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Mindfulness-based awareness training can help people learn to better control brain-computer interfaces. But a new study has found that a single guided mindfulness meditation exercise isn’t enough to boost performance. The findings, published in Frontiers in Human Neuroscience, suggest that a longer period of meditation is needed in order for people to experience observable improvements.

The authors of the research are interested in exploring the potential benefits of using mindfulness meditation as a training tool to improve the performance of brain-computer interfaces, which allow individuals to control machines or computers directly from their brain, bypassing the traditional neuromuscular pathway. These devices have the potential to greatly benefit people with conditions such as spinal cord injuries, stroke, and neurodegenerative diseases like amyotrophic lateral sclerosis (ALS).

Previous studies have shown that one of the most effective signals for brain-computer interface control is the sensorimotor rhythm produced in the primary sensorimotor areas during motor imagery. However, not everyone is able to effectively control brain-computer interfaces, with approximately 20% of the population being “BCI-inefficient” even with extensive training. Therefore, researchers are looking for ways to improve performance, and one potential method is through meditation.

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A team of medical scientists at The Catholic University of America, in Washington, D.C., working with a colleague from Purdue University, has developed a way to engineer the bacteriophage T4 to serve as a vector for molecular repair. The study is reported in the journal Nature Communications.

Prior research has shown that many human ailments arise due to : , Down syndrome, and hemophilia are just a few. Logic suggests that correcting such genetic mutations could cure these diseases. So researchers have been working toward developing gene editing tools that will allow for safe editing of genes.

One of the most promising is the CRISPR gene editing system. In this new effort, the research team took a more general approach to solving the problem by working to develop a vector that could be used to carry different kinds of tools to targeted cells and then enter them to allow for healing work to commence.

Researchers in Canada and the United States have used deep learning to derive an antibiotic that can attack a resistant microbe, acinetobacter baumannii, which can infect wounds and cause pneumonia. According to the BBC, a paper in Nature Chemical Biology describes how the researchers used training data that measured known drugs’ action on the tough bacteria. The learning algorithm then projected the effect of 6,680 compounds with no data on their effectiveness against the germ.

In an hour and a half, the program reduced the list to 240 promising candidates. Testing in the lab found that nine of these were effective and that one, now called abaucin, was extremely potent. While doing lab tests on 240 compounds sounds like a lot of work, it is better than testing nearly 6,700.

Interestingly, the new antibiotic seems only to be effective against the target microbe, which is a plus. It isn’t available for people yet and may not be for some time — drug testing being what it is. However, this is still a great example of how machine learning can augment human brainpower, letting scientists and others focus on what’s really important.

Contemporary DNA

DNA, or deoxyribonucleic acid, is a molecule composed of two long strands of nucleotides that coil around each other to form a double helix. It is the hereditary material in humans and almost all other organisms that carries genetic instructions for development, functioning, growth, and reproduction. Nearly every cell in a person’s body has the same DNA. Most DNA is located in the cell nucleus (where it is called nuclear DNA), but a small amount of DNA can also be found in the mitochondria (where it is called mitochondrial DNA or mtDNA).

Crispre cas 9.


A major issue in neuroscience is the poor translatability of research results from preclinical studies in animals to clinical outcomes. Comparative neuroscience can overcome this barrier by studying multiple species to differentiate between species-specific and general mechanisms of neural circuit functioning. Targeted manipulation of neural circuits often depends on genetic dissection, and use of this technique has been restricted to only a few model species, limiting its application in comparative research. However, ongoing advances in genomics make genetic dissection attainable in a growing number of species. To demonstrate the potential of comparative gene editing approaches, we developed a viral-mediated CRISPR/Cas9 strategy that is predicted to target the oxytocin receptor (Oxtr) gene in 80 rodent species. This strategy specifically reduced OXTR levels in all evaluated species (n = 6) without causing gross neuronal toxicity. Thus, we show that CRISPR/Cas9-based tools can function in multiple species simultaneously. Thereby, we hope to encourage comparative gene editing and improve the translatability of neuroscientific research.

The development of comparative gene editing strategies improves the translatability of animal research.