Introduction: Multiple risk factors of mortality have been identified in patients with COVID-19. Here, we sought to determine the effect of a history of neurological disorder and development of neurological manifestations on mortality in hospitalized patients with COVID-19.Methods: From March 20 to May 20, 2020, hospitalized patients with laboratory confirmed or highly suspected COVID-19 were identified at four hospitals in Ohio. Previous history of neurological disease was classified by severity (major or minor). Neurological manifestations during disease course were also grouped into major and minor manifestations. Encephalopathy, ischemic or hemorrhagic stroke, and seizures were defined as major manifestations, whereas minor neurological manifestations included headache, anosmia, dysgeusia, dizziness or vertigo, and myalgias. Multivariate logistic regression models were used to determine significant predictors of mortality in patients with COVID-19 infection.Results: 574/626 hospitalized patients were eligible for inclusion. Mean age of the 574 patients included in the analysis was 62.8 (SD 17.6), with 298 (51.9%) women. Of the cohort, 240(41.8%) patients had a prior history of neurological disease (HND), of which 204 (35.5%) had a major history of neurological disease (HND). Mortality rates were higher in patients with a major HND (30.9 vs. 15.4%; p = 0.00002), although this was not a significant predictor of death. Major neurological manifestations were recorded in 203/574 (35.4%) patients during disease course. The mortality rate in patients who had major neurological manifestations was 37.4% compared to 11.9% (p = 2 × 10 −12 ) in those who did not. In multivariate analysis, major neurological manifestation (OR 2.1,; p = 0.002) was a predictor of death. Conclusions: In this retrospective study, history of pre-existing neurological disease in hospitalized COVID-19 patients did not impact mortality; however, development of major neurological manifestations during disease course was found to be an independent predictor of death. Larger studies are needed to validate our findings.

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Multicore architecture is applied to contemporary avionics systems to deal with complex tasks. However, multicore architectures can cause interference by contention because the cores share hardware resources. This interference reduces the predictable execution time of safety-critical systems, such as avionics systems. To reduce this interference, methods of separating hardware resources or limiting capacity by core have been proposed. Existing studies have modified kernels to control hardware resources. Additionally, an execution model has been proposed that can reduce interference by adjusting the execution order of tasks without software modification. Avionics systems require several rigorous software verification procedures. Therefore, modifying existing software can be costly and time-consuming. In this work, we propose a method to apply execution models proposed in existing studies without modifying commercial real-time operating systems. We implemented the time-division multiple access (TDMA) and acquisition execution restitution (AER) execution models with pseudo-partition and message queuing on VxWorks 653. Moreover, we propose a multi-TDMA model considering the characteristics of the target hardware. For the interference analysis, we measured the L1 and L2 cache misses and the number of main memory requests. We demonstrated that the interference caused by memory sharing was reduced by at least 60% in the execution model. In particular, multi-TDMA doubled utilization compared to TDMA and also reduced the execution time by 20% compared to the AER model.

Background The purpose was to determine the association between infant exposure to humidifier disinfectant (HD) with neuropsychiatric problems in pre-school children. Methods A total of 2,150 children (age 4–11 months) were enrolled in the Panel Study of Korean Children (PSKC) study. The Korean version of the Child Behavior Checklist (CBCL) was used for assessments of neuropsychiatric problems. 1,113 children who participated in all the first to third PSKC studies and answered a question about HD exposure were finally enrolled. Results There were 717 (64.5%) children in non-HD group who were not exposed to HD and 396 (35.5%) in HD group with former exposure to HD. Exposure to HD was associated with total neuropsychiatric problems (adjusted odds ratio, aOR = 1.54, 95% CI = 1.15–2.06), being emotionally reactive (aOR = 1.55, 95% CI = 1.00–2.39), having attention problems (aOR = 1.96, 95% CI = 1.10–3.47), having oppositional defiant problems (aOR = 1.70, 95% CI = 1.07–2.71), and having attention deficit/hyperactivity problems (aOR = 11.57, 95% CI = 1.03–2.38). The risks for neuropsychiatric problems were clearly increased in boy, firstborn, and secondary smoker. Conclusions Exposure to HD during early childhood had a potential association with subsequent behavioral abnormalities.

When training a deep learning model with distributed training, the hardware resource utilization of each device depends on the model structure and the number of devices used for training. Distributed training has recently been applied to edge computing. Since edge devices have hardware resource limitations such as memory, there is a need for training methods that use hardware resources efficiently. Previous research focused on reducing training time by optimizing the synchronization process between edge devices or by compressing the models. In this paper, we monitored hardware resource usage based on the number of layers and the batch size of the model during distributed training with edge devices. We analyzed memory usage and training time variability as the batch size and number of layers increased. Experimental results demonstrated that, the larger the batch size, the fewer synchronizations between devices, resulting in less accurate training. In the shallow model, training time increased as the number of devices used for training increased because the synchronization between devices took more time than the computation time of training. This paper finds that efficient use of hardware resources for distributed training requires selecting devices in the context of model complexity and that fewer layers and smaller batches are required for efficient hardware use.

Periodic discharges seen in patients undergoing continuous EEG pose a challenge to physicians in falling into the interictal-ictal continuum and need for appropriate diagnostic and management approach. The authors herein present an 83-year-old male patient with an acute subdural hematoma who developed intermittent right upper and lower extremity weakness with change in mental status post craniotomy and hematoma evacuation. The patient was intubated and sedated with midazolam and propofol and placed on anti-seizure medications to control seizures. He was placed on continuous EEG monitoring and later developed lateralized periodic discharges. To guide treatment, a computed tomography perfusion study of the brain was performed. The results were consistent with an ictal phenomenon because of the presence of left hemispheric hyperemia. This increased cerebral blood volume and blood flow was used as a diagnostic tool to warrant more aggressive antiepileptic regimen. The patient was then started on an additional anti-seizure medication and dosages were further optimized. This led to an improvement in the patient's EEG and clinical state. There are numerous studies that show lateralized periodic discharges are seen as examples of subclinical status epilepticus and demonstrate hypermetabolism and hyperemia on positron emission tomography and single-photon emission computed tomography scans. When such imaging modalities are unavailable, cortical hyperperfusion on a computed tomography perfusion study might be a valid predictor of ongoing, subclinical, ictal activity and may be used as a complementary diagnostic tool to EEG to guide treatment.

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