A study and evaluation of image analysis techniques applied to remotely sensed data

An analysis of phenomena causing nonlinearities in the transformation from Landsat multispectral scanner coordinates to ground coordinates is presented. Experimental results comparing rms errors at ground control points indicated a slight improvement when a nonlinear (8-parameter) transformation was used instead of an affine (6-parameter) transformation. Using a preliminary ground truth map of a test site in Alabama covering the Mobile Bay area and six Landsat images of the same scene, several classification methods were assessed. A methodology was developed for automatic change detection using classification/cluster maps. A coding scheme was employed for generation of change depiction maps indicating specific types of changes. Inter- and intraseasonal data of the Mobile Bay test area were compared to illustrate the method. A beginning was made in the study of data compression by applying a Karhunen-Loeve transform technique to a small section of the test data set. The second part of the report provides a formal documentation of the several programs developed for the analysis and assessments presented

Plasma Krebs Cycle Intermediates in Nonalcoholic Fatty Liver Disease

Nonalcoholic liver disease (NAFLD) is manifested with a wide spectrum of clinical symptoms and is closely associated with the metabolic syndrome, inflammation, and mitochondrial dysfunction. Although the mechanism of mitochondrial dysfunction in NAFLD is still not fully elucidated, multiple studies have demonstrated evidence of molecular, biochemical, and biophysical mitochondrial abnormalities in NAFLD. Given the association between NAFLD and mitochondrial dysfunction, the aim of this study is to analyze circulating levels of Krebs cycle intermediates in a cohort of NAFLD-affected individuals and matching healthy controls and to correlate our findings with the liver function metrics. Standard serum biochemistry and Krebs cycle intermediates were analyzed in NAFLD (n = 22) and matched control (n = 67) cohorts. Circulating levels of isocitrate and citrate were significantly (p \u3c 0.05) elevated in the NAFLD cohort of patients. The area under the curve (AUROC) for these two metabolites exhibited a moderate clinical utility. Correlations between plasma Krebs cycle intermediates and standard clinical plasma metrics were explored by Pearson’s correlation coefficient. The data obtained for plasma Krebs cycle intermediates suggest pathophysiological insights that link mitochondrial dysfunction with NAFLD. Our findings reveal that plasma isocitrate and citrate can discriminate between normal and NAFLD cohorts and can be utilized as noninvasive markers of mitochondrial dysfunction in NAFLD. Future studies with large populations at different NAFLD stages are warranted

Influence of kNN-Based Load Forecasting Errors on Optimal Energy Production

This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast is used to compute the optimal on/off status and generation scheduling of the units. Finally, the influence of forecasting errors on both the status and generation level of the units over the scheduling period is studied

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

Combining Multiple Classifiers with Dynamic Weighted Voting

When a multiple classifier system is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the efficiency of this type of voting generally results affected negatively. In this paper, new functions for dynamic weighting in classifier fusion are introduced. Experimental results demonstrate the advantages of these novel strategies over the simple voting scheme

A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies

Background: Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms. Methods: We compare the classification performance of a number of important and widely used machine learning algorithms, namely the Random Forests (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbour (kNN). Using massively parallel processing on high-performance supercomputers, we compare the generalisation errors at various combinations of levels of several factors: number of features, training sample size, biological variation, experimental variation, effect size, replication and correlation between features. Results: For smaller number of correlated features, number of features not exceeding approximately half the sample size, LDA was found to be the method of choice in terms of average generalisation errors as well as stability (precision) of error estimates. SVM (with RBF kernel) outperforms LDA as well as RF and kNN by a clear margin as the feature set gets larger provided the sample size is not too small (at least 20). The performance of kNN also improves as the number of features grows and outplays that of LDA and RF unless the data variability is too high and/or effect sizes are too small. RF was found to outperform only kNN in some instances where the data are more variable and have smaller effect sizes, in which cases it also provide more stable error estimates than kNN and LDA. Applications to a number of real datasets supported the findings from the simulation study

Frailty in liver transplantation: An expert opinion statement from the American Society of Transplantation Liver and Intestinal Community of Practice

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149699/1/ajt15392_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149699/2/ajt15392.pd

Diagnostic and Prognostic Significance of Complement in Patients with Alcohol-associated Hepatitis

BACKGROUND and AIMS: Given the lack of effective therapies and high mortality in acute alcohol-associated hepatitis (AH), it is important to develop rationally-designed biomarkers for effective disease management. Complement, a critical component of the innate immune system, contributes to uncontrolled inflammatory responses leading to liver injury, but is also involved in hepatic regeneration. Here we investigated if a panel of complement proteins and activation products would provide useful biomarkers for severity of AH and aid in predicting 90 days mortality. APPROACH and RESULTS: Plasma samples collected at time of diagnosis from 254 patients with moderate and severe AH recruited from four medical centers and 31 healthy individuals were used to quantify complement proteins by ELISA and Luminex arrays. Components of the classical and lectin pathways, including complement factors C2, C4b and C4d, as well as complement factor I (CFI) and C5, were reduced in AH patients compared to healthy individuals. In contrast, components of the alternative pathway, including complement factor Ba (CFBa) and factor D (CFD), were increased. Markers of complement activation were also differentially evident, with C5a increased and the soluble terminal complement complex (sC5b9) decreased in AH. Mannose binding lectin (MBL), C4b, CFI, C5 and sC5b9 were negatively correlated with model for end-stage liver disease (MELD) score, while CFBa and CFD were positively associated with disease severity. Lower CFI and sC5b9 were associated with increased 90-day mortality in AH. CONCLUSIONS: Taken together, these data indicate that AH is associated with a profound disruption of complement. Inclusion of complement, especially CFI and sC5b9, along with other laboratory indicators, could improve diagnostic and prognostic indications of disease severity and risk of mortality for AH patients