Retaining System for a Dynamometer

Tato diplomová práce je zaměřena na optimalizaci sloupku zádržného systému užívaného na válcových dynamometrech. Dále pak na úpravu objímky sloužící k zavěšení háku řetězu držícího vůz. V úvodní části je vysvětlen důvod užití zádržných systémů, dále vznik problému, díky kterému je nutné současný sloupek optimalizovat, a nakonec rozdělení zádržných systémů. Druhá část obsahuje výpočty týkající se stávajícího řešení sloupku a výstupem je graf popisující rozsah užití tohoto řešení. Třetí část je zaměřena na experiment, jeho přípravu a samotné měření na zkušebním válcovém dynamometru. Čtvrtá část obsahuje návrh nového řešení sloupku a objímky na základě analytických výpočtů upravených dle poznatků z experimentu a obdobně vytvořeného MKP modelu.This master thesis is focused on optimalization of a retaining system column used on chassis dynamometers. Further on adjustment of a clip, which is used for hanging up of a chain, which retains a car. In the first part a reason of using retaining system is explained, after that an issue is described and whole part is finished by sorting of retaining systems. The second part contains calculations regards to the original column and output is a graph describing range of use of the original column. The third part is focused on preparation of an experiment, calibration of tenzometric senzors and measurement itself on a trial chassis dynamometer. The fourth part contains design of the new column and clip on base of analytical calculations adjusted according the experiment and likewise created FEM model.347 - Katedra částí a mechanismů strojůvýborn

The genomic epidemiology of multi-drug resistant invasive non-typhoidal Salmonella in selected sub-Saharan African countries

Funder: Swedish International Development Cooperation Agency (SIDA)Funder: Government of Republic of KoreaFunder: US Centers for Disease Control and PreventionBackground: Invasive non-typhoidal Salmonella (iNTS) is one of the leading causes of bacteraemia in sub-Saharan Africa. We aimed to provide a better understanding of the genetic characteristics and transmission patterns associated with multi-drug resistant (MDR) iNTS serovars across the continent. Methods: A total of 166 iNTS isolates collected from a multi-centre surveillance in 10 African countries (2010–2014) and a fever study in Ghana (2007–2009) were genome sequenced to investigate the geographical distribution, antimicrobial genetic determinants and population structure of iNTS serotypes–genotypes. Phylogenetic analyses were conducted in the context of the existing genomic frameworks for various iNTS serovars. Population-based incidence of MDR-iNTS disease was estimated in each study site. Results: Salmonella Typhimurium sequence-type (ST) 313 and Salmonella Enteritidis ST11 were predominant, and both exhibited high frequencies of MDR; Salmonella Dublin ST10 was identified in West Africa only. Mutations in the gyrA gene (fluoroquinolone resistance) were identified in S. Enteritidis and S. Typhimurium in Ghana; an ST313 isolate carrying blaCTX-M-15 was found in Kenya. International transmission of MDR ST313 (lineage II) and MDR ST11 (West African clade) was observed between Ghana and neighbouring West African countries. The incidence of MDR-iNTS disease exceeded 100/100 000 person-years-of-observation in children aged <5 years in several West African countries. Conclusions: We identified the circulation of multiple MDR iNTS serovar STs in the sampled sub-Saharan African countries. Investment in the development and deployment of iNTS vaccines coupled with intensified antimicrobial resistance surveillance are essential to limit the impact of these pathogens in Africa

Incidence of invasive salmonella disease in sub-Saharan Africa: a multicentre population-based surveillance study.

BACKGROUND: Available incidence data for invasive salmonella disease in sub-Saharan Africa are scarce. Standardised, multicountry data are required to better understand the nature and burden of disease in Africa. We aimed to measure the adjusted incidence estimates of typhoid fever and invasive non-typhoidal salmonella (iNTS) disease in sub-Saharan Africa, and the antimicrobial susceptibility profiles of the causative agents. METHODS: We established a systematic, standardised surveillance of blood culture-based febrile illness in 13 African sentinel sites with previous reports of typhoid fever: Burkina Faso (two sites), Ethiopia, Ghana, Guinea-Bissau, Kenya, Madagascar (two sites), Senegal, South Africa, Sudan, and Tanzania (two sites). We used census data and health-care records to define study catchment areas and populations. Eligible participants were either inpatients or outpatients who resided within the catchment area and presented with tympanic (≥38·0°C) or axillary temperature (≥37·5°C). Inpatients with a reported history of fever for 72 h or longer were excluded. We also implemented a health-care utilisation survey in a sample of households randomly selected from each study area to investigate health-seeking behaviour in cases of self-reported fever lasting less than 3 days. Typhoid fever and iNTS disease incidences were corrected for health-care-seeking behaviour and recruitment. FINDINGS: Between March 1, 2010, and Jan 31, 2014, 135 Salmonella enterica serotype Typhi (S Typhi) and 94 iNTS isolates were cultured from the blood of 13 431 febrile patients. Salmonella spp accounted for 33% or more of all bacterial pathogens at nine sites. The adjusted incidence rate (AIR) of S Typhi per 100 000 person-years of observation ranged from 0 (95% CI 0-0) in Sudan to 383 (274-535) at one site in Burkina Faso; the AIR of iNTS ranged from 0 in Sudan, Ethiopia, Madagascar (Isotry site), and South Africa to 237 (178-316) at the second site in Burkina Faso. The AIR of iNTS and typhoid fever in individuals younger than 15 years old was typically higher than in those aged 15 years or older. Multidrug-resistant S Typhi was isolated in Ghana, Kenya, and Tanzania (both sites combined), and multidrug-resistant iNTS was isolated in Burkina Faso (both sites combined), Ghana, Kenya, and Guinea-Bissau. INTERPRETATION: Typhoid fever and iNTS disease are major causes of invasive bacterial febrile illness in the sampled locations, most commonly affecting children in both low and high population density settings. The development of iNTS vaccines and the introduction of S Typhi conjugate vaccines should be considered for high-incidence settings, such as those identified in this study. FUNDING: Bill & Melinda Gates Foundation

The phylogeography and incidence of multi-drug resistant typhoid fever in sub-Saharan Africa.

There is paucity of data regarding the geographical distribution, incidence, and phylogenetics of multi-drug resistant (MDR) Salmonella Typhi in sub-Saharan Africa. Here we present a phylogenetic reconstruction of whole genome sequenced 249 contemporaneous S. Typhi isolated between 2008-2015 in 11 sub-Saharan African countries, in context of the 2,057 global S. Typhi genomic framework. Despite the broad genetic diversity, the majority of organisms (225/249; 90%) belong to only three genotypes, 4.3.1 (H58) (99/249; 40%), 3.1.1 (97/249; 39%), and 2.3.2 (29/249; 12%). Genotypes 4.3.1 and 3.1.1 are confined within East and West Africa, respectively. MDR phenotype is found in over 50% of organisms restricted within these dominant genotypes. High incidences of MDR S. Typhi are calculated in locations with a high burden of typhoid, specifically in children aged <15 years. Antimicrobial stewardship, MDR surveillance, and the introduction of typhoid conjugate vaccines will be critical for the control of MDR typhoid in Africa

Influenza laboratory testing and its application in timely Department of Defense biosurveillance

ObjectiveTo describe influenza laboratory testing and results in the Military Health System and how influenza laboratory results may be used in DoD Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE)IntroductionTimely influenza data can help public health decision-makers identify influenza outbreaks and respond with preventative measures. DoD ESSENCE has the unique advantage of ingesting multiple data sources from the Military Health System (MHS), including outpatient, inpatient, and emergency department (ED) medical encounter diagnosis codes and laboratory-confirmed influenza data, to aid in influenza outbreak monitoring. The Influenza-like Illness (ILI) syndrome definition includes ICD-9 or ICD-10 codes that may increase the number of false positive alerts. Laboratory-confirmed influenza data provides an increased positive predictive value (PPV). The gold standard for influenza testing is molecular assays or viral culture. However, the tests may take 3-10 days to result. Rapid influenza diagnostic tests (RIDTs) have a lower sensitivity, but the timeliness of receiving a result improves to within &lt;15 minutes. We evaluate the utility of RIDTs for routine ILI surveillance.MethodsAdministrative medical encounters for ILI and influenza laboratory-confirmed data were analyzed from the MHS from June 2013 – September 2017 (Figure 1). The medical encounters and laboratory data include outpatient, inpatient, and ED data. The ILI syndrome case definition is a medical encounter during the study period with an ICD-9 or ICD-10 codes in any diagnostic position (ICD-9 codes = 79.99, 382.9, 460, 461.9, 465.8, 465.9, 466.0, 486, 487.0, 487.1, 487.8, 488, 490, 780.6, or 786.2; ICD-10 codes = B97.89, H66.9, J00, J01.9, J06.9, J09, J09.X, J10, J10.0, J10.1, J10.2, J10.8, J11, J11.0, J11.1, J11.2, J11.8, J12.89, J12.9, J18, J20.9, J40, R05, R50.9). The ILI dataset was limited to care provided in the MHS as laboratory data is only available for direct care. We describe influenza laboratory testing practices in the MHS. We aggregated the ILI encounters and RIDT positive results into daily counts and generated a weekly Pearson’s correlation.ResultsInfluenza tests are ordered throughout the year; the mean weekly percentage of ILI encounters in which an influenza laboratory test is ordered is 5.62%, with a range from 0.68% in the off season to 19.2% during peak influenza activity. The mean weekly percentage of positive influenza laboratory results among all ILI encounters is 0.82%, with a range from 0.01% to 5.73% (Figure 2). The percent of ILI encounters in which a test is ordered increases as the influenza season progresses. Influenza laboratory tests conducted in the MHS include RIDTs, PCR, culture, and DFA. Among all influenza tests ordered in the MHS, 66.0% were RIDTs, 22.7% were PCR, and 11.3% were viral culture. Often, a confirmatory test is ordered following a RIDT; 20% of RIDTs have follow-up tests. The mean timeliness of influenza test result data in the MHS was 11.26 days for viral culture, 2.94 days for PCR, and 0.11 days for RIDTs. The RIDT results were moderately correlated with ILI encounters for the entire year (mean weekly Pearson correlation coefficient rho=0.60, 95% CI: 0.55, 0.66, Figure 3). During the influenza season, the mean weekly Pearson correlation coefficient increases to rho=0.75, 95% CI: 0.70, 0.79.ConclusionsThe DoD has the unique advantage of access to the electronic health record and laboratory tests and results of all MHS beneficiaries. This analysis provides evidence for increased utilization of positive RIDTs in ESSENCE. The moderate correlation between the ILI syndrome and positive RIDTs may be associated with ICD-10 codes included in the ILI syndrome definition that contribute to false positive influenza cases. Ongoing research is focused on improving this ILI syndrome definition using ICD-10 codes. Rapid influenza diagnostic tests provide more timely results than other influenza test types. In conjunction with ILI medical encounter data, positive RIDT data provides a more complete and timely picture of the true burden of influenza on the MHS population for early warning of influenza outbreaks

Evaluation of DoD Syndrome Mapping and Baseline for ICD-9-CM to ICD-10-CM Transition

ObjectiveThe transition from ICD-9-CM to ICD-10-CM requires evaluationof syndrome mappings to obtain a baseline for syndromic surveillancepurposes. Two syndrome mappings are evaluated in this report.IntroductionThe Department of Defense conducts syndromic surveillanceof health encounter visits of Military Health System (MHS)beneficiaries. Providers within the MHS assign up to 10 diagnosiscodes to each health encounter visit. The diagnosis codes are groupedinto syndrome and sub-syndrome categories. On October 1, 2015,the Health and Human Services-mandated transition from ICD-9-CM to ICD-10-CM required evaluation of the syndrome mappingsto establish a baseline of syndrome rates within the DoD. The DoDdata within the BioSense system currently utilizes DoD ESSENCEsyndrome mappings. The Master Mapping Reference Table (MMRT)was developed by the CDC to translate diagnostic codes across theICD-9-CM and ICD-10-CM encoding systems to prepare for thetransition. The DoD ESSENCE and MMRT syndrome definitions arepresented in this analysis for comparison.MethodsDoD data was pulled from the BioSense Platform through aRStudio server on October 11, 2016, querying data from October1, 2014 to September 30, 2016. This time period provides twelvemonths of ICD-9-CM data and twelve months of ICD-10-CM data.The ICD codes were binned to both DoD ESSENCE syndromes andMMRT macro syndromes for comparison. Although a patient visitmay contain up to 10 ICD codes, only the first four were includedfor this analysis. Providers are trained to prioritize diagnosis codesby position. Only 2.2% of visits had greater than 4 diagnostic codes.Each ICD code in a visit is binned to an applicable syndrome. Thetotal number of visits includes visits that binned and did not bin toa syndrome. Multiple syndromes may be assigned to one patient’shealth encounter visit if multiple ICD codes are binned. Additionally,more than one code per visit may bin to the same syndrome; however,only unique syndromes are counted in the total syndrome rate. Thetotal syndrome rate was calculated by total unique syndrome visitsas the numerator and total number of visits during the ICD-9-CM orICD-10-CM time period as the denominator. The rates per 1000 totalvisits were calculated.ResultsAmong the DoD ESSENCE syndromes, the ICD-9-CM ratefor ILI was 36.3 per 1,000 compared to the ICD-10-CM rate of38.6 per 1,000. The ICD-9-CM rate for neurological was 18.1 per1,000 compared to the ICD-10-CM rate of 0.2 per 1,000.Among the MMRT syndromes, the ICD-9-CM rate for ILI was16.7 per 1,000 compared to the ICD-10-CM rate of 38.4 per 1,000.The ICD-9-CM rate for mental disorders was 73.8 per 1,000 comparedto the ICD-10-CM rate of 73.2 per 1,000.ConclusionsThis analysis provides baseline rates of MMRT syndromes andsub-syndromes for syndromic surveillance during the ICD-9-CM toICD-10-CM transition. These data will serve for future comparisonand tracking of syndrome-specific trends for military-relevant healththreats

Modeling the risk of heat illness among basic training populations within the DoD, 2010–2017

ObjectiveTo identify predictors of the risk of developing exertional heat illness (EHI) among basic training populations in the Department of Defense.IntroductionAlthough effective preventive measures for heat-related illness have been recommended and mandated for military personnel, there continues to be incident cases. In 2016, there were 401 incident cases of heat stroke and 2,135 incident cases of “other heat illness” among all active component service members. Current military guidelines utilize the wet bulb globe temperature (WBGT) index to measure heat risk, guiding work/rest and hydration practices. The WBGT requires calibrated instrumentation and is based on fixed cutoff values. We propose using readily available meteorological data inputs and EHI cases to identify and validate an EHI risk prediction model. Prior studies have found that combinations of WBGT and the previous day’s WBGT and relative humidity and temperature have predictive value for EHI.1 We build upon prior work by using generalized additive models (GAMs).MethodsA case-control study was conducted among active component service members from all basic training installations from January 1, 2010 to May 31, 2017. Incident cases of EHI were identified utilizing diagnosis codes extracted from inpatient and outpatient medical encounters and confirmed reportable medical events. An equal number of random controls, matched by installation, were selected. Mean weather data during daylight hours from the Air Force Weather Squadron were provided for the closest weather station to the installation during the same time period. A GAM was used due to the non-linear association between EHI and weather predictors, to develop models for the risk of incident EHI. Training (75% of data) and test (25% of data) datasets were generated for model training and model validation. Three hundred sets of training and test datasets were randomly generated. For each set, sensitivity and specificity for EHI prediction was calculated. Four models with different combinations of predictors were compared: model 1 contains month, day of week, and installation; model 2 contains WBGT, month, day of week, and installation; model 3 contains WBGT, previous day’s WBGT, month, day of week, and installation; and model 4 contains relative humidity, temperature, month, day of week, and installation. Each predictor was significantly associated with EHI. The mean differences in sensitivity and specificity between all models and model 1 were compared and 95% confidence intervals were generated by bootstrapping. GAMs were generated using the mgcv package and odds ratios were generated using the oddsratio package in R.ResultsThere were 5,258 incident cases of EHI from 2010-2017 among active component service members stationed at basic training installations. There was not a significant difference in model performance when comparing the four models. The mean differences in sensitivity and specificity of each model compared to model 1 are displayed in Table 1. The association between log odds of EHI and WBGT, controlling for month, day of week, and installation (model 2) is displayed in Figure 1. There is not a single representative odds ratio generated for GAMs due to the non-linear relationship between predictors and the log odds of EHI. As an example, the odds ratio between two arbitrary WBGT points is displayed. The odds of EHI among those exposed to a mean WBGT of 85°F is 2.55 (95% CI: 2.45, 2.64) times the odds of EHI among those exposed to a mean WBGT of 80°F. The association between the log odds of EHI and relative humidity, controlling for month, day of week, installation, and temperature (model 4) is displayed in Figure 2. The odds of EHI among those exposed to 80% relative humidity is 1.36 (95% CI: 1.33, 1.39) times the odds of EHI among those exposed to 60% relative humidity.ConclusionsOur results provide evidence that there is no significant difference in model prediction of EHI utilizing various combinations of weather predictors. However, there is a significant non-linear association between weather predictors and EHI and examples of these relationships are given using different models. Model performance can be improved by including more granular exposure data (i.e. physical activity during EHI episode, biometric and physiological measures).References1. Wallace RF, Kriebel D, Punnett L, Wegman DH, Wenger CB, Gardner JW, Gonzalez RR. The effects of continuous hot weather training on risk of exertional heat illness. Med Sci Sports Exerc. 2005 Jan; 37(1):84-90

Civilian-military Collaboration: Department of Defense data in the BioSense Platform

ObjectiveThe Department of Defense data is available to NationalSyndromic Surveillance Program (NSSP) users to conduct syndromicsurveillance. This report summarizes the demographic characteristicsof DoD health encounter visits.IntroductionThe DoD provides daily outpatient and emergency room data feedsto the BioSense Platform within NSSP, maintained by the Centersfor Disease Control and Prevention. This data includes demographiccharacteristics and diagnosis codes for health encounter visits ofMilitary Health System beneficiaries, including active duty, activeduty family members, retirees, and retiree family members. NSSPfunctions through collaboration with local, state, and federal publichealth partners utilizing the BioSense Platform, an electronic healthinformation system.MethodsDoD data was pulled from the BioSense Platform through aRStudio server on October 11, 2016, querying data from November1, 2015 to September 30, 2016. Appointment type and beneficiarycategory data was not available in BioSense until November 1, 2015.Appointment type was categorized into clinic visits and telephoneconsults. Demographic characteristics (age group, gender, beneficiarycategory) are stratified by appointment type.ResultsDuring the time period of November 1, 2015 to September 30, 2016,data were received from 452 clinics. There is a military treatmentfacility located in 45 states and a military treatment facility mayhave one to 12 clinics. There were a total of 86,840,632 healthcareencounter records. The age group, 25-44 years, accounted for 39.4%of the medical encounters; the mean age was 33.9 (SD=19.1). Malesaccounted for 55.6% of the medical encounters. For the time periodfrom November 1, 2015 to September 30, 2016, 78.9% of medicalencounters were clinic visits. The remaining medical encounterswere telephone consults. Of the clinic visits, 53.7% of the medicalencounters were for active duty personnel.ConclusionsThis report highlights the DoD data available to NSSP users forcollaborative syndromic surveillance efforts, promoting a communityof practice. It is important to understand the population demographicsand limitations to the DoD data when conducting syndromicsurveillance