Darbo tikslas: įvertinti hologramos poveikį sveikų, jauno amžiaus asmenų širdies ir kraujagyslių sistemos funkciniams rodikliams, funkcinių judesių atlikimo stereotipui, dinaminio stabilumo ir fizinio pajėgumo rodikliams. Darbo tikslui įgyvendinti buvo iškelti šie uždaviniai: 1. Įvertinti hologramos poveikį sveikų, jauno amžiaus asmenų širdies ir kraujagyslių sistemos funkciniams rodikliams. 2. Įvertinti hologramos poveikį sveikų, jauno amžiaus asmenų funkcinių judesių atlikimo stereotipui. 3. Įvertinti hologramos poveikį sveikų, jauno amžiaus asmenų dinaminio stabilumo rodikliams. 4. Įvertinti hologramos poveikį sveikų, jauno amžiaus asmenų fizinio pajėgumo rodikliams. Tyrimas atliktas Lietuvos sveikatos mokslų universiteto Medicinos akademijos Slaugos fakulteto Sporto institute. Tyrime dalyvavo 31 asmuo. Moterų grupėje- 10 tiriamųjų (moterų amžiaus vidurkis 22,1 ±1,5 m. ( ±SEM, standartinė vidurkio įverčio paklaida), kūno masės indekso vidurkis 20,3 ±1,8 kg/m2). Vyrų grupėje - 21 tiriamasis ( amžiaus vidurkis 23,1 ± 1,2 m., kūno masės indekso vidurkis 23,6 ± 2,1 kg/m² ). Tyrimo dalyviai du kartus atliko pakopomis kas minutę didėjančio fizinio krūvio mėginį veloergometru, funkcinių judesių atlikimo stereotipo vertinimo (angl. Functional Movement Screen), Eurofito testų rinkinius ir Y pusiausvyros testą. Vieną kartą jie dėvėjo apyrankę su aktyvia holograma, o kitą kartą su neaktyvia holograma (placebo). Tarp atskirų testavimų buvo daroma savaitės pertrauka. Rezultatai:... [toliau žr. visą tekstą] / The aim of the study- evaluate the effect of hologram on young healthy human cardiovascular system, movement pattern quality, dynamic stability and physical performance. Goals of this research: 1. Evaluate hologram’s effect on young healthy human cardiovascular system. 2. Evaluate hologram’s effect on young healthy human movement pattern quality. 3. Evaluate hologram’s effect on young healthy human dynamic stability. 4. Evaluate holographic effect on young healthy human physical performance This study was carried out in the Lithuanian University of Health Sciences, Faculty of Nursery, Institute of Sport. 10 females (age average of 22,1 ± 1,5 m.(± SEM, standard error mean), body mass index average of 20,3 ± 1,8 kg/m2) and 21 males(age average of 23,1 ± 1,2 m., body mass index average of 23,6 ± 2,1 kg/m2) were tested. Reasearch type: each subject was tested 2 times – first time wearing wristband with active hologram and second time wearing wristband with non- active (placebo) hologram. One week interval was held between the testing in order to avoid the effect of previous testing. Tested person weren’t able to know what kind of wristband he or she is wearing during the testing. Holograms looked the same, with the same picture andthe same size. Participants performed increasing load veloergometer excirsice test, Functional Movement Screen and Eurofit test batteries, Y balance test. Results:. dynamic stability measured by Y balance testst in the anterior direction of the left... [to full text]

A method of holographic cross-connection is proposed for an Optical Ising machine system. The designed optical Ising machine based on multi-core fiber laser is introduced, including the theory of computation, history of optical computing, the concept of Ising model, the significance of optical Ising machine, the method to achieve Ising machine optically. The cross-connection part is based on computer-generated holograms (CGH), which is produced by Gerchburg-Saxton algorithm. The coupling coefficient between two channels as well as the phase change are controlled by CGHs. The design of holograms is discussed. The instrument used to display holograms is phase-only liquid crystal spatial light modulator (SLM) from HOLOEYE company. The optical system needed in this project, such as collimation lens and relay lens, is designed using Zemax. The system is first evaluated in Zemax simulation, and then constructed experimentally. The results show that we can control amplitude and phase of the reinjection beam at Multi-core fiber. Further experiment should be done to conclude that the control of the cross coupling between channels is achieved by displaying different holograms.

This report summarizes the work done to develop a 3D teleconferencing system, which enables remote participants anywhere in the world to be scanned in 3D, transmitted and displayed on a constructed 3D display with correct vertical and horizontal parallax, correct eye contact and eye gaze. The main focus of this report is the development of this system and especially how to in an efficient and general manner render to the novel 3D display. The 3D display is built out of modified commodity hardware and show a 3D scene for observers in up to 360 degrees around it and all heights. The result is a fully working 3D Teleconferencing system, resembling communication envisioned in movies such as holograms from Star Wars. The system transmits over the internet, at similar bandwidth requirements as concurrent 2D videoconferencing systems. / Project done at USC Institute for Creative Technologies, LA, USA. Presented at SIGGRAPH09.

Approved for public release; distribution is unlimited / This thesis describes the procedure used for the automatic measurement of particles from hologram taken in the combustion chamber of a rocket motor while firing. It describes the investigation done on two averaging techniques used to reduce speckle noise, capturing the image focused on a spinning mylar disk and software averaging of several image frames. The spinning disk technique proved superior for this application. The Kolmogorov-Smirnov two-sample test is applied to different particle samples in order to find an estimate of the number of particles required to obtain a stable distribution function. The number of particles is calculated and given. The last part of this study shows real particle distributions in the form of frequency histograms. / http://archive.org/details/automaticmeasure00carr / Major, Canadian Armed Forces

Cell-to-cell variations and intracellular processes such as cytoskeletal organization and organelle dynamics exhibit massive heterogeneity. Advances in imaging and optics have enabled researchers to access spatiotemporal information in living cells efficiently. Even though current imaging technologies allow us to acquire an unprecedented amount of cell images, it is challenging to extract valuable information from the massive and complex dataset to interpret heterogeneous biological processes. Machine learning (ML), referring to a set of computational tools to acquire knowledge from data, provides promising solutions to meet this challenge. In this dissertation, we developed ML pipelines for deconvolution of subcellular protrusion heterogeneity from live cell imaging and molecular diagnostic from lens-free digital in-line holography (LDIH) imaging. Cell protrusion is driven by spatiotemporally fluctuating actin assembly processes and is morphodynamically heterogeneous at the subcellular level. Elucidating the underlying molecular dynamics associated with subcellular protrusion heterogeneity is crucial to understanding the biology of cellular movement. Traditional ensemble averaging methods without characterizing the heterogeneity could mask important activities. Therefore, we established an ACF (auto-correlation function) based time series clustering pipeline called HACKS (deconvolution of heterogeneous activities in coordination of cytoskeleton at the subcellular level) to identify distinct subcellular lamellipodial protrusion phenotypes with their underlying actin regulator dynamics from live cell imaging. Using our method, we discover “accelerating protrusion”, which is driven by the temporally ordered coordination of Arp2/3 and VASP activities. Furthermore, deriving the merits of ML, especially Deep Learning (DL) to learn features automatically, we advanced our pipeline to learn fine-grained temporal features by integrating the prior ML analysis results with bi-LSTM (bi-direction long-short term memory) autoencoders to dissect variable-length time series protrusion heterogeneity. By applying it to subcellular protrusion dynamics in pharmacologically and metabolically perturbed epithelial cells, we discovered fine differential response of protrusion dynamics specific to each perturbation. This provides an analytical framework for detailed and quantitative understanding of molecular mechanisms hidden in their heterogeneity. Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microscopy. Numerical reconstruction for hologram images from large-field-of-view LDIH is extremely time-consuming. Until now, there are no effective manual-design features to interpret the lateral and depth information from complex diffraction patterns in hologram images directly, which limits LDIH utility for point-of-care applications. Inherited from advantages of DL to learn generalized features automatically, we proposed a deep transfer learning (DTL)-based approach to process LDIH images without reconstruction in the context of cellular analysis. Specifically, using the raw holograms as input, the features extracted from a well-trained network were able to classify cell categories according to the number of cell-bounded microbeads, which performance was comparable with that of object images as input. Combined with the developed DTL approach, LDIH could be realized as a low-cost, portable tool for point-of-care diagnostics. In summary, this dissertation demonstrate that ML applied to cell imaging can successfully dissect subcellular heterogeneity and perform cell-based diagnosis. We expect that our study will be able to make significant contributions to data-driven cell biological research.

Topics fragmentary , symbolism and memory are the basic elements of creation and thinking Adele Sobotková (1987 ) . In her installation made of clay and covered with a udusaných objects together with the soil can walk, touch , read, intervene . This raises a cryptographic magical space full of mysterious objects in the video that represent clusters of personal hieroglyphic symbols and structures without the possibility of accurate decryption. These enigmatic characters are like glass or water , are changing , floating landscapes and refer to feelings of uncertainty, and forgetting the past, which is not entirely clear - is revealed as a mirage . Hologram is a representation of reality or reality that no longer exists but in time, or never existed , dream hi - technology , the current symbol of immortality, infinite and preservation in time. " Holographic hieroglyph " is a fluid phenomenon with varying meanings depending on its reading , the transcript or constant metaphor memories and the zpřítomňování hand in hand zpřítomňování a revival oblivion . Unclear character that has certainly not decipher , and yet we have it all in front of him , we see him from all sides , but also through him is the present and yet unreal . Great recording of uncertainty. (press release of the exhibition Holographic Hieroglyph in the Youth Gallery in Brno)

This work describes a relatively new technique designed to write highly parallel programs, that name is OpenCL. It is intended for both GPU and CPU and other parallel processors. Libraries used by the processor architecture, which includes a large number of small cores. These cores are not as comprehensive as conventional processors and is therefore suitable for calculations, which are many and they are simple. It is this property could, under certain conditions, accelerate the calculation of the hologram, namely the calculation of the optical field. While the calculation itself is simple, but the amount of processed data is large and therefore slow. The work also contain the basic concepts of explanation of optical and digital holography.

Cell-to-cell variations and intracellular processes such as cytoskeletal organization and organelle dynamics exhibit massive heterogeneity. Advances in imaging and optics have enabled researchers to access spatiotemporal information in living cells efficiently. Even though current imaging technologies allow us to acquire an unprecedented amount of cell images, it is challenging to extract valuable information from the massive and complex dataset to interpret heterogeneous biological processes. Machine learning (ML), referring to a set of computational tools to acquire knowledge from data, provides promising solutions to meet this challenge. In this dissertation, we developed ML pipelines for deconvolution of subcellular protrusion heterogeneity from live cell imaging and molecular diagnostic from lens-free digital in-line holography (LDIH) imaging. Cell protrusion is driven by spatiotemporally fluctuating actin assembly processes and is morphodynamically heterogeneous at the subcellular level. Elucidating the underlying molecular dynamics associated with subcellular protrusion heterogeneity is crucial to understanding the biology of cellular movement. Traditional ensemble averaging methods without characterizing the heterogeneity could mask important activities. Therefore, we established an ACF (auto-correlation function) based time series clustering pipeline called HACKS (deconvolution of heterogeneous activities in coordination of cytoskeleton at the subcellular level) to identify distinct subcellular lamellipodial protrusion phenotypes with their underlying actin regulator dynamics from live cell imaging. Using our method, we discover “accelerating protrusion”, which is driven by the temporally ordered coordination of Arp2/3 and VASP activities. Furthermore, deriving the merits of ML, especially Deep Learning (DL) to learn features automatically, we advanced our pipeline to learn fine-grained temporal features by integrating the prior ML analysis results with bi-LSTM (bi-direction long-short term memory) autoencoders to dissect variable-length time series protrusion heterogeneity. By applying it to subcellular protrusion dynamics in pharmacologically and metabolically perturbed epithelial cells, we discovered fine differential response of protrusion dynamics specific to each perturbation. This provides an analytical framework for detailed and quantitative understanding of molecular mechanisms hidden in their heterogeneity. Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microscopy. Numerical reconstruction for hologram images from large-field-of-view LDIH is extremely time-consuming. Until now, there are no effective manual-design features to interpret the lateral and depth information from complex diffraction patterns in hologram images directly, which limits LDIH utility for point-of-care applications. Inherited from advantages of DL to learn generalized features automatically, we proposed a deep transfer learning (DTL)-based approach to process LDIH images without reconstruction in the context of cellular analysis. Specifically, using the raw holograms as input, the features extracted from a well-trained network were able to classify cell categories according to the number of cell-bounded microbeads, which performance was comparable with that of object images as input. Combined with the developed DTL approach, LDIH could be realized as a low-cost, portable tool for point-of-care diagnostics. In summary, this dissertation demonstrate that ML applied to cell imaging can successfully dissect subcellular heterogeneity and perform cell-based diagnosis. We expect that our study will be able to make significant contributions to data-driven cell biological research.

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