Development of a Personalized Quantitative Faculty Annual Evaluation System

Faculty annual evaluation is critical for faculty career development and department success. Our department developed a quantitative faculty evaluation metrics system that is more objective, consistent, flexible, personalized, transparent, and dynamic scoring system. We have implemented the system for 2 years and the outcomes are very encouraging in promoting faculty and department success.Cockrell School of Engineerin

The economic impact of global climate and tropospheric oxone on world agricultural production

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (leaves 72-74).The objective of my thesis is to analyze the economic impact on agriculture production from changes in climate and tropospheric ozone, and related policy interventions. The analysis makes use of the Emissions Prediction and Policy Analysis (EPPA) model, a computable general equilibrium model of the world economy and crop yield results from the Terrestrial Ecosystem Model (TEM), a biogeochemical model of terrestrial vegetation. I disaggregated the original EPPA model to capture the dynamic behaviors of crops, livestock and forestry within the agriculture sector. Further calibration was done to validate projections on future food shares according to Engel's Law. Results from AIDADS (An Implicit Direct Additive Demand System) were used to adjust the model, as the EPPA Agriculture Model was implemented using CES (Constant Elasticity of Substitution) consumption function that, other things equal, keeps the food share constant as income grows. My research shows that the direct effects of environmental change on yields are substantially moderated in terms of production effects as a result of crop sector adaptations and reallocation of resources within the economy. However, costs (or benefits) resulting from reallocation of resources show up as losses (or gains) in aggregate economic consumption. The findings also uncover additional benefits of policies that impose greenhouse gas emissions constraints as they mitigate damages from ozone pollutions. For example, in 2005 the consumption loss due to ozone damage is estimated to be 7.4 billions (5% of the value of crop production) for the United States, 16.5 billions (8.4%) for the European Union, and 17.8 billions (9.8%) for China.(cont.) In a scenario where greenhouse gas emissions are controlled, the consumption loss is reduced by 28%, 33%, and 23% for the US, the EU and China by 2050, respectively. Therefore, ozone pollution policy and climate policy (because it reduces ozone precursor emissions) are both effective in reducing ozone damages considerably.by Xiaodu (Dulles) Wang.S.M

Characterization of nonstructural protein 3 of a neurovirulent Japanese encephalitis virus strain isolated from a pig

<p>Abstract</p> <p>Background</p> <p>Japanese encephalitis virus (JEV), as a re-emerging virus that causes 10,000-15,000 human deaths from encephalitis in the world each year, has had a significant impact on public health. Pigs are the natural reservoirs of JEV and play an important role in the amplification, dispersal and epidemiology of JEV. The nonstructural protein 3 (NS3) of JEV possesses enzymatic activities of serine protease, helicase and nucleoside 5'-triphosphatase, and plays important roles in viral replication and pathogenesis.</p> <p>Results</p> <p>We characterized the NS3 protein of a neurovirulent strain of JEV (SH-JEV01) isolated from a field-infected pig. The NS3 gene of the JEV SH-JEV01 strain is 1857 bp in length and encodes protein of approximately 72 kDa with 99% amino acid sequence identity to that of the representative immunotype strain JaGAr 01. The NS3 protein was detectable 12 h post-infection in a mouse neuroblastoma cell line, Neuro-2a, and was distributed in the cytoplasm of cells infected with the SH-JEV01 strain of JEV. In the brain of mice infected with the SH-JEV01 strain of JEV, NS3 was detected in the cytoplasm of neuronal cells, including pyramidal neurons of the cerebrum, granule cells, small cells and Purkinje cells of the cerebellum.</p> <p>Conclusions</p> <p>The NS3 protein of a neurovirulent strain of JEV isolated from a pig was characterized. It is an approximately 72 kDa protein and distributed in the cytoplasm of infected cells. The Purkinje cell of the cerebellum is one of the target cells of JEV infection. Our data should provide some basic information for the study of the role of NS3 in the pathogenesis of JEV and the immune response.</p

Prediction of trabecular bone architectural features by deep learning models using simulated DXA images

Dual-energy X-ray absorptiometry (DXA) is widely used for clinical assessment of bone mineral density (BMD). Recent evidence shows that DXA images may also contain microstructural information of trabecular bones. However, no current image processing techniques could aptly extract the information. Inspired by the success of deep learning techniques in medical image analyses, we hypothesized in this study that DXA image-based deep learning models could predict the major microstructural features of trabecular bone with a reasonable accuracy. To test the hypothesis, 1249 trabecular cubes (6 mm × 6 mm × 6 mm) were digitally dissected out from the reconstruction of seven human cadaveric proximal femurs using microCT scans. From each cube, simulated DXA images in designated projections were generated, and the histomorphometric parameters (i.e., BV/TV, BS, Tb.Th, DA, Conn. D, and SMI) of the cube were determined using Image J. Convolutional neural network (CNN) models were trained using the simulated DXA images to predict the histomorphometric parameters of trabecular bone cubes. The results exhibited that the CNN models achieved high fidelity in predicting these histomorphometric parameters (from R = 0.80 to R = 0.985), showing that the DL models exhibited the capability of predicting the microstructural features using DXA images. This study also showed that the number and resolution of input simulated DXA images had considerable impacts on the prediction accuracy of the DL models. These findings support the hypothesis of this study and indicate a high potential of using DXA images in prediction of osteoporotic bone fracture risk

Re-emergence of H5N8 highly pathogenic avian influenza virus in wild birds, China

Re-emergence of H5N8 highly pathogenic avian influenza virus in wild birds, Chin

Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

In order to detect outliers in temperature time series data for improving data quality and decision-making quality related to design and operation, we proposed an algorithm based on sliding window prediction. Firstly, the time series are segmented based on the sliding window. Then, the prediction model is established based on the history data to predict the future value. If the difference between a predicted value and a measured value is larger than the preset threshold value, the sequence point will be judged to be an outlier and then corrected. In this paper, the sliding window and parameter settings of the algorithm are discussed and the algorithm is verified on actual data. This method does not need to pre classify the abnormal points and perform fast, and can handle large scale data. The experimental results show that the proposed algorithm can not only effectively detect outliers in the time series of meteorological data but also improves the correction efficiency notoriously

Inhomogeneity of Bone Mineral Distribution in 2D Projection Images of Trabecular Bone is Associated With Its Micro-Architecture and Biomechanical Properties

relationships with sill variance with R-squared values as high as 0.81 ( DISCUSSION Microarchitecture parameters and biomechanical properties of trabecular bone can be revealed from 2D projection images using the novel stochastic model to quantify the spatial variation of bone mineral distribution. This finding is significant since such a technique can be applied to 2D bone images of Dual-energy X-ray absorptiometry (DXA) scans to provide a convenient and economic solution to predict bone fragility based on parameters in addition to bone mineral density

Bioinspired design of hybrid composite materials

Mimicking the natural design motifs of structural biological materials is a promising approach to achieve a unique combination of strength and toughness for engineering materials. In this study, we proposed a 2D computational model, which is a two-hierarchy hybrid composite inspired by the ultrastructural features of bone. The model is composed of alternating parallel array of two subunits (A & B) mimicking ‘mineralized collagen fibril’ and ‘extrafibrillar matrix’ of bone at ultrastructural level. The subunit-A is formed by short stiff platelets embedded within a soft matrix. The subunit-B consists of randomly distributed stiff grains bonded by a thin layer of tough adhesive phase. To assess the performance of the bioinspired design, a conventional unidirectional long-fiber composite made with the same amount of hard and soft phases was studied. The finite element simulation results indicated that the toughness, strength and elastic modulus of the bioinspired composite was 312%, 83%, and 55% of that of the conventional composite, respectively. The toughness improvement was attributed to the prevalent energy-dissipating damage of adhesive phase in subunit-B and crack-bridging by subunit-A, the two major toughening mechanisms in the model. This study exemplifies some insights into natural design of materials to gain better material performance