Miracle-dz/UniFAR-FTUs huggingface dataset:
T‐Cell‐Mimicking Nanoparticles Can Neutralize HIV Infectivity

To improve human immunodeficiency virus (HIV) treatment and prevention, therapeutic strategies that can provide effective and broad‐spectrum neutralization against viral infection are highly desirable. Inspired by recent advances of cell‐membrane coating technology, herein, plasma membranes of CD4+ T cells are collected and coated onto polymeric cores. The resulting T‐cell‐membrane‐coated nanoparticles (denoted as “TNPs”) inherit T cell surface antigens critical for HIV binding, such as CD4 receptor and CCR5 or CXCR4 coreceptors. The TNPs act as decoys for viral attack and neutralize HIV by diverting the viruses away from their intended host targets. This decoy strategy, which simulates host cell functions for viral neutralization rather than directly suppressing viral replication machinery, has the potential to overcome HIV genetic diversity while not eliciting high selective pressure. In this study, it is demonstrated that TNPs selectively bind with gp120, a key envelope glycoprotein of HIV, and inhibit gp120‐induced killing of bystander CD4+ T cells. Furthermore, when added to HIV viruses, TNPs effectively neutralize the viral infection of peripheral mononuclear blood cells and human‐monocyte‐derived macrophages in a dose‐dependent manner. Overall, by leveraging natural T cell functions, TNPs show great potential as a new therapeutic agent against HIV infection.
APPIAN: Automated Pipeline for PET Image Analysis

APPIAN is an automated pipeline for user-friendly and reproducible analysis of positron emission tomography (PET) images with the aim of automating all processing steps up to the statistical analysis of measures derived from the final output images. The three primary processing steps are coregistration of PET images to T1-weighted magnetic resonance (MR) images, partial-volume correction (PVC), and quantification with tracer kinetic modeling. While there are alternate open-source PET pipelines, none offers all of the features necessary for making automated PET analysis as reliably, flexibly and easily extendible as possible. To this end, a novel method for automated quality control (QC) has been designed to facilitate reliable, reproducible research by helping users verify that each processing stage has been performed as expected. Additionally, a web browser-based GUI has been implemented to allow both the 3D visualization of the output images, as well as plots describing the quantitative results of the analyses performed by the pipeline. APPIAN also uses flexible region of interest (ROI) definition—with both volumetric and, optionally, surface-based ROI—to allow users to analyze data from a wide variety of experimental paradigms, e.g., longitudinal lesion studies, large cross-sectional population studies, multi-factorial experimental designs, etc. Finally, APPIAN is designed to be modular so that users can easily test new algorithms for PVC or quantification or add entirely new analyses to the basic pipeline. We validate the accuracy of APPIAN against the Monte-Carlo simulated SORTEO database and show that, after PVC, APPIAN recovers radiotracer concentrations within 93–100% accuracy.
Stability limit of human-in-the-loop model reference adaptive control architectures

ABSTRACT Model reference adaptive control (MRAC) offers mathematical and design tools to effectively cope with many challenges of real-world control problems such as exogenous disturbances, system uncertainties and degraded modes of operations. On the other hand, when faced with human-in-the-loop settings, these controllers can lead to unstable system trajectories in certain applications. To establish an understanding of stability limitations of MRAC architectures in the presence of humans, here a mathematical framework is developed whereby an MRAC is designed in conjunction with a class of linear human models including human reaction delays. This framework is then used to reveal, through stability analysis tools, the stability limit of the MRAC–human closed-loop system and the range of model parameters respecting this limit. An illustrative numerical example of an adaptive flight control application with a Neal–Smith pilot model is presented to demonstrate the effectiveness of developed approaches.
New Data Completing the Spectrum of the Ma, RMia, and RMja Genes for Resistance to Root-Knot Nematodes (Meloidogyne spp.) in Prunus.

Root-knot nematodes (RKN) (Meloidogyne spp.) are worldwide pests that affect a considerable number of plants, among which stone fruit (Prunus spp.) are severely attacked. Prevalent RKN species are Meloidogyne arenaria, M. incognita, and M. javanica in stone fruit but the emergent M. ethiopica and M. enterolobii are also reported to challenge perennial crops. In Prunus spp. , the complete-spectrum resistance (R) gene Ma from plum and the more restricted-spectrum R genes RMia from peach and RMja from almond completely inhibit nematode multiplication and gall formation of the RKN species that they control. This study aimed to update the resistance spectra of these three major genes by evaluating their activity toward one isolate of the yet-untested RKN species mentioned above. To state whether a given gene controls a particular species, the principle of our experiment was to genotype with appropriate markers a number of individuals segregating for this gene and then to phenotype these individuals. A perfect matching of the genotype and the phenotype of individuals indicates that the gene of interest is active against and, thus, controls the corresponding isolate of this RKN species. Segregating materials used were an Ma F1 plum progeny, an RMia F2 peach progeny, and an RMja F2 almond progeny. In addition to previous data, our results establish a clear spectrum for each of the three genes toward isolates from both the three prevalent species and the two emerging species. Ultimately, our results reveal that (i) Ma controls all of them, (ii) RMja controls all species except M. incognita and M. floridensis, and (iii) RMia controls M. arenaria, M. incognita, and M. ethiopica but not M. javanica or M. enterolobii. Our data should have wide implications for RKN resistance management and breeding and for deciphering the molecular mechanisms of the spectrum of RKN R genes.
Avoidance response by shrimps to a copper gradient: Does high population density prevent avoidance of contamination?

Bioassays using the nonforced exposure approach have been shown to be a relevant tool that might complement the traditional ecotoxicological risk assessment. Because the nonforced exposure approach is based on spatial displacement of organisms and the consequent habitat selection processes, the population density might play an important role in the decision to avoid or prefer an ecosystem. Therefore, the aim of the present study was to assess if the avoidance response to contamination, measured in a nonforced exposure system, is density‐dependent and how determinant contamination could be for the habitat‐selection process in comparison with the population density. The freshwater shrimp Atyaephyra desmarestii was exposed to a copper gradient in a nonforced exposure system formed by 7 interconnected compartments (total volume 600 mL), which contained different copper concentrations. The density treatments used were 3, 5, and 10 organisms per compartment, corresponding to 0.5, 0.8, and 1.7 organisms per 100 mL, respectively. Clearly, the avoidance response to copper was more intense in the population with the lower density: the highest population density showed the lowest avoidance. The concentrations that triggered an avoidance of 50% of the population were 47, 134, and 163 μg L−1. In summary, it was observed that shrimps were able to detect and avoid potentially toxic copper concentrations but that the avoidance response was affected by population density. Environ Toxicol Chem 2018;37:3095–3101. © 2018 SETAC
Neural Networks with Structural Resistance to Adversarial Attacks

In adversarial attacks to machine-learning classifiers, small perturbations are added to input that is correctly classified. The perturbations yield adversarial examples, which are virtually indistinguishable from the unperturbed input, and yet are misclassified. In standard neural networks used for deep learning, attackers can craft adversarial examples from most input to cause a misclassification of their choice. We introduce a new type of network units, called RBFI units, whose non-linear structure makes them inherently resistant to adversarial attacks. On permutation-invariant MNIST, in absence of adversarial attacks, networks using RBFI units match the performance of networks using sigmoid units, and are slightly below the accuracy of networks with ReLU units. When subjected to adversarial attacks, networks with RBFI units retain accuracies above 90% for attacks that degrade the accuracy of networks with ReLU or sigmoid units to below 2%. RBFI networks trained with regular input are superior in their resistance to adversarial attacks even to ReLU and sigmoid networks trained with the help of adversarial examples. The non-linear structure of RBFI units makes them difficult to train using standard gradient descent. We show that networks of RBFI units can be efficiently trained to high accuracies using pseudogradients, computed using functions especially crafted to facilitate learning instead of their true derivatives. We show that the use of pseudogradients makes training deep RBFI networks practical, and we compare several structural alternatives of RBFI networks for their accuracy.
Path Planning and Controlled Crash Landing of a Quadcopter in case of a Rotor Failure

This paper presents a framework for controlled emergency landing of a quadcopter, experiencing a rotor failure, away from sensitive areas. A complete mathematical model capturing the dynamics of the system is presented that takes the asymmetrical aerodynamic load on the propellers into account. An equilibrium state of the system is calculated around which a linear time-invariant control strategy is developed to stabilize the system. By utilizing the proposed model, a specific configuration for a quadcopter is introduced that leads to the minimum power consumption during a yaw-rate-resolved hovering after a rotor failure. Furthermore, given a 3D representation of the environment, an optimal flight trajectory towards a safe crash landing spot, while avoiding collision with obstacles, is developed using an RRT* approach. The cost function for determining the best landing spot consists of: (i) finding the safest landing spot with the largest clearance from the obstacles; and (ii) finding the most energy-efficient trajectory towards the landing spot. The performance of the proposed framework is tested via simulations.
Genito-Pelvic Pain Through a Dyadic Lens: Moving Toward an Interpersonal Emotion Regulation Model of Women’s Sexual Dysfunction

Researchers and clinicians alike widely acknowledge the inherently interpersonal nature of women’s sexual dysfunctions given that both partners impact and are impacted by these difficulties. Yet theoretical models for understanding the role of interpersonal factors in women’s sexual dysfunctions are severely lacking and have the potential to guide future research and inform more effective interventions. The most widely studied sexual dysfunction in women that has espoused a dyadic approach by including both members of affected couples is genito-pelvic pain/penetration disorder (GPPPD). In this article we use the example of GPPPD to introduce a novel interpersonal emotion regulation model of women’s sexual dysfunction. We first review current knowledge regarding distal and proximal interpersonal factors in GPPPD. Then, we describe our theoretical model and consider relevant pain and sex-related research on emotion regulation processes—emotional awareness, expression, and experience—in the context of GPPPD, including sexual function, satisfaction, and distress. Next, we review how existing theories from the fields of chronic pain and sex and relationships research have informed our model and how our model further builds on them. Finally, we discuss the implications of our model and its applications, including to other sexual dysfunctions in women.
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach

Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is to output high-accuracy posterior mean and uncertainty estimates. Classical Monte Carlo methods, particularly Markov Chain Monte Carlo, remain the gold standard for approximate Bayesian inference because they have a robust finite-sample theory and reliable convergence diagnostics. However, alternative methods, which are more scalable or apply to problems where Markov Chain Monte Carlo cannot be used, lack the same finite-data approximation theory and tools for evaluating their accuracy. In this work, we develop a flexible new approach to bounding the error of mean and uncertainty estimates of scalable inference algorithms. Our strategy is to control the estimation errors in terms of Wasserstein distance, then bound the Wasserstein distance via a generalized notion of Fisher distance. Unlike computing the Wasserstein distance, which requires access to the normalized posterior distribution, the Fisher distance is tractable to compute because it requires access only to the gradient of the log posterior density. We demonstrate the usefulness of our Fisher distance approach by deriving bounds on the Wasserstein error of the Laplace approximation and Hilbert coresets. We anticipate that our approach will be applicable to many other approximate inference methods such as the integrated Laplace approximation, variational inference, and approximate Bayesian computation
Designing Warnings to Reduce Identity Disclosure

ABSTRACT The objective of this research was to identify aspects of warnings that will reduce online disclosure of personal information, specifically aspects that people do not consider sensitive. The vast majority of online commercial websites collect personal data, and many consumers report cybercrime events, such that identity theft is a significant risk. Most people can be uniquely identified by the combination of their birth date, age, and gender. This is problematic, since people do not believe these identity elements to be important to safeguard. Participants were asked to provide personal information to receive an online insurance quote. Participants in experimental conditions were warned not to disclose their date of birth. The experimental manipulations were (a) the vividness of the warnings and (b) whether an alternative to disclosure was recommended. Analyses indicated that providing an alternative to disclosure proved to be an effective strategy to reduce disclosure. Personal information disclosure can be reduced through warnings, but warnings need to be carefully designed and tested. Clear recommendations for alternative behavior may be especially effective. Designers of warnings and alerts can use the principles identified in this research to make their messages more effective.
Transcriptional profiling reveals that a MYB transcription factor MsMYB4 contributes to the salinity stress response of alfalfa

MYB transcription factors are important regulators of the plant response to abiotic stress. Their participation in the salinity stress of the key forage legume species alfalfa (Medicago sativa) was investigated here by comparing the transcriptomes of the two cultivars Dryland (DL) and Sundory (SD), which differed with respect to their ability to tolerate salinity stress. When challenged by the stress, DL plants were better able than SD ones to scavenge reactive oxygen species. A large number of genes encoding transcription regulators, signal transducers and proteins involved in both primary and secondary metabolism were differentially transcribed in the two cultivars, especially when plants were subjected to salinity stress. The set of induced genes included 17 MYB family of transcription factors, all of which were subsequently isolated. The effect of constitutively expressing these genes on the salinity tolerance expressed by Arabidopsis thaliana was investigated. The introduction of MsMYB4 significantly increased the plants’ salinity tolerance in an abscisic acid-dependent manner. A sub-cellular localization experiment and a transactivation assay indicated that MsMYB4 was deposited in the nucleus and was able to activate transcription in yeast. Based on this information, we propose that the MsMYB4 products is likely directly involved in alfalfa’s response to salinity stress.
UCYN‐A3, a newly characterized open ocean sublineage of the symbiotic N2‐fixing cyanobacterium Candidatus Atelocyanobacterium thalassa

The symbiotic unicellular cyanobacterium Candidatus Atelocyanobacterium thalassa (UCYN-A) is one of the most abundant and widespread nitrogen (N2 )-fixing cyanobacteria in the ocean. Although it remains uncultivated, multiple sublineages have been detected based on partial nitrogenase (nifH) gene sequences, including the four most commonly detected sublineages UCYN-A1, UCYN-A2, UCYN-A3 and UCYN-A4. However, very little is known about UCYN-A3 beyond the nifH sequences from nifH gene diversity surveys. In this study, single cell sorting, DNA sequencing, qPCR and CARD-FISH assays revealed discrepancies involving the identification of sublineages, which led to new information on the diversity of the UCYN-A symbiosis. 16S rRNA and nifH gene sequencing on single sorted cells allowed us to identify the 16S rRNA gene of the uncharacterized UCYN-A3 sublineage. We designed new CARD-FISH probes that allowed us to distinguish and observe UCYN-A2 in a coastal location (SIO Pier; San Diego) and UCYN-A3 in an open ocean location (Station ALOHA; Hawaii). Moreover, we reconstructed about 13% of the UCYN-A3 genome from Tara Oceans metagenomic data. Finally, our findings unveil the UCYN-A3 symbiosis in open ocean waters suggesting that the different UCYN-A sublineages are distributed along different size fractions of the plankton defined by the cell-size ranges of their prymnesiophyte hosts.
Wholesale Pricing or Agency Pricing on Online Retail Platforms: The Effects of Customer Loyalty

ABSTRACT Traditionally, the online retailer has adopted the wholesale pricing model to work with heterogeneous manufacturers, that is, the retailer buys the products from the competitive manufacturers and then resells them to customers. Recently, the increasing prevalence of online retailing has given rise to a novel agency pricing model; that is, the manufacturers sell their products directly to customers on the retailer’s platform, and the retailer charges a commission fee for each sale. Meanwhile, customer loyalty is an important determinant of long-term business success for the retail platform. Considering the customer with loyalty, this paper investigates how the chosen pricing model (wholesale pricing model or agency pricing model) affects the online retailer’s profit, industry profit, consumer surplus, and social welfare. We find that the latter model always leads to a lower retail price and higher consumer surplus. More important, we also show that the industry profit and social welfare increase when the online retailer switches from the former model to the latter one. However, the online retailer’s profit critically depends on customer loyalty. Specifically, when customer loyalty is strong enough, the online retailer should adopt the agency pricing model. Otherwise, the wholesale pricing model dominates.
Effect of Inactivated Poliovirus Vaccine Campaigns, Pakistan, 2014–2017

Pakistan began using inactivated poliovirus vaccine alongside oral vaccine in mass campaigns to accelerate eradication of wild-type poliovirus in 2014. Using case-based and environmental surveillance data for January 2014–October 2017, we found that these campaigns reduced wild-type poliovirus detection more than campaigns that used only oral vaccine.
Co-Creative Level Design via Machine Learning

Procedural Level Generation via Machine Learning (PLGML), the study of generating game levels with machine learning, has received a large amount of recent academic attention. For certain measures these approaches have shown success at replicating the quality of existing game levels. However, it is unclear the extent to which they might benefit human designers. In this paper we present a framework for co-creative level design with a PLGML agent. In support of this framework we present results from a user study and results from a comparative study of PLGML approaches.
A Model-Driven Deep Learning Network for MIMO Detection

In this paper, we propose a model-driven deep learning network for multiple-input multiple-output (MIMO) detection. The structure of the network is specially designed by unfolding the iterative algorithm. Some trainable parameters are optimized through deep learning techniques to improve the detection performance. Since the number of trainable variables of the network is equal to that of the layers, the network can be easily trained within a very short time. Furthermore, the network can handle time-varying channel with only a single training. Numerical results show that the proposed approach can improve the performance of the iterative algorithm significantly under Rayleigh and correlated MIMO channels.
Informativeness of Auditory Stimuli Does Not Affect EEG Signal Diversity

Brain signal diversity constitutes a robust neuronal marker of the global states of consciousness. It has been demonstrated that, in comparison to the resting wakefulness, signal diversity is lower during unconscious states, and higher during psychedelic states. A plausible interpretation of these findings is that the neuronal diversity corresponds to the diversity of subjective conscious experiences. Therefore, in the present study we varied an information rate processed by the subjects and hypothesized that greater information rate will be related to richer and more differentiated phenomenology and, consequently, to greater signal diversity. To test this hypothesis speech recordings (excerpts from an audio-book) were presented to subjects at five different speeds (65, 83, 100, 117, and 135% of the original speed). By increasing or decreasing speed of the recordings we were able to, respectively, increase or decrease the presented information rate. We also included a backward (unintelligible) speech presentation and a resting-state condition (no auditory stimulation). We tested 19 healthy subjects and analyzed the recorded EEG signal (64 channels) in terms of Lempel-Ziv diversity (LZs). We report the following findings. First, our main hypothesis was not confirmed, as Bayes Factor indicates evidence for no effect when comparing LZs among five presentation speeds. Second, we found that LZs during the resting-state was greater than during processing of both meaningful and unintelligible speech. Third, an additional analysis uncovered a gradual decrease of diversity over the time-course of the experiment, which might reflect a decrease in vigilance. We thus speculate that higher signal diversity during the unconstrained resting-state might be due to a greater variety of experiences, involving spontaneous attention switching and mind wandering.
Arrestins and G proteins in cellular signaling: The coin has two sides

Arrestins maintain a balance between G protein– and arrestin-dependent ERK activation (Luttrell et al., in 25 September 2018 issue). Several studies have suggested that arrestin-mediated signaling by GPCRs requires G protein activation; however, in this issue of Science Signaling, Luttrell et al. documented arrestin-dependent activation of ERK1/2 by a number of GPCRs. These studies do not contradict each other, but illustrate the complexity of cellular signaling that cannot and should not be reduced to simplistic models.