Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying and Multi-Omics Integration of key-Metabolic Reactions from Untargeted Metabolomics

Metabolomics has become increasingly popular in biological and biomedical research, especially for multi-omics studies, due to the many associations of metabolism with diseases. This development is driven by improvements in metabolite identification and generating large amounts of data, increasing the need for computational solutions for data interpretation. In particular, only few computational approaches directly generating mechanistic hypotheses exist, making the biochemical interpretation of metabolomics data difficult. We present mantra, an approach to estimate how metabolic reactions change their activity between biological conditions without requiring absolute quantification of metabolites. Starting with a data-specific metabolic network we utilize linear models between substrates and products of a metabolic reaction to approximate deviations in activity. The obtained estimates can subsequently be used for network enrichment and integration with other omics data. By applying mantra to untargeted metabolomics measurements of Triple-Negative Breast Cancer biopsies, we show that it can accurately pinpoint biomarkers. On a dataset of stool metabolomics from Inflammatory Bowel Disease patients, we demonstrate that predictions on our proposed reaction metric generalize to an independent validation cohort and that it can be used for multi-omics network integration. By allowing mechanistic interpretation we facilitate knowledge extraction from metabolomics experiments.
A synthetic delivery vector for mucosal vaccination

The success of mRNA-based vaccines during the Covid-19 pandemic has highlighted the value of this new platform for vaccine development against infectious disease. However, the CD8+ T cell response remains modest with mRNA vaccines, and these do not induce mucosal immunity, which would be needed to prevent viral spread in the healthy population. To address this drawback, we developed a dendritic cell targeting mucosal vaccination vector, the homopentameric STxB. Here, we describe the highly efficient chemical synthesis of the protein, and its in vitro folding. This straightforward preparation led to a synthetic delivery tool whose biophysical and intracellular trafficking characteristics were largely indistinguishable from recombinant STxB. The chemical approach allowed for the generation of new variants with bioorthogonal handles. Selected variants were chemically coupled to several types of antigens derived from the mucosal viruses SARS-CoV-2 and type 16 human papillomavirus. Upon intranasal administration in mice, mucosal immunity, including resident memory CD8+ T cells and IgA antibodies was induced against these antigens. Our study thereby identifies a novel synthetic antigen delivery tool for mucosal vaccination with an unmatched potential to respond to an urgent medical need.
Time-resolved, deuterium-based fluxomics uncovers the hierarchy and dynamics of sugar processing by Pseudomonas putida

Pseudomonas putida, a soil bacterium widely used for synthetic biology and metabolic engineering, processes glucose through convergent peripheral pathways that ultimately yield 6-phosphogluconate. Such a periplasmic gluconate shunt (PGS), composed by glucose and gluconate dehydrogenases, sequentially transforms glucose into gluconate and 2-ketogluconate. Although the secretion of these organic acids by P. putida has been extensively recognized, the mechanism and spatiotemporal regulation of the PGS remained elusive thus far. To address this challenge, we have developed a novel methodology for metabolic flux analysis, D-fluxomics, based on deuterated sugar substrates. D-Fluxomics demonstrated that the PGS underscores a highly dynamic metabolic architecture in glucose-dependent batch cultures of P. putida, characterized by hierarchical carbon uptake by the PGS throughout the cultivation. Additionally, we show that gluconate and 2-ketogluconate accumulation and consumption can be solely explained as a result of the interplay between growth rate-coupled and decoupled metabolic fluxes. As a consequence, the formation of these acids in the PGS is inversely correlated to the bacterial growth rate—unlike the widely studied overflow metabolism of Escherichia coli and yeast. Our findings, which underline survival strategies of soil bacteria thriving in their natural environments, open new avenues for engineering P. putida towards efficient, sugar-based bioprocesses.
Inferring multi-locus selection in admixed populations

Admixture, the exchange of genetic information between distinct source populations, is thought to be a major source of adaptive novelty. Unlike mutation events, which periodically generate single alleles, admixture can introduce many selected alleles simultaneously. As such, the effects of linkage between selected alleles may be especially pronounced in admixed populations. However, existing tools for identifying selected mutations within admixed populations only account for selection at a single site, overlooking phenomena such as interference among proximal selected alleles. Here, we develop and extensively validate a method for identifying and quantifying the individual effects of multiple linked selected sites on a chromosome in admixed populations. Our approach numerically calculates the expected local ancestry landscape in an admixed population for a given multi-locus selection model, and then maximizes the likelihood of the model. After applying this method to admixed populations of Drosophila melanogaster, we found that the impacts between linked sites may be an important contributor to natural selection in admixed populations. Furthermore, for the situations we considered, the selection coefficients and number of selected sites are overestimated in analyses that do not consider the effects of linkage among selected sites. Our results imply that linkage among selected sites may be an important evolutionary force in admixed populations. This tool provides a powerful generalized method to investigate these crucial phenomena in diverse populations.
Rad53 regulates the lifetime of Rdh54 at homologous recombination intermediates

Rdh54 is a conserved DNA translocase that participates in homologous recombination (HR), DNA checkpoint adaptation, and chromosome segregation. Saccharomyces cerevisiae Rdh54 is a known target of the Mec1/Rad53 signaling axis, which globally protects genome integrity during DNA metabolism. While phosphorylation of DNA repair proteins by Mec1/Rad53 is critical for HR progression little is known about how specific post translational modifications alter HR reactions. Phosphorylation of Rdh54 is linked to protection of genomic integrity but the consequences of modification remain poorly understood. Here, we demonstrate that phosphorylation of the Rdh54 C-terminus by the effector kinase Rad53 regulates Rdh54 clustering activity as revealed by single molecule imaging. This stems from phosphorylation dependent and independent interactions between Rdh54 and Rad53. Genetic assays reveal that loss of phosphorylation leads to phenotypic changes resulting in loss-of-heterozygosity (LOH) outcomes. Our data highlight Rad53 as a key regulator of HR intermediates through activation and attenuation of Rdh54 motor function.
Controlling Neural Network Smoothness for Neural Algorithmic Reasoning

The modelling framework of neural algorithmic reasoning (Veličković & Blundell, 2021) postulates that a continuous neural network may learn to emulate the discrete reasoning steps of a symbolic algorithm. We investigate the underlying hypothesis in the most simple conceivable scenario – the addition of real numbers. Our results show that two layer neural networks fail to learn the structure of the task, despite containing multiple solutions of the true function within their hypothesis class. Growing the network’s width leads to highly complex error regions in the input space. Moreover, we find that the network fails to gener-alise with increasing severity i) in the training domain, ii) outside of the training domain but within its convex hull, and iii) outside the training domain’s convex hull. This behaviour can be emulated with Gaussian process regressors that use radial basis function kernels of decreasing length scale. Classical results establish an equivalence between Gaussian processes and infinitely wide neural networks. We demonstrate a tight linkage between the scaling of a network weights’ standard deviation and its effective length scale on a sinusoidal regression problem, suggesting simple modifications to control the length scale of the function learned by a neural network and, thus, its smoothness . This has important applications for the different generalisation scenarios suggested above, but it also suggests a partial remedy to the brittleness of neural network predictions as exposed by adversarial examples. We demonstrate the gains in adversarial robustness that our modification achieves on simple image classification problems. In conclusion, this work shows inherent problems of neural networks even for the simplest algorithmic tasks which, however, may be partially remedied through links to Gaussian processes.
Improved inference of population histories by integrating genomic and epigenomic data

With the availability of high quality full genome polymorphism (SNPs) data, it becomes feasible to study the past demographic and selective history of populations in exquisite detail. However, such inferences still suffer from a lack of statistical resolution for recent, e.g. bottlenecks, events, and/or for populations with small nucleotide diversity. Additional heritable (epi)genetic markers, such as indels, transposable elements, microsatellites or cytosine methylation, may provide further, yet untapped, information on the recent past population history. We extend the Sequential Markovian Coalescent (SMC) framework to jointly use SNPs and other hyper-mutable markers. We are able to 1) improve the accuracy of demographic inference in recent times, 2) uncover past demographic events hidden to SNP-based inference methods, and 3) infer the hyper-mutable marker mutation rates under a finite site model. As a proof of principle, we focus on demographic inference in A. thaliana using DNA methylation diversity data from 10 European natural accessions. We demonstrate that segregating Single Methylated Polymorphisms (SMPs) satisfy the modelling assumptions of the SMC framework, while Differentially Methylated Regions (DMRs) are not suitable as their length exceeds that of the genomic distance between two recombination events. Combining SNPs and SMPs while accounting for site- and region-level epimutation processes, we provide new estimates of the glacial age bottleneck and post glacial population expansion of the European A. thaliana population. Our SMC framework readily accounts for a wide range of heritable genomic markers, thus paving the way for next generation inference of evolutionary history by combining information from several genetic and epigenetic markers.
Parameter Efficient Node Classification on Homophilic Graphs

Deep Learning on Graphs was recently made possible with the introduction of Graph Neural Networks (GNNs). GNNs use learnable diffusion processes to propagate information through the graph and improve performance on downstream tasks. However, learning this diffusion process can be expensive in terms of memory and computation. While much research has gone into making these models more expressive and able to capture more complex patterns, in practice, edges in common benchmarking datasets often encode similarity of nodes with respect to the downstream task. This property is called homophily. We argue that for these homophilic graphs, learnable diffusion processes and large receptive fields are not required to achieve competitive performance. We propose Graph Non-Parametric Diffusion (GNPD) a method that outperforms traditional GNNs using only 2 linear models and non-parameteric diffusion. Our method takes ideas from Correct & Smooth (C&S) and the Scalable Inception Graph Network (SIGN) and combines them to create a simpler model that outperforms both of them on several datasets. Our method achieves unmatched parameter efficiency, competing with models with two orders of magnitude more parameters. Additionally GNPD can also forego spectral embeddings which are the computational bottleneck of the C&S method.
Learning to Look by Self-Prediction

We present a method for learning active vision skills, to move the camera to observe a robot’s sensors from informative points of view, without external rewards or labels. We do this by jointly training a visual predictor network, which predicts future returns of the sensors using pixels, and a camera control agent, which we reward using the negative error of the predictor. The agent thus moves the camera to points of view that are most predictive for a chosen sensor, which we select using a conditioning input to the agent. We observe that despite this noisy learned reward function, the learned policies avoid occlusions, and reliably frame the sensor in a specific location in the view, an emergent location which we call a behavioral fovea. We find that replacing the conventional camera with a foveal camera further increases the policies’ precision.
Improved Overparametrization Bounds for Global Convergence of SGD for Shallow Neural Networks

We study the overparametrization bounds required for the global convergence of stochastic gradient descent algorithm for a class of one hidden layer feed-forward neural networks equipped with ReLU activation function. We improve the existing state-of-the-art results in terms of the required hidden layer width. We introduce a new proof technique combining nonlinear analysis with properties of random initializations of the network
CHARACTERIZING POROUS CONCRETE MIXTURES FOR RIGID PAVEMENT

In highway engineering, the high porosity of Porous Concrete (PC) facilitates additional functions like noise reduction and urban ambient temperature minimizing, further, to hydroplaning control. The internal composited structure of PC plays the vital role in its mechanical behavior. However, increasing the concrete mixture’s porosity can adversely affect the mixture’s mechanical properties such as compressive and flexural strength, elasticity, and creep strains. This paper aims to demonstrate an experimental attempt to characterize the mechanical properties of porous concrete pavement (PCP) using local limestone aggregate. The attempt suggested a multistage process to optimize the best mixtures through five stages using different variables, namely: nominal maximum aggregate size (NMAS), w/c ratio, aggregate/paste ratio, superplasticizer dosage, and reactive powder dosage. Firstly, sample preparation was included paste preparation by mixing 75% of the specified mixing water (with SP if applicable) with OPC and SF (if applicable). In comparison, the remain 25% was used to wet the aggregate before continuing mixing. The prepared samples' mechanical and volumetric properties were determined to achieve this optimization process through compressive strength, flexural strength, porosity, coefficient of permeability, and density. The experimental results illustrated a wide range of variation in volumetric and mechanical properties of the produced mixes according to selected variables. The optimization is very significant to gain a PC mixture with an accepted property. Traditional PC mixture comparatively exhibited compressive strength of less than 15 MPa, and flexural strength of less than 3 MPa, while high performance PC mixture exhibited over 30 MPa compressive strength, 5 MPa flexural strength, 1.16 mm/s coefficient of permeability (for the mixture associated with 12.5 NMAS, 80% agg/paste ratio, 0.27 w/b, 0.7% (of binder weight) plasticizer, and 30% (of binder weight) silica fume). However, local limestone aggregate can offer PC mixture suitable for highway pavement when aggregate/cement, additives, and admixture are adequately optimized.
Integrating Bayesian Network Structure into Residual Flows and Variational Autoencoders

Deep generative models have become more popular in recent years due to their scalability and representation capacity. Unlike probabilistic graphical models, they typically do not incorporate speciﬁc domain knowledge. As such, this work explores incorporating arbitrary dependency structures, as speciﬁed by Bayesian networks, into variational autoen-coders (VAEs). This is achieved by developing a new type of graphical normalizing ﬂow, which extends residual ﬂows by encoding conditional independence through masking of the ﬂow’s residual block weight matrices, and using these to extend both the prior and inference network of the VAE. We show that the proposed graphical VAE provides a more inter-pretable model that generalizes better in data-sparse settings, when practitioners know or can hypothesize about certain latent factors in their domain. Furthermore, we show that graphical residual ﬂows provide not only density estimation and inference performance competitive with existing graphical ﬂows, but also more stable and accurate inversion in practice as a byproduct of the ﬂow’s Lipschitz bounds.
Bidirectional View based Consistency Regularization for Semi-Supervised Domain Adaptation

Distinguished from unsupervised domain adaptation (UDA), semi-supervised domain adaptation (SSDA) could access a few labeled target samples during learning additionally. Although achieving remarkable progress, target supervised information is easily overwhelmed by massive source supervised information, as there are many more labeled source samples than those in the target domain. In this work, we propose a novel method BVCR that better utilizes the supervised information by three schemes, i.e., modeling, exploration, and interaction. In the modeling scheme, BVCR models the source supervision and target supervision separately to avoid target supervised information being overwhelmed by source supervised information and better utilize the target supervision. Besides, as both supervised information naturally oﬀer distinct views for the target domain, the exploration scheme performs consistency regularization within each domain to better explore target information with bidirectional views. Moreover, as both views are complementary to each other, the interaction scheme performs consistency regularization across domains to exchange complementary
Optimal product line designing: A comparison of different discrete choice-based approaches

Nowadays, short product life cylcles force companies to frequently develop and offer new products. In order to gain revenues and stay competitive, companies have to carefully design their revenue-maximizing product lines. Often, individual consumer preferences derived from discrete choice experiments build the input for product line optimization. Basically, two different approaches, e.g., a simultaneous approach and a separate approach exist, that differ in their consideration of consumers’ preference heterogeneity. We compared these two types of product line design approaches w.r. t. to their performance to detect revenue-maximizing product lines. We use a Monte Carlo simulation study and found the simultaneous approach to outperform the separate approach and therefore being more robust to biases in the recovery of preference structures, e.g., part-worth utility estimation. Furthermore, we detect several experimental factors that determine the approaches’ performances.
Mixed effects in machine learning - A flexible mixedML framework to add random effects to supervised machine learning regression

Clustered data can frequently be found not only in social and behavioral sciences (e.g., multiple measurements of individuals) but also in typical machine learning problems (e.g., weather forecast in different cities, house prices in different regions). This implies dependencies for observations within one cluster, leading to violations of independent and identically distributed assumptions, biased estimates, and false inference. A typical approach to address this issue is to include random effects instead of fixed effects. We introduce the general mixed effects machine learning framework (mixedML), which includes random effects in supervised regression machine learning models, and present different estimation procedures. A segmentation of the problem allows to include random effects as an additional correction to the standard machine learning regression problem. Thus, the framework can be applied on top of the machine learning task, without the need to change the model or architecture, which distinguishes mixedML from other models in this field. With a simulation study and empirical data sets, we show that the framework produces comparable estimates to typical mixed effects frameworks in the linear case and increases the prediction quality and the gained information of the standard machine learning models in both the linear and non-linear case. Furthermore, the presented estimation procedures significantly decrease estimation time. Compared to other approaches in this area, the framework does not restrict the choice of machine learning algorithms and still includes random effects.
Impacts of heat, drought, and combined heat–drought stress on yield, phenotypic traits, and gluten protein traits: capturing stability of spring wheat in excessive environments

Wheat production and end-use quality are severely threatened by drought and heat stresses. This study evaluated stress impacts on phenotypic and gluten protein characteristics of eight spring wheat genotypes (Diskett, Happy, Bumble, SW1, SW2, SW3, SW4, and SW5) grown to maturity under controlled conditions (Biotron) using RGB imaging and size-exclusion high-performance liquid chromatography (SE-HPLC). Among the stress treatments compared, combined heat–drought stress had the most severe negative impacts on biomass (real and digital), grain yield, and thousand kernel weight. Conversely, it had a positive effect on most gluten parameters evaluated by SE-HPLC and resulted in a positive correlation between spike traits and gluten strength, expressed as unextractable gluten polymer (%UPP) and large monomeric protein (%LUMP). The best performing genotypes in terms of stability were Happy, Diskett, SW1, and SW2, which should be further explored as attractive breeding material for developing climate-resistant genotypes with improved bread-making quality. RGB imaging in combination with gluten protein screening by SE-HPLC could thus be a valuable approach for identifying climate stress–tolerant wheat genotypes.