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BackgroundThe autistic population is rapidly increasing; meanwhile, autistic adults face disproportionate risks for adverse COVID-19 outcomes. Limited research indicates that autistic individuals have been accepting of initial vaccination, but research has yet to document this population’s perceptions and acceptance of COVID-19 boosters. ObjectiveThis study aims to identify person-level and community characteristics associated with COVID-19 vaccination and booster acceptance among autistic adults, along with self-reported reasons for their stated preferences. Understanding this information is crucial in supporting this vulnerable population given evolving booster guidelines and the ending of the public health emergency for the COVID-19 pandemic. MethodsData are from a survey conducted in Pennsylvania from April 11 to September 12, 2022. Demographic characteristics, COVID-19 experiences, and COVID-19 vaccine decisions were compared across vaccination status groups. Chi-square analyses and 1-way ANOVA were conducted to test for significant differences. Vaccination reasons were ranked by frequency; co-occurrence was identified using phi coefficient correlation plots. ResultsMost autistic adults (193/266, 72.6%) intended to receive or received the vaccine and booster, 15% (40/266) did not receive or intend to receive any vaccine, and 12.4% (33/266) received or intended to receive the initial dose but were hesitant to accept booster doses. Reasons for vaccine acceptance or hesitancy varied by demographic factors and COVID-19 experiences. The most significant were previously contracting COVID-19, desire to access information about COVID-19, and discomfort with others not wearing a mask (all P=.001). County-level factors, including population density (P=.02) and percentage of the county that voted for President Biden (P=.001) were also significantly associated with differing vaccination acceptance levels. Reasons for accepting the initial COVID-19 vaccine differed among those who were or were not hesitant to accept a booster. Those who accepted a booster were more likely to endorse protecting others and trusting the vaccine as the basis for their acceptance, whereas those who were hesitant about the booster indicated that their initial vaccine acceptance came from encouragement from someone they trusted. Among the minority of those hesitant to any vaccination, believing that the vaccine was unsafe and would make them feel unwell were the most often reported reasons. ConclusionsIntention to receive or receiving the COVID-19 vaccination and booster was higher among autistic adults than the population that received vaccines in Pennsylvania. Autistic individuals who accepted vaccines prioritized protecting others, while autistic individuals who were vaccine hesitant had safety concerns about vaccines. These findings inform public health opportunities and strategies to further increase vaccination and booster rates among generally accepting autistic adults, to better support the already strained autism services and support system landscape. Vaccination uptake could be improved by leveraging passive information diffusion to combat vaccination misinformation among those not actively seeking COVID-19 information to better alleviate safety concerns.

Background: National guidelines recommend penicillins (PCN) as first-line treatment for many common pediatric infections in the outpatient setting. Although less than 1% of the United States population has a true, IgE-mediated PCN-allergy, approximately 10% of patients are labeled with a PCN-allergy. Accurate adverse drug reaction (ADR) documentation plays an important role in this over-labeling. We have previously shown that nurses feel assessment and documentation of PCN-allergies are critical to their role. However, additional evidence purports nurse hesitancy to interrogate allergy accuracy or reclassify parent’s response to side effect. Our objective was to explore frontline clinicians’ confidence in assessing, documenting, and responding to PCN-allergy labels. Methods: To expose barriers and prioritize improvement ideas for a multidisciplinary quality improvement (QI) project aimed to improve PCN-allergy labeling in our pediatric urgent care clinics, we deployed this investigator-developed survey to prescribers and nurses. It’s comprised of 14-questions scored on a 5-point Likert scale (4 demographic, 4 PCN/safety, 3 allergy types, 4 allergy documentations, 3 treatment options), and 1 optional free-text. We used descriptive statistics to compare survey responses between prescribers and nurses and evaluated free text comments for themes. Results: Eighty-seven clinicians across 3 sites participated, with a response rate of 35%, with variation by sites (25.3% to 41.4%). Forty-one percent of (n=36) responders have been in practice >15 years and 40.2% (n=35) have worked at our hospital > 15 years (Table 1). Overall, perceived knowledge of PCN-allergies and safety was favorable (Table 2). Prescribers reported higher confidence with: 1) perceiving many patients who believe they are allergic to PCN can safety take PCN (prescribers median=5 [IQR: 4, 5] vs. nurses median=4 [4,4], p = 0.003); and 2) perceiving that time pressures influenced their ability to reconcile allergies and side effects (prescribers median=4 [4, 5] vs. nurses median=3 [2, 4], p = 0.001). Both prescribers and nurses reported lower confidence in continuing to administer or prescribe an antibiotic in the setting of a reported ADR. Thirteen respondents (15%) provided comments with specific requests for additional family education and practice guidance, including the referral process to subspecialty clinics for PCN-allergy testing. Conclusions: Our survey results identified barriers to accurate PCN-allergy labels, including knowledge on documentation, time pressures, hesitancy to challenge parent report, and uncertainty on referral process for PCN-allergy testing. This survey will inform future drivers for our QI. Opportunities include electronic medical record refinement, improving referrals to PCN-allergy de-labeling clinics, and the development of scripted education to guide family discussions.

The probabilistic Bayesian neural network(BNN) is good at providing trustworthy outcomes that is important, e.g. in intrusion detection. Due to the complex of probabilistic BNN, it is looks like a “black box”. The explanation of its prediction is needed for improving its transparency. However, there is no explanatory method to explain the prediction of probabilistic BNN for the reason of uncertainty. For enhance the explainability of BNN model concerning uncertainty quantification, this paper proposes a Bayesian explanatory model that accounts for uncertainties inherent in Bayesian Autoencoder, encompassing both aleatory and epistemic uncertainties. Through global and local explanations, this Bayesian explanatory model is applied to intrusion detection scenarios. Fidelity and sensitivity analyses showcase that the proposed Bayesian explanatory model, which incorporates external uncertainty, effectively identifies key features and provides robust explanations.

Recent breakthroughs in hierarchical multi-agent deep reinforcement learning (HMADRL) are propelling the development of sophisticated multi-robot systems, particularly in the realm of complex coordination tasks. These advancements hold significant potential for addressing the intricate challenges inherent in fast-evolving sectors such as intelligent manufacturing. In this study, we introduce an innovative simulator tailored for a multi-robot pick-and-place (PnP) operation, built upon the OpenAI Gym framework. Our aim is to demonstrate the efficacy of HMADRL algorithms for multi robot coordination in a manufacturing setting, concentrating on their influence on the gripping rate, a crucial indicator for gauging system performance and operational efficiency.

IntroductionTAM receptor-mediated efferocytosis plays an important function in immune regulation and may contribute to antigen tolerance in the lungs, a site with continuous cellular turnover and generation of apoptotic cells. Some studies have identified failures in efferocytosis as a common driver of inflammation and tissue destruction in lung diseases. Our study is the first to characterize the in vivo function of the TAM receptors, Axl and MerTk, in the innate immune cell compartment, cytokine and chemokine production, as well as the alveolar macrophage (AM) phenotype in different settings in the airways and lung parenchyma.MethodsWe employed MerTk and Axl defective mice to induce acute silicosis by a single exposure to crystalline silica particles (20 mg/50 μL). Although both mRNA levels of Axl and MerTk receptors were constitutively expressed by lung cells and isolated AMs, we found that MerTk was critical for maintaining lung homeostasis, whereas Axl played a role in the regulation of silica-induced inflammation. Our findings imply that MerTk and Axl differently modulated inflammatory tone via AM and neutrophil recruitment, phenotype and function by flow cytometry, and TGF-β and CXCL1 protein levels, respectively. Finally, Axl expression was upregulated in both MerTk-/- and WT AMs, confirming its importance during inflammation.ConclusionThis study provides strong evidence that MerTk and Axl are specialized to orchestrate apoptotic cell clearance across different circumstances and may have important implications for the understanding of pulmonary inflammatory disorders as well as for the development of new approaches to therapy.

Background: Surgeon visual estimation of shoulder range of motion (ROM) is commonplace in the outpatient office setting and routinely reported in clinical research, but the reliability and accuracy of this practice remain unclear. The purpose of this study is to establish the reliability and accuracy of remote visual estimation of shoulder ROM in healthy volunteers and symptomatic patients among a large group of shoulder surgeons. Our hypothesis is that remote visual estimation would be reliable and accurate compared with the digital goniometer method. Methods: Fifty shoulder surgeon members of the PacWest Shoulder and Elbow Society independently determined the active shoulder forward flexion (FF), internal rotation at 90° abduction (IR90), external rotation at 90° abduction, external rotation at the side , and maximal spinal level reached with internal rotation (IRspine) through visual estimation of video recordings taken from 10 healthy volunteers and 10 symptomatic patients. Variations in measurements were quantified using the interobserver reliability through calculation of the intraclass correlation coefficient. Accuracy was determined through comparison with digital goniometer measurements obtained with an on-screen protractor application using Bland–Altman mean differences and 95% limits of agreement. Results: The interobserver reliability among examiners showed moderate to excellent correlation, with intraclass correlation coefficient ranging from 0.768 to 0.928 for the healthy volunteers and 0.739 to 0.878 for the symptomatic patients. Accuracy was limited, with upper limits of agreement exceeding the established minimal clinically important differences (MCIDs) for FF (20° vs. MCID of 14°) and IR90 (25° vs. 18°) in the healthy volunteers and for FF (33° vs. 16°), external rotation at 90° abduction (21° vs. 18°), and IR90 (31° vs. 20°) in the symptomatic patients. Conclusion: Despite generally high intersurgeon reliability in the visual estimation of shoulder ROM, there was questionable accuracy when compared to digital goniometer measurements,with measurement errors often exceeding established MCID values. Given the potential implications for the clinical response to treatment and the significance of research findings, the adoption of validated instruments to measure ROM and the standardization of examination procedures should be considered.

This research study represents a polydexterous deep reinforcement learning-based pick-and-place framework for industrial clutter scenarios. In the proposed framework, the agent tends to learn the pick-and-place of regularly and irregularly shaped objects in clutter by using the sequential combination of prehensile and non-prehensile robotic manipulations involving different robotic grippers in a completely self-supervised manner. The problem was tackled as a reinforcement learning problem; after the Markov decision process (MDP) was designed, the off-policy model-free Q-learning algorithm was deployed using deep Q-networks as a Q-function approximator. Four distinct robotic manipulations, i.e., grasp from the prehensile manipulation category and inward slide, outward slide, and suction grip from the non-prehensile manipulation category were considered as actions. The Q-function comprised four fully convolutional networks (FCN) corresponding to each action based on memory-efficient DenseNet-121 variants outputting pixel-wise maps of action-values jointly trained via the pixel-wise parametrization technique. Rewards were awarded according to the status of the action performed, and backpropagation was conducted accordingly for the FCN generating the maximum Q-value. The results showed that the agent learned the sequential combination of the polydexterous prehensile and non-prehensile manipulations, where the non-prehensile manipulations increased the possibility of prehensile manipulations. We achieved promising results in comparison to the baselines, differently designed variants, and density-based testing clutter.

ObjectiveHere, we demonstrate the first successful use of static neural stimulation patterns for specific information content. These static patterns were derived by a model that was applied to a subject’s own hippocampal spatiotemporal neural codes for memory.ApproachWe constructed a new model of processes by which the hippocampus encodes specific memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of targeted content into short-term memory. A memory decoding model (MDM) of hippocampal CA3 and CA1 neural firing was computed which derives a stimulation pattern for CA1 and CA3 neurons to be applied during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task.Main resultsMDM electrical stimulation delivered to the CA1 and CA3 locations in the hippocampus during the sample phase of DMS trials facilitated memory of images from the DMS task during a delayed recognition (DR) task that also included control images that were not from the DMS task. Across all subjects, the stimulated trials exhibited significant changes in performance in 22.4% of patient and category combinations. Changes in performance were a combination of both increased memory performance and decreased memory performance, with increases in performance occurring at almost 2 to 1 relative to decreases in performance. Across patients with impaired memory that received bilateral stimulation, significant changes in over 37.9% of patient and category combinations was seen with the changes in memory performance show a ratio of increased to decreased performance of over 4 to 1. Modification of memory performance was dependent on whether memory function was intact or impaired, and if stimulation was applied bilaterally or unilaterally, with nearly all increase in performance seen in subjects with impaired memory receiving bilateral stimulation.SignificanceThese results demonstrate that memory encoding in patients with impaired memory function can be facilitated for specific memory content, which offers a stimulation method for a future implantable neural prosthetic to improve human memory.

Abstract The role of the immune microenvironment in maintaining disease remission in patients with multiple myeloma (MM) is not well understood. In this study, we comprehensively profile the immune system in patients with newly diagnosed MM receiving continuous lenalidomide maintenance therapy with the aim of discovering correlates of long-term treatment response. Leveraging single-cell RNA sequencing and T cell receptor β sequencing of the peripheral blood and CyTOF mass cytometry of the bone marrow, we longitudinally characterize the immune landscape in 23 patients before and one year after lenalidomide exposure. We compare patients achieving sustained minimal residual disease (MRD) negativity to patients who never achieved or were unable to maintain MRD negativity. We observe that the composition of the immune microenvironment in both the blood and the marrow varied substantially according to both MRD negative status and history of autologous stem cell transplant, supporting the hypothesis that the immune microenvironment influences the depth and duration of treatment response.

The North Pacific rim was home to an extinct group of semiaquatic marine mammals, the order Desmostylia, which superficially resembled hippos. Desmostylians are an uncommon fossil vertebrate in most localities where they occur, and Oligocene taxa particularly so. Beyond the type dentition and two femora of Cornwallius sookensis, and the skull and postcrania of Behemotops proteus, both previously described from the region of Muir Creek locality of the upper Oligocene Sooke Formation of southern coast of Vancouver Island, British Columbia, Canada, no other records from this site have been reported. Additional specimens of teeth have now been found in the Royal British Columbia Museum and Canadian Museum of Nature, including a long-forgotten and unreported paratype. In total, three additional teeth, one of B. proteus and two possibly belonging to C. sookensis, are reported here from its type locality. More importantly, the upper molar reported here, as well as UCMP 36078 from Baja California and USNM 181740 from Oregon, all resemble each other but should be considered as belonging to Cornwallius only with caution.

Abstract Background Computational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease. Methods We proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses. Results The backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype. Conclusion We found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks.

Abstract Background Platelets are recognized as key immune effectors, but they are targets of bacterial virulence factors. In the present study, we aimed to examine the relationship between early platelet dynamics and the outcome of Staphylococcus aureus bacteremia (SAB). Method Electronic medical records of adult patients hospitalized for SAB between July 2012 and November 2020 were retrospectively reviewed for relevant demographic, laboratory, and clinical data. The outcome endpoints were mortality and microbial persistence. Results Among the 811 patients evaluated, 29% experienced thrombocytopenia on Day 1. Platelet count nadir occurred on Days 2–3 following SAB onset, and Day 4 was a determining point of platelet count trajectory and mortality. Mortality risk was 6% or less for those with normal platelet count by Day 4 regardless of whether they experienced thrombocytopenia on Day 1, but the risk increased to 16–21% for those who experienced thrombocytopenia on Day 4 regardless of whether they had normal platelet count on Day 1 or sustained thrombocytopenia. The duration of bacteremia was prolonged by one day (median 3 d vs. 2 d) for those with sustained thrombocytopenia compared to those without. Conclusion Early platelet dynamics during SAB have prognostic significance and represent an early window for potential platelet-directed therapeutic interventions to improve outcome.

Closed-circuit television (CCTV) systems have become pivotal tools in modern urban surveillance and traffic management, contributing significantly to road safety and security. This paper introduces an effective solution that capitalizes on CCTV video analytics and an event-driven framework to provide real-time updates on road traffic events, enhancing road safety. Furthermore, this system minimizes the storage requirements for visual data while retaining crucial details related to road traffic events. To achieve this, a two-step approach is employed: (1) training a Deep Convolutional Neural Network (DCNN) model using synthetic data for the classification of road traffic (accident) events and (2) generating video summaries for the classified events. Privacy laws make it challenging to obtain extensive real-world traffic data from open-source datasets, and this challenge is addressed by creating a customised synthetic visual dataset for training. The evaluation of the synthetically trained DCNN model is conducted on ten real-time videos under varying environmental conditions, yielding an average accuracy of 82.3% for accident classification (ranging from 56.7% to 100%). The test video related to the night scene had the lowest accuracy at 56.7% because there was a lack of synthetic data for night scenes. Furthermore, five experimental videos were summarized through the proposed system, resulting in a notable 23.1% reduction in the duration of the original full-length videos. Overall, this proposed system holds significant promise for event-based training of intelligent vehicles in Intelligent Transport Systems (ITS), facilitating rapid responses to road traffic incidents and the development of advanced context-aware systems.

Deep learning techniques have been widely adopted for cyber defence applications such as malware detection and anomaly detection. The ever-changing nature of cyber threats has made cyber defence a constantly evolving field. Smart manufacturing is critical to the broader thrust towards Industry 4.0 and 5.0. Developing advanced technologies in smart manufacturing requires enabling a paradigm shift in manufacturing, while cyber-attacks significantly threaten smart manufacturing. For example, a cyber attack (e.g., backdoor) occurs during the model’s training process. Cyber attack affects the models and impacts the resultant output to be misled. Therefore, this paper proposes a novel and comprehensive framework for smart cyber defence in deep learning security. The framework collectively incorporates a threat model, data, and model security. The proposed framework encompasses multiple layers, including privacy and protection of data and models. In addition to statistical and intelligent model techniques for maintaining data privacy and confidentiality, the proposed framework covers the structural perspective, i.e., policies and procedures for securing data. The study then offers different methods to make the models robust against attacks coupled with a threat model. Along with the model security, the threat model helps defend the smart systems against attacks by identifying potential or actual vulnerabilities and putting countermeasures and control in place. Moreover, based on our analysis, the study provides a taxonomy of the backdoor attacks and defences. In addition, the study provides a qualitative comparison of the existing backdoor attacks and defences. Finally, the study highlights the future directions for backdoor defences and provides a possible way for further research.

Investigations of detection canine performance across breeds has yielded mixed results, often reporting behavioral differences observed by researchers or reported by handlers through surveys. The present study tested 34 narcotics detection canines; 25 Belgian Malinois and 9 German Shepherds to determine if there were any differences between the breeds in overall rate of detection accuracy and the number of false alerts in searches. As previously determined, results of the present study support the continued use of narcotic detection canines for law enforcement agencies (i.e., positive alert percentage >90%; false alert percentage

IntroductionRituximab (R), an anti-CD20 monoclonal antibody (mAb) and the world’s first approved antibody for oncology patients, was combined with the CHOP chemotherapy regimen and markedly improved the prognosis of all B- cell–derived lymphomas, the most common hematological malignancy worldwide. However, there is a 35% disease recurrence with no advancement in the first-line treatment since R was combined with the archetypal CHOP chemotherapy regimen nearly 30 years ago. There is evidence that R synergizes with chemotherapy, but the pharmacological interactions between R and CHOP or between newer anti-CD20 mAbs and CHOP remain largely unexplored.MethodsWe used in vitro models to score pharmacological interactions between R and CHOP across various lymphoma cell lines. We compared these pharmacological interactions to ofatumumab, a second-generation anti-CD20 mAb, and CHOP. Lastly, we used RNA-sequencing to characterize the transcriptional profiles induced by these two antibodies and potential molecular pathways that mediate their different effects.ResultsWe discovered vast heterogeneity in the pharmacological interactions between R and CHOP in a way not predicted by the current clinical classification. We then discovered that R and ofatumumab differentially synergize with the cytotoxic and cytostatic capabilities of CHOP in separate distinct subsets of B-cell lymphoma cell lines, thereby expanding favorable immunochemotherapy interactions across a greater range of cell lines beyond those induced by R-CHOP. Lastly, we discovered these two mAbs differentially modulate genes enriched in the JNK and p38 MAPK family, which regulates apoptosis and proliferation.DiscussionOur findings were completely unexpected because these mAbs were long considered to be biological and clinical equivalents but, in practice, may perform better than the other in a patient-specific manner. This finding may have immediate clinical significance because both immunochemotherapy combinations are already FDA-approved with no difference in toxicity across phase I, II, and III clinical trials. Therefore, this finding could inform a new precision medicine strategy to provide additional therapeutic benefit to patients with B-cell lymphoma using immunochemotherapy combinations that already meet the clinical standard of care.

Background In primary care, low health literacy, particularly reading ability, is associated with worse health outcomes. Most physicians do not receive feedback on the reading levels of written communication that they may provide to patients, including result letters. Objective Our study compares the readability of result letters, written by resident versus attending physicians, to patients with positive or negative screens for reading ability, as determined by the single-item literacy screener (SILS). Methods Result letters to 50 patients at high risk and 50 patients at low risk of low reading ability were randomly selected starting from January 1st, 2020 at Albany Medical Center. Flesch–Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman–Liau Index (CLI), Simple Measure of Gobbledygook (SMOG), and Flesch Reading Ease (FRE) were used to compare the readability of resident versus attending result letters. Results For all SILS levels, attending physicians wrote result letters at a lower grade level than resident physicians based on the FKGL, GFI, and SMOG indices. The FKGL, GFI, and SMOG readability scores of result letters written to patients with SILS 3–5 were also lower when written by attending physicians compared to resident physicians. Conclusions Result letters written by attending physicians may be easier to read than result letters written by resident physicians, especially for patients with low reading ability. Future electronic health record (EHR) software should give physicians and providers feedback on the reading level of their written communication.

Summary: Recent literature suggests that tactile events are represented in the primary somatosensory cortex (S1) beyond its long-established topography; in addition, the extent to which S1 is modulated by vision remains unclear. To better characterize S1, human electrophysiological data were recorded during touches to the forearm or finger. Conditions included visually observed physical touches, physical touches without vision, and visual touches without physical contact. Two major findings emerge from this dataset. First, vision strongly modulates S1 area 1, but only if there is a physical element to the touch, suggesting that passive touch observation is insufficient to elicit neural responses. Second, despite recording in a putative arm area of S1, neural activity represents both arm and finger stimuli during physical touches. Arm touches are encoded more strongly and specifically, supporting the idea that S1 encodes tactile events primarily through its topographic organization but also more generally, encompassing other areas of the body.

Abstract Background Although genome-wide association studies (GWAS) have successfully located various genetic variants susceptible to Alzheimer’s Disease (AD), it is still unclear how specific variants interact with genes and tissues to elucidate pathologies associated with AD. Summary-data-based Mendelian Randomization (SMR) addresses this problem through an instrumental variable approach that integrates data from independent GWAS and expression quantitative trait locus (eQTL) studies in order to infer a causal effect of gene expression on a trait. Results Our study employed the SMR approach to integrate a set of meta-analytic cis-eQTL information from the Genotype-Tissue Expression (GTEx), CommonMind Consortium (CMC), and Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) consortiums with three sets of meta-analysis AD GWAS results. Conclusions Our analysis identified twelve total gene probes (associated with twelve distinct genes) with a significant association with AD. Four of these genes survived a test of pleiotropy from linkage (the HEIDI test).Three of these genes – RP11-385F7.1, PRSS36, and AC012146.7 – have not yet been reported differentially expressed in the brain in the context of AD, and thus are the novel findings warranting further investigation.

Abstract Background Genetic study of quantitative biomarkers in Alzheimer’s Disease (AD) is a promising method to identify novel genetic factors and relevant endophenotypes, which provides valuable information to deconvolute mechanistic complexity and better understand disease subtypes. Results Using the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we performed a genome-wide association study (GWAS) between 565,373 single nucleotide polymorphisms (SNPs) and 16 key AD biomarkers from 1,576 subjects at four visits. We identified a novel locus rs5011804 at 12p12.1 significantly associated with several AD biomarkers, including three cognitive traits (CDRSB, FAQ, ADAS13) and one imaging trait (fusiform volume). Additional mediation and interaction analyses investigated the relationships among this SNP, relevant biomarkers, and clinical diagnosis, confirming and further elaborating the genetic effects seen in the GWAS. Conclusion Our GWAS not only affirms key AD genes but also suggests the promising role of the SNP rs5011804 due to its associations with several AD cognitive and imaging outcomes. The SNP rs5011804 has a reported association with adult asthma and slightly affects intracranial volume but has not been associated with AD before. Our novel findings contribute to a more comprehensive view of the molecular mechanism behind AD.

Rationale: Deep brain stimulation (DBS) of the hippocampus is proposed for enhancement of memory impaired by injury or disease. Many pre-clinical DBS paradigms can be addressed in epilepsy patients undergoing intracranial monitoring for seizure localization, since they already have electrodes implanted in brain areas of interest. Even though epilepsy is usually not a memory disorder targeted by DBS, the studies can nevertheless model other memory-impacting disorders, such as Traumatic Brain Injury (TBI). Methods: Human patients undergoing Phase II invasive monitoring for intractable epilepsy were implanted with depth electrodes capable of recording neurophysiological signals. Subjects performed a delayed-match-to-sample (DMS) memory task while hippocampal ensembles from CA1 and CA3 cell layers were recorded to estimate a multi-input, multi-output (MIMO) model of CA3-to-CA1 neural encoding and a memory decoding model (MDM) to decode memory information from CA3 and CA1 neuronal signals. After model estimation, subjects again performed the DMS task while either MIMO-based or MDM-based patterned stimulation was delivered to CA1 electrode sites during the encoding phase of the DMS trials. Each subject was sorted (post hoc) by prior experience of repeated and/or mild-to-moderate brain injury (RMBI), TBI, or no history (control) and scored for percentage successful delayed recognition (DR) recall on stimulated vs. non-stimulated DMS trials. The subject’s medical history was unknown to the experimenters until after individual subject memory retention results were scored. Results: When examined compared to control subjects, both TBI and RMBI subjects showed increased memory retention in response to both MIMO and MDM-based hippocampal stimulation. Furthermore, effects of stimulation were also greater in subjects who were evaluated as having pre-existing mild-to-moderate memory impairment. Conclusion: These results show that hippocampal stimulation for memory facilitation was more beneficial for subjects who had previously suffered a brain injury (other than epilepsy), compared to control (epilepsy) subjects who had not suffered a brain injury. This study demonstrates that the epilepsy/intracranial recording model can be extended to test the ability of DBS to restore memory function in subjects who previously suffered a brain injury other than epilepsy, and support further investigation into the beneficial effect of DBS in TBI patients.

The perinucleolar compartment (PNC) is a small nuclear body that plays important role in tumorigenesis. PNC prevalence correlates with poor prognosis and cancer metastasis. Its expression in pediatric Ewing sarcoma (EWS) has not previously been documented. In this study, we analyzed 40 EWS tumor cases from Caucasian and Hispanic patients for PNC prevalence by immunohistochemical detection of polypyrimidine tract binding protein and correlated the prevalence with dysregulated microRNA profiles. EWS cases showed staining ranging from 0 to 100%, which were categorized as diffuse (≥77%, n = 9, high PNC) or not diffuse (p = 0.017) and in patients who relapsed with metastatic disease (n = 4; p = 0.011). High PNC was associated with significantly shorter disease-free survival and early recurrence compared to those with low PNC. Using NanoString digital profiling, high PNC tumors revealed upregulation of eight and downregulation of 18 microRNAs. Of these, miR-320d and miR-29c-3p had the most significant differential expression in tumors with high PNC. In conclusion, this is the first study that demonstrates the presence of PNC in EWS, reflecting its utility as a predictive biomarker associated with tumor metastasis, specific microRNA profile, Hispanic ethnic origin, and poor prognosis.

Dental microwear is used to investigate feeding ecology. Animals ingest geological material in addition to food. The full effect of geological abrasives on tooth wear is unknown. To evaluate mineralogical abrasives as tooth wear agents, rats were fed food manufactured with quartz silt, diatomaceous earth, and calcium carbonate. Rats were assigned to treatments and fed for 15 days. Molars were scanned with a Sensofar Plu Neox confocal microscope and evaluated using ISO-25178-2 parameters and traditional microwear variables using light microscopy. Using a pellet-diet as the control, all treatments had influence on microwear and discriminant function analyses indicated that unique surface textures had been produced. ISO variables with high discriminatory values were correlated to scratch and pit frequencies, but more ISO parameters identified changes associated with numbers of scratches than changes associated with pits. The microwear changes associated with the abrasive inclusions were co-dependent on the type of diet that the abrasives had been added to. The abrasives had less effect with pellets but produced more modified and more differentiated microwear when added to the transgenic dough. Although abrasives produce distinctive surface textures, some knowledge of the properties of food with the abrasives is needed to identify the abrasive agent.

Acute intermittent porphyria (AIP) is a rare autosomal dominant inherited disease, predominantly seen in female patients, caused by mutations in the hydroxymethylbilane synthase gene. When impaired, elevated heme biosynthesis precursor levels accumulate in the liver, resulting in neurological symptoms, psychiatric disturbances, darkened urine color, abdominal pain, nausea, vomiting, and ileus. We present a 22-year-old Hispanic female with diffuse abdominal pain and no bowel movements for 8 days. She reported recent antibiotic and oral contraceptive pill use. Computerized tomography of her abdomen revealed a dilated small bowel and marked colonic distension. A colonoscopy found mild nonspecific inflammation in the rectosigmoid and terminal ileum. Her abdominal pain persisted despite interventions and improvements in appetite, bowel movements, abdominal imaging, and treatment of an identified Clostridium difficile infection. A random urine porphobilinogen was then obtained and found to be elevated. Fractionation of plasma and urine porphyrins was suggestive of AIP. Her symptoms improved with 3 days of intravenous (IV) hematin and IV dextrose. This is a unique case of a rare disease due to her clinical presentation with ileus, unremarkable past medical history, family history, and the prehospitalization and intrahospitalization factors that likely exacerbated the patient AIP.

Background: There is significant evidence for cognitive decline following deep brain stimulation (DBS). Current stimulation paradigms utilize gamma frequency stimulation for optimal motor benefits; however, little has been done to optimize stimulation parameters for cognition. Recent evidence implicates subthalamic nucleus (STN) theta oscillations in executive function, and theta oscillations are well-known to relate to episodic memory, suggesting that theta frequency stimulation could potentially improve cognition in Parkinson’s disease (PD). Objective: To evaluate the acute effects of theta frequency bilateral STN stimulation on executive function in PD versus gamma frequency and off, as well as investigate the differential effects on episodic versus nonepisodic verbal fluency. Methods: Twelve patients (all males, mean age 60.8) with bilateral STN DBS for PD underwent a double-blinded, randomized cognitive testing during stimulation at (1) 130–135 Hz (gamma), (2) 10 Hz (theta) and (3) off. Executive functions and processing speed were evaluated using verbal fluency tasks (letter, episodic category, nonepisodic category, and category switching), color-word interference task, and random number generation task. Performance at each stimulation frequency was compared within subjects. Results: Theta frequency significantly improved episodic category fluency compared to gamma, but not compared to off. There were no significant differences between stimulation frequencies in other tests. Conclusion: In this pilot trial, our results corroborate the role of theta oscillations in episodic retrieval, although it is unclear whether this reflects direct modulation of the medial temporal lobe and whether similar effects can be found with more canonical memory paradigms. Further work is necessary to corroborate our findings and investigate the possibility of interleaving theta and gamma frequency stimulation for concomitant motor and cognitive effects.

Abstract Background Human induced pluripotent stem cells (hiPSCs) hold enormous promise in accelerating breakthroughs in understanding human development, drug screening, disease modeling, and cell and gene therapies. Their potential, however, has been bottlenecked in a mostly laboratory setting due to bioprocess challenges in the scale-up of large quantities of high-quality cells for clinical and manufacturing purposes. While several studies have investigated the production of hiPSCs in bioreactors, the use of conventional horizontal-impeller, paddle, and rocking-wave mixing mechanisms have demonstrated unfavorable hydrodynamic environments for hiPSC growth and quality maintenance. This study focused on using computational fluid dynamics (CFD) modeling to aid in characterizing and optimizing the use of vertical-wheel bioreactors for hiPSC production. Methods The vertical-wheel bioreactor was modeled with CFD simulation software Fluent at agitation rates between 20 and 100 rpm. These models produced fluid flow patterns that mapped out a hydrodynamic environment to guide in the development of hiPSC inoculation and in-vessel aggregate dissociation protocols. The effect of single-cell inoculation on aggregate formation and growth was tested at select CFD-modeled agitation rates and feeding regimes in the vertical-wheel bioreactor. An in-vessel dissociation protocol was developed through the testing of various proteolytic enzymes and agitation exposure times. Results CFD modeling demonstrated the unique flow pattern and homogeneous distribution of hydrodynamic forces produced in the vertical-wheel bioreactor, making it the opportune environment for systematic bioprocess optimization of hiPSC expansion. We developed a scalable, single-cell inoculation protocol for the culture of hiPSCs as aggregates in vertical-wheel bioreactors, achieving over 30-fold expansion in 6 days without sacrificing cell quality. We have also provided the first published protocol for in-vessel hiPSC aggregate dissociation, permitting the entire bioreactor volume to be harvested into single cells for serial passaging into larger scale reactors. Importantly, the cells harvested and re-inoculated into scaled-up vertical-wheel bioreactors not only maintained consistent growth kinetics, they maintained a normal karyotype and pluripotent characterization and function. Conclusions Taken together, these protocols provide a feasible solution for the culture of high-quality hiPSCs at a clinical and manufacturing scale by overcoming some of the major documented bioprocess bottlenecks.

This conceptual paper overviews how blockchain technology is involving the operation of multi-robot collaboration for combating COVID-19 and future pandemics. Robots are a promising technology for providing many tasks such as spraying, disinfection, cleaning, treating, detecting high body temperature/mask absence, and delivering goods and medical supplies experiencing an epidemic COVID-19. For combating COVID-19, many heterogeneous and homogenous robots are required to perform different tasks for supporting different purposes in the quarantine area. Managmnt and decentralizing multi-robot play a vital role in combating COVID-19 by reducing human interaction, monitoring, delivering goods. Blockchain technology can manage multi-robot collaboration in a decentralized fashion, improve the interaction among them to exchange information, share representation, share goals, and trust. We highlight the challenges and provide the tactical solutions enabled by integrating blockchain and multi-robot collaboration to combat the COVID-19 pandemic. The proposed conceptual framework can increase the intelligence, decentralization, and autonomous operations of connected multi-robot collaboration in the blockchain network. We overview blockchain potential benefits to defining a framework of multi-robot collaboration applications to combat COVID-19 epidemics such as monitoring and outdoor and hospital End to End (E2E) delivery systems. Furthermore, we discuss the challenges and opportunities of integrated blockchain, multi-robot collaboration, and the Internet of Things (IoT) for combating COVID-19 and future pandemics.

The purpose of smart surveillance systems for automatic detection of road traffic accidents is to quickly respond to minimize human and financial losses in smart cities. However, along with the self-evident benefits of surveillance applications, privacy protection remains crucial under any circumstances. Hence, to ensure the privacy of sensitive data, European General Data Protection Regulation (EU-GDPR) has come into force. EU-GDPR suggests data minimisation and data protection by design for data collection and storage. Therefore, for a privacy-aware surveillance system, this paper targets the identification of two areas of concern: (1) detection of road traffic events (accidents), and (2) privacy preserved video summarization for the detected events in the surveillance videos. The focus of this research is to categorise the traffic events for summarization of the video content, therefore, a state-of-the-art object detection algorithm, i.e., You Only Look Once (YOLOv5), has been employed. YOLOv5 is trained using a customised synthetic dataset of 600 annotated accident and non-accident video frames. Privacy preservation is achieved in two steps, firstly, a synthetic dataset is used for training and validation purposes, while, testing is performed on real-time data with an accuracy from 55% to 85%. Secondly, the real-time summarized videos (reduced video duration to 42.97% on average) are extracted and stored in an encrypted format to avoid un-trusted access to sensitive event-based data. Fernet, a symmetric encryption algorithm is applied to the summarized videos along with Diffie–Hellman (DH) key exchange algorithm and SHA256 hash algorithm. The encryption key is deleted immediately after the encryption process, and the decryption key is generated at the system of authorised stakeholders, which prevents the key from a man-in-the-middle (MITM) attack.

In this study, we develop a framework for an intelligent and self-supervised industrial pick-and-place operation for cluttered environments. Our target is to have the agent learn to perform prehensile and non-prehensile robotic manipulations to improve the efficiency and throughput of the pick-and-place task. To achieve this target, we specify the problem as a Markov decision process (MDP) and deploy a deep reinforcement learning (RL) temporal difference model-free algorithm known as the deep Q-network (DQN). We consider three actions in our MDP; one is ‘grasping’ from the prehensile manipulation category and the other two are ‘left-slide’ and ‘right-slide’ from the non-prehensile manipulation category. Our DQN is composed of three fully convolutional networks (FCN) based on the memory-efficient architecture of DenseNet-121 which are trained together without causing any bottleneck situations. Each FCN corresponds to each discrete action and outputs a pixel-wise map of affordances for the relevant action. Rewards are allocated after every forward pass and backpropagation is carried out for weight tuning in the corresponding FCN. In this manner, non-prehensile manipulations are learnt which can, in turn, lead to possible successful prehensile manipulations in the near future and vice versa, thus increasing the efficiency and throughput of the pick-and-place task. The Results section shows performance comparisons of our approach to a baseline deep learning approach and a ResNet architecture-based approach, along with very promising test results at varying clutter densities across a range of complex scenario test cases.

Alzheimer's disease (AD) is the leading cause of dementia; however, men and women face differential AD prevalence, presentation, and progression risks. Characterizing metabolomic profiles during AD progression is fundamental to understand the metabolic disruptions and the biological pathways involved. However, outstanding questions remain of whether peripheral metabolic changes occur equally in men and women with AD. Here, we evaluated differential effects of metabolomic and brain volume associations between sexes. We used three cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI), evaluated 1,368 participants, two metabolomic platforms with 380 metabolites in total, and six brain segment volumes. Using dimension reduction techniques, we took advantage of the correlation structure of the brain volume phenotypes and the metabolite concentration values to reduce the number of tests while aggregating relevant biological structures. Using WGCNA, we aggregated modules of highly co-expressed metabolites. On the other hand, we used partial least squares regression-discriminant analysis (PLS-DA) to extract components of brain volumes that maximally co-vary with AD diagnosis as phenotypes. We tested for differences in effect sizes between sexes in the association between single metabolite and metabolite modules with the brain volume components. We found five metabolite modules and 125 single metabolites with significant differences between sexes. These results highlight a differential lipid disruption in AD progression between sexes. Men showed a greater negative association of phosphatidylcholines and sphingomyelins and a positive association of VLDL and large LDL with AD progression. In contrast, women showed a positive association of triglycerides in VLDL and small and medium LDL with AD progression. Explicitly identifying sex differences in metabolomics during AD progression can highlight particular metabolic disruptions in each sex. Our research study and strategy can lead to better-tailored studies and better-suited treatments that take sex differences into account.

Background: Acromioclavicular (AC) separations are commonly seen shoulder injuries. Numerous surgical reconstruction techniques have been described. In this study, we present a series of patients who underwent an anatomic reconstruction using a synthetic ligament and allograft construct. Methods: We performed a retrospective review of patients with type IV or V AC separations who underwent primary or revision AC reconstruction with a luggage-tag synthetic ligament and a semitendinosus allograft placed through the anatomic insertion sites of the coracoclavicular ligaments. Patient-reported outcomes, as well as complication rates, were recorded at a minimum 2-year follow-up. Results: Ten patients with a mean age of 44.2 ± 14.9 years were included in the study. The mean Disabilities of the Arm, Shoulder and Hand score was 15.5 ± 15.4; mean Single Assessment Numeric Evaluation score, 81.8 ± 12.1; mean Simple Shoulder Test score, 11.4 ± 1.1; mean American Shoulder and Elbow Surgeons score, 84.6 ± 15.7; mean Constant score, 82.5 ± 11.6; and mean visual analog scale score, 2 ± 2.6. Conclusion: The technique using a luggage-tag synthetic ligament along with an anatomic allograft coracoclavicular ligament reconstruction is a safe, effective alternative to other techniques described in the literature.

Abstract Human induced pluripotent stem cells (hiPSCs) have generated a great deal of attention owing to their capacity for self‐renewal and differentiation into the three germ layers of the body. Their discovery has facilitated a new era in biomedicine for understanding human development, drug screening, disease modeling, and cell therapy while reducing ethical issues and risks of immune rejection associated with traditional embryonic stem cells. Bioreactor‐based processes have been the method of choice for the efficient expansion and differentiation of stem cells in controlled environments. Current protocols for the expansion of hiPSCs use horizontal impeller, paddle, or rocking wave mixing method bioreactors which require large static cell culture starting populations and achieve only moderate cell fold increases. This study focused on optimizing inoculation, agitation, oxygen, and nutrient availability for the culture of hiPSCs as aggregates in single‐use, low‐shear, vertical‐wheel bioreactors. Under optimized conditions, we achieved an expansion of more than 30‐fold in 6 days using a small starting population of cells and minimal media resources throughout. Importantly, we showed that that this optimized bioreactor expansion protocol could be replicated over four serial passages resulting in a cumulative cell expansion of 1.06E6‐fold in 28 days. Cells from the final day of the serial passage were of high quality, maintaining a normal karyotype, pluripotent marker staining, and the ability to form teratomas in vivo. These findings demonstrate that a vertical‐wheel bioreactor‐based bioprocess can provide optimal conditions for efficient, rapid generation of high‐quality hiPSCs to meet the demands for clinical manufacturing of therapeutic cell products.

Reflecting upon the constructionist model "learning-bymaking," prototyping (prototype making) as a product design and research approach is well recognised for assured development of innovative concepts in individual or collaborative working environments. A prototype is typically used as a tool to support experiments or interventions and to evaluate research goals. It also facilitates participatory design and user-centred design. However, it carries both coded and tacit knowledge that we, design educators and practitioners, find problematic to explain and instruct, particularly to non-designers. This paper amalgamates and argues the characteristics of prototyping including types, formats, and principles through literature review. Reflecting upon the designer’s intentions and the dual coding cognitive learning process, the author proposes a descriptive model that illustrates the dual actions experienced by the designer which can enable study on the improvement of the prototyping process.

Surveillance video recording is a powerful method of deterring unlawful activities. A robust data protection-by-design solution can be helpful in terms of making a captured video immutable, as such recordings cannot become a piece of evidence until proven to be unaltered. Similarly, video sharing from closed-circuit television video recording or in social media interaction requires self-authentication for responsible and reliable data sharing. This article presents a computationally inexpensive method of preserving a chain-of-evidence in surveillance videos by means of hashing and steganography. The method conforms to the data protection regulations, which are increasingly adopted by governments, and is applicable to network edge storage. Encryption keys are stored in a hardware wallet independently of the video capture device itself, while evidential information is stored steganographically within video frames themselves, independently of the content. Added protection is provided by hiding information within the two least-valued of pixel bitplanes, using a newly introduced technique that randomizes the pixel storage locations on a per video frame and video-capture device basis. Overall, the proposed method has turned out to not only preserve the integrity of stored video data but also results in minimal degradation of the video data resulting from steganography. Despite the inclusion of hidden information, video frames will still be available for common image-processing tasks such as tracking and classification, as their objective video quality is almost unchanged.

Video Surveillance (VS) systems are commonly deployed for real-time abnormal event detection and autonomous video analytics. Video captured by surveillance cameras in real-time often contains identifiable personal information, which must be privacy protected, sometimes along with the locations of the surveillance and other sensitive information. Within the Surveillance System, these videos are processed and stored on a variety of devices. The processing and storage heterogeneity of those devices, together with their network requirements, make real-time surveillance systems complex and challenging. This paper proposes a surveillance system, named as Multi-Level Video Security (MuLViS) for privacy-protected cameras. Firstly, a Smart Surveillance Security Ontology (SSSO) is integrated within the MuLViS, with the aim of autonomously selecting the privacy level matching the operating device's hardware specifications and network capabilities. Overall, along with its device-specific security, the system leads to relatively fast indexing and retrieval of surveillance video. Secondly, information within the videos are protected at the times of capturing, streaming, and storage by means of differing encryption levels. An extensive evaluation of the system, through visual inspection and statistical analysis of experimental video results, such as by the Encryption Space Ratio (ESR), has demonstrated the aptness of the security level assignments. The system is suitable for surveillance footage protection, which can be made General Data Protection Regulation (GDPR) compliant, ensuring that lawful data access respects individuals' privacy rights.

COVID-19 has profoundly impacted the global economy, particularly the agricultural sector. However, relatively little attention has been paid to the relationship between COVID-19 and the farmland market. A few descriptive studies have speculated about the impact of COVID-19 on farmland prices but presented no quantitative evidence. This study provides quantitative evidence on the causal effect of COVID-19 on farmland prices using the difference-in-differences method with population-based data on farmland transactions in Taiwan. While prior descriptive studies argued that increased farmland prices associated with COVID-19 were largely driven by macroeconomic conditions, we found that the onset of COVID-19 increased farmland prices by 5.1%, even after controlling for macroeconomic conditions and parcel-level farmland characteristics. Furthermore, we found that government payments are likely responsible for these increases in farmland prices. Financial assistance easing the economic burdens of the agricultural sector can also stabilize farmland prices.

The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is a tool that helps to detect intrusions by inspecting the network traffic. Although many researchers have studied and created new IDS solutions, IDS still needs improving in order to have good detection accuracy while reducing false alarm rates. In addition, many IDS struggle to detect zero-day attacks. Recently, machine learning algorithms have become popular with researchers to detect network intrusion in an efficient manner and with high accuracy. This paper presents the concept of IDS and provides a taxonomy of machine learning methods. The main metrics used to assess an IDS are presented and a review of recent IDS using machine learning is provided where the strengths and weaknesses of each solution is outlined. Then, details of the different datasets used in the studies are provided and the accuracy of the results from the reviewed work is discussed. Finally, observations, research challenges and future trends are discussed.

Commercially housed Pekin ducks (Anas platyrhynchos) are typically reared in same sex groups to facilitate separate diet provisioning. Several female ducklings are sometimes mixed into the otherwise all-male pens. This practice is thought to increase flock reproductive success. To evaluate this hypothesis, we reared ducklings in alternating same-sex groups (150 hens or 30 drakes/pen; 8 groups/sex) and evaluated the impacts of rearing on drake mounting behavior, testosterone levels, and flock fertility. At 12 days, three females were placed into four of the male duckling pens. At 20–22 weeks of age, adjacent male and female pens were moved into pens within a breeder barn, and combined to form mixed-sex pens. The number of correctly aligned mounts performed by 10 focal drakes per pen was evaluated over 3 days (12 h/day) at 26, 32, and 45 weeks of age. Circulating testosterone concentrations were analyzed from blood plasma samples collected from the focal drakes at 15 (baseline), 22, 28, 34 and 45 weeks of age. Pen-level fertility was determined at 33–34 and 45–46 weeks of age. Mount and testosterone data were analyzed using a Generalized Linear Mixed Model and a Linear Mixed Model in R 4.0.5, with duck in pen as a random effect. A Linear Mixed Model was used to analyze fertility data, with pen as a random effect. None of the measured variables were impacted by rearing treatment, but all varied with flock age. Physical access to female ducklings during rearing did not enhance flock reproductive success.

This retrospective observational study evaluated outpatient treatment patterns among patients with molecular-based viral diagnostic testing for suspected upper respiratory tract infections in the United States. Patients with a respiratory viral test were identified from 1 August 2016 to 1 July 2019 in a large national reference laboratory database linked to IQVIA’s prescription and medical claims databases. Antibiotic and influenza antiviral treatment patterns were reported up to 7 days post-test result. Predictors of antibiotic utilization were assessed using multivariable logistic regression. Among 9561 patients included in the study, 24.6% had evidence of ≥1 filled antibiotic prescription. Antibiotic utilization was higher in patients who tested negative for all viral targets (odds ratio [OR], 1.33; 95% confidence interval [CI], 1.17–1.50) and patients positive for non-influenza viruses (OR, 1.28; 95% CI, 1.09–1.51) compared with those influenza-positive only. Age ≥ 50 years and location outside of the northeast United States also predicted antibiotic utilization. Influenza antivirals were more common in influenza-positive patients compared with patients with other test results (32.5% vs. 3.6–9.0%). Thus, in this real-world study, antibiotic utilization was elevated in patients positive for non-influenza viruses, although antibiotics would generally not be indicated. Further research on pairing diagnostic tools with outpatient antibiotic stewardship programs is needed.

In this work, the oxygen transport and hydrodynamic flow of the PBS Vertical-Wheel MINI™ 0.1 bioreactor were characterized using experimental data and computational fluid dynamics simulations. Data acquired from spectroscopy-based oxygenation measurements was compared with data obtained from 3D simulations with a rigid-lid approximation and LES-WALE turbulence modeling, using the open-source software OpenFOAM-8. The mass transfer coefficients were determined for a range of stirring speeds between 10 and 100 rpm and for working volumes between 60 and 100 mL. Additionally, boundary condition, mesh refinement, and temperature variation studies were performed. Lastly, cell size, energy dissipation rate, and shear stress fields were calculated to determine optimal hydrodynamic conditions for culture. The experimental results demonstrate that the kL can be predicted using Sh=1.68Re0.551Sc13G1.18, with a mean absolute error of 2.08%. Using the simulations and a correction factor of 0.473, the expression can be correlated to provide equally valid results. To directly obtain them from simulations, a partial slip boundary condition can be tuned, ensuring better near-surface velocity profiles or, alternatively, by deeply refining the mesh. Temperature variation studies support the use of this correlation for temperatures up to 37 °C by using a Schmidt exponent of 1/3. Finally, the flow was characterized as transitional with diverse mixing mechanisms that ensure homogeneity and suspension quality, and the results obtained are in agreement with previous studies that employed RANS models. Overall, this work provides new data regarding oxygen mass transfer and hydrodynamics in the Vertical-Wheel bioreactor, as well as new insights for air-water mass transfer modeling in systems with low interface deformation, and a computational model that can be used for further studies.

Objective To assess the impact of diabetes, hypertension and cardiovascular diseases on inpatient mortality from COVID-19, and its relationship to ethnicity and social deprivation.Design Retrospective, single-centre observational studySetting Birmingham, UK.Participants 907 hospitalised patients with laboratory-confirmed COVID-19 from a multi-ethnic community, admitted between 1 March 2020 and 31 May 2020.Main outcome measures The primary analysis was an evaluation of cardiovascular conditions and diabetes in relation to ethnicity and social deprivation, with the end-point of inpatient death or death within 30 days of discharge. A multivariable logistic regression model was used to calculate HRs while adjusting for confounders.Results 361/907 (39.8%) died in hospital or within 30 days of discharge. The presence of diabetes and hypertension together appears to confer the greatest mortality risk (OR 2.75; 95% CI 1.80 to 4.21; p65 years (OR 3.32; 95% CI 2.15 to 5.11), male sex (OR 2.04; 95% CI 1.47 to 2.82), hypertension (OR 1.69; 95% CI 1.10 to 2.61) and cerebrovascular disease (OR 1.87; 95% CI 1.31 to 2.68) were independently associated with increased risk of death. The mortality risk did not differ between the quintiles of deprivation. High-sensitivity troponin I was the best predictor of mortality among biomarkers (OR 4.43; 95% CI 3.10 to 7.10). Angiotensin-receptor blockers (OR 0.57; 95% CI 0.33 to 0.96) and ACE inhibitors (OR 0.65; 95% CI 0.43 to 0.97) were not associated with adverse outcome. The Charlson Index of Comorbidity scores were significantly higher in non-survivors.Conclusions The combined prevalence of hypertension and diabetes appears to confer the greatest risk, where diabetes may have a modulating effect. Hypertension and cerebrovascular disease had a significant impact on inpatient mortality. Social deprivation and ethnicity did not have any effect once the patient was in hospital.

Abstract Background Since their inception, human induced pluripotent stem cells (hiPSCs) have held much promise for pharmacological applications and cell-based therapies. However, their potential can only be realised if large numbers of cells can be produced reproducibly on-demand. While bioreactors are ideal systems for this task, due to providing agitation and control of the culture parameters, the common impeller geometries were not designed for the expansion of mammalian cells, potentially leading to sub-optimal results. Results This work reports for the first time the usage of the novel Vertical-Wheel single-use bioreactors for the expansion of hiPSCs as floating aggregates. Cultures were performed in the PBS MINI 0.1 bioreactor with 60 mL of working volume. Two different culture media were tested, mTeSR1 and mTeSR3D, in a repeated batch or fed-batch mode, respectively, as well as dextran sulfate (DS) supplementation. mTeSR3D was shown to sustain hiPSC expansion, although with lower maximum cell density than mTeSR1. Dextran sulfate supplementation led to an increase in 97 and 106% in maximum cell number when using mTeSR1 or mTeSR3D, respectively. For supplemented media, mTeSR1 + DS allowed for a higher cell density to be obtained with one less day of culture. A maximum cell density of (2.3 ± 0.2) × 106 cells∙mL− 1 and a volumetric productivity of (4.6 ± 0.3) × 105 cells∙mL− 1∙d− 1 were obtained after 5 days with mTeSR1 + DS, resulting in aggregates with an average diameter of 346 ± 11 μm. The generated hiPSCs were analysed by flow cytometry and qRT-PCR and their differentiation potential was assayed, revealing the maintenance of their pluripotency after expansion. Conclusions The results here described present the Vertical-Wheel bioreactor as a promising technology for hiPSC bioprocessing. The specific characteristics of this bioreactor, namely in terms of the innovative agitation mechanism, can make it an important system in the development of hiPSC-derived products under current Good Manufacturing Practices.