A key variant in the cis-regulatory element of flowering gene Ghd8 associated with cold tolerance in rice.

Variations in the gene promoter play critical roles in the evolution of important adaptive traits in crops, but direct links of the regulatory mutation to the adaptive change are not well understood. Here, we examine the nucleotide variations in the promoter region of a transcription factor (Ghd8) that control grain number, plant height and heading date in rice. We find that a dominant promoter type of subspecies japonica displayed a high activity for Ghd8 expression in comparison with the one in indica. Transgenic analyses revealed that higher expression levels of Ghd8 delayed heading date and enhanced cold tolerance in rice. Furthermore, a single-nucleotide polymorphism (T1279G) at the position -1279 bp that locates on the potential GA-responsive motif in the Ghd8 promoter affected the expression of this gene. The 1279 T variant has elevated expression of Ghd8, thus conferring increased cold tolerance of rice seedlings. Nucleotide diversity analysis revealed that the approximately 25-kb genomic region surrounding Ghd8 in the subspecies japonica was under significant selection pressure. Our findings demonstrate that the join effects of the regulatory and coding variants largely contribute to the divergence of japonica and indica and increase the adaptability of japonica to the cold environment

E-learning tools for andragogy: a scale model of technology-based active learning

Andragogy is an educational philosophy on how to facilitate active learning for adult students. It requires instructors to engage students in various learning activities, including problem solving, essay writing, discussions, group projects, and so on. The challenge is how to facilitate student participation and assess learning outcomes. The emergence of e-learning tools, such as Discussion Board, Wiki, Blogs, and Wimba provide technical support for the new learning approach. Based on the review of information systems and education literature, this study develops a taxonomy of e-learning tools. In particular, it proposes a scale model based on the premise that e-learning tools must facilitate both content contribution and content appraisal for students. The taxonomy is validated with a simulation study based on the premises of media synchronicity theory. This framework provides a guideline on how to choose appropriate e-learning tools for various learning activities in the design of online and hybrid courses

Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition

The development of foundation vision models has pushed the general visual recognition to a high level, but cannot well address the fine-grained recognition in specialized domain such as invasive species classification. Identifying and managing invasive species has strong social and ecological value. Currently, most invasive species datasets are limited in scale and cover a narrow range of species, which restricts the development of deep-learning based invasion biometrics systems. To fill the gap of this area, we introduced Species196, a large-scale semi-supervised dataset of 196-category invasive species. It collects over 19K images with expert-level accurate annotations Species196-L, and 1.2M unlabeled images of invasive species Species196-U. The dataset provides four experimental settings for benchmarking the existing models and algorithms, namely, supervised learning, semi-supervised learning, self-supervised pretraining and zero-shot inference ability of large multi-modal models. To facilitate future research on these four learning paradigms, we conduct an empirical study of the representative methods on the introduced dataset. The dataset is publicly available at https://species-dataset.github.io/.Comment: Accepted by NeurIPS 2023 Track Datasets and Benchmark

Spatio-temporal Joint Modelling on Moderate and Extreme Air Pollution in Spain

Very unhealthy air quality is consistently connected with numerous diseases. Appropriate extreme analysis and accurate predictions are in rising demand for exploring potential linked causes and for providing suggestions for the environmental agency in public policy strategy. This paper aims to model the spatial and temporal pattern of both moderate and extremely poor PM10 concentrations (of daily mean) collected from 342 representative monitors distributed throughout mainland Spain from 2017 to 2021. We firstly propose and compare a series of Bayesian hierarchical generalized extreme models of annual maxima PM10 concentrations, including both the fixed effect of altitude, temperature, precipitation, vapour pressure and population density, as well as the spatio-temporal random effect with the Stochastic Partial Differential Equation (SPDE) approach and a lag-one dynamic auto-regressive component (AR(1)). Under WAIC, DIC and other criteria, the best model is selected with good predictive ability based on the first four-year data (2017--2020) for training and the last-year data (2021) for testing. We bring the structure of the best model to establish the joint Bayesian model of annual mean and annual maxima PM10 concentrations and provide evidence that certain predictors (precipitation, vapour pressure and population density) influence comparably while the other predictors (altitude and temperature) impact reversely in the different scaled PM10 concentrations. The findings are applied to identify the hot-spot regions with poor air quality using excursion functions specified at the grid level. It suggests that the community of Madrid and some sites in northwestern and southern Spain are likely to be exposed to severe air pollution, simultaneously exceeding the warning risk threshold

Degradation of methylene blue with magnetic Co-doped Fe\u3csub\u3e3\u3c/sub\u3eO\u3csub\u3e4\u3c/sub\u3e@FeOOH nanocomposites as heterogeneous catalysts of peroxymonosulfate

Magnetic Co-doped Fe3O4@FeOOH nanocomposites were prepared in one step using the hydrothermal synthesis process for catalyzing peroxymonosulfate (PMS) to degrade refractory methylene blue (MB) at a wide pH range (3.0–10.0). The catalysts\u27 physiochemical properties were characterized by different equipment; Fe3+/Fe2+ and Co3+/Co2+ were confirmed to coexist in the nanocomposite by X-ray photoelectron spectroscopy. The nanocomposite effectively catalyzed PMS\u27s decoloration (99.2%) and mineralization (64.7%) of MB. The formation of Co/Fe–OH complexes at the surface of nanoparticles was proposed to facilitate heterogeneous PMS activation. Compared with the observation for Fe3O4@FeOOH, the pseudo-first-order reaction constant was enhanced by 36 times due to Co substitution (0.1620 min–1 vs. 0.0045 min–1), which was assigned to the redox recycle of Fe3+/Fe2+ and Co3+/Co2+ in Co-doped Fe3O4@FeOOH. Besides, the catalyst could be easily reused by magnetic separation and exhibited relatively long-term stability

Credit Scoring Based on Hybrid Data Mining Classification

The credit scoring has been regarded as a critical topic. This study proposed four approaches combining with the NN (Neural Network) classifier for features selection that retains sufficient information for classification purpose. Two UCI data sets and different approaches combined with NN classifier were constructed by selecting features. NN classifier combines with conventional statistical LDA, Decision tree, Rough set and F-score approaches as features preprocessing step to optimize feature space by removing both irrelevant and redundant features. The procedure of the proposed algorithm is described first and then evaluated by their performances. The results are compared in combination with NN classifier and nonparametric Wilcoxon signed rank test will be held to show if there has any significant difference between these approaches. Our results suggest that hybrid credit scoring models are robust and effective in finding optimal subsets and the compound procedure is a promising method to the fields of data mining

Design earthquake ground motion prediction for Perth metropolitan area with microtremor measurements for site characterization

Perth is the largest city in Western Australia and home to three-quarters of the state\u27s residents. In recent decades, there have been a lot of earthquake activities just east of Perth in an area known as the South-West Seismic Zone. Previous numerical results of site response analyses based on limited available geology information for PMA indicated that Perth Basin might amplify the bedrock motion by more than 10 times at some frequencies and at some sites. Hence, more detailed studies on site characterization and amplification are necessary. The microtremor method using spatial autocorrelation (SPAC) processing is a useful tool for gaining thickness and shear wave velocity (SWV) of sediments and has been adopted in many previous studies. In this study, the response spectrum of rock site corresponding to the 475-year return period for PMA is defined according to the probabilistic seismic hazard analysis (PSHA) based on the latest ground motion attenuation model of Southwest Western Australia. Site characterization in PMA is performed using two microtremor measurements, namely SPAC technique and H/V method. The clonal selection algorithm (CSA) is introduced to perform direct inversion of SPAC curves to determine the soil profiles of representative PMA sites investigated in this study. Using the simulated bedrock motion as input, the responses of the soil sites are estimated using numerical method based on the shear-wave velocity vs. depth profiles determined from the SPAC technique. The response spectrum of the earthquake ground motion on surface of each site is derived from the numerical results of the site response analysis, and compared with the respective design spectrum defined in the Australian Earthquake Loading Code. The comparison shows that the code spectra are conservative in the short period range, but may slightly underestimate the response spectrum at some long period range. <br /