Nature 2025 science city ranking: six of top 10 in China, Qingdao is 31 between Munich and Berlin

https://www.nature.com/nature-index/supplements/nature-index-2025-science-cities/tables/overall

https://en.people.cn/n3/2025/1118/c90000-20391615.html

The newly released “Nature Index 2025 Science Cities” supplement shows that the number of Chinese cities in the global top ten rose from five in 2023 to six in 2024, marking the first time China holds a majority in the rankings.

The supplement draws on the Nature Index database, which tracks research articles published from 2015 to 2024. Its analysis uses “Share”, a fractional count reflecting institutional contribution to publications, as the primary metric, with time-series data adjusted to 2024 levels. Each city’s Share is calculated by summing the contributions of all affiliated institutions located within that city.

According to the Nature Index, the world’s leading science cities overall are: Beijing, Shanghai, New York metropolitan area (U.S.), Boston metropolitan area (U.S.), Nanjing (China), Guangzhou (China), San Francisco Bay Area (U.S.), Wuhan (China), Baltimore-Washington metropolitan area (U.S.), and Hangzhou (China).

Further analysis shows that Chinese cities hold a strong advantage in chemistry, physical sciences, and earth and environmental sciences, leading the global rankings in all three fields. Notably, Chinese cities claimed all of the top ten positions in chemistry for the first time. In the other two subject areas, they secured six of the top ten spots, with Beijing ranking first worldwide across all three domains.

European cities in the ranking start at 19 (London), followed by Zurich (28), Cambridge (29), Munich (30) and Berlin (32), following Qingdao at position 31.

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https://english.news.cn/20260606/de8eff009a94407c8eeeb1fdab13d675/c.html

https://www.cell.com/cell/abstract/S0092-8674(26)00571-4?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867426005714%3Fshowall%3Dtrue

A joint research led by the CAS Institute of Oceanology in collaboration with the Hong Kong-based Chinese University of Hong Kong and Northwestern Polytechnical University in Xi’an deciphered the mechanism of ultra-long starvation tolerance in deep-sea isopods and provides an important paradigm for understanding how life balances growth and survival in extreme environments.

The deep sea is cold, dark, and almost entirely devoid of reliable nutrition, making long-term survival a remarkable evolutionary feat. To survive the abyss, the isopod possesses an enormous stomach that occupies about two-thirds of its body and acts like a deep-freeze pantry, allowing it to gorge when food is available and store the haul for months or even years. Second, it maintains an exceptionally low basal metabolic rate, essentially putting itself on permanent energy-saving mode. Together, these traits turn opportunistic binge eating into an ultra-long energy reserve.

In addition, a key gene involved in this metabolic slowdown, named ND1, is not originally part of the isopod’s own genome. The isopod “hijacks” it from an external symbiotic bacterium through horizontal gene transfer.

To verify ND1’s function, the researchers inserted the gene into zebrafish, nematodes, and human cells in the lab. Under normal temperatures, the gene recipients burned energy faster and became less tolerant of starvation. However, under cold conditions that mimic the isopod’s deep-sea home, ND1 suppressed energy metabolism, reduced mitochondrial activity, and boosted starvation endurance in zebrafish by a remarkable 37 percent.

This temperature-dependent switch solves the so-called “energy paradox” — how can a giant animal with high energy demands survive where food is extremely scarce? The ND1 acts as a metabolic thermostat, fine-tuning energy burn in response to environmental conditions. It provides a solution to the trade-off between body size and food scarcity.

http://english.cas.cn/newsroom/research-news/202606/t20260608_1161380.shtml

https://onlinelibrary.wiley.com/doi/10.1002/mlf2.70089

Researchers from the CAS Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) and Shenzhen Third People’s Hospital have developed a Ramanome-based phenotypic platform to improve the efficiency of bacteriophage evaluation for potential clinical use.

By combining Raman spectroscopy with a random forest model, the researchers introduced the Ramanome-based Phage Susceptibility Test (RPST). This phenotypic method reduces the turnaround time for host range verification to approximately one hour, compared to the 11–21 hours typically required by traditional plaque-based assays.

Bacteriophages offer a precise alternative to antibiotics in the fight against antimicrobial resistance. However, matching phages to clinical bacterial isolates remains challenging due to their narrow host ranges and the slow, qualitative nature of conventional assays.

The RPST framework monitors bacterial metabolic changes within 40 minutes of phage-host co-incubation and identifies four conserved Raman spectral biomarker regions linked to nucleic acids, proteins, and lipids. Combining these biomarkers into a Composite Infection Index (CII), the system achieved a 96.0% concordance rate across 25 phage-host pairs.

Unlike static assays, the continuous CII metric estimates the fraction of infected cells, enabling researchers to rank phage potency and determine the minimum MOI required to sustain infection.

While the method shows operational promise, the researchers acknowledge the need for large-scale, multi-center validation across different instruments to ensure long-term clinical reproducibility.

https://j.people.com.cn/n3/2026/0527/c94476-20460938.html

The Haier Group has announced an ultra-lightweight, artificial intelligence (AI)-powered exoskeleton robot designed to assist with movement. The company claims that using this robot can reduce physical energy expenditure by up to 37%.

The W3 features a “full carbon fiber + titanium alloy” design, resulting in a main unit weight of just 1.75 kilograms (kg). Equipped with the AI ​​Gait Algorithm 3.0 and built-in multi-dimensional sensors, the device can interpret a user’s movement intentions in milliseconds. Furthermore, it utilizes a “high-torque dual-motor + high-energy battery” system; the maximum assistive force per leg reaches 16 Newton-meters (N·m), effectively reducing the physical load on the body by approximately 5 kg.

According to Haier, the robot also features a “short-stride walking” mode designed to accommodate the specific gait characteristics of elderly individuals—namely, reduced muscle strength and a shortened stride length. By precisely compensating for muscle weakness, the device aims to enable a more stable and secure walking experience.

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