Search Results (201)

This single case study examines the implementation of a co-designed fifth-grade science unit enhanced by using Virtual Reality (VR) and integrating translingual and transmodal scaffolding strategies to support students’ participation and quality of talk during scientific sensemaking. The co-designed science unit covered physical and chemical changes as part of the fifth-grade science curriculum. The research involves a fifth-grade science teacher and her class of 22 students comprising multilingual learners (ML) and English monolingual learners (EML). This study examines the learning experience of 3 student pairs grouped as ML-ML, EML-ML and EML-EML. Using content analysis, we analyzed 911 min of video data on the six students’ learning in this unit. The results indicate that when the teacher used translingual and transmodal scaffolding strategies introduced during the co-design process, equal participation across MLs and EMLs was observed. The VR pair programming worked well for student pairs in increasing active participation regardless of the pairing, although active participation did not necessarily lead to high quality science talk. Findings of this study provide implications and recommendations for leveraging the scaffolding from teachers, materials, and VR pair programing activity to support the equal participation and quality of talk among all learners during scientific sensemaking. Full article

This study presents SmartMix Web3, a framework combining ensemble machine learning and blockchain technology to optimize low-carbon concrete design. It addresses two key challenges: (1) the limitations of conventional models in predicting concrete performance, and (2) ensuring data reliability and overcoming collaboration issues in AI-driven sustainable construction. Validated with 61 real-world experiments in Cameroon and 752 mix designs, the framework shows major improvements in predictive accuracy and decentralized trust. To address the first research question, a stacked ensemble model comprising Extreme Gradient Boosting (XGBoost)–Random Forest and a Convolutional Neural Network (CNN) was developed, achieving a 22% reduction in Root Mean Square Error (RMSE) for compressive strength prediction and embodied carbon estimation compared to traditional methods. The 29% reduction in Mean Absolute Error (MAE) results confirms the superiority of Extreme Learning Machine (EML) in low-carbon concrete performance prediction. For the second research question, SmartMix Web3 employs blockchain to ensure tamper-proof traceability and promote collaboration. Deployed on Ethereum, it automates verification of tokenized Environmental Product Declarations via smart contracts, reducing disputes and preserving data integrity. Federated learning supports decentralized training across nine batching plants, with Secure Hash Algorithm (SHA)-256 checks ensuring privacy. Field implementation in Cameroon yielded annual cost savings of FCFA 24.3 million and a 99.87 kgCO₂/m³ reduction per mix design. By uniting EML precision with blockchain transparency, SmartMix Web3 offers practical and scalable benefits for sustainable construction in developing economies. Full article

Levitation techniques reduce the available heterogeneous nucleation sites and provide stable access to deeply undercooled melts. However, some samples have repeatably demonstrated that, in the presence of strong stirring, solidification may be induced at moderate, sub-critical undercoolings. Dynamic nucleation is a mechanism by which solidification may be induced through flow effects within a sub-critically undercooled melt. In this mechanism, collapsing cavities within the melt produce very high-pressure shocks, which shift the local melting temperature. In these regions of locally shifted melt temperatures, thermodynamic conditions enable nuclei to grow and trigger solidification of the full sample. By deepening the local undercooling, dynamic nucleation enables solidification to occur in conditions where classical nucleation does not. Dynamic nucleation has been observed in several zirconium and zirconium-based samples in the Electromagnetic Levitator onboard the International Space Station (ISS-EML). The experiments presented here address conditions in which a zirconium sample alloyed with 2.5 atomic percent niobium spontaneously solidifies during electromagnetic levitation experiments with strong melt stirring. In these experimental conditions, classical nucleation predicts the sample to remain liquid. This solidification behavior is consistent with the solidification behavior observed in prior experiments on pure zirconium. Full article

This paper reinforces the previously proposed moving linear (ML) model approach for time series analysis by introducing theoretically grounded enhancements. The ML model flexibly decomposes a time series into constrained and remaining components, enabling the extraction of trends and fluctuations with minimal structural assumptions. Building on this framework, we present two key improvements. First, we develop a theoretically justified evaluation criterion that facilitates coherent estimation of model parameters, particularly the width of the time interval. Second, we enhance the extended ML (EML) model by introducing a new outlier detection and estimation method that identifies both the number and locations of outliers by maximizing the reduction in AIC. Unlike the earlier version, the reinforced EML model simultaneously estimates outlier effects and improves model fit within a unified, likelihood-based framework. Empirical applications to economic time series illustrate the method’s superior ability to detect meaningful anomalies and produce stable, interpretable decompositions. These contributions offer a generalizable and theoretically supported approach to modeling nonstationary time series with structural disturbances. Full article

Background: Comprehensive molecular profiling is essential for precision oncology in non-small cell lung cancer (NSCLC). However, genomic data from Eastern European populations, including Romania, remain limited. Methods: We analyzed 398 consecutive NSCLC cases tested at the PATHOS Molecular Pathology Laboratory (Cluj-Napoca, Romania) between April 2024 and February 2025 using the Ion Torrent™ Genexus™ System and the Oncomine™ Dx Target Test, which evaluates SNVs/indels in 46 genes, fusions in 23 genes, and CNVs in 19 genes from FFPE samples. Results: The cohort was predominantly male (66%) with a median age of 67 years. Adenocarcinoma represented 70% of cases with known histology. Genomic profiling revealed a high frequency of actionable alterations. KRAS mutations were the most common (29.1%), with p.G12C detected in 10.3% of all the cases. EGFR mutations were present in 14.3% of patients, mostly exon 19 deletions and L858R substitutions. BRAF alterations (5.3%) included both V600E and non-V600E variants. RET alterations were detected as eight missense mutations, two canonical fusions (KIF5B–RET, CCDC6–RET), one amplification, and three transcript imbalances. EML4-ALK fusions (1.77%), ERBB2 mutations/amplifications (3.0%), and FGFR1/FGFR3 amplifications were also observed. Conclusions: This study provides the first large-scale molecular snapshot of NSCLC in Romania. While the overall genomic profiles align with Western populations, the higher frequency of KRAS p.G12C and FGFR amplifications highlights the value of region-specific data to support targeted therapies in Eastern Europe. Full article

Background: Medicines affect the environment throughout their lifecycle, from production and distribution to use and disposal. They contribute to the pollution of air, water, and soil, impacting ecosystems and human health. Recognizing these risks, regulatory bodies and organizations have highlighted pharmaceutical pollution as a global concern, emphasizing the need for environmental risk assessments and sustainable practices. Methods: This study reviewed the essential medicines lists (EMLs) from 158 countries and examined the available ecotoxicological data. Medicines with high bioaccumulation, persistence, and toxicity were identified and cross-referenced with their inclusion in EMLs. Additionally, we analyzed the presence of alternative medicines with similar therapeutic effects but potentially lower environmental risks. Results: Five medicines—ciprofloxacin, ethinylestradiol, levonorgestrel, ibuprofen, and sertraline—were selected as illustrative examples due to their high environmental persistence and toxicity. All were listed in the 2023 WHO model list, with ciprofloxacin appearing in 94.3% of national EMLs. Conclusions: This study underscores the limited availability of ecotoxicological data, which hinders environmental risk assessment for medicines. EMLs could serve as a tool to enhance the awareness and data mobilization of pharmaceutical pollution. Incorporating environmental criteria into EMLs could support more sustainable medicine selection and regulatory practices. Full article

Ovarian tissue is critical for goose reproduction. This study aimed to investigate gene regulation by DNA methylation in relation to the reproductive traits of geese. We performed whole-genome bisulfite sequencing (WGBS) on ovarian tissues from Sichuan white geese (high-laying-rate group: HLRG, ♀ = 3; low-laying-rate group: LLRG, ♀ = 3) during the laying period. The results showed a higher level of hypermethylated differentially methylated regions (DMRs) in the HLRG, indicating a higher overall methylation level compared to the LLRG. In total, we identified 2831 DMRs and 733 differentially methylated genes (DMGs), including 363 genes with upregulated methylation. These DMGs were significantly enriched in pathways related to microtubule function (GO:0005874; GO:0000226), GnRH secretion, thyroid hormone signaling, ECM-receptor interaction, and PI3K–Akt signaling. Integration with RNA-seq data identified eight overlapping genes between DMGs and differentially expressed genes (DEGs), with five genes (CUL9, MEGF6, EML6, SYNE2, AK1BA) exhibiting a correlation between hypomethylation and high expression. EML6, in particular, emerged as a promising candidate, potentially regulating follicle growth and development in Sichuan white geese. Future studies should focus on further verifying the role of the EML6 gene. In conclusion, this study provides important insights into the regulatory mechanisms of DNA methylation influencing reproductive traits in geese, offering novel candidate markers for future goose breeding programs. Full article

With the rapid growth of data center demand driven by AI, high-speed optical modules (such as 800G and 1.6T) have become critical components. Traditional 800G modules face issues such as complex processes and large sizes due to the separate packaging of EML chips, AlN substrates, and capacitors. This study proposes a high-speed EML module based on silicon integration, where resistors, capacitors, and AuSn soldering areas are integrated onto the silicon substrate, enabling the bonding of the EML chip, reducing packaging costs, and enhancing scalability. Key achievements include: the development of a 100G EML chip; the fabrication of a high-speed silicon integrated carrier; successful Chip-on-Carrier (COC) packaging and testing, with a laser output power of 10 mW, extinction ratio of 10 dB, and bandwidth greater than 40 GHz; and reliability verified through 500 h of aging tests. This study provides an expandable solution for next-generation high-speed optical interconnects. Full article

Background/Objectives: This study presents a novel double-weighted Bayesian Ensemble Machine Learning (DW-EML) model aimed at improving the classification and prediction of metabolomics data. This discipline, which involves the comprehensive analysis of metabolites in a biological system, provides valuable insights into complex biological processes and disease states. As metabolomics assumes an increasingly prominent role in the diagnosis of human diseases and in precision medicine, there is a pressing need for more robust artificial intelligence tools that can offer enhanced reliability and accuracy in medical applications. The proposed DW-EML model addresses this by integrating multiple classifiers within a double-weighted voting scheme, which assigns weights based on the cross-validation accuracy and classification confidence, ensuring a more reliable prediction framework. Methods: The model was applied to publicly available datasets derived from studies on critical illness in children, chronic typhoid carriage, and early detection of ovarian cancer. Results: The results demonstrate that the DW-EML approach outperformed methods traditionally used in metabolomics, such as the Partial Least Squares Discriminant Analysis in terms of accuracy and predictive power. Conclusions: The DW-EML model is a promising tool for metabolomic data analysis, offering enhanced robustness and reliability for diagnostic and prognostic applications and potentially contributing to the advancement of personalized and precision medicine. Full article

Small cell lung cancer (SCLC) is an aggressive neuroendocrine tumor. Lung cancer patients with ALK and EML4 fusions respond significantly to ALK inhibitors. The EML4-ALK fusion gene mutation is the result of an inversion of chromosome 2, which juxtaposes the 5 end of the EML4 gene with the 3 end of the ALK gene. In SCLC, the frequency of fusion genes is very low, and to the best of our knowledge, only four cases of ALK fusion gene mutations in SCLC have been reported. In this report, we describe the treatment of a 74-year-old female patient with SCLC who developed recurrence of hilar lymph node metastasis three years after surgical resection. Postoperative NGS showed that this patient is a SCLC patient harboring a rare EML4-ALK fusion mutation, and a satisfactory 43-month overall survival (OS) was achieved after treatment with ensartinib targeting the EML4-ALK fusion gene mutation. The ALK-TKI may be a new treatment option for these patients. This article provides a therapeutic reference. Full article

In this paper, an EML (electro-magnetic log) integrated navigation algorithm based on the HMM (hidden Markov model) and CNLKF (cross-noise linear Kalman filter) is proposed, which is suitable for SINS (strapdown inertial navigation system)/EML/GNSS (global navigation satellite system) integrated navigation systems for small or medium-sized AUV (autonomous underwater vehicle). The algorithm employs the following five techniques: ① the HMM-based pre-processing algorithm of EML data; ② the CNLKF-based fusion algorithm of SINS/EML information; ③ the MALKF (modified adaptive linear Kalman filter)-based algorithm of GNSS-based calibration; ④ the estimation algorithm of the current speed based on output from MALKF and GNSS; ⑤ the feedback correction of LKF (linear Kalman filter). The principle analysis of the algorithm, the modeling process, and the flow chart of the algorithm are given in this paper. The sea trial of a small-sized AUV shows that the endpoint positioning error of the proposed/traditional algorithm by this paper is 20.5 m/712.1 m. The speed of the water current could be relatively accurately estimated by the proposed algorithm. Therefore, the algorithm has the advantages of high accuracy, strong anti-interference ability (it can effectively shield the outliers of EML and GNSS), strong adaptability to complex environments, and high engineering practicality. In addition, compared with the traditional DVL (Doppler velocity log), EML has the advantages of great concealment, low cost, light weight, small size, and low power consumption. Full article

Since the discovery of the first-generation ALK inhibitor, many other tyrosine kinase inhibitors have been demonstrated to be effective in the first line or further lines of treatment in patients with advanced non-small cell lung cancer with EMLA4-ALK translocation. This review traces the main milestones in the treatment of ALK-positive metastatic patients and the survival outcomes in the first-line and second-line settings with different ALK inhibitors. It presents the two options available for first-line treatment at the present time: sequencing different ALK inhibitors versus using the most potent inhibitor in front-line treatment. The efficacy outcomes of different ALK inhibitors in the first-line setting; the molecular profile of the disease, including mutation resistances and ALK variants and co-mutations; and patients’ co-morbidities and inhibitor toxicities should be taken into account to address the choice of the first-line treatment, as suggested in this review. Full article

Our recently created RNA-sequence-based microRNA (miRNA) expression signature in breast cancer clinical specimens revealed that some miR-30 family members were significantly downregulated in cancer tissues. Based on TCGA database analyses, we observed that among the miR-30 family members, miR-30a-3p (the passenger strand derived from pre-miR-30a) was significantly downregulated in breast cancer (BC) clinical specimens, and its low expression predicted worse prognoses. Ectopic expression assays showed that miR-30a-3p transfected cancer cells (MDA-MB-157 and MDA-MB-231) had their aggressive phenotypes significantly suppressed, e.g., their proliferation, migration, and invasion abilities. These data indicated that miR-30a-3p acted as a tumor-suppressive miRNA in BC cells. Our subsequent search for miR-30a-3p controlled molecular networks in BC cells yielded a total of 189 genes. Notably, among those 189 genes, cell-cycle-related genes (ANLN, MKI67, CCNB1, NCAPG, ZWINT, E2F7, PDS5A, RIF1, BIRC5, MAD2L1, CACUL1, KIF23, UBE2S, EML4, SEPT10, CLTC, and PCNP) were enriched according to a GeneCodis 4 database analysis. Moreover, the overexpression of four genes (ANLN, CCNB1, BIRC5, and KIF23) significantly predicted worse prognoses for patients with BC according to TCGA analyses. Finally, our assays demonstrated that the overexpression of ANLN had cancer-promoting functions in BC cells. The involvement of miR-30a-3p (the passenger strand) in BC molecular pathogenesis is a new concept in cancer research, and the outcomes of our study strongly indicate the importance of analyzing passenger strands of miRNAs in BC cells. Full article

The EML4–ALK oncogene drives tumour progression in approximately 5% of cases of non-small-cell lung cancers. At least 15 EML4–ALK variants have been identified, which elicit differential responses to conventional ALK inhibitors. Unfortunately, most, if not all, patients eventually acquire resistance to these inhibitors and succumb to the disease, which warrants the need for alternative targets to be identified. The most aggressive variant, EML4–ALK variant 3 (V3), assembles into a complex on interphase microtubules together with the NEK9 and NEK7 kinases, which leads to the downstream phosphorylation of NEK7 substrates. Overall, this promotes an elongated cell morphology and an enhanced migratory phenotype, which likely contributes to the increased metastasis often seen in V3 patients. Here, using two separate approaches to displace V3 from microtubules and a variety of in vitro assays, we show that microtubule association of EML4–ALK V3 is required for both V3 phenotypes, as removal of the oncogenic fusion protein from microtubules led to the dissociation of the V3–NEK9–NEK7 complex and the reversal of both phenotypic changes. Overall, we propose that targeting the interaction between EML4–ALK V3 and microtubules might offer a novel therapeutic option, independent of ALK activity, for V3+ NSCLC patients with acquired resistance to ALK inhibitors. Full article

FGF8, ALK, and EML4 have been identified as promising biomarkers in a number of malignancies. The aim of this study was to examine the prognostic role of FGF8, ALK, and EML4 in esophageal squamous cell carcinoma (ESCC). Methods: Consecutive patients with ESCC who underwent upfront resection were included in this study. ALK and EML4 gene status was evaluated by fluorescence in situ hybridization (FISH) using a triple-color break-apart single-fusion probe and a probe against 2p11. FGF8, ALK, and EML4 protein expression was determined by immunohistochemistry. Results: A total of 122 patients were included in this study. Multivariate analysis revealed that FGF8 overexpression is an independent negative prognostic factor for patients’ overall survival (OS) (p = 0.04). Furthermore, a significant correlation between the expression of FGF8, and ALK (p = 0.04) and EML4 (p = 0.01) alteration was found. Conclusions: FGF8 overexpression is an adverse independent prognostic factor in patients with upfront resected ESCC. Furthermore, FGF8 expression significantly correlates with ALK and EML4 amplification and may therefore qualify as a future therapeutic target. Full article