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Hidradenitis suppurativa (HS) is a chronic skin condition affecting approximately 1% of the US population. HS skin lesions are highly inflammatory and characterized by a large immune infiltrate. While B cells and plasma cells comprise a major component of this immune milieu, the biology and the contribution of these cells in HS pathogenesis are unclear. We aimed to investigate the dynamics and microenvironmental interactions of B cells within cutaneous HS lesions. Combining histological analysis, single-cell RNA sequencing, and spatial transcriptomics profiling of HS lesions, we defined the tissue microenvironment relative to B cell activity within this disease. Our findings identified tertiary lymphoid structures (TLSs) within HS lesions and described organized interactions among T cells, B cells, antigen-presenting cells, and skin stroma. We found evidence that B cells within HS TLSs actively underwent maturation, including participation in germinal center reactions and class switch recombination. Moreover, skin stroma and accumulating T cells were primed to support the formation of TLSs and facilitate B cell recruitment during HS. Our data definitively demonstrated the presence of TLSs in lesional HS skin and point to ongoing cutaneous B cell maturation through class switch recombination and affinity maturation during disease progression in this inflamed nonlymphoid tissue.

Lightning talks of the Workflows in Support of Large-Scale Science (WORKS) workshop are a venue where the workflow community (researchers, developers, and users) can discuss work in progress, emerging technologies and frameworks, and training and education materials. This paper summarizes the WORKS 2021 lightning talks, which cover four broad topics: (i) libEnsemble, a Python library to coordinate the concurrent evaluation of dynamic ensembles of calculations; (ii) Edu WRENCH, a set of online pedagogic modules that provides simulation-driven hands-on activity in the browser; (iii) VisDict, an envisioned visual dictionary framework that will translate terms, jargon, and concepts between research domains and workflow providers; and (iv) Pegasus Kickstart, a lightweight tool for capturing workflow tasks' performance, including performance metrics from Nvidia GPUs.

The present studies examined whether priming distinct motivational states influenced persistence at a task designed to promote repeated failure, and post-task plans for engaging in self-regulatory activity. Two double-blind, between-subject experiments (Study 1: N = 58; Study 2: N = 92) involved participants being randomized to Autonomous Motivation, Controlling Motivation, or Neutral prime conditions using a scrambled-sentence test. Participants then attempted an impossible persistence task that promoted repeated failure. Following, participants reported their plans to engage in exercise. Using frequentist and Bayesian analyses, Study 1, Study 2, and an internal meta-analysis showed no differences in persistence or planned exercise across priming conditions, thus contrasting with previous research. Unanticipated moderation effects or motivational priming effects being smaller than those inputted into a priori power analyses may be the most likely reasons for these findings., Author(s): Stephen L. Murphy [sup.1] , Ian M. Taylor [sup.2] Author Affiliations: (1) grid.5570.7, 0000 0004 0490 981X, Department of Social Psychology, Faculty of Psychology, Ruhr-University Bochum, , Universitätsstraße 150, [...]

Record linkage is the task of combining records from multiple files which refer to overlapping sets of entities when there is no unique identifying field. In streaming record linkage, files arrive sequentially in time and estimates of links are updated after the arrival of each file. This problem arises in settings such as longitudinal surveys, electronic health records, and online events databases, among others. The challenge in streaming record linkage is to efficiently update parameter estimates as new data arrive. We approach the problem from a Bayesian perspective with estimates calculated from posterior samples of parameters and present methods for updating link estimates after the arrival of a new file that are faster than fitting a joint model with each new data file. In this article, we generalize a two-file Bayesian Fellegi-Sunter model to the multi-file case and propose two methods to perform streaming updates. We examine the effect of prior distribution on the resulting linkage accuracy as well as the computational tradeoffs between the methods when compared to a Gibbs sampler through simulated and real-world survey panel data. We achieve near-equivalent posterior inference at a small fraction of the compute time. for this article are available online. [ABSTRACT FROM AUTHOR]

Minimizing the temporal gap between behavior and reward enhances persistence, but the effect of other outcomes is unknown. Two concurrently run studies aimed to investigate whether persistence on a physical task would be influenced according to whether participants expected immediate versus delayed goal feedback. Furthermore, whether this effect occurs via intrinsic motivation (Studies 1 and 2) or delaying the desire–goal conflict (Study 2) was examined. Using a counterbalanced within-person design, 34 participants in each study (Study 1: 16 males, 18 females; Study 2: 15 males, 19 females) completed two wall-sit persistence tasks, one with immediate feedback expected (regarding the participant's position on a leader board) and the other with feedback expected to be provided 1 week later. A two-way mixed analysis of variance found no significant differences in persistence between conditions in either study. Furthermore, no indirect effects were found via intrinsic motivation or delayed desire–goal conflict. Study findings did not support the hypothesis that the timing of expected feedback enhances persistence. [ABSTRACT FROM AUTHOR]

Existing robotic systems have a tension between generality and precision. Deployed solutions for robotic manipulation tend to fall into the paradigm of one robot solving a single task, lacking "precise generalization," or the ability to solve many tasks without compromising on precision. This paper explores solutions for precise and general pick and place. In precise pick and place, or kitting, the robot transforms an unstructured arrangement of objects into an organized arrangement, which can facilitate further manipulation. We propose SimPLE (Simulation to Pick Localize and placE) as a solution to precise pick and place. SimPLE learns to pick, regrasp, and place objects given the object's computer-aided design model and no prior experience. We developed three main components: task-aware grasping, visuotactile perception, and regrasp planning. Task-aware grasping computes affordances of grasps that are stable, observable, and favorable to placing. The visuotactile perception model relies on matching real observations against a set of simulated ones through supervised learning to estimate a distribution of likely object poses. Last, we computed a multistep pick-and-place plan by solving a shortest-path problem on a graph of hand-to-hand regrasps. On a dual-arm robot equipped with visuotactile sensing, SimPLE demonstrated pick and place of 15 diverse objects. The objects spanned a wide range of shapes, and SimPLE achieved successful placements into structured arrangements with 1-mm clearance more than 90% of the time for six objects and more than 80% of the time for 11 objects. Editor's summary: In robotic manipulation, there is often a trade-off between high accuracy for a repetitive motion and reliability in an unstructured environment. To teach a robot to move objects into an organized arrangement, Bauza et al. have developed a framework called SimPLE, which stands for Simulation to Pick, Localize, and placE. Given only a model of the object, the framework generates training data by sampling grasps in simulation. The SimPLE framework was tested with a set of 15 objects of different geometries on a dual-arm robot equipped with tactile sensors and an external depth camera. Using hand-to-hand regrasps, the robot successfully relocated the objects into structured arrangements, demonstrating the possibility of transferring a model learned in simulation to a real robot. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Prior self-control exertion is consistently reported to cause decrements in subsequent physical performance. However, research into the explanatory mechanisms underpinning the effect is limited and has not been assessed under a meta-analytical lens. Therefore, the present study reports a meta-analysis examining the effects of self-control exertion on subsequent physical performance, as well as the mechanisms underpinning the effect. A systematic search of relevant databases was conducted to identify studies that utilized the sequential task paradigm, involving self-control manipulations lasting 30 minutes or less, and examined an aspect of physical performance. Random effects meta-analysis demonstrated that the prior exertion of self-control resulted in a statistically significant medium sized negative effect of prior self-control exertion on subsequent physical performance (g = −0.55). Further analysis revealed a small increase in initial perceptions of pain (g = 0.18) and a medium sized reduction in self-efficacy (g = −0.48), while motivation and RPE were unaffected following the exertion of self-control. The present study provides a novel insight into the mechanisms underpinning the effects of prior self-control exertion on subsequent physical performance. Initial perceptions of pain and self-efficacy appear important mechanisms and thus could be targeted in future interventions aimed at attenuating the effects of self-control exertion to enhance subsequent physical performance. [ABSTRACT FROM AUTHOR]

SAMHD1 regulates cellular nucleotide homeostasis, controlling dNTP levels by catalysing their hydrolysis into 2'-deoxynucleosides and triphosphate. In differentiated CD4+ macrophage and resting T-cells SAMHD1 activity results in the inhibition of HIV-1 infection through a dNTP blockade. In cancer, SAMHD1 desensitizes cells to nucleoside-analogue chemotherapies. Here we employ time-resolved cryogenic-EM imaging and single-particle analysis to visualise assembly, allostery and catalysis by this multi-subunit enzyme. Our observations reveal how dynamic conformational changes in the SAMHD1 quaternary structure drive the catalytic cycle. We capture five states at high-resolution in a live catalytic reaction, revealing how allosteric activators support assembly of a stable SAMHD1 tetrameric core and how catalysis is driven by the opening and closing of active sites through pairwise coupling of active sites and order-disorder transitions in regulatory domains. This direct visualisation of enzyme catalysis dynamics within an allostery-stabilised platform sets a precedent for mechanistic studies into the regulation of multi-subunit enzymes. SAMHD1 is a regulator of dNTP homeostasis and an HIV restriction factor. The authors use time-resolved cryo-EM to visualise dynamic conformational changes that drive the catalytic cycle and allosteric regulation of this multi-subunit enzyme. [ABSTRACT FROM AUTHOR]