Optimal control of ribosome population for gene expression under periodic nutrient intake.

Translation of proteins is a fundamental part of gene expression that is mediated by ribosomes. As ribosomes significantly contribute to both cellular mass and energy consumption, achieving efficient management of the ribosome population is also crucial to metabolism and growth. Inspired by biological evidence for nutrient-dependent mechanisms that control both ribosome-active degradation and genesis, we introduce a dynamical model of protein production, that includes the dynamics of resources and control over the ribosome population. Under the hypothesis that active degradation and biogenesis are optimal for maximizing and maintaining protein production, we aim to qualitatively reproduce empirical observations of the ribosome population dynamics. Upon formulating the associated optimization problem, we first analytically study the stability and global behaviour of solutions under constant resource input, and characterize the extent of oscillations and convergence rate to a global equilibrium. We further use these results to simplify and solve the problem under a quasi-static approximation. Using biophysical parameter values, we find that optimal control solutions lead to both control mechanisms and the ribosome population switching between periods of feeding and fasting, suggesting that the intense regulation of ribosome population observed in experiments allows to maximize and maintain protein production. Finally, we find some range for the control values over which such a regime can be observed, depending on the intensity of fasting.

Interval estimation in three-class receiver operating characteristic analysis: A fairly general approach based on the empirical likelihood.

The empirical likelihood is a powerful nonparametric tool, that emulates its parametric counterpart-the parametric likelihood-preserving many of its large-sample properties. This article tackles the problem of assessing the discriminatory power of three-class diagnostic tests from an empirical likelihood perspective. In particular, we concentrate on interval estimation in a three-class receiver operating characteristic analysis, where a variety of inferential tasks could be of interest. We present novel theoretical results and tailored techniques studied to efficiently solve some of such tasks. Extensive simulation experiments are provided in a supporting role, with our novel proposals compared to existing competitors, when possible. It emerges that our new proposals are extremely flexible, being able to compete with contestants and appearing suited to accommodating several distributions, such, for example, mixtures, for target populations. We illustrate the application of the novel proposals with a real data example. The article ends with a discussion and a presentation of some directions for future research.

AlignOT: An Optimal Transport Based Algorithm for Fast 3D Alignment With Applications to Cryogenic Electron Microscopy Density Maps.

Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly and challenging, with standard alignment tools being potentially stuck in local minima, we propose here a new procedure, called AlignOT, which relies on the use of computational optimal transport (OT) to align EM maps in 3D space. By embedding a fast estimation of OT maps within a stochastic gradient descent algorithm, our method searches for a rotation that minimizes the Wasserstein distance between two maps, represented as point clouds. We quantify the impact of various parameters on the precision and accuracy of the alignment, and show that AlignOT can outperform the standard local alignment methods, with an increased range of rotation angles leading to proper alignment. We further benchmark AlignOT on various pairs of experimental maps, which account for different types of conformational heterogeneities and geometric properties. As our experiments show good performance, we anticipate that our method can be broadly applied to align 3D EM maps.

Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets.

Conventional dimensionality reduction methods like Multidimensional Scaling (MDS) are sensitive to the presence of orthogonal outliers, leading to significant defects in the embedding. We introduce a robust MDS method, called DeCOr-MDS (Detection and Correction of Orthogonal outliers using MDS), based on the geometry and statistics of simplices formed by data points, that allows to detect orthogonal outliers and subsequently reduce dimensionality. We validate our methods using synthetic datasets, and further show how it can be applied to a variety of large real biological datasets, including cancer image cell data, human microbiome project data and single cell RNA sequencing data, to address the task of data cleaning and visualization.

CAT PETR: a graphical user interface for differential analysis of phosphorylation and expression data.

Antibody microarray data provides a powerful and high-throughput tool to monitor global changes in cellular response to perturbation or genetic manipulation. However, while collecting such data has become increasingly accessible, a lack of specific computational tools has made their analysis limited. Here we present CAT PETR, a user friendly web application for the differential analysis of expression and phosphorylation data collected via antibody microarrays. Our application addresses the limitations of other GUI based tools by providing various data input options and visualizations. To illustrate its capabilities on real data, we show that CAT PETR both replicates previous findings, and reveals additional insights, using its advanced visualization and statistical options.

Quantitative Nanoscale Secondary Ion Mass Spectrometry (NanoSIMS) Imaging of Individual Vesicles to Investigate the Relation between Fraction of Chemical Release and Vesicle Size.

We used correlative transmission electron microscopy (TEM) and nanoscale secondary ion mass spectrometry (NanoSIMS) imaging to quantify the contents of subvesicular compartments, and to measure the partial release fraction of 13 C-dopamine in cellular nanovesicles as a function of size. Three modes of exocytosis comprise full release, kiss-and-run, and partial release. The latter has been subject to scientific debate, despite a growing amount of supporting literature. We tailored culturing procedures to alter vesicle size and definitively show no size correlation with the fraction of partial release. In NanoSIMS images, vesicle content was indicated by the presence of isotopic dopamine, while vesicles which underwent partial release were identified by the presence of an 127 I-labelled drug, to which they were exposed during exocytosis allowing entry into the open vesicle prior to its closing again. Demonstration of similar partial release fractions indicates that this mode of exocytosis is predominant across a wide range of vesicle sizes.

Geometric differences in the ribosome exit tunnel impact the escape of small nascent proteins.

The exit tunnel is the subcompartment of the ribosome that contains the nascent polypeptide chain and, as such, is involved in various vital functions, including regulation of translation and protein folding. As the geometry of the tunnel shows important differences across species, we focus on key geometrical features of eukaryote and prokaryote tunnels. We used a simple coarse-grained molecular dynamics model to study the role of the tunnel geometry in the post-translational escape of short proteins (short open reading frames [sORFs]) with lengths ranging from 6 to 56 amino acids. We found that the probability of escape for prokaryotes is one for all but the 12-mer chains. Moreover, proteins of this length have an extremely low escape probability in eukaryotes. A detailed examination of the associated single trajectories and energy profiles showed that these variations can be explained by the interplay between the protein configurational space and the confinement effects introduced by the constriction sites of the ribosome exit tunnel. For certain lengths, either one or both of the constriction sites can lead to the trapping of the protein in the "pocket" regions preceding these sites. As the distribution of existing sORFs indicates some bias in length that is consistent with our findings, we finally suggest that the constraints imposed by the tunnel geometry have impacted the evolution of sORFs.

RiboXYZ: a comprehensive database for visualizing and analyzing ribosome structures.

Recent advances in Cryo-EM led to a surge of ribosome structures deposited over the past years, including structures from different species, conformational states, or bound with different ligands. Yet, multiple conflicts of nomenclature make the identification and comparison of structures and ortholog components challenging. We present RiboXYZ (available at https://ribosome.xyz), a database that provides organized access to ribosome structures, with several tools for visualisation and study. The database is up-to-date with the Protein Data Bank (PDB) but provides a standardized nomenclature that allows for searching and comparing ribosomal components (proteins, RNA, ligands) across all the available structures. In addition to structured and simplified access to the data, the application has several specialized visualization tools, including the identification and prediction of ligand binding sites, and 3D superimposition of ribosomal components. Overall, RiboXYZ provides a useful toolkit that complements the PDB database, by implementing the current conventions and providing a set of auxiliary tools that have been developed explicitly for analyzing ribosome structures. This toolkit can be easily accessed by both experts and non-experts in structural biology so that they can search, visualize and compare structures, with various potential applications in molecular biology, evolution, and biochemistry.

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data.

In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more common in every country around the globe, causing a significant burden on society and patients' families. MHDs come in many forms with various severities of symptoms and differing periods of suffering, and as a result it is difficult to differentiate between them and simple to confuse them with each other. Therefore, we propose a support system that employs deep learning (DL) with wearable device data to provide physicians with an objective reference resource by which to make differential diagnoses and plan treatment. We conducted experiments on open datasets containing activity motion signal data from wearable devices to identify schizophrenia and mood disorders (bipolar and unipolar), the datasets being named Psykose and Depresjon. The results showed that, in both workflow approaches, the proposed framework performed well in comparison with the traditional machine learning (ML) and DL methods. We concluded that applying DL models using activity motion signal data from wearable devices represents a prospective objective support system for MHD differentiation with a good performance.

Application of transport-based metric for continuous interpolation between cryo-EM density maps.

Cryogenic electron microscopy (cryo-EM) has become widely used for the past few years in structural biology, to collect single images of macromolecules "frozen in time". As this technique facilitates the identification of multiple conformational states adopted by the same molecule, a direct product of it is a set of 3D volumes, also called EM maps. To gain more insights on the possible mechanisms that govern transitions between different states, and hence the mode of action of a molecule, we recently introduced a bioinformatic tool that interpolates and generates morphing trajectories joining two given EM maps. This tool is based on recent advances made in optimal transport, that allow efficient evaluation of Wasserstein barycenters of 3D shapes. As the overall performance of the method depends on various key parameters, including the sensitivity of the regularization parameter, we performed various numerical experiments to demonstrate how MorphOT can be applied in different contexts and settings. Finally, we discuss current limitations and further potential connections between other optimal transport theories and the conformational heterogeneity problem inherent with cryo-EM data.

End-to-end pipeline for differential analysis of pausing in ribosome profiling data.

Ribosome profiling is a powerful technique which maps the distribution of ribosomes along mRNAs to analyze translation genome-wide. Ribosome density can be affected by multiple factors, such as changes to translation initiation or elongation rates. We describe the application of a metric for identifying genes rate-limited by these rates by analyzing the relative distribution of ribosome footprints along transcripts. This protocol also details two sample analyses comparing gene translation efficiencies and the distribution of ribosome densities on downloadable datasets. For complete details on the use and execution of this protocol, please refer to Flanagan et al. (2022).

FMRP-dependent production of large dosage-sensitive proteins is highly conserved.

Mutations in FMR1 are the most common heritable cause of autism spectrum disorder. FMR1 encodes an RNA-binding protein, FMRP, which binds to long, autism-relevant transcripts and is essential for normal neuronal and ovarian development. In contrast to the prevailing model that FMRP acts to block translation elongation, we previously found that FMRP activates the translation initiation of large proteins in Drosophila oocytes. We now provide evidence that FMRP-dependent translation is conserved and occurs in the mammalian brain. Our comparisons of the mammalian cortex and Drosophila oocyte ribosome profiling data show that translation of FMRP-bound mRNAs decreases to a similar magnitude in FMRP-deficient tissues from both species. The steady-state levels of several FMRP targets were reduced in the Fmr1 KO mouse cortex, including a ∼50% reduction of Auts2, a gene implicated in an autosomal dominant autism spectrum disorder. To distinguish between effects on elongation and initiation, we used a novel metric to detect the rate-limiting ribosome stalling. We found no evidence that FMRP target protein production is governed by translation elongation rates. FMRP translational activation of large proteins may be critical for normal human development, as more than 20 FMRP targets including Auts2 are dosage sensitive and are associated with neurodevelopmental disorders caused by haploinsufficiency.

Influence of ethanol-gasoline blended fuel on performance and emission characteristics of the test motorcycle engine.

This research conducted the effect of different ethanol blends in a conventional gasoline engine under various load and speed conditions for its performance and emission characteristics. The experiment was tested on the test motorcycle designed initially to run by gasoline fuel. The experimental procedure was performed in the motorcycle testbed equipped, measuring power, fuel consumption, and exhaust emissions. NOx and HC emissions of the test vehicle using ethanol blends are lower than gasoline. However, NOx emissions have an opposite trend; they increased approximately 7.4% with E5, 12.3% with E10, and 18.51% with E20 due to the temperature increase. Furthermore, the ethanol contents provide the leaning effect to enhance the air-fuel equivalence ratio to a more excellent value and result in the burning closer to stoichiometric. As a result, improved combustion and increased power output are possible.Implications: This paper aims to find out the solution to solve the problem related to the energy crisis and polluted environment from the motorcycles in Vietnam.

Receiver operating characteristic estimation and threshold selection criteria in three-class classification problems for clustered data.

Statistical evaluation of diagnostic tests, and, more generally, of biomarkers, is a constantly developing field, in which complexity of the assessment increases with the complexity of the design under which data are collected. One particularly prevalent type of data is clustered data, where individual units are naturally nested into clusters. In these cases, Bias can arise from omission, in the evaluation process, of cluster-level effects and/or individual covariates. Focusing on the three-class case and for continuous-valued diagnostic tests, we investigate how to exploit the clustered structure of data within a linear-mixed model approach, both when the assumption of normality holds and when it does not. We provide a method for the estimation of covariate-specific receiver operating characteristic surfaces and discuss methods for the choice of optimal thresholds, proposing three possible estimators. A proof of consistency and asymptotic normality of the proposed threshold estimators is given. All considered methods are evaluated by extensive simulation experiments. As an application, we study the use of the Lysosomal Associated Membrane Protein Family Member 5 gene expression as a biomarker to distinguish among three types of glutamatergic neurons.

Ruptured tuberculous aneurysms of the abdominal aorta: Two case series.

BACKGROUND: Ruptured aneurysms secondary to the tuberculous infection of the aorta are a rare and life-threatening disease. We report a single-center experience of two patients with ruptured infrarenal tuberculous aneurysms. CASE PRESENTATION: We report 2 patients with ruptures of the tuberculous aneurysm. All patients had acute abdominal pain and were diagnosed by echography then CT scan preoperatively. The first patient (male, 50 years old) had a ruptured saccular aneurysm. The second patient (male, 43 years old) had a retroperitoneal contained rupture. All were treated by open prosthetic repair, by vascular surgeons. The two patients were well after operations. The diagnosis was confirmed by pathology examination. Antituberculous treatment was introduced after the operation. CONCLUSIONS: Ruptured tuberculous aneurysms are rare but life-threatening. The diagnosis requires a high degree of suspicion. The treatment includes early diagnosis and emergent surgical intervention, extensive excision of infected field, aortic reconstruction, and prolonged antituberculous drug therapy.

Visualization of Partial Exocytotic Content Release and Chemical Transport into Nanovesicles in Cells.

For decades, "all-or-none" and "kiss-and-run" were thought to be the only major exocytotic release modes in cell-to-cell communication, while the significance of partial release has not yet been widely recognized and accepted owing to the lack of direct evidence for exocytotic partial release. Correlative imaging with transmission electron microscopy and NanoSIMS imaging and a dual stable isotope labeling approach was used to study the cargo status of vesicles before and after exocytosis; demonstrating a measurable loss of transmitter in individual vesicles following stimulation due to partial release. Model secretory cells were incubated with 13C-labeled l-3,4-dihydroxyphenylalanine, resulting in the loading of 13C-labeled dopamine into their vesicles. A second label, di-N-desethylamiodarone, having the stable isotope 127I, was introduced during stimulation. A significant drop in the level of 13C-labeled dopamine and a reduction in vesicle size, with an increasing level of 127I-, was observed in vesicles of stimulated cells. Colocalization of 13C and 127I- in several vesicles was observed after stimulation. Thus, chemical visualization shows transient opening of vesicles to the exterior of the cell without full release the dopamine cargo. We present a direct calculation for the fraction of neurotransmitter release from combined imaging data. The average vesicular release is 60% of the total catecholamine. An important observation is that extracellular molecules can be introduced to cells during the partial exocytotic release process. This nonendocytic transport process appears to be a general route of entry that might be exploited pharmacologically.

Analysis of Ribosome Profiling Data.

Ribosome profiling methods are based on high-throughput sequencing of ribosome-protected mRNA footprints and allow to study in detail translational changes. Bioinformatic and statistical tools are necessary to analyze sequencing data. Here, we describe our developed methods for a fast and reliable quality control of ribosome profiling data, to efficiently visualize ribosome positions and to estimate ribosome speed in an unbiased way. The methodology described here is applicable to several genetic and environmental conditions including stress and are based on the R package RiboVIEW and calculation of quantitative estimates of local and global translation speed, based on a biophysical model of translation dynamics.

Defining an action of SO(d)-rotations on images generated by projections of d-dimensional objects: Applications to pose inference with Geometric VAEs.

Recent advances in variational autoencoders (VAEs) have enabled learning latent manifolds as compact Lie groups, such as SO(d). Since this approach assumes that data lies on a subspace that is homeomorphic to the Lie group itself, we here investigate how this assumption holds in the context of images that are generated by projecting a d-dimensional volume with unknown pose in SO(d). Upon examining different theoretical candidates for the group and image space, we show that the attempt to define a group action on the data space generally fails, as it requires more specific geometric constraints on the volume. Using geometric VAEs, our experiments confirm that this constraint is key to proper pose inference, and we discuss the potential of these results for applications and future work.

Les récents progrès dans le domaine des autoencodeurs variationnels (VAEs) ont permis l'apprentissage de variétés latentes sur des groupes de Lie compacts, tels que SO(d). Une telle approche supposant l'espace des données homéomorphe au groupe de Lie, nous étudions ici la validité de cette hypothèse dans le contexte d'images générées par projection d'un volume de dimension d, dont la pose dans SO(d) est inconnue. Après examen de différents candidats définissant l'espace des images et groupe, on montre que l'on ne peut de manière générale obtenir une action de groupe, sans une contrainte supplémentaire sur le volume. En appliquant des VAEs géométriques, nos expériences confirment que ces contraintes géométriques sont essentielles pour l'inférence de la pose associée au volume projeté, et nous discutons pour conclure des applications potentielles de ces résultats.

Deep Ensemble Learning Approaches in Healthcare to Enhance the Prediction and Diagnosing Performance: The Workflows, Deployments, and Surveys on the Statistical, Image-Based, and Sequential Datasets.

With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. Patient data can be collected, retrieved, and stored in real time. These data are valuable and meaningful for monitoring, diagnosing, and further applications in data analysis and decision-making. Essentially, the data can be divided into three types, namely, statistical, image-based, and sequential data. Each type has a different method of retrieval, processing, and deployment. Additionally, the application of machine learning (ML) and deep learning (DL) in healthcare support systems is growing more rapidly than ever. Numerous high-performance architectures are proposed to optimize decision-making. As reliability and stability are the most important factors in the healthcare support system, enhancing the predicted performance and maintaining the stability of the model are always the top priority. The main idea of our study comes from ensemble techniques. Numerous studies and data science competitions show that by combining several weak models into one, ensemble models can attain outstanding performance and reliability. We propose three deep ensemble learning (DEL) approaches, each with stable and reliable performance, that are workable on the above-mentioned data types. These are deep-stacked generalization ensemble learning, gradient deep learning boosting, and deep aggregation learning. The experiment results show that our proposed approaches achieve more vigorous and reliable performance than traditional ML and DL techniques on statistical, image-based, and sequential benchmark datasets. In particular, on the Heart Disease UCI dataset, representing the statistical type, the gradient deep learning boosting approach dominates the others with accuracy, recall, F1-score, Matthews correlation coefficient, and area under the curve values of 0.87, 0.81, 0.83, 0.73, and 0.91, respectively. On the X-ray dataset, representing the image-based type, the deep aggregation learning approach shows the highest performance with values of 0.91, 0.97, 0.93, 0.80, and 0.94, respectively. On the Depresjon dataset, representing the sequence type, the deep-stacked generalization ensemble learning approach outperforms the others with values of 0.91, 0.84, 0.86, 0.8, and 0.94, respectively. Overall, we conclude that applying DL models using our proposed approaches is a promising method for the healthcare support system to enhance prediction and diagnosis performance. Furthermore, our study reveals that these approaches are flexible and easy to apply to achieve optimal performance.

Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network.

Since 2015, a fast growing number of deep learning-based methods have been proposed for protein-ligand binding site prediction and many have achieved promising performance. These methods, however, neglect the imbalanced nature of binding site prediction problems. Traditional data-based approaches for handling data imbalance employ linear interpolation of minority class samples. Such approaches may not be fully exploited by deep neural networks on downstream tasks. We present a novel technique for balancing input classes by developing a deep neural network-based variational autoencoder (VAE) that aims to learn important attributes of the minority classes concerning nonlinear combinations. After learning, the trained VAE was used to generate new minority class samples that were later added to the original data to create a balanced dataset. Finally, a convolutional neural network was used for classification, for which we assumed that the nonlinearity could be fully integrated. As a case study, we applied our method to the identification of FAD- and FMN-binding sites of electron transport proteins. Compared with the best classifiers that use traditional machine learning algorithms, our models obtained a great improvement on sensitivity while maintaining similar or higher levels of accuracy and specificity. We also demonstrate that our method is better than other data imbalance handling techniques, such as SMOTE, ADASYN, and class weight adjustment. Additionally, our models also outperform existing predictors in predicting the same binding types. Our method is general and can be applied to other data types for prediction problems with moderate-to-heavy data imbalances.