Background Pathologic evidence of Alzheimer disease (AD) is detectable years before onset of clinical symptoms. Imaging-based identification of structural changes of the brain in people at genetic risk for early-onset AD may provide insights into how genes influence the pathologic cascade that leads to dementia. Purpose To assess structural connectivity differences in cortical networks between cognitively normal autosomal dominant Alzheimer disease (ADAD) mutation carriers versus noncarriers and to determine the cross-sectional relationship of structural connectivity and cortical amyloid burden with estimated years to symptom onset (EYO) of dementia in carriers. Materials and Methods In this exploratory analysis of a prospective trial, all participants enrolled in the Dominantly Inherited Alzheimer Network between January 2009 and July 2014 who had normal cognition at baseline, T1-weighted MRI scans, and diffusion tensor imaging (DTI) were analyzed. Amyloid PET imaging using Pittsburgh compound B was also analyzed for mutation carriers. Areas of the cerebral cortex were parcellated into three cortical networks: the default mode network, frontoparietal control network, and ventral attention network. The structural connectivity of the three networks was calculated from DTI. General linear models were used to examine differences in structural connectivity between mutation carriers and noncarriers and the relationship between structural connectivity, amyloid burden, and EYO in mutation carriers. Correlation network analysis was performed to identify clusters of related clinical and imaging markers. Results There were 30 mutation carriers (mean age ± standard deviation, 34 years ± 10; 17 women) and 38 noncarriers (mean age, 37 years ± 10; 20 women). There was lower structural connectivity in the frontoparietal control network in mutation carriers compared with noncarriers (estimated effect of mutation-positive status, -0.0266; P = .04). Among mutation carriers, there was a correlation between EYO and white matter structural connectivity in the frontoparietal control network (estimated effect of EYO, -0.0015, P = .01). There was no significant relationship between cortical global amyloid burden and EYO among mutation carriers (P > .05). Conclusion White matter structural connectivity was lower in autosomal dominant Alzheimer disease mutation carriers compared with noncarriers and correlated with estimated years to symptom onset. Clinical trial registration no. NCT00869817 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by McEvoy in this issue.

The paper presents a methodology for analyzing time series of gene expression data collected from the leaves of potato virus Y (PVY) infected and non-infected potato plants, with the aim to identify significant differences between the two sets of potato plants' characteristic for various time points. We aim at identifying differentially-expressed genes whose expression values are statistically significantly different in the set of PVY infected potato plants compared to non-infected plants, and which demonstrate also statistically significant changes of expression values of genes of PVY infected potato plants in time. The novelty of the approach includes stratified data randomization used in estimating the statistical properties of gene expression of the samples in the control set of non-infected potato plants. A novel estimate that computes the relative minimal distance between the samples has been defined that enables reliable identification of the differences between the target and control datasets when these sets are small. The relevance of the outcomes is demonstrated by visualizing the relative minimal distance of gene expression changes in time for three different types of potato leaves for the genes that have been identified as relevant by the proposed methodology.

The heterogeneity of Alzheimer’s disease contributes to the high failure rate of prior clinical trials. We analyzed 5-year longitudinal outcomes and biomarker data from 562 subjects with mild cognitive impairment (MCI) from two national studies (ADNI) using a novel multilayer clustering algorithm. The algorithm identified homogenous clusters of MCI subjects with markedly different prognostic cognitive trajectories. A cluster of 240 rapid decliners had 2-fold greater atrophy and progressed to dementia at almost 5 times the rate of a cluster of 184 slow decliners. A classifier for identifying rapid decliners in one study showed high sensitivity and specificity in the second study. Characterizing subgroups of at risk subjects, with diverse prognostic outcomes, may provide novel mechanistic insights and facilitate clinical trials of drugs to delay the onset of AD.

Exploratory Clustering is a novel general purpose clustering tool which is especially appropriate for medical domains in which we need to identify subpopulations that are similar in two different data layers. The tool implements the multi-layer clustering algorithm in a framework that enables iterative experiments by the user in his search for relevant patient subpopulations. A unique property of the tool is integration of clustering and feature selection algorithms. Differences in values of most relevant attributes are used to demonstrate decisive properties of constructed clusters. Usefulness of the tool is illustrated on a task of discovering groups of patients with similar cognitive impairment.

Background: Identification of biomarkers for the Alzheimer's disease (AD) is a challenge and a very difficult task both for medical research and data analysis.Methods: We applied a novel clustering tool with the goal to identify subpopulations of the AD patients that are homogeneous in respect of available clinical as well as in respect of biological descriptors.Results: The main result is identification of three clusters of patients with significant problems with dementia. The evaluation of properties of these clusters demonstrates that brain atrophy is the main driving force of dementia. The unexpected result is that the largest subpopulation that has very significant problems with dementia has besides mild signs of brain atrophy also large ventricular, intracerebral and whole brain volumes. Due to the fact that ventricular enlargement may be a consequence of brain injuries and that a large majority of patients in this subpopulation are males, a potential hypothesis is that such medical status is a consequence of a combination of previous traumatic events and degenerative processes.Conclusions: The results may have substantial consequences for medical research and clinical trial design. The clustering methodology used in this study may be interesting also for other medical and biological domains.

This paper presents homogeneous clusters of patients, identified in the Alzheimer's Disease Neuroimaging Initiative (ADNI) data population of 317 females and 342 males, described by a total of 243 biological and clinical descriptors. Clustering was performed with a novel methodology, which supports identification of patient subpopulations that are homogeneous regarding both clinical and biological descriptors. Properties of the constructed clusters clearly demonstrate the differences between female and male Alzheimer's disease patient groups. The major difference is the existence of two male subpopulations with unexpected values of intracerebral and whole brain volumes.

Heart rate variability (HRV) gives information on the sympathetic-parasympathetic autonomic balance. The aim of the study was to analyze sympathovagal balance after acute spinal cord injury (SCI), demonstrated by linear measures in time and frequency domain of HRV and to analyze the effect of corticosteroids on HRV parameters in SCI. The study included 40 tetraplegic patients with acute SCI and 40 healthy subjects as control group. In the SCI group, 29 patients received and 11 patients did not receive corticosteroid therapy. All patients underwent 24-hour Holter monitoring for evaluation of HRV. Cardiac autonomic balance was evaluated by analysis of HRV in time and frequency domain. Sympathovagal balance (LF/HF) was significantly reduced in the groups of acute SCI patients, both with and without corticosteroid therapy, as compared with controls. However, there was no statistically significant difference between the two SCI groups (1.74 (0524) with and 1.75 (0534) without corticosteroid therapy). This study showed the sympathovagal balance to be altered in the acute phase of cervical spinal cord trauma. Finally, there was no effect of corticosteroid therapy on HRV parameters in SCI patients.

Identification of biomarkers for the Alzheimer's disease is a challenge and a very difficult task both for medical research and data analysis. In this work we present results obtained by application of a novel clustering tool. The goal is to identify subpopulations of the Alzheimer's disease (AD) patients that are homogeneous in respect of available clinical and biological descriptors. The result presents a segmentation of the Alzheimer's disease patient population and it may be expected that within each subpopulation separately it will be easier to identify connections between clinical and biological descriptors. Through the evaluation of the obtained clusters with AD subpopulations it has been noticed that for two of them relevant biological measurements (whole brain volume and intracerebral volume) change in opposite directions. If this observation is actually true it would mean that the diagnosed severe dementia problems are results of different physiological processes. The observation may have substantial consequences for medical research and clinical trial design. The used clustering methodology may be interesting also for other medical and biological domains.

The paper presents experiments with a novel clustering methodology that enables identification of subpopulations of the Alzheimer’s disease patients that are homogeneous in respect of both clinical and biological descriptors. It is expected that recognition of relevant connections between clinical and biological descriptors will be easier within such subpopulations. Our dataset includes 317 female and 342 male patients from the ADNI study that are described by a total of 243 biological and clinical descriptors recorded at baseline evaluation. The constructed clusters clearly demonstrate differences between female and male patient subpopulations. An interesting result is identification of a cluster of male Alzheimer’s disease patients that are, surprisingly, characterized by increased intracerebral and whole brain volumes. The finding suggests existence of two different biological pathways for the Alzheimer’s disease.

This study examines the impact of bilateral investment treaties (BITs) on Swiss foreign direct investment (FDI). It also investigates the role of BITs as protective tools of Swiss investment. This paper is based on secondary data analysis; data is obtained from various official entities. This study uses statistical and machine learning techniques in order to detect meaningful relationships between BITs and FDI flows. Our findings suggest that the implementation of BITs have an insignificant impact on the increase of Swiss FDI flows. However, from our examination, two interesting findings have emerged suggesting that the completion of BITs may have an impact on the increase of political stability and rule of law of partner countries. Resumen Este estudio examina el impacto de los tratados bilaterales de inversion (TBI) de la inversion extranjera directa suizo (IED). Tambien indaga el papel de los TBI como herramientas de proteccion de inversiones. Esta investigacion se basa en analisis de datos secundarios, los datos son obtenidos de diversas entidades oficiales. Este estudio utiliza tecnicas de aprendizaje estadistico con el fin de detectar relaciones significativas entre los TBI y los flujos de IED. Nuestros resultados sugieren que la implementacion de tratados bilaterales de inversion tiene un impacto insignificante en el aumento de IED suizos. Sin embargo, desde nuestro examen,dos hallazgos interesantes han surgido, proponiendo que los TBI puedan tener un impacto en el aumento de la estabilidad politica y el estado de derecho de los paises asociados.

This study examines the impact of bilateral investment treaties (BITs) on Swiss foreign direct investment (FDI). It also investigates the role of BITs as protective tools of Swiss investment. This paper is based on secondary data analysis; data is obtained from various official entities. This study uses statistical and machine learning techniques in order to detect meaningful relationships between BITs and FDI flows. Our findings suggest that the implementation of BITs have an insignificant impact on the increase of Swiss FDI flows. However, from our examination, two interesting findings have emerged suggesting that the completion of BITs may have an impact on the increase of political stability and rule of law of partner countries.

Methods We have developed a three step procedure consisting of: A) transformation of ECG signals into a set of instances with 5 msec distance, so that each instance is defined by 93 features that describe characteristics of signals in the concrete time slot, B) evaluation of a multi-rule model on the set of instances so that a value in the range 200 to +200 is generated which is proportional to the probability that the instance is a fetal QRS event, C) transformation of a string of generated values into a string of QRS events taking into account that typical distance between fetal QRS is 250-600 msec. The central part of the approach is the preparation of the multi-rule model that consists of about 70,000 rules that vote either yes or no for fetal QRS [1]. Probability of fetal QRS is proportional to the difference between yes and no votes. The model is constructed by a machine learning approach from a set of 10,000 examples described by the same set of features. Positive examples are coming from time slots with known fetal QRS events, while negative examples are from time slots that are 50 msec far from the positive examples.

Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based noise ranking methodology for explicit noise and outlier identification, named Noise-Rank, which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of noise detection algorithms on data with randomly injected noise. A methodology for visual performance evaluation of noise detection algorithms in the precision-recall space, named Viper, is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new noise detection algorithms into the proposed noise detection and performance evaluation workflows.

The topic of this work is the presentation of a novel clustering methodology based on instance similarity in two or more attribute layers. The work is motivated by multi-view clustering and redescription mining algorithms. In our approach we do not construct descriptions of subsets of instances and we do not use conditional independence assumption of different views. We do bottom up merging of clusters only if it enables reduction of an example variability score for all layers. The score is defined as a two component sum of squared deviates of example similarity values. For a given set of instances, the similarity values are computed by execution of an artificially constructed supervised classification problem. As a final result we identify a small but coherent clusters. The methodology is illustrated on a real life discovery task aimed at identification of relevant subgroups of countries with similar trading characteristics in respect of the type of commodities they export.

Introduction Intelligent data analysis (IDA) is an important tool for data based medical research. It is often combined with statistical techniques. The primary goal of IDA is data understanding and hypothesis creation while statistics is used for hypotheses validation. Aim: Presentation of novel approaches for pattern recognition, example clustering, and data understanding tasks in cardiological applications.

Two of the world's largest markets, the EU and China, have initiated negotiations to lower down their markets' barriers in pursuit of a multi-billion-dollar free-trade deal. Although the European Commission has proposed a new generation of competitiveness-driven bilateral free trade agreements with emerging and developing countries, some EU member countries are skeptical of China's trade practices which have halted Brussels and Beijing negotiations. However, some EU officials have suggested that this agreement will be a stepping stone for future liberalization of the EU economy and that a free trade agreement with China is only a matter of time. The purpose of this study is to investigate the comparative advantage of EU members and China, how complementary trade is between the EU and China and how similar are the exports of these prospective FTA partners. This is a research paper based on secondary data analysis; data is obtained from various official entities. This study uses statistical and machine learning techniques in order to detect meaningful trade relationships between the EU and China.

The purpose of this paper is to contribute to the theoretical frameworks of the interaction of tourism and international development; this study also provides a critical evaluation of the effectiveness of international development initiatives and poverty reduction, using the Nicaraguan case. The evaluation incorporates aspects related to the millennium development goals (MDGs) and the heavily indebted poor countries (HIPCs) initiatives. By using data analysis techniques, we evaluated the ratio between investment in the tourism industry and economic growth in the period 1995–2010. From this analysis, a predictive model has been developed. Tourism represents one of the main sources of income for many developing countries. We argue that tourism pro–poor must be integrated within national development strategies and should be present in international economic development and poverty reduction.

The starting point for the research has been the list of 147 banking crises within the period 1976-2011 prepared by the International Monetary Fund. The countries with crises have been analysed with respect to publicly available World Bank indicators in the periods of three years before the crises. The machine learning methodology for subgroup discovery has been used for the analysis. It enabled identification of five subsets of crises. Two of them have been identified as especially useful for the characterization of EU countries with banking crises in the year 2008. Fast growing credit activity is characteristic for the first subgroup while socioeconomic problems recognized by non-increasing quality of public health are decisive for the second subgroup. Comparative analysis of EU countries included into these subgroups demonstrated statistically significant differences with respect to World Bank good governance indicator values for the period before the crisis. Control of corruption, rule of law, and government effectiveness are the indicators which are statistically different for these sets of countries. The significance of the result is in the segmentation of the corpus of countries with banking crises and the recognition of connections between banking crises, socioeconomic problems, and governance effectiveness in some EU countries.

An interdisciplinary literature suggests that institutional environment features are significant determinants in the location of foreign direct investment. Weak institutional environment features are considered to have a negative effect on capital inflows. Conversely, empirical studies have found that investors locate in host countries providing high rates of return despite weak institutional environment features. Switzerland is home to some of the most important global investors and one of Europe's largest global investors. Swiss foreign direct investment is an interesting case to examine since it has been nurtured in a well-established institutional environment. In this paper, we evaluate Swiss foreign direct investment located in 56 countries over the period 2005-2009 using statistical and machine learning techniques. From our analysis two models emerge suggesting that Swiss investment favours countries with high political stability and high accountability. However, we also find that Swiss investment does not necessarily discriminate against countries with weak institutional environments.