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The COVID-19 pandemic accelerated the development of decentralized clinical trials (DCT). DCT’s are an important and pragmatic method for assessing health outcomes yet comprise only a minority of clinical trials, and few published methodologies exist. In this report, we detail the operational components of COVID-OUT, a decentralized, multicenter, quadruple-blinded, randomized trial that rapidly delivered study drugs nation-wide. The trial examined three medications (metformin, ivermectin, and fluvoxamine) as outpatient treatment of SARS-CoV-2 for their effectiveness in preventing severe or long COVID-19. Decentralized strategies included HIPAA-compliant electronic screening and consenting, prepacking investigational product to accelerate delivery after randomization, and remotely confirming participant-reported outcomes. Of the 1417 individuals with the intention-to-treat sample, the remote nature of the study caused an additional 94 participants to not take any doses of study drug. Therefore, 1323 participants were in the modified intention-to-treat sample, which was the a priori primary study sample. Only 1.4% of participants were lost to follow-up. Decentralized strategies facilitated the successful completion of the COVID-OUT trial without any in-person contact by expediting intervention delivery, expanding trial access geographically, limiting contagion exposure, and making it easy for participants to complete follow-up visits. Remotely completed consent and follow-up facilitated enrollment.
Several hypotheses may explain the association between substance use, posttraumatic stress disorder (PTSD), and depression. However, few studies have utilized a large multisite dataset to understand this complex relationship. Our study assessed the relationship between alcohol and cannabis use trajectories and PTSD and depression symptoms across 3 months in recently trauma-exposed civilians.
In total, 1618 (1037 female) participants provided self-report data on past 30-day alcohol and cannabis use and PTSD and depression symptoms during their emergency department (baseline) visit. We reassessed participant's substance use and clinical symptoms 2, 8, and 12 weeks posttrauma. Latent class mixture modeling determined alcohol and cannabis use trajectories in the sample. Changes in PTSD and depression symptoms were assessed across alcohol and cannabis use trajectories via a mixed-model repeated-measures analysis of variance.
Three trajectory classes (low, high, increasing use) provided the best model fit for alcohol and cannabis use. The low alcohol use class exhibited lower PTSD symptoms at baseline than the high use class; the low cannabis use class exhibited lower PTSD and depression symptoms at baseline than the high and increasing use classes; these symptoms greatly increased at week 8 and declined at week 12. Participants who already use alcohol and cannabis exhibited greater PTSD and depression symptoms at baseline that increased at week 8 with a decrease in symptoms at week 12.
Our findings suggest that alcohol and cannabis use trajectories are associated with the intensity of posttrauma psychopathology. These findings could potentially inform the timing of therapeutic strategies.
In this chapter, we review computer models of cognition that have focused on the use of neural networks. These architectures were inspired by research into how computation works in the brain. The approach is called connectionism because it proposes that processing is characterized by patterns of activation across simple processing units connected together into complex networks, with knowledge stored in the strength of the connections between units. We place connectionism in its historical context, describing the “three ages” of artificial neural network research: from the genesis of the first formal theories of computation in the 1930s and 1940s, to the parallel distributed processing (PDP) models of cognition of the 1980s and 1990s, and the advances in “deep” neural networks emerging in the mid-2000s. Transition between the ages has been triggered by new insights into how to create and train more powerful artificial neural networks. We discuss important foundational cognitive models that illustrate some of the key properties of connectionist systems, and indicate how the novel theoretical contributions of these models arose from their key computational properties. We consider how connectionist modeling has influenced wider theories of cognition, and how in the future, connectionist modeling of cognition may progress by integrating further constraints from neuroscience and neuroanatomy.
OBJECTIVES/GOALS: The goal of this proposal is to develop a technology that combines calcium imaging via confocal microscopy, and force measurement via monolayer stress microscopy to perform simultaneous quantitative measurements of agonist-induced Ca2+ and mechanical signals in HASMCs. METHODS/STUDY POPULATION: The methods by which second messenger signals and changes in mechanical forces determine specific physiological responses are complex. Recent studies point to the importance of temporal and spatial encoding in determining signal specificity. Hence, approaches that probe both chemical and mechanical signals are needed. We combine hyperspectral imaging for second messenger signal measurements, monolayer stress microscopy for mechanical force measurements, and S8 analysis software for quantifying localized signals. Imaging was performed using an excitation-scanning hyperspectral microscope. Hyperspectral images were unmixed to identify signals from fluorescent labels and microparticles. Images were analyzed to quantify localized force dynamics through monolayer stress microscopy. RESULTS/ANTICIPATED RESULTS: Results indicate that localized and transient cellular signals can be quantified and mapped within cell populations. Importantly, these results establish a method for simultaneous interrogation of cellular signals and mechanical forces that may play synergistic roles in regulating downstream cellular physiology in confluent monolayers. DISCUSSION/SIGNIFICANCE: We will measure the distribution of chemical and mechanical signals within cells, providing insight into the dynamics of cell signaling. Studies will have implication in the understanding of infections, drug delivery in which non-uniform distributions of drugs are a certainty, and in understanding coordinated responses in cellular systems.
An emergent volume electron microscopy technique called cryogenic serial plasma focused ion beam milling scanning electron microscopy (pFIB/SEM) can decipher complex biological structures by building a three-dimensional picture of biological samples at mesoscale resolution. This is achieved by collecting consecutive SEM images after successive rounds of FIB milling that expose a new surface after each milling step. Due to instrumental limitations, some image processing is necessary before 3D visualization and analysis of the data is possible. SEM images are affected by noise, drift, and charging effects, that can make precise 3D reconstruction of biological features difficult. This article presents Okapi-EM, an open-source napari plugin developed to process and analyze cryogenic serial pFIB/SEM images. Okapi-EM enables automated image registration of slices, evaluation of image quality metrics specific to pFIB-SEM imaging, and mitigation of charging artifacts. Implementation of Okapi-EM within the napari framework ensures that the tools are both user- and developer-friendly, through provision of a graphical user interface and access to Python programming.
Reward processing has been proposed to underpin the atypical social feature of autism spectrum disorder (ASD). However, previous neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social reward processing in ASD.
Utilising a large sample, we aimed to assess reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD.
Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6–30.6 years of age) and 181 typically developing participants (7.6–30.8 years of age).
Across social and monetary reward anticipation, whole-brain analyses showed hypoactivation of the right ventral striatum in participants with ASD compared with typically developing participants. Further, region of interest analysis across both reward types yielded ASD-related hypoactivation in both the left and right ventral striatum. Across delivery of social and monetary reward, hyperactivation of the ventral striatum in individuals with ASD did not survive correction for multiple comparisons. Dimensional analyses of autism and attention-deficit hyperactivity disorder (ADHD) scores were not significant. In categorical analyses, post hoc comparisons showed that ASD effects were most pronounced in participants with ASD without co-occurring ADHD.
Our results do not support current theories linking atypical social interaction in ASD to specific alterations in social reward processing. Instead, they point towards a generalised hypoactivity of ventral striatum in ASD during anticipation of both social and monetary rewards. We suggest this indicates attenuated reward seeking in ASD independent of social content and that elevated ADHD symptoms may attenuate altered reward seeking in ASD.
To derive and validate a model for risk of resistance to first-line community-acquired pneumonia (CAP) therapy.
We developed a logistic regression prediction model from a large multihospital discharge database and validated it versus the Drug Resistance in Pneumonia (DRIP) score in a holdout sample and another hospital system outside that database. Resistance to first-line CAP therapy (quinolone or third generation cephalosporin plus macrolide) was based on blood or respiratory cultures.
This study was conducted using data from 177 Premier Healthcare database hospitals and 11 Cleveland Clinic hospitals.
Adults hospitalized for CAP.
Risk factors for resistant infection.
Among 138,762 eligible patients in the Premier database, 12,181 (8.8%) had positive cultures and 5,200 (3.8%) had organisms resistant to CAP therapy. Infection with a resistant organism in the previous year was the strongest predictor of resistance; markers of acute illness (eg, receipt of mechanical ventilation or vasopressors) and chronic illness (eg, pressure ulcer, paralysis) were also associated with resistant infections. Our model outperformed the DRIP score with a C-statistic of 0.71 versus 0.63 for the DRIP score (P < .001) in the Premier holdout sample, and 0.65 versus 0.58 (P < .001) in Cleveland Clinic hospitals. Clinicians at Premier facilities used broad-spectrum antibiotics for 20%–30% of patients. In discriminating between patients with and without resistant infections, physician judgment slightly outperformed the DRIP instrument but not our model.
Our model predicting infection with a resistant pathogen outperformed both the DRIP score and physician practice in an external validation set. Its integration into practice could reduce unnecessary use of broad-spectrum antibiotics.
To document changes in evaluation protocols for acute invasive fungal sinusitis during the coronavirus disease 2019 pandemic, and to analyse concordance between clinical and histopathological diagnoses based on new practice guidelines.
Protocols for the evaluation of patients with suspected acute invasive fungal sinusitis both prior and during the coronavirus disease 2019 period are described. A retrospective analysis of patients presenting with suspected acute invasive fungal sinusitis from 1 May to 30 June 2021 was conducted, with assessment of the concordance between clinical and final diagnoses.
Among 171 patients with high clinical suspicion, 160 (93.6 per cent) had a final histopathological diagnosis of invasive fungal sinusitis, concordant with the clinical diagnosis, with sensitivity of 100 per cent, positive predictive value of 93.6 per cent and negative predictive value of 100 per cent.
The study highlights a valuable screening tool with good accuracy, involving emphasis on ‘red flag’ signs in high-risk populations. This could be valuable in situations demanding the avoidance of aerosol-generating procedures and in resource-limited settings facilitating early referral to higher level care centres.
A field experiment was conducted in 2019 and 2020 that included six site-years and four locations in Arkansas to determine the optimal sequence and timing of dicamba and glufosinate applications when applied alone, sequentially, or in combination to control Palmer amaranth by size: labeled (<10 cm height) and non-labeled (13 to 25 cm height). Single applications of dicamba, glufosinate, and dicamba plus glufosinate (not labeled) resulted in less than 80% Palmer amaranth control, regardless of weed size. The mixture of dicamba plus glufosinate was antagonistic for Palmer amaranth control and percent mortality. Sequential applications, averaged over all time intervals and herbicides, improved the percentage of Palmer amaranth control 11 to 17 percentage points over a single application, regardless of weed size at application 28 d after final application (DAFA). Palmer amaranth control with glufosinate followed by (fb) glufosinate and dicamba fb dicamba, pending weed size, were optimized at intervals of 7 d, and 14 to 21 d, respectively. Because single site of action (SOA) postemergence herbicide systems increase the likelihood of the development of resistant biotypes and are not a best management practice (BMP) in that regard; sequential applications involving both dicamba and glufosinate were more effective. Furthermore, the sequence of application mattered with a preference for applying dicamba first. Dicamba fb glufosinate at a 14-d interval was profit-maximizing and the only herbicide treatment that resulted in 100% weed control when size was <10 cm. For larger weed sizes, economic analysis revealed that dicamba fb dicamba performed better than dicamba fb glufosinate when no penalty was assigned for using a single SOA. This resulted in greater yield loss risk and soil weed seed bank in comparison to timelier weed control with the smaller weed size. Hence, timely weed control and two SOAs to control Palmer amaranth are recommended as BMPs that reduce producer risk.
Fluting is a technological and morphological hallmark of some of the most iconic North American Paleoindian stone points. Through decades of detailed artifact analyses and replication experiments, archaeologists have spent considerable effort reconstructing how flute removals were achieved, and they have explored possible explanations of why fluting was such an important aspect of early point technologies. However, the end of fluting has been less thoroughly researched. In southern North America, fluting is recognized as a diagnostic characteristic of Clovis points dating to approximately 13,000 cal yr BP, the earliest widespread use of fluting. One thousand years later, fluting occurs more variably in Dalton and is no longer useful as a diagnostic indicator. How did fluting change, and why did point makers eventually abandon fluting? In this article, we use traditional 2D measurements, geometric morphometric (GM) analysis of 3D models, and 2D GM of flute cross sections to compare Clovis and Dalton point flute and basal morphologies. The significant differences observed show that fluting in Clovis was highly standardized, suggesting that fluting may have functioned to improve projectile durability. Because Dalton points were used increasingly as knives and other types of tools, maximizing projectile functionality became less important. We propose that fluting in Dalton is a vestigial technological trait retained beyond its original functional usefulness.
From 2014 to 2020, we compiled radiocarbon ages from the lower 48 states, creating a database of more than 100,000 archaeological, geological, and paleontological ages that will be freely available to researchers through the Canadian Archaeological Radiocarbon Database. Here, we discuss the process used to compile ages, general characteristics of the database, and lessons learned from this exercise in “big data” compilation.
Numerous theories posit different core features to borderline personality disorder (BPD). Recent advances in network analysis provide a method of examining the relative centrality of BPD symptoms, as well as examine the replicability of findings across samples. Additionally, despite the increase in research supporting the validity of BPD in adolescents, clinicians are reluctant to diagnose BPD in adolescents. Establishing the replicability of the syndrome across adolescents and adults informs clinical practice and research. This study examined the stability of BPD symptom networks and centrality of symptoms across samples varying in age and clinical characteristics.
Cross-sectional analyses of BPD symptoms from semi-structured diagnostic interviews from the Collaborative Longitudinal Study of Personality Disorders (CLPS), the Methods to Improve Diagnostic Assessment and Service (MIDAS) study, and an adolescent clinical sample. Network attributes, including edge (partial association) strength and node (symptom) expected influence, were compared.
The three networks were largely similar and strongly correlated. Affective instability and identity disturbance emerged as relatively central symptoms across the three samples, and relationship difficulties across adult networks. Differences in network attributes were more evident between networks varying both in age and in BPD symptom severity level.
Findings highlight the relative importance of affective, identity, and relationship symptoms, consistent with several leading theories of BPD. The network structure of BPD symptoms appears generally replicable across multiple large samples including adolescents and adults, providing further support for the validity of the diagnosis across these developmental phases.
This study quantified CO2 emissions from tropical peat swamp soils in Brunei Darussalam. At each site, soil was collected from areas of intact and degraded peat and CO2 flux, and total organic content were measured ex situ. Soil organic content (~20–99%) was not significantly different between intact and degraded forest samples. CO2 flux was higher for intact forest samples than degraded forest samples (~1.0 vs. ~0.6 μmol CO2 m−2 s−1, respectively) but did not differ among forest locations. From our laboratory experiments, we estimated a potential emissions of ~10–20 t CO2 ha−1 y−1 which is in the lower range of values reported for other tropical peat swamps. However, our results are likely affected by unmeasured variation in root respiration and the lability of resident carbon. Overall, these findings provide experimental evidence to support that clearance of tropical peat swamp forests can increase CO2 emissions due to faster rates of decomposition.
Wisdom is a personality trait comprising seven components: self-reflection, pro-social behaviors, emotional regulation, acceptance of diverse perspectives, decisiveness, social advising, and spirituality. Wisdom, a potentially modifiable trait, is strongly associated with well-being. We have published a validated 28-item San Diego Wisdom Scale, the SD-WISE-28. Brief scales are necessary for use in large population-based studies and in clinical practice. The present study aimed to create an abbreviated 7-item version of the SD-WISE.
Participants included 2093 people, aged 20-82 years, recruited and surveyed through the online crowdsourcing platform Amazon Mechanical Turk. The participants’ mean age was 46 years, with 55% women. Participants completed the SD-WISE-28 as well as validation scales for various positive and negative constructs. Psychometric analyses (factor analysis and item response theory) were used to select one item from each of the seven SD-WISE-28 subscales.
We selected a combination of items that produced acceptable unidimensional model fit and good reliability (ω = 0.74). Item statistics suggested that all seven items were strong indicators of wisdom, although the association was weakest for spirituality. Analyses indicated that the 28-item and 7-item SD-WISE are both very highly correlated (r = 0.92) and produce a nearly identical pattern of correlations with demographic and validity variables.
The SD-WISE-7, and its derived Jeste-Thomas Wisdom Index (JTWI) score, balances reliability and brevity for research applications.
We summarize some of the past year's most important findings within climate change-related research. New research has improved our understanding about the remaining options to achieve the Paris Agreement goals, through overcoming political barriers to carbon pricing, taking into account non-CO2 factors, a well-designed implementation of demand-side and nature-based solutions, resilience building of ecosystems and the recognition that climate change mitigation costs can be justified by benefits to the health of humans and nature alone. We consider new insights about what to expect if we fail to include a new dimension of fire extremes and the prospect of cascading climate tipping elements.
A synthesis is made of 10 topics within climate research, where there have been significant advances since January 2020. The insights are based on input from an international open call with broad disciplinary scope. Findings include: (1) the options to still keep global warming below 1.5 °C; (2) the impact of non-CO2 factors in global warming; (3) a new dimension of fire extremes forced by climate change; (4) the increasing pressure on interconnected climate tipping elements; (5) the dimensions of climate justice; (6) political challenges impeding the effectiveness of carbon pricing; (7) demand-side solutions as vehicles of climate mitigation; (8) the potentials and caveats of nature-based solutions; (9) how building resilience of marine ecosystems is possible; and (10) that the costs of climate change mitigation policies can be more than justified by the benefits to the health of humans and nature.
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We present the data and initial results from the first pilot survey of the Evolutionary Map of the Universe (EMU), observed at 944 MHz with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The survey covers
of an area covered by the Dark Energy Survey, reaching a depth of 25–30
rms at a spatial resolution of
11–18 arcsec, resulting in a catalogue of
220 000 sources, of which
180 000 are single-component sources. Here we present the catalogue of single-component sources, together with (where available) optical and infrared cross-identifications, classifications, and redshifts. This survey explores a new region of parameter space compared to previous surveys. Specifically, the EMU Pilot Survey has a high density of sources, and also a high sensitivity to low surface brightness emission. These properties result in the detection of types of sources that were rarely seen in or absent from previous surveys. We present some of these new results here.