Cells that produce useful animal and plant proteins and can be cultured using photosynthesis could offer new methods of producing drugs and meat.
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Tiny cracks and a 9-volt battery could help fight deadly superbugs
As superbugs become harder to treat and antibiotic resistance continues to rise worldwide, Flinders University scientists have uncovered an unlikely ally in the fight to prevent infections before they take hold. They have found that carefully controlled microscopic cracks can enhance the performance of an antibacterial coating, which helps destroy dangerous bacteria when activated with a small electrical charge.
The discovery could lead to smart wound dressings and wearable devices that monitor health while protecting against infection.
The paper, “Stress-Programmed Microcrack Networks in Gallium–Carbon Nanotube Thin Films for Plasma-Active and Electroresponsive Antibacterial Interfaces,” is published in Small Structures.
Physicists Propose a ‘Supercurrent’ To Read Qubits Without Destroying Their Quantum State
The qubits at the center of quantum computing systems are the equivalent of the binary bits that classical computing processors use – but while classical bits can only be 1 or 0, qubits can be 1, 0, or a blend of both (superposition), a flexibility that promises to transform the power of our computers.
There’s one nagging problem with superposition, which is that as soon as a qubit is accessed and measured, its value then gets fixed to 1 or 0. The instant it’s checked up on, the most useful characteristic of the qubit disappears.
Now researchers from China and Brazil, publishing their work in Physical Review B, think they have a solution to the problem. Essentially, it involves reading the electrical current produced by a qubit, rather than the qubit itself.
Leveraging Data-Driven Decision-Making to Enhance Healthcare Organizational Performance and Service Quality
Data-driven decision-making (DDDM) is increasingly important in healthcare management as organizations seek to enhance organizational performance, service quality, patient outcomes, and resource utilization. This qualitative study examines DDDM within the Jamaican healthcare setting, where healthcare leaders operate within a resource-constrained environment and must use clinical, financial, operational, and patient-experience data to inform managerial decisions and service delivery. Although healthcare organizations may have access to substantial amounts of data, data availability alone does not guarantee effective decision-making. Leaders must analyze relevant information, monitor performance indicators, identify service-delivery gaps, and translate findings into evidence-based strategies. The study explores how healthcare leaders in Jamaica use DDDM to support strategic planning, quality improvement, resource allocation, performance monitoring, and organizational accountability. Findings indicate that effective use of data enables healthcare leaders to identify trends, assess performance, monitor service delivery, and implement timely interventions. However, barriers such as inadequate data quality, limited data access, technological limitations, organizational culture, and gaps in leadership and data-analytic competencies may constrain effective data use. Strengthening data literacy, analytical capacity, data governance, and an evidence-based organizational culture may help healthcare leaders translate data into actionable strategies to improve organizational performance and service quality. The implications of these findings are particularly relevant to healthcare organizations in Jamaica. They may be transferable to other healthcare settings only where organizational, technological, leadership, and resource conditions are sufficiently comparable.
Data-Driven Decision Making, Healthcare Management, Healthcare Performance, Service Quality, Quality Improvement, Performance Monitoring, Healthcare Leadership, Data Analytics, Transformational Leadership.
AI based study demonstrates stimulation of brown fat by blood-pressure drug
Scientists have discovered that the drug losartan, commonly prescribed for high blood pressure, boosts brown adipose tissue activity in mice, enhancing their metabolism. This indicates that repurposing losartan—an angiotensin receptor blocker (ARB) with a well-known safety profile—may be a clinically viable method of stimulating brown fat, which has been linked to improved cardiovascular health and, reported for the first time in this study, psychiatric health.
In a paper published in Nature, researchers found that across all diagnoses, those conditions were less prevalent in individuals with brown fat.
The researchers initially discovered the connection between brown fat and ARBs through an analysis of medical records from 100,000 patients. The work builds on discoveries in the lab made in 2021 in which they characterized roughly 50,000 patients, finding that people with more brown fat had significantly lower odds of cardiometabolic diseases, including coronary artery disease and type II diabetes.
Imagine Finding Out Your Whole Life Was a TV Show | The Truman Show
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Optical system maps multiple infrared fields onto one small detector
Researchers from the Changchun Institute of Optics, Fine Mechanics and Physics of the Chinese Academy of Sciences have developed an optical system that focuses light from several subfields onto a single small-format detector. By analyzing field-dependent spot shapes, the system identifies which subfield an isolated infrared target comes from. In sequence-level tests, it identified the correct field in about 89% of cases. The study is published in Optics and Laser Technology.
Wide-field infrared imaging typically uses large focal-plane arrays, where each part of the scene occupies a distinct detector region. Larger arrays increase demands on detector fabrication, cooling, electronic readout and system integration. The researchers pursued another route by reusing the same detector area for several viewing regions. This enables the optical field to expand without increasing the detector format in direct proportion, which may reduce the hardware burden of wide-field systems designed for sparse point-like targets.
In scenes with only a few isolated targets, much of a large array may carry no useful target signal at a given moment. The new design uses that sparsity by mapping several regions to shared detector pixels.
A likely supermassive neutron star might shed light on the mass gap between neutron stars and black holes
A team of astronomers recently identified a red giant star with an unseen binary companion after examining data from the Large Sky Area MultiObject Fiber Spectroscopic Telescope (LAMOST) and the Apache Point Observatory Galactic Evolution Experiment (APOGEE). Although the companion object can’t be observed visually, astronomers can see the gravitational effects it has on the red giant star.
All of this is nothing new—researchers often discover binary systems with effectively invisible companions thanks to gravitational effects. However, the inferred mass of the unseen object, along with the system’s other characteristics, makes it a bit of an anomaly. The research on this pair, published in Science Advances, suggests the unseen companion is likely an unusually massive neutron star that is pushing the boundaries of how massive neutron stars can get.
Unstable plasma heats itself with waves it generates, experiments reveal
Fusion power is expected to be a sustainable energy source that emits no carbon dioxide during electricity generation. To achieve this, research is underway worldwide to confine high-temperature plasma using magnetic fields. To initiate a fusion reaction, high-energy particles or waves must be injected from outside to heat the plasma to extremely high temperatures.
Once the fusion reaction begins, “self-heating” occurs, in which high-energy particles generated by the reaction further heat the plasma, allowing the high-temperature state to be maintained even if external heating is reduced.
This self-heating process is complex, and large-scale experimental facilities such as ITER are necessary to demonstrate it in its entirety. Demonstration of self-heating is expected around 2040. However, one of the key processes underpinning self-heating—the phenomenon in which “waves generated by plasma instabilities heat the plasma itself”—can be verified even with current experimental facilities.
AI models can answer differently when told they are being watched
Large language models (LLMs), the AI systems underpinning the functioning of ChatGPT, Gemini and other similar platforms, are now widely used worldwide. While the responses provided by these models can be useful in many situations, they can also change under specific conditions.
Researchers at TCS Research and KIIT Bhubaneswar in India recently carried out an experiment with 10 different LLMs to understand the extent to which these models follow rules under different conditions. Their findings, reported in an arXiv preprint, suggest that some LLMs can change their responses when they are told that their answers might be monitored, yet how much their responses vary depends on the model and the value it is asked to set aside.
“Language models can follow a directive when they believe they are monitored and abandon it when they believe they are not, a pattern termed alignment faking,” wrote Manodeep Ray, Vivek Srivastava and their colleagues in their paper.