Mosesramirez3951

uture clinical guidelines for these patients.

Patients affected by SMA type 2 presented significantly higher apnea-hypopnea indices than controls; differences in sleep architecture identified include decreased total sleep time, increased percentage of stage N1 of NREM sleep as well as increased sleep fragmentation seen in the SMA type 2 group, due to respiratory related arousals. We would like to point out that validated pediatric sleep questionnaires in general population, may not be useful tools when screening for SDB in these patients. This should be taken into consideration in clinical practice and in the elaboration of future clinical guidelines for these patients.

To develop a 2-stage discrete events simulation (DES) based framework for the evaluation of elective surgery cancellation strategies and resumption scenarios across multiple operational outcomes.

Study data was derived from the data warehouse and domain knowledge on the operational process of the largest tertiary hospital in Singapore. 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 were extracted for the study. A clustering approach was used in stage 1 of the modelling framework to develop the groups of surgeries that followed distinctive postponement patterns. These clusters were then used as inputs for stage 2 where the DES model was used to evaluate alternative phased resumption strategies considering the outcomes of OR utilization, waiting times to surgeries and the time to clear the backlogs.

The tool enabled us to understand the elective postponement patterns during the COVID-19 partial lockdown period, and evaluate the be disciplines performed from 1 January 2019 to 31 May 2020 captured in the Singapore General Hospital (SGH) enterprise data warehouse. The outcomes evaluated were OR utilization, waiting times to surgeries and time to clear the backlogs. A user-friendly visualization interface was developed to enable decision makers to determine the most promising surgery resumption strategy across these outcomes. Hospitals globally can make use of the modelling framework to adapt to their own surgical systems to evaluate strategies for postponement and resumption of elective surgeries.

Patients admitted to the emergency department (ED) with COVID-19 symptoms are routinely required to have chest radiographs and computed tomography (CT) scans. COVID-19 infection has been directly related to the development of acute respiratory distress syndrome (ARDS) and severe infections could lead to admission to intensive care and increased risk of death. The use of clinical data in machine learning models available at time of admission to ED can be used to assess possible risk of ARDS, the need for intensive care (admission to the Intensive Care Unit; ICU) as well as risk of mortality. In addition, chest radiographs can be inputted into a deep learning model to further assess these risks.

This research aimed to develop machine and deep learning models using both structured clinical data and image data from the electronic health record (EHR) to predict adverse outcomes following ED admission.

Light Gradient Boosting Machine (LightGBM) was used as the main machine learning algorithm using all clinicants admitted to the ED with or without COVID-19 symptoms.

The results can help in clinical decision making, especially when addressing ARDS and mortality, during the assessment of patients admitted to the ED with or without COVID-19 symptoms.

To accelerate healthcare/genomic medicine research and facilitate quality improvement, researchers have started cross-institutional collaborations to use artificial intelligence on clinical/genomic data. However, there are real-world risks of incorrect models being submitted to the learning process, due to either unforeseen accidents or malicious intent. This may reduce the incentives for institutions to participate in the federated modeling consortium. Existing methods to deal with this "model misconduct" issue mainly focus on modifying the learning methods, and therefore are more specifically tied with the algorithm.

In this paper, we aim at solving the problem in an algorithm-agnostic way by (1) designing a simulator to generate various types of model misconduct, (2) developing a framework to detect the model misconducts, and (3) providing a generalizable approach to identify model misconducts for federated learning. We considered the following three categories Plagiarism, Fabrication, and Falsification, and then developed a detection framework with three components Auditing, Coefficient, and Performance detectors, with greedy parameter tuning.

We generated 10 types of misconducts from models learned on three datasets to evaluate our detection method. Our experiments showed high recall with low added computational cost. Our proposed detection method can best identify the misconduct on specific sites from any learning iteration, whereas it is more challenging to precisely detect misconducts for a specific site and at a specific iteration.

We anticipate our study can support the enhancement of the integrity and reliability of federated machine learning on genomic/healthcare data.

We anticipate our study can support the enhancement of the integrity and reliability of federated machine learning on genomic/healthcare data.This paper examines the mental health and substance use impacts of the COVID-19 pandemic among sexual and gender minority (SGM) populations as compared to non-SGM populations, and identifies risk factors for mental health and substance use impacts among SGM groups. Data were drawn from two rounds of a repeated cross-sectional monitoring survey of 6027 Canadian adults, with Round 1 conducted May 14-19, 2020 and Round 2 conducted September 14-21, 2020. Bivariate cross-tabulations with chi-square tests were utilized to identify differences in mental health and substance use outcomes between SGM and non-SGM groups. Separate multivariable logistic regression models were used to identify risk factors for mental health and substance use outcomes for all SGM respondents. Compared to non-SGM respondents, a greater proportion of SGM participants reported mental health and substance use impacts of the COVID-19 pandemic, including deterioration in mental health, poor coping, suicidal thoughts, self-harm, alcohol and cannabis use, and use of substances to cope. Among SGM respondents, various risk factors, including having a pre-existing mental health condition, were identified as associated with mental health and substance use impacts. These widening inequities demonstrate the need for tailored public mental health actions during and beyond the pandemic.Granules recovered from a highly reduced anaerobic digester were capable of active nitrogen removal in the absence of exogenous electron donors, averaging 0.25 mg mgNO3--N /gVSS/d over 546 days of operation. Electron mass balance indicated that about half the influent nitrate was converted to ammonia via DNRA and another half denitrified. TVB-3166 This capacity was associated with an onion-like structure of multiple layers enriched in reduced iron and sulfur, and a complex microbial community shown by metagenomic sequencing to consist of multiple physiological groups and associated activities, including methanogenesis, denitrification, dissimilatory nitrate reduction to ammonia (DNRA), iron oxidation and reduction, and sulfur reduction and oxidation. Nitrate reduction was supported by both entrained organic material and reduced iron and sulfur species, corresponding to 2.13 mg COD/gVSS/d. Batch incubations showed that approximately 15% of denitrified nitrate was coupled to the oxidation of sulfur derived from both sulfate respiration and granular material enriched in iron-sulfide. Inhibition of sulfate reduction resulted in redirection of electron flow to methanogenesis and, in combination with other batch tests, showed that these granules supported a complex microbial community in which cryptic redox cycles linked carbon, sulfur, and iron oxidation with nitrate, sulfate, iron, and carbon dioxide reduction. This system shows promise for treatment of nitrate contaminated ground water without addition of an external organic carbon source as well as wastewater treatment in combination with (granular) sludge elimination leading in a net reduction of solid treatment costs.Wastewater treatment plant effluents and releases from rainwater overflow basins can contribute to the input of genotoxic micropollutants in aquatic ecosystems. Predominantly lipophilic genotoxic compounds tend to sorb to particulate matter, making sediment a source and a sink of pollution. Therefore, the present study aims to investigate the genotoxic potential of freshwater sediments (i) during the dry period and (ii) after extensive rain events by collecting sediment samples in one small anthropogenically impacted river in Germany up- and downstream of the local wastewater treatment plant. The Micronucleus and Ames fluctuation assays with Salmonella typhimurium strains TA98, TA100, YG1041, and YG1042 were used to assess the genotoxic potential of organic sediment extracts. For evaluation of possible genotoxicity drivers, target analysis for 168 chemical compounds was performed. No clastogenic effects were observed, while the genotoxic potential was observed at all sampling sites primarily driven by polycyclic aromatic hydrocarbons, nitroarenes, aromatic amines, and polycyclic heteroarenes. Freshwater sediments' genotoxic potential increased after extensive rain events due to sediment perturbation and the rainwater overflow basin release. In the present study, the rainwater overflow basin was a significant source for particle-bound pollutants from untreated wastewater, suggesting its role as a possible source of genotoxic potential. The present study showed high sensitivity and applicability of the bacterial Salmonella typhimurium strains YG1041 and YG1042 to organic sediment extracts to assess the different classes of genotoxic compounds. A combination of effect-based methods and a chemical analysis was shown as a suitable tool for a genotoxic assessment of freshwater sediments.Riverbank filtration is an established and quantitatively important approach to mine high-quality raw water for drinking water production. Bacterial fecal indicators are routinely used to monitor hygienic raw water quality, however, their applicability in viral contamination has been questioned repeatedly. Additionally, there are concerns that the increasing frequency and intensity of meteorological and hydrological events, i.e., heavy precipitation and droughts leading to high and low river levels, may impair riverbank filtration performance. In this study, we explored the removal of adenovirus compared with several commonly used bacterial and viral water quality indicators during different river levels. In a seasonal study, water from the Rhine River, a series of groundwater monitoring wells, and a production well were regularly collected and analyzed for adenovirus, coliphages, E. coli, C. perfringens, coliform bacteria, the total number of prokaryotic cells (TCC), and the number of virus-like particles (TVPC) using molecular and cultivation-based assays.