Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of reading engagement on scientific literacy – an analysis based on the XGBoost method

Scientific literacy is a key factor of personal competitiveness, and reading is the most common activity in daily learning life, and playing the influence of reading on individuals day by day is the most convenient way to improve the level of scientific literacy of all people. Reading engagement is one of the important student characteristics related to reading literacy, which is highly malleable and is jointly reflected by behavioral, cognitive, and affective engagement, and it is of theoretical and practical significance to explore the relationship between reading engagement and scientific literacy using reading engagement as an entry point. In this study, we used PISA2018 data from China to explore the relationship between reading engagement and scientific literacy with a sample of 15-year-old students in mainland China. 36 variables related to reading engagement and background variables (gender, grade, and socioeconomic and cultural status of the family) were selected from the questionnaire as the independent variables, and the score of the Scientific Literacy Assessment (SLA) was taken as the outcome variable, and supervised machine learning method, the XGBoost algorithm, to construct the model. The dataset is randomly divided into training set and test set to optimize the model, which can verify that the obtained model has good fitting degree and generalization ability. Meanwhile, global and local personalized interpretation is done by introducing the SHAP value, a cutting-edge machine model interpretation method. It is found that among the three major components of reading engagement, cognitive engagement is the more influential factor, and students with high reading cognitive engagement level are more likely to get high scores in scientific literacy assessment, which is relatively dominant in the model of this study. On the other hand, this study verifies the feasibility of the current popular machine learning model, i.e., XGBoost, in a large-scale international education assessment program, with a better model adaptability and conditions for global and local interpretation.
MISNet: multi-source information-shared EEG emotion recognition network with two-stream structure

Introduction When constructing machine learning and deep neural networks, the domain shift problem on different subjects complicates the subject independent electroencephalography (EEG) emotion recognition. Most of the existing domain adaptation methods either treat all source domains as equivalent or train source-specific learners directly, misleading the network to acquire unreasonable transfer knowledge and thus resulting in negative transfer. Methods This paper incorporates the individual difference and group commonality of distinct domains and proposes a multi-source information-shared network (MISNet) to enhance the performance of subject independent EEG emotion recognition models. The network stability is enhanced by employing a two-stream training structure with loop iteration strategy to alleviate outlier sources confusing the model. Additionally, we design two auxiliary loss functions for aligning the marginal distributions of domain-specific and domain shared features, and then optimize the convergence process by constraining gradient penalty on these auxiliary loss functions. Furthermore, the pre-training strategy is also proposed to ensure that the initial mapping of shared encoder contains sufficient emotional information. Results We evaluate the proposed MISNet to ascertain the impact of several hyper-parameters on the domain adaptation capability of network. The ablation experiments are conducted on two publically accessible datasets SEED and SEED-IV to assess the effectiveness of each loss function. Discussion The experimental results demonstrate that by disentangling private and shared emotional characteristics from differential entropy features of EEG signals, the proposed MISNet can gain robust subject independent performance and strong domain adaptability.
Interleukins: pathophysiological role in acute pancreatitis

Acute pancreatitis (AP) is a common inflammatory state characterized by a clinical course that can lead to serious local and extrapancreatic organ malfunction and failure. Interleukins (ILs) are biologically active glycoproteins primarily produced by macrophages and lymphocytes. According to the literature, there are many ILs. However, this article represents a summary of the role of ILs in AP, such as IL-1, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-11, IL-12, IL-13, IL-14, IL-15, IL-16, IL-17, IL-18, IL-19, and IL-20. The ways to modulate IL activity to reduce inflammation and improve outcomes in individuals with this condition are under investigation. Drugs that target specific ILs might be developed to mitigate the effects of AP.
Overexpression of miR-506-3p reversed doxorubicin resistance in drug-resistant osteosarcoma cells

Background and objective: Osteosarcoma is a common primary malignant tumor of bone, and doxorubicin is one of the most widely used therapeutic drugs. While the problem of doxorubicin resistance limits the long-term treatment benefits in osteosarcoma patients. The role of miRNAs and their target genes in osteosarcoma have become increasingly prominent. Currently, there is no report on miR-506-3p reversing doxorubicin resistance by targeting STAT3 in osteosarcoma. The purpose of this study was to investigate the molecular mechanism that overexpression of miR-506-3p reverses doxorubicin resistance in drug-resistant osteosarcoma cells. Methods: Doxorubicin-resistant osteosarcoma cells (U-2OS/Dox) were constructed by intermittent stepwise increasing stoichiometry. The target genes of miR-506-3p were predicted by bioinformatics approach and the targeting relationship between miR-506-3p and STAT3 was detected using dual luciferase reporter assay. U-2OS/Dox cells were treated with miR-506-3p overexpression and STAT3 silencing respectively. Then Western blot and RT-qPCR were used to detect the protein and mRNA expression levels of JAK2/STAT3 signaling pathway, drug-resistant and apoptotic associated molecules. The migration and invasion were assessed by cell scratch assay and transwell assay. The cell proliferative viability and apoptosis were investigated by CCK8 assay and flow cytometry assay. Results: U-2OS/Dox cells were successfully constructed with a 14.4-fold resistance. MiR-506-3p is directly bound to the 3′-UTR of STAT3 mRNA. Compared with U-2OS cells, the mRNA expression of miR-506-3p was reduced in U-2OS/Dox cells. Overexpression of miR-506-3p decreased the mRNA expression levels of JAK2, STAT3, MDR1/ABCB1, MRP1/ABCC1, Survivin and Bcl-2, and decreased the protein expression levels of p-JAK2, STAT3, MDR1/ABCB1, MRP1/ABCC1, Survivin and Bcl-2, and conversely increased Bax expression. It also inhibited the proliferation, migration and invasion of U-2OS/Dox cells and promoted cells apoptosis. The results of STAT3 silencing experiments in the above indicators were consistent with that of miR-506-3p overexpression. Conclusion: Overexpression of miR-506-3p could inhibit the JAK2/STAT3 pathway and the malignant biological behaviors, then further reverse doxorubicin resistance in drug-resistant osteosarcoma cells. The study reported a new molecular mechanism for reversing the resistance of osteosarcoma to doxorubicin chemotherapy and provided theoretical support for solving the clinical problems of doxorubicin resistance in osteosarcoma.
Mayfly Burrows in Firmground of Recent Rivers from the Czech Republic and Poland, with Some Comments on Ephemeropteran Burrows in General

ABSTRACT U-shaped, pouch-like burrows with parallel limbs, covered with short scratches arranged in sets, occur in the thalweg of the Ohře river in NW Czech Republic. Similar, but smaller burrows with rare scratches, not arranged in sets, occur in the thalweg of the Drwęca river in N Poland. Probably, they are produced by larvae and/or nymphs of Palingenia and Polymitarcis (Ephoron), respectively. In both localities, they burrowed in firmground surfaces at shallow depths. The burrowed surfaces were emerged during low water levels. A review of recent mayfly burrows shows that they are 1) U-shaped pouches with parallel limbs and septum, which may be covered with short scratches and are oriented perpendicular to the bottom, irrespective of its inclination, or 2) wide U-shape burrows with divergent limbs, which may be branched. In the fossil record, the ichnogenera Fuersichnus, Asthenopodichnium, and Rhizocorallium are partly ascribed to mayfly burrows, but their comparison to the recent burrows shows that such interpretations are somewhat problematic. The mayfly burrows are potentially good indicators of aquatic, non-marine, well oxygenated, clean water environments.
HGNN-QSSA: Heterogeneous Graph Neural Networks With Quantitative Sampling and Structure-Aware Attention

Heterogeneous information networks provide abundant structural and semantic information. Two main strategies for leveraging this data include meta-path-based and meta-path-free methods. The effectiveness of the former heavily depends on the quality of manually defined meta-paths, which may lead to the instability of the model. However, the existing meta-path-free methods lack of neighbor screening during aggregating, and there is also an overemphasis on attribute information. To address these issues, we propose the Heterogeneous Graph Neural Network model by incorporating Quantitative Sampling and Structure-aware Attention. We introduce a Quantitative Sampling Module that calculates the similarity between neighbors of the target nodes and target nodes, enabling us to select the top k nodes with the strongest relevance to the target node based on this measure, and incorporate a Structure-aware Attention Module during the aggregation of neighbor information. This module combines both structural and attribute information to aggregate the neighbor information effectively. By implementing these improvements, our proposed model exhibits superior performance compared to several state-of-the-art methods on two real-world datasets.
Timing and Nutrient Type of Isocaloric Snacks Impacted Postprandial Glycemic and Insulinemic Responses of the Subsequent Meal in Healthy Subjects

The aim of the study was to explore the impact of both the macronutrient composition and snacking timing on the postprandial glycemic insulinemic responses and food intake. Seventeen healthy female volunteers completed the randomized crossover trials. The volunteers were provided a standard breakfast and lunch at 8:00 and 13:00, respectively, and an ad libitum dinner at 18:00. Provided at either 10:30 (midmorning) or 12:30 (preload), the glycemic effects of the three types of 70 kcal snacks, including chicken breast (mid-C and pre-C), apple (mid-A and pre-A), and macadamia nut (mid-M and pre-M), were compared with the non-snack control (CON), evaluated by continuous glucose monitoring (CGM). The mid-M showed increased insulin resistance after lunch compared with CON, while the pre-M did not. The pre-A stabilized the glycemic response in terms of all variability parameters after lunch, while the mid-A had no significant effect on postprandial glucose control. Both the mid-C and pre-C improved the total area under the glucose curve, all glycemic variability parameters, and the insulin resistance within 2 h after lunch compared with CON. The pre-C attained the lowest energy intake at dinner, while the mid-A and the mid-M resulted in the highest. In conclusion, the chicken breast snack effectively stabilized postprandial glycemic excursion and reduced insulin resistance while the macadamia snack did not, regardless of ingestion time. Only as a preload could the apple snack mitigate the glucose response after the subsequent meal.
Synergistic Effects of Temozolomide and Doxorubicin in the Treatment of Glioblastoma Multiforme: Enhancing Efficacy through Combination Therapy

Glioblastoma multiforme (GBM), a grade IV (WHO classification) malignant brain tumor, poses significant challenges in treatment. The current standard treatment involves surgical tumor removal followed by radiation and chemotherapeutic interventions. However, despite these efforts, the median survival for GBM patients remains low. Temozolomide, an alkylating agent capable of crossing the blood–brain barrier, is currently the primary drug for GBM treatment. Its efficacy, however, is limited, leading to the exploration of combination treatments. In this study, we have investigated the synergistic effects of combining temozolomide with doxorubicin, a chemotherapeutic agent widely used against various cancers. Our experiments, conducted on both temozolomide-sensitive (U87) and -resistant cells (GBM43 and GBM6), have demonstrated a synergistic inhibition of brain cancer cells with this combination treatment. Notably, the combination enhanced doxorubicin uptake and induced higher apoptosis in temozolomide-resistant GBM43 cells. The significance of our findings lies in the potential application of this combination treatment, even in cases of temozolomide resistance. Despite doxorubicin’s inability to cross the blood–brain barrier, our results open avenues for alternative delivery methods, such as conjugation with carriers like albumin or local administration at the surgical site through a hydrogel application system. Our study suggests that the synergistic interaction between temozolomide and doxorubicin holds promise for enhancing the efficacy of glioblastoma treatment. The positive outcomes observed in our experiments provide confidence in considering this strategy for the benefit of patients with glioblastoma.
Loss of NSD2 causes dysregulation of synaptic genes and altered H3K36 dimethylation in mice

Background: Epigenetic disruptions have been implicated in neurodevelopmental disorders. NSD2 is associated with developmental delay/intellectual disability; however, its role in brain development and function remains unclear. Methods: We performed transcriptomic and epigenetic analyses using Nsd2 knockout mice to better understand the role of NSD2 in the brain. Results and discussion: Transcriptomic analysis revealed that the loss of NSD2 caused dysregulation of genes related to synaptic transmission and formation. By analyzing changes in H3 lysine 36 dimethylation (H3K36me2), NSD2-mediated H3K36me2 mainly marked quiescent state regions and the redistribution of H3K36me2 occurred at transcribed genes and enhancers. By integrating transcriptomic and epigenetic data, we observed that H3K36me2 changes in a subset of dysregulated genes related to synaptic transmission and formation. These results suggest that NSD2 is involved in the regulation of genes important for neural function through H3K36me2. Our findings provide insights into the role of NSD2 and improve our understanding of epigenetic regulation in the brain.
Three-day changes in resting metabolism after a professional young rugby league match

Professional collision-sport athletes report uniquely large energy expenditures across the season (1-4), as determined by gold standard assessment of resting metabolic rate (RMR (5)) and total energy expenditure (TEE (6)). Such expenditures are possibly a consequence of strenuous match demands, which repeatedly expose players to substantial exercise-and collision-induced muscle damage (7). Recovery from such large perturbations of homeostasis (8) are likely to be energetically expensive (9), in part determining the distinct in-season energetic demands of professional collision-sport athletes. Aim. Accurately determining the effect of match play on resting metabolism is essential to optimise acute manipulation of energy balance, player recovery and long-term athlete development. Therefore, for the first time this case report investigated the metabolic cost of a professional young rugby league match.
Investigation of a neural implicit representation tomography method for flow diagnostics

In this work, a new gridless approach to tomographic reconstruction of 3D flow fields is introduced and investigated. The approach, termed here as FluidNeRF, is based on the concept of volume representation through Neural Radiance Fields (NeRF). NeRF represents a 3D volume as a continuous function using a deep neural network. In FluidNeRF, the neural network is a function of 3D spatial coordinates in the volume and produces an intensity of light per unit volume at that position. The network is trained using the loss between measured and rendered 2D projections similar to other multi-camera tomography techniques. Projections are rendered using an emission-based integrated line-of-sight method where light rays are traced through the volume; the network is used to determine intensity values along the ray. This paper investigates the influence of the NeRF hyperparameters, camera layout and spacing, and image noise on the reconstruction quality as well as the computational cost. A DNS-generated synthetic turbulent jet is used as a ground-truth representative flow field. Results obtained with FluidNeRF are compared to an adaptive simultaneous algebraic reconstruction technique (ASART), which is representative of a conventional reconstruction technique. Results show that FluidNeRF matches or outperforms ASART in reconstruction quality, is more robust to noise, and offers several advantages that make it more flexible and thus suitable for extension to other flow measurement techniques and scaling to larger-scale problems.
Analysis of Diode Reverse Recovery Effect on ZVS Condition for GaN-Based LLC Resonant Converter

LLC resonant converter can achieve zero voltage switching (ZVS) for primary-side devices and zero current switching (ZCS) for secondary-side rectifiers. However, the reverse recovery and junction capacitance (<italic>C<sub>j</sub></italic>) of secondary-side diode critically affect the ZVS condition of primary-side switches. The effect of <italic>C<sub>j</sub></italic> has been discussed in literature, but not the reverse recovery. In this paper, the reverse recovery charge (<italic>Q</italic><sub>rr</sub>) is converted to an equivalent capacitance <inline-formula><tex-math notation="LaTeX">$(C_{{\rm rr}\_{\rm eq}})$</tex-math></inline-formula> for the study of primary-side ZVS performance. An accurate model during deadtime is derived and further applied to characterize ZVS performance with different reverse recovery charges in different regions. The concept of establishing parameter <italic>C</italic><sub>total</sub> to consider both <italic>C<sub>j</sub></italic> and <inline-formula><tex-math notation="LaTeX">$C_{{\rm rr}\_{\rm eq}}$</tex-math></inline-formula> is proposed to evaluate the effect of the secondary-side rectifiers. This concept provides the guideline for diode and synchronous rectification <sc>mosfet</sc> selection to ensure ZVS condition for LLC converters. To verify the concept and the derived model, a 200/400 V 400 W LLC resonant converter prototype operating from 200 to 700 kHz is built and its ZVS performances with different diodes are compared. Two issues caused by <italic>Q</italic><sub>rr</sub> effect, including <inline-formula><tex-math notation="LaTeX">$V_{\rm ds}$</tex-math></inline-formula> reverse charging and asymmetrical waveform during deadtime, are explained thoroughly as well.
Ecological Roles of Exploited Sea Cucumbers

Sea cucumbers (Echinodermata: Holothuroidea) are large and abundant members of marine benthic communities. Overexploitation worldwide has raised concern because they have important functions within ecosystems. The ecological roles of commercially exploited sea cucumbers (Aspidochirotida and Dendrochirotida) are reviewed here, focusing on recent literature. Of the more than 70 species commercially exploited, at least 12 regularly bury into sand and mud, playing major roles in bioturbation. Most aspidochirotids are deposit-feeders, reducing the organic load and redistributing surface sediments, making them bioremediators for coastal mariculture. Sea cucumbers excrete inorganic nitrogen and phosphorus, enhancing the productivity of benthic biota. This form of nutrient recycling is crucial in ecosystems in oligotrophic waters such as coral reefs. Feeding and excretion by sea cucumbers also act to increase seawater alkalinity which contributes to local buffering of ocean acidification. Sea cucumbers host more than 200 species of parasitic and commensal symbionts from seven phyla, thereby enhancing ecosystem biodiversity. They are preyed on by many taxa, thereby transferring animal tissue and nutrients (derived from detritus and microalgae) to higher trophic levels. Overexploitation of sea cucumbers is likely to decrease sediment health, reduce nutrient recycling and potential benefits of deposit-feeding to seawater chemistry, diminish biodiversity of associated symbionts, and reduce the transfer of organic matter from detritus to higher trophic levels. Ecosystem-based fisheries management needs to consider the importance of sea cucumbers in marine ecosystems and implement regulatory measures to safeguard their ecological roles.
Large-Scale Structured Output Classification via Multiple Structured Support Vector Machine by Splitting

Structured support vector machine (SSVM) is an effective method on coping with problems involving complex outputs such as multiple dependent output variables and structured output spaces. However, its training process is very time consuming for large-scale data with complex structure and many classes. In this paper, to improve the efficiency of SSVM, we propose a multiple structured support vector machine (MSSVM) for structured output classification via the idea of splitting large into small. By constructing novel classification loss for each class, MSSVM solves a series of smaller optimization problems rather than one large-size optimization problem in SSVM. Therefore, MSSVM greatly reduces the training speed of SSVM. In addition, the structured output label information and discriminative information are embedded in the introduced losses in a simple but effective way. Experiments on multiclass classification, ordinal regression and hierarchical classification datasets demonstrate the efficiency and effectiveness of the proposed MSSVM.