Educational Attainment, Non-English Language Usage, and Ability to Communicate in English in 30 Massachusetts Cities/Towns

Data regarding an individual\u27s ability, or the ability of members of a household to speak English, primary language spoken at home, educational attainment, and the level of literacy proficiency should be taken into consideration when designing and implementing policies regarding health care initiatives and the publication of health care information. This report highlights data collected from three sources: 1) The National Adult Literacy Survey; 2) The 1990 Federal Census; and 3) The Massachusetts Institute for Social and Economic Research

Thermomechanical properties of amorphous metallic tungsten-oxygen and tungsten-oxide coatings

In this work, we investigate the correlation between morphology, composition, and the mechanical properties of metallic amorphous tungsten-oxygen and amorphous tungsten-oxide films deposited by Pulsed Laser Deposition. This correlation is investigated by the combined use of Brillouin Spectroscopy and the substrate curvature method. The stiffness of the films is strongly affected by both the oxygen content and the mass density. The elastic moduli show a decreasing trend as the mass density decreases and the oxygen-tungsten ratio increases. A plateaux region is detected in correspondence of the transition between metallic and oxide films. The compressive residual stresses, moderate stiffness and high local ductility that characterize compact amorphous tungsten-oxide films make them promising for applications involving thermal or mechanical loads. The coefficient of thermal expansion is quite high (i.e. 8.9 $\cdot$ 10$^{-6}$ K$^{-1}$), being strictly correlated to the amorphous structure and stoichiometry of the films. Under thermal treatments they show a quite low relaxation temperature (i.e. 450 K). They crystallize into the $\gamma$ monoclinic phase of WO$_3$ starting from 670 K, inducing an increase by about 70\% of material stiffness.Comment: The research leading to these results has also received funding from the European Research Council Consolidator Grant ENSURE (ERC-2014-CoG No. 647554). The views and opinions expressed herein do not necessarily reflect those of the European Commissio

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solution for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements

Host range of mammalian orthoreovirus type 3 widening to alpine chamois

Mammalian orthoreoviruses (MRV) type 3 have been recently identified in human and several animal hosts, highlighting the apparent lack of species barriers. Here we report the identification and genetic characterization of MRVs strains in alpine chamois, one of the most abundant wild ungulate in the Alps. Serological survey was also performed by MRV neutralization test in chamois population during five consecutive years (2008-2012). Three novel MRVs were isolated on cell culture from chamois lung tissues. No respiratory or other clinical symptoms neither lung macroscopic lesions were observed in the chamois population. MRV strains were classified as MRV-3 within the lineage III, based on S1 phylogeny, and were closely related to Italian strains identified in dog, bat and diarrheic pig. The full genome sequence was obtained by next-generation sequencing and phylogenetic analyses showed that other segments were more similar to MRVs of different geographic locations, serotypes and hosts, including human, highlighting genome reassortment and lack of host specific barriers. By using serum neutralization test, a high prevalence of MRV-3 antibodies was observed in chamois population throughout the monitored period, showing an endemic level of infection and suggesting a self-maintenance of MRV and/or a continuous spill-over of infection from other animal species

BVDV permissiveness and lack of expression of co-stimulatory molecules on PBMCs from calves pre-infected with BVDV

Bovine viral diarrhea virus (BVDV) has been detected in peripheral blood mononuclear cells (PBMCs) of immunocompetent animals, not being clear whether the development of a specific humoral immune response can prevent BVDV infection. The aim of this study was to evaluate the ability of non-cytopathic BVDV to replicate and produce infectious virus in PBMCs from calves pre-infected with BVDV and to elucidate the immunomodulatory effect of BVDV on these cells in an in vitro model. Quantification of virus was by quantitative PCR, while its replicative capacity and shedding into the extracellular environment was evaluated by viral titration. Apoptosis was assessed by flow cytometry analysis of annexin V and propidium iodide, and by expression of caspase-3/7. Flow cytometry was used to analyze the expression of CD14/CD11b/CD80, CD4/CD8/CD25, MHC-I/MHC-II and B-B2 markers. Our results showed that PBMCs from cattle naturally infected with BVDV were more susceptible to in vitro BVDV infection and showed a more severe apoptosis response than those from na\uefve animals. Non-cytopathic BVDV in vitro infection also resulted in a lack of effect in the expression of antigen presentation surface markers. All these findings could be related to the immunosuppressive capacity of BVDV and the susceptibility of cattle to this infection

Control Words of String Rewriting P Systems

P systems with controlled computations have been introduced and investigated in the recent past, by assigning labels to the rules in the regions of the P system and guiding the computations by control words. Here we consider string rewriting cell-like transition P system with label assigned rules working in acceptor mode and compare the obtained family of languages of control words over the rule labels with certain well-known language families. An application to chain code picture generation is also pointed out

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided

Use of artificial intelligence to automatically predict the optimal patient-specific inversion time for late gadolinium enhancement imaging. Tool development and clinical validation

Introduction With the worldwide diffusion of cardiac magnetic resonance (CMR), demand on image quality has grown. CMR late gadolinium enhancement (LGE) imaging provides critical diagnostic and prognostic information, and guides management. The identification of optimal Inversion Time (TI), a time-sensitive parameter closely linked to contrast kinetics, is pivotal for correct myocardium nulling. However, determining the optimal TI can be challenging in some diseases and for less experienced operators. Purpose To develop and test an artificial intelligence tool to automatically predict the personalised optimal TI in LGE imaging. Methods The tool, named THAITI, consists of a Random Forest regression model. It considers, as input parameters, patient-specific TI determinants (age, gender, weight, height, kidney function, heart rate) and CMR scan-specific TI determinants (B0, contrast type and dose, time elapsed from contrast injection). THAITI was trained on 219 patients (3585 images) with mixed conditions who underwent CMR (1.5T; Gadobutrol; averaged, MOCO, free-breathing true-FISP IR [1]) for clinical reasons. The dataset was split with a 90–10 policy: 90% of data for training, and 10% for testing. THAITI’s hyperparameters were optimised by embedding k-fold cross validation into an evolutionary computation algorithm, and the best performing model was finally evaluated on the test set. A graphical user interface was also developed. Clinical validation was performed on 55 consecutive patients, randomised to experimental (THAITI-set TI) vs control (operator-set TI) group. Image quality was assessed blindly by 2 independent experienced operators by a 4-points Likert scale, and by means of the contrast/enhancement ratio (CER) (i.e., signal intensity of enhanced/remote myocardium ratio). Results In the testing set, the TI predicted by THAITI differed from the ground truth by ≥ 5ms in 16% of cases. At clinical validation, myocardial nulling quality did not differ between the experimental vs the control group either by CER or visual assessment, with an overall "optimal" or "good" nulling in 96% vs 93%, respectively. Conclusions Using main determinants of contrast kinetics, THAITI efficiently predicted the optimal TI for CMR-LGE imaging. The tool works as a stand-alone on laptops/mobile devices, not requiring adjunctive scanner technology and thus has great potential for diffusion, including in small or recently opened CMR services, and in low-resource settings. Additional development is ongoing to increase generalisability (multi-vendor, multi-sequence, multi-contrast) and to test its potential to further improve CMR-LGE image quality and reduce the need for repeated imaging for inexperienced operators. Figure 1. Top: THAITI interface. Bottom: examples of experimental group CMR-LGE imaging. Table 1. Control vs experimental group. Data expressed as absolute number (%), mean ± SD, median [IQR]. ⧧ T-test; * Chi-square

Macroglobulinemia de Waldeström

Esta enfermedad que fue descrita por primera vez por Waldestróm, en 1944, es una afección que está íntimamente emparentada con el mieloma múltiple y es una alteración irreversible de las proteínas con el carácter de una paraproteinemia. Waldestróm señaló como signos característicos: a) La presencia en el plasma de una macroglobulina, es decir, una globulina de peso molecular muv elevado, con constante de sedimentación S20 en cantidad mayor del 15 % de las globulinas. b) La infiltración y proliferación en la médula ósea y demás órganos del SRE, de un tipo de células de estirpe reticular, pero que en su morfología recuerda a la serie linfocítica y a la plasmocítica, es decir, sería una reticulosis plasmo-linfocitaria. Dichas células elaboran un exceso de inmunoglobulinas o fragmentos de las mismas que en 1940 fueron denominadas por Apitz de “paraproteínas” consideradas por este autor como afines aunque diferentes de las gammaolobulinas normales.Facultad de Ciencias Médica

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since tumor's frequency and severity differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks' adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training.Comment: 44 pages, 6 figures, Accepted to Neurocomputing, Co-first authors: Leonardo Rundo and Changhee Ha