Ectopic lymphoid-organ development occurs through interleukin 7-mediated enhanced survival of lymphoid-tissue-inducer cells.

Development of Peyer's patches and lymph nodes requires the interaction between CD4+ CD3- IL-7Ralpha+ lymphoid-tissue inducer (LTi) and VCAM-1+ organizer cells. Here we showed that by promoting their survival, enhanced expression of interleukin-7 (IL-7) in transgenic mice resulted in accumulation of LTi cells. With increased IL-7 availability, de novo formation of VCAM-1+ Peyer's patch anlagen occurred along the entire fetal gut resulting in a 5-fold increase in Peyer's patch numbers. IL-7 overexpression also led to formation of multiple organized ectopic lymph nodes and cecal patches. After immunization, ectopic lymph nodes developed normal T cell-dependent B cell responses and germinal centers. Mice overexpressing IL-7 but lacking either RORgamma, a factor required for LTi cell generation, or lymphotoxin alpha1beta2 had neither Peyer's patches nor ectopic lymph nodes. Therefore, by controlling LTi cell numbers, IL-7 can regulate the formation of both normal and ectopic lymphoid organs.

Iowa Homeland Security and Emergency Management, 2015 Enhanced 911 Annual Report, January 14, 2016

The Iowa Department of Homeland Security and Emergency Management (HSEMD) submits this enhanced 911 (E911) annual report to the Iowa General Assembly’s standing committees on government oversight pursuant to Iowa Code § 34A.7A (3) (a). This section of the Code requires the E911 program manager to advise the General Assembly of the status of E911 wireline and wireless implementation and operations, the distribution of surcharge receipts, and an accounting of revenue and expenses of the E911 program.

Iowa Homeland Security and Emergency Management, 2014 Enhanced 911 Annual Report, January 15, 2015

The Iowa Department of Homeland Security and Emergency Management (HSEMD) submits this enhanced 911 (E911) annual report to the Iowa General Assembly’s standing committees on government oversight pursuant to Iowa Code § 34A.7A (3) (a). This section of the Code requires the E911 program manager to advise the General Assembly of the status of E911 wireline and wireless implementation and operations, the distribution of surcharge receipts, and an accounting of revenue and expenses of the E911 program.

Kernel-PCA data integration with enhanced interpretability

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Soybean crop area estimation by Modis/Evi data

The objective of this work was to develop a procedure to estimate soybean crop areas in Rio Grande do Sul state, Brazil. Estimations were made based on the temporal profiles of the enhanced vegetation index (Evi) calculated from moderate resolution imaging spectroradiometer (Modis) images. The methodology developed for soybean classification was named Modis crop detection algorithm (MCDA). The MCDA provides soybean area estimates in December (first forecast), using images from the sowing period, and March (second forecast), using images from the sowing and maximum crop development periods. The results obtained by the MCDA were compared with the official estimates on soybean area of the Instituto Brasileiro de Geografia e Estatística. The coefficients of determination ranged from 0.91 to 0.95, indicating good agreement between the estimates. For the 2000/2001 crop year, the MCDA soybean crop map was evaluated using a soybean crop map derived from Landsat images, and the overall map accuracy was approximately 82%, with similar commission and omission errors. The MCDA was able to estimate soybean crop areas in Rio Grande do Sul State and to generate an annual thematic map with the geographic position of the soybean fields. The soybean crop area estimates by the MCDA are in good agreement with the official agricultural statistics.

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

How we experience immersive virtual environments: the concept of presence and its measurement

This paper reviews the concept of presence in immersive virtual environments, the sense of being there signalled by people acting and responding realistically to virtual situations and events. We argue that presence is a unique phenomenon that must be distinguished from the degree of engagement, involvement in the portrayed environment. We argue that there are three necessary conditions for presence: the (a) consistent low latency sensorimotor loop between sensory data and proprioception; (b) statistical plausibility: images must be statistically plausible in relation to the probability distribution of images over natural scenes. A constraint on this plausibility is the level of immersion;(c) behaviour-response correlations: Presence may be enhanced and maintained over time by appropriate correlations between the state and behaviour of participants and responses within the environment, correlations that show appropriate responses to the activity of the participants. We conclude with a discussion of methods for assessing whether presence occurs, and in particular recommend the approach of comparison with ground truth and give some examples of this.

Land use/cover classification in the Brazilian Amazon using satellite images

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Impact of enhanced compliance initiatives on the efficacy of rosuvastatin in reducing low density lipoprotein cholesterol levels in patients with primary hypercholesterolaemia.

The effectiveness of lipid-lowering medication critically depends on the patients' compliance and the efficacy of the prescribed drug. The primary objective of this multicentre study was to compare the efficacy of rosuvastatin with or without access to compliance initiatives, in bringing patients to the Joint European Task Force's (1998) recommended low-density lipoprotein cholesterol (LDL-C) level goal (LDL-C, <3.0 mmol/L) at week 24. Secondary objectives were comparison of the number and percentage of patients achieving European goals (1998, 2003) for LDL-C and other lipid parameters. Patients with primary hypercholesterolaemia and a 10-year coronary heart disease risk of >20% received open label rosuvastatin treatment for 24 weeks with or without access to compliance enhancement tools. The initial daily dosage of 10 mg could be doubled at week 12. Compliance tools included: a) a starter pack for subjects containing a videotape, an educational leaflet, a passport/goal diary and details of the helpline and/or website; b) regular personalised letters to provide message reinforcement; c) a toll-free helpline and a website. The majority of patients (67%) achieved the 1998 European goal for LDL-C at week 24. 31% required an increase in dosage of rosuvastatin to 20 mg at week 12. Compliance enhancement tools did not increase the number of patients achieving either the 1998 or the 2003 European target for plasma lipids. Rosuvastatin was well tolerated during this study. The safety profile was comparable with other drugs of the same class. 63 patients in the 10 mg group and 58 in the 10 mg Plus group discontinued treatment. The main reasons for discontinuation were adverse events (39 patients in the 10 mg group; 35 patients in the 10 mg Plus group) and loss to follow-up (13 patients in the 10 mg group; 9 patients in the 10 mg Plus group). The two most frequently reported adverse events were myalgia (34 patients, 3% respectively) and back pain (23 patients, 2% respectively). The overall rate of temporary or permanent study discontinuation due to adverse events was 9% (n = 101) in patients receiving 10 mg rosuvastatin and 3% (n = 9) in patients titrated up to 20 mg rosuvastatin. Rosuvastatin was effective in lowering LDL-C values in patients with hypercholesterolaemia to the 1998 European target at week 24. However, compliance enhancement tools did not increase the number of patients achieving any European targets for plasma lipids.

Geometrically accurate time series of archived aerial images and airborne lidar data in a forest environment

Abstract

Vessel centerline tracking and boundary sgementation in coronary MRA with minimal manual interaction.

Magnetic resonance angiography (MRA) provides a noninvasive means to detect the presence, location and severity of atherosclerosis throughout the vascular system. In such studies, and especially those in the coronary arteries, the vessel luminal area is typically measured at multiple cross-sectional locations along the course of the artery. The advent of fast volumetric imaging techniques covering proximal to mid segments of coronary arteries necessitates automatic analysis tools requiring minimal manual interactions to robustly measure cross-sectional area along the three-dimensional track of the arteries in under-sampled and non-isotropic datasets. In this work, we present a modular approach based on level set methods to track the vessel centerline, segment the vessel boundaries, and measure transversal area using two user-selected endpoints in each coronary of interest. Arterial area and vessel length are measured using our method and compared to the standard Soap-Bubble reformatting and analysis tool in in-vivo non-contrast enhanced coronary MRA images.

Qualitative grading of severity of lumbar spinal stenosis based on the morphology of the dural sac on magnetic resonance images.

STUDY DESIGN.: Retrospective radiologic study on a prospective patient cohort. OBJECTIVE.: To devise a qualitative grading of lumbar spinal stenosis (LSS), study its reliability and clinical relevance. SUMMARY OF BACKGROUND DATA.: Radiologic stenosis is assessed commonly by measuring dural sac cross-sectional area (DSCA). Great variation is observed though in surfaces recorded between symptomatic and asymptomatic individuals. METHODS.: We describe a 7-grade classification based on the morphology of the dural sac as observed on T2 axial magnetic resonance images based on the rootlet/cerebrospinal fluid ratio. Grades A and B show cerebrospinal fluid presence while grades C and D show none at all. The grading was applied to magnetic resonance images of 95 subjects divided in 3 groups as follows: 37 symptomatic LSS surgically treated patients; 31 symptomatic LSS conservatively treated patients (average follow-up, 2.5 and 3.1 years); and 27 low back pain (LBP) sufferers. DSCA was also digitally measured. We studied intra- and interobserver reliability, distribution of grades, relation between morphologic grading and DSCA, as well relation between grades, DSCA, and Oswestry Disability Index. RESULTS.: Average intra- and interobserver agreement was substantial and moderate, respectively (k = 0.65 and 0.44), whereas they were substantial for physicians working in the study originating unit. Surgical patients had the smallest DSCA. A larger proportion of C and D grades was observed in the surgical group. Surface measurementsresulted in overdiagnosis of stenosis in 35 patients and under diagnosis in 12. No relation could be found between stenosis grade or DSCA and baseline Oswestry Disability Index or surgical result. C and D grade patients were more likely to fail conservative treatment, whereas grades A and B were less likely to warrant surgery. CONCLUSION.: The grading defines stenosis in different subjects than surface measurements alone. Since it mainly considers impingement of neural tissue it might be a more appropriate clinical and research tool as well as carrying a prognostic value.

Reversible and high capacity data hiding in medical images

In this paper we introduce a highly efficient reversible data hiding system. It is based on dividing the image into tiles and shifting the histograms of each image tile between its minimum and maximum frequency. Data are then inserted at the pixel level with the largest frequency to maximize data hiding capacity. It exploits the special properties of medical images, where the histogram of their nonoverlapping image tiles mostly peak around some gray values and the rest of the spectrum is mainlyempty. The zeros (or minima) and peaks (maxima) of the histograms of the image tiles are then relocated to embed the data. The grey values of some pixels are therefore modified.High capacity, high fidelity, reversibility and multiple data insertions are the key requirements of data hiding in medical images. We show how histograms of image tiles of medical images can be exploited to achieve these requirements. Compared with data hiding method applied to the whole image, our scheme can result in 30%-200% capacity improvement and still with better image quality, depending on the medical image content. Additional advantages of the proposed method include hiding data in the regions of non-interest and better exploitation of spatial masking.