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The advent of generative artificial intelligence (AI) has ignited an increase in discussions about generative AI tools in education. In this study, a human-centered learning and teaching framework that uses generative AI tools for self-regulated learning development through domain knowledge learning was proposed to catalyze changes in educational practices. The framework illustrates how generative AI tools can revolutionize educational practices and transform the processes of teaching and learning to become human-centered. It emphasizes the evolving roles of teachers, who increasingly become skillful facilitators and humanistic storytellers who craft differentiated instructions and attempt to develop students' individualized learning. Drawing upon insights from neuroscience, the framework guides students to employ generative AI tools to augment their attentiveness, stimulate active engagement in learning, receive immediate feedback, and encourage self-reflection. The pedagogical approach is also reimagined; teachers equipped with generative AI tools and AI literacy can refine their teaching strategies to better equip students to meet future challenges. The practical application of the framework is demonstrated in a case study involving the development of Chinese language writing ability among primary students within a K-12 educational context. This article also reports the results of a 60-h development programme for teachers. Specifically, providing in-service teachers with cases involving uses of the proposed framework helped them to better understand the generative AI concepts and integrate them into their teaching and learning and increased their perceived ability to design AI-integrated courses that would enhance students' attention, engagement, confidence, and satisfaction.

Abstract Background Differences of sex development (DSD) are congenital conditions in which chromosomal, gonadal, or phenotypic sex is atypical. In more than 50% of human DSD cases, a molecular diagnosis is not available. In intensively farmed pig populations, the incidence of XX DSD pigs is relatively high, leading to economic losses for pig breeders. Interestingly, in the majority of 38, XX DSD pigs, gonads still develop into testis-like structures or ovotestes despite the absence of the testis-determining gene (SRY). However, the current understanding of the molecular background of XX DSD pigs remains limited. Methods Anatomical and histological characteristics of XX DSD pigs were analysed using necropsy and HE staining. We employed whole-genome sequencing (WGS) with 10× Genomics technology and used de novo assembly methodology to study normal female and XX DSD pigs. Finally, the identified variants were validated in 32 XX DSD pigs, and the expression levels of the candidate variants in the gonads of XX DSD pigs were further examined. Results XX DSD pigs are characterised by the intersex reproductive organs and the absence of germ cells in the seminiferous tubules of the gonads. We identified 4,950 single-nucleotide polymorphisms (SNPs) from non-synonymous mutations in XX DSD pigs. Cohort validation results highlighted two specific SNPs, “c.218T > C” in the “Interferon-induced transmembrane protein 1 gene (IFITM1)” and “c.1043C > G” in the “Newborn ovary homeobox gene (NOBOX)”, which were found exclusively in XX DSD pigs. Moreover, we verified 14 candidate structural variants (SVs) from 1,474 SVs, identifying a 70 bp deletion fragment in intron 5 of the WW domain-containing oxidoreductase gene (WWOX) in 62.5% of XX DSD pigs. The expression levels of these three candidate genes in the gonads of XX DSD pigs were significantly different from those of normal female pigs. Conclusion The nucleotide changes of IFITM1 (c.218T > C), NOBOX (c.1043 C > G), and a 70 bp deletion fragment of the WWOX were the most dominant variants among XX DSD pigs. This study provides a theoretical basis for better understanding the molecular background of XX DSD pigs. Plain language summary DSD are conditions affecting development of the gonads or genitalia. These disorders can happen in many different types of animals, including pigs, goats, dogs, and people. In people, DSD happens in about 0.02–0.13% of births, and in pigs, the rate is between 0.08% and 0.75%. Pigs have a common type of DSD where the animal has female chromosomes (38, XX) but no SRY gene, which is usually found on the Y chromosome in males. XX DSD pigs may look like both males and females on the outside and have testis-like or ovotestis (a mix of ovary and testis) gonads inside. XX DSD pigs often lead to not being able to have piglets, slower growth, lower chance of survival, and poorer meat quality. Here, we used a method called whole-genome de novo sequencing to look for variants in the DNA of XX DSD pigs. We then checked these differences in a larger group of pigs. Our results reveal the nucleotide changes in IFITM1 (c.218T > C), NOBOX (c.1043 C > G), and a 70 bp deletion fragment in intron 5 of the WWOX, all linked to XX DSD pigs. The expression levels of these three genes were also different in the gonads of XX DSD pigs compared to normal female pigs. These variants are expected to serve as valuable molecular markers for XX DSD pigs. Because pigs are a lot like humans in their genes, physiology, and body structure, this research could help us learn more about what causes DSD in people.

The effect of technology on primary students’ self-regulated vocabulary learning (SRVL) over time and its dynamic relationship with vocabulary outcomes have been scarcely studied. This quasi-experimental study reports a longitudinal inquiry into the effect of a mobile-assisted self-regulation scheme on primary students’ SRVL and the relationship between the changes in the perceived SRVL skills and vocabulary learning outcome. The study lasted seven months. Participants were 174 Grade 4 students (89 girls) from four classes at a primary school in Mainland China. Two classes were randomly assigned to the experimental and control groups. Students in both groups used the app named Vocab+, the former with and the latter without a mobile-assisted self-regulation scheme. Data collection included questionnaires on students’ perceived SRVL skills and vocabulary tests. Latent Growth Modelling (LGM) was used to analyse data. The results showed that perceived SRVL skills and vocabulary learning outcomes increased over time. The findings further revealed that students in the experimental group exhibited a steeper increasing trend in perceived SRVL skills and vocabulary learning outcomes. Besides, the association between the growth rates of students’ perception of SRVL skills and vocabulary learning outcomes was stronger with the experimental group than with the control group. Our results provided theoretical implications for understanding the relationship between SRVL skills and vocabulary learning outcomes from a developmental perspective.

Abstract The ankle joint, which connects the lower limbs and the sole of the foot, is prone to sprain during walking and sports, which leads to ankle arthritis. Supratroleolar osteotomy is an ankle preserving operation for the treatment of ankle arthritis, in which the osteotomy is an important fixing and supporting part. In order to avoid stress shielding effect as much as possible, the osteotomy block is designed as a porous structure. In this study, the osteotomy block was designed based on three-period minimal surface, and the designed structure was manufactured by 3D printing. The mechanical properties of different structures were studied by mechanical test and finite element simulation. In mechanical tests, the Gyroid structure showed a progressive failure mechanism from bottom to bottom, while the Diamond structure showed a shear failure zone at 45° Angle, which was not conducive to energy absorption and was more prone to brittle fracture than the Gyroid structure. Therefore, the Gyroid structure is valuable for further research in the development of porous osteotomy.

Background: Liquid biopsy-based biomarkers, including circulating tumor DNA (ctDNA) and blood tumor mutational burden (bTMB), are recognized as promising predictors of prognoses and responses to immune checkpoint inhibitors (ICIs), despite insufficient sensitivity of single biomarker detection. This research aims to determine whether the combinatorial utility of longitudinal ctDNA with bTMB analysis could improve the prognostic and predictive effects. Methods: This prospective two-center cohort trial, consisting of discovery and validation datasets, enrolled unresectable locally advanced non-small-cell lung cancer (LA-NSCLC) patients and assigned them to chemoradiotherapy (CRT) or CRT + consolidation ICI cohorts from 2018 to 2022. Blood specimens were collected pretreatment, 4 weeks post-CRT, and at progression to assess bTMB and ctDNA using 486-gene next-generation sequencing. Dynamic ∆bTMB was calculated as post-CRT bTMB minus baseline bTMB levels. Decision curve analyses were performed to calculate Concordance index (C-index). Results: One hundred twenty-eight patients were enrolled. In the discovery dataset (n = 73), patients treated with CRT and consolidation ICI had significantly longer overall survival (OS; median not reached [NR] vs 20.2 months; P < 0.001) and progression-free survival (PFS; median 25.2 vs 11.4 months; P = 0.011) than those without ICI. Longitudinal analysis demonstrated a significant decrease in ctDNA abundance post-CRT (P < 0.001) but a relative increase with disease progression. Post-CRT detectable residual ctDNA correlated with significantly shorter OS (median 18.3 months vs NR; P = 0.001) and PFS (median 7.3 vs 25.2 months; P < 0.001). For patients with residual ctDNA, consolidation ICI brought significantly greater OS (median NR vs 14.8 months; P = 0.005) and PFS (median 13.8 vs 6.2 months; P = 0.028) benefit, but no significant difference for patients with ctDNA clearance. Dynamic ∆bTMB was predictive of prognosis. Patients with residual ctDNA and increased ∆bTMB (∆bTMB > 0) had significantly worse OS (median 9.0 vs 23.0 months vs NR; P < 0.001) and PFS (median 3.4 vs 7.3 vs 25.2 months; P < 0.001). The combinatorial model integrating post-CRT ctDNA with ∆bTMB had optimal predictive effects on OS (C-index = 0.723) and PFS (C-index = 0.693), outperforming individual features. In the independent validation set, we confirmed residual ctDNA predicted poorer PFS (median 50.8 vs 14.3 months; P = 0.026) but identified more consolidation ICI benefit (median NR vs 8.3 months; P = 0.039). The combined model exhibited a stable predictive advantage (C-index = 0.742 for PFS). Conclusions: The multiparameter assay integrating qualitative residual ctDNA testing with quantitative ∆bTMB dynamics improves patient prognostic risk stratification and efficacy predictions, allowing for personalized consolidation therapy for LA-NSCLC.

Lately, as a subset of human-centric studies, vision-oriented human action recognition has emerged as a pivotal research area, given its broad applicability in fields like healthcare, video surveillance, autonomous driving, sports, and education. This brief applies Lie algebra and standard bone length data to represent human skeleton data. A multi-layer long short-term memory (LSTM) recurrent neural network and convolutional neural network (CNN) are applied for human motion recognition. Finally, the trained network weights are converted into the crossbar-based memristor circuit, which can accelerate the network inference, reduce energy consumption, and obtain an excellent computing performance.

By integrating the successful case of the European Union emissions trading system, this study proposes a water emissions trading system, a novel method of reducing water pollution. Assuming that upstream governments allocate initial quotas to upstream businesses as the compensation standard, this approach defines the foundational principles of market trading mechanisms and establishes a robust watershed ecological compensation model to address challenges in water pollution prevention. To be specific, the government establishes a reasonable initial quota for upstream enterprises, which can be used to limit the emissions of upstream pollution. When enterprises exceed their allocated emissions quota, they face financial penalties. Conversely, these emissions rights can be transformed into profitable assets by participating in the trading market as a form of ecological compensation. Numerical simulations demonstrate that various pollutant emissions from upstream businesses will have various effects on the profits of other businesses. Businesses in the upstream region received reimbursement from the assigned emission rights through the market mechanism, demonstrating that ecological compensation for the watershed can be achieved through the market mechanism. This novel market trading system aims at controlling emissions management from the perspectives of individual enterprises and ultimately optimizing the aquatic environment. HIGHLIGHT The article establishes a water emissions trading mechanism. We establish this mechanism to control the amount of emission from each enterprise. Then we achieve the purpose of optimizing the water environment.;

Reliable prediction of traffic accidents is crucial for the identification of potential hazards in advance, formulation of effective preventative measures, and reduction of accident incidence. Existing neural network-based models generally suffer from a limited field of perception and poor long-term dependency capturing abilities, which severely restrict their performance. To address the inherent shortcomings of current traffic prediction models, we propose the Traffic Transformer for multidimensional, multi-step traffic accident prediction. Initially, raw datasets chronicling sporadic traffic accidents are transformed into multivariate, regularly sampled sequences that are amenable to sequential modeling through a temporal discretization process. Subsequently, Traffic Transformer captures and learns the hidden relationships between any elements of the input sequence, constructing accurate prediction for multiple forthcoming intervals of traffic accidents. Our proposed Traffic Transformer employs the sophisticated multi-head attention mechanism in lieu of the widely used recurrent architecture. This significant shift enhances the model's ability to capture long-range dependencies within time series data. Moreover, it facilitates a more flexible and comprehensive learning of diverse hidden patterns within the sequences. It also offers the versatility of convenient extension and transference to other diverse time series forecasting tasks, demonstrating robust potential for further development in this field. Extensive comparative experiments conducted on a real-world dataset from Qatar demonstrate that our proposed Traffic Transformer model significantly outperforms existing mainstream time series forecasting models across all evaluation metrics and forecast horizons. Notably, its Mean Absolute Percentage Error reaches a minimal value of only 4.43%, which is substantially lower than the error rates observed in other models. This remarkable performance underscores the Traffic Transformer's state-of-the-art level of in predictive accuracy.

B-cell Chronic Lymphocytic Leukemia (B-CLL) is a malignancy caused by the clonal expansion of mature B lymphocytes bearing a CD5+CD19+ (B1) phenotype. However, the origin of B-CLL remains controversial. We showed previously that STYK1/NOK transgenic mice develop a CLL-like disease. Using this model system in this study, we attempt to define the stage of CLL initiation. Here, we show that the phenotype of STYK1/NOK-induced B-CLL is heterogeneous. The expanded B1 lymphocyte pool was detected within peripheral lymphoid organs and was frequently associated with the expansions of memory B cells. Despite this immunophenotypic heterogeneity, suppression of B cell development at an early stage consistently occurred within the bone marrow (BM) of STYK1/NOK-tg mice. Overall, we suggest that enforced expression of STYK1/NOK in transgenic mice might significantly predispose BM hematopoietic stem cells (HSCs) towards the development of B-CLL.

Raptors can change the shape and area of their wings to an exceptional degree in a fast and efficient manner, surpassing other birds, insects, or bats. Some researchers have focused on the functional properties of muscle skeletons, mechanics, and flapping robot design. However, the wing motion of the birds of prey has not been measured quantitatively, and synthetic bionic wings with morphing abilities similar to raptors are far from reality. Therefore, in the current study, a 3D suspension system for holding bird carcasses was designed and fabricated to fasten the wings of Falco Peregrinus with a series of morphing postures. Subsequently, the wing skeleton of the falcon was scanned during extending motions using the computed tomography (CT) approach to obtain three consecutive poses. Subsequently, the skeleton was reconstructed to identify the contribution of the forelimb bones to the extending/folding motions. Inspired by these findings, we propose a simple mechanical model with four bones to form a wing-morphing mechanism using the proposed pose optimisation method. Finally, a bionic wing mechanism was implemented to imitate the motion of the falcon wing—divided into inner and outer wings with folding and twisting motions. The results show that the proposed four-bar mechanism can track bone motion paths with high fidelity.

Abstract Background Following China’s official designation as malaria-free country by WHO, the imported malaria has emerged as a significant determinant impacting the malaria reestablishment within China. The objective of this study is to explore the application prospects of machine learning algorithms in imported malaria risk assessment of China. Methods The data of imported malaria cases in China from 2011 to 2019 was provided by China CDC; historical epidemic data of malaria endemic country was obtained from World Malaria Report, and the other data used in this study are open access data. All the data processing and model construction based on R, and map visualization used ArcGIS software. Results A total of 27,088 malaria cases imported into China from 85 countries between 2011 and 2019. After data preprocessing and classification, clean dataset has 765 rows (85 * 9) and 11 cols. Six machine learning models was constructed based on the training set, and Random Forest model demonstrated the best performance in model evaluation. According to RF, the highest feature importance were the number of malaria deaths and Indigenous malaria cases. The RF model demonstrated high accuracy in forecasting risk for the year 2019, achieving commendable accuracy rate of 95.3%. This result aligns well with the observed outcomes, indicating the model’s reliability in predicting risk levels. Conclusions Machine learning algorithms have reliable application prospects in risk assessment of imported malaria in China. This study provides a new methodological reference for the risk assessment and control strategies adjusting of imported malaria in China.

The primary objective of this study was to examine the aromatic composition characteristics of six varieties of sugar-roasted 'Yanshan chestnuts' and to discern the distinctions in taste and odor between them. The volatile flavor constituents and pivotal aroma compounds of these sugar-roasted Chinese chestnuts were scrutinized using headspace solid-phase microextraction (HS-SPME) in conjunction with gas chromatography-mass spectrometry (GC-MS) and the odor activity value (OAV) method. Additionally, the volatile odor components and taste were assessed using electronic nose and tongue technologies. Results showed that, a total of 57 compounds were detected via GC-MS in the volatile components of these six types of sugar-roasted Castanea mollissima, which included 19 esters, 12 alcohols, 5 aldehydes, 5 ketones, 4 alkenes, 4 benzodiazepines, 2 amines, and 6 other components. Among these, 23 constituents with an OAV greater than 1 were identified as the key aroma components of the sugar-roasted chestnuts. The electronic nose evaluation illustrated that the predominant volatile odor components of the six sugar-roasted Castanea mollissima were alcohols, aldehydes, ketones, nitrogen oxides, organic sulfides, inorganic sulfides, and methyl groups. Furthermore, the electronic tongue analysis indicated that these six roasted chestnuts exhibited similar taste indices. The richness and umami sensor demonstrated the most substantial response to the different roasted chestnuts, while the sour and astringent tastes were the least prominent. The main taste indices of the roasted chestnuts were determined to be richness, umami, sweetness, saltiness, bitterness, and a lingering bitter aftertaste. The volatile flavor components and their quantities differed among the six types of sugar-roasted chestnuts, implying that the volatile flavor components vary among different sugar-roasted chestnut varieties, and the levels of the same compound also differ between varieties. The use of principal component analysis (PCA), radar chromatogram analysis, and GC-MS technology combined with electronic nose/tongue technologies proved to be an effective means to distinguish the differences in flavor among different varieties of sugar-roasted Chinese chestnuts and identify the key aroma components. This research provides a theoretical foundation for future flavor research on different sugar-roasted Chinese chestnut varieties.

BackgroundKnee osteoarthritis (KOA) is one of most prevalent and fastest-growing causes of pain, impaired mobility, and poor quality of life in the rapidly aging population worldwide. There is a lack of high-quality evidence on the efficacy of traditional Chinese medicine (TCM), particularly acupuncture, and a lack of KOA practice guidelines that are tailored to unique population demographics and tropical climates. ObjectiveOur HARMOKnee (Heat and Acupuncture to Manage Osteoarthritis of the Knee) trial aims to address these gaps by evaluating the short- and medium-term clinical and cost-effectiveness of acupuncture with heat therapy in addition to standard care, compared to standard care alone. Through a robust process and economic evaluation, we aim to inform evidence-based practice for patients with KOA to facilitate the large-scale implementation of a comprehensive and holistic model of care that harmonizes elements of Western medicine and TCM. We hypothesize that acupuncture with heat therapy as an adjunct to standard care is clinically more effective than standard care alone. MethodsA multicenter, pragmatic, parallel-arm, single-blinded, effectiveness-implementation hybrid randomized controlled trial will be conducted. We intend to recruit 100 patients with KOA randomized to either the control arm (standard care only) or intervention arm (acupuncture with heat therapy, in addition to standard care). The inclusion criteria are being a community ambulator and having primary KOA, excluding patients with secondary arthritis or previous knee replacements. The primary outcome measure is the Knee Osteoarthritis Outcome Score at 6 weeks. Secondary outcome measures include psychological, physical, quality of life, satisfaction, and global outcome measures at 6, 12, and 26 weeks. A mixed method approach through an embedded process evaluation will facilitate large-scale implementation. An economic evaluation will be performed to assess financial sustainability. ResultsPatient enrollment has been ongoing since August 2022. The recruitment process is anticipated to conclude by July 2024, and the findings will be analyzed and publicized as they are obtained. As of November 6, 2023, our patient enrollment stands at 65 individuals. ConclusionsThe findings of our HARMOKnee study will contribute substantial evidence to the current body of literature regarding the effectiveness of acupuncture treatment for KOA. Additionally, we aim to facilitate the creation of standardized national guidelines for evidence-based practice that are specifically tailored to our unique population demographics. Furthermore, we seek to promote the adoption and integration of acupuncture and heat therapy into existing treatment models. Trial RegistrationClinicalTrials.gov NCT05507619; https://clinicaltrials.gov/study/NCT05507619 International Registered Report Identifier (IRRID)DERR1-10.2196/54352