Artificial intelligence methods enhance the discovery of RNA interactions

Understanding how RNAs interact with proteins, RNAs, or other molecules remains a challenge of main interest in biology, given the importance of these complexes in both normal and pathological cellular processes. Since experimental datasets are starting to be available for hundreds of functional interactions between RNAs and other biomolecules, several machine learning and deep learning algorithms have been proposed for predicting RNA-RNA or RNA-protein interactions. However, most of these approaches were evaluated on a single dataset, making performance comparisons difficult. With this review, we aim to summarize recent computational methods, developed in this broad research area, highlighting feature encoding and machine learning strategies adopted. Given the magnitude of the effect that dataset size and quality have on performance, we explored the characteristics of these datasets. Additionally, we discuss multiple approaches to generate datasets of negative examples for training. Finally, we describe the best-performing methods to predict interactions between proteins and specific classes of RNA molecules, such as circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), and methods to predict RNA-RNA or RNA-RBP interactions independently of the RNA type

Experimental Realization of Optimal Time-Reversal on an Atom Chip for Quantum Undo Operations

The authors report the use of the dressed chopped random basis optimal control algorithm to realize time-reversal procedures. The latter are aimed for the implementation of quantum undo operations in quantum technology contexts as quantum computing and quantum communications. The last performed operation can be time-reversed via the undo command so as to perfectly restore a condition in which any new operation, chosen by the external user, can be applied. By generalizing this concept, the undo command can also allow for the reversing of a quantum operation in a generic time instant of the past. Here, thanks to optimal time-reversal routines, all these functionalities are experimentally implemented on the fivefold (Formula presented.) Hilbert space of a Bose–Einstein condensate of non-interacting 87Rb atoms in the ground state, realized with an atom chip. Each time-reversal transformation is attained by designing an optimal modulated radio frequency field, achieving on average an accuracy of around 92% in any performed test. The experimental results are accompanied by a thermodynamic interpretation based on the Loschmidt echo. These findings are expected to promote the implementation of time-reversal operations in a real scenario of gate-based quantum computing with a more complex structure than the five-level system considered here

Experimental Realization of Optimal Time-Reversal on an Atom Chip for Quantum Undo Operations

We report on the use of the dCRAB optimal control algorithm to realize time-reversal procedures for the implementation of quantum undo operations, to be applied in quantum technology contexts ranging from quantum computing to quantum communications. By means of the undo command, indeed, the last performed operation can be time-reversed so as to perfectly restore a condition in which an arbitrary new operation, chosen by the external user, can be applied. Moreover, by further generalizing this concept, the undo command can also allow for the reversing of a quantum operation in a generic instant of the past. Here, thanks to optimal time-reversal routines, all these functionalities are experimentally implemented on the five-fold F=2 Hilbert space of a Bose-Einstein condensate (BEC) of non-interacting $^{87}$Rb atoms in the ground state, realized with an atom chip. Specifically, each time-reversal transformation is attained by designing an optimal modulated radio frequency field, achieving on average an accuracy of around 92% in any performed test. The experimental results are accompanied by a thermodynamic interpretation based on the Loschmidt echo. Our findings are expected to promote the implementation of time-reversal operations in a real scenario of gate-based quantum computing with a more complex structure than the five-level system here considered

Carotid Ultrasound Boundary Study (CUBS): An Open Multicenter Analysis of Computerized Intima–Media Thickness Measurement Systems and Their Clinical Impact

Common carotid intima–media thickness (CIMT) is a commonly used marker for atherosclerosis and is often computed in carotid ultrasound images. An analysis of different computerized techniques for CIMT measurement and their clinical impacts on the same patient data set is lacking. Here we compared and assessed five computerized CIMT algorithms against three expert analysts’ manual measurements on a data set of 1088 patients from two centers. Inter- and intra-observer variability was assessed, and the computerized CIMT values were compared with those manually obtained. The CIMT measurements were used to assess the correlation with clinical parameters, cardiovascular event prediction through a generalized linear model and the Kaplan–Meier hazard ratio. CIMT measurements obtained with a skilled analyst's segmentation and the computerized segmentation were comparable in statistical analyses, suggesting they can be used interchangeably for CIMT quantification and clinical outcome investigation. To facilitate future studies, the entire data set used is made publicly available for the community at http://dx.doi.org/10.17632/fpv535fss7.1

GateFinder: projection-based gating strategy optimization for flow and mass cytometry

Motivation: High-parameter single-cell technologies can reveal novel cell populations of interest, but studying or validating these populations using lower-parameter methods remains challenging.Results: Here, we present GateFinder, an algorithm that enriches high-dimensional cell types with simple, stepwise polygon gates requiring only two markers at a time. A series of case studies of complex cell types illustrates how simplified enrichment strategies can enable more efficient assays, reveal novel biomarkers and clarify underlying biology

Differential Control of Yersinia pestis Biofilm Formation In Vitro and in the Flea Vector by Two c-di-GMP Diguanylate Cyclases

Yersinia pestis forms a biofilm in the foregut of its flea vector that promotes transmission by flea bite. As in many bacteria, biofilm formation in Y. pestis is controlled by intracellular levels of the bacterial second messenger c-di-GMP. Two Y. pestis diguanylate cyclase (DGC) enzymes, encoded by hmsT and y3730, and one phosphodiesterase (PDE), encoded by hmsP, have been shown to control biofilm production in vitro via their opposing c-di-GMP synthesis and degradation activities, respectively. In this study, we provide further evidence that hmsT, hmsP, and y3730 are the only three genes involved in c-di-GMP metabolism in Y. pestis and evaluated the two DGCs for their comparative roles in biofilm formation in vitro and in the flea vector. As with HmsT, the DGC activity of Y3730 depended on a catalytic GGDEF domain, but the relative contribution of the two enzymes to the biofilm phenotype was influenced strongly by the environmental niche. Deletion of y3730 had a very minor effect on in vitro biofilm formation, but resulted in greatly reduced biofilm formation in the flea. In contrast, the predominant effect of hmsT was on in vitro biofilm formation. DGC activity was also required for the Hms-independent autoaggregation phenotype of Y. pestis, but was not required for virulence in a mouse model of bubonic plague. Our results confirm that only one PDE (HmsP) and two DGCs (HmsT and Y3730) control c-di-GMP levels in Y. pestis, indicate that hmsT and y3730 are regulated post-transcriptionally to differentially control biofilm formation in vitro and in the flea vector, and identify a second c-di-GMP-regulated phenotype in Y. pestis