Lower Average Mean Research Articles

Safety and Effectiveness of Selexipag in Pediatric Pulmonary Hypertension: A Retrospective Multicenter Cohort Study

ObjectiveTo describe the safety and effectiveness of treating pediatric patients who have pulmonary arterial hypertension (PAH) with selexipag in a real-world, multi-center cohort, given that data supporting its use in pediatric PAH are sparse. Study designWe report a multi-center, retrospective, cohort study of children with PAH treated with selexipag. Demographic and clinical variables were extracted from the medical records. Clinical parameters were analyzed at 3 timepoints: pre-selexipag, 3-12 months post-selexipag, and >12 months follow-up. ResultsEighty-seven patients were included, 32 received selexipag as add-on to background therapy, and 55 transitioned from another prostanoid. Median starting and final doses were 4.7 and 28.5 μg/kg/dose BID, respectively. Add-on patients demonstrated improved indexed pulmonary to systemic vascular resistance ratio after selexipag initiation (PVRi/SVRi, 0.62v0.53, p=0.034) with a lower average mean pulmonary artery pressure (MPAP, 46v39 mmHg, p=NS), and oxygen consumption (VO2 max 27.8v30.9 mL/kg/min, p=NS). Transition patients demonstrated stable MPAP (47v45 mmHg, p=NS) and a lower mean PVRi (10.9v8.2 Wood units*m2, p=NS) but late functional worsening in some with VO2 max decreased at follow-up (26.0v19.5 ml/kg/min). Side effects were noted in 40% of the cohort but prompted discontinuation in only 2%. ConclusionsIn a large, multi-center cohort, the oral prostacyclin agonist selexipag demonstrates favorable tolerability and effectiveness. Add-on patients demonstrated early hemodynamic improvement. Transition patients demonstrated early stability with risk of late functional worsening, highlighting the importance of ongoing monitoring.

A deep learning framework for prediction of crop yield in Australia under the impact of climate change

Accurate prediction of crop yields is essential to ensure food security. In this study, a new deep neural networks framework is developed to predict crop yields in Australia, considering the impact of climate change, fertilizer use, and crop area. It is implemented for oats, corn, rice, and wheat crops, and its forecasting performance is benchmarked against five statistical and machine learning methods. All the software codes for the implementation of the proposed framework are freely available. The proposed framework shows the highest forecasting performance for all the considered crop types. It provides 23%, 38%, 39%, and 40% lower average mean absolute error than the benchmark methods for oat, corn, rice, and wheat crops, respectively. The reductions in average root mean squared error are 19%, 25%, 37%, and 29% over the benchmark methods. Then, it is used to predict yields of the considered crops in Australia towards 2025 under six different climate change scenarios. It is observed that although climate change has some boosting impact on crop yield, it is not sustainable to meet the demand. However, it is possible to keep crop yields rising while mitigating climate change.

Carbon emissions of power transmission and transformation projects in the whole life cycle for smart sustainable energy systems

The study investigates the optimization of life cycle carbon emissions in smart sustainable energy systems through power transformation and transmission project power load predictions. Firstly, a multi-task learning-based short-term user load forecasting technique is developed, where the power load curves of multiple residential customers are grouped and classified using the K-means clustering method. Additionally, the Bidirectional Long Short-Term Memory (BiLSTM) technique is introduced to anticipate the power load intelligently. Secondly, a life cycle carbon emission assessment model for the power transmission and transformation project (PTTP) is constructed based on the life cycle assessment (LCA) method, which divides the project's life cycle into four stages: production, installation and construction, operation and maintenance, and demolition. Finally, an experimental evaluation of this model is conducted. The results demonstrate that compared with the baseline model Long Short-Term Memory (LSTM), this model achieves a significantly lower average Mean Absolute Error (MAE) at 3.62% while achieving significantly higher accuracy in power load forecasting at 94.34%. A comprehensive examination of carbon emissions across all four phases reveals that overall carbon emissions are highest during the operation and maintenance stage followed by the equipment production stage and installation/construction stage, with the lowest overall carbon emissions observed. Hence, this study endeavors to forecast power load demand with precision and identify the principal determinants of carbon emissions in power engineering. By discerning and managing these key factors, an optimal, energy-efficient intelligent power load scheme can be derived.

A multitask encoder–decoder model for quality prediction in injection moulding

It is desirable in injection moulding that every yielded product is of high quality and precision. Several studies are conducted on modelling the relationship between the process parameters e.g., holding pressure, mould temperature, etc. that are either time series or non-time series, and the output product quality characteristics e.g., weight, dimensions, and warpage. To build a more comprehensive/generalized quality prediction model with online learning, this study for the first time takes time series process parameters from 2 pairs of in-mould pressure and temperature sensors simultaneously with 18 non-time series parameters and introduces a single deep learning architecture to perform multitask learning i.e., to leverage information from the related sources to simultaneously predict multiple product quality characteristics on a real injection moulding machine. This along with the discussed modern containerization practices develops a fully automated software part of the quality control process. The non-time series process parameters are learned by fully connected neural network layers of the model while the time series process parameters are related through an attention-based encoder–decoder architecture. This model is ablated as well as compared with a benchmark model to validate the performance advantage in terms of the reduced error asymptote. The proposed model offers an order of magnitude lower average mean squared error than the benchmark model, and 1.2 to 3 times smaller mean squared errors than its ablated versions.

3D Radio Map Reconstruction Based on Generative Adversarial Networks Under Constrained Aircraft Trajectories

Three-dimensional (3D) radio map, which characterizes the spatial distribution of the received signal strength (RSS) across a 3D space, can be a significant tool for wireless resource management and spectrum surveillance. However, incomplete 3D radio map is usually obtained in practice due to the infinite number of positions where aircraft perform RSS measurement suffering from constrained trajectories. In order to tackle this problem, we propose a 3D radio map reconstruction scheme based on generative adversarial networks (GANs), where a novel GAN variant with unsupervised learning is proposed. Specifically, the cost function of the variant integrates a reconstruction loss and an adversarial loss, while the network structure exploits ResNet and dilated convolution under the idea of patchGAN. Numerical results reveal that the network structure and the cost function can benefit the reconstruction scheme, yielding low-volatility and lower average mean squared error (MSE). Moreover, it is shown that the proposed scheme can outperform baselines in terms of the average MSE, even if the number of measured samples for inference significantly decreases.

Data‐Driven Design of Electrically Conductive Nanocomposite Materials: A Case Study of Acrylonitrile–Butadiene–Styrene/Carbon Nanotube Binary Composites

The field of polymer‐based nanoscience has always been of significant interest in the search for polymer/carbon nanotube (CNT) nanocomposites with optimized material properties for new applications. Herein, it is demonstrated that data collected from the online literature can be used to develop an efficient deep learning model to design acrylonitrile–butadiene–styrene (ABS)/CNT binary composites. A dataset of 14 945 data points is constructed from 110 studies. The results demonstrate that compared with a vanilla deep regression model, the proposed model achieves a 26% lower average mean absolute error and an accuracy of 80.6%, which is 16.2% higher than the vanilla deep regression model in predicting the six electrical and mechanical properties of target ABS/CNT composites. In addition, a Monte Carlo simulation integrated with the developed deep neural network (DNN) model effectively screens input variables for users and thus appropriately guides them to manufacture a composite product with desired physical properties.

Body-fitted bi-directional evolutionary structural optimization using nonlinear diffusion regularization

The bi-directional evolutionary structural optimization (BESO) method effectively uses basic strategies of removing and adding material based on element sensitivity. However, challenges remain in generating smooth boundaries to improve the finite element analysis accuracy and achieve structural aesthetics. This work develops a body-fitted triangular/tetrahedral mesh generation algorithm to yield smooth boundaries in the BESO method. The optimization problem is regularized by adding a diffusion term in the objective function. We found that the first has the best regularization effect of Lorentzian, Tikhonov, Perona–Malik, Huber, and Tukey functions. The void elements are excluded from spatial optimization to save computation costs and computer memory. Numerical examples show that the proposed method converges quickly, only taking dozens of iterations to converge. Also, the smooth boundaries of the optimized structures in 2D/3D scenarios are naturally obtained from the proposed method, not from smoothing post-processing. Compared with the optimization toolbox in Abaqus, the example of the automotive control arm demonstrates smoother boundaries and lower average mean compliance.

Comparative Contact Angle Analysis of 1% Chlorhexidine and 2% Metronidazole Mouthgel - An In Vitro Study

Introduction: Chlorhexidine is considered to be gold standard drug in treating various oral infections. Metronidazole is effective for treating various anaerobic infections, including dental abscess and periodontal abscess. In current technical applications and material sciences, contact angle is a thermodynamic characteristic that describes the wettability of solid surfaces. The aim of this study is to compare the contact angle of chlorhexidine and metronidazole mouth gel.
 Materials and Methods: Total 10 samples of gel was used, involving 5 samples of 1% chlorhexidine gel and 5 samples of 2 % metronidazole gel. The contact angles were measured with the Ossila Goniometer after each sample was dispensed from a microsyringe on the facial surface of the microtomed tooth. The obtained values from the samples were determined, and the data was statistically analysed using SPSS software version 20.0. The paired t test was used to analyse the data.
 Results: From the data collected it was found that the average contact angle of the mouth gels that is chlorhexidine and metronidazole gel were 51.58 & 67.04 respectively. T test showed p value of 0.157, (p value < 0.05). Hence, it is statistically not significant. Chlorhexidine gel had a lower average mean of contact angle than metronidazole gel, which means chlorhexidine gel had got better wettability when compared to metronidazole gel.
 Conclusion: Thus, it can be concluded that chlorhexidine gel has a better tendency to wet the tooth or oral cavity surfaces when compared to metronidazole gel, thereby it can have better action in treating various oral conditions.

The Effect of a Denser City over the Urban Microclimate: The Case of Toronto

In the last decades, several studies have revealed how critical the urban heat island (UHI) effect can be in cities located in cold climates, such as the Canadian one. Meanwhile, many researchers have looked at the impact of the city design over the urban microclimate, and have raised concerns about the development of too dense cities. Under the effect of the “Places to Growth” plan, the city of Toronto is experiencing one of the highest rates of building development in North America. Over 48,000 and 33,000 new home permits were issued in 2012 and 2013 respectively, and at the beginning of 2015, almost 500 high-rise proposals across the Greater Toronto Area were released. In this context, it is important to investigate how new constructions will affect the urban microclimate, and to propose strategies to mitigate possible UHI effects. Using the software ENVI-met, microclimate simulations for the Church-Yonge corridor both in the current situation and with the new constructions are reported in this paper. The outdoor air temperature and the wind speed are the parameters used to assess the outdoor microclimate changes. The results show that the new constructions could increase the wind speed around the buildings. However, high-rise buildings will somewhat reduce the air temperature during day-time, as they will create large shadow areas, with lower average mean radiant temperature.

Diabetes, Glucose Control, and 9-Year Cognitive Decline Among Older Adults Without Dementia

To determine if prevalent and incident diabetes mellitus (DM) increase risk of cognitive decline and if, among elderly adults with DM, poor glucose control is related to worse cognitive performance. DESIGN Prospective cohort study. Health, Aging, and Body Composition Study at 2 community clinics. A total of 3069 elderly adults (mean age, 74.2 years; 42% black; 52% female). Participants completed the Modified Mini-Mental State Examination (3MS) and Digit Symbol Substitution Test (DSST) at baseline and selected intervals over 10 years. Diabetes mellitus status was determined at baseline and during follow-up visits. Glycosylated hemoglobin A1c level was measured at years 1 (baseline), 4, 6, and 10 from fasting whole blood. At baseline, 717 participants (23.4%) had prevalent DM and 2352 (76.6%) were without DM, 159 of whom developed incident DM during follow-up. Participants with prevalent DM had lower baseline test scores than participants without DM (3MS: 88.8 vs 90.9; DSST: 32.5 vs 36.3, respectively; t = 6.09; P = .001 for both tests). Results from mixed-effects models showed a similar pattern for 9-year decline (3MS: -6.0- vs -4.5-point decline; t = 2.66; P = .008; DSST: -7.9- vs -5.7-point decline; t = 3.69; P = .001, respectively). Participants with incident DM tended to have baseline and 9-year decline scores between the other 2 groups but were not statistically different from the group without DM. Multivariate adjustment for demographics and medical comorbidities produced similar results. Among participants with prevalent DM, glycosylated hemoglobin A1c level was associated with lower average mean cognitive scores (3MS: F = 8.2; P for overall = .003; DSST: F = 3.4; P for overall = .04), even after multivariate adjustment. Among well-functioning older adults, DM and poor glucose control among those with DM are associated with worse cognitive function and greater decline. This suggests that severity of DM may contribute to accelerated cognitive aging.

Larval rearing of ide (Leuciscus idus (L.)) using decapsulated Artemia

The use of decapsulated Artemia cysts as food for ide, Leuciscus idus (L.), larviculture was investigated. Three days after hatching, the larvae were fed on different diets: (a) dried decapsulated Artemia cysts, (b) Artemia nauplii, (c) Artemia nauplii for 7 days and then trout starter, (d) diet for marine larvae (manufactured artificial diet), (e) trout starter. After a 21-day rearing period, the highest survival rate was obtained with the larvae receiving decapsulated Artemia cysts. Feeding of the larvae with artificial diets resulted in a significantly lower survival rate compared to the other groups. At the end of the experiment, the larvae fed on Artemia nauplii gained significantly higher length compared to the other groups. Feeding different artificial diets resulted in significantly lower average mean length and weight compared to the other groups.

SU-E-T-759: Evaluation of Pencil Beam Convolution and Anisotropic Analytical Algorithm in Stereotactic Lung Irradiation

Purpose: To evaluate dose distributions in stereotactic body radiation therapy (SBRT) treatment plans for lung tumors calculated with pencil beam convolution (PBC) algorithm with modified Batho power law (MBPL) against heterogeneity corrected anisotropic analytical algorithm (AAA) of the Varian Eclipse treatment planning system. Methods: The 4D CT image data from twenty patients with low stage non-small cell lung cancer were used to create treatment plans with 5 to 7 fields 6 MV coplanar non-opposing beams. Internal target volumes (ITVs) were given a 0.7 cm margin to create planning target volumes (PTVs). Plans were generated with PBC (MBPL) for tissue heterogeneity and optimized with a prescribed dose of 60 Gy in 3 fractions to at least 95% of the PTV. These optimized plans were recalculated with AAA, with identical beam arrangements, fluences and monitor units, to generate comparison plans. Results: The AAA plans had lower average minimum, mean and maximum PTV doses by 17%, 8.2% and 2.1% respectively compared to PBC plans. The average dose to 95% of the PTV volume (D95) for AAA plans was lower by ∼14% compared to PBC. The average percentage of uninvolved lung volume receiving minimum doses of 10 Gy (V10) and 20 Gy (V20) was below 10.3% and 5.3% for both PBC and AAA plans. The AAA plans had lower average mean and maximum uninvolved lung dose by 8.6% and 8.9% respectively compared to PBC plans. Conclusions: The average D95 of 51.6 Gy calculated with AAA compared to 60 Gy with PBC in this study agrees well with the recent suggestion by the quality assurance working group of phase III Rosel study of prescription dose reduction of 10% from 60 Gy to 54 Gy when utilizing AAA instead of PBC.

A tale of two bridges: Effect of the bloodless bridge on renal function and blood pressure in neonates managed with venoarterial extracorporeal membrane oxygenation

To investigate if a change in bridge design of the extracorporeal membrane oxygenation (ECMO) circuit had an impact on renal function and blood pressure in neonates requiring venoarterial ECMO support. : Retrospective chart review. A tertiary care neonatal intensive care unit and ECMO center. The medical records of neonates admitted to the neonatal intensive care unit and treated with venoarterial ECMO were reviewed. Data were collected on 50 consecutive neonates treated previous to (prebridge group) and following (postbridge group) transition to a new bridge design on the ECMO circuit. None. Gestational age, gender, racial distribution, and use of hypertensive therapy were similar between the two groups. Daily blood urea nitrogen, serum creatinine, urine output, fluid balance, and average and maximum systolic and mean arterial blood pressures were recorded for the first 3 days on bypass. The postbridge group had lower maximum mean arterial blood pressure and systolic blood pressure on day 2 of ECMO and lower average mean arterial blood pressure and systolic blood pressure on days 2 and 3 of ECMO. These differences remained significant after controlling for covariates in a multiple regression model. A higher percentage of patients were hypertensive (mean arterial blood pressure >60) in the prebridge group compared with the postbridge group. There were no differences in blood urea nitrogen, serum creatinine, fluid balance, and urine output between the two groups. Patients managed on venoarterial ECMO after the transition to the "bloodless" bridge had less hypertension compared with those managed before the bridge change. This may reflect improved maintenance of renal perfusion associated with transition to an ECMO bridge design that does not require intermittent circulation with associated arterial-venous shunting.

Comparison of Clogging in Drip Emitters During Application of Sewage Effluent and Groundwater

Drip irrigation can be an efficient and safe method of applying water from sewage effluent; however, clogging of emitters is one of the most serious problems associated with drip irrigation systems using sewage effluent. The objectives of this study were to investigate the temporal variations of the emitter discharge rate and the distribution of clogged emitters in the system and to quantify the impact of emitter clogging on system performance. In the experiments, six types of emitters with or without a pressure-compensation device and two types of water sources (stored secondary sewage effluent and groundwater) were assessed by measuring the emitter discharge rate of the system at approximately ten-day intervals. The total duration of irrigation was 83 days, with a daily application of 12 h. The water source had a very significant influence on the level of drip emitter clogging. Of all the emitters tested over the entire period of the experiments, the emitters applying sewage effluent were clogged much more severely, producing a 26% lower average mean discharge rate than those applying groundwater. Different types of emitters had different susceptibilities to clogging, but the clogging sensitivity was inversely proportional to the pathway area in the emitter and the emitter's manufacturing coefficient of variability (Mfg CV). The distribution of clogged emitters along the lateral was controlled by the emitter type and the water source. For groundwater application, the clogged emitters tended to be generally located at the terminals of the laterals for emitters without a pressure-compensation device, while randomly distributed clogged emitters were found for pressure-compensating emitters. A more random distribution of clogged emitters was found for the sewage application. Clogging of emitters could seriously degrade system performance. The values of the uniformity coefficient (CU) and the statistical uniformity coefficient (Us) decreased linearly with the mean clogging degree of the emitters. To maintain a high system performance, more frequent chemical treatments should be applied to drip irrigation systems applying the sewage effluent than to systems applying the groundwater.

Sulcal morphology changes and their relationship with cortical thickness and gyral white matter volume in mild cognitive impairment and Alzheimer's disease

We investigated the changes of sulcal shape (average mean curvature in folded regions and sulcal depth) in mild cognitive impairment (MCI) and Alzheimer's disease (AD) using quantitative surface-based methods in a large sample of magnetic resonance imaging data. Moreover, we observed their relationships with cortical thickness and gyral white matter (WM) volume, while considering age effect. This study involved 85 normal controls ( n [men/women]: 36/49, age [mean ± SD]: 71.1 ± 4.9 years), and 100 MCI (44/56, 71.8 ± 6.5) and 145 AD subjects (53/92, 72.7 ± 7.3). We found significantly lower average mean curvature (greater sulcal widening) and shallower sulcal depth with disease progression from controls to MCI and MCI to AD. The most remarkable change in MCI and AD was sulcal widening observed in the temporal lobe (average mean curvature, control [mean]: 0.564, MCI: 0.534 (5.3% decrease from control), AD: 0.486 (13.8% and 9.0% decrease from control and MCI respectively)). Of the four measurements, the sulcal widening measurement showed the highest sensitivity in revealing group differences between control and MCI, which might be useful for detecting early dementia. Significant reductions in cortical thickness and gyral WM volume also occurred in MCI and AD. Multiple regression analysis demonstrated that a wider and shallower sulcal shape was primarily associated with decreased cortical thickness and gyral WM volume in each group. Age-related trends for the sulcal shape were not strongly found when cortical thickness and gyral WM volume were considered.

Chiropody May Prevent Amputations in Diabetic Patients on Peritoneal Dialysis

A multidisciplinary approach has been shown to be of benefit in the prevention of lower limb ulceration and amputation in patients with diabetes, but there is less information on the role of such an approach in patients receiving dialysis treatment. The purpose of the present study was to determine whether the institution of a chiropody program would result in fewer amputations in diabetic patients on peritoneal dialysis (PD). Retrospective chart review. The PD program at a tertiary-care hospital. Patients with diabetes that were enrolled in the PD program between January 1997 and December 1999, inclusive, that were offered the opportunity to see a chiropodist, and that agreed to be seen. A total of 132 patients were included. Education about foot care, assessment, and, in some instances, treatment by a chiropodist. Patients with an amputation were more likely to be male (p < 0.01) and have peripheral vascular disease (p < 0.001) compared to those without an amputation. They also had a lower average mean arterial pressure (p < 0.05), lower weekly creatinine clearance (p < 0.01), higher mean erythropoietin dose (p < 0.05), and longer duration of end-stage renal disease (p < 0.001). Factors that were predictive of shorter time to death or amputation were older age [hazard ratio (HR) = 1.03, p < 0.05], peripheral vascular disease (HR = 2.66, p< 0.01), and cerebrovascular disease (HR = 2.70, p< 0.01). Being seen by a chiropodist was protective (HR = 0.39, p < 0.01). The current study suggests that a chiropody program may help to prevent amputation in patients with diabetes on PD.

Using Spatial Statistics to Select Model Complexity

Testing for nonindependence among the residuals from a regression or time series model is a common approach to evaluating the adequacy of a fitted model. This idea underlies the familiar Durbin–Watson statistic, and previous works illustrate how the spatial autocorrelation among residuals can be used to test a candidate linear model. We propose here that a version of Moran's I statistic for spatial autocorrelation, applied to residuals from a fitted model, is a practical general tool for selecting model complexity under the assumption of iid additive errors. The “space” is defined by the independent variables, and the presence of significant spatial autocorrelation in residuals is evidence that a more complex model is needed to capture all of the structure in the data. An advantage of this approach is its generality, which results from the fact that no properties of the fitted model are used other than consistency. The problem of smoothing parameter selection in nonparametric regression is used to illustrate the performance of model selection based on residual spatial autocorrelation (RSA). In simulation trials comparing RSA with established selection criteria based on minimizing mean square prediction error, smooths selected by RSA exhibit fewer spurious features such as minima and maxima. In some cases, at higher noise levels, RSA smooths achieved a lower average mean square error than smooths selected by GCV. We also briefly describe a possible modification of the method for non-iid errors having short-range correlations, for example, time-series errors or spatial data. Some other potential applications are suggested, including variable selection in regression models.

An infrared survey for embedded young stars in the Perseus molecular cloud complex

view Abstract Citations (65) References (60) Co-Reads Similar Papers Volume Content Graphics Metrics Export Citation NASA/ADS An Infrared Survey for Embedded Young Stars in the Perseus Molecular Cloud Complex Ladd, E. F. ; Lada, E. A. ; Myers, P. C. Abstract We searched the Perseus molecular cloud complex for isolated, embedded, and presumably young stars using the survey data collected with the IRAS and previously published visual extinction data. Of the 23 source positions which satisfied both our IRAS color and visual extinction tests, we imaged 18 and identified the 2 micron counterparts of 11 IRAS sources. We have estimated the luminosities and mean frequencies of these sources and compare these properties with a similarly selected group of sources from the Taurus-Auriga cloud. We find that the isolated sources in Perseus cloud have a slightly higher average luminosity and a distinctly lower average mean frequency than the sources in our Taurus sample. These results suggest that the Perseus cloud is intermediate in its star-forming character between the predominantly low-mass star-forming regions like Taurus and those which are capable of producing very high mass stars, like the clouds in Orion. Publication: The Astrophysical Journal Pub Date: June 1993 DOI: 10.1086/172735 Bibcode: 1993ApJ...410..168L Keywords: Early Stars; Infrared Astronomy; Molecular Clouds; Star Formation; Infrared Astronomy Satellite; Interstellar Extinction; Point Sources; Spectral Energy Distribution; Stellar Luminosity; Astrophysics; INFRARED: STARS; ISM: CLOUDS; STARS: PRE--MAIN-SEQUENCE full text sources ADS | data products SIMBAD (77)

Peroperative measurements of blood flow and pressure in occlusion and/or stenosis of the subclavian artery and the brachiocephalic trunk.

Preoperative haemodynamic studies were performed in 60 out of 64 patients operated upon for occlusive disease of the subclavian artery. The pressure differences over the occlusive lesion were recorded in 53 patients. Blood flow was studied in 56 patients with the aid of electromagnetic flowmetry. The average mean pressure difference was 30 mmHg in 6 patients with occlusion of the brachiocephalic trunk and 20 mmHg in 20 patients with left subclavian artery occlusion. In 3 patients with right subclavian occlusion it was 17 mmHg. The highest individual mean pressure differences were found in patients with multiple occlusive lesions in extracranial cephalic arteries. Stenoses of the brachiocephalic trunk and the subclavian arteries in general caused a lower average mean pressure difference than the occlusions. Vertebral blood flow in cases of occlusion of the proximal part of the subclavian artery was usually reversed.