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Unmodelled searches and reconstruction is a critical aspect of gravitational wave data analysis, requiring sophisticated software tools for robust data analysis. This paper introduces PycWB, a user-friendly and modular Python-based framework developed to enhance such analyses based on the widely used unmodelled search and reconstruction algorithm Coherent Wave Burst (cWB). The main features include a transition from C++ scripts to YAML format for user-defined parameters, improved modularity, and a shift from complex class-encapsulated algorithms to compartmentalized modules. The PycWB architecture facilitates efficient dependency management, better error-checking, and the use of parallel computation for performance enhancement. Moreover, the use of Python harnesses its rich library of packages, facilitating post-production analysis and visualization. The PycWB framework is designed to improve the user experience and accelerate the development of unmodelled gravitational wave analysis.

Gastric cancer (GC) is the fourth most common malignant cancer and is a lifethreatening disease worldwide. Phytochemicals have been shown to be a rational, safe, non-toxic, and very promising approach to the prevention and treatment of cancer. It has been found that phytochemicals have protective effects against GC through inhibiting cell proliferation, inducing apoptosis and autophagy, suppressing cell invasion and migration, anti-angiogenesis, inhibit Helicobacter pylori infection, regulating the microenvironment. In recent years, the role of phytochemicals in the occurrence, development, drug resistance and prognosis of GC has attracted more and more attention. In order to better understand the relationship between phytochemicals and gastric cancer, we briefly summarize the roles and functions of phytochemicals in GC tumorigenesis, development and prognosis. This review will probably help guide the public to prevent the occurrence and development of GC through phytochemicals, and develop functional foods or drugs for the prevention and treatment of gastric cancer. [ABSTRACT FROM AUTHOR]

The rapid progress in point-of-care testing (POCT) has become a promising decentralized patient-centered approach for the control of infectious diseases, especially in resource-limited settings. POCT devices should be inexpensive, rapid, simple operation and preferably require no power supply. Here, we developed a simple bacterial sensing platform that can be operated by a smartphone for bacteria identification and antimicrobial susceptibility testing (AST) based on using a polydiacetylene (PDA) arrayed membrane chip. Each PDA array produced a unique color ‘fingerprint’ pattern for each bacteria based on different modes of action of toxins from bacteria on biomimetic lipid bilayers within PDA-lipid assemblies. We show that the PDA-based device can detect viable cells of bacteria as low as 104 ​CFU/mL within 1.5 ​h compared with several days of conventional bacterial identification, with the aid of a smartphone app. The device can also be used for an antimicrobial susceptibility test (AST) for at least two broad-spectrum antimicrobials within 4 ​h and provide identification of antimicrobial susceptibility and resistance, enabling the selection of appropriate therapies. This PDA-based sensing platform provides an alternative way for bacterial detection and could be used as a portable and inexpensive POCT device for the rapid detection of bacterial infection in limited-resource settings.