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Abstract Recently, deep neural network-based cognitive models such as deep knowledge tracing have been introduced into the field of learning analytics and educational data mining. Despite an accurate predictive performance of such models, it is challenging to interpret their behaviors and obtain an intuitive insight into latent student learning status. To address these challenges, this paper proposes a new learner modeling framework named the EAKT, which embeds a structured cognitive model into a transformer. In this way, the EAKT not only can achieve an excellent prediction result of learning outcome but also can depict students’ knowledge state on a multi-dimensional knowledge component(KC) level. By performing the fine-grained analysis of the student learning process, the proposed framework provides better explanatory learner models for designing and implementing intelligent tutoring systems. The proposed EAKT is verified by experiments. The performance experiments show that the EAKT can better predict the future performance of student learning(more than 2.6% higher than the baseline method on two of three real-world datasets). The interpretability experiments demonstrate that the student knowledge state obtained by EAKT is closer to ground truth than other models, which means EAKT can more accurately trace changes in the students’ knowledge state.

The suspicious noise points are taken as center to construct a certain size neighborhood in this paper. Morphological close filtering is performed by pepper noise neighborhood. Then, morphological open filtering is applied by neighborhood salt noise point. The gray values of suspected noise points are replaced by filtered gray value of neighborhood center point. During the transforming process, gray values of non-suspicious noise points remain unchanged. Experiments show that this scheme is practical and plausible. The filtered image has advantages of less mean square error and high peak signal-to-noise ratio. Compared with the median filtering algorithm, the morphological open-close filtering algorithm，and the adaptive filtering algorithm, this proposed improved algorithm provides a better filtering property.

Agricultural chemical residue in the environment is one of the biggest concerns for biosafety and human health worldwide. Here, we develop a carrier for pesticides by the chemical combination of UiO-66 and sodium alginate (Alg). When clothianidin (CTD) was encapsulated in UiO-66/Alg, they showed the ability to act as controlled-release pesticides with double stimuli-responsive. Although only 62.74% of CTD was released at 27 °C in 144 h, 93.52% was released at 45 °C at the same time, and the release could be tuned by adding phosphate at the time of greatest requirement. Moreover, the UV-irradiating stability of CTD in UiO-66/Alg was >3.5 times greater than for free CTD, which combined with the outstanding biosafety of UiO-66/Alg-CTD illustrates its potential for development as a bio-friendly, stable and intelligent carrier of pesticides to improve the utilization efficiency of pesticides.

The dye-sensitized solar cell (DSSC) is one candidate among the third-generation solar cells. The performance of most DSSCs based on TiO2 photoanode was limited by the low electron mobility within TiO2. To produce a much higher power conversion efficiency, Sn-doped TiO2 nanowire arrays were successfully prepared using a simple hydrothermal process. It was found that Sn doping augments electron mobility well and raises the flat band potential to improve the performance of DSSCs. The power conversion efficiency (η) of a DSSC based on the reasonable Sn-doped TiO2, N719 dye, platinized counter electrode and iodide/triiodide electrolyte reaches 8.75%. Furthermore, with an anatase TiO2 light scattering layer, a DSSC based on the Sn-doped TiO2 NWAs exhibits a remarkable power conversion efficiency of 9.43%, which is especially useful in weak light conditions.

Pressure overload-induced pathological cardiac hypertrophy eventually leads to heart failure (HF). Unfortunately, lack of effective targeted therapies for HF remains a challenge in clinical management. Mixed-lineage leukemia 4 (MLL4) is a member of the SET family of histone methyltransferase enzymes, which possesses histone H3 lysine 4 (H3K4)-specific methyltransferase activity. However, whether and how MLL4 regulates cardiac function is not reported in adult HF. Here we report that MLL4 is required for endoplasmic reticulum (ER) stress homeostasis of cardiomyocytes and protective against pressure overload-induced cardiac hypertrophy and HF. We observed that MLL4 is increased in the heart tissue of HF mouse model and HF patients. The cardiomyocyte-specific deletion of Mll4 (Mll4-cKO) in mice leads to aggravated ER stress and cardiac dysfunction following pressure overloading. MLL4 knockdown neonatal rat cardiomyocytes (NRCMs) also display accelerated decompensated ER stress and hypertrophy induced by phenylephrine (PE). The combined analysis of Cleavage Under Targets and Tagmentation sequencing (CUT&Tag-seq) and RNA sequencing (RNA-seq) data reveals that, silencing of Mll4 alters the chromatin landscape for H3K4me1 modification and gene expression patterns in NRCMs. Interestingly, the deficiency of MLL4 results in a marked reduction of H3K4me1 and H3K27ac occupations on Thrombospondin-4 (Thbs4) gene loci, as well as Thbs4 gene expression. Mechanistically, MLL4 acts as a transcriptional activator of Thbs4 through mono-methylation of H3K4 and further regulates THBS4-dependent ER stress response, ultimately plays a role in HF. Our study indicates that pharmacologically targeting MLL4 and ER stress might be a valid therapeutic approach to protect against cardiac hypertrophy and HF.

Background: From 2004 onwards, the Chinese government has freely offered complimentary Chinese herbal medicine (CHM) to Chinese HIV/AIDS patients, alongside the prescribed first line therapy of highly active antiretroviral therapy (HAART). Thus, we aimed to explore the effectiveness and safety of CHM for patients with HIV/AIDS. Methods: The data from the Guangxi pilot database and antiviral treatment sites database have been respectively developed into two datasets in this prospective cohort real-world study, the CHM combined HAART group (the integrated group) and the HAART group. A 1:1 propensity score matching (PSM) was performed and the longitudinal data were analyzed using a generalized estimating equation (GEE) model with an autocorrelation matrix and log link function attached to the Gamma distribution. Results: A final sample of 629 patients, 455 and 174 in the integrated group and HAART group respectively, were obtained from the full dataset. As covariates for PSM, gender, age, baseline CD4+ and CD4+/ CD8+ were assessed based on the results of the logistic regression analyses. Following PSM, 166 pairs from the full dataset were matched successfully, with 98 pairs in the baseline CD4+ > 200 subgroup, and 55 pairs in the baseline CD4+ ≤ 200 subgroup. In the full dataset, HAART group achieved higher CD4+ count (OR = 1.119, 95%CI [1.018, 1.230]) and CD4+/CD8+ ratio (OR = 1.168, 95%CI [1.045, 1.305]) than the integrated group, so did in the CD4+ > 200 subgroup. For the CD4+ ≤ 200 subgroup, the CD4+ (OR = 0.825, 95%CI [0.694, 0.980]) and CD4+/CD8+ (OR = 0.826, 95%CI [0.684, 0.997]) of the integrated group were higher than those of the HAART group. The safety outcomes showed that there were no significant differences in BUN, ALT and AST levels between the groups but Cr showed significantly higher levels in HAART groups of all three datasets. Conclusions: Compared to HAART alone, CHMs combined with HAART had better effects in improving the immune function of HIV/AIDS in patients with baseline CD4+ count ≤ 200. The results of the two subgroups are in opposite directions, and chance does not explain the apparent subgroup effect. A study with larger sample size and longer follow-up period is warranted in order to increase study credibility. [ABSTRACT FROM AUTHOR]

Silencers are repressive cis -regulatory elements that play crucial roles in transcriptional regulation. Experimental methods for identifying silencers are always costly and time-consuming. Computational methods, which relies on genomic sequence features, have been introduced as alternative approaches. However, silencers do not have significant epigenomic signature. Therefore, we explore a new way to computationally identify silencers, by incorporating chromatin structural information. We propose the SilenceREIN method, which focuses on finding silencers on anchors of chromatin loops. By using graph neural networks, we extracted chromatin structural information from a regulatory element interaction network. SilenceREIN integrated the chromatin structural information with linear genomic signatures to find silencers. The predictive performance of SilenceREIN is comparable or better than other states-of-the-art methods. We performed a genome-wide scanning to systematically find silencers in human genome. Results suggest that silencers are widespread on anchors of chromatin loops. In addition, enrichment analysis of transcription factor binding motif support our prediction results. As far as we can tell, this is the first attempt to incorporate chromatin structural information in finding silencers. All datasets and source codes of SilenceREIN have been deposited in a GitHub repository (https://github.com/JianHPan/SilenceREIN). [ABSTRACT FROM AUTHOR]