of dynamical behaviors on the stable component.
Advanced material system for the design of an intermediate band solar cell: Type-II CdTe quantum dots in a ZnCdSe matrix

We explore CdTe fractional monolayer quantum dots (QDs) in a ZnCdSe host matrix for potential application in an intermediate band solar cell device. Careful consideration has been taken during the ...
Transcriptomic signature of rapidly evolving immune genes in a highland fish

Recent genome-wide studies have begun to elucidate the genomic basis of hypoxia, long-term cold and high saline and alkaline adaptation in highland fish, and a number of key genes contributed to its highland adaptation were identified. An increasing number of studies indicated that immune genes of Tibetan endemic fish species underwent positive selection towards functional shift, while the insight into immune gene repertoire of Tibetan highland fishes from genome-wide studies has largely lagged behind. In this study, we performed one of the first comparative genomics study in particular focusing on the signatures of immune genes in a highland fish, Gymnocypris przewalskii based on immune-relevant tissue transcriptome assemblies. We identified seven putative rapidly evolving immune genes with elevated molecular evolutionary rate (dN/dS) relative to lowland fish species. Using tissue-transcriptome data, we found most of rapidly evolving immune genes were broadly expressed in head-kidney, spleen, gills and skin tissues, which significantly enriched for complement activation and inflammatory response processes. In addition, we found a set of complement activation related genes underwent accelerated evolution and showed consistently repressed expression patterns in response to parasite Ichthyophthirius multifiliis infection. Moreover, we detected a number of immune genes involved in adaptive immune system exhibited distinct signature of upregulated expression patterns after parasite infection. Taken together, this study provided putative transcriptomic signatures of rapidly evolving immune genes, and will gain the insight into Schizothoracine fish adaptation to high-altitude extreme aquatic environments including diversified pathogen challenge.
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations

Accurately learning from user data while providing quantifiable privacy guarantees provides an opportunity to build better ML models while maintaining user trust. This paper presents a formal approach to carrying out privacy preserving text perturbation using the notion of d_χ-privacy designed to achieve geo-indistinguishability in location data. Our approach applies carefully calibrated noise to vector representation of words in a high dimension space as defined by word embedding models. We present a privacy proof that satisfies d_χ-privacy where the privacy parameter $\varepsilon$ provides guarantees with respect to a distance metric defined by the word embedding space. We demonstrate how $\varepsilon$ can be selected by analyzing plausible deniability statistics backed up by large scale analysis on GloVe and fastText embeddings. We conduct privacy audit experiments against $2$ baseline models and utility experiments on 3 datasets to demonstrate the tradeoff between privacy and utility for varying values of varepsilon on different task types. Our results demonstrate practical utility (
Numerical Results for the Hubbard Model: Implications for the High Tc Pairing Mechanism

Numerical studies of the Hubbard model and its strong-coupling form, the t-J model, show evidence for antiferromagnetic, \(d_{x^{\text{2}} - y^2 } \)-pairing and stripe correlations which remind one of phenomena seen in the layered cuprate materials. Here, we ask what these numerical results imply about various scenarios for the pairing mechanism.
The Arp2/3 complex and the formin, Diaphanous, are both required to regulate the size of germline ring canals in the developing egg chamber

Intercellular bridges are an essential structural feature found in both germline and somatic cells throughout the animal kingdom. Because of their large size, the germline intercellular bridges, or ring canals, in the developing fruit fly egg chamber are an excellent model to study the formation, stabilization, and growth of these structures. Within the egg chamber, the germline ring canals connect 15 supporting nurse cells to the developing oocyte, facilitating the transfer of materials required for successful oogenesis. The ring canals are derived from a stalled actomyosin contractile ring; once formed, additional actin and actin-binding proteins are recruited to the ring to support the 20-fold growth that accompanies oogenesis. These behaviors provide a unique model system to study the actin regulators that control incomplete cytokinesis, intercellular bridge formation, and growth. By temporally controlling their expression in the germline, we have demonstrated that the Arp2/3 complex and the formin, Diaphanous (Dia), coordinately regulate ring canal size and growth throughout oogenesis. Dia is required for successful incomplete cytokinesis and the initial stabilization of the germline ring canals. Once ring canals have formed, the Arp2/3 complex and Dia cooperate to determine ring canal size and maintain stability. Our data suggest that nurse cells must maintain a precise balance between the activity of these two nucleators during oogenesis.
Balancing Queueing and Retransmission: Latency-Optimal Massive MIMO Design

One fundamental challenge in 5G URLLC is how to optimize massive MIMO systems for achieving low latency and high reliability. A natural design choice to maximize reliability and minimize retransmission is to select the lowest allowed target error rate. However, the overall latency is the sum of queueing latency and retransmission latency, hence choosing the lowest target error rate does not always minimize the overall latency. In this paper, we minimize the overall latency by jointly designing the target error rate and transmission rate adaptation, which leads to a fundamental tradeoff point between queueing and retransmission latency. This design problem can be formulated as a Markov decision process, which is theoretically optimal, but its complexity is prohibitively high for real-system deployments. We managed to develop a low-complexity closed-form policy named Large-arraY Reliability and Rate Control (LYRRC), which is proven to be asymptotically latency-optimal as the number of antennas increases. In LYRRC, the transmission rate is twice of the arrival rate, and the target error rate is a function of the antenna number, arrival rate, and channel estimation error. With simulated and measured channels, our evaluations find LYRRC satisfies the latency and reliability requirements of URLLC in all the tested scenarios.
Sequence-Independent Self-Assembly of Germ Granule mRNAs into Homotypic Clusters

Summary mRNAs enriched in membraneless condensates provide functional compartmentalization within cells. The mechanisms that recruit transcripts to condensates are under intense study; however, how mRNAs organize once they reach a granule remains poorly understood. Here, we report on a self-sorting mechanism by which multiple mRNAs derived from the same gene assemble into discrete homotypic clusters. We demonstrate that in vivo mRNA localization to granules and self-assembly within granules are governed by different mRNA features: localization is encoded by specific RNA regions, whereas self-assembly involves the entire mRNA, does not involve sequence-specific, ordered intermolecular RNA:RNA interactions, and is thus RNA sequence independent. We propose that the ability of mRNAs to self-sort into homotypic assemblies is an inherent property of an messenger ribonucleoprotein (mRNP) that is augmented under conditions that increase RNA concentration, such as upon enrichment in RNA-protein granules, a process that appears conserved in diverse cellular contexts and organisms.
Sparse Array Design for Maximizing the Signal-to-Interference-plus-Noise-Ratio by Matrix Completion

Abstract We consider sparse array beamformer design achieving maximum signal-to interference plus noise ratio (MaxSINR). Both array configuration and weights are attuned to the changing sensing environment. This is accomplished by simultaneously switching among antenna positions and adjusting the corresponding weights. The sparse array optimization design requires estimating the data autocorrelations at all spatial lags across the array aperture. Towards this end, we adopt low rank matrix completion under the semidefinite Toeplitz constraint for interpolating those autocorrelation values corresponding to the missing lags. We compare the performance of matrix completion approach with that of the fully augmentable sparse array design acting on the same objective function. The optimization tool employed is the regularized l 1 -norm successive convex approximation (SCA). Design examples with simulated data are presented using different operating scenarios, along with performance comparisons among various configurations.
New Query Lower Bounds for Submodular Function Minimization

We consider submodular function minimization in the oracle model: given black-box access to a submodular set function f:2^[n] → ℝ, find an element of arg min_S {f(S)} using as few queries to f(⋅) as possible. State-of-the-art algorithms succeed with O(n²) queries [Yin Tat Lee et al., 2015], yet the best-known lower bound has never been improved beyond n [Nicholas J. A. Harvey, 2008]. We provide a query lower bound of 2n for submodular function minimization, a 3n/2-2 query lower bound for the non-trivial minimizer of a symmetric submodular function, and a binom{n}{2} query lower bound for the non-trivial minimizer of an asymmetric submodular function. Our 3n/2-2 lower bound results from a connection between SFM lower bounds and a novel concept we term the cut dimension of a graph. Interestingly, this yields a 3n/2-2 cut-query lower bound for finding the global mincut in an undirected, weighted graph, but we also prove it cannot yield a lower bound better than n+1 for s-t mincut, even in a directed, weighted graph.
The impact of star formation and active nuclei on the interstellar medium in ultraluminous infrared galaxies

The energy input into the interstellar medium in UltraluminousInfrared Galaxies (ULIRGs) is enormous, regardless of the nature ofthe power source. I discuss some of the major consequences for thestructure and energetics of the ISM in these galaxies. Observationally, the column densities in the nuclear regions of ULIRGsare known to be very high, which makes distinguishing starbursts fromAGN quite difficult. The level of energy and momentum injection meansthat the pressure in the ISM must be extremely high, at least 3-4orders of magnitude larger than in the local ISM or typical giantmolecular clouds. It also means that the luminosity of GMCs in ULIRGsmust be very high, as they must radiate many times their bindingenergy over their lifetimes. I briefly review the influence whichX-ray irradiation can have on the ISM in AGN-powered ULIRGs. Finally,I show that the presence of PAH features in ULIRGs does not imply thatthey must be starburst-dominated, since at the column densities andpressures typical of the ISM in ULIRGs PAHs can survive even at tensof parsec distances from the AGN.
Drug-drug interaction prediction based on co-medication patterns and graph matching

High-order drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) are common, particularly for elderly people, and therefore represent a significant public health problem. In this paper, the problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered. To solve this problem, novel kernels over drug combinations of arbitrary orders are developed within support vector machines (SVMs) for the prediction. Graph matching methods are used in the novel kernels to measure the similarities among drug combinations, in which drug co-medication patterns are leveraged to measure single drug similarities. The experimental results on a real-world dataset demonstrated that the new kernels achieve an area under the curve (AUC) value 0.912 for the prediction problem. The new methods with drug co-medication based single drug similarities can accurately predict whether a drug combination is likely to induce adverse drug reactions of interest.
Sparse filtered SIRT for electron tomography

Abstract Electron tomographic reconstruction is a method for obtaining a three-dimensional image of a specimen with a series of two dimensional microscope images taken from different viewing angles. Filtered backprojection, one of the most popular tomographic reconstruction methods, does not work well under the existence of image noises and missing wedges. This paper presents a new approach to largely mitigate the effect of noises and missing wedges. We propose a novel filtered backprojection that optimizes the filter of the backprojection operator in terms of a reconstruction error. This data-dependent filter adaptively chooses the spectral domains of signals and noises, suppressing the noise frequency bands, so it is very effective in denoising. We also propose the new filtered backprojection embedded within the simultaneous iterative reconstruction iteration for mitigating the effect of missing wedges. Our numerical study is presented to show the performance gain of the proposed approach over the state-of-the-art.
Criticality of Lagrange multipliers in extended nonlinear optimization

The paper is devoted to the study and applications of criticality of Lagrange multipliers in variational systems, which are associated with the class of problems in composite optimization known as ...
Estimating the power of sequence covariation for detecting conserved RNA structure.

Pairwise sequence covariations are a signal of conserved RNA secondary structure. We describe a method for distinguishing when lack of covariation signal can be taken as evidence against a conserved RNA structure, as opposed to when a sequence alignment merely has insufficient variation to detect covariations. We find that alignments for several long non-coding RNAs previously shown to lack covariation support do have adequate covariation detection power, providing additional evidence against their proposed conserved structures. Availability and implementation The R-scape web server is at eddylab.org/R-scape, with a link to download the source code. Supplementary information Supplementary data are available at Bioinformatics online.
Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations

We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to the simulations or systems plus three algorithmic areas: particle dynamics, agent-based models and partial differential equations. The patterns are further divided into three action areas: Improving simulation with Configurations and Integration of Data, Learn Structure, Theory and Model for Simulation, and Learn to make Surrogates.
Four Years in Review: Statistical Practices of Likert Scales in Human-Robot Interaction Studies

As robots become more prevalent, the importance of the field of human-robot interaction (HRI) grows accordingly. As such, we should endeavor to employ the best statistical practices. Likert scales are commonly used metrics in HRI to measure perceptions and attitudes. Due to misinformation or honest mistakes, most HRI researchers do not adopt best practices when analyzing Likert data. We conduct a review of psychometric literature to determine the current standard for Likert scale design and analysis. Next, we conduct a survey of four years of the International Conference on Human-Robot Interaction (2016 through 2019) and report on incorrect statistical practices and design of Likert scales. During these years, only 3 of the 110 papers applied proper statistical testing to correctly-designed Likert scales. Our analysis suggests there are areas for meaningful improvement in the design and testing of Likert scales. Lastly, we provide recommendations to improve the accuracy of conclusions drawn from Likert data.
Merging ab initio theory and few-body approach for (d, p) reactions

A framework for A(d, p)B reactions is introduced by merging the microscopic approach to computing the properties of the nucleon-target systems and the three-body n + p + A reaction formalism, thus providing a consistent link between the reaction cross sections and the underlying microscopic structure. In this first step toward a full microscopic description, we focus on the inclusion of the neutron-target microscopic properties. The properties of the neutron-target subsystem are encapsulated in the Green's function which is computed with the coupled Cluster theory using a chiral nucleon-nucleon and three-nucleon interactions. Subsequently, this many-body information is introduced in the few-body Green's Function Transfer approach to (d, p) reactions. Our benchmarks on stable targets 40,48Ca show an excellent agreement with the data. We then proceed to make specific predictions for (d, p) on neutron rich 52,54Ca isotopes. These predictions are directly relevant to testing the new magic numbers N = 32, 34 and are expected to be feasible in the first campaign of the projected FRIB facility.
Both Configuration and QM Region Size Matter: Zinc Stability in QM/MM Models of DNA Methyltransferase

Quantum-mechanical/molecular-mechanical (QM/MM) methods are essential to the study of metalloproteins, but the relative importance of sampling and degree of QM treatment in achieving quantitative predictions is poorly understood. We study the relative magnitude of configurational and QM-region sensitivity of energetic and electronic properties in a representative Zn2+ metal binding site of a DNA methyltransferase. To quantify property variations, we analyze snapshots extracted from 250 ns of molecular dynamics simulation. To understand the degree of QM-region sensitivity, we perform analysis using QM regions ranging from a minimal 49-atom region consisting only of the Zn2+ metal and its four coordinating Cys residues up to a 628-atom QM region that includes residues within 12 A of the metal center. Over the configurations sampled, we observe that illustrative properties (e.g., rigid Zn2+ removal energy) exhibit large fluctuations that are well captured with even minimal QM regions. Nevertheless, for both energetic and electronic properties, we observe a slow approach to asymptotic limits with similarly large changes in absolute values that converge only with larger (ca. 300-atom) QM region sizes. For the smaller QM regions, the electronic description of Zn2+ binding is incomplete: the metal binds too tightly and is too stabilized by the strong electrostatic potential of MM point charges, and the Zn-S bond covalency is overestimated. Overall, this work suggests that efficient sampling with QM/MM in small QM regions is an effective method to explore the influence of enzyme structure on target properties. At the same time, accurate descriptions of electronic and energetic properties require a larger QM region than the minimal metal-coordinating residues in order to converge treatment of both metal-local bonding and the overall electrostatic environment.
Some effects of bias errors in redundant flight control systems.

The controllability and steady-state response of parallel-redundant flight control systems are examined. It is found that state components which appear in the parallel signal paths, e.g., individual actuator-commands, are not controllable, although the sum of the command signals is well behaved. If the response modes associated with these components are not stable, bias errors can cause the components to diverge, leading to the possibility of nuisance trips in failure detection/isolation logic and eventual control system lockup (at saturation). Combining the inputs to the control computers assures that sensor bias will not cause divergence, while cross-strapping control strings bounds divergent response to all bias error inputs.
Detonation tube impulse in subatmospheric environments

Thrust from a multicycle pulse detonation engine operating at practical flight altitudes will vary with surrounding environment pressure. We have carried out the first experimental study using a detonation tube hung in a ballistic pendulum arrangement within a large pressure vessel to determine the effect that the environment has on the single-cycle impulse. Air pressure decreased below 100 kPa, whereas initial pressure of the stoichiometric ethylene-oxygen mixture inside the tube varied between 100 and 30 kPa. The original impulse model (Wintenberger et al., Journal of Propulsion and Power, Vol. 19, No. 1, 2002, pp. 22-38) was modified to predict the observed increase in impulse and blowdown time as the environmental pressure decreased below 1 atm. Comparisons between the impulse from detonation tubes and ideal steady-flow rockets indicate incomplete expansion of the detonation tube exhaust, resulting in a 37% difference in impulse at a pressure ratio (ratio of pressure behind the Taylor wave to environmental pressure) of 100.
On the Potential of Neuroeconomics: A Critical (but Hopeful) Appraisal †

This paper attempts to identify and articulate the specific ways in which the emerging field of neuroeconomics might shed light on traditional positive and normative questions in economics, as well as the likely limitations of its potential contributions. It sets forth both reservations and reasons for guarded optimism.
Chapter 10 – Self-Defense

Publisher Summary Self-defense shows that evolution has used many different strategies and tactics to provide animals with tools for avoiding predation. One important theme has been that behavior interacts with nonbehavioral defenses. For example, hiding is behavior, but being well hidden requires the adaptations of color and shape involved in camouflage. Being a convincing mimic may start with color and shape, but for mimicry to work, the mimic needs to behave like its model. Avoidance of predators has three elements: avoiding detection, evading capture, and fighting back. Crypsis (camouflage) can employ background matching, countershading, brokenness, and disruptive coloration. These different patterns of color and form typically have the same result: they make the animal difficult to see. Mimicry and diversion are other ways to escape notice. Mimicry differs from camouflage in that the mimic usually appears to be another living organism. Diversion may direct the predator toward a different (and more expendable) prey body area, or it may direct predators away from nest locations and young. It is found that many of the behaviors that are expressed in the avoidance and deterrence of predation have parallels in animals' relationships with parasites and pathogens. If parasitized or infected with a pathogen, behavioral responses of the host can play an important role in survival.
A multi-method analysis of the PCAOB’s relationship with the audit profession

Abstract Responsive regulation (RR) theory posits that effective regulators enforce compliance by escalating penalties only if persuasion fails, otherwise risking formation of a culture of resentment. Using RR theory as a lens, we examine the interactions between large audit firms and the PCAOB during the initial years of PCAOB regulation, when annually-inspected auditors utilized negative tone within their inspection response letters to express public disagreement with their inspection reports. Consistent with our expectations, we find that negative tone within response letters is positively associated with (1) future Part I inspection findings that result in a restatement of a client’s financial statements and (2) the likelihood that Part II of a future inspection report will be publicly disclosed. We triangulate these results and the underlying theory in semi-structured interviews of eight PCAOB inspectors and six audit partners involved in these early interactions between the largest firms and the PCAOB. Consistent with RR theory, the PCAOB viewed public disagreement as indicators of noncompliance, incorporated public disagreement into subsequent inspections, and escalated penalties for noncompliance. Contrary to RR theory’s prescriptions, the PCAOB did not use persuasion as a first reaction to noncompliance. In response, the firms stopped publicly disagreeing, but may have formed a culture of resentment toward the PCAOB.
Category-Level Articulated Object Pose Estimation

This paper addresses the task of category-level pose estimation for articulated objects from a single depth image. We present a novel category-level approach that correctly accommodates object instances previously unseen during training. We introduce Articulation-aware Normalized Coordinate Space Hierarchy (ANCSH) – a canonical representation for different articulated objects in a given category. As the key to achieve intra-category generalization, the representation constructs a canonical object space as well as a set of canonical part spaces. The canonical object space normalizes the object orientation, scales and articulations (e.g. joint parameters and states) while each canonical part space further normalizes its part pose and scale. We develop a deep network based on PointNet++ that predicts ANCSH from a single depth point cloud, including part segmentation, normalized coordinates, and joint parameters in the canonical object space. By leveraging the canonicalized joints, we demonstrate: 1) improved performance in part pose and scale estimations using the induced kinematic constraints from joints; 2) high accuracy for joint parameter estimation in camera space.
Ginger cannot cure cancer: Battling fake health news with a comprehensive data repository

Nowadays, Internet is a primary source of attaining health information. Massive fake health news which is spreading over the Internet, has become a severe threat to public health. Numerous studies and research works have been done in fake news detection domain, however, few of them are designed to cope with the challenges in health news. For instance, the development of explainable is required for fake health news detection. To mitigate these problems, we construct a comprehensive repository, FakeHealth, which includes news contents with rich features, news reviews with detailed explanations, social engagements and a user-user social network. Moreover, exploratory analyses are conducted to understand the characteristics of the datasets, analyze useful patterns and validate the quality of the datasets for health fake news detection. We also discuss the novel and potential future research directions for the health fake news detection.
Resilience of Urban Transport Network-of-Networks under Intense Flood Hazards Exacerbated by Targeted Attacks

Natural hazards including floods can trigger catastrophic failures in interdependent urban transport network-of-networks (NoNs). Population growth has enhanced transportation demand while urbanization and climate change have intensified urban floods. However, despite the clear need to develop actionable insights for improving the resilience of critical urban lifelines, the theory and methods remain underdeveloped. Furthermore, as infrastructure systems become more intelligent, security experts point to the growing threat of targeted cyber-physical attacks during natural hazards. Here we develop a hypothesis-driven resilience framework for urban transport NoNs, which we demonstrate on the London Rail Network (LRN). We find that topological attributes designed for maximizing efficiency rather than robustness render the network more vulnerable to compound natural-targeted disruptions including cascading failures. Our results suggest that an organizing principle for post-disruption recovery may be developed with network science principles. Our findings and frameworks can generalize to urban lifelines and more generally to real-world spatial networks.