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Some of the most efficacious antiviral therapeutics are ribonucleos(t)ide analogs. The presence of a 3’-to-5’ proofreading exoribonuclease (ExoN) in coronaviruses diminishes the potency of many ribonucleotide analogs. The ability to interfere with ExoN activity will create new possibilities for control of SARS-CoV-2 infection. ExoN is formed by a 1:1 complex of nsp14 and nsp10 proteins. We have purified and characterized ExoN using a robust, quantitative system that reveals determinants of specificity and efficiency of hydrolysis. Double-stranded RNA is preferred over single-stranded RNA. Nucleotide excision is distributive, with only one or two nucleotides hydrolyzed in a single binding event. The composition of the terminal basepair modulates excision. A stalled SARS-CoV-2 replicase in complex with either correctly or incorrectly terminated products prevents excision, suggesting that a mispaired end is insufficient to displace the replicase. Finally, we have discovered several modifications to the 3’-RNA terminus that interfere with or block ExoN-catalyzed excision. While a 3’-OH facilitates hydrolysis of a nucleotide with a normal ribose configuration, this substituent is not required for a nucleotide with a planar ribose configuration such as that present in the antiviral nucleotide produced by viperin. Design of ExoN-resistant, antiviral ribonucleotides should be feasible.

Most machine learning algorithms require a fixed length input to be able to perform commonly desired tasks such as classification, clustering, and regression. For natural language processing, the inherently unbounded and recursive nature of the input poses a unique challenge when deriving such fixed length representations. Although today there is a general consensus on how to generate fixed length representations of individual words which preserve their meaning, the same cannot be said for sequences of words in sentences, paragraphs, or documents. In this work, we study the encoders commonly used to generate fixed length representations of natural language sequences, and analyze their effectiveness across a variety of high and low level tasks including sentence classification and question answering. Additionally, we propose novel improvements to the existing Skip-Thought and End-to-End Memory Network architectures and study their performance on both the original and auxiliary tasks. Ultimately, we show that the setting in which the encoders are trained, and the corpus used for training, have a greater influence of the final learned representation than the underlying sequence encoders themselves.

CONTEXT: Pain can be a debilitating side effect of radiation therapy (RT). Data from the general population has shown that sleep disturbance can influence pain incidence and severity; however, less is known about this relationship in breast cancer patients receiving RT. OBJECTIVES: This secondary analysis examined the association of pre-treatment moderate/severe levels of sleep disturbance with subsequent RT-induced pain after adjusting for pre-RT pain. METHODS: We report on 573 female breast cancer patients undergoing RT from a previously completed phase II clinical trial for radiation dermatitis. Sleep disturbance, total pain, and pain subdomains – sensory pain, affective pain, and perceived pain intensity were assessed at pre- and post-RT. At pre-RT, patients were dichotomized into 2 groups: those with moderate/severe sleep disturbance (N=85) vs. those with no/mild sleep disturbance (control; N=488). RESULTS: At pre-RT, women with moderate/severe sleep disturbance were younger, less likely to be married, more likely to have had mastectomy and chemotherapy, and more likely to have depression/anxiety disorder and fatigue than the control group (all p’s

Background: Microdosing psychedelics is the practice of taking small, sub-hallucinogenic doses of lysergic acid diethylamide or psilocybin-containing mushrooms. Despite its surging popularity, little is known about the specific intentions to start microdosing and the effects of this practice. Aims: First, we aimed to replicate previous findings regarding the subjective benefits and challenges reported for microdosing. Second, we assessed whether people who microdose test their substances before consumption. Third, we examined whether having an approach-intention to microdosing was predictive of more reported benefits. Methods: The Global Drug Survey runs the world’s largest online drug survey. Participants who reported last year use of lysergic acid diethylamide or psilocybin in the Global Drug Survey 2019 were offered the opportunity to answer a sub-section on microdosing. Results: Data from 6753 people who reported microdosing at least once in the last 12 months were used for analyses. Our results suggest a partial replication of previously reported benefits and challenges among the present sample often reporting enhanced mood, creativity, focus and sociability. Counter to our prediction, the most common challenge participants associated with microdosing was ‘None’. As predicted, most participants reported not testing their substances. Counter to our hypothesis, approach-intention – microdosing to approach a desired goal – predicted less rather than more benefits. We discuss alternate frameworks that may better capture the reasons people microdose. Conclusion: Our results suggest the perceived benefits associated with microdosing greatly outweigh the challenges. Microdosing may have utility for a variety of uses while having minimal side effects. Double-blind, placebo-controlled experiments are required to substantiate these reports.

Science requires replicable tools to measure its intended constructs. Attention research has developed tools that have been used in mind-wandering research, but mind-wandering measures often rely on response-inhibition, which introduces speed-accuracy trade-offs that may conflate errors for mind-wandering. We sought to replicate three studies that used an improved mind-wandering measure: the Metronome Response Task (MRT). In a large (N=300) multisite sample, the primary MRT finding was replicated, showing that continuous rhythmic response time variability reliably predicted self-reported mind-wandering. Our findings also show previously undetected differences between intentional and unintentional mind-wandering. While previously reported mediators (motivation) and moderators (confidence) did not replicate, additional covariates add predictive value and additional constructs (e.g., boredom, effort) demonstrate convergent validity. The MRT is useful for inducing and measuring mind-wandering and provides an especially replicable tool. The MRT's measurement of attention could support future models of the complete cycle of sustained attention.

The hydroperoxidation of alkylidenecyclopropanes and other strained alkenes using an N-hydroxylamine and molecular oxygen occurred in the absence of catalyst, initiator, or light. The oxidation rea...

How can we measure the absence of awareness? Attention research has developed tools for measuring self-caught meta-awareness restoration and behavioral mind-wandering, but we lack a way to dynamically track the loss of meta-awareness. The present pre-registered study sought to bring together three extant paradigms into one tool designed to dynamically measure meta-awareness: the Metronome Counting Task (MCT). The MCT is a continuous performance task wherein participants tap along to a steady beat while counting to 20, indicating the final count by a special button press. This sample (N = 74) provides evidence that participants could self-catch their failures in the task, that a response variability metric measuring mind-wandering depth was successfully recreated in this new tool, and that dynamic performance changes may be useful for detecting meta-awareness loss before participants become internally aware of the loss or are caught by external errors. The MCT was conceived as a tool that will support neuroimaging models of dynamic fluctuations during sustained attention, providing a link between the phenomenology of meta-awareness, the behavior measured by a replicable index of task engagement, and a continuous performance task on time-scales relevant for MRI. We discuss the possibility that meta-awareness may exist on a continuum and that conceptions of mind-wandering as attention failures may plausibly be reconceived as changes in goal priority manifesting as shifting task engagement.

High performance calorimetry conducted at future hadron colliders, such as the FCC-hh, poses a significant challenge for applying current detector technologies due to unprecedented beam luminosities and radiation fields. Solutions include developing scintillators that are capable of separating events at the sub-fifty picosecond level while also maintaining performance after extreme and constant neutron and ionizing radiation exposure. The RADiCAL is an approach that incorporates radiation tolerant materials in a sampling 'shashlik' style calorimeter configuration, using quartz capillaries filled with organic liquid or polymer-based wavelength shifters embedded in layers of tungsten plates and LYSO crystals. This novel design intends to address the Priority Research Directions (PRD) for calorimetry listed in the DOE Basic Research Needs (BRN) workshop for HEP Instrumentation. Here we report preliminary results from an experimental run at the Fermilab Test Beam Facility in June 2022. These tests demonstrate that the RADiCAL concept is capable of < 50 ps timing resolution., 5 pages, 10 figures, SCINT22 conference

Background Microdosing psychedelics is the practice of consuming very low, sub-hallucinogenic doses of a psychedelic substance, such as lysergic acid diethylamide (LSD) or psilocybin-containing mushrooms. According to media reports, microdosing has grown in popularity, yet the scientific literature contains minimal research on this practice. There has been limited reporting on adverse events associated with microdosing, and the experiences of microdosers in community samples have not been categorized. Methods In the present study, we develop a codebook of microdosing benefits and challenges (MDBC) based on the qualitative reports of a real-world sample of 278 microdosers. Results We describe novel findings, both in terms of beneficial outcomes, such as improved mood (26.6%) and focus (14.8%), and in terms of challenging outcomes, such as physiological discomfort (18.0%) and increased anxiety (6.7%). We also show parallels between benefits and drawbacks and discuss the implications of these results. We probe for substance-dependent differences, finding that psilocybin-only users report the benefits of microdosing were more important than other users report. Conclusions These mixed-methods results help summarize and frame the experiences reported by an active microdosing community as high-potential avenues for future scientific research. The MDBC taxonomy reported here informs future research, leveraging participant reports to distil the highest-potential intervention targets so research funding can be efficiently allocated. Microdosing research complements the full-dose literature as clinical treatments are developed and neuropharmacological mechanisms are sought. This framework aims to inform researchers and clinicians as experimental microdosing research begins in earnest in the years to come. Electronic supplementary material The online version of this article (10.1186/s12954-019-0308-4) contains supplementary material, which is available to authorized users.

The cloud and telecommunications industry is in the midst of a transition towards the edge. There is a tremendous opportunity for the research community to influence this transformation, but doing so requires understanding industry momentum, and making a concerted effort to align with that momentum. We believe there are three keys to doing this: (1) focus on the intersection of the cloud and access networks, (2) contribute to the relevant open source projects, and (3) address the challenge of operationalizing the results. The paper puts forward a concrete proposal for all three, and discusses the opportunity to influence how the Internet evolves at the edge and enable innovative edge applications.

Much of the scientific research in the United States is conducted in the public interest and funded through tax dollars, with exact funding allocations controlled through a variety of high-level processes, including through legislation. Scientists can provide expert opinions to government officials and their staff in this and other processes of establishing and enforcing governmental legislation, policies, and regulations, although such expertise is often underutilized. This is partly due to the paucity of scientists who have training to engage policymakers. To address this deficit, many scientific societies now provide organized training in science policy, including how to seek out opportunities to engage with policymakers. One such example is the Entomological Society of America’s Science Policy Fellowship (ESA SPF) program. This article is written by ESA SPF members and discusses the importance of interfacing with policymakers, some challenges this group encountered, and lessons learned through our experiences in the program. Through efforts like this, we hope to inspire other scientists to engage their societies in advocacy work and make their voice heard.

Meditation has become a cultural phenomenon, and modern scientific research on the topic has exploded. Thousands of scientific articles report various benefits of meditation including clinical, physiological and well-being outcomes. Despite these benefits, drop-out rates in mindfulness-based interventions remain a problem and little work has studied the drawbacks of meditation. Reports of adverse reactions to meditation have emerged and critical voices have begun advocating caution, rather than enthusiasm, for meditation training. Furthermore, the experiences of meditators outside interventions and conventional lab studies are not well understood. Here we develop an empirical codebook of and framework for meditation benefits and drawbacks (MBDs), discussing the actionable implications for meditation training in the real world. These data reveal the major drawbacks hindering real-world meditators, including several less intuitive drawbacks. We also report the major benefits of meditation and generate a structural framework from which they can be understood in parallel with drawbacks. We investigate whether meditation styles affect MBDs and report comparisons between current and former meditators. These results bring cogent structure to the variety of meditation outcomes laypeople experience. As the number of meditators continues to increase, we need such structures to inform how meditation is taught, ensuring ethically informed consent and optimizing practice-fit. Mixed-methods research such as this study allows a greater practical understanding of how meditation is experienced in situ. This work complements the literature on the clinical benefits and neurophysiological mechanisms of meditation and this framework will inform clinicians, researchers and meditation teachers as best practices are reviewed in the coming years.

This study seeks to understand the value of ventilation imaging in pregnant patients imaged for suspected pulmonary embolism (PE). Ventilation-perfusion (VQ) scans in this high-risk population were compared to ventilation-only scans. We hypothesize that in this relatively healthy population, the exclusion of ventilation scans will not impact the rate of scans interpreted as positive. This retrospective blinded comparative reader study on collated VQ scans performed on pregnant patients in the course of routine clinical care in a 5 year period (03/2012 to 07/2017). Each set of VQ and perfusion only (Q) studies were reviewed by 8 readers (4 nuclear radiology fellows and 4 nuclear medicine faculty) in random order; the Q scans simply omitted the ventilation images. Readers recorded each study as PE, no PE, or non-diagnostic (prospective investigative study of acute PE diagnosis classifications). Logistic mixed effects models were used to test the association between scan type (VQ vs Q). 203 pairs of studies in 197 patients were included (6 patients had 2 scans). Subjects ranged from 14 to 45 years of age, with a median 28 years. A significant association between scan type and positive/negative classification. Q-scans received more positive classifications than VQ-scans (median of 7.6% vs 6.7%). No association was seen between scan type and positive/indeterminate classification, nor between scan type and negative/indeterminate classification. The exclusion of ventilation images in VQ-scans was associated with a higher rate of positive studies, but this difference was small (1%). Given the overwhelmingly normal percentage of Q-exams (90% in our study), and the benefits of omitting ventilation imaging, perfusion-only imaging should be considered a reasonable option for imaging the pregnant patient to exclude PE.

Linux has become the de-facto operating system of our age, but its vulnerabilities are a constant threat to service availability, user privacy, and data integrity. While one might scrap Linux and start over, the cost of that would be prohibitive due to Linux's ubiquitous deployment. In this paper, we propose an alternative, incremental route to a safer Linux through proper modularization and gradual replacement module by module. We lay out the research challenges and potential solutions for this route, and discuss the open questions ahead.

Today's computing systems pay a heavy "OS tax", as kernel execution accounts for a significant amount of resource footprint. This is not least because today's kernels abound with hardcoded heuristics that are designed with unstated assumptions, which rarely generalize well for diversifying applications and device technologies. We propose the concept of reconfigurable kernel datapaths that enables kernels to self-optimize dynamically. In this architecture, optimizations are computed from empirical data using machine learning (ML), and they are integrated into the kernel in a safe and systematic manner via an in-kernel virtual machine. This virtual machine implements the reconfigurable match table (RMT) abstraction, where tables are installed into the kernel at points where performance-critical events occur, matches look up the current execution context, and actions encode context-specific optimizations computed by ML, which may further vary from application to application. Our envisioned architecture will support both offline and online learning algorithms, as well as varied kernel subsystems. An RMT verifier will check program well-formedness and model efficiency before admitting an RMT program to the kernel. An admitted program can be interpreted in bytecode or just-in-time compiled to optimize the kernel datapaths.

This article considers the data on donations to New Zealand political parties collected by the Electoral Commission. The purpose is to address who gets what, and why. Relatively small amounts are donated. A little may buy considerable influence. There is limited evidence of strong upward trends in political donations, suggesting a systemic equilibrium. The plurality of donations is received by unsuccessful parties, suggesting that money is insufficient for political success. Most donations come from individuals (mostly men) or families. Cross-political spectrum donations are mostly from businesses and to the two dominant parties, suggesting that businesses are trying to buy the ear of the major power in government.

One of the principal challenges associated with decarbonization is the temporal variability of renewable energy generation, which is creating the need to better balance load on the grid by shaving peak demand. We analyzed how innovative load-shifting technologies can be used by large institutions like the University of Virginia to shift load and support statewide efforts to decarbonize. To do this, we focused on the University's plans for expansion of the Fontaine Research Park, which is a good model for understanding how these technologies could distribute energy load behind the meter. First, we worked to develop a predictive model to forecast when peak demands will occur and understand how interventions, including heat recovery chillers and thermal storage tanks, might be used to balance load. Then, we extended a statewide energy systems model using the Tools for Energy Modeling Optimization and Analysis (TEMOA) to simulate the ways in which these types of interventions might be scaled to the whole state. Using the energy demand model in conjunction with aggregated institutional energy use data, the team evaluated the effects that broader adoption of distributed energy technologies in Virginia could have on the grid's ability to handle the energy transition. Our study showed implementing distributed energy sources on a state-scale had insignificant effect on balancing load. However, on a microgrid scale, such technologies prove to be a useful resource to decrease peak demand which would allow for further clean energy projects and possible cost reductions.