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Abstract Introduction The 2010 release of an abuse deterrent formulation (ADF) of OxyContin, a brand name prescription opioid, has been cited as a major driver for the reduction in prescription drug misuse and the associated increasing illicit opioid use and overdose rates. However, studies of this topic often do not account for changes in supplies of other prescription opioids that were widely prescribed before and after the ADF OxyContin release, including generic oxycodone formulations and hydrocodone. We therefore sought to compare the impact of the ADF OxyContin release to that of decreasing prescription opioid supplies in West Virginia (WV). Methods Opioid tablet shipment and overdose data were extracted from The Washington Post ARCOS (2006–2014) and the WV Forensic Drug Database (2005–2020), respectively. Locally estimated scatterplot smoothing (LOESS) was used to estimate the point when shipments of prescription opioids to WV began decreasing, measured via dosage units and morphine milligram equivalents (MMEs). Interrupted time series analysis (ITSA) was used to compare the impact LOESS-identified prescription supply changes and the ADF OxyContin release had on prescription (oxycodone and hydrocodone) and illicit (heroin, fentanyl, and fentanyl analogues) opioid overdose deaths in WV. Model fit was compared using Akaike Information Criteria (AIC). Results The majority of opioid tablets shipped to WV from 2006 to 2014 were generic oxycodone or hydrocodone, not OxyContin. After accounting for a 6-month lag from ITSA models using the LOESS-identified change in prescription opioid shipments measured via dosage units (2011 Q3) resulted in the lowest AIC for both prescription (AIC = -188.6) and illicit opioid-involved overdoses (AIC = -189.4), indicating this intervention start date resulted in the preferred model. The second lowest AIC was for models using the ADF OxyContin release as an intervention start date. Discussion We found that illicit opioid overdoses in WV began increasing closer to when prescription opioid shipments to the state began decreasing, not when the ADF OxyContin release occurred. Similarly, the majority of opioid tablets shipped to the state for 2006–2014 were generic oxycodone or hydrocodone. This may indicate that diminishing prescription supplies had a larger impact on opioid overdose patterns than the ADF OxyContin release in WV.

IntroductionBilateral microprocessor-controlled prosthetic knee (MPK) users have unique needs in traversing environmental barriers compared to unilateral users. An enhancement to the Genium™/Genium X3™ MPK which included an updated ruleset, hydraulics, and new bilateral parameter presets was made to improve safety while stumbling and the smoothness of gait for all users while also improving the experience of bilateral users. The purpose of the study was to evaluate the effectiveness of the enhancements in a sample with unilateral and bilateral amputation.MethodsA convenience sample of MPK users was recruited from two sites in the USA in two phases. Assessments included the L-Test of Functional Mobility, Activity-specific Balance Confidence Scale, Prosthetic Limb User Survey of Mobility, a study-specific questionnaire, and the Comparative Activities of Daily Living (ADL) Questionnaire. Statistical significance of extracted data was tested with the Wilcoxon Rank-Sum Test for independent data and Wilcoxon Signed-Rank for paired data with an a priori significance level of p

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

Self-replicating RNA viruses such as alphaviruses, flaviviruses, paramyxoviruses, and rhabdoviruses have been engineered as expression vectors for vaccine development. The prominent feature of self-replicating RNA viruses is their RNA-dependent RNA polymerase activity, which generates massive self-amplification of RNA in the cytoplasm of infected host cells leading to extreme levels of transgene expression. Infectious diseases have been targeted by overexpression of surface proteins of pathogens as antigens for vaccine development. Moreover, overexpression of tumor-associated antigens and immunostimulatory genes has been the basis for cancer vaccines. Proof-of-concept of robust antigen-specific immune responses and protection against challenges with lethal doses of infectious agents have been demonstrated. Likewise, vaccine development against various cancers has elicited strong immune responses and resulted in tumor regression and eradication, cure, and prolonged survival in animal tumor models. Good safety and immune responses have been achieved in clinical trials. The ERVEBO® vaccine, based on the vesicular stomatitis virus, has been approved for immunization against the Ebola virus disease.

Abstract We have investigated the efficacy of superparamagnetic iron oxide nanoparticles (SPIONs) as positive T1 contrast agents for low-field magnetic resonance imaging (MRI) at 64 millitesla (mT). Iron oxide-based agents, such as the FDA-approved ferumoxytol, were measured using a variety of techniques to evaluate T1 contrast at 64 mT. Additionally, we characterized monodispersed carboxylic acid-coated SPIONs with a range of diameters (4.9–15.7 nm) in order to understand size-dependent properties of T1 contrast at low-field. MRI contrast properties were measured using 64 mT MRI, magnetometry, and nuclear magnetic resonance dispersion (NMRD). We also measured MRI contrast at 3 T to provide comparison to a standard clinical field strength. SPIONs have the capacity to perform well as T1 contrast agents at 64 mT, with measured longitudinal relaxivity (r1) values of up to 67 L mmol−1 s−1, more than an order of magnitude higher than corresponding r1 values at 3 T. The particles exhibit size-dependent longitudinal relaxivities and outperform a commercial Gd-based agent (gadobenate dimeglumine) by more than eight-fold at physiological temperatures. Additionally, we characterize the ratio of transverse to longitudinal relaxivity, r2/r1 and find that it is ~ 1 for the SPION based agents at 64 mT, indicating a favorable balance of relaxivities for T1-weighted contrast imaging. We also correlate the magnetic and structural properties of the particles with models of nanoparticle relaxivity to understand generation of T1 contrast. These experiments show that SPIONs, at low fields being targeted for point-of-care low-field MRI systems, have a unique combination of magnetic and structural properties that produce large T1 relaxivities.

BackgroundA large proportion of United States (U.S.) youth play basketball, baseball, softball, or T-ball. Each of the activities poses a documented risk of craniofacial and neck injuries. However, few studies have assessed the national prevalence of pediatric craniofacial and neck injuries in this population, particularly following the COVID-19 pandemic.MethodsThe National Electronic Injury Surveillance System (NEISS) dataset was used to identify pediatric craniofacial and neck injuries associated with basketball, baseball, softball, or T-ball from 2003-2022 in a cross-sectional study. The annual number of injuries before and after the onset of the COVID-19 pandemic with 95% confidence intervals were calculated. Interrupted time series analysis (ITSA) was used to estimate the pandemic's impact on the monthly number of injuries incurred nationally.ResultsBoth overall and stratified by sport involvement, the annual number and rate of injuries identified in NEISS decreased significantly after the COVID-19 pandemic. ITSA demonstrated that the monthly number of injuries decreased -4094.4 (95% CI = -5100, -3088.7) immediately after the beginning of the pandemic. The number of injuries began increasing towards pre-pandemic levels at a rate of 110.6 (95% CI = 64, 157.2) injuries per month after the initial plunge.ConclusionPrior to the-pandemic, there was a steady decline in craniofacial and neck injuries due to basketball, baseball, softball and T-ball among children, aged

Abstract Background Evidence suggests that rates of occupational injuries in the US are decreasing. As several different occupational injury surveillance systems are used in the US, more detailed investigation of this trend is merited. Furthermore, studies of this decrease remain descriptive and do not use inferential statistics. The aim of this study was to provide both descriptive and inferential statistics of temporal trends of occupational injuries treated in US emergency departments (EDs) for 2012 to 2019. Methods Monthly non-fatal occupational injury rates from 2012 to 2019 were estimated using the national electronic injury surveillance system—occupational supplement (NEISS-Work) dataset, a nationally representative sample of ED-treated occupational injuries. Rates were generated for all injuries and by injury event type using monthly full-time worker equivalent (FTE) data from the US Current Population Survey as a denominator. Seasonality indices were used to detect seasonal variation in monthly injury rates. Trend analysis using linear regression adjusted for seasonality was conducted to quantify changes in injury rates from 2012 to 2019. Results Occupational injuries occurred at an average rate of 176.2 (95% CI = ± 30.9) per 10,000 FTE during the study period. Rates were highest in 2012 and declined to their lowest level in 2019. All injury event types occurred at their highest rate in summer months (July or August) apart from falls, slips, and trips, which occurred at their highest rate in January. Trend analyses indicated that total injury rates decreased significantly throughout the study period (− 18.5%; 95% CI = ± 14.5%). Significant decreases were also detected for injuries associated with contact with foreign object and equipment (− 26.9%; 95% CI = ± 10.5%), transportation incidents (− 23.2%; 95% CI = ± 14.7%), and falls, slips, and trips (− 18.1%; 95% CI = ± 8.9%). Conclusions This study supports evidence that occupational injuries treated in US EDs have decreased since 2012. Potential contributors to this decrease include increased workplace mechanization and automation, as well as changing patterns in US employment and health insurance access.

Chronic brain recordings suggest that seizure risk is not uniform, but rather varies systematically relative to daily (circadian) and multiday (multidien) cycles. Here, one human and seven dogs with naturally occurring epilepsy had continuous intracranial EEG (median 298 days) using novel implantable sensing and stimulation devices. Two pet dogs and the human subject received concurrent thalamic deep brain stimulation (DBS) over multiple months. All subjects had circadian and multiday cycles in the rate of interictal epileptiform spikes (IES). There was seizure phase locking to circadian and multiday IES cycles in five and seven out of eight subjects, respectively. Thalamic DBS modified circadian (all 3 subjects) and multiday (analysis limited to the human participant) IES cycles. DBS modified seizure clustering and circadian phase locking in the human subject. Multiscale cycles in brain excitability and seizure risk are features of human and canine epilepsy and are modifiable by thalamic DBS.

A number of studies have explored whether language is grounded in action and perception, however little attention has been given to the sense of smell. Here we directly test whether olfactory information is necessary for comprehension of odor-related language, by comparing language performance in a group of participants with no sense of smell (anosmics) with a group of control participants with an intact sense of smell. We found no difference in comprehension of odor- and taste-related language between anosmics and controls using a lexical decision task, suggesting olfaction is not crucial to semantic representations of odor-related language. However, we did find that anosmics were better at remembering odor-related words than controls, and they also rated odor- and taste-related words as more positively valenced than control participants. We suggest odor-related language is more salient and emotional to anosmics because it reminds them of their missing sense. Overall, this study supports the proposal that odor-related language is not grounded in odor perception.

This study demonstrates how a team of librarians sustained authentic assessment across multiple studies in order to inform changes to an information literacy curriculum. It demonstrates the cyclical and action-based nature of assessment, including closing one loop only to reopen another and begin the assessment process again, emphasizing the importance of sustainability and making changes that increase student learning. Researchers analyzed 79 English composition papers for evidence of information literacy skills, expanding upon a previous study which established information literacy skill benchmarks. Findings from the previous study led to the development of new library instruction lessons, which targeted skills students struggled with -- mainly topic refinement and information synthesis. To measure the impact of the modifications, the authors used two rubrics as well as a citation analysis to identify shifts in student learning. Findings indicate that the new lessons contribute to student improvements in synthesis, topic refinement, and source variety. This study illustrates the importance of engaging in an ongoing cycle of assessment and continually making improvements to instruction practices while implementing evidence-based decisions.

Assignment design provides a potential niche for librarians to fill in improving research assignments and in providing opportunities for interdisciplinary collaboration on teaching, but this can be difficult work to claim as librarians. In the 2016-2017 academic year, a team of three librarians at Utah State University, a mid-size research university, piloted an assignment design workshop for faculty. Based on a model developed by the National Institute for Learning Outcomes Assessment (NILOA), our workshop's core component was a structured, librarian-facilitated small group discussion among three to four faculty members from a range of academic departments. Interdisciplinary conversation about teaching research skills thrived in these discussions (called "charrettes"), with librarians uniquely positioned to encourage knowledge sharing in service of student learning and success. This article presents three iterations of our workshops as a case study in information literacy intervention outside traditional classroom instruction sessions, extending and redefining the role of the academic librarian as a partner in teaching and learning.

Anomaly detection in multivariate time series is valuable for many applications. In this context, unsupervised and semi-supervised deep learning methods that estimate how normal a new observation is have shown promising results on benchmark datasets. These methods are dependent on a threshold that determines which points should be regarded as anomalous and not be anomalous. However, finding the optimal threshold is not easy since no information about the ground truth is known in advance, which implies that there are limitations to automatic threshold-setting methods available today. An alternative is to utilize the expertise of users that can interact in a threshold-setting procedure, but for this to be practically feasible, the method needs to be both accurate and efficient in relation to the state-of-the-art automatic methods. Therefore, this study develops an interactive threshold-setting schema and examines to what extent it can outperform the current state-of-the-art automatic threshold-setting methods. The result of the study strongly indicates that the suggested method with little effort can provide higher accuracy than the automatic threshold-setting methods on a general basis.

[This corrects the article DOI: 10.1371/journal.pcbi.1010527.].

Vaccine development against SARS-CoV-2 has been highly successful in slowing down the COVID-19 pandemic. A wide spectrum of approaches including vaccines based on whole viruses, protein subunits and peptides, viral vectors, and nucleic acids has been developed in parallel. For all types of COVID-19 vaccines, good safety and efficacy have been obtained in both preclinical animal studies and in clinical trials in humans. Moreover, emergency use authorization has been granted for the major types of COVID-19 vaccines. Although high safety has been demonstrated, rare cases of severe adverse events have been detected after global mass vaccinations. Emerging SARS-CoV-2 variants possessing enhanced infectivity have affected vaccine protection efficacy requiring re-design and re-engineering of novel COVID-19 vaccine candidates. Furthermore, insight is given into preparedness against emerging SARS-CoV-2 variants.

Teaching roles in academic libraries can be accompanied by a great deal of anxiety. This study surveyed librarian attitudes toward their teaching role and librarians' experiences with teaching anxiety. Sixty-four percent of librarians participating in the survey said they experienced teaching anxiety, including 65.10 percent experiencing physical symptoms and 73.43 percent of librarians who experienced teaching anxiety experiencing psychological symptoms. Findings indicate tension between sustaining a work-life balance and managing physical and psychological symptoms. Based on these findings, the researchers propose supports that can help mitigate the adverse effects of teaching anxiety on library instructors.

The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.

Self-replicating RNA viruses have been commonly used for preventive and therapeutic interventions in the fields of infectious diseases and cancers. Both RNA viruses with single-stranded RNA genomes of positive and negative polarity have been utilized. Expression of viral surface proteins from self-replicating RNA virus vectors has elicited strong immune responses and provided protection against challenges with lethal doses of pathogens in various animal models using recombinant viral particles, RNA replicons, or plasmid-based replicon vectors. Similarly, immunization with self-replicating RNA virus vectors expressing tumor antigens has induced tumor-specific antibody (Ab) responses, inhibited tumor growth, eradicated tumors, and protected immunized animals against tumor challenges. Clinical trials have demonstrated good safety and tolerance of self-replicating RNA viruses. Although the number of clinical trials is low, robust immune responses and protection against challenges with pathogens and tumor cells have been achieved. The Ervebo vaccine against Ebola virus disease has been approved by both the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA).

The scientific, private, and industrial sectors use a wide variety of technological platforms available to achieve protection against SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), including vaccines. However, the virus evolves continually into new highly virulent variants, which might overcome the protection provided by vaccines and may re-expose the population to infections. Mass vaccinations should be continued in combination with more or less mandatory non-pharmaceutical interventions. Therefore, the key questions to be answered are: (i) How to identify the primary and secondary infections of SARS-CoV-2? (ii) Why are neutralizing antibodies not long-lasting in both cases of natural infections and post-vaccinations? (iii) Which are the factors responsible for this decay in neutralizing antibodies? (iv) What strategy could be adapted to develop long-term herd immunity? (v) Is the Spike protein the only vaccine target or is a vaccine cocktail better?

Since its initial publication in 1986, ISO 5439 has been unclear on the evaluation of isolated and repeated shock vibration. In 2015, an expert’s workshop was held in conjunction with the 13th International Conference that identified features of a vibration signal that may be important for predicting health outcomes, including high-frequency vibration and shock. Since then, ISO standards have been drafted on the measurement of shock and measurement of high-frequency vibrations, and in the European Union, a proposed revision of machinery supply legislation will require manufacturers to provide information on the average peak amplitude of acceleration. The Nancy Workshop introduced the issue of hand-transmitted (mechanical) shock (HTS) and work currently active within International Standards groups, and discussed options for defining HTS measurement. The workshop concluded that HTS is likely to present different risks to those of continuous hand–arm vibration, and that, therefore, there is a need for different metrics, and that high-frequency vibration is an important component of many HTS sources, e.g., impact wrenches, nail guns, riveting hammers, etc.; therefore, HTS evaluation should include higher frequencies of vibration, possibly up to 10 kHz.

The importance of anomaly detection in multivariate time series has led to the development of several prominent deep learning solutions. As a part of the anomaly detection process, the scoring method has shown to be of significant importance when separating non-anomalous points from anomalous ones. At this time, most of the solutions utilize an aggregated score which means that relevant information created by the anomaly detection model might be lost. Therefore, this study has set out to examine to what extent anomaly detection in multivariate time series based on deep learning can be improved if all the residuals from each individual channel is considered in the anomaly score. To achieve this, an aggregated and separated scoring method has been applied with a simple denoising convolutional autoencoder. In addition, the performance has been compared with other state-of-the-art methods. The result showed that the separated approach has the potential to generate a significantly higher performance than the aggregated one. At the same time, there were some indications suggesting that an aggregated scoring is better at generalizing when no labels are available to select the anomaly thresholds. Therefore, the result should serve as an encouragement to use a separated scoring approach together with a small sample of labeled anomalies to optimize the thresholds. Lastly, due to the impact of the anomaly score, the result suggests that future research within this field should consider applying the same anomaly scoring method when comparing the performance of deep learning algorithms.

The relationship between macroscale electrophysiological recordings and the dynamics of underlying neural activity remains unclear. We have previously shown that low frequency EEG activity (