cssi/SciSciGPT-SciSciNet huggingface dataset:
Privacy and security in transnational data processing systems

Transnational data processing systems are international value-added, public, special-interest community, or private computer-communications networks that operate computers in several countries and provide computing services to users in these and other countries. Examples of such systems are the North American-based Telenet, Infonet, Mark III, Tymnet, and Datapac, and the European developmental Euronet and the Nordic Data Network. Among computer networks operated by international communities are SITA, an airlines reservation system, and SWIFT, a worldwide interbank financial telecommunications network. In development is the European Informatics Network (EIN) which will provide information services and data base access to users in the countries of the European Economic Community (EEC). Finally, private computer-communications systems are operated over international data carrier networks by multinational corporations in many countries.
Regulating TNCs: Should Uber and Lyft set their own rules?

Abstract We evaluate the impact of three proposed regulations of transportation network companies (TNCs) like Uber, Lyft and Didi: (1) A minimum wage for drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip congestion tax. The impact is assessed using a queuing theoretic equilibrium model which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. We show that a floor placed under driver earnings can push the ride-hailing platform to hire more drivers and offer more rides, at the same time that passengers enjoy faster rides and lower total cost, while platform rents are reduced. Contrary to standard competitive labor market theory, enforcing a minimum wage for drivers benefits both drivers and passengers, and promotes the efficiency of the entire system. This surprising outcome holds for almost all model parameters, and it occurs because the wage floor curbs TNC labor market power. In contrast to a wage floor, imposing a cap on the number of vehicles hurts drivers, because the platform reaps all the benefits of limiting supply. The congestion tax has the expected impact: fares increase, wages and platform revenue decrease. We also construct variants of the model to briefly discuss platform subsidy, platform competition, and autonomous vehicles.
Utility of echocardiography in predicting mortality in infants with severe bronchopulmonary dysplasia.

To determine the relationship between interventricular septal position (SP) and right ventricular systolic pressure (RVSP) and mortality in infants with severe BPD (sBPD). Infants with sBPD in the Children’s Hospitals Neonatal Database who had echocardiograms 34–44 weeks’ postmenstrual age (PMA) were included. SP and RVSP were categorized normal, abnormal (flattened/bowed SP or RVSP > 40 mmHg) or missing. Of 1157 infants, 115 infants (10%) died. Abnormal SP or RVSP increased mortality (SP 19% vs. 8% normal/missing, RVSP 20% vs. 9% normal/missing, both p < 0.01) in unadjusted and multivariable models, adjusted for significant covariates (SP OR 1.9, 95% CI 1.2–3.0; RVSP OR 2.2, 95% CI 1.1–4.7). Abnormal parameters had high specificity (SP 82%; RVSP 94%), and negative predictive value (SP 94%, NPV 91%) for mortality. Abnormal SP or RVSP is independently associated with mortality in sBPD infants. Negative predictive values distinguish infants most likely to survive.
Cech approximation to the Brown-Gersten spectral sequence

In this paper, we show that the etale index of a torsion cohomological Brauer class is divisible by the period of the class. The tool used to make this computation is the Cech approximation of the title. To create the approximation, we use the folklore theorem that the homotopy limit and Postnikov spectral sequences for a cosimplicial space agree beginning with the E2-page. As far we know, this folklore theorem has no proof in the literature, so we include a proof.
Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics

SUMMARY Our knowledge of transcriptional heterogeneities in epithelial stem/progenitor cell compartments is limited. Epidermal basal cells sustain cutaneous tissue maintenance and drive wound healing. Previous studies have probed basal cell heterogeneity in stem/progenitor potential, but a non-biased dissection of basal cell dynamics during differentiation is lacking. Using single-cell RNA-sequencing coupled with RNAScope and fluorescence lifetime imaging, we identify three non-proliferative and one proliferative basal cell transcriptional states in homeostatic skin that differ in metabolic preference and become spatially partitioned during wound re-epithelialization. Pseudotemporal trajectory and RNA velocity analyses produce a quasi-linear differentiation hierarchy where basal cells progress from Col17a1high/Trp63high state to early response state, proliferate at the juncture of these two states, or become growth arrested before differentiating into spinous cells. Wound healing induces plasticity manifested by dynamic basal-spinous interconversions at multiple basal states. Our study provides a systematic view of epidermal cellular dynamics supporting a revised “hierarchical-lineage” model of homeostasis.
The Inseparability of Race and Partisanship in the United States

Many recent studies consider the overlapping nature of major political identities. Drawing on this research, we posit that partisanship and race are so enmeshed in the public mind that events which independently trigger one of these identities can also activate the other. We find support for this in three behavioral game experiments with 5496 respondents. These studies reveal what we refer to as the “parallel updating” of out-group affect. Shifts in racial affect are accompanied by simultaneous movement in attitudes and behavior towards members of the other political party. Conversely, changes in partisan affect co-occur with movement in views of racial out-groups. Our results speak to the inseparability of racial and partisan affect in the United States and suggest an important link between studies of racial animus and partisan affective polarization.
Generating Pareto Optimal Dose Distributions for Radiation Therapy Treatment Planning

Radiotherapy treatment planning currently requires many trial-and-error iterations between the planner and treatment planning system, as well as between the planner and physician for discussion/consultation. The physician’s preferences for a particular patient cannot be easily quantified and precisely conveyed to the planner. In this study we present a real-time volumetric Pareto surface dose generation deep learning neural network that can be used after segmentation by the physician, adding a tangible and quantifiable endpoint to portray to the planner. From 70 prostate patients, we first generated 84,000 intensity modulated radiation therapy plans (1,200 plans per patient) sampling the Pareto surface, representing various tradeoffs between the planning target volume (PTV) and the organs-at-risk (OAR), including bladder, rectum, left femur, right femur, and body. We divided the data to 10 test patients and 60 training/validation patients. We then trained a hierarchically densely connected convolutional U-net (HD U-net), to take the PTV and avoidance map representing OARs masks and weights, and predict the optimized plan. The HD U-net is capable of accurately predicting the 3D Pareto optimal dose distributions, with average [mean, max] dose errors of [3.4%, 7.7%](PTV), [1.6%, 5.6%](bladder), [3.7%, 4.2%](rectum), [3.2%, 8.0%](left femur), [2.9%, 7.7%](right femur), and [0.04%, 5.4%](body) of the prescription dose. The PTV dose coverage prediction was also very similar, with errors of 1.3% (D98) and 2.0% (D99). Homogeneity was also similar, differing by 0.06 on average. The neural network can predict the dose within 1.7 s. Clinically, the optimization and dose calculation is much slower, taking 5–10 min.
High Flux Passive Imaging With Single-Photon Sensors

Single-photon avalanche diodes (SPADs) are an emerging technology with a unique capability of capturing individual photons with high timing precision. SPADs are being used in several active imaging systems (e.g., fluorescence lifetime microscopy and LiDAR), albeit mostly limited to low photon flux settings. We propose passive free-running SPAD (PF-SPAD) imaging, an imaging modality that uses SPADs for capturing 2D intensity images with unprecedented dynamic range under ambient lighting, without any active light source. Our key observation is thatthe precise inter-photon timing measured by a SPAD can be used for estimating scene brightness under ambient lighting conditions, even for very bright scenes. We develop a theoretical model for PF-SPAD imaging, and derive a scene brightness estimator based on the average time of darkness between successive photons detected by a PF-SPAD pixel. Our key insight is that due to the stochastic nature of photon arrivals, this estimator does not suffer from a hard saturation limit. Coupled with high sensitivity at low flux, this enables a PF-SPAD pixel to measure a wide range of scene brightnesses, from very low to very high, thereby achieving extreme dynamic range. We demonstrate an improvement of over 2 orders of magnitude over conventional sensors by imaging scenes spanning a dynamic range of 10^6:1.
Recycled ADMM: Improving the Privacy and Accuracy of Distributed Algorithms

Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems. In distributed settings, each node performs computation with its local data and the local results are exchanged among neighboring nodes in an iterative fashion. During this iterative process the leakage of data privacy arises and can accumulate significantly over many iterations, making it difficult to balance the privacy-accuracy tradeoff. We propose Recycled ADMM (R-ADMM), where a linear approximation is applied to every even iteration, its solution directly calculated using only results from the previous, odd iteration. It turns out that under such a scheme, half of the updates incur no privacy loss and require much less computation compared to the conventional ADMM. Moreover, R-ADMM can be further modified (MR-ADMM) such that each node independently determines its own penalty parameter over iterations. We obtain a sufficient condition for the convergence of both algorithms and provide the privacy analysis based on objective perturbation. It can be shown that the privacy-accuracy tradeoff can be improved significantly compared with conventional ADMM.
Parsing Heterogeneity in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder with Individual Connectome Mapping.

Abstract Traditional diagnostic systems for neurodevelopmental disorders define diagnostic categories that are heterogeneous in behavior and underlying neurobiological alterations. The goal of this...
Legofit: estimating population history from genetic data

Our current understanding of archaic admixture in humans relies on statistical methods with large biases, whose magnitudes depend on the sizes and separation times of ancestral populations. To avoid these biases, it is necessary to estimate these parameters simultaneously with those describing admixture. Genetic estimates of population histories also confront problems of statistical identifiability: different models or different combinations of parameter values may fit the data equally well. To deal with this problem, we need methods of model selection and model averaging, which are lacking from most existing software. The Legofit software package allows simultaneous estimation of parameters describing admixture, and the sizes and separation times of ancestral populations. It includes facilities for data manipulation, estimation, analysis of residuals, model selection, and model averaging. Legofit uses genetic data to study the history of a subdivided population. It is unaffected by recent history and can therefore focus on the deep history of population size, subdivision, and admixture. It outperforms several statistical methods that have been widely used to study population history and should be useful in any species for which DNA sequence data is available from several populations.
DSL: Discriminative Subgraph Learning via Sparse Self-Representation.

The goal in network state prediction (NSP) is to classify the global state (label) associated with features embedded in a graph. This graph structure encoding feature relationships is the key distinctive aspect of NSP compared to classical supervised learning. NSP arises in various applications: gene expression samples embedded in a protein-protein interaction (PPI) network, temporal snapshots of infrastructure or sensor networks, and fMRI coherence network samples from multiple subjects to name a few. Instances from these domains are typically ``wide'' (more features than samples), and thus, feature sub-selection is required for robust and generalizable prediction. How to best employ the network structure in order to learn succinct connected subgraphs encompassing the most discriminative features becomes a central challenge in NSP. Prior work employs connected subgraph sampling or graph smoothing within optimization frameworks, resulting in either large variance of quality or weak control over the connectivity of selected subgraphs. In this work we propose an optimization framework for discriminative subgraph learning (DSL) which simultaneously enforces (i) sparsity, (ii) connectivity and (iii) high discriminative power of the resulting subgraphs of features. Our optimization algorithm is a single-step solution for the NSP and the associated feature selection problem. It is rooted in the rich literature on maximal-margin optimization, spectral graph methods and sparse subspace self-representation. DSL simultaneously ensures solution interpretability and superior predictive power (up to 16% improvement in challenging instances compared to baselines), with execution times up to an hour for large instances.
On the criteria to be used in decomposing systems into modules

This paper discusses modularization as a mechanism for improving the flexibility and comprehensibility of a system while allowing the shortening of its development time. The effectiveness of a "modularization" is dependent upon the criteria used in dividing the system into modules. A system design problem is presented and both a conventional and unconventional decomposition are described. It is shown that the unconventional decompositions have distinct advantages for the goals outlined. The criteria used in arriving at the decompositions are discussed. The unconventional decomposition, if implemented with the conventional assumption that a module consists of one or more subroutines, will be less efficient in most cases. An alternative approach to implementation which does not have this effect is sketched.
Network-based heuristics for constraint satisfaction problems

Many AI tasks can be formulated as Constraint-Satisfaction problems (CSP), i.e., the assignment of values to variables subject to a set of constraints. While some CSPs are hard, those that are easy can often be mapped into sparse networks of constraints which, in the extreme case, are trees. This paper identifies classes of problems that lend themselves to easy solutions, and develops algorithms that solve these problems optimally. The paper then presents a method of generating heuristic advice to guide the order of value assignments based on both the sparseness found in the constraint network and the simplicity of tree-structured CSPs. The advice is generated by simplifying the pending subproblems into trees, counting the number of consistent solutions in each simplified subproblem, and comparing these counts to decide among the choices pending in the original problem.
Distinguishing deformation mechanisms in elastocapillary experiments.

Soft materials are known to deform due to a variety of mechanisms, including capillarity, buoyancy, and swelling. In this paper, we present experiments on polyvinylsiloxane gel threads partially-immersed in three liquids with different solubility, wettability, and swellability. Our results demonstrate that deformations due to capillarity, buoyancy, and swelling can be of similar magnitude as such threads come to static equilibrium. To account for all three effects being present in a single system, we derive a model capable of explaining the observed data and use it to determine the force law at the three-phase contact line. The results show that the measured forces are consistent with the expected Young–Dupre equation, and do not require the inclusion of a tangential contact line force.
Optoelectronic optimization of graded-bandgap thin-film AlGaAs solar cells

An optoelectronic optimization was carried out for an $ {{\rm Al}_\xi }{{\rm Ga}_{1 - \xi }}{\rm As} $AlξGa1−ξAs (AlGaAs) solar cell containing (i) an $ n $n-AlGaAs absorber layer with a graded bandgap and (ii) a periodically corrugated Ag backreflector combined with localized ohmic Pd–Ge–Au backcontacts. The bandgap of the absorber layer was varied either sinusoidally or linearly. An efficiency of 33.1% with the 2000-nm-thick $ n $n-AlGaAs absorber layer is predicted with linearly graded bandgap along with silver backreflector and localized ohmic backcontacts, in comparison to 27.4% efficiency obtained with homogeneous bandgap and a continuous ohmic backcontact. Sinusoidal grading of the bandgap is predicted to enhance the maximum efficiency to 34.5%. Thus, grading the bandgap of the absorber layer, along with a periodically corrugated Ag backreflector and localized ohmic Pd-Ge-Au backcontacts, can help realize ultrathin and high-efficient AlGaAs solar cells for terrestrial applications.
Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation

We consider the task of tensor estimation, i.e. estimating a low-rank 3-order $n \times n \times n$ tensor from noisy observations of randomly chosen entries in the sparse regime. In the context of matrix (2-order tensor) estimation, a variety of algorithms have been proposed and analyzed in the literature including the popular collaborative filtering algorithm that is extremely well utilized in practice. However, in the context of tensor estimation, there is limited progress. No natural extensions of collaborative filtering are known beyond “flattening” the tensor into a matrix and applying standard collaborative filtering. As the main contribution of this work, we introduce a generalization of the collaborative filtering algorithm for the setting of tensor estimation and argue that it achieves sample complexity that (nearly) matches the conjectured lower bound on the sample complexity. Interestingly, our generalization uses the matrix obtained from the “flattened” tensor to compute similarity as in the classical collaborative filtering but by defining a novel “graph” using it. The algorithm recovers the tensor with mean-squared-error (MSE) decaying to 0 as long as each entry is observed independently with probability $p= \Omega(n^{-3/2+\epsilon})$ for any arbitrarily small $\epsilon > 0$. It turns out that $p = \Omega(n^{-3/2})$ is the conjectured lower bound as well as “connectivity threshold” of graph considered to compute similarity in our algorithm.
NUMERICAL INTEGRATION ON GRAPHS: WHERE TO SAMPLE AND HOW TO WEIGH.

Let G = (V,E,w) be a finite, connected graph with weighted edges. We are interested in the problem of finding a subset W ⊂ V of vertices and weights aw such that 1|V|∑v∈Vf(v)∼∑w∈Wawf(w) for functions f:V→ℝ that are 'smooth' with respect to the geometry of the graph; here ~ indicates that we want the right-hand side to be as close to the left-hand side as possible. The main application are problems where f is known to vary smoothly over the underlying graph but is expensive to evaluate on even a single vertex. We prove an inequality showing that the integration problem can be rewritten as a geometric problem ('the optimal packing of heat balls'). We discuss how one would construct approximate solutions of the heat ball packing problem; numerical examples demonstrate the efficiency of the method.
Comparison of Derivative Estimation Methods in Solving Optimal Control Problems Using Direct Collocation

A study is conducted to evaluate four derivative estimation methods when solving a large sparse nonlinear programming problem that arises from the approximation of an optimal control problem using a direct collocation method. In particular, the Taylor series-based finite-difference, bicomplex-step, and hyper-dual derivative estimation methods are evaluated and compared alongside a well known automatic differentiation method. The performance of each derivative estimation method is assessed based on the number of iterations, the computation time per iteration, and the total computation time required to solve the nonlinear programming problem. The efficiency of each of the four derivative estimation methods is compared by solving three benchmark optimal control problems. It is found that while central finite-differencing is typically more efficient per iteration than either the hyper-dual or bicomplex-step, the latter two methods have significantly lower overall computation times due to the fact that fewer iterations are required by the nonlinear programming problem when compared with central finite-differencing. Furthermore, while the bicomplex-step and hyper-dual methods are similar in performance, the hyper-dual method is significantly easier to implement. Moreover, the automatic differentiation method is found to be substantially less computationally efficient than any of the three Taylor series-based methods. The results of this study show that the hyper-dual method offers several benefits over the other three methods both in terms of computational efficiency and ease of implementation.
The Pheromone and Pheromone Receptor Mating-Type Locus Is Involved in Controlling Uniparental Mitochondrial Inheritance in Cryptococcus

Mitochondria are inherited uniparentally during sexual reproduction in the majority of eukaryotic species studied, including humans, mice, and nematodes, as well as many fungal species. Mitochondrial uniparental inheritance (mito-UPI) could be beneficial in that it avoids possible genetic conflicts between organelles with different genetic backgrounds, as recently shown in mice, and it could prevent the spread of selfish genetic elements in the mitochondrial genome. Despite the prevalence of observed mito-UPI, the underlying mechanisms and the genes involved in controlling this non-Mendelian inheritance are poorly understood in many species. In Cryptococcus neoformans, a human pathogenic basidiomyceteous fungus, mating types (MATα and MATa) are defined by alternate alleles at the single MAT locus that evolved from fusion of the two MAT loci (P/R encoding pheromones and pheromone receptors, and HD encoding homeodomain transcription factors) that are the ancestral state in the basidiomycota. Mitochondria are inherited uniparentally from the MATa parent in C. neoformans, and this requires the SXI1α and SXI2a HD factors encoded by MAT. However, there is evidence that additional genes contribute to the control of mito-UPI in Cryptococcus. Here, we show that in C. amylolentus, a sibling species of C. neoformans with unlinked P/R and HD MAT loci, mito-UPI is controlled by the P/R locus and is independent of the HD locus. Consistently, by replacing the MATα alleles of the pheromones (MF) and pheromone receptor (STE3) with the MATa alleles, we show that these P/R locus-defining genes indeed affect mito-UPI in C. neoformans during sexual reproduction. Additionally, we show that during early stages of C. neoformans sexual reproduction, conjugation tubes are always produced by the MATα cells, resulting in unidirectional migration of the MATα nucleus into the MATa cell during zygote formation. This process is controlled by the P/R locus and could serve to physically restrict movement of MATα mitochondria in the zygotes, and thereby contribute to mito-UPI. We propose a model in which both physical and genetic mechanisms function in concert to prevent the coexistence of mitochondria from the two parents in the zygote, and subsequently in the meiotic progeny, thus ensuring mito-UPI in pathogenic Cryptococcus, as well as in closely related nonpathogenic species. The implications of these findings are discussed in the context of the evolution of mito-UPI in fungi and other more diverse eukaryotes.
The transcriptional correlates of divergent electric organ discharges in Paramormyrops electric fish

Understanding the genomic basis of phenotypic diversity can be greatly facilitated by examining adaptive radiations with hypervariable traits. In this study, we focus on a rapidly diverged species group of mormyrid electric fish in the genus Paramormyrops, which are characterized by extensive phenotypic variation in electric organ discharges (EODs). The main components of EOD diversity are waveform duration, complexity and polarity. Using an RNA-sequencing based approach, we sought to identify gene expression correlates for each of these EOD waveform features by comparing 11 specimens of Paramormyrops that exhibit variation in these features. Patterns of gene expression among Paramormyrops are highly correlated, and 3274 genes (16%) were differentially expressed. Using our most restrictive criteria, we detected 145–183 differentially expressed genes correlated with each EOD feature, with little overlap between them. The predicted functions of several of these genes are related to extracellular matrix, cation homeostasis, lipid metabolism, and cytoskeletal and sarcomeric proteins. These genes are of significant interest given the known morphological differences between electric organs that underlie differences in the EOD waveform features studied. In this study, we identified plausible candidate genes that may contribute to phenotypic differences in EOD waveforms among a rapidly diverged group of mormyrid electric fish. These genes may be important targets of selection in the evolution of species-specific differences in mate-recognition signals.
Modeling the effect of maturity on the elastic moduli of kerogen using atomistic simulations

The mechanical properties of kerogen, the organic constituent of shale source rocks, change as it becomes progressively buried under sediment over geologic time. While these changes are due to both...
Behavior-Based Quality Discrimination

Problem definition: New technology enables firms to recognize customers from their purchase histories and then provide different quality levels of product features or services for repeat and new cu...
Cathepsin S activation contributes to elevated CX3CL1 (fractalkine) levels in tears of a Sjögren's syndrome murine model

Autoimmune dacryoadenitis and altered lacrimal gland (LG) secretion are features of Sjogren’s syndrome (SS). Activity of cathepsin S (CTSS), a cysteine protease, is significantly and specifically increased in SS patient tears. The soluble chemokine, CX3CL1 (fractalkine), is cleaved from membrane-bound CX3CL1 by proteases including CTSS. We show that CX3CL1 is significantly elevated by 2.5-fold in tears (p = 0.0116) and 1.4-fold in LG acinar cells (LGAC)(p = 0.0026) from male NOD mice, a model of autoimmune dacryoadenitis in SS, relative to BALB/c controls. Primary mouse LGAC and human corneal epithelial cells (HCE-T cells) exposed to interferon-gamma, a cytokine elevated in SS, showed up to 9.6-fold (p ≤ 0.0001) and 25-fold (p ≤ 0.0001) increases in CX3CL1 gene expression, and 1.9-fold (p = 0.0005) and 196-fold (p ≤ 0.0001) increases in CX3CL1 protein expression, respectively. Moreover, exposure of HCE-T cells to recombinant human CTSS at activity equivalent to that in SS patient tears increased cellular CX3CL1 gene and protein expression by 2.8-fold (p = 0.0021) and 5.1-fold (p ≤ 0.0001), while increasing CX3CL1 in culture medium by 5.8-fold (p ≤ 0.0001). Flow cytometry demonstrated a 4.5-fold increase in CX3CR1-expressing immune cells (p ≤ 0.0001), including increased T-cells and macrophages, in LG from NOD mice relative to BALB/c. CTSS-mediated induction/cleavage of CX3CL1 may contribute to ocular surface and LG inflammation in SS.
A new approach to closed-loop linear system identification via a vector autoregressive model.

A new vector autoregressive (VAR) model algorithm is developed for closed-loop identification. The new VAR approach is an extension of a recently developed algorithm, named the optimal parameter search (OPS), thus, we call the new technique VOPS, for vector OPS. Monte Carlo simulations of closed-loop systems were performed to compare the performance of VOPS to the widely utilized vector least squares (VLS) and vector fast orthogonal search (VFOS) approaches. In addition, we examined the effect on parameter estimates obtained via open-loop identification techniques, when using data from closed-loop systems. Comparative results show that both the VOPS and VFOS algorithms produce far more accurate parameter estimates than does the VLS. Furthermore, open-loop identification via univariate OPS and to a lesser extent univariate FOS for closed-loop systems, does not adversely affect the accuracy of the parameter estimates. An open-loop identification via the univariate least-squares method for closed-loop systems showed the most deleterious effect on the accuracy of the parameter estimates. In addition to developing the VOPS algorithm, we also developed approaches termed constrained OPS (COPS) and constrained FOS (CFOS). For closed-loop systems considered in this paper, both COPS and CFOS resulted in more accurate parameter estimates (less biased and more efficient) than did VLS, VFOS, and VOPS. © 2002 Biomedical Engineering Society. PAC2002: 8780-y, 0705Kf
Sleep in the Natural Environment: A Pilot Study

Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, numerous commercial digital devices have been developed that record physiological data, such as movement, heart rate, and respiratory rate, which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. The sleep-related output metrics from these devices include sleep staging and total sleep duration and are derived via proprietary algorithms that utilize a variety of these physiological recordings. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether sleep metric outputs from self-reported sleep metrics (SRSMs) and four sensors, specifically Fitbit Surge (a smart watch), Withings Aura (a sensor pad that is placed under a mattress), Hexoskin (a smart shirt), and Oura Ring (a smart ring), were related to known cognitive and psychological metrics, including the n-back test and Pittsburgh Sleep Quality Index (PSQI). We analyzed correlation between multiple device-related sleep metrics. Furthermore, we investigated relationships between these sleep metrics and cognitive scores across different timepoints and SRSM through univariate linear regressions. We found that correlations for sleep metrics between the devices across the sleep cycle were almost uniformly low, but still significant (P < 0.05). For cognitive scores, we found the Withings latency was statistically significant for afternoon and evening timepoints at P = 0.016 and P = 0.013. We did not find any significant associations between SRSMs and PSQI or cognitive scores. Additionally, Oura Ring's total sleep duration and efficiency in relation to the PSQI measure was statistically significant at P = 0.004 and P = 0.033, respectively. These findings can hopefully be used to guide future sensor-based sleep research.
Dissecting Fertility Functions of Drosophila Y Chromosome Genes with CRISPR.

Gene-poor, repeat-rich regions of the genome are poorly understood and have been understudied due to technical challenges and the misconception that they are degenerating "junk." Yet multiple lines of evidence indicate these regions may be an important source of variation that could drive adaptation and species divergence, particularly through regulation of fertility. The ∼40 Mb Y chromosome of Drosophila melanoga st er contains only 16 known protein-coding genes, and is highly repetitive and entirely heterochromatic. Most of the genes originated from duplication of autosomal genes and have reduced nonsynonymous substitution rates, suggesting functional constraint. We devised a genetic strategy for recovering and retaining stocks with sterile Y-linked mutations and combined it with CRISPR to create mutants with deletions that disrupt three Y-linked genes. Two genes, PRY and FDY, had no previously identified functions. We found that PRY mutant males are subfertile, but FDY mutant males had no detectable fertility defects. FDY, the newest known gene on the Y chromosome, may have fertility effects that are conditional or too subtle to detect. The third gene, CCY, had been predicted but never formally shown to be required for male fertility. CRISPR targeting and RNA interference of CCY caused male sterility. Surprisingly, however, our CCY mutants were sterile even in the presence of an extra wild-type Y chromosome, suggesting that perturbation of the Y chromosome can lead to dominant sterility. Our approach provides an important step toward understanding the complex functions of the Y chromosome and parsing which functions are accomplished by genes vs. repeat elements.
The knot Floer cube of resolutions and the composition product

We examine the relationship between the oriented cube of resolutions for knot Floer homology and HOMFLY-PT homology. By using a filtration induced by additional basepoints on the Heegaard diagram f...
Enhanced recovery after surgery outcomes in minimally invasive nonhysterectomy gynecologic procedures

Background Improved patient outcomes and satisfaction associated with enhanced recovery after surgery protocols have increasingly replaced traditional perioperative anesthesia care. Fast-track surgery pathways have been extensively validated in patients undergoing hysterectomies, yet the impact on fertility-sparing laparoscopic gynecologic operations, particularly those addressing chronic pain conditions, has not been examined. Objective The objective of the study was to determine the effects of enhanced recovery after surgery pathway implementation compared with conventional perioperative care in women undergoing laparoscopic minimally invasive nonhysterectomy gynecologic procedures. Study Design We conducted a retrospective cohort study of women undergoing uterine-sparing laparoscopic gynecologic procedures for benign conditions (tubal/adnexal pathology, endometriosis, or leiomyomas) during a 24 month period before and after enhanced recovery after surgery implementation at a tertiary care center. We compared immediate perioperative outcomes and 30 day complications. The primary outcome was same-day discharge rates. Factors influencing unplanned admissions, postoperative pain, sedation, nausea, and vomiting represented secondary analyses. Results A total of 410 women (enhanced recovery after surgery, n = 196; conventional perioperative care, n = 214) met inclusion criteria. Following enhanced recovery after surgery implementation, same-day discharge rates increased by 9.4% (P = .001). Reductions in postoperative pain and nausea/vomiting represented the primary driving factor behind lower unplanned admissions. Higher preoperative antiemetic medication administration in the enhanced recovery after surgery group resulted in a 57% reduction in postanesthesia care unit antiemetics (P Conclusion Enhanced recovery after surgery implementation resulted in increased same-day discharge rates and improved perioperative outcomes without affecting 30 day morbidity in women undergoing laparoscopic minimally invasive nonhysterectomy gynecologic procedures.
Zero dark current in H2RG detectors: it is all multiplexer glow

We present the analysis of James Webb Space Telescope near-infrared H2RG detectors with a 5-μm cutoff, which shows that, at temperatures <60 K, there is no measurable dark current. Instead, the observed signal in dark exposures is almost entirely due to multiplexer glow that arises as each pixel is selected. We are able to separate the per-sample glow from the time-dependent dark current by comparing the observed signal in both continuous and sparsely sampled dark exposures. Such explicit tests are required to break the degeneracy between dark current and uniform amplifier glow. We show that the glow is lower within the regions of the detector that are missing the epoxy back fill (voids). We also find that the glow from each pixel extends out to a radius of several pixels. Because of the higher sampling frequency of subarray observations, the per-sample glow leads to a higher apparent dark current in subarray exposures. Finally, we show that the magnitude of the glow is affected by the pixel source follower current, the pixel clocking rate, and the number of outputs running in parallel. Our measurement of an insignificant dark current shows that the detector noise is no longer limited by the quality of the mercury cadmium telluride layer but instead by the multiplexer and readout electronics.
Structure-Guided Mutagenesis Alters Deubiquitinating Activity and Attenuates Pathogenesis of a Murine Coronavirus.

Coronaviruses express a multifunctional papain-like protease, termed papain-like protease 2 (PLP2). PLP2 acts as a protease that cleaves the viral replicase polyprotein and as a deubiquitinating (DUB) enzyme which removes ubiquitin (Ub) moieties from ubiquitin-conjugated proteins. Previous in vitro studies implicated PLP2/DUB activity as a negative regulator of the host interferon (IFN) response, but the role of DUB activity during virus infection was unknown. Here, we used X-ray structure-guided mutagenesis and functional studies to identify amino acid substitutions within the ubiquitin-binding surface of PLP2 that reduced DUB activity without affecting polyprotein processing activity. We engineered a DUB mutation (Asp1772 to Ala) into a murine coronavirus and evaluated the replication and pathogenesis of the DUB mutant virus (DUBmut) in cultured macrophages and in mice. We found that the DUBmut virus replicates similarly to the wild-type (WT) virus in cultured cells, but the DUBmut virus activates an IFN response at earlier times compared to the wild-type virus infection in macrophages, consistent with DUB activity negatively regulating the IFN response. We compared the pathogenesis of the DUBmut virus to that of the wild-type virus and found that the DUBmut-infected mice had a statistically significant reduction (P < 0.05) in viral titer in liver and spleen at day 5 postinfection (d p.i.), although both wild-type and DUBmut virus infections resulted in similar liver pathology. Overall, this study demonstrates that structure-guided mutagenesis aids the identification of critical determinants of the PLP2-ubiquitin complex and that PLP2/DUB activity plays a role as an interferon antagonist in coronavirus pathogenesis.IMPORTANCE Coronaviruses employ a genetic economy by encoding multifunctional proteins that function in viral replication and also modify the host environment to disarm the innate immune response. The coronavirus papain-like protease 2 (PLP2) domain possesses protease activity, which cleaves the viral replicase polyprotein, and also DUB activity (deconjugating ubiquitin/ubiquitin-like molecules from modified substrates) using identical catalytic residues. To separate the DUB activity from the protease activity, we employed a structure-guided mutagenesis approach and identified residues that are important for ubiquitin binding. We found that mutating the ubiquitin-binding residues results in a PLP2 that has reduced DUB activity but retains protease activity. We engineered a recombinant murine coronavirus to express the DUB mutant and showed that the DUB mutant virus activated an earlier type I interferon response in macrophages and exhibited reduced replication in mice. The results of this study demonstrate that PLP2/DUB is an interferon antagonist and a virulence trait of coronaviruses.
Branching Ratios, Radiative Lifetimes, and Transition Dipole Moments for YbOH

Medium resolution (Δν~ 3 GHz) laser-induced fluorescence (LIF) excitation spectra of a rotationally cold sample of YbOH in the 17300-17950 cm-1 range have been recorded using two-dimensional (excit...
JunB Controls Intestinal Effector Programs in Regulatory T Cells.

Foxp3-expressing regulatory T (Treg) cells are critical mediators of immunological tolerance to both self and microbial antigens. Tregs activate context-dependent transcriptional programs to adapt effector function to specific tissues; however, the factors controlling tissue-specific gene expression in Tregs remain unclear. Here, we find that the AP-1 transcription factor JunB regulates the intestinal adaptation of Tregs by controlling select gene expression programs in multiple Treg subsets. Treg-specific ablation of JunB results in immune dysregulation characterized by enhanced colonic T helper cell accumulation and cytokine production. However, in contrast to its classical binding-partner BATF, JunB is dispensable for maintenance of effector Tregs as well as most specialized Treg subsets. In the Peyer’s patches, JunB activates a transcriptional program facilitating the maintenance of CD25- Tregs, leading to the complete loss of T follicular regulatory cells in the absence of JunB. This defect is compounded by loss of a separate effector program found in both major colonic Treg subsets that includes the cytolytic effector molecule granzyme B. Therefore, JunB is an essential regulator of intestinal Treg effector function through pleiotropic effects on gene expression.
Phonetic Feedback for Speech Enhancement with and Without Parallel Speech Data

While deep learning systems have gained significant ground in speech enhancement research, these systems have yet to make use of the full potential of deep learning systems to provide high-level feedback. In particular, phonetic feedback is rare in speech enhancement research even though it includes valuable top-down information. We use the technique of mimic loss to provide phonetic feedback to an off-the-shelf enhancement system, and find gains in objective intelligibility scores on CHiME-4 data. This technique takes a frozen acoustic model trained on clean speech to provide valuable feedback to the enhancement model, even in the case where no parallel speech data is available. Our work is one of the first to show intelligibility improvement for neural enhancement systems without parallel speech data, and we show phonetic feedback can improve a state-of-the-art neural enhancement system trained with parallel speech data.
Intra-striatal AAV2.retro administration leads to extensive retrograde transport in the rhesus macaque brain: implications for disease modeling and therapeutic development

Recently, AAV2.retro, a new capsid variant capable of efficient retrograde transport in brain, was generated in mice using a directed evolution approach. However, it remains unclear to what degree transport will be recapitulated in the substantially larger and more complex nonhuman primate (NHP) brain. Here, we compared the biodistribution of AAV2.retro with its parent serotype, AAV2, in adult macaques following delivery into the caudate and putamen, brain regions which comprise the striatum. While AAV2 transduction was primarily limited to the injected brain regions, AAV2.retro transduced cells in the striatum and in dozens of cortical and subcortical regions with known striatal afferents. We then evaluated the capability of AAV2.retro to deliver disease-related gene cargo to biologically-relevant NHP brain circuits by packaging a fragment of human mutant HTT, the causative gene mutation in Huntington's disease. Following intra-striatal delivery, pathological mHTT-positive protein aggregates were distributed widely among cognitive, motor, and limbic cortico-basal ganglia circuits. Together, these studies demonstrate strong retrograde transport of AAV2.retro in NHP brain, highlight its utility in developing novel NHP models of brain disease and suggest its potential for querying circuit function and delivering therapeutic genes in the brain, particularly where treating dysfunctional circuits, versus single brain regions, is warranted.
Agent-Level Pandemic Simulation (ALPS) for Analyzing Effects of Lockdown Measures

Abstract This paper develops an agent-level stochastic simulation model, termed RAW-ALPS, for simulating the spread of an epidemic in a confined community. The mechanism of transmission is agent-to-agent contact, using parameters reported for COVID-19 pandemic. When unconstrained, the agents follow independent random walks and catch infections via physical proximity with infected agents. The main goal of the RAW-ALPS simulation is to help quantify effects of preventive measures – timing and durations of lockdowns – on infections, fatalities and recoveries. Three types of lockdown measures are considered: (1) whole population (except essential workers), (2) only the infected agents, and (3) only the symptomatic agents. An infected agent under quarantine can only infect a co-inhabitant, thus causing a decline in infections during lockdowns. The model provides quantifications of changes in infection rates and casualties by imposition and maintenance of restrictive measures in place. The results show that the most effective use of lockdown measures is when all infected agents, including both symptomatic and asymptomatic, are quarantined, while allowing for free movements of all uninfected agents. This calls for regular and extensive testing of the population to isolate and restrict all infected agents.
11 – Rust Diseases

Publisher Summary This chapter discusses rust diseases. Rust diseases are caused by a specialized group of fungi called the rust fungi; these fungi are obligate parasites. Rust fungi have several spore stages in their life cycles. Rust fungi can infect leaves, twigs, branches, and the trunk. These fungi cause spots and blisters on leaves and cankers and cause galls on branches and the main stem. Rust diseases are separated into three categories: canker rusts, gall rusts, and leaf rusts. The chapter describes the life cycle of rust disease fungi. The life cycle of rust disease fungi includes five spore stages on two different hosts. Gall rusts and canker rusts are serious disease problems while leaf rusts are of minor importance. The chapter discusses the treatment of rust diseases. The alternate host such as currants and gooseberries should be removed in an approximate radius of 900 feet from any susceptible white pine tree. This local removal of the alternate host will prevent infection because the thin-walled spores that infect pine needles can only survive transport short distances in the air.
Quantification of Adeno-Associated Virus with Safe Nucleic Acid Dyes.

Adeno-associated virus (AAV) is the most commonly used viral vector for both biological and gene therapeutic applications. Although many methods have been developed to measure quantity attributes o...