Improving medical image perception by hierarchical clustering based segmentation

It has been well documented that radiologists' performance is not perfect: they make both false positive and false negative decisions. For example, approximately thirty percent of early lung cancer is missed on chest radiographs when the evidence is clearly visible in retrospect. Currently computer-aided detection (CAD) uses software, designed to reduce errors by drawing radiologists' attention to possible abnormalities by placing prompts on images. Alberdi et al examined the effects of CAD prompts on performance, comparing the negative effect of no prompt on a cancer case with prompts on a normal case. They showed that no prompt on a cancer case can have a detrimental effect on reader sensitivity and that the reader performs worse than if the reader was not using CAD. This became particularly apparent when difficult cases were being read. They suggested that the readers were using CAD as a decision making tool instead of a prompting aid. They conclude that "incorrect CAD can have a detrimental effect on human decisions". The goal of this paper is to explore the possibility of using hierarchical clustering based segmentation (HSC), as a perceptual aid, to improve the performance of the reader

Improving medical image perception by hierarchical clustering based segmentation

It has been well documented that radiologists' performance is not perfect: they make both false positive and false negative decisions. For example, approximately thirty percent of early lung cancer is missed on chest radiographs when the evidence is clearly visible in retrospect [1]. Currently Computer-Aided Detection (CAD) uses software, designed to reduce errors by drawing radiologists' attention to possible abnormalities by placing prompts on images. Alberdi et al examined the effects of CAD prompts on performance, comparing the negative effect of no prompt on a cancer case with prompts on a normal case. They showed that no prompt on a cancer case can have a detrimental effect on reader sensitivity and that the reader performs worse than if the reader was not using CAD. This became particularly apparent when difficult cases were being read. They suggested that the readers were using CAD as a decision making tool instead of a prompting aid. They conclude that "incorrect CAD can have a detrimental effect on human decisions" [2]. The goal of this paper is to explore the possibility of using Hierarchical Clustering based Segmentation (HCS) [3], as a perceptual aid, to improve the performance of the reader

Adaptive sampling technique for computer network traffic parameters using a combination of fuzzy system and regression model

In order to evaluate the effectiveness of wired and wireless networks for multimedia communication, suitable mechanisms to analyse their traffic are needed. Sampling is one such mechanism that allows a subset of packets that accurately represents the overall traffic to be formed thus reducing the processing resources and time. In adaptive sampling, unlike fixed rate sampling, the sample rate changes in accordance with transmission rate or traffic behaviour and thus can be more optimal. In this study an adaptive sampling technique that combines regression modelling and a fuzzy inference system has been developed. It adjusts the sampling according to the variations in the traffic characteristics. The method's operation was assessed using a computer network simulated in the NS-2 package. The adaptive sampling evaluated against a number of non-adaptive sampling gave an improved performance

Abiotic controls on macroscale variations of humid tropical forest height

Spatial variation of tropical forest tree height is a key indicator of ecological processes associated with forest growth and carbon dynamics. Here we examine the macroscale variations of tree height of humid tropical forests across three continents and quantify the climate and edaphic controls on these variations. Forest tree heights are systematically sampled across global humid tropical forests with more than 2.5 million measurements from Geoscience Laser Altimeter System (GLAS) satellite observations (2004–2008). We used top canopy height (TCH) of GLAS footprints to grid the statistical mean and variance and the 90 percentile height of samples at 0.5 degrees to capture the regional variability of average and large trees globally. We used the spatial regression method (spatial eigenvector mapping-SEVM) to evaluate the contributions of climate, soil and topography in explaining and predicting the regional variations of forest height. Statistical models suggest that climate, soil, topography, and spatial contextual information together can explain more than 60% of the observed forest height variation, while climate and soil jointly explain 30% of the height variations. Soil basics, including physical compositions such as clay and sand contents, chemical properties such as PH values and cation-exchange capacity, as well as biological variables such as the depth of organic matter, all present independent but statistically significant relationships to forest height across three continents. We found significant relations between the precipitation and tree height with shorter trees on the average in areas of higher annual water stress, and large trees occurring in areas with low stress and higher annual precipitation but with significant differences across the continents. Our results confirm other landscape and regional studies by showing that soil fertility, topography and climate may jointly control a significant variation of forest height and influencing patterns of aboveground biomass stocks and dynamics. Other factors such as biotic and disturbance regimes, not included in this study, may have less influence on regional variations but strongly mediate landscape and small-scale forest structure and dynamics.The research was funded by Gabon National Park (ANPN) under the contract of 011-ANPN/2012/SE-LJTW at UCLA. We thank IIASA, FAO, USGS, NASA, Worldclim science teams for making their data available. (011-ANPN/2012/SE-LJTW - Gabon National Park (ANPN) at UCLA

Improved pitting corrosion resistance of S.S 316L by Pulsed Current Gas Tungsten Arc Welding

In this study, S.S 316L was welded using Direct Current Gas Tungsten Arc Welding (DGTAW) and Pulsed Current Gas Tungsten Arc Welding (PGTAW) methods. Optical observations, scanning electron microscopy (SEM) and X-ray diffraction analysis (XRD) were employed to study the effect of continuous and pulse currents on microstructure and phase transformation in weld metal (WM). In addition pits morphology were evaluated by SEM. The corrosion behaviour was analyzed using cyclic polarizaton tests and Mott-schottky measurements. The pulse current resulted in finer grain and more ferrite in WM. This can be due to the decrease in heat input and higher cooling rate encouraged by pulse current. Cyclic potentiodynamic polarization tests showed that the WM of sample produced by pulse current show higher corrosion and pitting resistances than that in sample produced by continuous current. The reason is attributed to lower segregation of solute elements such as chromium and molybdenum into the delta-ferrite and also finer grain size produced in WM due to lower heat input and higher cooling rate. Both of these factors increase the stability of passive layer formed. The results showed that the corrosion behaviour of WM in both conditions (pulse and continuous current) is higher than the base metal (BM). This fact is attributed to the presence of ferrite bands formed in BM due to the segregation of alloy elements. The Mott-schottky plots confirmed that the passive layer formed on welded samples was an n-type semiconductor. The results showed that the samples showed less pitting resistance contained more oxygen vacancies in their passive film structure. It is also concluded that the breakdown of passive layer and pitting formation obey point defect model (PDM). Keywords: S.S 316L, Pulsed Current Gas Tungsten Arc Welding (PGTAW), lacy ferrite, vermicular ferrite, Pitting corrosion, Mott- Schottky

Onboard Data Processor for Change-Detection Radar Imaging

A computer system denoted a change-detection onboard processor (CDOP) is being developed as a means of processing the digitized output of a synthetic-aperture radar (SAR) apparatus aboard an aircraft or spacecraft to generate images showing changes that have occurred in the terrain below between repeat passes of the aircraft or spacecraft over the terrain. When fully developed, the CDOP is intended to be capable of generating SAR images and/or SAR differential interferograms in nearly real time. The CDOP is expected to be especially useful for understanding some large-scale natural phenomena and/or mitigating natural hazards: For example, it could be used for near-real-time observation of surface changes caused by floods, landslides, forest fires, volcanic eruptions, earthquakes, glaciers, and sea ice movements. It could also be used to observe such longer-term surface changes as those associated with growth of vegetation (relevant to estimation of wildfire fuel loads). The CDOP is, essentially, an interferometric SAR processor designed to operate aboard a radar platform

Genomic Prediction using Single or Multi-Breed Reference Populations in US Maine-Anjou Beef Cattle

The objective of this study was to estimate accuracies of genomic predictions based on 50K SNP genotypes for 8 nationally evaluated traits in US Maine-Anjou beef cattle using single or multi-breed reference populations. The accuracies of direct genomic values (DGV) ranged from 0.22 to 0.45 for 8 studied traits when the reference populations comprised only 573 Maine-Anjou animals. Accuracies were slightly reduced and ranged from 0.21 to 0.38 when the reference population included over 9,000 animals from many other breeds as well as Maine-Anjou. These results demonstrate that including data from other populations does not generally increase accuracy of prediction in one particular population. This means every breed association must develop its own reference population if it intends to offer genomic prediction

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

Uncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products

Comparison of the effects of acupressure and touch on the headache caused by spinal anesthesia after cesarean section

Introduction: Post-Dural puncture headache (PDPH) is one of the common complications of spinal anesthesia, especially after cesarean section. It is better to treat this headache by non-pharmaceutical methods. One of the non-pharmaceutical pain control methods is the use of acupressure. But so far, its impact on headache after spinal anesthesia has not been studied. Therefore, this study was performed with aim to evaluate the effect of acupressure and touch on headache caused by spinal anesthesia after cesarean section. Methods: This randomized clinical trial with control group was conducted on 90 patients who underwent cesarean section by spinal anesthesia in Semnan Amir AlMomenin (A) Hospital in 2015. These patients were randomly divided into acupressure, touch and control groups. Pain severity was measured by Visual Analog Scale (VAS). Then, changes of pain severity pre and post intervention was measured and recorded. Data was analyzed by SPSS software (version 16) and Pearson and Spearman correlation coefficient tests, Kruskal-Wallis test, ANOVA and paired t-test. P0.05). After the intervention, there was a statistically significant decrease in mean pain score in acupressure and touch groups in comparison with control group (P<0.001). Also, after intervention, there was a statistically significant decrease in mean of headache scores in the acupressure group compared with touch group (P<0.001). Conclusion: Comparing with touch, acupressure was more effective for headache-relieving after spinal anesthesia in women undergoing cesarean section. Therefore, it is suggested that acupressure along with other conventional treatments be used to control and treat such headaches. © 2016, Mashhad University of Medical Sciences. All Rights Reserved

Association of plasma total testosterone level and metabolic syndrome in adult males

Introduction: Low testosterone level has strongly been correlated with body fat accumulation and abdominal obesity in men. Objectives: This study aimed to evaluate testosterone level in men with and without metabolic syndrome to determine the relationship between testosterone and metabolic syndrome. Patients and Methods: This case-control study was conducted on 172 cases of metabolic syndrome and 172 participants as a control group in Rasoul Akram hospital, Tehran, Iran. Demographic characteristics, fasting blood sugar (FBS), high-density lipoprotein (HDL), low-density lipoprotein (LDL), cholesterol, triglyceride (TG), and testosterone levels were recorded. SPSS version 21.0 and SAS version 9.1 were used for statistical analysis. Level of significance was considered 0.05. Results: The mean age of the two groups were 45.1 ± 9.3 years and 41.5 ± 11.2 years, respectively. There was a significant difference in serum testosterone levels between both groups and low testosterone levels were associated with metabolic syndrome (P < 0.001). Serum testosterone levels showed a significant negative correlation with age in the metabolic syndrome group (r =-0.16, P = 0.02). The relationship between metabolic syndrome and total plasma testosterone level using logistic regression model showed that, by increasing the total plasma testosterone level, the odds ratio for metabolic syndrome was 0.076 (95 CI: 0.027-0.216; P < 0.001). Conclusion: According to the results, low level of testosterone was related to the presence of metabolic syndrome in adult males. Future studies can investigate diagnostic value of testosterone level in this syndrome. © 2020 The Author(s)