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Background Misdiagnosis of multiple sclerosis (MS) and neuromyelitis optica (NMO) may delay the treatment, resulting in poor prognosis. However, the precise identification of these two diseases is still challenging in clinical practice. We aimed to evaluate the value of quantitative radiomic features extracted from the brain white matter lesions for differential diagnosis of MS and NMO. Methods We recruited 116 CNS demyelinating patients including 78 MS, and 38 NMO. Three neuroradiologists performed visual differential diagnosis based on brain MRI for comparison purpose. A multi-level scheme was designed to harness the selection of discriminative and stable radiomics features extracted from brain while mater lesions in T1-MPRAGE, T2 sequences and clinical factors. Based on the imaging phenotype composed of the selected radiomic and clinical features, Multi-parametric Multivariate Random Forest (MM-RF) model was constructed and verified with both 10-fold cross-validation and independent testing. Result interpretation was provided to build trust in diagnostic decisions. Results Eighty-six patients were randomly selected to form the training set while the rest 30 patients for independent testing. On the training set, our MM-RF model achieved accuracy 0.849 and AUC 0.826 in 10-fold cross-validation, which were significantly higher than clinical visual analysis (0.709 and 0.683, p p of all Conclusions Multi-parametric radiomic features have potential as practical quantitative imaging biomarkers for differentiating MS from NMO.

The direct hydrogenation of CO 2 to higher alcohols has the potential to turn the main contributor of global warming into a valuable feedstock. However, for this technology to become attractive, more efficient and, especially, selective catalysts are required. Here we present a high throughput study on the influence of different promoters on the CO 2 hydrogenation performance of Rh‐SiO 2 catalysts. Fe and K promoters were found to improve ethanol selectivity at the expense of undesired CH 4 . The best‐performing catalyst, with a composition 2 wt.% K, 20 wt.% Fe, and 5 wt.% Rh, displays an EtOH selectivity of 16% at CO 2 conversion level of 18.4% and CH 4 selectivity of 46%. The combination of different characterization techniques and catalyst screening allowed us to unravel the role of each catalyst component in this complex reaction mechanism.

We investigate the surface deformation pattern of GPS station at MPGO Ghuttu (GHUT) to find out the cause of anomalous behavior in the continuous GPS time series. Seven years (2007-2013) of GPS data has been analyzed using GAMIT/GLOBK software and generated the daily position time series. The horizontal translational motion at GHUT is 43.7 ± 1 mm/yr at an angle of 41°± 3° towards NE, while for the IGS station at LHAZ, the motion is 49.4 ±1 mm/yr at 18 ± 2.5° towards NEE. The estimated velocity at GHUT station with respect to IISC is 12 ± 1 mm/yr towards SW. Besides, we have also examined anomalous changes in the time series of GHUT before, after and during the occurrences of local earthquakes by considering the empirical strain radius; such that, a possible relationship between the strain radius and the occurrences of earthquakes have been explored. We considered seven local earthquakes on the basis of Dobrovolsky strain radius condition having magnitude from 4.5 to 5.7, which occurred from 2007 to 2011. Results show irrespective of the station strain radius, pre-seismic surface deformational anomalies are observed roughly 70 to 80 days before the occurrence of a Moderate or higher magnitude events. This has been observed for the cases of those events originated from the Uttarakashi and the Chamoli seismic zones in the Garhwal and Kumaun Himalaya. Occurrences of short (< 100 days) and long (two years) inter-seismic events in the Garhwal region plausibly regulating and diffusing the regional strain accumulation.

Background Acute necrotizing encephalopathy of children (ANEC) is a rare fulminant type of acute encephalopathy that mainly occurs in children with a characteristic clinico-radiological pattern. It is commonly preceded by viral infections. The condition carries a poor prognosis with high morbidity and mortality rates. We highlight the relationship between diagnostic multi-parametric magnetic resonance imaging (MRI) findings and correlate them with the clinical outcome of children with ANEC by enrollment of MRI scoring. Results The thalami were involved in all 30 patients, brain stem in 80%, basal ganglia (13.3%), cerebral white matter (WM) in 73%, and cerebellar WM in 33%. Hemorrhage was present in 86.7% patients, edema 80%, and necrosis in 13.3%. We found that the patients having the highest MRI scores were in the poor outcome category; whereas the patients with lower MR score (1 or 2) had better outcomes. Statistically positive correlation (r= 0.1198) was found between the multi-parametric MR score and the outcome category. Conclusion An extended multi-parametric MRI should be performed in ANEC, allowing early detection and scoring of the disease for better prognosis. There is a positive correlation between the clinical outcome and the MR scoring.

Purpose To assess arterial spin labelling (ASL) perfusion and diffusion MR imaging (DWI) in the differentiation of grade II from grade III gliomas. Material and methods A prospective cohort study was done on 36 patients (20 male and 16 female) with diffuse gliomas, who underwent ASL and DWI. Diffuse gliomas were classified into grade II and grade III. Calculation of tumoural blood flow (TBF) and apparent diffusion coefficient (ADC) of the tumoral and peritumoural regions was made. The ROC curve was drawn to differentiate grade II from grade III gliomas. Results There was a significant difference in TBF of tumoural and peritumoural regions of grade II and III gliomas (p = 0.02 and p =0.001, respectively). Selection of 26.1 and 14.8 ml/100 g/min as the cut-off for TBF of tumoural and peritumoural regions differentiated between both groups with area under curve (AUC) of 0.69 and 0.957, and accuracy of 77.8% and 88.9%, respectively. There was small but significant difference in the ADC of tumoural and peritumoural regions between grade II and III gliomas (p = 0.02 for both). The selection of 1.06 and 1.36 × 10-3 mm2/s as the cut-off of ADC of tumoural and peritumoural regions was made, to differentiate grade II from III with AUC of 0.701 and 0.748, and accuracy of 80.6% and 80.6%, respectively. Combined TBF and ADC of tumoural regions revealed an AUC of 0.808 and accuracy of 72.7%. Combined TBF and ADC for peritumoural regions revealed an AUC of 0.96 and accuracy of 94.4%. Conclusion TBF and ADC of tumoural and peritumoural regions are accurate non-invasive methods of differentiation of grade II from grade III gliomas.

The apparent resistivity imaging at multiparametric geophysical observatory (MPGO), Ouguri Hills, Tezpur was carried out since 31st August, 2016 for fixed interval time of 3 days to investigate precursory signature prior to earthquake events. Anomalies in apparent resistivity prior to earthquake events are observed. The anomalies are not influenced by the rainfall activity in the region during the investigation period. We adopt Weibull distribution technique with observed apparent resistivity data to look for and revalidate the observations. The Weibull parameters, i.e. K and m are estimated to be 0.26 and 1.04, respectively. No precise relation between magnitude and earthquake precursory time is found. Simultaneously, an attempt has been made to establish a relation between the earthquake magnitude (M) and precursory time (T) and a relation between the earthquake precursory time (T) and distance of the earthquake (Δ) from the observation point. Weibull probability distribution indicate the probability of an earthquake occurrence exceeds the measure up to 80% (9 days) after the precursory signal is observed. A comparison of probability analysis ascertains a mean precursory time and its standard deviation.

Conventional staging for bladder cancer involves CT and TURBT, and is thought to understage up to fifty-percent of T1 tumours. This report details the case of a 64-year-old male who whilst on cystoscopic surveillance for high grade bladder cancer, underwent a multi-parametric MRI Bladder due to clinical suspicion of occult muscle invasive disease. Despite minimal urothelial changes at cystoscopy, MRI demonstrated a well-defined T3 lesion. The patient proceeded to radical cystectomy and final pathology verified the MRI findings. The role of MRI in bladder cancer is yet to be defined but should be considered if clinical suspicion for understaging exists.

A method for calculating amplitudes and constructing frequency characteristics of forced and self-excited vibrations of a rotor-fluid-foundation system on rolling bearings with a non-linear characteristic based on the method of complex amplitudes and harmonic balance has been developed. Non-linear equations of motion of the rotor-fluid-foundation system are derived, and analytical methods of their solution are presented. Frequencies of fundamental and ultra-harmonic resonances are determined. The intervals between self-oscillation frequencies are estimated. The dependence of amplitudes on the amount of fluid in the rotor cavity, the mass of the foundation, linear imbalance, the value of the stiffness coefficient, and the damping coefficient is shown.

T1 and T2 relaxation times combined with 31 P spectroscopy have been proven efficient for muscular disease characterization as well as for pre- and post-muscle stimulation measurements. Even though 31 P spectroscopy can already be performed during muscle exercise, no method for T1 and T2 measurement enables this possibility. In this project, a complete setup and protocol for multi-parametrical MRI of the rat gastrocnemius before, during and after muscle stimulation at 4.7 and 7 T is presented. The setup is fully MRI compatible and is composed of a cradle, an electro-stimulator and an electronic card in order to synchronize MRI sequences with muscle stimulation. A 2D triggered radial-encoded Look-Locker sequence was developed, and enabled T1 measurements in less than 2 min on stimulated muscle. Also, a multi-slice multi-echo sequence was adapted and synchronized for T2 measurements as well as 31 P spectroscopy acquisitions in less than 4 min in both cases on stimulated muscle. Methods were validated on young rats using different stimulation paradigms. Then they were applied on older rats to compare quantification results, using the different stimulation paradigms, and allowed observation of metabolic changes related to aging with good reproducibility. The robustness of the whole setup shows wide application opportunities.

Summary Background Topical moisturizing products are widely used to alleviate the problems associated with xerotic skin. Their use affects many properties of the stratum corneum (SC) in a complex and interrelated manner. The range of measurement techniques available to the researcher has increased in recent years. However, few studies have looked for correlations between the different techniques for assessing how aspects of xerotic skin change over time as a result of topical moisturizer usage. Objectives A 3‐week in vivo study using an oil‐in‐water based moisturizing product and an untreated site was conducted to determine the clinical significance of and any correlations between a range of different approaches for the measurement of skin lipid content and also skin hydration and visual grading of dry skin. Methods A range of traditional and more recently developed skin measurement techniques have been used to examine a variety of SC properties in normal and xerotic skin during topical moisturizer usage. Results In vivo confocal Raman spectroscopy and analysis of SC lipids from tape strips both showed an increase in SC lipid level and organization after 3 weeks of moisturizer usage on xerotic skin. Hydration, measured both optically and electrically, also increased and skin barrier function improved, with strong correlations between the different measures of dryness being observed. Conclusions Strong correlations were observed between the skin measurements for lipid assessment and skin hydration with regard to the assessment of xerotic skin, providing valuable new information for future in vivo clinical research into dry and atopic skin. Keywords biophysical assessment, skin barrier, skin hydration, topical moisturizers, Xerosis

Objectives: Comparing the diagnostic accuracy of different MRI techniques in characterization of breast lesions. Methods: The study was performed using 1.5T MRI and included 35 females with detected 47 breast lesions. Lesions were analyzed regarding their morphology, kinetic curve pattern, DWI and tCho peak measurement. The results of each MRI parameter were correlated to histo-pathology. Results: The study included 35 females with 47 breast lesions. Sensitivity and specificity were 87.5% and 93.3% using morphology alone. By using curve pattern, they were 96.9% and 73.3%. DWI was 96.9% sensitive, 66.7% specific, and MRS was 74.2% sensitive and 71.4% specific. The cut-off ADC value was 1.063 × 10−3 mm2/s. Choline cut-off value was (0.36 mmol/l). Sensitivity and specificity were increased to 100% and 93.3% after combining either kinetic curve or DWI with morphology. Sensitivity was increased to 90.3%, after combining MRS with morphology, however this slightly reduced the specificity to 92.9%. Conclusion: MRI is very sensitive for detection of breast lesions, however it lacks specificity. Post-processing tools and functional MRI have improved the specificity. MRS has increased the sensitivity, however specificity has been slightly decreased. Advances in knowledge: Functional MRI is very helpful for characterization of different breast lesions, thus avoiding unnecessary intervention. Keywords: Breast lesions, MR breast BIRADS lexicon, DWI, ADC value, MR spectroscopy, Choline peak

ObjectivesTo investigate the efficacy of multi-parametric MRI-based radiomics nomograms for preoperative distinction between benign and malignant sinonasal tumors.MethodsData of 244 patients with sinonasal tumor (training set, n=192; test set, n=52) who had undergone pre-contrast MRI, and 101 patients who underwent post-contrast MRI (training set, n=74; test set, n=27) were retrospectively analyzed. Independent predictors of malignancy were identified and their performance were evaluated. Seven radiomics signatures (RSs) using maximum relevance minimum redundancy (mRMR), and the least absolute shrinkage selection operator (LASSO) algorithm were established. The radiomics nomograms, comprising the clinical model and the RS algorithms were built: one based on pre-contrast MRI (RNWOC); the other based on pre-contrast and post-contrast MRI (RNWC). The performances of the models were evaluated with area under the curve (AUC), calibration, and decision curve analysis (DCA) respectively.ResultsThe efficacy of the clinical model (AUC=0.81) of RNWC was higher than that of the model (AUC=0.76) of RNWOC in the test set. There was no significant difference in the AUC of radiomic algorithms in the test set. The RS-T1T2 (AUC=0.74) and RS-T1T2T1C (RSWC, AUC=0.81) achieved a good distinction efficacy in the test set. The RNWC and the RNWOC showed excellent distinction (AUC=0.89 and 0.82 respectively) in the test set. The DCA of the nomograms showed better clinical usefulness than the clinical models and radiomics signatures.ConclusionsThe radiomics nomograms combining the clinical model and RS can be accurately, safely and efficiently used to distinguish between benign and malignant sinonasal tumors.

With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets. Up to eight contrasts were included from each brain MRI and each image volume was reformatted to common resolution to accommodate for differences between scanners. Following reviewing the radiology reports, three neuroradiologists assigned each study to abnormal vs normal, and identified three critical findings including acute infarction, acute hemorrhage, and mass effect. A deep convolutional neural network was constructed by a combination of localization feature extraction (LFE) modules and global classifiers to identify the presence of 4 variables in brain MRIs including abnormal, acute infarction, acute hemorrhage and mass effect. Training, validation and testing sets were randomly defined on a patient basis. Training was performed on 9845 studies using balanced sampling to address class imbalance. Receiver operating characteristic (ROC) analysis was performed. The ROC analysis of our models for 1050 studies within our internal test data showed AUC/sensitivity/specificity of 0.91/83%/86% for normal versus abnormal brain MRI, 0.95/92%/88% for acute infarction, 0.90/89%/81% for acute hemorrhage, and 0.93/93%/85% for mass effect. For 1072 studies within our external test data, it showed AUC/sensitivity/specificity of 0.88/80%/80% for normal versus abnormal brain MRI, 0.97/90%/97% for acute infarction, 0.83/72%/88% for acute hemorrhage, and 0.87/79%/81% for mass effect. Our proposed deep convolutional network can accurately identify abnormal and critical intracranial findings on individual brain MRIs, while addressing the fact that some MR contrasts might not be available in individual studies.

ObjectivesTo systematically evaluate and compare the predictive capability for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on radiomics from multi-parametric MRI (mp-MRI) including six sequences when used individually or combined, and to establish and validate the optimal combined model.MethodsA total of 195 patients confirmed HCC were divided into training (n = 136) and validation (n = 59) datasets. All volumes of interest of tumors were respectively segmented on T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, artery phase, portal venous phase, and delay phase sequences, from which quantitative radiomics features were extracted and analyzed individually or combined. Multivariate logistic regression analyses were undertaken to construct clinical model, respective single-sequence radiomics models, fusion radiomics models based on different sequences and combined model. The accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of different models.ResultsAmong nine radiomics models, the model from all sequences performed best with AUCs 0.889 and 0.822 in the training and validation datasets, respectively. The combined model incorporating radiomics from all sequences and effective clinical features achieved satisfactory preoperative prediction of MVI with AUCs 0.901 and 0.840, respectively, and could identify the higher risk population of MVI (P < 0.001). The Delong test manifested significant differences with P < 0.001 in the training dataset and P = 0.005 in the validation dataset between the combined model and clinical model.ConclusionsThe combined model can preoperatively and noninvasively predict MVI in HCC patients and may act as a usefully clinical tool to guide subsequent individualized treatment.

The plate boundary between the American and Eurasian plates runs in southwest Iceland along a 5-10 km wide seismicity zone on the Reykjanes Peninsula. There, tectonic spreading events take place as continuous seismic release and seismic episodes (swarms and individual large events) with recurrence interval of about 40 years and volcanic episodes at intervals of 800-1000 years. The crust in Reykjanes is, therefore, particularly thin and hot and geothermal energy is currently harnesses in two areas on the western part of the peninsula in Reykjanes and Svartsengi. Since January 2020, earthquake swarms with larger events up to M5.6 have been occurring frequently over the entire Reykjanes Peninsula, accompanied by unusual uplift (up to 12 cm) and subsidence cycles in the Svartsengi-Eldvörp fissure swarm. This raises the question whether we might be at the beginning of a new volcanic episode. In order to classify such processes at an early stage, multidisciplinary geophysical measurements are particularly valuable. The Icelandic Meteorological Office (IMO), University of Iceland as well as ISOR and several partners responded immediately after the unrest began. As soon as January 2020, GFZ proposed a rapid response field campaign (MAGIC: MultidisciplinAry imaGIng and Characterization of the magma/fluid reservoir beneath Svartsengi). Only one week after the uplift start and first earthquake swarm, we connected a Distributed Acoustic Sensing interrogator to a 21 km long telecommunication fibre optic cable which crosses the uplift and swarm area. In addition, while we complied to strict constraints due to the Covid-19 pandemic, the rapid response activities comprised deployment of several additional sensors including broadband seismology, rotational seismology and we performed repeated surveys including gas-, gravity-, electromagnetic-, airborne and ground magnetic- measurements. We present preliminary results from various techniques and discuss their role in discriminating different scenarios aiming at explaining the magma-tectonic unrest phase. In particular, we analyze how the combination of airborne snapshots of ground morphology can be combined with the high temporal and spatial resolution deformation fields along the fibre optic cable., EGUsphere

Poor indoor air quality (IAQ) can cause several respiratory diseases and symptoms. In this study, IAQ of 121 homes located in 3 different towns of Canakkale, Turkey was monitored throughout a year. Target air pollutants were particulate matter, volatile organic compounds (VOCs), bioaerosols, and Carbon dioxide (CO2), as well as air temperature and humidity. Moreover, pulmonary functions of the occupants were measured on a monthly basis. Also, occurrence of sick building syndrome (SBS) symptoms and other health related data were gathered from the occupants by a detailed questionnaire. The SBS is a situation related to indoor air pollution in which the occupants of a building experience health or comfort related adverse effects that appear to be associated directly to the time spent in the "sick" building. Results of this study showed that the highest indoor air pollutant levels were observed in Can town and the lowest levels were observed in Central town. Indoor levels of bioaerosols, particulate matter, benzene, Cladosporium spp., and Penicillium spp. as well as pulmonary functions of the occupants showed statistically significant differences between the locations of the homes (p < 0.001). Factor analysis implied that both indoor and outdoor originated sources contributed to IAQ. Even though the predominant SBS symptoms varied seasonally and spatially among the study sites, fatigue, cold-flu like symptoms, and difficulty in concentration occurred frequently. Correlations were found among the occurrence of SBS symptoms, measured IAQ parameters, and personal factors (p < 0.05). Performing more studies from the health and IAQ points of view improve public awareness.

PURPOSE Non-invasive and quantitative biomarkers of somatic mutations derived from multi-parametric MRI (MP-MRI) could potentially help in predicting the response of patients to therapy, leading to development of targeted and personalized treatments. In this study, we developed radiogenomic signatures of multiple driver genes using artificial intelligence (AI) methods. METHODS In this study, 2740 radiomic features, including shape and volumetric measures computed for different tumorous regions, and characteristics derived from histograms and gray-level co-occurrence matrix (GLCM), were extracted from pre-operative MP-MRI (T1, T1Gd, T2, T2-FLAIR, DTI, and DSC-MRI) scans of 161 patients with newly diagnosed glioblastoma. The tumor samples, collected surgically from these patients, were sequenced using an in-house targeted next generation sequencing (NGS) panel of genes. We constructed quantitative imaging signatures of somatic mutations in several genes from 161 IDH-wildtype glioblastoma patients, including ATRX, FGFR2, EGFR, MET, NF1, PDGFRA, PIK3CA, PTEN, RB1, TP53, using cross-validated SVM classifiers. RESULTS The cross-validated classification performance for each signature was assessed by area under the receiver operating characteristic (ROC) curve (AUC), indicating the following results: PTEN (n = 69, AUC = 0.64), EGFR (n = 52, AUC = 0.72), TP53 (n = 51, AUC = 0.67), NF1 (n = 33, AUC = 0.74), ATRX (n = 22; AUC = 0.74), FGFR2 (n = 6, AUC = 0.82), MET (n = 26, AUC = 0.77), PDGFRA (n = 14, AUC = 0.82), PIK3CA (n = 14, AUC = 0.78), RB1 (n = 14, AUC = 0.81). CONCLUSION Using multi-parametric MRI, we developed quantitative non-invasive in vivo signatures with the potential for pre-operative assessment of a glioblastoma’s molecular characteristics. These non-invasive radiogenomic biomarkers may be useful for understanding the molecular composition of a glioblastoma prior to surgical resection, thus enabling earlier selection of patients for targeted therapy trials and possible neoadjuvant treatment.

International audience; This article presents an open source software able to convert, display and process medical images. It differentiates itself from the existing software by its ability to design complex processing pipelines and to wisely execute them on a large database. An MP3 pipeline can contain unlimited homemade or ready-made processes and can be carried out with a parallel execution system. As a viewer, MP3 allows to display up to 4 images together and to draw Regions Of Interest (ROI). Two applications showing the strengths of the software are here exposed: a preclinical study involving Magnetic Resonance Imaging (MRI) data and a clinical one involving Computed Tomography (CT) images. MP3 is downloadable at https://github.com/nifm-gin/MP3.

Stroke is the second leading cause of death and disability worldwide, with ischemic stroke as the most common type. The preferred diagnostic procedure at the acute stage is the acquisition of multi-parametric magnetic resonance imaging (MRI). This type of imaging not only detects and locates the stroke lesion, but also provides the blood flow dynamics that helps clinicians in assessing the risks and benefits of reperfusion therapies. However, evaluating the outcome of these risky therapies beforehand is a complicated task due to the variability of lesion location, size, shape, and cerebral hemodynamics involved. Though the fully automated model for predicting treatment outcomes using multi-parametric imaging would be highly valuable in clinical settings, MRI datasets acquired at the acute stage are mostly scarce and suffer high class imbalance. In this paper, parallel multi-parametric feature embedded siamese network (PMFE-SN) is proposed that can learn with few samples and can handle skewness in multi-parametric MRI data. Moreover, five suitable evaluation metrics that are insensitive to imbalance are defined for this problem. The results show that PMFE-SN not only outperforms other state-of-the-art techniques in all these metrics but also can predict the class with a small number of samples, as well as the class with high number of samples. An accuracy of 0.67 on leave one cross out testing has been achieved with only two samples (minority class) for training and accuracy of 0.61 with the highest number of samples (majority class). In comparison, state-of-the-art using hand crafted features has 0 accuracy for minority class and 0.33 accuracy for majority class.

High Content Screening (HCS) is a technology that allows life scientists to analyze the effect of bioactive molecules on cellular phenotypes. It is a subset of High Throughput Screening (HTS), where the limitation is in the number of features that can be extracted from one well using 96, 384 or 1536-well microplates. Data acquired using HCS is using the same principle but can produce much more in-depth data. In HCS, generated data sets can be very large and complex. The utility of HCS is based on the fact that the profile of the extracted numeric features can be used to compare phenotypes. The technology is widely used in drug discovery projects, academia and the pharmaceutical industry. This thesis investigates the data analysis workflow in HCS. It became evident that the majority of screening facilities do not use appropriate software for the analysis of HCS data with the result that a relatively small proportion of the generated data is actually used for data analysis. The main research question of this PhD thesis therefore is: “How can multi-parametric data analysis contribute to effective knowledge discovery in High Content Screening?” This research consists of six papers, mapped to the industry-standard framework of Knowledge Discovery in Databases (KDD). In Chapter two, we start with an abstract view that describes the entire process of High Throughput Screening (HTS). This reveals the main problems in the field. Then we examine the area of HCS, a subset of HTS, and target the problems that were identified in Chapter two. In Chapter three, we develop a workflow based on unsupervised data analytics methods and is implemented as web-based software called HC StratoMineR. The method and implementation are tested, validated and reported. Chapter four is centered around gains and losses of using supervised data analytics methods in the field of HCS. Chapter five showcases the usage of interactive visualizations in the field of HCS. It involves an experiment in which bioinformatics students performed data analysis exercises using static or interactive data visualizations. The results demonstrate the effectiveness of using interactive visualizations. Chapter six is focused on the burden of preprocessing data in the field of data science. A standard data analysis protocol is developed to automate this process and is made available in an R package. The protocol is implemented, tested, validated and reported in this chapter. Finally, chapter seven is a typical example in laboratory practice that applies and validates the methods introduced in chapter three to a chemical screen. The results demonstrate and verify the potential of the methods introduced in this thesis.

Quantitative MRI and MRS of muscle are increasingly being used to measure individual pathophysiological processes in Becker muscular dystrophy (BMD). In particular, muscle fat fraction was shown to be highly associated with functional tests in BMD. However, the muscle strength per unit of contractile cross‐sectional area is lower in patients with BMD compared with healthy controls. This suggests that the quality of the non‐fat‐replaced (NFR) muscle tissue is lower than in healthy controls. Consequently, a measure that reflects changes in muscle tissue itself is needed. Here, we explore the potential of water T 2 relaxation times, diffusion parameters and phosphorus metabolic indices as early disease markers in patients with BMD. For this purpose, we examined these measures in fat‐replaced (FR) and NFR lower leg muscles in patients with BMD and compared these values with those in healthy controls. Quantitative proton MRI (three‐point Dixon, multi‐spin‐echo and diffusion‐weighted spin‐echo echo planar imaging) and 2D chemical shift imaging 31P MRS data were acquired in 24 patients with BMD (age 18.8‐66.2 years) and 13 healthy controls (age 21.3‐63.6 years). Muscle fat fractions, phosphorus metabolic indices, and averages and standard deviations (SDs) of the water T 2 relaxation times and diffusion tensor imaging (DTI) parameters were assessed in six individual leg muscles. Phosphodiester levels were increased in the NFR and FR tibialis anterior, FR peroneus and FR gastrocnemius lateralis muscles. No clear pattern was visible for the other metabolic indices. Increased T 2 SD was found in the majority of FR muscles compared with NFR and healthy control muscles. No differences in average water T 2 relaxation times or DTI indices were found between groups. Overall, our results indicate that primarily muscles that are further along in the disease process showed increases in T 2 heterogeneity and changes in some metabolic indices. No clear differences were found for the DTI indices between groups., In this study, diffusion measures and the means and standard deviations of the water T 2 relaxation time and 31P metabolic indices were explored as early markers for muscle tissue changes in the lower leg muscles of patients with Becker muscular dystrophy. Primarily muscles that were further along in the disease process as defined by elevated fat fractions showed increased T 2 heterogeneity in the majority of lower leg muscles and some changes in 31P metabolic indices.

Capable of imaging blood perfusion, oxygenation, and flow simultaneously at the microscopic level, multi-parametric photoacoustic microscopy (PAM) has quickly emerged as a powerful tool for studying hemodynamic and metabolic changes due to physiological stimulations or pathological processes. However, the low scanning speed poised by the correlation-based blood flow measurement impedes its application in studying rapid microvascular responses. To address this challenge, we have developed a new, to the best of our knowledge, multi-parametric PAM system. By extending the optical scanning range with a cylindrically focused ultrasonic transducer (focal zone, 76 µ m × 4.5 m m ) for simultaneous acquisition of 500 B-scans, the new system is 112 times faster than our previous multi-parametric system that uses a spherically focused transducer (focal diameter, 40 µm) and enables high-resolution imaging of blood perfusion, oxygenation, and flow over an area of 4.5 × 1 m m 2 at a frame rate of 1 Hz. We have demonstrated the feasibility of this system in the living mouse ear. Further development of this system into reflection mode will enable real-time cortex-wide imaging of hemodynamics and metabolism in the mouse brain.

This study aimed to develop a diagnostic algorithm for preoperative differentiating uterine sarcoma from leiomyoma through a supervised machine-learning method using multi-parametric MRI. A total of 65 participants with 105 myometrial tumors were included: 84 benign and 21 malignant lesions (belonged to 51 and 14 patients, respectively; based on their postoperative tissue diagnosis). Multi-parametric MRI including T1-, T2-, and diffusion-weighted (DW) sequences with ADC-map, contrast-enhanced images, as well as MR spectroscopy (MRS), was performed for each lesion. Thirteen singular MRI features were extracted from the mentioned sequences. Various combination sets of selective features were fed into a machine classifier (coarse decision-tree) to predict malignant or benign tumors. The accuracy metrics of either singular or combinational models were assessed. Eventually, two diagnostic algorithms, a simple decision-tree and a complex one were proposed using the most accurate models. Our final simple decision-tree obtained accuracy = 96.2%, sensitivity = 100% and specificity = 95%; while the complex tree yielded accuracy, sensitivity and specificity of 100%. To summarise, the complex diagnostic algorithm, compared to the simple one, can differentiate tumors with equal sensitivity, but a higher specificity and accuracy. However, it needs some further time-consuming modalities and difficult imaging calculations. Trading-off costs and benefits in appropriate situations must be determinative.

Estimating the quantity of CO2 diffusively emitted from the Earth’s surface has important implications for volcanic surveillance and global atmospheric CO2 budgets. However, the identification and quantification of non-hydrothermal contributions to CO2 release can be ambiguous. Here, we describe a multi-parametric approach employed at the Nisyros caldera, Aegean Arc, Greece, to assess the relative influence of deep and shallow gases released from the soil. In April 2019, we measured diffuse soil surface CO2 fluxes, together with their carbon isotope compositions, and at a depth of 80 cm, the CO2 concentration, soil temperature, and the activities of radon and thoron. The contributions of deep CO2 and biogenic CO2 fluxes were distinguished on the basis of their carbon isotope compositions. A Principal Component Analysis (PCA), performed on the measured parameters, effectively discriminates between a deep- and a shallow degassing component. The total CO2 output estimated from a relatively small testing area was two times higher with respect to that observed in a previous survey (October 2018). The difference is ascribed to variation in the soil biogenic CO2 production, that was high in April 2019 (a wet period) and low or absent in October 2018 (a dry period). Accounting for seasonal biogenic activity is therefore critical in monitoring and quantifying CO2 emissions in volcanic areas, because they can partially- or completely overwhelm the volcanic-hydrothermal signal.

With the aim to develop a multi-parametric system devote to improving our present capability to assess seismic hazard particularly in the short-medium term, the preliminary step is to identify those parameters that have demonstrated their non-casual relation with earthquake occurrences and whose anomalous variations can be associated to the complex seismic process. Since 80, fluctuations of Earth thermally emitted radiation, measured by satellite sensors in the Thermal InfraRed (TIR) spectral region, have been reported by several authors in some connection with the occurrence of earthquakes; they and can be considered as one of the possible parameters to include within a multi-parametric system. In this paper, the Robust Satellite Techniques (RST) approach has been exploited to highlight Significant Sequences of TIR Anomalies (SSTAs) possibly related to seismic events happened in Italy in the period June 2004-December 2014. In particular, we evaluated the level of correlation between occurrence of earthquakes with M>=4 and RST-based TIR anomalies using two different spatial resolutions of MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager) images. Results of the two RST analyses show that the 82% of the identified SSTAs does not disappear when downscaled spatial resolution SEVIRI TIR records were used. For both analyses, more than 60% of SSTAs is apparently in connection with the occurrence of M>=4 earthquakes; more than the 80% of them has tendency to anticipate the occurrence of earthquakes. Although about the 40% of SSTAs is not apparently related to documented seismic activity (false positives), results of random tests (i.e. Molchan's error diagrams) indicate a non-casual correlation between SSTAs and earthquake occurrences. These results confirm that the parameter "RST-based satellite TIR anomalies" is one of the possible candidates to be included in a multi-parametric system for time-Dependent Assessment of Seismic Hazard (t-DASH). © 2020 the Istituto Nazionale di Geofisica e Vulcanologia. All rights reserved.

Multi-parametric asymptotic analysis of dynamic phenomena in lightweight three-layered structures is performed. The presence of high contrast in densities of skin and core layers may lead to the small value of the lowest shear thickness resonance frequency, allowing a two-mode long wave low-frequency approximation of the exact dispersion relation. The range of uniform asymptotic validity of the two-mode approximation with respect to relative thickness and density are established. The theoretical predictions are illustrated numerically.

Background Atherosclerosis is prevalent in cardiovascular disease, but present imaging modalities have limited capabilities for characterizing lesion stage, progression and response to intervention. This study tests whether intravascular magnetic resonance imaging (IVMRI) measures of relaxation times (T1, T2) and proton density (PD) in a clinical 3 Tesla scanner could characterize vessel disease, and evaluates a practical strategy for accelerated quantification. Methods IVMRI was performed in fresh human artery segments and swine vessels in vivo, using fast multi-parametric sequences, 1–2 mm diameter loopless antennae and 200–300 μm resolution. T1, T2 and PD data were used to train a machine learning classifier (support vector machine, SVM) to automatically classify normal vessel, and early or advanced disease, using histology for validation. Disease identification using the SVM was tested with receiver operating characteristic curves. To expedite acquisition of T1, T2 and PD data for vessel characterization, the linear algebraic method (‘SLAM’) was modified to accommodate the antenna’s highly-nonuniform sensitivity, and used to provide average T1, T2 and PD measurements from compartments of normal and pathological tissue segmented from high-resolution images at acceleration factors of R ≤ 18-fold. The results were validated using compartment-average measures derived from the high-resolution scans. Results The SVM accurately classified ~80% of samples into the three disease classes. The ‘area-under-the-curve’ was 0.96 for detecting disease in 248 samples, with T1 providing the best discrimination. SLAM T1, T2 and PD measures for R ≤ 10 were indistinguishable from the true means of segmented tissue compartments. Conclusion High-resolution IVMRI measures of T1, T2 and PD with a trained SVM can automatically classify normal, early and advanced atherosclerosis with high sensitivity and specificity. Replacing relaxometric MRI with SLAM yields good estimates of T1, T2 and PD an order-of-magnitude faster to facilitate IVMRI-based characterization of vessel disease. Electronic supplementary material The online version of this article (doi:10.1186/s12968-017-0399-6) contains supplementary material, which is available to authorized users.

Response assessment in light chain (AL) amyloidosis is challenging given the low level of circulating free light chains usually seen. Multi-parametric flow cytometry (MFC) from a marrow aspirate was demonstrated to retain a prognostic significance in several recent studies. In this work, 82 AL patients who had MFC study at end of therapy were analysed based on whether clonal plasma cells were detected or not. Among patients who achieved deep response (i.e. very good partial response or complete response) to first-line therapy, lack of clonal marrow plasma cells as measured by MFC was associated with improved progression-free survival (PFS) compared to patients with residual clonal plasma cells (3-year PFS 88% vs. 46%, p = .003), particularly among patients who achieved a complete response (3-year PFS 100% vs. 33%, p = .001). Absence of clonal plasma cells by MFC compared with patients with detectable clonal plasma cells among deep responders was associated with lower level of involved light chain (involved free light chain (iFLC), median 1.1 vs. 1.7 mg/dL; p = .02) and higher frequency of renal response (100% vs. 68%; p = .005). Further studies are needed to determine if MFC should be incorporated into response criteria in AL amyloidosis.

In this paper a new optical, Fiber Bragg Grating (FBG) based, pressure sensor is presented. The sensor adopts a stainless steel membrane with a FBG sensor attached and is thought to be used in a multi parametric multi-sensor system aimed at the detection of water leakages in potable water networks. The sensor has been experimentally characterized on a reference plant in the range of pressure 0–6 bar. Main metrological characteristics of the lab-scale prototype developed are: a sensitivity of 0.314 nm/bar, an accuracy of about 39 mbar and a resolution of about 1.5 mbar.

Natural calamities observation, study and simulation has always been a prime concern for disaster management agencies. Billions of dollars are spent annually to explore geo-seismic movements especially earthquakes but it has always been a unique accident. The real-time study of seismic waves, ground motions, and earthquakes always needed a programmable mechanical structure capable of physically producing the identical geo-seismic motions with seismology domain definitions. A programmable multi-parametric five degrees of freedom electromechanical seismic wave events simulation platform to study and experiment seismic waves and earthquakes realization in the form of geo-mechanic ground motions is exhibited in this work. The proposed platform was programmed and interfaced through an IoT cloud-based Web application. The geo-mechanics was tested in the range of i) frequencies of extreme seismic waves from 0.1Hz to 178Hz; ii) terrestrial inclinations from -10.000° to 10.000°; iii) velocities of 1km/s to 25km/s iv) variable arrival times 1us to 3000ms; v) magnitudes M1.0 to M10.0 earthquake; vi) epi-central and hypo-central distances of 290+ and 350+ kilometers. Wadati and triangulation methods have been used for entire platform dynamics design and implementation as one of key contributions in this work. This platform is as an enabler for a variety of applications such as training self-balancing and calibrating seismic-resistant designs and structures in addition to studying and testing seismic detection devices as well as motion detection sensors. Nevertheless, it serves as an adequate training colossus for machine learning algorithms and event management expert systems.

Relative oxygen extraction fraction (rOEF) is a fundamental indicator of cerebral metabolic function. An easily applicable method for magnetic resonance imaging (MRI) based rOEF mapping is the multi-parametric quantitative blood oxygenation level dependent (mq-BOLD) approach with separate acquisitions of transverse relaxation times [Formula: see text] and T2 and dynamic susceptibility contrast (DSC) based relative cerebral blood volume (rCBV). Given that transverse relaxation and rCBV in white matter (WM) strongly depend on nerve fiber orientation, mq-BOLD derived rOEF is expected to be affected as well. To investigate fiber orientation related rOEF artefacts, we present a methodological study characterizing anisotropy effects of WM as measured by diffusion tensor imaging (DTI) on mq-BOLD in 30 healthy volunteers. Using a 3T clinical MRI-scanner, we performed a comprehensive correlation of all parameters ([Formula: see text], T2, [Formula: see text], rCBV, rOEF, where [Formula: see text]=1/[Formula: see text]–1/T2) with DTI-derived fiber orientation towards the main magnetic field (B0). Our results confirm strong dependencies of transverse relaxation and rCBV on the nerve fiber orientation towards B0, with anisotropy-driven variations up to 37%. Comparably weak orientation-dependent variations of mq-BOLD derived rOEF (3.8%) demonstrate partially counteracting influences of [Formula: see text] and rCBV effects, possibly suggesting applicability of rOEF as an oxygenation sensitive biomarker. However, unresolved issues warrant caution when applying mq-BOLD to WM.

Advances in microscopy with new visualization possibilities often bring dramatic progress to our understanding of the intriguing cellular machinery. Picosecond optoacoustic micro-spectroscopy is an optical technique based on ultrafast pump-probe generation and detection of hypersound on time durations of picoseconds and length scales of nanometers. It is experiencing a renaissance as a versatile imaging tool for cell biology research after a plethora of applications in solid-state physics. In this emerging context, this work reports on a dual-probe architecture to carry out real-time parallel detection of the hypersound propagation inside a cell that is cultured on a metallic substrate, and of the hypersound reflection at the metal/cell adhesion interface. Using this optoacoustic modality, several biophysical properties of the cell can be measured in a noncontact and label-free manner. Its abilities are demonstrated with the multiple imaging of a mitotic macrophage-like cell in a single run experiment.

In determination of flexible pavement layers moduli using Falling Weight Deflectometer (FWD), the pavement depth temperature should be determined and then the moduli should be corrected into a reference temperature. As direct measurement of pavement temperature is time consuming and is difficult to be determined in trafficked roads, some models are developed to predict temperature of asphalt layers through pavement depth, including BELLS model. The objective of this research is to determine correlation between actual measurement and prediction of temperature variations through asphalt layers with applying BELLS model. Ten new and rehabilitated pavement sites were selected in hot climate regions in Khuzestan and Kerman provinces in southern part of Iran. In typical hot summer days, pavement temperatures were measured at half and at one-third of the depth of asphalt layers and FWD testing were performed. Results indicated that a linear regression analysis of BELLS predicted temperatures versus measured values, provides very good correlation with actual field measurements of temperatures through the asphalt layers. Furthermore, predictions were more precise in rehabilitated pavements rather than in newly constructed pavements. Finally, using multi parametric linear fitting analysis, a new model was developed to accurately predict the temperature of asphalt layers in new pavements.

Objective To report an initial experience of cognitive transrectal ultrasonography-guided targeted prostate needle biopsies based on multi-parametric magnetic resonance imaging (mpMRI) findings. Random biopsy of the prostate is a crude way to identify prostate cancer hoping to hit the area of disease and hence the yield has never exceeded 30%. The introduction of mpMRI has tremendously enhanced our ability to identify specific suspicious areas with significant disease [Prostate Imaging - Reporting and Data System (PI-RADS) 4 and 5] and to target those areas thus improving the detection of significant disease with fewer biopsies. Methods In all, 97 consecutive patients with elevated prostate-specific antigen levels and suspicious mpMRI underwent targeted biopsies from the suspicious lesion ( ∼ five) and two random biopsies from the non-suspicious areas. The biopsies were performed using cognitive identification of the suspected area on MRI by a team comprised of a Urologist and Radiologist. All patients had routine pre-biopsy preparation. Results In all, 55 of 97 patients (57%) had positive biopsies, whilst 41/97 patients (42%) had negative biopsies, and two patients had atypical small acinar proliferation. The median number of cores taken was seven. Of the 55 patients with positive biopsies, 42 had significant Gleason grade ⩾7 disease, 27 had bilateral disease and in 11 of these 27 one contralateral side was negative on MRI but positive at biopsy. Conclusion Cognitive-targeted biopsies of the prostate based on pre-biopsy MRI mapping for suspicious areas has tremendously enhanced our yield of important disease detection and not insignificant ones. In addition, this is done with fewer biopsies thus reducing potential complications.

Purpose Quantifying multiple NMR properties of sodium could be of benefit to assess changes in cellular viability in biological tissues. A proof of concept of Quantitative Imaging using Configuration States (QuICS) based on a SSFP sequence with multiple contrasts was implemented to extract simultaneously 3D maps of applied flip angle (FA), total sodium concentration, T1, T2, and Apparent Diffusion Coefficient (ADC). Methods A 3D Cartesian Gradient Recalled Echo (GRE) sequence was used to acquire 11 non-balanced SSFP contrasts at a 6 × 6 × 6 mm3 isotropic resolution with carefully-chosen gradient spoiling area, RF amplitude and phase cycling, with TR/TE = 20/3.2 ms and 25 averages, leading to a total acquisition time of 1 h 18 min. A least-squares fit between the measured and the analytical complex signals was performed to extract quantitative maps from a mono-exponential model. Multiple sodium phantoms with different compositions were studied to validate the ability of the method to measure sodium NMR properties in various conditions. Results Flip angle maps were retrieved. Relaxation times, ADC and sodium concentrations were estimated with controlled precision below 15%, and were in accordance with measurements from established methods and literature. Conclusion The results illustrate the ability to retrieve sodium NMR properties maps, which is a first step toward the estimation of FA, T1, T2, concentration and ADC of 23Na for clinical research. With further optimization of the acquired QuICS contrasts, scan time could be reduced to be suitable with in vivo applications.

Using automated supervised behavioral assessment software, we recorded and analyzed 24 h non-interrupted recordings of mice for a duration of 11 days. With the assistance of free R programming, we used correlation matrix-based hierarchical clustering and factor analysis to separate the 33 activities into meaningful clusters and groups without losing the exhaustive nature of the findings. These groups represent novel meaningful behavioral patterns exhibited by mice in home cage. Thirty-three activities were separated into 5 clusters based on dissimilarity between activities and 6 factors based on statistical modeling. Using these two methods, we describe and compare behavioral arrays of two groups of animals: 1. Continuously recorded for 11 days in social isolation and 2. Intermittently socially isolated for recording on days 1, 3, 5, 8, and 10, while socializing on the other days. This is the first work to our knowledge that interprets mouse home cage activities throughout a 24 h period and proposes a base line of a daily routine of a healthy C57Bl/6J mouse that can be used for various experimental paradigms, including disease, neuroinflammation, or drug testing to trace behavioral changes that follow intervention. In this work, we defined the necessary acclimatization period for the 24 h recording paradigm of home cage behavior. We demonstrated the behavioral changes that are associated with the effect of social isolation, intermittent socialization, and re-introduction to a familiar home cage. We provide the full description of the codes used in R.

International audience; Introduction: Magnetic resonance imaging (MRI) shows slight spatial variations in brain white matter (WM). We used quantitative multi-parametric MRI to evaluate in what respect these inhomogeneities could correspond to WM subtypes with specific characteristics and spatial distribution.Materials and methods: Twenty-six controls (12 women, 38 ±9 Y) took part in a 60-min session on a 3T scanner measuring 7 parameters: R1 and R2, diffusion tensor imaging which allowed to measure Axial and Radial Diffusivity (AD, RD), magnetization transfer imaging which enabled to compute the Macromolecular Proton Fraction (MPF), and a susceptibility-weighted sequence which permitted to quantify R2* and magnetic susceptibility (χm). Spatial independent component analysis was used to identify WM subtypes with specific combination of quantitative parameters values.Results: Three subtypes could be identified. t-WM (track) mostly mapped on well-formed projection and commissural tracts and came with high AD values (all p < 10−18). The two other subtypes were located in subcortical WM and overlapped with association fibers: f-WM (frontal) was mostly anterior in the frontal lobe whereas c-WM (central) was underneath the central cortex. f-WM and c-WM had higher MPF values, indicating a higher myelin content (all p < 1.7 10−6). This was compatible with their larger χm and R2, as iron is essentially stored in oligodendrocytes (all p < 0.01). Although R1 essentially showed the same, its higher value in t-WM relative to c-WM might be related to its higher cholesterol concentration.Conclusions: Thus, f- and c-WMs were less structured, but more myelinated and probably more metabolically active regarding to their iron content than WM related to fasciculi (t-WM). As known WM bundles passed though different WM subtypes, myelination might not be uniform along the axons but rather follow a spatially consistent regional variability. Future studies might examine the reproducibility of this decomposition and how development and pathology differently affect each subtype.

The objectives of the study were to develop a framework for automatic outer and inner breast tissue segmentation using multi-parametric MRI images of the breast tumor patients; and to perform breast density and tumor tissue analysis. MRI of the breast was performed on 30 patients at 3T-MRI. T1, T2 and PD-weighted(W) images, with and without fat saturation(WWFS), and dynamic-contrast-enhanced(DCE)-MRI data were acquired. The proposed automatic segmentation approach was performed in two steps. In step-1, outer segmentation of breast tissue from rest of body parts was performed on structural images (T2-W/T1-W/PD-W without fat saturation images) using automatic landmarks detection technique based on operations like profile screening, Otsu thresholding, morphological operations and empirical observation. In step-2, inner segmentation of breast tissue into fibro-glandular(FG), fatty and tumor tissue was performed. For validation of breast tissue segmentation, manual segmentation was carried out by two radiologists and similarity coefficients(Dice and Jaccard) were computed for outer as well as inner tissues. FG density and tumor volume were also computed and analyzed. The proposed outer and inner segmentation approach worked well for all the subjects and was validated by two radiologists. The average Dice and Jaccard coefficients value for outer segmentation using T2-W images, obtained by two radiologists, were 0.977 and 0.951 respectively. These coefficient values for FG tissue were 0.915 and 0.875 respectively whereas for tumor tissue, values were 0.968 and 0.95 respectively. The volume of segmented tumor ranged over 2.1 cm3–7.08 cm3. The proposed approach provided automatic outer and inner breast tissue segmentation, which enables automatic calculations of breast tissue density and tumor volume. This is a complete framework for outer and inner breast segmentation method for all structural images.

Data from a multi-parametric MRI study of patients with possible early-stage prostate cancer was assessed with a view to creating an efficient clinical protocol. Based on a correlation analysis suggesting that diffusion-weighted imaging (DWI) scores are more strongly correlated with overall PIRADS scores than other modalities such as dynamic contrast enhanced imaging or spectroscopy, we investigate the combination of T2-weighted imaging (T2w) and DWI as a potential diagnostic tool for prostate cancer detection, staging and guided biopsies. Quantification of the noise floor in the DWI images and careful fitting of the data suggests that the mono-exponential model provides a very good fit to the data and there is no evidence of non-Gaussian diffusion for $b$-values up to 1000 s/mm$^2$. This precludes the use of kurtosis or other non-Gaussian measures as a biomarker for prostate cancer in our case. However, the ADC scores for healthy and probably malignant regions are significantly lower for the latter in all 20 but one patient. The results suggest that a simplified mp-MRI protocol combining T2w and DWI may be a good compromise for a cost and time efficient, early-stage prostate cancer diagnostic programme, combining robust MR biomarkers for prostate cancer that can be reliably quantified and appear well-suited for general clinical practice., Comment: to appear in MRI

PurposeConventional MRI methods are insufficient to accurately differentiate between local tumour recurrence and radiation-induced necrosis. Advanced MRI techniques such as perfusion imaging, diffusion imaging and spectroscopy have individually been shown to aid the assessment of response following SRS treatment of brain metastases [1–3]. However, the potential added-value of a multi-parametric approach using a combination of these techniques has not been investigated. Materials and MethodsA retrospective analysis was performed on 10 multi-parametric MRI examinations following SRS for metastatic brain lesions in a cohort of 7 patients with varying primary malignant diseases (melanoma, n=3; breast, n=3; small cell lung, n=1). Multi-parametric MRI examination consisted of dynamic susceptibility contrast (DSC) perfusion imaging, diffusion weighted imaging (DWI) and both single- and multi-voxel MR spectroscopy (MRS) (TE = 30 ms). Relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) maps were calculated from DSC and DWI data respectively using Siemens Syngo software and an rCBV ratio (lesion: contralateral) calculated. Analysis of MRS was performed using TARQUIN [4]. Correlations between rCBV ratio, ADC and Cho:Cr were investigated. Predictions of response based on each parameter were tested using radiological and clinical follow up. ResultsCho:Cr demonstrates a strong positive correlation with rCBV ratio (r = 0.89, p < 0.01). This is in agreement with previous work in pre-treatment metastases [5]. No significant correlations were observed between ADC and rCBV or Cho:Cr. Applying a reported threshold for rCBV ratio of 2.1 [1], patients were divided into two groups showing suspected tumour recurrence (n=4) and suspected radiation necrosis (n=3). ConclusionPreliminary results suggest that a combination of high rCBV, low ADC and high Cho: Cr can clearly identify local tumour recurrence and that multi-parametric MRI may show improved accuracy compared with any of the techniques in isolation.

This chapter focuses on the conception and design of an autonomous and disposable multi-parametric analyzer for the early diagnosis of diabetes mellitus and its different associated risk factors.

Deception Island volcano (Antarctica) is one of the most closely monitored and studied volcanoes on the region. In January 2005, a multi-parametric international experiment was conducted that encompassed both Deception Island and its surrounding waters. We performed this experiment from aboard the Spanish oceanographic vessel ‘Hesperides’, and from five land-based locations on Deception Island (the Spanish scientific Antarctic base ‘Gabriel de Castilla’ and four temporary camps). This experiment allowed us to record active seismic signals using a large network of seismic stations that were deployed both on land and on the seafloor. In addition, other geophysical data were acquired, including bathymetric high precision multi-beam data, and gravimetric and magnetic profiles. To date, the seismic and bathymetric data have been analysed but the magnetic and gravimetric data have not. We provide P-wave arrival-time picks and seismic tomography results in velocity and attenuation. In this manuscript, we describe the main characteristics of the experiment, the instruments, the data, and the repositories from which data and information can be obtained. Machine-accessible metadata file describing the reported data (ISA-Tab format)

[EN] Ecological networks, both displaying mutualistic or antagonistic interactions, seem to share common structural traits: the presence of nestedness and modularity. A variety of model approaches and hypothesis have been formulated concerning the significance and implications of these properties. In phage-bacteria bipartite infection networks, nestedness seems to be the rule in many different contexts. Modeling the coevolution of a diverse virus¿host ensemble is a difficult task, given the dimensionality and multi parametric nature of a standard continuous approximation. Here, we take a different approach, by using a neutral, toy model of host¿phage interactions on a spatial lattice. Each individual is represented by a bit string (a digital genome) but all strings in each class (i.e. hosts or phages) share the same sets of parameters. A matching allele model of phage-virus recognition rule is enough to generate a complex, diverse ecosystem with heterogeneous patterns of interaction and nestedness, provided that interactions take place under a spatially constrained setting. It is found that nestedness seems to be an emergent property of the co-evolutionary dynamics. Our results indicate that the enhanced diversity resulting from localized interactions strongly promotes the presence of nested infection matrices., The authors would like to thank the members of the Complex Systems Lab and our colleagues at the Santa Fe Institute for fruitful discussions. This work has been supported by the Botin Foundation by Banco Santander through its Santander Universities Global Division. This work was supported by the grants BFU2015-65037-P (S.F.E.) and FIS2016-77447-R (S.V.) from Spain Ministerio de Economia, Industria y Competitividad, AEI/MINEICO/FEDER and UE. The authors also thank the Santa Fe Institute, wheremost of this work was done

International audience; Prostate cancer (CaP) is the second most diagnosed cancer in men all over the world. In the last decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed improving diagnosis. In practice, diagnosis is affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and diagnosis (CAD) systems are being designed to help radiologists in their clinical practice. We propose a CAD system taking advantage of all MRI modalities (i.e., T2-W-MRI, DCE-MRI, diffusion weighted (DW)-MRI, MRSI). The aim of this CAD system was to provide a probabilistic map of cancer location in the prostate. We extensively tested our proposed CAD using different fusion approaches to combine the features provided by each modality. The source code and the dataset have been released.

International audience