Conventional Frequency Analysis Research Articles

Using remote sensing technology in estimating flood discharge

Floods are the most natural disasters occurring on earth. Recently, Egypt has been subjected to more frequent floods due to climate change. The main objective of this paper is creating hydrological analysis and estimating water discharge due to a flood hitting a hazardous zone near Safaga city. The research is divided into four main stages; 1) Morphological study, 2) Meteorological study, 3) Losses estimation, and 4) Flood discharge estimation. First, morphological study in which watershed boundaries, main streams and morphological data are delineated using geographic information system (GIS) with the aid of Landsat-8 satellite images and 30-m resolution digital elevation model (DEM). Second, meteorological study in which conventional rainfall frequency analysis is generated to estimate rainfall depth at different return periods using 15-years rainfall historical data from weather stations. Third, losses estimation is determined by using soil conservation service-curve number method (SCS-CN). Fourth, water discharge volume is carried out through hydrological modelling using a hydrologic engineering centre model. The estimated water volume is an appropriate information for decision makers to propose the adequate protection works to mitigate flood impacts.

A machine learning approach combined with wavelet analysis for automatic detection of Pc5 geomagnetic pulsations observed at geostationary orbits

Pc5 geomagnetic pulsations are ultra-low frequency (ULF) waves whose period lies within 150–600 s. Their detection is crucial for accurate space weather modelling, monitoring, and analysis. Identification of these pulsations using manual approaches is difficult and time-consuming because of the smaller magnitudes and the limited durations within which they occur. To overcome this challenge, we propose a robust Cascade Forward Neural Network (CFNN) model combined with the wavelet technique for automatically detecting Pc5 events from satellite datasets observed at geostationary orbits. The dataset used in this study is the magnetic field vector measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) from 2000 to 2009. Pc5 geomagnetic pulsations were extracted from the toroidal component of the field perturbation using a bandpass Butterworth filter. Continuous wavelet transform (CWT) analysis using the mother Morlet wavelet was utilized to validate the integrity of the extracted signal in the time–frequency domain. The extracted Pc5 signal was decomposed into details and approximations using Daubechies wavelet transform to separate the intelligible signal from the incoherent noise that left their trace on the magnetic field time series. The detail signal was subjected to denoising using the heuristic Stein Unbiased Risk Estimate (SURE) approach with soft thresholding to obtain the denoised Pc5 events. The denoised Pc5 events were utilized as the target in the machine learning whereas the inputs were obtained from 5-dimensional space transformation of the toroidal component of the time series. The developed algorithm performed well and demonstrated a detection accuracy of 80 % and a Root Mean Square Error (RMSE) of 0.13 nT indicating a high performance in detecting Pc5 events. For effective validation of the model, Pc5 events detected by our model were correlated with the Kp index and the amplitude of the Pc5 events observed at geostationary orbit. A good correlation was obtained in both cases, making our model a practical and preferred choice for Pc5 pulsation detection in contrast to conventional frequency analysis tools which take much time for signal processing on large data sets.

Dynamic flood frequency analysis for west flowing rivers of Kerala, India

Conventional Flood Frequency Analysis (FFA) may underestimate flood quantiles and increase hydraulic infrastructure vulnerability in changing climates. This study uses annual maximum streamflow data from 17 hydrologic stations along west-flowing rivers in Kerala, India, for Non-Stationary (NS) FFA. The Generalized Extreme Value model with a linear temporal location parameter worked effectively for five stations. Kidangoor and Pattazhy stations must account for non-stationarity for longer return periods (RPs) (>50 years), whereas Neeleeswaram and Perumannu stations must for shorter RPs (<50 years). An extensive study was conducted for Neeleswaram station (Periyar basin) by simulating NS models incorporating four large-scale climate oscillations as covariates. The stationary assumption underestimated flood return levels of 2-year RP by about 61% which increases the flood risk leading to failure of hydraulic infrastructures. It was observed that the best fitted climate-based NS model achieves stationary return level of 150-years RP at 25-years RP itself. The study proved that the climate-based NS models captured the 2018 August Floods in Periyar basin better than stationary and time-based models. The regional variability in FF curve behaviour concludes that NSFFA for Kerala cannot be generalised and must be done at a local-scale.

A mixed distribution approach for low-flow frequency analysis – Part 1: Concept, performance, and effect of seasonality

Abstract. In seasonal climates with a warm and a cold season, low flows are generated by different processes so that the annual extreme series will be a mixture of summer and winter low-flow events. This leads to a violation of the homogeneity assumption for all statistics derived from the annual series and gives rise to inaccurate conclusions. In this first part of a two-paper series, a mixed distribution approach to perform frequency analysis in catchments with mixed low-flow regimes is proposed. We formulate the theoretical basis of the mixed distribution approach for the lower extremes based on annual minima series. The main strength of the model is that it allows the user to estimate return periods of summer low flows, winter low flows, and annual return periods in a theoretically sound and consistent way. Using archetypal examples, we show how the model behaves for a range of low-flow regimes, from distinct winter and summer regimes to mixed regimes where seasonal occurrence in summer and winter is equally likely. The examples show in a qualitative way the loss in accuracy one has to expect with conventional extreme value statistics performed with the annual extremes series. The model is then applied to a comprehensive Austrian data set to quantify the expected gain of using the mixed distribution approach compared to conventional frequency analysis. Results indicate that the gain of using a mixed distribution approach is indeed large. On average, the relative deviation is 21 %, 39 %, and 63 % when estimating the low flow with a 20-, 50-, and 100-year return period. For the 100-year event, 75 % of stations show a performance gain of &gt;10 %, 41 % of stations &gt; 50 %, and 25 % of stations &gt; 80.6 %. This points to a broad relevance of the approach that goes beyond highly mixed seasonal regimes to include the strongly seasonal ones. We finally correlate the performance gain with seasonality indices in order to show the expected gain conditional to the strength of seasonality expressed by the ratio of average summer and winter low flow seasonality ratio (SR). For the 100-year event, the expected gain is about 70 % for SR=1.0, 20 % for SR=1.5, and 10 % for SR=2.0. The performance gain is further allocated to the spatial patterns of SR in the study area. The results suggest that the mixed estimator is relevant not only for mountain forelands but to a much wider range of catchment typologies. The mixed distribution approach provides one consistent approach for summer, winter, and annual probabilities and should be used by default in seasonal climates with a cold winter season where summer and winter low flows can occur.

Crack type analysis and damage evaluation of BFRP-repaired pre-damaged concrete cylinders using acoustic emission technique

Fiber-reinforced polymer (FRP) composites have been widely used in the retrofitting of concrete columns after earthquakes. However, FRP composites prevent traditional field inspection techniques from observing concrete cracks inside the FRP-retrofitted columns caused by external loading. In the present study, axial compressive loading tests were performed on basalt FRP (BFRP)-repaired pre-damaged cylindrical concrete accompanied by real-time acoustic emission (AE) monitoring. Three damage stages of BFRP-jacketed cylinders were defined based on the proposed strain energy-based damage index and predicted using fractal theory. The conventional RA value-average frequency (RA-AF) analysis and Gaussian mixture model (GMM) analysis effectively revealed the evolution of crack types of BFRP-repaired pre-damaged cylinders. GMM method verified the effectiveness of the RA-AF analysis and provided a better approximation for the classification of crack types. As the pre-damage degree increased, RA value increased and AF value decreased. The shear cracks developed significantly at the severe damage stage in two-layer BFRP-jacketed cylinders.

A storm-centered multivariate modeling of extreme precipitation frequency based on atmospheric water balance

Abstract. Conventional rainfall frequency analysis faces several limitations. These include difficulty incorporating relevant atmospheric variables beyond precipitation and limited ability to depict the frequency of rainfall over large areas that is relevant for flooding. This study proposes a storm-based model of extreme precipitation frequency based on the atmospheric water balance equation. We developed a storm tracking and regional characterization (STARCH) method to identify precipitation systems in space and time from hourly ERA5 precipitation fields over the contiguous United States from 1951 to 2020. Extreme “storm catalogs” were created by selecting annual maximum storms with specific areas and durations over a chosen region. The annual maximum storm precipitation was then modeled via multivariate distributions of atmospheric water balance components using vine copula models. We applied this approach to estimate precipitation average recurrence intervals for storm areas from 5000 to 100 000 km2 and durations from 2 to 72 h in the Mississippi Basin and its five major subbasins. The estimated precipitation distributions show a good fit to the reference data from the original storm catalogs and are close to the estimates from conventional univariate GEV distributions. Our approach explicitly represents the contributions of water balance components in extreme precipitation. Of these, water vapor flux convergence is the main contributor, while precipitable water and a mass residual term can also be important, particularly for short durations and small storm footprints. We also found that ERA5 shows relatively good water balance closure for extreme storms, with a mass residual on average 10 % of precipitation. The approach can incorporate nonstationarities in water balance components and their dependence structures and can benefit from further advancements in reanalysis products and storm tracking techniques.

Vibration analysis process based on spectrogram using gradient class activation map with selection process of CNN model and feature layer

This paper presents a vibration analysis process using a convolutional neural network (CNN) model and a gradient class activation map (Grad-CAM), based on a model and feature layer selection process. The main problem associated with conventional bearing fault diagnosis is the need to calculate defect frequencies, which requires that all bearing-related information should be known beforehand. The extraction of information regarding the mechanical equipment and the subsequent generation of a database requires significant time and cost. The proposed methodology can detect frequencies without any prior information by visualizing the activation area of the CNN model. The proposed approach helps overcome the drawbacks of the conventional diagnosis using Grad-CAM, and it indicates the rationale of classification, thus explaining the cause of vibration data for a defect diagnosis. The paper presents a process that visualizes and analyzes the activated region through the conversion of vibration signals into spectrogram images and use of CNN models. The proposed method further serves as a process of selecting the appropriate CNN model and its feature layers among several models. The vibration data were collected from a motor using an accelerometer and IoT module. The feasibility of the proposed methodology was verified using the recorded data. Spectrogram images for five situations (normal, inner fault, outer fault, ball fault, and cage fault) were generated from the acquired data. A VGG-19 model with an accuracy of 99.92% was selected based on transfer learning of three CNN models (ResNet50, VGG-16, VGG-19) using these spectrogram images. Finally, the feature layer of dimension 28 × 28 is selected for the Grad-CAM activation model. The visualization model was analyzed and compared to conventional defect frequency analysis methods. The results analyzed through the activation area near the defect frequency region of the conventional frequency analysis show that the use of deep learning in the proposed model helps evaluate the frequency intervals required to determine defects without any prior information regarding mechanical specifications.

Impact of the COVID-19 pandemic on clinical radiography education: Perspective of students and educators from a low resource setting

IntroductionThe COVID-19 pandemic has had significant impact on clinical radiography educational activities including teaching, research, and clinical placement. The study aimed to investigate the impact of the COVID-19 pandemic on clinical radiography education in a low-resource setting from the perspectives of students and educators. MethodsA cross-sectional online survey was conducted between December 2020 and March 2021. Participants included student radiographers and educators from six clinical radiography training institutions in Ghana. Conventional descriptive statistics, frequency, and thematic text analysis were undertaken. Results291 valid responses were received involving 277 students and 14 educators. Most of the respondents, predominantly students did not find the online platforms for learning user-friendly. Key challenges to clinical radiography training during the pandemic included poor internet connectivity (73.8%), inadequate time for academic discussions (50.9%), and the reluctance of health facilities to accept students for clinical placement (51%). Challenges with data collection and the provision of supervision were issues associated with research activities. DiscussionThe findings suggest the COVID-19 pandemic has had significant impact on clinical radiography education in Ghana and other countries of similar resource availability. ConclusionClinical radiography training institutions need to customize approaches to education. Adopting blended learning approaches as well as the use of simulation to augment practical placement opportunities are new modes of instruction that will maximize learning experience.

Improvements to Flood Frequency Analysis on Alluvial Rivers Using Paleoflood Data

AbstractHydrologists and engineers routinely use flood frequency analyses to compute flood probabilities for mitigation, infrastructure planning, and emergency management. Conventional flood frequency analyses—in which annual discharge maxima from a stream gage are fit to a statistical probability distribution—often encounter large uncertainties when estimating extreme flood levels. Most gage records span relatively short periods of time (&lt;100 years), and thus the most extreme and infrequently occurring flood events tend to be poorly represented in instrumental data sets. Here, we demonstrate how a new generation of paleoflood records derived from floodplain sediments can be used to improve the accuracy and precision of extreme flood probability estimates along alluvial rivers. We use a series of simulation experiments to show that incorporating large numbers of paleoflood events in flood frequency analyses can significantly reduce the uncertainty of extreme flood estimates when the paleoflood data are sufficiently accurate and precise. Our results illustrate that robust paleoflood records can improve the shape parameter of flood frequency distributions, which determine the thickness of distribution tails, when as many as 50 paleoflood events are incorporated. We conclude by demonstrating how an alluvial paleoflood data set reduces uncertainty in a flood frequency analysis for a gage on the lower Mississippi River. Finally, we provide recommendations for how to incorporate paleoflood information into flood frequency analysis to improve the accuracy of extreme flood probabilities.

Incorporation of Cost-Benefit Analysis Considering Epistemic Uncertainty for Calculating the Optimal Design Flood

Design flood via flood frequency analysis provides basic information for designing hydraulic structures. Quantification of uncertainty in flood frequency analysis has become an important issue during the past three decades. However, few studies have considered practical procedures for selecting a single design flood in the uncertainty range. Cost-benefit analysis can be incorporated to select a single design flood by calculating the optimal value in the total expected cost function. In particular, in this study, the relationship between conventional flood frequency analysis and cost-benefit analysis is addressed. Additionally, the parameter uncertainty is quantified by the Metropolis-Hastings algorithm to find the optimal design floods considering parameter uncertainty. The annual maximum (AM) series and partial duration (PD) series were used to identify the effect of various types of data. The optimal design floods obtained by the cost-benefit analysis considering parameter uncertainty were systematically larger than the design flood obtained by the conventional flood frequency analysis. Regarding the types of data, the generalized Pareto distribution (GPD) had the largest values in all return periods, while the Gumbel distribution had the smallest values in all cases.

Estimating the design flood under the influence of check dams by removing nonstationarity from the flood peak discharge series

Abstract The construction of check dams in northwestern China has resulted in nonstationary changes in flood peak discharge series; the stationary assumption of the conventional hydrological frequency analysis is no longer satisfied. According to the characteristics of the construction and operation of check dams, the nonstationarity of flood peak discharge series are largely induced by changes in the effective runoff generation area (i.e., the basin area minus the area controlled by check dams). Knowing the power function relationship between the flood peak discharge and the basin area, we can remove the influence of the effective runoff generation area and convert the original nonstationary series into a stationary series. This de-nonstationarity method can achieve stationarity in the first and second moments simultaneously. Therefore, we can calculate the design value of the reconstructed series using the conventional frequency analysis method. According to the effective runoff generation area under design conditions, we can then obtain the corresponding design flood of the original series. We applied this method to the Mahuyu River basin to obtain the design flood under nonstationarity. Due to the consideration of the deterministic influence of check dams during the de-nonstationarity process, the uncertainty analyzed by the bootstrap method is obviously small.

Two novel approaches to the content analysis of school mathematics textbooks

ABSTRACT The analysis of the content of school textbooks, particularly in a time of cross-cultural borrowing, is a growing field restricted by the tools currently available. In this paper, drawing on the analyses of three English year-one mathematics textbooks, we show how two approaches to the analysis of sequential data not only supplement conventional frequency analyses but highlight trends in the content of such textbooks hidden from frequency analyses alone. The first, moving averages, is conventionally used in science to eliminate noise and demonstrate trends in data. The second, Lorenz curves, is typically found in the social sciences to compare different forms of social phenomena. Both, as we show, extend the range of questions that can be meaningfully asked of textbooks. Finally, we speculate as to how both approaches can be used with other forms of ordered classroom data.

Return Period of Low Tide Level in the Yangtze Estuary based on Nonstationarity Analysis

Due to the influence of climate change and human activities, stationarity of hydrologic time series is being challenged. The Yangtze Estuary is a region with highly developed hydraulic structures and shipping. Stationarity analysis of water level in the Yangtze Estuary is of great significance. In this study, the return period of low tide level in the Yangtze Estuary is estimated with nonstationarity considered. Conventional frequency analysis and the time-varying moment method are used to analyze the annual minimum tide level (AMTL) records of Wusongkou Station and Baozhen Station on the basis of temporal change analysis. Abrupt changes are detected at 1996 and 1990 for Wusongkou Station and Baozhen Station separately. The GEV distribution with linear time-varying parameter fits best for Wusongkou Station and Baozhen Station. The AMTL series of both stations reveal a slight increasing trend. The 100-year low tide level of Wusongkou Station is approximately 0.261 meters, which is about -0.041 meters for Baozhen Station. The corresponding return period is 150 years and 119 years respectively, which indicates the existing channel standard more secure and the recalculation of design water level necessary in the Yangtze Estuary.

Using lake sediment archives to improve understanding of flood magnitude and frequency: Recent extreme flooding in northwest UK

AbstractWe present the first quantitative reconstruction of palaeofloods using lake sediments for the UK and show that for a large catchment in NW England the cluster of devastating floods from 1990 to present is without precedent in this 558‐year palaeo‐record. Our approach augments conventional flood magnitude and frequency (FMF) analyses with continuous lake sedimentary data to provide a longer‐term perspective on flood magnitude recurrence probabilities. The 2009 flood, the largest in &gt;558 years, had a recurrence interval larger (1:2,200 year) than revealed by conventional flood estimation using shorter duration gauged single station records (1:1,700 year). Flood‐rich periods are non‐stationary in their correlation with climate indices, but the 1990‐2018 cluster is associated with warmer Northern Hemisphere Temperatures and positive Atlantic Multidecadal Oscillation. Monitored records rarely capture the largest floods and our palaeoflood series shows, for this catchment, such omissions undermine evaluations of future risk. Our approach provides an exemplar of how to derive centennial palaeoflood reconstructions from lakes coupled well with their catchments around the world. © 2019 The Authors. Earth Surface Processes and Landforms Published by John Wiley &amp; Sons Ltd.

Nonstationary frequency pairing reveals a highly sensitive peak flow regime to harvesting across a wide range of return periods

Decades of pairing peak flows of forested and harvested watersheds by equal chronology when evaluating the effects of forest harvesting on peak flows in paired watershed studies lately came under criticisms; and suggestions have been made to abandon the practice and pair by equal frequency instead. However, the stationarity assumption imposed by conventional frequency analyses complicates the use of frequency pairing because peak flows contain change-point and trend-shifting nonstationarities caused by continuous harvesting and forest regrowth. Here we introduce a new nonstationary frequency pairing method for evaluating harvesting effects by allowing the parameters of peak flow frequency distributions to change in time using physically based covariates. The method’s demonstration on a paired watershed study in the snow environment of British Columbia, Canada, reveals how both small (return periods < 10 years) and large (return periods > 10 years) peak flows are highly sensitive to harvesting within the critical mid-elevation south-exposed slopes of the watershed. Such outcomes challenge an age-old deterministic wisdom, but are consistent with the emerging probabilistic understanding of how forests affect peak flows. Advantages of the new method include: (i) bypassing the need for the calibration equation traditionally used in paired watershed studies, thereby eliminating some associated sources of uncertainty; (ii) making use of longer peak flow records by explicitly accounting for the physical causes of the nonstationarities, and thus more explicit inferences about the effects of harvesting on the larger peak flows; and (iii) allowing for the estimation of harvesting effects on peak flows at different points in time during the disturbance history of a watershed, thus providing a direct evaluation of the hydrologic recovery. This study falls within the newly emerging field of “attribution science” which uses a combination of observations and models to identify separately the factors that contribute to extremes. It paves the way to a wider range of applications, not just in the wider hydrology but also other geosciences.

Reducing uncertainty of design floods of two-component mixture distributions by utilizing flood timescale to classify flood types in seasonally snow covered region

The conventional flood frequency analysis typically assumes the annual maximum flood series (AMFS) result from a homogeneous flood population. However, actually AMFS are frequently generated by distinct flood generation mechanisms (FGMs), which are controlled by the interaction between different meteorological triggers (e.g., thunderstorms, typhoon, snowmelt) and properties of underlying surface (e.g., antecedent soil moisture and land-cover types). To consider the possibility of two FGMs in flood frequency analysis, researchers often use the two-component mixture distributions (TCMD) without explicitly linking each component distribution to a particular FGM. To improve the mixture distribution modeling in seasonally snow covered regions, an index called flood timescale (FT), defined as the ratio of the flood volume to peak value and chosen to reflect the relevant FGM, is employed to classify each flood into one of two types, i.e., the snowmelt-induced long-duration floods and the rainfall-induced short-duration floods, thus identifying the weighting coefficient of each component distribution beforehand. In applying the FT-based TCMD to model the AMFS of 34 watersheds in Norway, ten types of mixture distributions are considered. The design floods and associated confidence intervals are calculated using parametric bootstrap method. The results indicate that the FT-based TCMD model reduces the uncertainty in the estimation of design floods for high return periods by up to 40% with respect to the traditional TCMD. The improved predictive ability of the FT-based TCMD model is attributed to its explicit recognition of distinct generation mechanisms of floods, thereby being able to identify the weighting coefficient and FGM of each component distribution without optimization.

Assessing the Impacts of Univariate and Bivariate Flood Frequency Approaches to Flood Risk Accounting for Reservoir Operation

Flood frequency analysis plays a fundamental role in dam planning, reservoir operation, and risk assessment. However, conventional univariate flood frequency analysis carried out by flood peak inflow or volume does not account for the dependence between flood properties. In this paper, we proposed an integrated approach to estimate reservoir risk by combining the copula-based bivariate flood frequency (peak and volume) and reservoir routing. Through investigating the chain reaction of “flood frequency—reservoir operation-flood risk”, this paper demonstrated how to simulate flood hydrographs using different frequency definitions (copula “Or” and “And” scenario), and how these definitions affect flood risks. The approach was applied to the Meishan reservoir in central China. A set of flood hydrographs with 0.01 frequency under copula “Or” and “And” definitions were constructed, respectively. Upstream and downstream flood risks incorporating reservoir operation were calculated for each scenario. Comparisons between flood risks from univariate and bivariate flood frequency analysis showed that bivariate flood frequency analysis produced less diversity in the results, and thus the results are more reliable in risk assessment. More importantly, the peak-volume combinations in a bivariate approach can be adjusted according to certain prediction accuracy, providing a flexible estimation of real-time flood risk under different prediction accuracies and safety requirements.

Prediction of atrial fibrillation recurrence before catheter ablation using an adaptive nonlinear and non-stationary surface ECG analysis

The recurrence rate of atrial fibrillation (AF) following catheter ablation is high. Successful non-recurrence prediction before ablation is financially and physically beneficial. Atrial fibrillation cycle length (AFCL) has been used for such predictions. The study explored a nonlinear and non-stationary signal analysis technique, ensemble empirical model decomposition (EEMD), to capture intrinsic AFCL pattern. Twenty-eight AF patients underwent regular catheter ablation protocols and follow-up treatments. Surface ECG were recorded before ablation procedures, and preprocessed with QRS-T cancellation method to extract intrinsic AF signals, which were then analyzed with EEMD to calculate AFCL adaptively. Conventional Fourier-based dominant frequency (DF) analysis was conducted for comparison. During 12 months follow-up, 17 patients maintained sinus rhythm while 11 patients had AF recurrence. Patients who maintained sinus rhythm had longer AFCL (152.2 (145.0, 157.6) ms) than those with AF recurrence (138.8 (135.0, 145.2) ms). Patients with AFCL >152.1 ms had no recurrence (Kaplan–Meier curve analysis, P=0.006, hazard ratio: 5.68). With AFCL as a predictor of AF non-recurrence at 12 months, the area under ROC curve is 0.80, while with DF analysis, the area is 0.66. EEMD-based intrinsic AFCL measured from surface ECG may serve as an effective non-invasive catheter ablation screening tool for AF non-recurrence.

Analysis of motor fan radiated sound and vibration waveform by automatic pattern recognition technique using \u201cMahalanobis distance\u201d

In recent years, as the weight of IT equipment has been reduced, the demand for motor fans for cooling the interior of electronic equipment is on the rise. Sensory test technique by inspectors is the mainstream for quality inspection of motor fans in the field. This sensory test requires a lot of experience to accurately diagnose differences in subtle sounds (sound pressures) of the fans, and the judgment varies depending on the condition of the inspector and the environment. In order to solve these quality problems, development of an analysis method capable of quantitatively and automatically diagnosing the sound/vibration level of a fan is required. In this study, it was clarified that the analysis method applying the MT system based on the waveform information of noise and vibration is more effective than the conventional frequency analysis method for the discrimination diagnosis technology of normal and abnormal items. Furthermore, it was found that due to the automation of the vibration waveform analysis system, there was a factor influencing the discrimination accuracy in relation between the fan installation posture and the vibration waveform.

Combined effect of glutamine at position 70 of HLA-DRB1 and alanine at position 57 of HLA-DQB1 in type 1 diabetes: An epitope analysis.

The contribution of specific HLA Class II alleles in type 1 diabetes is determined by polymorphic amino acid epitopes that direct antigen binding therefore, along with conventional allele frequency analysis, epitope analysis can provide important insights into disease susceptibility. We analyzed the highly heterogeneous Cypriot population for the HLA class II loci of T1DM patients and controls and we report for the first time their allele frequencies. Within our patient cohort we identified a subgroup that did not carry the DRB1*03:01-DQA1*05:01-DQB1*02:01 and DRB1*04:xx-DQA1*03:01-DQB1*03:02 risk haplotypes but a novel recombinant one, DRB1*04:XX-DQA1*03:01-DQB1*02:01 designated DR4-DQ2.3. Through epitope analysis we identified established susceptibility (DQB1 A57, DRB1 H13) and resistance (DQB1 D57) residues as well as other novel susceptibility residues DRB1 Q70, DQB1 L26 and resistance residues DRB1 D70, R70 and DQB1 Y47. Prevalence of susceptibility epitopes was higher in patients and was not exclusively a result of linkage disequilibrium. Residues DRB1 Q70, DQB1 L26 and A57 and a 10 amino acid epitope of DQA1 were the most significant in discriminating risk alleles. An extended haplotype containing these epitopes was carried by 92% of our patient cohort. Sharing of susceptibility epitopes could also explain the absence of risk haplotypes in patients. Finally, many significantly associated epitopes were non-pocket residues suggesting that critical immune functions may exist spanning further from the binding pockets.