Characterizing Spatiotemporal Dynamics of Methane Emissions from Rice Paddies in Northeast China from 1990 to 2010

BACKGROUND: Rice paddies have been identified as major methane (CH(4)) source induced by human activities. As a major rice production region in Northern China, the rice paddies in the Three-Rivers Plain (TRP) have experienced large changes in spatial distribution over the recent 20 years (from 1990 to 2010). Consequently, accurate estimation and characterization of spatiotemporal patterns of CH₄ emissions from rice paddies has become an pressing issue for assessing the environmental impacts of agroecosystems, and further making GHG mitigation strategies at regional or global levels. METHODOLOGY/PRINCIPAL FINDINGS: Integrating remote sensing mapping with a process-based biogeochemistry model, Denitrification and Decomposition (DNDC), was utilized to quantify the regional CH(4) emissions from the entire rice paddies in study region. Based on site validation and sensitivity tests, geographic information system (GIS) databases with the spatially differentiated input information were constructed to drive DNDC upscaling for its regional simulations. Results showed that (1) The large change in total methane emission that occurred in 2000 and 2010 compared to 1990 is distributed to the explosive growth in amounts of rice planted; (2) the spatial variations in CH₄ fluxes in this study are mainly attributed to the most sensitive factor soil properties, i.e., soil clay fraction and soil organic carbon (SOC) content, and (3) the warming climate could enhance CH₄ emission in the cool paddies. CONCLUSIONS/SIGNIFICANCE: The study concluded that the introduction of remote sensing analysis into the DNDC upscaling has a great capability in timely quantifying the methane emissions from cool paddies with fast land use and cover changes. And also, it confirmed that the northern wetland agroecosystems made great contributions to global greenhouse gas inventory

Front-like entire solutions for monostable reaction-diffusion systems

This paper is concerned with front-like entire solutions for monostable reactiondiffusion systems with cooperative and non-cooperative nonlinearities. In the cooperative case, the existence and asymptotic behavior of spatially independent solutions (SIS) are first proved. Combining a SIS and traveling fronts with different wave speeds and directions, the existence and various qualitative properties of entire solutions are then established using comparison principle. In the non-cooperative case, we introduce two auxiliary cooperative systems and establish some comparison arguments for the three systems. The existence of entire solutions is then proved via the traveling fronts and SIS of the auxiliary systems. Our results are applied to some biological and epidemiological models. To the best of our knowledge, it is the first work to study the entire solutions of non-cooperative reaction-diffusion systems

Interaction between CRHR1 and BDNF Genes Increases the Risk of Recurrent Major Depressive Disorder in Chinese Population

BACKGROUND: An important etiological hypothesis about depression is stress has neurotoxic effects that damage the hippocampal cells. Corticotropin-releasing hormone (CRH) regulates brain-derived neurotrophic factor (BDNF) expression through influencing cAMP and Ca2+ signaling pathways during the course. The aim of this study is to examine the single and combined effects of CRH receptor 1 (CRHR1) and BDNF genes in recurrent major depressive disorder (MDD). METHODOLOGY/PRINCIPAL FINDING: The sample consists of 181 patients with recurrent MDD and 186 healthy controls. Whether genetic variations interaction between CRHR1 and BDNF genes might be associated with increased susceptibility to recurrent MDD was studied by using a gene-based association analysis of single-nucleotide polymorphisms (SNPs). CRHR1 gene (rs1876828, rs242939 and rs242941) and BDNF gene (rs6265) were identified in the samples of patients diagnosed with recurrent MDD and matched controls. Allelic association between CRHR1 rs242939 and recurrent MDD was found in our sample (allelic: p = 0.018, genotypic: p = 0.022) with an Odds Ratio 0.454 (95% CI 0.266-0.775). A global test of these four haplotypes showed a significant difference between recurrent MDD group and control group (chi-2 = 13.117, df = 3, P = 0.016. Furthermore, BDNF and CRHR1 interactions were found in the significant 2-locus, gene-gene interaction models (p = 0.05) using a generalized multifactor dimensionality reduction (GMDR) method. CONCLUSION: Our results suggest that an interaction between CRHR1 and BDNF genes constitutes susceptibility to recurrent MDD

Spin Caloritronics

This is a brief overview of the state of the art of spin caloritronics, the science and technology of controlling heat currents by the electron spin degree of freedom (and vice versa).Comment: To be published in "Spin Current", edited by S. Maekawa, E. Saitoh, S. Valenzuela and Y. Kimura, Oxford University Pres

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches

Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels

Construction of Vascular Tissues with Macro-Porous Nano-Fibrous Scaffolds and Smooth Muscle Cells Enriched from Differentiated Embryonic Stem Cells

Vascular smooth muscle cells (SMCs) have been broadly used for constructing tissue-engineered blood vessels. However, the availability of mature SMCs from donors or patients is very limited. Derivation of SMCs by differentiating embryonic stem cells (ESCs) has been reported, but not widely utilized in vascular tissue engineering due to low induction efficiency and, hence, low SMC purity. To address these problems, SMCs were enriched from retinoic acid induced mouse ESCs with LacZ genetic labeling under the control of SM22α promoter as the positive sorting marker in the present study. The sorted SMCs were characterized and then cultured on three-dimensional macro-porous nano-fibrous scaffolds in vitro or implanted subcutaneously into nude mice after being seeded on the scaffolds. Our data showed that the LacZ staining, which reflected the corresponding SMC marker SM22α expression level, was efficient as a positive selection marker to dramatically enrich SMCs and eliminate other cell types. After the sorted cells were seeded into the three-dimensional nano-fibrous scaffolds, continuous retinoic acid treatment further enhanced the SMC marker gene expression level while inhibited pluripotent maker gene expression level during the in vitro culture. Meanwhile, after being implanted subcutaneously into nude mice, the implanted cells maintained the positive LacZ staining within the constructs and no teratoma formation was observed. In conclusion, our results demonstrated the potential of SMCs derived from ESCs as a promising cell source for therapeutic vascular tissue engineering and disease model applications

Accurate Prediction of Protein Structural Class

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods

Retrospective analysis of nosocomial infections in the intensive care unit of a tertiary hospital in China during 2003 and 2007

<p>Abstract</p> <p>Background</p> <p>Nosocomial infections are a major threat to patients in the intensive care unit (ICU). Limited data exist on the epidemiology of ICU-acquired infections in China. This retrospective study was carried out to determine the current status of nosocomial infection in China.</p> <p>Methods</p> <p>A retrospective review of nococomial infections in the ICU of a tertiary hospital in East China between 2003 and 2007 was performed. Nosocomial infections were defined according to the definitions of Centers for Disease Control and Prevention. The overall patient nosocomial infection rate, the incidence density rate of nosocomial infections, the excess length of stay, and distribution of nosocomial infection sites were determined. Then, pathogen and antimicrobial susceptibility profiles were further investigated.</p> <p>Results</p> <p>Among 1980 patients admitted over the period of time, the overall patient nosocomial infection rate was 26.8% or 51.0 per 1000 patient days., Lower respiratory tract infections (LRTI) accounted for most of the infections (68.4%), followed by urinary tract infections (UTI, 15.9%), bloodstream (BSI, 5.9%), and gastrointestinal tract (GI, 2.5%) infections. There was no significant change in LRTI, UTI and BSI infection rates during the 5 years. However, GI rate was significantly decreased from 5.5% in 2003 to 0.4% in 2007. In addition, <it>A. baumannii, C. albicans </it>and <it>S. epidermidis </it>were the most frequent pathogens isolated in patients with LRTIs, UTIs and BSIs, respectively. The rates of isolates resistant to commonly used antibiotics ranged from 24.0% to 93.1%.</p> <p>Conclusion</p> <p>There was a high and relatively stable rate of nosocomial infections in the ICU of a tertiary hospital in China through year 2003–2007, with some differences in the distribution of the infection sites, and pathogen and antibiotic susceptibility profiles from those reported from the Western countries. Guidelines for surveillance and prevention of nosocomial infections must be implemented in order to reduce the rate.</p