Dictionary learning allows model-free pseudotime estimation of transcriptomic data

Background Pseudotime estimation from dynamic single-cell transcriptomic data enables characterisation and understanding of the underlying processes, for example developmental processes. Various pseudotime estimation methods have been proposed during the last years. Typically, these methods start with a dimension reduction step because the low-dimensional representation is usually easier to analyse. Approaches such as PCA, ICA or t-SNE belong to the most widely used methods for dimension reduction in pseudotime estimation methods. However, these methods usually make assumptions on the derived dimensions, which can result in important dataset properties being missed. In this paper, we suggest a new dictionary learning based approach, dynDLT, for dimension reduction and pseudotime estimation of dynamic transcriptomic data. Dictionary learning is a matrix factorisation approach that does not restrict the dependence of the derived dimensions. To evaluate the performance, we conduct a large simulation study and analyse 8 real-world datasets. Results The simulation studies reveal that firstly, dynDLT preserves the simulated patterns in low-dimension and the pseudotimes can be derived from the low-dimensional representation. Secondly, the results show that dynDLT is suitable for the detection of genes exhibiting the simulated dynamic patterns, thereby facilitating the interpretation of the compressed representation and thus the dynamic processes. For the real-world data analysis, we select datasets with samples that are taken at different time points throughout an experiment. The pseudotimes found by dynDLT have high correlations with the experimental times. We compare the results to other approaches used in pseudotime estimation, or those that are method-wise closely connected to dictionary learning: ICA, NMF, PCA, t-SNE, and UMAP. DynDLT has the best overall performance for the simulated and real-world datasets. Conclusions We introduce dynDLT, a method that is suitable for pseudotime estimation. Its main advantages are: (1) It presents a model-free approach, meaning that it does not restrict the dependence of the derived dimensions; (2) Genes that are relevant in the detected dynamic processes can be identified from the dictionary matrix; (3) By a restriction of the dictionary entries to positive values, the dictionary atoms are highly interpretable.

Land as a Basis for Recent Progress in the Study of Urbanization Dynamics

Urbanization is one of the most dynamic processes occurring on the Earth [...]

Impact of the Revetment on the Seashore in the Region of Babie Doły (KM 93.6–93.9), Poland

The present paper addresses the litho-dynamic and morpho-dynamic processes in the coastal zone of Babie Doły (KM 93.6–93.9), Poland. As a background, the history of coastal engineering measures in this area is described. The impact of post-war structures on the seashore is analysed on the basis of historical maps, supported by results of the sediment transport modelling. Shore regression is caused by the so-called downstream erosion behind the headland with remains of rock palisade structures. The possible consequences for the seashore resulting from the removal of the analysed revetment are discussed. The paper also presents recommendations to the relevant authorities for the future.

Profiling transcription factor activity dynamics using intronic reads in time-series transcriptome data

Activities of transcription factors (TFs) are temporally modulated to regulate dynamic cellular processes, including development, homeostasis, and disease. Recent developments of bioinformatic tools have enabled the analysis of TF activities using transcriptome data. However, because these methods typically use exon-based target expression levels, the estimated TF activities have limited temporal accuracy. To address this, we proposed a TF activity measure based on intron-level information in time-series RNA-seq data, and implemented it to decode the temporal control of TF activities during dynamic processes. We showed that TF activities inferred from intronic reads can better recapitulate instantaneous TF activities compared to the exon-based measure. By analyzing public and our own time-series transcriptome data, we found that intron-based TF activities improve the characterization of temporal phasing of cycling TFs during circadian rhythm, and facilitate the discovery of two temporally opposing TF modules during T cell activation. Collectively, we anticipate that the proposed approach would be broadly applicable for decoding global transcriptional architecture during dynamic processes.

Mobility of kinetochore proteins measured by FRAP analysis in living cells

The kinetochore is essential for faithful chromosome segregation during mitosis and is assembled through dynamic processes involving numerous kinetochore proteins. Various experimental strategies have been used to understand kinetochore assembly processes. Fluorescence recovery after photobleaching (FRAP) analysis is also a useful strategy for revealing the dynamics of kinetochore assembly. In this study, we introduced fluorescence protein-tagged kinetochore protein cDNAs into each endogenous locus and performed FRAP analyses in chicken DT40 cells. Centromeric protein (CENP)-C was highly mobile in interphase, but immobile during mitosis. CENP-C mutants lacking the CENP-A-binding domain became mobile during mitosis. In contrast to CENP-C, CENP-T and CENP-H were immobile during both interphase and mitosis. The mobility of Dsn1, which is a component of the Mis12 complex and directly binds to CENP-C, depended on CENP-C mobility during mitosis. Thus, our FRAP assays provide dynamic aspects of how the kinetochore is assembled.

The Pusher as an Object of the Simulation Model in the Tasks of Studying Cyclic Mechanisms Dynamics

The cyclic mechanism is one of the basic automatic machines components. It is the cyclic mechanisms that define the performance and reliability limits of these machines. The methods for assessing the automatic machines reliability are based on data of dynamic forces acting on the mechanism's links, leading to their deformation and possible destruction under certain operating conditions. Simulation modeling of dynamic processes in cyclic mechanisms is based on its representation in the form of interconnected typical objects, the allowable properties and limits of which are known. The article presents the dynamics study results one of the basic high-speed automatic machines mechanism in the object-oriented representation. Mechanism performs translational motion and allows the possibility of one-way kinematic connection breaking.

Nonlinear Mathematical Models of Metamaterials Defined as “Mass-in-Mass” and “Damper-in-Mass” Chains

To describe dynamic processes in an acoustic (mechanical) metamaterial, there are proposed models that are a one-dimensional chain containing the same masses connected by linearly elastic (or nonlinearly elastic) elements (springs) with the same stiffness. In this case, it is assumed that each mass contains inside itself a series connection of another mass and an elastic element or viscous element (damper).