青岛 Qingdao
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https://www.cas.cn/syky/202602/t20260226_5102870.shtml

https://doi.org/10.1186/s40168-026-02339-3

A research team at the CAS Qingdao Institute of Bioenergy and Bioprocess Technology has developed RamEx, an integrated analysis framework for Ramanome big data. This platform, tailored to the characteristics of Raman spectroscopy data, establishes a one-stop workflow from data reading and standardized preprocessing to downstream data mining, centered on automated quality control algorithms and efficient parallel computing processes. It also demonstrates a systematic analysis of microbial metabolomical heterogeneity and metabolic pattern differentiation at the single-cell level.

Raman genomics deep analysis can track the dynamic changes in the composition of macromolecules such as lipids, proteins, and nucleic acids in different cells, thus revealing the differentiation and succession patterns of microbial metabolic states at the population scale with single-cell precision. This provides new research ideas and technical pathways for understanding the functional organization and environmental adaptation mechanisms of complex communities.

http://english.cas.cn/newsroom/research-news/202602/t20260224_1151116.shtml

https://link.springer.com/article/10.1186/s40168-026-02339-3

Scientists from the CAS Qingdao Institute of Bioenergy and Bioprocess Technology have developed a novel computational tool, RamEx, designed to resolve the computational bottleneck in high-throughput microbial Ramanomics.

RamEx streamlines the full Ramanomic analysis pipeline, from data preprocessing and automated quality control to advanced data mining. An Iterative Convolutional Outlier Detection (ICOD) algorithm tackles spectral noise in an unsupervised manner to dynamically identify and eliminate spectral artifacts, ensuring high-quality input for downstream analysis.

The platform’s performance was validated using diverse datasets, including pathogenic bacteria, probiotics, and yeast fermentation systems. Notably, RamEx successfully captured phenotypic heterogeneity in genetically identical yeast cells by detecting subtle metabolic fluctuations and tracking the dynamic accumulation of intracellular macromolecules, including lipids, proteins, and nucleic acids.

https://j.people.com.cn/n3/2026/0203/c95952-20422094.html

A professor at Qingdao University in Shandong Province has developed a groundbreaking system that generates electricity when people blink, supplying power to glasses that allow patients with amyotrophic lateral sclerosis (ALS) to control their wheelchairs simply by moving their eyes.

With conventional eye tracking devices, patients who wanted to operate a wheelchair and move around had to wear a heavy device on their head and be connected to a long electrical cord. Furthermore, alarms of low battery levels did discouraging patients from moving around on their own.

The eye tracking system of the team generates and supplies electricity by attaching dimethylpolysiloxane (PDMS)  to the surface of the user’s eyeball like a contact lens, creating a “micro-friction generator.” When the user blinks or moves their eyeball, friction occurs between the eyeball and PDMS, continuously generating electricity.

In an eyeglass frame worn by the user, transparent electrodes made of indium tin oxide are embedded, acting as a transducer. The transparent electrodes precisely track the distribution and changes of electric charge through electrostatic induction and convert it into a recognizable electrical signal in real time. This signal is then transmitted to an external device via a control circuit, ultimately enabling highly precise control.

Before this technology can leave the lab and be widely applied, however, a series of hurdles must be overcome for industrialization.

An illustration of controlling a wheelchair through blinking and eye movements (photo courtesy of interviewee).

https://doi.org/10.1016/j.biortech.2025.133788

http://english.cas.cn/newsroom/research_news/life/202601/t20260114_1145714.shtml