The Acquisition of Words’ Meaning Based on Constructivism

Students do need opportunities and guidance to acquire the meaning of the word and make the words active. Constructivism stresses that word meaning cannot be assimilated by the child in a ready-made form but have to undergo a certain development. The acquisition of the words meaning depends on the cooperation between the student and the teacher. The aim of this paper is to expound how we can take advantage of the theory of Constructivism to help the students acquire the meaning of the words. Constructivists hold that education should be concerned with helping people to make their own meanings and teachers should present learners with problem-solving activities. Students are hosts. Teachers are instructors and helpers. They emphasize students’ important role in learning. Only when we adopt this, can we improve students’ thinking ability and realize the sustainable development in students’ acquisition of vocabulary

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy

Posterior sampling, i.e., exponential mechanism to sample from the posterior distribution, provides $\varepsilon$-pure differential privacy (DP) guarantees and does not suffer from potentially unbounded privacy breach introduced by $(\varepsilon,\delta)$-approximate DP. In practice, however, one needs to apply approximate sampling methods such as Markov chain Monte Carlo (MCMC), thus re-introducing the unappealing $\delta$-approximation error into the privacy guarantees. To bridge this gap, we propose the Approximate SAample Perturbation (abbr. ASAP) algorithm which perturbs an MCMC sample with noise proportional to its Wasserstein-infinity ($W_\infty$) distance from a reference distribution that satisfies pure DP or pure Gaussian DP (i.e., $\delta=0$). We then leverage a Metropolis-Hastings algorithm to generate the sample and prove that the algorithm converges in W$_\infty$ distance. We show that by combining our new techniques with a careful localization step, we obtain the first nearly linear-time algorithm that achieves the optimal rates in the DP-ERM problem with strongly convex and smooth losses

Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the development of biomedical research. KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction. However, previous researches pay less attention to the prior knowledge existing in KBs. This paper proposes a neural network-based attention model (NAM) for CDR extraction, which makes full use of context information in documents and prior knowledge in KBs. For a pair of entities in a document, an attention mechanism is employed to select important context words with respect to the relation representations learned from KBs. Experiments on the BioCreative V CDR dataset show that combining context and knowledge representations through the attention mechanism, could significantly improve the CDR extraction performance while achieve comparable results with state-of-the-art systems.Comment: Published on IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11 pages, 5 figure

Microbial electrochemical enhanced composting of sludge and kitchen waste: electricity generation, composting efficiency and health risk assessment for land use

To realize the energy and resource utilization from organic solid waste, a two-phase microbial desalination cell (TPMDC) was constructed using dewatered sludge and kitchen waste as the anode substrate. The performance of electricity generation and composting efficacy was investigated, along with a comprehensive assessment of the potential health risks associated with the land use of the resulting mixed compost products. Experimental outcomes revealed a maximum open-circuit voltage of 0.893 ± 0.005 V and a maximum volumetric power density of 0.797 ± 0.009 W/m³. After 90 days of composting enhanced by microbial electrochemistry, a significant organic matter removal rate of 31.13 ± 0.44 % was obtained, and the anode substrate electric conductivity was reduced by 30.02 ± 0.04 % based on the anode desalination. Simultaneously, there was an increase in the content of available nitrogen, phosphorus, and potassium, as well as an improvement in the seed germination index. The forms of heavy metals shifted from bioavailable to stable residual states. The non-carcinogenic hazard index (HI) values for heavy metals and polycyclic aromatic hydrocarbons (PAHs) during the land use of compost products were less than 1, and the total carcinogenic risk (TCR) values for heavy metals and PAHs were below the acceptable threshold of 10−4. The occupational population risk of infection from five pathogens was higher than that of the general public, with all risk values ranging from 8.67 × 10−8 to 1, where the highest risk was attributed to occupational exposure to Legionella. These outcomes demonstrated that the mixture of dewatered sludge and kitchen waste was an appropriate anode substrate to enhance TPMDC stability for electricity generation, and its compost products have promising land use suitability and acceptable land use risk, which will provide important guidance for the safe treatment and disposal of organic solid waste.The authors gratefully acknowledge funding from Project 52270122 and 51608155 supported by National Nature Science Foundation of China, Project LH2021E097 supported by the Natural Science Foundation of Heilongjiang Province, Project QMPT-2007 supported by Harbin Medical University, support of China Scholarship Council.Heliyo

Crk proteins transduce FGF signaling to promote lens fiber cell elongation

Specific cell shapes are fundamental to the organization and function of multicellular organisms. Fibroblast Growth Factor (FGF) signaling induces the elongation of lens fiber cells during vertebrate lens development. Nonetheless, exactly how this extracellular FGF signal is transmitted to the cytoskeletal network has previously not been determined. Here, we show that the Crk family of adaptor proteins, Crk and Crkl, are required for mouse lens morphogenesis but not differentiation. Genetic ablation and epistasis experiments demonstrated that Crk and Crkl play overlapping roles downstream of FGF signaling in order to regulate lens fiber cell elongation. Upon FGF stimulation, Crk proteins were found to interact with Frs2, Shp2 and Grb2. The loss of Crk proteins was partially compensated for by the activation of Ras and Rac signaling. These results reveal that Crk proteins are important partners of the Frs2/Shp2/Grb2 complex in mediating FGF signaling, specifically promoting cell shape changes