Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image

As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because ELM does not use the spatial information which is very important for HSI classification. In view of this, this paper proposes a new framework for spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based on lots of experiments and analysis, we found out that the LELM is a better choice than nonlinear ELM for spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learn such distribution using the LBP. The proposed method not only maintain the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines and Pavia University, demonstrate the good performances of the proposed method.Comment: 13 pages,8 figures,3 tables,articl

Tetramethyl pyrazine exerts anti-apoptotic and antioxidant effects in a mouse model of MPTP-induced Parkinson's disease via regulation of the expressions of Bax, Bcl-2, Nrf2 and GCLC

Purpose: To investigate the effect of tetramethyl pyrazine (TMP) on MPTP)-mediated neuronal apoptosis and oxidative imbalance in mice, and the mechanism of action involved. Methods: Forty-five mice were assigned evenly to blank control, MPTP and TMP groups. The protein concentrations of Bax, Bcl-2, cytochrome C (Cyt c), Nrf2, GCLC and cleaved caspase-3; and levels of glutathione (GSH) and thiobarbituric acid reactive products (TBARS) were evaluated and compared amongst the groups. Results: Cyt c, Bax, and cleaved caspase-3 protein levels in TMP group were significantly lower than those in MPTP group, while Bcl-2 protein expression was higher in TMP group than in MPTP mice (p < 0.05). Furthermore, TBARS was lower in TMP group than in MPTP group, while GSH level increased, relative to MPTP mice. The levels of Nrf2 and GCLC were significantly higher in TMP group than in MPTP group (p < 0.05). Conclusion: Tetramethyl pyrazine exerts anti-apoptotic and antioxidant effects on MPTP-mediated Parkinsonism via regulation of the expressions of Bax, Bcl-2, Nrf2 and glutamate-cysteine ligase catalytic subunit. Thus, TMP has potential for use in the treatment Parkinson’s disease

The Impacts of Transportation Investment on Economic Growth in the Twin Cities

The transportation system plays a critical role in fostering economic growth. Although previous studies have shed light on the impacts of transportation investments, their results are not readily adapted to predicting economic impacts of individual transportation projects. This study aimed to (1) investigate the impacts of transportation investments on economic growth (wages and employment) in the Twin Cities and (2) develop a method that practitioners can apply to predict economic growth resulting from investments in individual projects (as well as disinvestments). The capacity of such predictions is critical for the economy of the Twin Cities because transportation infrastructure lasts for decades once built. The method is expected to be used by practitioners of planning, programming, and finance at MnDOT and DEED, as well as at the Metropolitan Council. This study contributes to the base of knowledge by offering new empirical evidence on intra-urban patterns of agglomeration based on small-scale geographic data on job density from the Twin Cities. Our findings indicate that in general urbanization effects tend to dominate localization effects across a range of industries

Longitudinal Plasma Metabolomics Profile in Pregnancy—A Study in an Ethnically Diverse U.S. Pregnancy Cohort

Amino acids, fatty acids, and acylcarnitine metabolites play a pivotal role in maternal and fetal health, but profiles of these metabolites over pregnancy are not completely established. We described longitudinal trajectories of targeted amino acids, fatty acids, and acylcarnitines in pregnancy. We quantified 102 metabolites and combinations (37 fatty acids, 37 amino acids, and 28 acylcarnitines) in plasma samples from pregnant women in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies-Singletons cohort (n = 214 women at 10-14 and 15-26 weeks, 107 at 26-31 weeks, and 103 at 33-39 weeks). We used linear mixed models to estimate metabolite trajectories and examined variation by body mass index (BMI), race/ethnicity, and fetal sex. After excluding largely undetected metabolites, we analyzed 77 metabolites and combinations. Levels of 13 of 15 acylcarnitines, 7 of 25 amino acids, and 18 of 37 fatty acids significantly declined over gestation, while 8 of 25 amino acids and 10 of 37 fatty acids significantly increased. Several trajectories appeared to differ by BMI, race/ethnicity, and fetal sex although no tests for interactions remained significant after multiple testing correction. Future studies merit longitudinal measurements to capture metabolite changes in pregnancy, and larger samples to examine modifying effects of maternal and fetal characteristics

Genomic monitoring of SARS-CoV-2 uncovers an Nsp1 deletion variant that modulates type I interferon response

The SARS-CoV-2 virus, the causative agent of COVID-19, is undergoing constant mutation. Here, we utilized an integrative approach combining epidemiology, virus genome sequencing, clinical phenotyping, and experimental validation to locate mutations of clinical importance. We identified 35 recurrent variants, some of which are associated with clinical phenotypes related to severity. One variant, containing a deletion in the Nsp1-coding region (D500-532), was found in more than 20% of our sequenced samples and associates with higher RT-PCR cycle thresholds and lower serum IFN-beta levels of infected patients. Deletion variants in this locus were found in 37 countries worldwide, and viruses isolated from clinical samples or engineered by reverse genetics with related deletions in Nsp1 also induce lower IFN-beta responses in infected Calu-3 cells. Taken together, our virologic surveillance characterizes recurrent genetic diversity and identified mutations in Nsp1 of biological and clinical importance, which collectively may aid molecular diagnostics and drug design.Peer reviewe

Agglomeration Economies

Economists have long recognized the importance of urban areas as focal points of economic production and exchange. In recent decades, they have also come to better understand the productivity benefits of firms being located in large urban areas. A variety of advantages may accrue to firms that cluster together in large cities relating, for example, to access to specialized labor, information spillovers, and interactions with customers or suppliers. These types of advantages are often referred to as examples of agglomeration economies in urban areas. Empirically, these gains have been shown to be potentially quite large, with reviews of the literature suggesting that doubling the size of an urban area’s population may be associated with productivity gains on the order of several percentage points. While economic research on this topic has greatly advanced our understanding of the concepts, theory, and likely quantitative implications for urban economies, there has been comparatively little emphasis on the spatial nature of agglomeration economies within urban areas. This is an important distinction, as different sources of agglomeration economies may have different spatial characteristics, and some may be sensitive to transport costs in ways that can be affected by the performance of urban transportation networks. Our research was an effort to link these concepts by operationalizing two specific types of agglomeration economies, localization and urbanization economies, and to investigate their relationship to employment density across several economic sectors within the Twin Cities.The University Metropolitan Consortium supported this researc

Identifying Leaf Phenology of Deciduous Broadleaf Forests from PhenoCam Images Using a Convolutional Neural Network Regression Method

Vegetation phenology plays a key role in influencing ecosystem processes and biosphere-atmosphere feedbacks. Digital cameras such as PhenoCam that monitor vegetation canopies in near real-time provide continuous images that record phenological and environmental changes. There is a need to develop methods for automated and effective detection of vegetation dynamics from PhenoCam images. Here we developed a method to predict leaf phenology of deciduous broadleaf forests from individual PhenoCam images using deep learning approaches. We tested four convolutional neural network regression (CNNR) networks on their ability to predict vegetation growing dates based on PhenoCam images at 56 sites in North America. In the one-site experiment, the predicted phenology dated to after the leaf-out events agree well with the observed data, with a coefficient of determination (R2) of nearly 0.999, a root mean square error (RMSE) of up to 3.7 days, and a mean absolute error (MAE) of up to 2.1 days. The method developed achieved lower accuracies in the all-site experiment than in the one-site experiment, and the achieved R2 was 0.843, RMSE was 25.2 days, and MAE was 9.3 days in the all-site experiment. The model accuracy increased when the deep networks used the region of interest images rather than the entire images as inputs. Compared to the existing methods that rely on time series of PhenoCam images for studying leaf phenology, we found that the deep learning method is a feasible solution to identify leaf phenology of deciduous broadleaf forests from individual PhenoCam images

Total flavonoids and total phenol content and their antioxidant activities of fern <i>Adiantum flabellulatum</i>

The contents of total flavonoids and total phenol of fern Adiantum flabellulatum L. were determined. The DPPH and ABTS free radical scavenging activities and Fe reduction force were determined and analyzed as well. The results showed:1) The total flavonoid content of A. flabellulatum ranged from 6%~8%,while the total phenol contents ranged from 7%~14%. Total flavonoid and total phenol contents in subterranean parts were both higher than those in aerial parts. And total flavonoid and total phenol contents were different for material from different localities;2) The extracts from A. flabellulatum have certain ferric reducing ability of plasma and DPPH and ABTS radicals scavenging activities. And ABTS scavenging activity and ferric reducing ability of plasma both strengthen along with the increasing of concentration of total flavonoids and total phenol respectively