In operable non-small cell lung cancer (NSCLC), molecular residual disease (MRD) detection via circulating tumor DNA (ctDNA) has emerged as a promising approach to identify individuals at high risk of disease recurrence and potentially guide adjuvant therapy decisions. A recent prospective observational study conducted at Guangdong Provincial People’s Hospital, China, aimed to elucidate the role of MRD detection in patients with operable NSCLC harboring oncogenic fusions, MET exon skipping, or de novo MET amplification following surgical resection and explore the association between postoperative MRD mutations and clinical outcomes.

This research introduces new techniques to significantly improve the field of view of three-photon microscopy. Scientists can now observe the activity of large groups of neurons in deep brain regions with unprecedented detail. This advancement enables a deeper understanding of brain function and neural networks.

A prospective cross-section study by BGI Genomics and Siriraj Hospital demonstrates high accuracy of multitarget stool DNA testing for colorectal cancer detection in Thai individuals, offering a non-invasive alternative to colonoscopy.

Researchers have implemented the enhanced YOLOv7 in the state identification of railway sliding chairs. This approach is capable of precisely discerning the states of the sliding chairs, thereby enabling the formulation of corresponding maintenance strategies.

Recent advances in optoelectronics have propelled 3D imaging technology into new realms of application, particularly in sectors such as industrial inspection, solid mechanics, and biomedicine. Nevertheless, the 3D imaging speed is fundamentally limited by the intrinsic frame rate of the imaging sensor. Overcoming this barrier is essential for the development of high-speed or even ultra-high-speed 3D imaging capabilities, thereby pushing the boundaries of current 3D imaging technologies. Leveraging frequency multiplexing and deep learning, this study substantially increases the 3D imaging speed of the traditional fringe projection profilometry (FPP) technique, exceeding the detector frame rate by nearly an order of magnitude without compromising the spatial resolution. This new 3D imaging paradigm opens a gate for slow-scan cameras to quantitatively study a multitude of dynamic processes with both high spatial and temporal resolution.

    Researchers at Purdue University Indianapolis have developed a groundbreaking capacitorless solid-state power filter (SSPF) for DC-AC converters, addressing the limitations of traditional passive filtering methods. This innovative SSPF generates a sinusoidal AC voltage without capacitors, utilizing a planar transformer and a high-frequency H-bridge converter to inject voltage harmonics, resulting in minimal harmonic distortion. By eliminating capacitors, which are prone to failure, this design enhances the reliability and efficiency of power conversion systems, with potential applications in renewable energy, electric vehicles, and industrial automation.

    High-efficient isolated DC/DC converters with a high-efficiency synchronous reluctance generator (SRG) are the ultimate solutions in DC microgrid systems. The design and modeling of isolated DC/DC converters with the performance of SRG are carried out. Reactive and active powers are used as PWM control variables on the generator side. Further, the flux estimator is used. Three-phase pulse width modulation (PWM) rectifier is used by applying space vector modulation (SVM) with a constant switching frequency for direct power control. Further, the paper also includes the experimental validation of the results.

    The paper also proposes that highly efficient power converters and synchronous reluctance generators are required to achieve high performance for hybrid renewable energy systems applications.

the study highlights that LTV is more effective than NTV and ETV against M^pro mutants, making it a promising candidate for treating drug-resistant strains of SARS-CoV-2.

The team conducted the first extensive study of automated issue classification on 9 state-of-the-art issue classification approaches.