Global S&T Development Trend Analysis Platform of Resources and Environment
The interiors of giant planets remain poorly understood. Even for the planets in the Solar System, difficulties in observation lead to large uncertainties in the properties of planetary cores. Exoplanets that have undergone rare evolutionary processes provide a route to understanding planetary interiors. Planets found in and near the typically barren hot-Neptune '
Observations of TOI-849b reveal a radius smaller than Neptune'
Acute myeloid leukaemia (AML) is a heterogeneous disease characterized by transcriptional dysregulation that results in a block in differentiation and increased malignant self-renewal. Various epigenetic therapies aimed at reversing these hallmarks of AML have progressed into clinical trials, but most show only modest efficacy owing to an inability to effectively eradicate leukaemia stem cells (LSCs)(1). Here, to specifically identify novel dependencies in LSCs, we screened a bespoke library of small hairpin RNAs that target chromatin regulators in a unique ex vivo mouse model of LSCs. We identify the MYST acetyltransferase HBO1 (also known as KAT7 or MYST2) and several known members of the HBO1 protein complex as critical regulators of LSC maintenance. Using CRISPR domain screening and quantitative mass spectrometry, we identified the histone acetyltransferase domain of HBO1 as being essential in the acetylation of histone H3 at K14. H3 acetylated at K14 (H3K14ac) facilitates the processivity of RNA polymerase II to maintain the high expression of key genes (including Hoxa9 and Hoxa10) that help to sustain the functional properties of LSCs. To leverage this dependency therapeutically, we developed a highly potent small-molecule inhibitor of HBO1 and demonstrate its mode of activity as a competitive analogue of acetyl-CoA. Inhibition of HBO1 phenocopied our genetic data and showed efficacy in a broad range of human cell lines and primary AML cells from patients. These biological, structural and chemical insights into a therapeutic target in AML will enable the clinical translation of these findings.
Breakdown of the blood-brain barrier in individuals carrying the epsilon 4 allele of the APOE gene, but not the epsilon 3 allele, increases with and predicts cognitive impairment and is independent of amyloid beta or tau pathology.
Vascular contributions to dementia and Alzheimer'
A video-based deep learning algorithm-EchoNet-Dynamic-accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease(1), screening for cardiotoxicity(2) and decisions regarding the clinical management of patients with a critical illness(3). However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training(4,5). Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.
Self-organized criticality is an elegant explanation of how complex structures emerge and persist throughout nature(1), and why such structures often exhibit similar scale-invariant properties(2-9). Although self-organized criticality is sometimes captured by simple models that feature a critical point as an attractor for the dynamics(10-15), the connection to real-world systems is exceptionally hard to test quantitatively(16-21). Here we observe three key signatures of self-organized criticality in the dynamics of a driven-dissipative gas of ultracold potassium atoms: self-organization to a stationary state that is largely independent of the initial conditions
A driven-dissipative gas of ultracold potassium atoms is used to demonstrate three key signatures of self-organized criticality, and provides a system in which the phenomenon can be experimentally tested.
