Ihmisten asennon tunnistus syvyyskameralla

Human pose estimation has many applications from activity analysis to autonomous cars. Modern advances in deep learning research have enabled real time multi-person pose estimation in complex environments. In this thesis, a state of the art deep learning architecture is adapted to work with depth sensors. A dataset is generated using computer graphics instead of annotating thousands of images by hand. Results are promising; trained neural network detects humans in multi-person environments even when occlusion is present. However, there are challenges rising from the difference between the real world and the synthetic data generation, which has to be addressed.Automaattisella ihmisten asennon tunnistuksella on lukuisia sovelluksia aktiviteettianalyysistä itsenäisiin autoihin. Nykyaikainen kehitys syvien neuroverkkojen alalla on mahdollistanut useamman ihmisen reaaliaikaisen asennon tunnistamisen monimutkaisissa ympäristöissä. Tässä diplomityössä adaptoidaan nykyaikainen neuroverkko arkkitehtuuri toimimaan syvyyskameralla saaduilla kuvilla. Neuroverkon opetukseen tarvittava opetusdata generoidoon tietokonegrafiikan avulla sen sijaan, että opetusdata luotaisiin käsityönä. Saadut tulokset ovat lupaavia; opetettu neuroverkko kykenee tunnistamaan samanaikaisesti usean ihmisen monimutkaisessa ympäristössä. Kaikesta huolimatta, simuloidun datan ja todellisen maailman välinen eroavaisuus aiheuttaa ongelmia, jotka täytyy ottaa huomioon

Distributed hybrid-indexing of compressed pan-genomes for scalable and fast sequence alignment

Computational pan-genomics utilizes information from multiple individual genomes in large-scale comparative analysis. Genetic variation between case-controls, ethnic groups, or species can be discovered thoroughly using pan-genomes of such subpopulations. Whole-genome sequencing (WGS) data volumes are growing rapidly, making genomic data compression and indexing methods very important. Despite current space-efficient repetitive sequence compression and indexing methods, the deployed compression methods are often sequential, computationally time-consuming, and do not provide efficient sequence alignment performance on vast collections of genomes such as pan-genomes. For performing rapid analytics with the ever-growing genomics data, data compression and indexing methods have to exploit distributed and parallel computing more efficiently. Instead of strict genome data compression methods, we will focus on the efficient construction of a compressed index for pan-genomes. Compressed hybrid-index enables fast sequence alignments to several genomes at once while shrinking the index size significantly compared to traditional indexes. We propose a scalable distributed compressed hybrid-indexing method for large genomic data sets enabling pan-genome-based sequence search and read alignment capabilities. We show the scalability of our tool, DHPGIndex, by executing experiments in a distributed Apache Spark-based computing cluster comprising 448 cores distributed over 26 nodes. The experiments have been performed both with human and bacterial genomes. DHPGIndex built a BLAST index for n = 250 human pan-genome with an 870:1 compression ratio (CR) in 342 minutes and a Bowtie2 index with 157:1 CR in 397 minutes. For n = 1,000 human pan-genome, the BLAST index was built in 1520 minutes with 532:1 CR and the Bowtie2 index in 1938 minutes with 76:1 CR. Bowtie2 aligned 14.6 GB of paired-end reads to the compressed (n = 1,000) index in 31.7 minutes on a single node. Compressing n = 13,375,031 (488 GB) GenBank database to BLAST index resulted in CR of 62:1 in 575 minutes. BLASTing 189,864 Crispr-Cas9 gRNA target sequences (23 MB in total) to the compressed index of human pan-genome (n = 1,000) finished in 45 minutes on a single node. 30 MB mixed bacterial sequences were (n = 599) were blasted to the compressed index of 488 GB GenBank database (n = 13,375,031) in 26 minutes on 25 nodes. 78 MB mixed sequences (n = 4,167) were blasted to the compressed index of 18 GB E. coli sequence database (n = 745,409) in 5.4 minutes on a single node.Peer reviewe

Magnetic microrheometry of tumor-relevant stiffness levels and probabilistic quantification of viscoelasticity differences inside 3D cell culture matrices.

The progression of breast cancer involves cancer-cell invasions of extracellular matrices. To investigate the progression, 3D cell cultures are widely used along with different types of matrices. Currently, the matrices are often characterized using parallel-plate rheometry for matrix viscoelasticity, or liquid-like viscous and stiffness-related elastic characteristics. The characterization reveals averaged information and sample-to-sample variation, yet, it neglects internal heterogeneity within matrices, experienced by cancer cells in 3D culture. Techniques using optical tweezers and magnetic microrheometry have measured heterogeneity in viscoelasticity in 3D culture. However, there is a lack of probabilistic heterogeneity quantification and cell-size-relevant, microscale-viscoelasticity measurements at breast-tumor tissue stiffness up to ≃10 kPa in Young's modulus. Here, we have advanced methods, for the purpose, which use a magnetic microrheometer that applies forces on magnetic spheres within matrices, and detects the spheres displacements. We present probabilistic heterogeneity quantification using microscale-viscoelasticity measurements in 3D culture matrices at breast-tumor-relevant stiffness levels. Bayesian multilevel modeling was employed to distinguish heterogeneity in viscoelasticity from the effects of experimental design and measurement errors. We report about the heterogeneity of breast-tumor-relevant agarose, GrowDex, GrowDex-collagen and fibrin matrices. The degree of heterogeneity differs for stiffness, and phase angle (i.e. ratio between viscous and elastic characteristics). Concerning stiffness, agarose and GrowDex show the lowest and highest heterogeneity, respectively. Concerning phase angle, fibrin and GrowDex-collagen present the lowest and the highest heterogeneity, respectively. While this heterogeneity information involves softer matrices, probed by ≃30 μm magnetic spheres, we employ larger ≃100 μm spheres to increase magnetic forces and acquire a sufficient displacement signal-to-noise ratio in stiffer matrices. Thus, we show pointwise microscale viscoelasticity measurements within agarose matrices up to Young's moduli of 10 kPa. These results establish methods that combine magnetic microrheometry and Bayesian multilevel modeling for enhanced heterogeneity analysis within 3D culture matrices

Fibrin Stiffness Regulates Phenotypic Plasticity of Metastatic Breast Cancer Cells

The extracellular matrix (ECM)-regulated phenotypic plasticity is crucial for metastatic progression of triple negative breast cancer (TNBC). While ECM faithful cell-based models are available for in situ and invasive tumors, such as cell aggregate cultures in reconstituted basement membrane and in collagenous gels, there are no ECM faithful models for metastatic circulating tumor cells (CTCs). Such models are essential to represent the stage of metastasis where clinical relevance and therapeutic opportunities are significant. Here, CTC-like DU4475 TNBC cells are cultured in mechanically tunable 3D fibrin hydrogels. This is motivated, as in circulation fibrin aids CTC survival by forming a protective coating reducing shear stress and immune cell-mediated cytotoxicity and promotes several stages of late metastatic processes at the interface between circulation and tissue. This work shows that fibrin hydrogels support DU4475 cell growth, resulting in spheroid formation. Furthermore, increasing fibrin stiffness from 57 to 175 Pa leads to highly motile, actin and tubulin containing cellular protrusions, which are associated with specific cell morphology and gene expression patterns that markedly differ from basement membrane or suspension cultures. Thus, mechanically tunable fibrin gels reveal specific matrix-based regulation of TNBC cell phenotype and offer scaffolds for CTC-like cells with better mechano-biological properties than liquid.Peer reviewe