The increase of fungal infection

A strain of Candida auris cultured in a petri dish at a CDC laboratory.
Photo Credit: Shawn Lockhart / Centers for Disease Control / Public Domain

Late last year the WHO published a report highlighting the first-ever list of fungal "priority pathogens" – a catalogue of the 19 fungi that represent the greatest threat to public health. The premise behind the publication is both because fungi are a significant and increasing threat to public health and because there is little global R&D into fungi or their treatment.

According to Professor Ana Traven, from the Biomedicine Discovery Institute, fungi can range from the benign (skin and nail infections and vaginal thrush) to the deadly (Candida, Aspergillus), “and they have been largely ignored because deadly fungal infections predominantly target people who are immunosuppressed, they are generally not transmitted in human-to-human contact.”

Discovering Unique Microbes Made Easy with DOE Systems Biology Knowledgebase (KBase)

Overview of Metagenome-Assembled Genome Extraction data and analysis workflow using KBase apps.
Image courtesy of Chivian, D. et al. Metagenome-assembled genome extraction and analysis from microbiomes using KBase. Nature Protocols 18 (2022).

Microbes are foundational for life on Earth. These tiny organisms play a major role in everything from transforming sunlight into the fundamental molecules of life. They help to produce much of the oxygen in our atmosphere. They even cycle nutrients between air and soil. Scientists are constantly finding interactions between microbes and plants, animals, and other macroscopic lifeforms. As genomic sequencing has advanced, researchers can investigate not only isolated microbes, but also whole communities of microorganisms – known as microbiomes – based on DNA found in an environment. The genomes extracted from these communities (metagenomic sequences) can identify the organisms that carry out biogeochemical processes, contribute to health or disease in human gastrointestinal microbiomes, or interact with plant roots in the rhizosphere. The Department of Energy Systems Biology Knowledgebase (KBase) recently released a suite of features and a protocol for performing sophisticated microbiome analysis that can accelerate research in microbial ecology.

Ancestral variation guides future environmental adaptations

A sea campion in its natural habitat on the coast.
Photo Credit: Bangor University

The humble sea campion flower can show us how species adapt.

The speed of environmental change is very challenging for wild organisms. When exposed to a new environment individual plants and animals can potentially adjust their biology to better cope with new pressures they are exposed to - this is known as phenotypic plasticity.

Plasticity is likely to be important in the early stages of colonizing new places or when exposed to toxic substances in the environment. New research published in Nature Ecology & Evolution, shows that early plasticity can influence the ability to subsequently evolve genetic adaptations to conquer new habitats.

A.I. used to predict space weather like Coronal Mass Ejections

 Dr Andy Smith of Northumbria University
Photo Credit: Northumbria University/Simon Veit-Wilson.

A physicist from Northumbria University has received over £500,000 to create AI that will safeguard the Earth from destructive space storms.

Coronal Mass Ejections, which are solar eruptions from the Sun, can send plasma hurtling towards Earth at high speeds. These space storms can cause severe disruptions to power grids and communication systems.

With our increasing reliance on technology, solar storms pose a serious threat to our everyday lives, leading to severe space weather being added to the UK National Risk Assessment for the first time in 2011.

Researcher and his team analyzed huge amounts of data from satellites and space missions over the last 20 years to gain a better understanding of the conditions under which storms are likely to occur.

Patients with brain cancer may benefit from treatment to boost white blood cells

A new study led by Washington University School of Medicine reveals at least one cause of low white blood cell counts in patients treated for glioblastoma and demonstrates a potential treatment strategy that improves survival in mice.
Photo Credit: Tima Miroshnichenko

Patients with glioblastoma, a devastating brain cancer, receive treatment that frequently leads to the unfortunate side effect of low white blood cell counts that lasts six months to a year. The low numbers of white blood cells are associated with shorter survival — but the specific reason for the prolonged drop in white blood cells and the link with shorter survival has vexed scientists.

A new study led by Washington University School of Medicine in St. Louis reveals at least one cause of low white blood cell counts in patients treated for glioblastoma and demonstrates a potential treatment strategy that improves survival in mice.

The study is published in the journal Science Translational Medicine.

Patients with this cancer typically do not survive longer than 18 months. The standard treatment is radiation and chemotherapy, after which many patients develop severely low numbers of lymphocytes — a type of white blood cell — in the bloodstream. The cause of these low lymphocyte counts has been something of a mystery because the therapy does not target the bone marrow, where these cells originate, and not all patients experience the problem.

Volcano-like rupture could have caused magnetar slowdown

An artist's impression of a magnetar eruption. 
Illustration Credit: NASA's Goddard Space Flight Center

On Oct. 5, 2020, the rapidly rotating corpse of a long-dead star about 30,000 light years from Earth changed speeds. In a cosmic instant, its spinning slowed. And a few days later, it abruptly started emitting radio waves.

Thanks to timely measurements from specialized orbiting telescopes, Rice University astrophysicist Matthew Baring and colleagues were able to test a new theory about a possible cause for the rare slowdown, or “anti-glitch,” of SGR 1935+2154, a highly magnetic type of neutron star known as a magnetar.

In a study published this month in Nature Astronomy, Baring and co-authors used X-ray data from the European Space Agency’s X-ray Multi-Mirror Mission ( XMM-Newton) and NASA’s Neutron Star Interior Composition Explorer ( NICER) to analyze the magnetar’s rotation. They showed the sudden slowdown could have been caused by a volcano-like rupture on the surface of the star that spewed a “wind” of massive particles into space. The research identified how such a wind could alter the star’s magnetic fields, seeding conditions that would be likely to switch on the radio emissions that were subsequently measured by China’s Five-hundred-meter Aperture Spherical Telescope ( FAST).

Not just mood swings but premenstrual depression

The scientists took images of the womens’ brain with positron emission tomography (PET) at different cycle times. 
Image Credit: © MPI CBS

Researchers find serotonin transporter in the brain increased

Scientists led by Julia Sacher from Max Planck Institute for Human Cognitive and Brain Sciences and Osama Sabri from the Leipzig University Hospital have discovered in an elaborate patient study that the transport of the neurotransmitter serotonin in the brain increases in women with premenstrual dysphoric disorder (PMDD) shortly before menstruation. Their findings provide the basis for a more targeted therapy of this specific mood disorder, in which patients only have to take antidepressants for a few days.

PMS, or premenstrual syndrome, is now a familiar term to many - about 50 per cent of all women experience these symptoms a few days before onset of their menstruation. The more severe form, called premenstrual dysphoric disorder (PMDD), affects eight percent of women of childbearing age and is associated with physical symptoms such as sleep disturbances or breast pain as well as psycho-emotional symptoms, including depression, loss of control, irritability, aggressiveness and concentration problems. As a result, many women with PMDD experience disruptions in their personal and professional lives.

New method to control electron spin paves the way for efficient quantum computers

Researchers at the University of Rochester developed a new method for manipulating information in quantum systems by controlling the spin of electrons in silicon quantum dots. Electrons in silicon experience a phenomenon called spin-valley coupling between their spin (up and down arrows) and valley states (blue and red orbitals). When researchers apply a voltage (blue glow) to electrons in silicon, they harness the spin-valley coupling effect and can manipulate the spin and valley states, controlling the electron spin.
Illustration Credit: Michael Osadciw / University of Rochester

The method, developed by Rochester scientists, overcomes the limitations of electron spin resonance.

Quantum science has the potential to revolutionize modern technology with more efficient computers, communication, and sensing devices. Challenges remain in achieving these technological goals, however, including how to precisely manipulate information in quantum systems.

In a paper published in Nature Physics, a group of researchers from the University of Rochester, including John Nichol, an associate professor of physics, outlines a new method for controlling electron spin in silicon quantum dots—tiny, nanoscale semiconductors with remarkable properties—as a way to manipulate information in a quantum system.

“The results of the study provide a promising new mechanism for coherent control of qubits based on electron spin in semiconductor quantum dots, which could pave the way for the development of a practical silicon-based quantum computer,” Nichol says.

The coupling of two quantum dots was successful for the first time

Arne Ludwig was responsible for the design and manufacture of the semiconductor structures for the experiment.
Photo Credit: RUB, Kramer

This means a big step towards the technical applicability of quantum technology, for example for arithmetic operations.

A tiny change means a big breakthrough in quantum physics: an international research team from Bochum and Copenhagen has managed to couple two quantum dots in one nanochip. After exciting a quantum point using a laser, a signal is sent out, the origin of which can no longer be related to one of the quantum points, as if both had each sent half of the signal in the form of a single photon. "At first that sounds like a little success, but this signal entanglement, which sits on a single photon, is more than the sum of its parts," says Dr. Arne Ludwig from the Chair of Solid-State Physics at the Ruhr University Bochum. “It represents a big step towards the usability of quantum technology for computer operations. "Together with researchers from the Niels Bohr Institute at the University of Copenhagen, the Bochum team published the results in the journal Science from 27. Published January 2023.

Machine learning identifies drugs that could potentially help smokers quit

Penn State College of Medicine researchers helped identify eight medications that may be repurposed to help people quit smoking. A team of more than 70 researchers contributed to the analysis of genetic and smoking behavior data from more than 1.3 million people.
Image Credit: Scientific Frontline

Medications like dextromethorphan, used to treat coughs caused by cold and flu, could potentially be repurposed to help people quit smoking cigarettes, according to a study by Penn State College of Medicine and University of Minnesota researchers. They developed a novel machine learning method, where computer programs analyze data sets for patterns and trends, to identify the drugs and said that some of them are already being tested in clinical trials.

Cigarette smoking is risk factor for cardiovascular disease, cancer and respiratory diseases and accounts for nearly half a million deaths in the United States each year. While smoking behaviors can be learned and unlearned, genetics also plays a role in a person’s risk for engaging in those behaviors. The researchers found in a prior study that people with certain genes are more likely to become addicted to tobacco.

Using genetic data from more than 1.3 million people, Dajiang Liu, Ph.D., professor of public health sciences, and of biochemistry and molecular biology and Bibo Jiang, Ph.D., assistant professor of public health sciences, co-led a large multi-institution study that used machine learning to study these large data sets — which include specific data about a person’s genetics and their self-reported smoking behaviors.