cssi/SciSciGPT-SciSciNet huggingface dataset:
Synthetic and genomic regulatory elements reveal aspects of cis-regulatory grammar in mouse embryonic stem cells

Transcription factors are proteins that flip genetic switches; their role is to control when and where genes are active. They do this by binding to short stretches of DNA called cis-regulatory sequences. Each sequence can have several binding sites for different transcription factors, but it is largely unclear whether the transcription factors binding to the same regulatory sequence actually work together. It is possible that each transcription factor may work independently and there only needs to be critical mass of transcription factors bound to throw the genetic switch. If this is the case, the most important features of a cis-regulatory sequence should be the number of binding sites it contains, and how tightly the transcription factors bind to those sites. The more transcription factors and the more strongly they bind, the more active the gene should be. An alternative option is that certain transcription factors may work better together, enhancing each other's effects such that the total effect is more than the sum of its parts. If this is true, the order, orientation and spacing of the binding sites within a sequence should matter more than the number. One way to investigate to distinguish between these possibilities is to study mouse embryonic stem cells, which have a core set of four transcription factors. Looking directly at a real genome, however, can be confusing and it is difficult to measure the effects of different cis-regulatory sequences because genes differ in so many other ways. To tackle this problem, King et al. created a synthetic set of cis-regulatory sequences based on the four core transcription factors found in mouse stem cells. The synthetic set had every combination of two, three or four of the binding sites, with each site either facing forwards or backwards along the DNA strand. King et al. attached each of the synthetic cis-regulatory sequences to a reporter gene to find out how well each sequence performed. This revealed that the cis-regulatory sequences with the most binding sites and the tightest binding affinities work best, suggesting that transcription factors mainly work independently. There was evidence of some interaction between some transcription factors, because, of the synthetic sequences with four binding sites, some worked better than others, and there were patterns in the most effective binding site combinations. However, these effects were small and when King et al. went on to test sequences from the real mouse genome, the most important factor by far was the number of binding sites. Synthetic libraries of DNA sequences allow researchers to examine gene regulation more clearly than is possible in real genomes. Yet this approach does have its limitations and it is impossible to capture every type of cis-regulatory sequence in one library. The next step to extend this work is to combine the two approaches, taking sequences from the real genome and manipulating them one by one. This could help to unravel the rules that govern how cis-regulatory sequences work in real cells.
Sparse Multiresolution Representations With Adaptive Kernels

Reproducing kernel Hilbert spaces (RKHSs) are key components of non-parametric toolsused in signal processing, statistics, and machine learning. In this work, we aim to address three issues of the classical RKHS-based techniques. First, they require the RKHS to be known a priori , which is unrealistic in many applications. Furthermore, the choice of RKHS affects the shape and smoothness of the solution, thus impacting its performance. Second, RKHSs are ill-equipped to deal with heterogeneous degrees of smoothness, i.e., with functions that are smooth in some parts of their domain but vary rapidly in others. Finally, the computational complexity of evaluating the solution of these methods grows with the number of data points in the training sample, rendering these techniques infeasible for many applications. Although kernel learning, local kernel adaptation, and sparsity have been used to address these issues, many of these approaches are computationally intensive or forgo optimality guarantees. We tackle these problems by leveraging a novel integral representation of functions in RKHSs that allows for arbitrary centers and different kernels at each center. To address the complexity issue, we then write the function estimation problem as a sparse functional program that explicitly minimizes the support of the representation leading to low complexity solutions. Despite their non-convexity and infinite dimensionality, we show these problems can be solved exactly and efficiently by leveraging duality, and illustrate this new approach in simulated and real data.
Quantitative Immersability of Riemann Metrics and the Infinite Hierarchy of Prestrained Shell Models

We propose results that relate the following two contexts: For each n, we identify conditions for the validity of the scaling $$h^{2n}$$, in terms of the vanishing of Riemann curvatures of G up to appropriate orders, and in terms of the matched isometry expansions. We also establish the asymptotic behaviour of the minimizing immersions as $$h\rightarrow 0$$.
Rényi Differentially Private ADMM for Non-Smooth Regularized Optimization

In this paper we consider the problem of minimizing composite objective functions consisting of a convex differentiable loss function plus a non-smooth regularization term, such as $L_1$ norm or nuclear norm, under Renyi differential privacy (RDP). To solve the problem, we propose two stochastic alternating direction method of multipliers (ADMM) algorithms: ssADMM based on gradient perturbation and mpADMM based on output perturbation. Both algorithms decompose the original problem into sub-problems that have closed-form solutions. The first algorithm, ssADMM, applies the recent privacy amplification result for RDP to reduce the amount of noise to add. The second algorithm, mpADMM, numerically computes the sensitivity of ADMM variable updates and releases the updated parameter vector at the end of each epoch. We compare the performance of our algorithms with several baseline algorithms on both real and simulated datasets. Experimental results show that, in high privacy regimes (small e), ssADMM and mpADMM outperform baseline algorithms in terms of classification and feature selection performance, respectively.
Surrogate Optimal Control for Strategic Multi-Agent Systems

This paper studies how to design a platform to optimally control constrained multi-agent systems with a single coordinator and multiple strategic agents. In our setting, only the coordinator applies control inputs, however, it must do so based on information provided by the agents. One major challenge is that if the platform is not correctly designed then the agents may provide false information to the coordinator in order to achieve improved outcomes for themselves at the expense of the overall system efficiency. Here, we design an interaction mechanism between the agents and the coordinator such that the mechanism: ensures agents truthfully report their information, has low communication requirements, and leads to a control action that achieves efficiency by achieving a Nash equilibrium. We illustrate our proposed mechanism in a model predictive control (MPC) application involving heating, ventilation, air-conditioning (HVAC) control by a building manager of an apartment building.
The realistic job preview: Five key elements and their importance for research and practice

This paper suggests the way RJPs have been operationalized does not reflect the theoretical framework upon which they are based. Thus, it is suggested that the benefits of providing RJPs may be severely underestimated. We discuss five key elements of an RJP and suggest the best RJP source has not been utilized. In closing, specific suggestions for developing and providing RJPs are offered.
Acute Plasmodium Infection Promotes Interferon-Gamma-Dependent Resistance to Ebola Virus Infection.

Summary During the 2013–2016 Ebola virus (EBOV) epidemic, a significant number of patients admitted to Ebola treatment units were co-infected with Plasmodium falciparum, a predominant agent of malaria. However, there is no consensus on how malaria impacts EBOV infection. The effect of acute Plasmodium infection on EBOV challenge was investigated using mouse-adapted EBOV and a biosafety level 2 (BSL-2) model virus. We demonstrate that acute Plasmodium infection protects from lethal viral challenge, dependent upon interferon gamma (IFN-γ) elicited as a result of parasite infection. Plasmodium-infected mice lacking the IFN-γ receptor are not protected. Ex vivo incubation of naive human or mouse macrophages with sera from acutely parasitemic rodents or macaques programs a proinflammatory phenotype dependent on IFN-γ and renders cells resistant to EBOV infection. We conclude that acute Plasmodium infection can safeguard against EBOV by the production of protective IFN-γ. These findings have implications for anti-malaria therapies administered during episodic EBOV outbreaks in Africa.
An iterative method for classification of binary data

In today's data driven world, storing, processing, and gleaning insights from large-scale data are major challenges. Data compression is often required in order to store large amounts of high-dimensional data, and thus, efficient inference methods for analyzing compressed data are necessary. Building on a recently designed simple framework for classification using binary data, we demonstrate that one can improve classification accuracy of this approach through iterative applications whose output serves as input to the next application. As a side consequence, we show that the original framework can be used as a data preprocessing step to improve the performance of other methods, such as support vector machines. For several simple settings, we showcase the ability to obtain theoretical guarantees for the accuracy of the iterative classification method. The simplicity of the underlying classification framework makes it amenable to theoretical analysis and studying this approach will hopefully serve as a step toward developing theory for more sophisticated deep learning technologies.
Lipoproteins and Lipid Transport

Lipids are transported in blood plasma in association with proteins. Some polar lipids are transported by albumin (free fatty acids, bile acids) or by specific binding proteins (retinol-binding protein). Nonpolar lipids are transported in large complexes containing polar lipids and specific apoproteins (which serve functions beyond that of “packaged”). These complexes comprise the macromolecular particles that we generally think of when we apply the term “lipoproteins” to blood plasma and other extracellular fluids. These lipoproteins and their role in transport of nonpolar lipids are the subject of this short and eclectic review.
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

Recent progress in Natural Language Understanding (NLU) is driving fast-paced advances in Information Retrieval (IR), largely owed to fine-tuning deep language models (LMs) for document ranking. While remarkably effective, the ranking models based on these LMs increase computational cost by orders of magnitude over prior approaches, particularly as they must feed each query-document pair through a massive neural network to compute a single relevance score. To tackle this, we present ColBERT, a novel ranking model that adapts deep LMs (in particular, BERT) for efficient retrieval. ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity. By delaying and yet retaining this fine-granular interaction, ColBERT can leverage the expressiveness of deep LMs while simultaneously gaining the ability to pre-compute document representations offline, considerably speeding up query processing. Crucially, ColBERT's pruning-friendly interaction mechanism enables leveraging vector-similarity indexes for end-to-end retrieval directly from millions of documents. We extensively evaluate ColBERT using two recent passage search datasets. Results show that ColBERT's effectiveness is competitive with existing BERT-based models (and outperforms every non-BERT baseline), while executing two orders-of-magnitude faster and requiring up to four orders-of-magnitude fewer FLOPs per query.
Diagnostic Tests, Multiple†

In medical practice, diagnostic tests are rarely used in isolation. Typically, the evidence for forming a diagnosis comes from multiple sources, in the form of signs and symptoms and other patient characteristics, in addition to specific tests that are ordered to rule in or rule out a candidate diagnosis. Thus, the evidence from individual diagnostic tests must be used collectively in forming the diagnosis, and the statistical dependency of the information from these multiple sources becomes an important factor in assessing the weight of evidence. In this article, we consider the two settings in which data from many diagnostic tests are relevant. In the first, we consider discriminant analysis, where one wishes to develop diagnostic rules on the basis of an available data set encompassing information on multiple tests. In the second setting, we consider the problem of sequentially updating diagnostic probabilities as new tests are selected and performed in the course of making a diagnosis for an individual patient. Keywords: discriminant analysis; sequential testing
Measurement of Stratified Distributions of Dielectric Properties and Dependent Physical Parameters

Recent advances in ω-k (frequency-wavenumber) interdigital dielectrometry are described. Using this technology, information about the microstructure of dielectric materials is obtained by applying to the sensor-dielectric interface a spatially periodic electric potential swept in frequency from 0.005 Hz to 10,000 Hz. The penetration depth of the electric field is proportional to the spatial wavelength of the electric potential. Application of multi-wavelength electrode arrangements allows measurement of stratified distributions of complex dielectric permittivity. Calibration techniques serve to relate the distributed dielectric properties of materials to other physical variables, such as density, porosity, cracking, lamination, and diffusion of contaminants into the material. The specific problem treated in this paper is in the measurement of moisture concentration distribution in transformer pressboard during the diffusion of water molecules from ambient transformer oil. The output of interdigital sensors is strongly influenced by the microgranularity of the material’s surface. Although this dependence complicates interpretation of the measurements in some applications, the variation of the output may also be used to characterize the shape of the surface on the microscale.
Measurement Error in Epidemiologic Studies

This article concerns measurement error in epidemilogic studies, often called errors-in-variables. I discuss what measurement error is, its impact on parameter estimates and hypothesis tests, and how to perform statistical analyses that account for the measurement error. Keywords: measurement error; errors-in-variables; attenuation; misclassification; instrumental variables; latent variables; regression calibration; SIMEX (simulation extrapolation)
MYOPIC LOSS AVERSION AND THE EQUITY PREMIUM PUZZLE

The equity premium puzzle refers to the empirical fact that stocks have outperformed bonds over the last century by a surprisingly large margin. We offer a new explanation based on two behavioral concepts. First, investors are assumed to be "loss averse," meaning that they are distinctly more sensitive to losses than to gains. Second, even long-term investors are assumed to evaluate their portfolios frequently. We dub this combination "myopic loss aversion." Using simulations, we find that the size of the equity premium is consistent with the previously estimated parameters of prospect theory if investors evaluate their portfolios annually. There is an enormous discrepancy between the returns on stocks and fixed income securities. Since 1926 the annual real return on stocks has been about 7 percent, while the real return on treasury bills has been less than 1 percent. As demonstrated by Mehra and Prescott [1985], the combination of a high equity premium, a low risk-free rate, and smooth consumption is difficult to explain with plausible levels of investor risk aversion. Mehra and Prescott estimate that investors would have to have coefficients of relative risk aversion in excess of 30 to explain the historical equity premium, whereas previous estimates and theoretical arguments suggest that the actual figure is close to 1.0. We are left with a pair of questions: why is the equity premium so large, or why is anyone willing to hold bonds? The answer we propose in this paper is based on two concepts from the psychology of decision-making. The first concept is loss aversion. Loss aversion refers to the tendency for individuals to be more sensitive to reductions in their levels of well-being than to increases. The concept plays a central role in Kahneman and Tversky's [1979] descriptive theory of decision-making under
Instability of the flow of two immiscible liquids with different viscosities in a pipe

We study the flow of two immiscible fluids of different viscosities and equal density through a pipe under a pressure gradient. This problem has a continuum of solutions corresponding to arbitrarily prescribed interface shapes. The question therefore arises as to which of these solutions are stable and thus observable. Experiments have shown a tendency for the thinner fluid to encapsulate the thicker one. This has been ‘explained’ by the viscous-dissipation principle, which postulates that the amount of viscous dissipation is minimized for a given flow rate. For a circular pipe, this predicts a concentric configuration with the more viscous fluid located at the core. A linear stability analysis, which is carried out numerically, shows that while this configuration is stable when the more viscous fluid occupies most of the pipe, it is not stable when there is more of the thin fluid. Therefore the dissipation principle does not always hold, and the volume ratio is a crucial factor.
Chapter 22 – Breastfeeding Support Groups and Community Resources

Abstract Breastfeeding success requires support for the mother from individuals, groups, and organizations. Peer breastfeeding support is important in any of its forms—personal, group, and professional (e.g., lactation consultants). The needs of breastfeeding mothers and their families call for the continued growth and development of governmental and nongovernmental organizations for the protection, promotion, and support of breastfeeding.
Evidence of an Unusual Poly(A) RNA Signature Detected by High-Throughput Chemical Mapping

Homopolymeric adenosine RNA plays numerous roles in both cells and noncellular genetic material. We report herein an unusual poly(A) signature in chemical mapping data generated by the Eterna Massi...
Scalable, two‐stage, autoinduction of recombinant protein expression in E. coli utilizing phosphate depletion

We report the scalable production of recombinant proteins in Escherichia coli, reliant on tightly controlled autoinduction, triggered by phosphate depletion in the stationary phase. The method, reliant on engineered strains and plasmids, enables improved protein expression across scales. Expression levels using this approach have reached as high as 55% of the total cellular protein. The initial use of the method in instrumented fed-batch fermentations enables cell densities of ∼30 gCDW/L and protein titers up to 8.1 ± 0.7 g/L (∼270 mg/gCDW). The process has also been adapted to an optimized autoinduction media, enabling routine batch production at culture volumes of 20 μl (384-well plates), 100 μl (96-well plates), 20 ml, and 100 ml. In batch cultures, cell densities routinely reach ∼5-7 gCDW/L, offering protein titers above 2 g/L. The methodology has been validated with a set of diverse heterologous proteins and is of general use for the facile optimization of routine protein expression from high throughput screens to fed-batch fermentation.
The effects of direct brain stimulation in humans depend on frequency, amplitude, and white-matter proximity.

Abstract Background Researchers have used direct electrical brain stimulation to treat a range of neurological and psychiatric disorders. However, for brain stimulation to be maximally effective, clinicians and researchers should optimize stimulation parameters according to desired outcomes. Objective The goal of our large-scale study was to comprehensively evaluate the effects of stimulation at different parameters and locations on neuronal activity across the human brain. Methods To examine how different kinds of stimulation affect human brain activity, we compared the changes in neuronal activity that resulted from stimulation at a range of frequencies, amplitudes, and locations with direct human brain recordings. We recorded human brain activity directly with electrodes that were implanted in widespread regions across 106 neurosurgical epilepsy patients while systematically stimulating across a range of parameters and locations. Results Overall, stimulation most often had an inhibitory effect on neuronal activity, consistent with earlier work. When stimulation excited neuronal activity, it most often occurred from high-frequency stimulation. These effects were modulated by the location of the stimulating electrode, with stimulation sites near white matter more likely to cause excitation and sites near gray matter more likely to inhibit neuronal activity. Conclusion By characterizing how different stimulation parameters produced specific neuronal activity patterns on a large scale, our results provide an electrophysiological framework that clinicians and researchers may consider when designing stimulation protocols to cause precisely targeted changes in human brain activity.
Meta-learning for mixed linear regression

In modern supervised learning, there are a large number of tasks, but many of them are associated with only a small amount of labeled data. These include data from medical image processing and robotic interaction. Even though each individual task cannot be meaningfully trained in isolation, one seeks to meta-learn across the tasks from past experiences by exploiting some similarities. We study a fundamental question of interest: When can abundant tasks with small data compensate for lack of tasks with big data? We focus on a canonical scenario where each task is drawn from a mixture of $k$ linear regressions, and identify sufficient conditions for such a graceful exchange to hold; The total number of examples necessary with only small data tasks scales similarly as when big data tasks are available. To this end, we introduce a novel spectral approach and show that we can efficiently utilize small data tasks with the help of $\tilde\Omega(k^{3/2})$ medium data tasks each with $\tilde\Omega(k^{1/2})$ examples.
Tensor denoising and completion based on ordinal observations

Higher-order tensors arise frequently in applications such as neuroimaging, recommendation system, social network analysis, and psychological studies. We consider the problem of low-rank tensor estimation from possibly incomplete, ordinal-valued observations. Two related problems are studied, one on tensor denoising and the other on tensor completion. We propose a multi-linear cumulative link model, develop a rank-constrained M-estimator, and obtain theoretical accuracy guarantees. Our mean squared error bound enjoys a faster convergence rate than previous results, and we show that the proposed estimator is minimax optimal under the class of low-rank models. Furthermore, the procedure developed serves as an efficient completion method which guarantees consistent recovery of an order-$K$ $(d,\ldots,d)$-dimensional low-rank tensor using only $\tilde{\mathcal{O}}(Kd)$ noisy, quantized observations. We demonstrate the outperformance of our approach over previous methods on the tasks of clustering and collaborative filtering.
ACE2 and TMPRSS2 are expressed on the human ocular surface, suggesting susceptibility to SARS-CoV-2 infection.

Abstract Purpose Conjunctival signs and symptoms are observed in a subset of patients with COVID-19, and SARS-CoV-2 has been detected in tears, raising concerns regarding the eye both as a portal of entry and carrier of the virus. The purpose of this study was to determine whether ocular surface cells possess the key factors required for cellular susceptibility to SARS-CoV-2 entry/infection. Methods We analyzed human post-mortem eyes as well as surgical specimens for the expression of ACE2 (the receptor for SARS-CoV-2) and TMPRSS2, a cell surface-associated protease that facilitates viral entry following binding of the viral spike protein to ACE2. Results Across all eye specimens, immunohistochemical analysis revealed expression of ACE2 in the conjunctiva, limbus, and cornea, with especially prominent staining in the superficial conjunctival and corneal epithelial surface. Surgical conjunctival specimens also showed expression of ACE2 in the conjunctival epithelium, especially prominent in the superficial epithelium, as well as weak or focal expression in the substantia propria. All eye and conjunctival specimens also expressed TMPRSS2. Finally, Western blot analysis of protein lysates from human corneal epithelium obtained during refractive surgery confirmed expression of ACE2 and TMPRSS2. Conclusions Together, these results suggest that ocular surface cells including conjunctiva are susceptible to infection by SARS-CoV-2, and could therefore serve as a portal of entry as well as a reservoir for person-to-person transmission of this virus. This highlights the importance of safety practices including face masks and ocular contact precautions in preventing the spread of COVID-19 disease.
Learning Selection Strategies in Buchberger’s Algorithm

Studying the set of exact solutions of a system of polynomial equations largely depends on a single iterative algorithm, known as Buchberger's algorithm. Optimized versions of this algorithm are crucial for many computer algebra systems (e.g., Mathematica, Maple, Sage). We introduce a new approach to Buchberger's algorithm that uses reinforcement learning agents to perform S-pair selection, a key step in the algorithm. We then study how the difficulty of the problem depends on the choices of domain and distribution of polynomials, about which little is known. Finally, we train a policy model using proximal policy optimization (PPO) to learn S-pair selection strategies for random systems of binomial equations. In certain domains, the trained model outperforms state-of-the-art selection heuristics in total number of polynomial additions performed, which provides a proof-of-concept that recent developments in machine learning have the potential to improve performance of algorithms in symbolic computation.
The optimization of a jet turbojet engine by PSO and searching algorithms

The turbojet engine operates on the ideal Brayton cycle (gas turbine) and consists of six main parts: diffusers, compressors, combustion chambers, turbines, afterburners and nozzles. Using computer code writing in MATLAB software environment, exergy analysis on all selected turbojet engine components, exergy analysis on J85-GE-21 turbojet engine for selective height of 1000-8000 meters above sea level at speeds of 200 m/s and temperatures of 10°C, 20°C and 40°C have been provided and then, according to the system functions, the system is optimized based on the PSO method. For the purpose of optimization, variables of Mach number, efficiency of the compressor, turbine, nozzle and compressor pressure ratio are considered in the range of 0.6 to 1.4, 0.8 to 0.95, 0.8 to 0.95 and 7 to 10, respectively. The highest exergy efficiency of different parts of the engine at sea level with an inlet air velocity of 200 m/s corresponds to a diffuser with 73.1%. Then, the nozzle and combustion chamber are respectively 68.6% and 51.5%. The lowest exergy efficiency is related to compressor with 4%. After that, the afterburner is ranked second with 11.6%. Also, the values of entropy produced and the efficiency of the second law before optimization were 1176.99 and 479 w/k respectively and the same values after optimization were 1129 and 51.4 w/k respectively which is identified. After the optimization process, the amount of entropy produced is reduced and the efficiency of the second law of thermodynamics has increased.
An Exploratory Study of Argumentative Writing by Young Students: A transformer-based Approach

We present a computational exploration of argument critique writing by young students. Middle school students were asked to criticize an argument presented in the prompt, focusing on identifying and explaining the reasoning flaws. This task resembles an established college-level argument critique task. Lexical and discourse features that utilize detailed domain knowledge to identify critiques exist for the college task but do not perform well on the young students’ data. Instead, transformer-based architecture (e.g., BERT) fine-tuned on a large corpus of critique essays from the college task performs much better (over 20% improvement in F1 score). Analysis of the performance of various configurations of the system suggests that while children’s writing does not exhibit the standard discourse structure of an argumentative essay, it does share basic local sequential structures with the more mature writers.
The matching number and Hamiltonicity of graphs

The matching number of a graph G is the size of a maximum matching in the graph. In this note, we present a sufficient condition involving the matching number for the Hamiltonicity of graphs.
ebony Affects Pigmentation Divergence and Cuticular Hydrocarbons in Drosophila americana and D. novamexicana

Drosophila pigmentation has been a fruitful model system for understanding the genetic and developmental mechanisms underlying phenotypic evolution. For example, prior work has shown that divergence of the tan gene contributes to pigmentation differences between two members of the virilis group: Drosophila novamexicana, which has a light yellow body color, and D. americana, which has a dark brown body color. Quantitative trait locus (QTL) mapping and expression analysis has suggested that divergence of the ebony gene might also contribute to pigmentation differences between these two species. Here, we directly test this hypothesis by using CRISPR/Cas9 genome editing to generate ebony null mutants in D. americana and D. novamexicana and then using reciprocal hemizygosity testing to compare the effects of each species’ ebony allele on pigmentation. We find that divergence of ebony does indeed contribute to the pigmentation divergence between species, with effects on both the overall body color as well as a difference in pigmentation along the dorsal abdominal midline. Motivated by recent work in D. melanogaster, we also used the ebony null mutants to test for effects of ebony on cuticular hydrocarbon (CHC) profiles. We found that ebony affects CHC abundance in both species, but does not contribute to qualitative differences in the CHC profiles between these two species. Additional transgenic resources for working with D. americana and D. novamexicana, such as white mutants of both species and yellow mutants in D. novamexicana, were generated in the course of this work and are also described. Taken together, this study advances our understanding of loci contributing to phenotypic divergence and illustrates how the latest genome editing tools can be used for functional testing in non-model species.
Keep It Real: a Window to Real Reality in Virtual Reality

This paper proposed a new interaction paradigm in the virtual reality (VR) environments, which consists of a virtual mirror or window projected onto a virtual surface, representing the correct perspective geometry of a mirror or window reflecting the real world. This technique can be applied to various videos, live streaming apps, augmented and virtual reality settings to provide an interactive and immersive user experience. To support such a perspective-accurate representation, we implemented computer vision algorithms for feature detection and correspondence matching. To constrain the solutions, we incorporated an automatically tuning scaling factor upon the homography transform matrix such that each image frame follows a smooth transition with the user in sight. The system is a real-time rendering framework where users can engage their real-life presence with the virtual space.
Investigating a spectral deception loss metric for training machine learning-based evasion attacks

Adversarial evasion attacks have been very successful in causing poor performance in a wide variety of machine learning applications. One such application is radio frequency spectrum sensing. While evasion attacks have proven particularly successful in this area, they have done so at the detriment of the signal's intended purpose. More specifically for real-world applications of interest, the resulting perturbed signal that is transmitted to evade an eavesdropper must not deviate far from the original signal, less the intended information is destroyed. Recent work by the authors and others has demonstrated an attack framework that allows for intelligent balancing between these conflicting goals of evasion and communication. However, while these methodologies consider creating adversarial signals that minimize communications degradation, they have been shown to do so at the expense of the spectral shape of the signal. This opens the adversarial signal up to defenses at the eavesdropper such as filtering, which could render the attack ineffective. To remedy this, this work introduces a new spectral deception loss metric that can be implemented during the training process to force the spectral shape to be more in-line with the original signal. As an initial proof of concept, a variety of methods are presented that provide a starting point for this proposed loss. Through performance analysis, it is shown that these techniques are effective in controlling the shape of the adversarial signal.
COVID-19 and Inequity: a Comparative Spatial Analysis of New York City and Chicago Hot Spots.

There have been numerous reports that the impact of the ongoing COVID-19 epidemic has disproportionately impacted traditionally vulnerable communities associated with neighborhood attributes, such as the proportion of racial and ethnic minorities, migrants, and the lower income households. The goal of this ecological cross-sectional study is to examine the demographic and economic nature of spatial hot and cold spots of SARS-CoV-2 rates in New York City and Chicago as of April 13, 2020 using data from the New York City Department of Health and Mental Hygiene, Illinois Department of Public Health, and the American Community Survey. In both cities, cold spots (clusters of low SARS-CoV-2 rate ZIP code tabulation areas as identified by the Getis-Ord (GI*) statistic) demonstrated social determinants of health characteristics typically associated with better health outcomes and the ability to maintain physical distance (“social distancing”). These neighborhoods tended to be wealthier, have higher educational attainment, higher proportions of non-Hispanic white residents, and more workers in managerial occupations (all p values < 0.01 using Wilcoxon two-sample test). Hot spots (clusters of high SARS-CoV-2 rate ZIP code tabulation areas) had similarities as well, such as lower rates of college graduates and higher proportions of people of color. It also appears that household size (more people per household), rather than overall population density (people per square mile), is more strongly associated with hot spots. New York City had an average of 3.0 people per household in hot spots and 2.1 in cold spots (p < 0.01), and Chicago had 2.8 people per household in hot spots and 2.0 in cold spots (p = 0.03). However, hotspots were located in neighborhoods that were significantly less dense (New York City: 22,900 people per square mile in hot spots and 68,900 in cold spots (p < 0.01); Chicago: 10,000 people per square mile in hot spots and 23,400 in cold spots (p = 0.03)). Findings suggest important differences between the cities’ hot spots as well. NYC hot spots can be described as working-class and middle-income communities, perhaps indicative of greater concentrations of service workers and other occupations (including those classified as “essential services” during the pandemic) that may not require a college degree but pay wages above poverty levels. Chicago’s hot spot neighborhoods, on the other hand, are among the city’s most vulnerable, low-income neighborhoods with extremely high rates of poverty, unemployment, and non-Hispanic Black residents.
Computationally-Guided Investigation of Dual Amine/pi Lewis Acid Catalysts for Direct Additions of Aldehydes and Ketones to Unactivated Alkenes and Alkynes

Dual amine/pi Lewis acid catalyst systems have been reported for intramolecular direct additions of aldehydes/ketones to unactivated alkynes and occasionally alkenes, but related intermolecular reactions are rare and not presently of significant synthetic utility, likely due to undesired coordination of enamine intermediates to the metal catalyst. We reasoned that bulky metal ligands and bulky amine catalysts could minimize catalyst poisoning and could facilitate certain examples of direct intermolecular additions of aldehyde/ketones to alkenes/alkynes. Density Functional Theory (DFT) calculations were performed that suggested that PyBOX-Pt(II) catalysts for alkene/alkyne activation could be combined with MacMillan’s imidazolidinone organocatalyst for aldehyde/ketone activation to facilitate desirable C-C bond formations, and certain reactions were calculated to be more exergonic than catalyst poisoning pathways. As calculated, preformed enamines generated from the MacMillan imidazolidinone did not displace ethylene from a biscationic (t-Bu)PyBOX-Pt2+complex, but neither were the desired C-C bond formations observed under several different conditions.
A multicenter, multidisciplinary evaluation of 1701 healthcare professional students' LGBT cultural competency: Comparisons between dental, medical, occupational therapy, pharmacy, physical therapy, physician assistant, and social work students.

Background Efforts to characterize healthcare professional students’ lesbian, gay, bisexual, and transgender (LGBT) cultural competency are necessary to recommend educational initiatives. Very few studies have evaluated LGBT cultural competency across multiple healthcare disciplines, and no known studies have included students of other healthcare disciplines such as occupational therapy, pharmacy, physical therapy, and physician assistant. Methods Healthcare professional students (N = 1701) at three universities across the United States completed a survey consisting of demographics, experiential variables (i.e., LGBT patients and LGBT curricular hours), and the 7-point Likert LGBT-Development of Clinical Skills Scale (LGBT-DOCSS). LGBT-DOCSS scores, annual LGBT patients, and annual LGBT curricular hours were compared across healthcare disciplines. Results While students reported very high Attitudinal Awareness (M = 6.48, SD = 0.92), they endorsed moderate Basic Knowledge (M = 5.54, SD = 1.16) and low Clinical Preparedness (M = 3.78, SD = 1.28). After controlling for several demographic and experiential variables, there were significant differences among healthcare disciplines on LGBT-DOCSS scores, with social work students reporting the highest on all scores, and dental students reporting the lowest on all scores except Clinical Preparedness. There were also significant differences among healthcare disciplines on annual LGBT patients [mean range: 0.57 (dental) to 7.59 (physician assistant)] and annual LGBT curricular hours [mean range: 0.51 (occupational therapy) to 5.64 (social work)]. Experiential variables were significant predictors for Overall LGBT-DOCSS, Clinical Preparedness, and Basic Knowledge (all p < 0.001); LGBT patients was also a significant predictor for Attitudinal Awareness (p < 0.05). Conclusions Taken together, significant differences in LGBT cultural competency exist across healthcare disciplines, which may result from inadequate experiences with LGBT patients and LGBT curricular education. Future efforts should consider increasing LGBT patient contact hours and LGBT formal education hours to enhance healthcare students’ LGBT cultural competency.
Mass Error-Correction Codes for Polymer-Based Data Storage

We consider the problem of correcting mass readout errors in information encoded in binary polymer strings. Our work builds on results for string reconstruction problems using composition multisets [1] and the unique string reconstruction framework proposed in [2]. Binary polymer-based data storage systems [3] operate by designing two molecules of significantly different masses to represent the symbols {0,1} and perform readouts through noisy tandem mass spectrometry. Tandem mass spectrometers fragment the strings to be read into shorter substrings and only report their masses, often with errors due to imprecise ionization. Modeling the fragmentation process output in terms of composition multisets allows for designing asymptotically optimal codes capable of unique reconstruction and the correction of a single mass error [2] through the use of derivatives of Catalan paths. Nevertheless, no solutions for multiple-mass error-corrections are currently known. Our work addresses this issue by describing the first multiple-error correction codes that use the polynomial factorization approach for the Turnpike problem [4] and the related factorization described in [1]. Adding Reed-Solomon type coding redundancy into the corresponding polynomials allows for correcting t mass errors in polynomial time using ${\mathcal{O}}\left( {{t^2}\log k} \right)$ redundant bits, where k is the information string length. The redundancy can be improved to ${\mathcal{O}}(t + \log k)$. However, no decoding algorithm that runs polynomial-time in both t and n for this scheme are currently known, where n is the length of the coded string.
Broadcasting on trees near criticality

We revisit the problem of broadcasting on d-ary trees: starting from a Bernoulli(1/2) random variable X 0 at a root vertex, each vertex forwards its value across binary symmetric channels BSC δ to d descendants. The goal is to reconstruct X 0 given the vector X Lh of values of all variables at depth h. It is well known that reconstruction (better than a random guess) is possible as h →∞ if and only if δ c (d). In this paper, we study the behavior of the mutual information and the probability of error when δ is slightly subcritical. The innovation of our work is application of the recently introduced "less-noisy" channel comparison techniques. For example, we are able to derive the positive part of the phase transition (reconstructability when δ c ) using purely information-theoretic ideas. This is in contrast with previous derivations, which explicitly analyze distribution of the Hamming weight of X Lh (a so-called Kesten-Stigum bound).
Augmented Lagrangian optimization under fixed-point arithmetic

Abstract In this paper, we propose an inexact Augmented Lagrangian Method (ALM) for the optimization of convex and nonsmooth objective functions subject to linear equality constraints and box constraints where errors are due to fixed-point data. To prevent data overflow we also introduce a projection operation in the multiplier update. We analyze theoretically the proposed algorithm and provide convergence rate results and bounds on the accuracy of the optimal solution. Since iterative methods are often needed to solve the primal subproblem in ALM, we also propose an early stopping criterion that is simple to implement on embedded platforms, can be used for problems that are not strongly convex, and guarantees the precision of the primal update. To the best of our knowledge, this is the first fixed-point ALM that can handle non-smooth problems, data overflow, and can efficiently and systematically utilize iterative solvers in the primal update. Numerical simulation studies on a logistic regression problem are presented that illustrate the proposed method.
Transformative school-community collaboration as a positive school climate to prevent school absenteeism.

School absenteeism has become a prevalent problem that affects student development and future societies across the world. We examined whether and how the framework for transformative school-community collaboration (TSCC) can be utilized to effectively reduce school absenteeism. To achieve this goal, we analyzed clustered data involving 3428 students within 14 schools that collaborated with communities in providing out-of-school time programs. A generalized ordered logit analysis with clustered standard errors showed that overall TSCC significantly decreased the likelihood of students' school absenteeism. Democratic and empowering structures in the collaboration were particularly significant for reducing the higher level of school absenteeism. We conclude our article with practice implications to translate the core dimensions of TSCC into effective practice.
Once Racialized, Now “Immigrationized”? Explaining the Immigration-Welfare Link in American Public Opinion

Recent research claims that American public opinion about welfare, long racialized, has become “immigrationized,” or formed on the basis of people’s feelings about immigration. However, the evidenc...
Single-axon level automatic segmentation and feature extraction from immuhistochemical images of peripheral nerves

Quantitative descriptions of the morphology and structure of peripheral nerves is central in the development of bioelectronic devices interfacing the nerves. While histological procedures and microscopy techniques yield high-resolution detailed images of individual axons, automated methods to extract relevant information at the single-axon level are not widely available. We implemented a segmentation algorithm that allows for subsequent feature extraction in immunohistochemistry (IHC) images of peripheral nerves at the single fiber scale. These features include short and long cross-sectional diameters, area, perimeter, thickness of surrounding myelin and polar coordinates of single axons within a nerve or nerve fascicle. We evaluated the performance of our algorithm using manually annotated IHC images of 27 fascicles of the swine cervical vagus; the accuracy of single-axon detection was 82%, and of the classification of fiber myelination was 89%.
Monosynaptic Projections to Excitatory and Inhibitory preBötzinger Complex Neurons

The key driver of breathing rhythm is the preBotzinger Complex (preBotC) whose activity is modulated by various functional inputs, e.g., volitional, physiological, and emotional. While the preBotC is highly interconnected with other regions of the breathing central pattern generator (bCPG) in the brainstem, there is no data about the direct projections to either excitatory and inhibitory preBotC subpopulations from other elements of the bCPG or from suprapontine regions. Using modified rabies tracing, we identified neurons throughout the brain that send monosynaptic projections to identified excitatory and inhibitory preBotC neurons in mice. Within the brainstem, neurons from sites in the bCPG, including the contralateral preBotC, Botzinger Complex, the nucleus of the solitary tract (NTS), parafacial region (pF L /pF V ), and parabrachial nuclei (PB), send direct projections to both excitatory and inhibitory preBotC neurons. Suprapontine inputs to the excitatory and inhibitory preBotC neurons include the superior colliculus, red nucleus, amygdala, hypothalamus, and cortex; these projections represent potential direct pathways for volitional, emotional, and physiological control of breathing.