217 research outputs found
Neural Network Methods for Radiation Detectors and Imaging
Recent advances in image data processing through machine learning and
especially deep neural networks (DNNs) allow for new optimization and
performance-enhancement schemes for radiation detectors and imaging hardware
through data-endowed artificial intelligence. We give an overview of data
generation at photon sources, deep learning-based methods for image processing
tasks, and hardware solutions for deep learning acceleration. Most existing
deep learning approaches are trained offline, typically using large amounts of
computational resources. However, once trained, DNNs can achieve fast inference
speeds and can be deployed to edge devices. A new trend is edge computing with
less energy consumption (hundreds of watts or less) and real-time analysis
potential. While popularly used for edge computing, electronic-based hardware
accelerators ranging from general purpose processors such as central processing
units (CPUs) to application-specific integrated circuits (ASICs) are constantly
reaching performance limits in latency, energy consumption, and other physical
constraints. These limits give rise to next-generation analog neuromorhpic
hardware platforms, such as optical neural networks (ONNs), for high parallel,
low latency, and low energy computing to boost deep learning acceleration
Biological characterization of Philadelphia chromosome-positive acute lymphoblastic leukemia
The prognosis of Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia (ALL) has significantly improved with the introduction of tyrosine kinase inhibitors (TKIs). As the incidence of Ph-positivity increases with age, a substantial number of elderly Ph+ ALL patients are ineligible for intensive treatment modalities. Currently, a proportion of patients experience prolonged survival with TKI-based therapies only, and many succumb eventually to non leukemia-related causes.
The aim of this thesis was to identify potential predictive biomarkers for more personalized risk stratification in Ph+ ALL, including characterization of the immune microenvironment in ALL bone marrow (BM). We also wanted to assess the drug sensitivity of primary patient samples to identify potential novel or repurposed drugs, with especially non-fit patients in mind, and to study the prevalence of copy number alterations and other secondary mutations.
In study I, we collected archived formalin-fixed and paraffin-embedded BM biopsies from Ph+ (n = 31) and Philadelphia chromosome-negative (Ph−; n = 21) ALL patients and non-leukemic controls (n = 14). The samples were constructed to tissue microarrays and analyzed with multiplex immunohistochemistry and automated image analysis. The immune contexture of Ph+ and Ph− ALL BM did not differ significantly. Instead, ALL BM was characterized by an increased amount of immune cells associated with immunosuppression when compared to healthy controls. Further, the higher proportion of CD4+PD1+TIM3+ T cells, older age, and lower platelet count at diagnosis segregated a group with poor survival.
In study II, we analyzed the drug sensitivity of 18 primary B-ALL BM samples (Ph+ n=10, Ph− n=8) to a selection of 64 drugs by using a well-established drug sensitivity and resistance testing assay. The results were combined with whole transcriptome sequencing and publicly available gene expression data. Apoptosis-modulating BCL2 inhibitors and MDM2 inhibitors were widely effective. BCL2-selective venetoclax was more effective in Ph− samples, whereas BCL2, BCL-W, and BCL-XL targeting navitoclax showed uniform potency. BCL2 expression was significantly higher in Ph− ALL, whereas BCL-W and BCL-XL were overexpressed in Ph+ ALL, explaining the differential drug responses. In addition, the sequencing strategies recognized three previously undiagnosed Ph-like patients with a sensitivity to TKIs.
In study III, we investigated the frequency and significance of copy number alterations (CNAs) and other secondary mutations in Ph+ ALL by applying targeted next-generation sequencing (NGS) gene panel and multiplex ligation-dependent probe amplification to diagnostic (n=40) and relapse-phase (n=11) BM samples. We also assessed the prevalence of subclonal T315I kinase domain mutations. The results were combined with clinical registry data. Deletions of IKZF1 together with deletions in CDKN2A/B and/or PAX5 were common, and they stratified a group with dismal outcome. Other secondary mutations at diagnosis were rare.
In conclusion, this thesis shows Ph+ ALL BM immune contexture did not differ from Ph− ALL. Instead, ALL BM immune microenvironment differs from healthy controls, and immune profiling can serve as a tool in identifying novel prognostic biomarkers. Copy number alterations (CNA) defined a subset in Ph+ ALL with dismal outcome, and we recommend incorporating CNA analysis to routine diagnostic procedures. In addition, with ex vivo drug testing, we identified several potential compounds to be further tested in clinical trials.Tyrosiinikinaasiestäjät (TKE) ovat parantaneet merkittävästi Philadelphia-kromosomipositiivisen (Ph+) akuutin lymfaattisen leukemian (ALL) ennustetta. Koska Ph+ ALL :n yleisyys kasvaa iän myötä, merkittävää osaa näistä iäkkäämmistä tai heikkokuntoisemmista potilaista ei voida kuitenkaan hoitaa tavanomaisilla intensiivisillä hoito-ohjelmilla hoitoon liittyvien haittojen vuoksi. Toisaalta osa potilaista saa hyvän vasteen pelkälle TKE-pohjaiselle kevennetylle hoidolle, ja monet menehtyvät lopulta leukemiaan liittymättömiin syihin.
Tämän väitöskirjatyön tavoitteena oli selvittää potentiaalisia biomarkkereita Ph+ ALL :n yksilöllisemmän riskinarvioinnin kehittämiseksi, sekä kuvata immuunijärjestelmän koostumusta ALL :n luuytimen mikroympäristössä. Analysoimme myös potilasnäytteiden herkkyyttä lupaaville lääkeaineille ajatellen erityisesti hauraampien potilaiden ilmeistä tarvetta tehokkaille ja samalla turvallisille lääkehoidoille. Arvioimme myös kopiolukumuutosten ja muiden sekundaaristen mutaatioiden esiintyvyyttä Ph+ ALL:ssa.
Ensimmäisessä osatyössä keräsimme vanhoja luuydinbiopsioita Ph+ (n=31) ja Philadelphia-kromosominegatiivista (Ph−; n=21) ALL:ia sairastavilta potilailta sekä terveiltä kontrolleilta (n=14). Näytteistä koostetut kudosblokit värjättiin multipleksatulla immunohistokemialla ja analysoitiin käyttäen apuna automatisoitua kuva-analyysia. Ph+ ja Ph− ALL -potilaiden luuytimen immunologinen mikroympäristö ei eronnut merkittävästi toisistaan. Sen sijaan ALL-potilailla immuunivasteen heikentämiseen liittyvien solutyyppien osuus oli korostunut verrattuna terveisiin kontrolleihin. Lisäksi CD4+PD1+TIM3+ T-solujen suurempi osuus, korkeampi ikä sekä matalampi verihiutaleiden määrä diagnoosihetkellä erottelivat monimuuttujamallissa ALL-potilaista huonoennusteisen ryhmän.
Toisessa osatyössä analysoimme 18 potilasnäytteen (Ph+ n=10, Ph− n=8) herkkyyttä 64 eri lääkeaineelle käyttämällä vakiintunutta lääkeherkkyystestausmenetelmää. Näytteistä tehtiin myös RNA-sekvensointi, sekä tulokset yhdistettiin julkisista tietokannoista saatavilla olevaan geenien ilmentymistä kuvaavaan dataan. Ohjelmoitua solukuolemaa edistävät BCL2:n ja MDM2:n estäjät olivat tehokkaita valtaosassa näytteitä. Valikoivasti BCL2:een kohdistuva venetoklaksi oli tehokkaampi Ph− näytteissä, kun taas laajemmin BCL2:een, BCL-W:een sekä BCL-XL:ään kohdistuva navitoklaksi oli tehokas lähes kaikissa näytteissä. BCL2-geenin ilmentyminen oli lisääntynyt Ph− ALL-potilailla, kun taas BCL-W- ja BCL-XL-geenien ilmentymistasot olivat korkeampia Ph+ ALL:ssa tarjoten samalla mekanistisen selityksen eroille lääkevasteissa. Sekvensointi tunnisti lisäksi kolmen Ph− potilaan näytteessä geneettisiä muutoksia, jotka aiheuttivat herkkyyttä TKE-lääkkeille.
Kolmannessa osatyössä selvitimme kopiolukumuutosten ja muiden sekundaaristen geneettisen muutosten yleisyyttä ja merkitystä Ph+ ALL:ssa hyödyntämällä kohdennettua syväsekvensointia sekä MLPA-menetelmää (MLPA, multiplex ligation-dependent probe amplification) diagnoosi- (n=40) ja relapsivaiheen (n=11) luuydinnäytteissä. Arvioimme myös subklonaalisten T315I kinaasialueen mutaatioiden esiintyvyyttä. Tulokset analysoitiin yhdessä kliinisen rekisteridatan kanssa. IKZF1-geenin deleetiot yhdessä CDKN2A/B ja/tai PAX5-geenin deleetioiden kanssa olivat yleisiä ja erottelivat erityisen huonon ennusteen ryhmän. Muita sekundaarisia geneettisiä muutoksia esiintyi lähinnä relapsivaiheen näytteissä.
Tässä väitöskirjatyössä osoitimme, että Ph+ ALL:ia ja Ph− ALL:ia sairastavien potilaiden luuytimen immunologinen mikroympäristö ei eronnut merkittävästi toisistaan. Sen sijaan ALL:n luuytimen immunologinen mikroympäristö erosi terveistä kontrolleista, ja immuunijärjestelmän profilointia voidaan hyödyntää etsittäessä uusia ennusteeseen vaikuttavia biomarkkereita. Yhdistelmä epäsuotuisia kopiolukumuutoksia erotteli huonon ennusteen alaryhmän Ph+ ALL:ssa, ja suosittelemme kopiolukumuutosten rutiininomaista määrittämistä diagnoosivaiheessa. Lisäksi tunnistimme ex vivo -lääkeherkkyystestauksella useita ALL:n kliinisiin lääketutkimuksiin soveltuvia, lupaavia lääkeaineita
2022 Review of Data-Driven Plasma Science
Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required
Unstained Tissue Imaging and Virtual Hematoxylin and Eosin Staining of Histologic Whole Slide Images
Tissue structures, phenotypes, and pathology are routinely investigated based on histology. This includes chemically staining the transparent tissue sections to make them visible to the human eye. Although chemical staining is fast and routine, it permanently alters the tissue and often consumes hazardous reagents. On the other hand, on using adjacent tissue sections for combined measurements, the cell-wise resolution is lost owing to sections representing different parts of the tissue. Hence, techniques providing visual information of the basic tissue structure enabling additional measurements from the exact same tissue section are required. Here we tested unstained tissue imaging for the development of computational hematoxylin and eosin (HE) staining. We used unsupervised deep learning (CycleGAN) and whole slide images of prostate tissue sections to compare the performance of imaging tissue in paraffin, as deparaffinized in air, and as deparaffinized in mounting medium with section thicknesses varying between 3 and 20 μm. We showed that although thicker sections increase the information content of tissue structures in the images, thinner sections generally perform better in providing information that can be reproduced in virtual staining. According to our results, tissue imaged in paraffin and as deparaffinized provides a good overall representation of the tissue for virtually HE-stained images. Further, using a pix2pix model, we showed that the reproduction of overall tissue histology can be clearly improved with image-to-image translation using supervised learning and pixel-wise ground truth. We also showed that virtual HE staining can be used for various tissues and used with both 20× and 40× imaging magnifications. Although the performance and methods of virtual staining need further development, our study provides evidence of the feasibility of whole slide unstained microscopy as a fast, cheap, and feasible approach to producing virtual staining of tissue histology while sparing the exact same tissue section ready for subsequent utilization with follow-up methods at single-cell resolution.publishedVersionPeer reviewe
Automatic understanding of multimodal content for Web-based learning
Web-based learning has become an integral part of everyday life for all ages and backgrounds. On the one hand, the advantages of this learning type, such as availability, accessibility, flexibility, and cost, are apparent. On the other hand, the oversupply of content can lead to learners struggling to find optimal resources efficiently. The interdisciplinary research field Search as Learning is concerned with the analysis and improvement of Web-based learning processes, both on the learner and the computer science side.
So far, automatic approaches that assess and recommend learning resources in Search as Learning (SAL) focus on textual, resource, and behavioral features. However, these approaches commonly ignore multimodal aspects. This work addresses this research gap by proposing several approaches that address the question of how multimodal retrieval methods can help support learning on the Web. First, we evaluate whether textual metadata of the TIB AV-Portal can be exploited and enriched by semantic word embeddings to generate video recommendations and, in addition, a video summarization technique to improve exploratory search. Then we turn to the challenging task of knowledge gain prediction that estimates the potential learning success given a specific learning resource. We used data from two user studies for our approaches. The first one observes the knowledge gain when learning with videos in a Massive Open Online Course (MOOC) setting, while the second one provides an informal Web-based learning setting where the subjects have unrestricted access to the Internet. We then extend the purely textual features to include visual, audio, and cross-modal features for a holistic representation of learning resources. By correlating these features with the achieved knowledge gain, we can estimate the impact of a particular learning resource on learning success.
We further investigate the influence of multimodal data on the learning process by examining how the combination of visual and textual content generally conveys information. For this purpose, we draw on work from linguistics and visual communications, which investigated the relationship between image and text by means of different metrics and categorizations for several decades. We concretize these metrics to enable their compatibility for machine learning purposes. This process includes the derivation of semantic image-text classes from these metrics. We evaluate all proposals with comprehensive experiments and discuss their impacts and limitations at the end of the thesis.Web-basiertes Lernen ist ein fester Bestandteil des Alltags aller Alters- und Bevölkerungsschichten geworden. Einerseits liegen die Vorteile dieser Art des Lernens wie Verfügbarkeit, Zugänglichkeit, Flexibilität oder Kosten auf der Hand. Andererseits kann das Überangebot an Inhalten auch dazu führen, dass Lernende nicht in der Lage sind optimale Ressourcen effizient zu finden. Das interdisziplinäre Forschungsfeld Search as Learning beschäftigt sich mit der Analyse und Verbesserung von Web-basierten Lernprozessen.
Bisher sind automatische Ansätze bei der Bewertung und Empfehlung von Lernressourcen fokussiert auf monomodale Merkmale, wie Text oder Dokumentstruktur. Die multimodale Betrachtung ist hingegen noch nicht ausreichend erforscht. Daher befasst sich diese Arbeit mit der Frage wie Methoden des Multimedia Retrievals dazu beitragen können das Lernen im Web zu unterstützen. Zunächst wird evaluiert, ob textuelle Metadaten des TIB AV-Portals genutzt werden können um in Verbindung mit semantischen Worteinbettungen einerseits Videoempfehlungen zu generieren und andererseits Visualisierungen zur Inhaltszusammenfassung von Videos abzuleiten. Anschließend wenden wir uns der anspruchsvollen Aufgabe der Vorhersage des Wissenszuwachses zu, die den potenziellen Lernerfolg einer Lernressource schätzt. Wir haben für unsere Ansätze Daten aus zwei Nutzerstudien verwendet. In der ersten wird der Wissenszuwachs beim Lernen mit Videos in einem MOOC-Setting beobachtet, während die zweite eine informelle web-basierte Lernumgebung bietet, in der die Probanden uneingeschränkten Internetzugang haben. Anschließend erweitern wir die rein textuellen Merkmale um visuelle, akustische und cross-modale Merkmale für eine ganzheitliche Darstellung der Lernressourcen. Durch die Korrelation dieser Merkmale mit dem erzielten Wissenszuwachs können wir den Einfluss einer Lernressource auf den Lernerfolg vorhersagen.
Weiterhin untersuchen wir wie verschiedene Kombinationen von visuellen und textuellen Inhalten Informationen generell vermitteln. Dazu greifen wir auf Arbeiten aus der Linguistik und der visuellen Kommunikation zurück, die seit mehreren Jahrzehnten die Beziehung zwischen Bild und Text untersucht haben. Wir konkretisieren vorhandene Metriken, um ihre Verwendung für maschinelles Lernen zu ermöglichen. Dieser Prozess beinhaltet die Ableitung semantischer Bild-Text-Klassen. Wir evaluieren alle Ansätze mit umfangreichen Experimenten und diskutieren ihre Auswirkungen und Limitierungen am Ende der Arbeit
A sense of self for power side-channel signatures: instruction set disassembly and integrity monitoring of a microcontroller system
Cyber-attacks are on the rise, costing billions of dollars in damages, response, and investment annually. Critical United States National Security and Department of Defense weapons systems are no exception, however, the stakes go well beyond financial. Dependence upon a global supply chain without sufficient insight or control poses a significant issue. Additionally, systems are often designed with a presumption of trust, despite their microelectronics and software-foundations being inherently untrustworthy. Achieving cybersecurity requires coordinated and holistic action across disciplines commensurate with the specific systems, mission, and threat.
This dissertation explores an existing gap in low-level cybersecurity while proposing a side-channel based security monitor to support attack detection and the establishment of trusted foundations for critical embedded systems. Background on side-channel origins, the more typical side-channel attacks, and microarchitectural exploits are described. A survey of related side-channel efforts is provided through side-channel organizing principles. The organizing principles enable comparison of dissimilar works across the side-channel spectrum. We find that the maturity of existing side-channel security monitors is insufficient, as key transition to practice considerations are often not accounted for or resolved.
We then document the development, maturation, and assessment of a power side-channel disassembler, Time-series Side-channel Disassembler (TSD), and extend it for use as a security monitor, TSD-Integrity Monitor (TSD-IM). We also introduce a prototype microcontroller power side-channel collection fixture, with benefits to experimentation and transition to practice. TSD-IM is finally applied to a notional Point of Sale (PoS) application for proof of concept evaluation. We find that TSD and TSD-IM advance state of the art for side-channel disassembly and security monitoring in open literature.
In addition to our TSD and TSD-IM research on microcontroller signals, we explore beneficial side-channel measurement abstractions as well as the characterization of the underlying microelectronic circuits through Impulse Signal Analysis (ISA). While some positive results were obtained, we find that further research in these areas is necessary. Although the need for a non-invasive, on-demand microelectronics-integrity capability is supported, other methods may provide suitable near-term alternatives to ISA
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