cssi/SciSciGPT-SciSciNet huggingface dataset:
Cellular life from the three domains and viruses are transcriptionally active in a hypersaline desert community

Microbial communities play essential roles in the biosphere and understanding the mechanisms underlying their functional adaptations to environmental conditions is critical for predicting their behaviour. This aspect of microbiome function has not been well characterized in natural high-salt environments. To address this knowledge gap, and to build a general framework relating the genomic and transcriptomic components in a microbiome, we performed a meta-omic survey of extremophile communities inhabiting halite (salt) nodules in the Atacama Desert. We found that the major phyla of this halophilic community have different levels of total transcriptional activity, at the selected time-points, and that different metabolic pathways were activated in their transcriptomes. We report that a novel Dolichomastix alga-the only eukaryote found in this system-was the most active community member. It produced the vast majority of the community's photosynthetic transcripts despite being outnumbered by Cyanobacteria. The divergence in the transcriptional landscapes of these segregated communities, compared with the relatively stable metagenomic functional potential, suggests that microbiomes in each salt nodule undergo unique transcriptional adjustments to adapt to local conditions. We also report the characterization of several previously unknown halophilic viruses, many of which exhibit transcriptional activity indicative of host infection.
Integrated printed circuit board device for cell lysis and nucleic acid extraction.

Preparation of raw, untreated biological samples remains a major challenge in microfluidics. We present a novel microfluidic device based on the integration of printed circuit boards and an isotachophoresis assay for sample preparation of nucleic acids from biological samples. The device has integrated resistive heaters and temperature sensors as well as a 70 µm × 300 µm × 3.7 cm microfluidic channel connecting two 15 µL reservoirs. We demonstrated this device by extracting pathogenic nucleic acids from 1 µL dispensed volume of whole blood spiked with Plasmodium falciparum. We dispensed whole blood directly onto an on-chip reservoir, and the system's integrated heaters simultaneously lysed and mixed the sample. We used isotachophoresis to extract the nucleic acids into a secondary buffer via isotachophoresis. We analyzed the convective mixing action with micro particle image velocimetry (micro-PIV) and verified the purity and amount of extracted nucleic acids using off-chip quantitative polymerase chain reaction (PCR). We achieved a clinically relevant limit of detection of 500 parasites per microliter. The system has no moving parts, and the process is potentially compatible with a wide range of on-chip hybridization or amplification assays.
Inter-Temporal Investment in Climate Change Adaptation and Mitigation

Increasingly there appears to be recognition of the need to simultaneously implement adaptation and mitigation. However, the optimal combination of adaptation and mitigation is an open question. The literature on this is rather small, and is inconsistent in conclusions. Here we explore the temporal optimal investment mix between adaptation and mitigation and their relative contributions to damage reduction. We do this using a conceptual framework that integrates mitigation and adaptation considering one time flow and persistent stock adaptation. We also consider unadaptable damages and investment competition. Results suggest that stock and flow adaptation are dominant damage reduction means in the first 120 years while mitigation dominates from thereon. We also find that the adaptation/mitigation mix changes markedly toward mitigation when climate change damages per unit atmospheric CO2 concentration rises.
Treatment of severe and intractable constipation.

A careful clinical evaluation, exclusion of secondary causes (eg, colonic obstruction, metabolic conditions, and drug-induced constipation), and assessments of colonic transit and rectal evacuation are necessary to ascertain whether constipation is attributable to normal colonic transit, delayed colonic transit (ie, slow-transit constipation), or a rectal evacuation disorder (with or without delayed colonic transit). Idiopathic slow-transit constipation is a clinical syndrome predominantly affecting women and is characterized by intractable constipation and delayed colonic transit. This syndrome is attributed to disordered colonic motor function and spans a spectrum of variable severity ranging from patients who have relatively mild delays in transit, but are otherwise indistinguishable from irritable bowel syndrome, at one extreme to patients with colonic inertia or chronic megacolon at the other extreme. Most patients are treated with one or more pharmacological agent. A subtotal colectomy is effective and occasionally indicated for patients with medically refractory severe slow-transit constipation, provided that pelvic floor dysfunction has been excluded or treated. Pelvic floor dysfunction can be diagnosed by the clinical features and anorectal testing. Most patients with pelvic floor dysfunction will respond to pelvic floor retraining by biofeedback therapy.
Inflation Stabilization and Welfare

This paper derives loss functions for analyses of optimal monetary policy that are grounded in the welfare of private agents, in the case of explicit optimizing models of private-sector behavior in which the real effects of monetary policy result from nominal price rigidity. A quadratic approximation to the utility-based welfare criterion is developed that allows comparison between this criterion and the ad hoc quadratic loss functions typically assumed in the literature on monetary policy evaluation. It is shown that the goal of inflation stabilization, generally presumed to be an important (and perhaps the preeminent) goal of monetary policy, can in fact be justified in such a framework, insofar as variable inflation results in real distortions when prices are not adjusted throughout the economy in a perfectly synchronized fashion. The exact sense in which inflation variability matters for welfare, however, depends upon the details of price-setting behavior.
One cost of the Chilean capital controls: Increased financial constraints for smaller traded firms

There is growing support for taxes on short-term capital inflows in emerging markets, such as the encaje adopted by Chile from 1991 to 1998. This paper assesses whether the Chilean capital controls increased financial constraints for different-sized, publicly-traded firms. It uses an Euler-equation framework and shows that during the encaje, smaller traded firms experienced significant financial constraints. These constraints decreased as firm size increased. Both before and after the encaje, however, smaller firms did not experience significant financial constraints, and there is no relationship between firm size and financial constraints. Although Chilean-style capital controls may yield some benefits, any such benefits should be weighed against this cost of increasing financial constraints for small and mid-sized firms.
Cases Relating to Chapter 1 – History and Development of Rules of Evidence

Publisher Summary This chapter presents the case study of Funk versus United States. The case is related to the history and development of rules of evidence. The sole inquiry made in this case is whether in a federal court the wife of the defendant on trial for a criminal offense is a competent witness in his behalf. The rules of the common law which disqualified as witnesses persons having an interest, long since, in the main, have been abolished both in England and in this country; and what was once regarded as a sufficient ground for excluding the testimony of such persons altogether has come to be uniformly and more sensibly regarded as affecting the credit of the witness only. A refusal to permit the wife upon the ground of interest to testify in behalf of her husband, while permitting him, who has the greater interest, to testify for himself, presents a manifest incongruity.
Personal-bankruptcy cycles

This paper estimates the dynamics of the personal-bankruptcy rate over the business cycle by exploiting large cross-state variation. We find that bankruptcy rates are significantly higher than normal during a recession and rise as a recession persists. After a recession ends, there is a hangover whereby bankruptcy rates begin to fall but remain above normal for several more quarters. Recovery periods see a strong bounce-back effect with bankruptcy rates significantly below normal for several quarters. Despite the significant increases in bankruptcies during recessions, the largest contributor to rising bankruptcies during these periods has tended to be the longstanding upward trend.
Sensitivity operators for linear time-varying systems

Publisher Summary This chapter discusses sensitivity operators for linear time-varying systems. A major problem in analysis and design of dynamical systems is the influence of parameter variations on system performance. It is customary to use feedback to counteract the affects of parameter variations. Although feedback can lead to an improvement in steady state accuracy, it is known that for single-input, single-output linear time-invariant systems, the effects of parameter variations on the system frequency response may be greater for the feedback system than for an open loop system with the same nominal transfer function. However, the feedback structure can be designed to be less sensitive to parameter variations than an open loop structure. This can be carried out by incorporating sensitivity functions in the design procedure. Sensitivity matrices have also been used to develop criteria for comparing feedback and open loop performances for multi-input, multi-output linear time-invariant systems. The chapter presents a direct comparison of open and closed loop errors via the sensitivity operator.
The Redistributive Effects of Political Reservation for Minorities: Evidence from India

Abstract We examine the impact of political reservation for disadvantaged minority groups on poverty. To address the concern that political reservation is endogenous, we take advantage of the state-time variation in reservation in state legislative assemblies in India generated by national policies that cause reservations to be revised and the time lags with which revised reservations are implemented. Using data on sixteen major Indian states for the period 1960–2000, we find that increasing the share of seats reserved for Scheduled Tribes significantly reduces poverty while increasing the share of seats reserved for Scheduled Castes has no impact on poverty. Political reservation for Scheduled Tribes has a greater effect on rural poverty than urban poverty, and appears to benefit people near the poverty line as well as those far below it.
The effects of cylindrical surface modifications on turbulent boundary layers

Etude experimentale par visualisation a l'aide de bulles d'hydrogene et mesures par anemometrie a film chaud
School Closures in Chicago: What Happened to the Teachers?:

In 2013, the Chicago Board of Education closed 47 elementary schools, directly affecting 13,000 students and 900 teachers. The closures created employment uncertainty for closed-school teachers, an...
Dose and duration of interferon γ pre-licensing interact with donor characteristics to influence the expression and function of indoleamine-2,3-dioxygenase in mesenchymal stromal cells

Human mesenchymal stromal cells (MSCs) are a leading cell therapy candidate for the treatment of immune and inflammatory diseases due to their potent regulation of immune cells. MSC expression of indoleamine-2,3-dioxygenase (IDO) upon interferon γ (IFNγ) exposure has been proposed as both a sentinel marker and key mediator of MSC immunomodulatory potency. Rather than wait for in vivo exposure to cytokines, MSCs can be pre-licensed during manufacturing to enhance IDO expression. In this study, we systematically examine the relative role that the dose of IFNγ, the duration of pre-licensing and the donor of origin play in dictating MSC production of functional IDO. We find that across three human MSC donors, MSCs increase their expression of IDO in response to both increased dose of IFNγ and duration of pre-licensing. However, with extended pre-licensing, the expression of IDO no longer predicts MSCs ability to suppress activated peripheral blood mononuclear cells. In addition, pre-licensing dose and duration are revealed to be minor modifiers of MSCs inherent potency, and thus cannot be manipulated to boost poor donors to the levels of high-performing donors. Thus, the dose and duration of pre-licensing should be tailored to optimize performance of specific donors and an emphasis on donor selection is needed to realize significant benefits of pre-licensing.
HybridPose: 6D Object Pose Estimation Under Hybrid Representations

We introduce HybridPose, a novel 6D object pose estimation approach. HybridPose utilizes a hybrid intermediate representation to express different geometric information in the input image, including keypoints, edge vectors, and symmetry correspondences. Compared to a unitary representation, our hybrid representation allows pose regression to exploit more and diverse features when one type of predicted representation is inaccurate (e.g., because of occlusion). Different intermediate representations used by HybridPose can all be predicted by the same simple neural network, and outliers in predicted intermediate representations are filtered by a robust regression module. Compared to state-of-the-art pose estimation approaches, HybridPose is comparable in running time and is significantly more accurate. For example, on Occlusion Linemod dataset, our method achieves a prediction speed of 30 fps with a mean ADD(-S) accuracy of 79.2%, representing a 67.4% improvement from the current state-of-the-art approach.
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling

State-of-the-art deep learning systems rely on iterative distributed training to tackle the increasing complexity of models and input data. The iteration time in these communication-heavy systems depends on the computation time, communication time and the extent of overlap of computation and communication. In this work, we identify a shortcoming in systems with graph representation for computation, such as TensorFlow and PyTorch, that result in high variance in iteration time --- random order of received parameters across workers. We develop a system, TicTac, to improve the iteration time by fixing this issue in distributed deep learning with Parameter Servers while guaranteeing near-optimal overlap of communication and computation. TicTac identifies and enforces an order of network transfers which improves the iteration time using prioritization. Our system is implemented over TensorFlow and requires no changes to the model or developer inputs. TicTac improves the throughput by up to $37.7\%$ in inference and $19.2\%$ in training, while also reducing straggler effect by up to $2.3\times$. Our code is publicly available.
Remarks on optimal rates of convergence in periodic homogenization of linear elliptic equations in non-divergence form

We study and characterize the optimal rates of convergence in periodic homogenization of linear elliptic equations in non-divergence form. We obtain that the optimal rate of convergence is either $$O(\varepsilon )$$ or $$O(\varepsilon ^2)$$ depending on the diffusion matrix A, source term f, and boundary data g. Moreover, we show that the set of diffusion matrices A that give optimal rate $$O(\varepsilon )$$ is open and dense in the set of $$C^2$$ periodic, symmetric, and positive definite matrices, which means that generically, the optimal rate is $$O(\varepsilon )$$ .
Generators for the $C^m$-closures of ideals

Let R denote the ring of real polynomials on Rn. Fix m≥0, and let A1,…,AM∈R. The \emph{Cm-closure} of (A1,…,AM), denoted here by [A1,…,AM;Cm], is the ideal of all f∈R expressible in the form f=F1A1+⋯+FMAM with each Fi∈Cm(Rn). In this paper we exhibit an algorithm for computing generators for [A1,…,AM;Cm].
Probing quantum walks through coherent control of high-dimensionally entangled photons

Control over the duration of a quantum walk is critical to unlocking its full potential for quantum search and the simulation of many-body physics. Here we report quantum walks of biphoton frequency combs where the duration of the walk, or circuit depth, is tunable over a continuous range without any change to the physical footprint of the system—a feature absent from previous photonic implementations. In our platform, entangled photon pairs hop between discrete frequency modes with the coupling between these modes mediated by electro-optic modulation of the waveguide refractive index. Through control of the phase across different modes, we demonstrate a rich variety of behavior: from walks exhibiting enhanced ballistic transport or strong energy confinement, to subspaces featuring scattering centers or local traps. We also explore the role of entanglement dimensionality in the creation of energy bound states, which illustrates the potential for these walks to quantify high-dimensional entanglement.
Asymptotically Hyperbolic Manifolds with Boundary Conjugate Points but no Interior Conjugate Points

We construct non-trapping asymptotically hyperbolic manifolds with boundary conjugate points but no interior conjugate points
Geometric Heat Flow Method for Legged Locomotion Planning

We propose in this letter a motion planning method for legged robot locomotion based on Geometric Heat Flow framework. The task is challenging due to the hybrid nature of dynamics and contact constraints. We encode the hybrid dynamics and constraints into Riemannian inner product, and this inner product is defined so that short curves correspond to admissible motions for the system. We rely on the affine geometric heat flow to deform an arbitrary path connecting the desired initial and final states to this admissible motion. The method is able to automatically find the trajectory of robot’s center of mass, feet contact positions and forces on uneven terrain.
Fake News Early Detection: A Theory-driven Model

Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this article, a theory-driven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level, and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information.
Discovering a sparse set of pairwise discriminating features in high-dimensional data

Motivation Recent technological advances produce a wealth of high-dimensional descriptions of biological processes, yet extracting meaningful insight and mechanistic understanding from these data remains challenging. For example, in developmental biology, the dynamics of differentiation can now be mapped quantitatively using single-cell RNA sequencing, yet it is difficult to infer molecular regulators of developmental transitions. Here, we show that discovering informative features in the data is crucial for statistical analysis as well as making experimental predictions. Results We identify features based on their ability to discriminate between clusters of the data points. We define a class of problems in which linear separability of clusters is hidden in a low-dimensional space. We propose an unsupervised method to identify the subset of features that define a low-dimensional subspace in which clustering can be conducted. This is achieved by averaging over discriminators trained on an ensemble of proposed cluster configurations. We then apply our method to single-cell RNA-seq data from mouse gastrulation, and identify 27 key transcription factors (out of 409 total), 18 of which are known to define cell states through their expression levels. In this inferred subspace, we find clear signatures of known cell types that eluded classification prior to discovery of the correct low-dimensional subspace. Availability and implementation https://github.com/smelton/SMD. Supplementary information Supplementary data are available at Bioinformatics online.
Sum of squares bounds for the ordering principle

In this paper, we analyze the sum of squares hierarchy (SOS) on the ordering principle on n elements (which has N = Θ(n2) variables). We prove that degree O([MATH HERE]log(n)) SOS can prove the ordering principle. We then show that this upper bound is essentially tight by proving that for any ∈ > 0, SOS requires degree Ω(n1/2-∈) to prove the ordering principle.
Coupled-cluster computations of optical potential for medium-mass nuclei

Recent progress in the numerical resolution of the nuclear many-body problem and in the development of nuclear Hamiltonians rooted in Quantum Chromodynamics, have opened the door to first-principles computation of nuclear reactions. In this article, we discuss the current status of ab initio calculations of nucleon-nucleus optical potential for medium-mass systems, with a focus on results obtained with the coupled-cluster method.
Concise Chemoenzymatic Total Synthesis and Identification of Cellular Targets of Cepafungin I.

Summary The natural product cepafungin I was recently reported to be one of the most potent covalent inhibitors of the 20S proteasome core particle through a series of in vitro activity assays. Here, we report a short chemoenzymatic total synthesis of cepafungin I featuring the use of a regioselective enzymatic oxidation to prepare a key hydroxylated amino acid building block in a scalable fashion. The strategy developed herein enabled access to a chemoproteomic probe, which in turn revealed the exceptional selectivity and potency of cepafungin I toward the β2 and β5 subunits of the proteasome. Further structure-activity relationship studies suggest the key role of the hydroxyl group in the macrocycle and the identity of the lipid tail in modulating the potency of this natural product family. This study lays the groundwork for further medicinal chemistry exploration to fully realize the anticancer potential of cepafungin I.
Quantifying Assurance in Learning-Enabled Systems

Dependability assurance of systems embedding machine learning (ML) components—so called learning-enabled systems (LESs)—is a key step for their use in safety-critical applications. In emerging standardization and guidance efforts, there is a growing consensus in the value of using assurance cases for that purpose. This paper develops a quantitative notion of assurance that an learning-enabled system (LES) is dependable, as a core component of its assurance case, also extending our prior work that applied to ML components. Specifically, we characterize LES assurance in the form of assurance measures: a probabilistic quantification of confidence that an LES possesses system-level properties associated with functional capabilities and dependability attributes. We illustrate the utility of assurance measures by application to a real world autonomous aviation system, also describing their role both in i) guiding high-level, runtime risk mitigation decisions and ii) as a core component of the associated dynamic assurance case.
Asynchronous Schemes for Stochastic and Misspecified Potential Games and Nonconvex Optimization

In “Asynchronous Schemes for Stochastic and Misspecified Potential Games and Nonconvex Optimization,” Lei and Shanbhag consider a class of convex stochastic Nash games, possibly corrupted by parame...
Rapid Adaptation to Remote Didactics and Learning in GME

Weekly didactic conference in emergency medicine education has traditionally united residents and faculty for learning and fostered community within the residency program The global pandemic Coronavirus Disease-19 (COVID-19) has fueled a rapid transition to remote learning that has disrupted the typical in-person format To maintain ACGME1 educational experiences and requirements for residents in a safe manner, many residencies have moved to videoconferencing platforms such as Zoom?, Teams?, and WebEX ?
Space-time imaging, magnification, and time reversal of matter waves

An imaging system is proposed for matter-wave functions, which is based on producing a quadratic phase modulation on the wavefunction of a charged particle, analogous to that produced by a space or time lens. The modulation is produced by co-propagating the wavepacket within an extremum of the harmonic vector and scalar potentials associated with a slow-wave electromagnetic structure. By preceding and following this interaction with appropriate dispersion, which is characteristic of a solution to the time-dependent Schrodinger equation, a system results that is capable of magnifying (i.e., stretching or compressing the space- and time-scales) and time-reversing an arbitrary quantum wavefunction.
Associations of mitochondrial genomic variation with corticobasal degeneration, progressive supranuclear palsy, and neuropathological tau measures

Mitochondrial health is important in ageing and dysfunctional oxidative phosphorylation (OXPHOS) accelerates ageing and influences neurodegeneration. Mitochondrial DNA (mtDNA) codes for vital OXPHOS subunits and mtDNA background has been associated with neurodegeneration; however, no study has characterised mtDNA variation in Progressive supranuclear palsy (PSP) or Corticobasal degeneration (CBD) risk or pathogenesis. In this case–control study, 910 (42.6% male) neurologically-healthy controls, 1042 (54.1% male) pathologically-confirmed PSP cases, and 171 (52.0% male) pathologically-confirmed CBD cases were assessed to determine how stable mtDNA polymorphisms, in the form of mtDNA haplogroups, were associated with risk of PSP, risk of CBD, age of PSP onset, PSP disease duration, and neuropathological tau pathology measures for neurofibrillary tangles (NFT), neuropil threads (NT), tufted astrocytes (TA), astrocytic plaques (AP), and oligodendroglial coiled bodies (CB). 764 PSP cases and 150 CBD cases had quantitative tau pathology scores. mtDNA was genotyped for 39 unique SNPs using Agena Bioscience iPlex technologies and mitochondrial haplogroups were defined to mitochondrial phylogeny. After adjustment for multiple testing, we observed an association with risk of CBD for mtDNA sub-haplogroup H4 (OR = 4.51, P = 0.001) and the HV/HV0a haplogroup was associated with a decreased severity of NT tau pathology in PSP cases (P = 0.0023). Our study reports that mitochondrial genomic background may be associated with risk of CBD and may be influencing tau pathology measures in PSP. Replication of these findings will be important.
Khovanov homotopy type, Burnside category and products

We give a new construction of a Khovanov stable homotopy type, or spectrum. We show that this construction gives a space stably homotopy equivalent to the Khovanov spectra constructed by Lipshitz and Sarkar (J. Amer. Math. Soc. 27 (2014) 983–1042) and Hu, Kriz and Kriz (Topology Proc. 48 (2016) 327–360) and, as a corollary, that those two constructions give equivalent spectra. We show that the construction behaves well with respect to disjoint unions, connected sums and mirrors, verifying several of Lipshitz and Sarkar’s conjectures. Finally, combining these results with Lipshitz and Sarkar’s computations (J. Topol. 7 (2014) 817–848) and refined s–invariant (Duke Math. J. 163 (2014) 923–952), we obtain new results about the slice genera of certain knots.
On a type of permutation rational functions over finite fields

Abstract Let p be a prime and n be a positive integer. Let f b ( X ) = X + ( X p − X + b ) − 1 , where b ∈ F p n is such that Tr p n / p ( b ) ≠ 0 . In 2008, Yuan et al. [12] showed that for p = 2 , 3 , f b permutes F p n for all n ≥ 1 . Using the Hasse-Weil bound, we show that when p > 3 and n ≥ 5 , f b does not permute F p n . For p > 3 and n = 2 , we prove that f b permutes F p 2 if and only if Tr p 2 / p ( b ) = ± 1 . We conjecture that for p > 3 and n = 3 , 4 , f b does not permute F p n .
Adaptive Incentive Design

We apply control theoretic and optimization techniques to adaptively design incentives for principal-agent problems in which the principal faces adverse selection in its interaction with multiple agents. In particular, the principal's objective depends on data from strategic decision makers (agents) whose decision-making process is unknown a priori . We consider both the cases where agents play best response to one another (Nash) and where they employ myopic update rules. By parametrizing the agents’ utility functions and the incentives offered, we develop an algorithm that the principal can employ to learn the agents’ decision-making processes while simultaneously designing incentives to change their response to one that is more desirable. We provide convergence results for this algorithm both in the noise-free and noisy cases and present illustrative examples.
Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy

Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e.g. indoor, underground. Conventionally, the performance of visual-inertial SLAM is evaluated with open-loop analysis, with a focus on the drift level of SLAM systems. In this paper, we raise the question on the importance of visual estimation latency in closed-loop navigation tasks, such as accurate trajectory tracking. To understand the impact of both drift and latency on visualinertial SLAM systems, a closed-loop benchmarking simulation is conducted, where a robot is commanded to follow a desired trajectory using the feedback from visual-inertial estimation. By extensively evaluating the trajectory tracking performance of representative state-of-the-art visual-inertial SLAM systems, we reveal the importance of latency reduction in visual estimation module of these systems. The findings suggest directions of future improvements for visual-inertial SLAM.
Perceived Risk and Intentions to Practice Health Protective Behaviors in a Mining-Impacted Region.

Effective risk communication strategies are critical to reducing lead exposure in mining-impacted communities. Understanding the strength of the associations between perceived risk and individuals’ behavioral intentions to protect their health is important for developing these strategies. We conducted a survey within three communities of northern Idaho, USA (n = 306) in or near a Superfund Megasite with legacy mining contamination. Survey data were used to test a theoretical model based on the Health Belief Model. Respondents had higher intentions to practice health protective behaviors when they perceived the risk of lead contamination as severe and recognized the benefits of practicing health protective behaviors. Women reported higher behavioral intentions than men, but age and mining affiliation were not significantly associated with behavioral intentions. Although managing lead hazards in communities impacted by mining is challenging due to widely distributed contamination, effective health risk messages, paired with remediation, are powerful tools to protect the health and safety of residents.
Renin-angiotensin-aldosterone system inhibitors and COVID-19 infection or hospitalization: a cohort study

BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may increase the risk of COVID-19 infection or affect disease severity. Prior studies have not examined the association of medication dose with risks. METHODS: This retrospective cohort study included people aged ≥18 years enrolled in a US integrated healthcare system for at least 4 months as of 2/29/2020. Current ACEI and ARB use was identified from pharmacy data, and the estimated daily dose was calculated and standardized across medications. COVID-19 infections and hospitalizations were identified through 6/14/2020 from laboratory and hospitalization data. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals, adjusting for race/ethnicity, obesity and other covariates. RESULTS: Among 322,044 individuals, 826 developed COVID-19 infection. Among people using ACEI/ARBs, 204/56,105 developed COVID-19 (3.6 per 1000 individuals) compared with 622/265,939 without ACEI/ARB use (2.3 per 1000), yielding an adjusted OR of 0.91 (95% CI 0.74-1.12). For use of < 1 defined daily dose vs. nonuse, the adjusted OR for infection was 0.92 (95% CI 0.66-1.28); for 1 to < 2 defined daily doses, 0.89 (95% CI 0.66-1.19); and for ≥2 defined daily doses, 0.92 (95% CI 0.72-1.18). The OR was similar for ACEIs and ARBs and in subgroups by age and sex. 26% of people with COVID-19 infection were hospitalized; the adjusted OR for hospitalization in relation to ACEI/ARB use was 0.98 (95% CI 0.63-1.54), and there was no association with dose. CONCLUSIONS: These findings support current recommendations that individuals on these medications continue their use.
Ab initio Folding Potentials for Proton-Nucleus Scattering with NCSM Nonlocal One-Body Densities

Based on the spectator expansion of the multiple scattering series we employ a nonlocal translationally invariant nuclear density derived from a chiral next-to-next-to-leading order (NNLO) and the very same interaction for consistent full-folding calculations of the effective (optical) potential for nucleon-nucleus scattering for light nuclei.
Quantum operator growth bounds for kicked tops and semiclassical spin chains

We present a framework for understanding the dynamics of operator size, and bounding the growth of out-of-time-ordered correlators, in models of large-$S$ spins. Focusing on the dynamics of a single spin, we show the finiteness of the Lyapunov exponent in the large-$S$ limit; our bounds are tighter than the best known Lieb-Robinson-type bounds on these systems. We numerically find our upper bound on Lyapunov exponents is within an order of magnitude of numerically computed values in classical and quantum kicked top models. Generalizing our results to coupled large-$S$ spins on lattices, we show that the butterfly velocity, which characterizes the spatial speed of quantum information scrambling, is finite as $S\ensuremath{\rightarrow}\ensuremath{\infty}$. We emphasize qualitative differences between operator growth in semiclassical large-spin models and quantum holographic systems including the Sachdev-Ye-Kitaev model.
Light-induced static magnetization: Nonlinear Edelstein effect

Light can interact with magnetism in materials. Motivated by the Edelstein effect, whereby a static electric field can generate magnetization in metals, in this work we theoretically and computationally demonstrate that static magnetization can also be generated through light in semiconductors. Such an effect is essentially a second-order nonlinear response and can be considered as a generalization of the Edelstein effect. This nonlinear Edelstein effect (NLEE) applies to semiconductors under both linearly and circularly polarized light, and there are no constraints from either spatial inversion or time-reversal symmetry. With ab initio calculations, we reveal several prominent features of NLEE. We find that the light-induced orbital magnetizations can be significantly greater than the spin magnetizations, in contrast to standard intrinsic magnetism where the orbital magnetic moment is strongly quenched under crystal field. We show that in multilayer (multisublattice) materials, different ferromagnetic and ferrimagnetic structures can be realized under photon pumping, depending on the interlayer (intersublattice) symmetry. It is also possible to switch the magnetic ordering in antiferromagnetic materials. The relationship between NLEE and other magneto-optic effects, including the inverse Faraday effect and inverse Cotton-Mouton effect, is also discussed.
Diversity of tRNA Clusters in the Chloroviruses

Viruses rely on their host’s translation machinery for the synthesis of their own proteins. Problems belie viral translation when the host has a codon usage bias (CUB) that is different from an infecting virus due to differences in the GC content between the host and virus genomes. Here, we examine the hypothesis that chloroviruses adapted to host CUB by acquisition and selection of tRNAs that at least partially favor their own CUB. The genomes of 41 chloroviruses comprising three clades, each infecting a different algal host, have been sequenced, assembled and annotated. All 41 viruses not only encode tRNAs, but their tRNA genes are located in clusters. While differences were observed between clades and even within clades, seven tRNA genes were common to all three clades of chloroviruses, including the tRNAArg gene, which was found in all 41 chloroviruses. By comparing the codon usage of one chlorovirus algal host, in which the genome has been sequenced and annotated (67% GC content), to that of two of its viruses (40% GC content), we found that the viruses were able to at least partially overcome the host’s CUB by encoding tRNAs that recognize AU-rich codons. Evidence presented herein supports the hypothesis that a chlorovirus tRNA cluster was present in the most recent common ancestor (MRCA) prior to divergence into three clades. In addition, the MRCA encoded a putative isoleucine lysidine synthase (TilS) that remains in 39/41 chloroviruses examined herein, suggesting a strong evolutionary pressure to retain the gene. TilS alters the anticodon of tRNAMet that normally recognizes AUG to then recognize AUA, a codon for isoleucine. This is advantageous to the chloroviruses because the AUA codon is 12–13 times more common in the chloroviruses than their host, further helping the chloroviruses to overcome CUB. Among large DNA viruses infecting eukaryotes, the presence of tRNA genes and tRNA clusters appear to be most common in the Phycodnaviridae and, to a lesser extent, in the Mimiviridae.
Siah2 integrates mitogenic and extracellular matrix signals linking neuronal progenitor ciliogenesis with germinal zone occupancy

Evidence is lacking as to how developing neurons integrate mitogenic signals with microenvironment cues to control proliferation and differentiation. We determine that the Siah2 E3 ubiquitin ligase functions in a coincidence detection circuit linking responses to the Shh mitogen and the extracellular matrix to control cerebellar granule neurons (CGN) GZ occupancy. We show that Shh signaling maintains Siah2 expression in CGN progenitors (GNPs) in a Ras/Mapk-dependent manner. Siah2 supports ciliogenesis in a feed-forward fashion by restraining cilium disassembly. Efforts to identify sources of the Ras/Mapk signaling led us to discover that GNPs respond to laminin, but not vitronectin, in the GZ microenvironment via integrin β1 receptors, which engages the Ras/Mapk cascade with Shh, and that this niche interaction is essential for promoting GNP ciliogenesis. As GNPs leave the GZ, differentiation is driven by changing extracellular cues that diminish Siah2-activity leading to primary cilia shortening and attenuation of the mitogenic response.
The K-property for subadditive equilibrium states

By generalizing Ledrappier's criterion [Mesures d'equilibre d'entropie completement positive, in Systemes dynamiques II – Varsovie, number 50 in Asterisque, Societe mathematique de France, 1977, pp...
Cross-Domain Adaptation with Discrepancy Minimization for Text-Independent Forensic Speaker Verification.

Forensic audio analysis for speaker verification offers unique challenges due to location/scenario uncertainty and diversity mismatch between reference and naturalistic field recordings. The lack of real naturalistic forensic audio corpora with ground-truth speaker identity represents a major challenge in this field. It is also difficult to directly employ small-scale domain-specific data to train complex neural network architectures due to domain mismatch and loss in performance. Alternatively, cross-domain speaker verification for multiple acoustic environments is a challenging task which could advance research in audio forensics. In this study, we introduce a CRSS-Forensics audio dataset collected in multiple acoustic environments. We pre-train a CNN-based network using the VoxCeleb data, followed by an approach which fine-tunes part of the high-level network layers with clean speech from CRSS-Forensics. Based on this fine-tuned model, we align domain-specific distributions in the embedding space with the discrepancy loss and maximum mean discrepancy (MMD). This maintains effective performance on the clean set, while simultaneously generalizes the model to other acoustic domains. From the results, we demonstrate that diverse acoustic environments affect the speaker verification performance, and that our proposed approach of cross-domain adaptation can significantly improve the results in this scenario.
Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes

We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.
Backward-forward search for manipulation planning

In this paper we address planning problems in high-dimensional hybrid configuration spaces, with a particular focus on manipulation planning problems involving many objects. We present the hybrid backward-forward (HBF) planning algorithm that uses a backward identification of constraints to direct the sampling of the infinite action space in a forward search from the initial state towards a goal configuration. The resulting planner is probabilistically complete and can effectively construct long manipulation plans requiring both prehensile and nonprehensile actions in cluttered environments.
Fast permutation tests and related methods, for association between rare variants and binary outcomes.

In large-scale genetic studies, a primary aim is to test for an association between genetic variants and a disease outcome. The variants of interest are often rare and appear with low frequency among subjects. In this situation, statistical tests based on standard asymptotic results do not adequately control the type I error rate, especially if the case : control ratio is unbalanced. In this article, we propose the use of permutation and approximate unconditional tests for testing association with rare variants. We use novel analytical calculations to efficiently approximate the true type I error rate under common study designs, and in numerical studies show that the proposed classes of tests significantly improve upon standard testing methods. We also illustrate our methods in data from a recent case-control study for genetic causes of a severe side effect of a common drug treatment.
Free-Free Emission at Low Radio Frequencies

We discuss free-free radio emission from ionized gas in the intergalactic medium. Because the emissivity is proportional to the square of the electron density, the mean background is strongly sensitive to the spatial clumping of free electrons. Using several existing models for the clumping of ionized gas, we find that the expected free-free distortion to the cosmic microwave background blackbody spectrum is at a level detectable with upcoming experiments such as the Absolute Radiometer for Cosmology, Astrophysics, and Diffuse Emission experiment. However, the dominant contribution to the distortion comes from clumpy gas at z 3, and the integrated signal does not strongly constrain the epoch of reionization. In addition to the mean emission, we consider spatial fluctuations in the free-free background and the extent to which these anisotropies confuse the search for fluctuations in 21 cm line emission from neutral hydrogen during and prior to reionization. This background is smooth in frequency space and hence can be removed through frequency differencing, but only so long as the 21 cm signal and the free-free emission are uncorrelated. We show that because the free-free background is generated primarily at low redshifts, the cross-correlation between the two fields is smaller than a few percent. Thus, multifrequency cleaning should be an effective way to eliminate the free-free confusion.
A CHARACTERIZATION OF THE BRIGHTNESS OSCILLATIONS DURING THERMONUCLEAR BURSTS FROM 4U 1636(536

The discovery of nearly coherent brightness oscillations during thermonuclear X-ray bursts from six neutron star low-mass X-ray binaries has opened up a new way to study the propagation of thermonuclear burning and may ultimately lead to greater understanding of thermonuclear propagation in other astrophysical contexts, such as in Type Ia supernovae. Here, we report detailed analyses of the ~580 Hz brightness oscillations during bursts from 4U 1636-536. We investigate the bursts as a whole and, in more detail, the initial portions of the bursts. We analyze the ~580 Hz oscillations in the initial 0.75 s of the five bursts that were used in a previous search for a brightness oscillation at the expected ~290 Hz spin frequency. We find that, although it is appropriate to use an arbitrarily complicated model of the ~580 Hz oscillations to generate a candidate waveform for the ~290 Hz oscillations, models with more than two parameters are not required by the data. For the bursts as a whole, we show that the characteristics of the brightness oscillations vary greatly from burst to burst. We find, however, that in at least one of the bursts, and possibly in three of the four that have strong brightness oscillations throughout the burst, the oscillation frequency decreases well after the peak of the burst. This behavior poses a challenge to the standard burning layer expansion explanation for the frequency changes.