Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging

In clinical diagnostics, it is of outmost importance to correctly identify the source of a metastatic tumor, especially if no apparent primary tumor is present. Tissue-based proteomics might allow correct tumor classification. As a result, we performed MALDI imaging to generate proteomic signatures for different tumors. These signatures were used to classify common cancer types. At first, a cohort comprised of tissue samples from six adenocarcinoma entities located at different organ sites (esophagus, breast, colon, liver, stomach, thyroid gland, n = 171) was classified using two algorithms for a training and test set. For the test set, Support Vector Machine and Random Forest yielded overall accuracies of 82.74 and 81.18%, respectively. Then, colon cancer liver metastasis samples (n = 19) were introduced into the classification. The liver metastasis samples could be discriminated with high accuracy from primary tumors of colon cancer and hepatocellular carcinoma. Additionally, colon cancer liver metastasis samples could be successfully classified by using colon cancer primary tumor samples for the training of the classifier. These findings demonstrate that MALDI imaging-derived proteomic classifiers can discriminate between different tumor types at different organ sites and in the same site.

Assessing and mapping LD and SLM to directly support the planning and scaling up of SLM interventions to combat desertification

Global investment in Sustainable Land Management (SLM) has been substantial, but knowledge gaps remain. Overviews of where land degradation (LD) is taking place and how land users are addressing the problem using SLM are still lacking for most individual countries and regions. Relevant maps focus more on LD than SLM, and they have been compiled using different methods. This makes it impossible to compare the benefits of SLM interventions and prevents informed decision-making on how best to invest in land. To fill this knowledge gap, a standardised mapping method has been collaboratively developed by the World Overview of Conservation Approaches and Technologies (WOCAT), FAO’s Land Degradation Assessment in Drylands (LADA) project, and the EU’s Mitigating Desertification and Remediating Degraded Land (DESIRE) project. The method generates information on the distribution and characteristics of LD and SLM activities and can be applied at the village, national, or regional level. It is based on participatory expert assessment, documents, and surveys. These data sources are spatially displayed across a land-use systems base map. By enabling mapping of the DPSIR framework (Driving Forces-Pressures-State-Impacts-Responses) for degradation and conservation, the method provides key information for decision-making. It may also be used to monitor LD and conservation following project implementation. This contribution explains the mapping method, highlighting findings made at different levels (national and local) in South Africa and the Mediterranean region. Keywords: Mapping, Decision Support, Land Degradation, Sustainable Land Management, Ecosystem Services, Participatory Expert Assessment

Impaired top-down modulation of saccadic latencies in patients with schizophrenia but not in first-degree relatives

Impaired eye movements have a long history in schizophrenia research and meet the criteria of a reliable biomarker. However, the effects of cognitive load and task difficulty on saccadic latencies (SL) are less understood. Recent studies showed that SL are strongly task dependent: SL are decreased in tasks with higher cognitive demand, and increased in tasks with lower cognitive demand. The present study investigates SL modulation in patients with schizophrenia and their first-degree relatives. A group of 13 patients suffering from ICD-10 schizophrenia, 10 first-degree relatives, and 24 control subjects performed two different types of visual tasks: a color task and a Landolt ring orientation task. We used video-based oculography to measure SL. We found that patients exhibited a similar unspecific SL pattern in the two different tasks, whereas controls and relatives exhibited 20–26% shorter average latencies in the orientation task (higher cognitive demand) compared to the color task (lower cognitive demand). Also, classification performance using support vector machines suggests that relatives should be assigned to the healthy controls and not to the patient group. Therefore, visual processing of different content does not modulate SL in patients with schizophrenia, but modulates SL in the relatives and healthy controls. The results reflect a specific oculomotor attentional dysfunction in patients with schizophrenia that is a potential state marker, possibly caused by impaired top-down disinhibition of the superior colliculus by frontal/prefrontal areas such as the frontal eye fields.

Brain-borne IL-1 adjusts glucoregulation and provides fuel support to astrocytes and neurons in an autocrine/paracrine manner.

It is still controversial which mediators regulate energy provision to activated neural cells, as insulin does in peripheral tissues. Interleukin-1β (IL-1β) may mediate this effect as it can affect glucoregulation, it is overexpressed in the 'healthy' brain during increased neuronal activity, and it supports high-energy demanding processes such as long-term potentiation, memory and learning. Furthermore, the absence of sustained neuroendocrine and behavioral counterregulation suggests that brain glucose-sensing neurons do not perceive IL-1β-induced hypoglycemia. Here, we show that IL-1β adjusts glucoregulation by inducing its own production in the brain, and that IL-1β-induced hypoglycemia is myeloid differentiation primary response 88 protein (MyD88)-dependent and only partially counteracted by Kir6.2-mediated sensing signaling. Furthermore, we found that, opposite to insulin, IL-1β stimulates brain metabolism. This effect is absent in MyD88-deficient mice, which have neurobehavioral alterations associated to disorders in glucose homeostasis, as during several psychiatric diseases. IL-1β effects on brain metabolism are most likely maintained by IL-1β auto-induction and may reflect a compensatory increase in fuel supply to neural cells. We explore this possibility by directly blocking IL-1 receptors in neural cells. The results showed that, in an activity-dependent and paracrine/autocrine manner, endogenous IL-1 produced by neurons and astrocytes facilitates glucose uptake by these cells. This effect is exacerbated following glutamatergic stimulation and can be passively transferred between cell types. We conclude that the capacity of IL-1β to provide fuel to neural cells underlies its physiological effects on glucoregulation, synaptic plasticity, learning and memory. However, deregulation of IL-1β production could contribute to the alterations in brain glucose metabolism that are detected in several neurologic and psychiatric diseases.Molecular Psychiatry advance online publication, 8 December 2015; doi:10.1038/mp.2015.174.

The Trypanosoma brucei MitoCarta and its regulation and splicing pattern during development

It has long been known that trypanosomes regulate mitochondrial biogenesis during the life cycle of the parasite; however, the mitochondrial protein inventory (MitoCarta) and its regulation remain unknown. We present a novel computational method for genome-wide prediction of mitochondrial proteins using a support vector machine-based classifier with approximately 90% prediction accuracy. Using this method, we predicted the mitochondrial localization of 468 proteins with high confidence and have experimentally verified the localization of a subset of these proteins. We then applied a recently developed parallel sequencing technology to determine the expression profiles and the splicing patterns of a total of 1065 predicted MitoCarta transcripts during the development of the parasite, and showed that 435 of the transcripts significantly changed their expressions while 630 remain unchanged in any of the three life stages analyzed. Furthermore, we identified 298 alternatively splicing events, a small subset of which could lead to dual localization of the corresponding proteins.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Low social support and poor emotional regulation are associated with increased stress hormone reactivity to mental stress in systemic hypertension

CONTEXT: There is strong evidence for a physiological hyperreactivity to stress in systemic hypertension, but data on associated or potentially moderating psychological factors are scarce. OBJECTIVE: The objective of the study was to identify psychological correlates of physiological stress reactivity in systemic hypertension. DESIGN: This was a cross-sectional, quasi-experimentally controlled study. Study participants underwent an acute standardized psychosocial stress task combining public speaking and mental arithmetic in front of an audience. SETTING: The study was conducted in the population in the state of Zurich, Switzerland. SUBJECTS: Subjects included 22 hypertensive and 26 normotensive men (mean +/- sem 44 +/- 2 yr). MAIN OUTCOME MEASURES: We assessed the psychological measures social support, emotional regulation, and cognitive appraisal of the stressful situation. Moreover, we measured salivary cortisol and plasma epinephrine and norepinephrine before and after stress and several times up to 60 min thereafter as well as blood pressure and heart rate. RESULTS: We found poorer hedonistic emotional regulation (HER) and lower perceived social support in hypertensives, compared with normotensives (P < 0.01). Compared with normotensives, hypertensives showed higher cortisol, epinephrine, and norepinephrine secretions after stress (P < 0.038) as well as higher systolic and diastolic blood pressure (P < 0.001). Cortisol reactivity and norepinephrine secretion were highest in hypertensive men with low HER (P < 0.05). In contrast, hypertensives with high HER did not significantly differ from normotensives in both cortisol and norepinephrine secretion after stress. Epinephrine secretion was highest in hypertensives with low social support but was not different between hypertensives with high social support and normotensives. CONCLUSIONS: The findings suggest that both low social support and low HER are associated with elevated stress hormone reactivity in systemic hypertension.

Extracorporeal membrane oxygenation (ECMO): extended indications for artificial support of both heart and lungs

Extracorporeal membrane oxygenation (ECMO) was used to achieve temporary artificial support in cardiac and pulmonary function in 22 patients from 1987 to September 1990. Standard indications were postcardiotomy cardiogenic shock (n = 4), neonatal (n = 1) and adult respiratory distress syndrome (n = 4). ECMO was also used for extended indications, such as graft failure following heart (n = 11) or lung transplantation (n = 2). In six of these cases ECMO was instituted as a bridge device to subsequent retransplantation of either the heart (n = 4) or one lung (n = 2). One out of nine patients supported by ECMO for standard indications, and two out of 13 patients supported for extended indications are long-term survivors. This series illustrates the results with ECMO in emergency situations, in patients under immunosuppressive protocols, or in patients with advanced lung failure requiring almost complete artificial gas exchange. In such complex situations, ECMO does provide stabilization until additional therapeutic measures are in effect. ECMO cannot be recommended for postoperative cardiogenic shock but short-term ECMO support is an accepted method in most cases with graft failure or pulmonary failure or other origin.

Independent association between lower level of social support and higher coagulation activity before and after acute psychosocial stress

OBJECTIVE: To investigate the relationship between social support and coagulation parameter reactivity to mental stress in men and to determine if norepinephrine is involved. Lower social support is associated with higher basal coagulation activity and greater norepinephrine stress reactivity, which in turn, is linked with hypercoagulability. However, it is not known if low social support interacts with stress to further increase coagulation reactivity or if norepinephrine affects this association. These findings may be important for determining if low social support influences thrombosis and possible acute coronary events in response to acute stress. We investigated the relationship between social support and coagulation parameter reactivity to mental stress in men and determined if norepinephrine is involved. METHODS: We measured perceived social support in 63 medication-free nonsmoking men (age (mean +/- standard error of the mean) = 36.7 +/- 1.7 years) who underwent an acute standardized psychosocial stress task combining public speaking and mental arithmetic in front of an audience. We measured plasma D-dimer, fibrinogen, clotting Factor VII activity (FVII:C), and plasma norepinephrine at rest as well as immediately after stress and 20 minutes after stress. RESULTS: Independent of body mass index, mean arterial pressure, and age, lower social support was associated with higher D-dimer and fibrinogen levels at baseline (p < .012) and with greater increases in fibrinogen (beta = -0.36, p = .001; DeltaR(2) = .12), and D-dimer (beta = -0.21, p = .017; DeltaR(2) = .04), but not in FVII:C (p = .83) from baseline to 20 minutes after stress. General linear models revealed significant main effects of social support and stress on fibrinogen, D-dimer, and norepinephrine (p < .035). Controlling for norepinephrine did not change the significance of the reported associations between social support and the coagulation measures D-dimer and fibrinogen. CONCLUSIONS: Our results suggest that lower social support is associated with greater coagulation activity before and after acute stress, which was unrelated to norepinephrine reactivity.

Climate change and infectious diseases of wildlife: Altered interactions between pathogens, vectors and hosts

Infectious diseases result from the interactions of host, pathogens, and, in the case of vector-borne diseases, also vectors. The interactions involve physiological and ecological mechanisms and they have evolved under a given set of environmental conditions. Environmental change, therefore, will alter host-pathogen-vector interactions and, consequently, the distribution, intensity, and dynamics of infectious diseases. Here, we review how climate change may impact infectious diseases of aquatic and terrestrial wildlife. Climate change can have direct impacts on distribution, life cycle, and physiological status of hosts, pathogens and vectors. While a change in either host, pathogen or vector does not necessarily translate into an alteration of the disease, it is the impact of climate change on the interactions between the disease components which is particularly critical for altered disease risks. Finally, climate factors can modulate disease through modifying the ecological networks host-pathogen-vector systems are belonging to, and climate change can combine with other environmental stressors to induce cumulative effects on infectious diseases. Overall, the influence of climate change on infectious diseases involves different mechanisms, it can be modulated by phenotypic acclimation and/or genotypic adaptation, it depends on the ecological context of the host-pathogen-vector interactions, and it can be modulated by impacts of other stressors. As a consequence of this complexity, non-linear responses of disease systems under climate change are to be expected. To improve predictions on climate change impacts on infectious disease, we suggest that more emphasis should be given to the integration of biomedical and ecological research for studying both the physiological and ecological mechanisms which mediate climate change impacts on disease, and to the development of harmonized methods and approaches to obtain more comparable results, as this would support the discrimination of case-specific versus general mechanisms