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Non-small-cell lung cancer (NSCLC) frequently harbors mutations in the epidermal growth factor receptor (EGFR), with exon 20 insertions comprising 1–10% of these mutations. EGFR exon 20 insertions are less responsive to conventional tyrosine kinase inhibitors (TKIs), leading to the development of targeted agents. This review explores key therapeutic agents, such as Amivantamab, Mobocertinib, Poziotinib, Zipalertinib, and Sunvozertinib, which have shown promise in treating NSCLC with EGFR exon 20 insertions. Amivantamab, a bispecific antibody-targeting EGFR and c-MET, demonstrates significant efficacy, particularly when combined with chemotherapy. Mobocertinib, a TKI, selectively targets EGFR exon 20 mutations but faces limitations in efficacy. Poziotinib, another oral TKI, shows mixed results due to mutation-specific responses. Zipalertinib and Sunvozertinib have emerged as potent TKIs with promising clinical data. Despite these advances, challenges in overcoming resistance mutations and improving central nervous system penetration remain. Future research should focus on optimizing first-line combination therapies and enhancing diagnostic strategies for comprehensive mutation profiling.

Distinct viral gene expression characterizes Epstein-Barr virus (EBV) infection in EBV-producing marmoset B-cell (B95-8) and EBV-associated gastric carcinoma (SNU719) cell lines. CCCTC-binding factor (CTCF) is a structural chromatin factor that coordinates chromatin interactions in the EBV genome. Chromatin immunoprecipitation followed by sequencing against CTCF revealed 16 CTCF binding sites in the B95-8 and SNU719 EBV genomes. The biological function of one CTCF binding site (S13 locus) located on the BamHI A right transcript (BART) miRNA promoter was elucidated experimentally. Microscale thermophoresis assay showed that CTCF binds more readily to the stable form than the mutant form of the S13 locus. EBV BART miRNA clusters encode 22 miRNAs, whose roles are implicated in EBV-related cancer pathogenesis. The B95-8 EBV genome lacks a 11.8-kb EcoRI C fragment, whereas the SNU719 EBV genome is full-length. ChIP-PCR assay revealed that CTCF, RNA polymerase II, H3K4me3 histone, and H3K9me3 histone were more enriched at S13 and S16 (167-kb) loci in B95-8 than in the SNU719 EBV genome. 4C-Seq and 3C-PCR assays using B95-8 and SNU719 cells showed that the S13 locus was associated with overall EBV genomic loci including 3-kb and 167-kb region in both EBV genomes. We generated mutations in the S13 locus in bacmids with or without the 11.8-kb BART transcript unit (BART(+/-)). The S13 mutation upregulated BART miRNA expression, weakened EBV latency, and reduced EBV infectivity in the presence of EcoRI C fragment. Another 3C-PCR assay using four types of BART(+/-)·S13(wild-type(Wt)/mutant(Mt)) HEK293-EBV cells revealed that the S13 mutation decreased DNA associations between the 167-kb region and 3-kb in the EBV genome. Based on these results, CTCF bound to the S13 locus along with the 11.8-kb EcoRI C fragment is suggested to form an EBV 3-dimensional DNA loop for coordinated EBV BART miRNA expression and infectivity.

Luminous efficacy model uses solar radiation data to generate illuminance data, and its performance also depends on the quality of solar radiation data. Various luminous efficacy models are reviewed and evaluated to select a universal luminous efficacy model. Since most luminous efficacy models are fitted with specific local climate characteristics, the model that has the least locality as well as accuracy is a mandatory requirement. Three representative luminous efficacy models are selected and evaluated with measured solar radiation and illuminance data from four worldwide cities. It was found that all the evaluated models provide good predictions (over 0.96 R2 value) for both global and diffuse illuminance. Among them, the Perez luminous efficacy model shows the highest performance in terms of accuracy and bias. However, illuminance data prediction based on estimated solar radiation data is more common practice rather than those from measured solar data. The performance of the selected luminous efficacy models is evaluated when recently proposed universal solar radiation model supplies predicted solar radiation data. The result indicates that the quality of estimated solar radiation data has a much deeper impact on the performance of the luminous efficacy model. Within the current limited technology and measured data resource, the consecutive processing of the modified Zhang and Huang solar model and Perez luminous efficacy model could provide the best option to predict both global and diffuse solar radiation and illuminance. But, users of the model-based illuminance data should interpret their simulation results with the error (30%~40% in RMSE and ±6% in MBE) in predicting global and diffuse illuminance.

To improve the energy prediction performance of a building energy model, the occupancy status information is very important. This is more important in real buildings, rather than under construction buildings, because actual building occupancy can significantly influence its energy consumption. In this study, a machine learning based framework for a consecutive occupancy estimation is proposed by utilizing internet of things data, such as indoor temperature and luminance, CO2 density, electricity consumption of lighting, HVAC (heating, ventilation, and air conditioning), electric appliances, etc. Three machine learning based occupancy estimation algorithms (decision tree, support vector machine, artificial neural networks) are selected and evaluated in terms of the performance of estimating the occupancy status for each season. The selection process of the input variables that have crucial impact on the algorithms’ performance are described in detail. Finally, an occupancy estimation framework that can repeat model training and estimation consecutively in a situation when time-series data are continuously provided over the entire measurement period is suggested. In addition, the performance of the framework is evaluated to identify how it improves the energy prediction performance of the building energy model compared to conventional energy modeling practices. The suggested framework is distinguished from similar previous studies in two ways: (1) The proposed framework reveals that input variables for the occupancy estimation model can be occasionally changed by an occupant response to certain times and seasons, and (2) the framework incorporates time-series indirect occupancy sensing data and classification algorithms to consecutively provide occupancy information for the energy modeling effort.

A novel design concept for implementing an antenna on a compact LTCC module is proposed for a Bluetooth application. The integrated antenna consists of an LTCC module and a two-layered radiating element, which occupies a volume of 5 mm × 6.3 mm × 0.8 mm. The antenna has a 7.5 dB return loss bandwidth of 100 MHz, from 2.39 to 2.49 GHz and has nearly omni-directional radiation patterns with good antenna gain in spite of its small size. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 180–183, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23051 [ABSTRACT FROM AUTHOR]

This paper summarizes the results of a comparative analysis for four models utilized to predict solar radiation in Kuwait City, Kuwait. The four models include the Kasten model, Zhang and Huang model, Muneer model, and neural network based model. The analysis was based on hourly measured solar data for Kuwait City. The measured hourly solar radiation data are obtained for the year 1994 and include global, direct, and diffuse solar radiations. Nonsolar weather data for the same year and site are obtained from the US National Climatic Data Center (NCDC). Weather files suitable for building energy simulation are developed using measuered data as well as predictions from the four solar models. A series of simulation analysis to determine the impact of solar model selection for the weather file on the energy uses predictions from a whole-building simulation program using office buildings in Kuwait. The results of the validation analysis and the simulation evaluation indicate that Zhang and Huang model is suitable for the predicting hourly solar radiation suitable for energy analysis of buildings in Kuwait. [ABSTRACT FROM AUTHOR]