Zonal Soil Type Determines Soil Microbial Responses to Maize Cropping and Fertilization.

Soil types heavily influence ecological dynamics. It remains controversial to what extent soil types shape microbial responses to land management changes, largely due to lack of in-depth comparison across various soil types. Here, we collected samples from three major zonal soil types spanning from cold temperate to subtropical climate zones. We examined bacterial and fungal community structures, as well as microbial functional genes. Different soil types had distinct microbial biomass levels and community compositions. Five years of maize cropping (growing corn or maize) changed the bacterial community composition of the Ultisol soil type and the fungal composition of the Mollisol soil type but had little effect on the microbial composition of the Inceptisol soil type. Meanwhile, 5 years of fertilization resulted in soil acidification. Microbial compositions of the Mollisol and Ultisol, but not the Inceptisol, were changed and correlated (P < 0.05) with soil pH. These results demonstrated the critical role of soil type in determining microbial responses to land management changes. We also found that soil nitrification potentials correlated with the total abundance of nitrifiers and that soil heterotrophic respiration correlated with the total abundance of carbon degradation genes, suggesting that changes in microbial community structure had altered ecosystem processes. IMPORTANCE Microbial communities are essential drivers of soil functional processes such as nitrification and heterotrophic respiration. Although there is initial evidence revealing the importance of soil type in shaping microbial communities, there has been no in-depth, comprehensive survey to robustly establish it as a major determinant of microbial community composition, functional gene structure, or ecosystem functioning. We examined bacterial and fungal community structures using Illumina sequencing, microbial functional genes using GeoChip, microbial biomass using phospholipid fatty acid analysis, as well as functional processes of soil nitrification potential and CO2 efflux. We demonstrated the critical role of soil type in determining microbial responses to land use changes at the continental level. Our findings underscore the inherent difficulty in generalizing ecosystem responses across landscapes and suggest that assessments of community feedback must take soil types into consideration. Author Video: An author video summary of this article is available

Automatic categorization of diverse experimental information in the bioscience literature

Background: Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. Results: We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at WormBase for automatic association of newly published papers with ten data types including RNAi, antibody, phenotype, gene regulation, mutant allele sequence, gene expression, gene product interaction, overexpression phenotype, gene interaction, and gene structure correction. Conclusions: Our methods are applicable to a variety of data types with training set containing several hundreds to a few thousand documents. It is completely automatic and, thus can be readily incorporated to different workflow at different literature-based databases. We believe that the work presented here can contribute greatly to the tremendous task of automating the important yet labor-intensive biocuration effort

English Cooperative Learning Mode in a Rural Junior High School in China

Cooperative learning is one of the most recognized and fruitful research areas in modern education practice. It has been widely used in many countries as an effective teaching strategy to improve class efficiency and students’ comprehensive language ability since the 1990’s. This paper takes JA Junior High School, a rural junior high school in Nantong, China, as a case to explore its English cooperative learning mode. A questionnaire was designed based on nine factors namely learning expectation, learning interest, learning initiative, emotional experience, cooperative awareness, cooperative ability, learning effectiveness, learning evaluation and English usage level. The purpose is to try to find whether gender, grade and academic achievements have an effect on English cooperative learning. 515 valid questionnaires were collected and analyzed by t-test and One-way ANOVA. After analysis, it turned out that these three factors have an impact on the effectiveness of English cooperative learning. The results showed that the differences of gender, grade and academic achievements should be taken into consideration in accordance with the characteristics of rural middle school in constructing the English cooperative learning mode

PSR J1926-0652: A Pulsar with Interesting Emission Properties Discovered at FAST

We describe PSR J1926-0652, a pulsar recently discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Using sensitive single-pulse detections from FAST and long-term timing observations from the Parkes 64-m radio telescope, we probed phenomena on both long and short time scales. The FAST observations covered a wide frequency range from 270 to 800 MHz, enabling individual pulses to be studied in detail. The pulsar exhibits at least four profile components, short-term nulling lasting from 4 to 450 pulses, complex subpulse drifting behaviours and intermittency on scales of tens of minutes. While the average band spacing P3 is relatively constant across different bursts and components, significant variations in the separation of adjacent bands are seen, especially near the beginning and end of a burst. Band shapes and slopes are quite variable, especially for the trailing components and for the shorter bursts. We show that for each burst the last detectable pulse prior to emission ceasing has different properties compared to other pulses. These complexities pose challenges for the classic carousel-type models.Comment: 13pages with 12 figure

Comparison of Two Suspension Arrays for Simultaneous Detection of Five Biothreat Bacterial in Powder Samples

We have developed novel Bio-Plex assays for simultaneous detection of Bacillus anthracis, Yersinia pestis, Brucella spp., Francisella tularensis, and Burkholderia pseudomallei. Universal primers were used to amplify highly conserved region located within the 16S rRNA amplicon, followed by hybridized to pathogen-specific probes for identification of these five organisms. The other assay is based on multiplex PCR to simultaneously amplify five species-specific pathogen identification-targeted regions unique to individual pathogen. Both of the two arrays are validated to be flexible and sensitive for simultaneous detection of bioterrorism bacteria. However, universal primer PCR-based array could not identify Bacillus anthracis, Yersinia pestis, and Brucella spp. at the species level because of the high conservation of 16S rDNA of the same genus. The two suspension arrays can be utilized to detect Bacillus anthracis sterne spore and Yersinia pestis EV76 from mimic “write powder” samples, they also proved that the suspension array system will be valuable tools for diagnosis of bacterial biothreat agents in environmental samples

Forecasting tourism demand with an improved mixed data sampling model

Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalised dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperform the former

Scenario Forecasting for Global Tourism

This study provides innovative forecasts of the probabilities of certain scenarios of tourism demand. The scenarios of interest are constructed in relation to tourism growth and economic growth. The probability forecasts based on these scenarios provide valuable information for destination policy makers. The time-varying parameter panel vector autoregressive (TVP-PVAR) model is adopted for scenario forecasting. Both the accuracy rate and the Brier score are used to evaluate the forecasting performance. A global set of 25 tourism destinations is empirically examined, and the results confirm that the TVP-PVAR model with a time-varying error covariance matrix is generally a promising tool for forecasting. Our study contributes to tourism forecasting literature in advocating the use of scenario forecasting to facilitate industry decision making in situations wherein forecasts are defined by two or more dimensions simultaneously. In addition, it is the first study to introduce the TVP-PVAR model to tourism demand forecasting