A deep-sea salmon farming facility near Qingdao

https://jp.news.cn/20250116/9dc88fa0753f468da2fc4772c53548ec/c.html?page=1

In a large-scale deep sea smart fishery farming facility “Deep Blue 2” in the Qingdao National Deep Sea and Ocean Green Aquaculture Test Area, approximately 400,000 salmon are farmed in farming cages, with a high survival rate and healthy growth.

According to Gu Qihuan, production manager at Shandong Caijing Wanzefeng Marine Technology Co., Ltd., salmon farming requires strict environmental conditions, and it is very difficult to find suitable sea areas for large-scale farming. However, this location is home to 130,000 square kilometers of Yellow Sea cold water mass, and the water temperature in summer is 10 to 16 degrees Celsius, which is very suitable for salmon growth. Deep Blue 2 sinks to the level of the cold water mass less than 30 meters in summer and rises to the surface again in winter.

Deep Blue 2 is 71.5 meters high, 70 meters in diameter, and has a fully submerged farming area of ​​90,000 cubic meters. It is equipped with multiple smart farming equipment such as an automatic feeding system and an underwater photography system, making unmanned farming in the deep sea and distant ocean possible.

For harvest, schools of salmon are sucked up one after another onto work boats, passed through a fish-water separator, and transported to a workshop for processing. Workers place the salmon in insulated boxes filled with ice and transport them to land overnight for processing and sale.

The freshly caught salmon weigh an average of 3-4 kilograms each, and each harvest is about 5,000 fish. At the earliest, they can be delivered to major cities in China in just over 30 hours.

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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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