Automation and the worker

Thesis (M.B.A.)--Boston Universit

PADTUN - using semantic technologies in tunnel diagnosis and maintenance domain

YesA Decision Support System (DSS) in tunnelling domain deals with identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key area in diagnosing pathologies is to identify regions of interest (ROI). In practice, tunnel experts intuitively abstract regions of interest by selecting tunnel portions that are susceptible to the same types of pathologies with some distance approximation. This complex diagnosis process is often subjective and poorly scales across cases and transport structures. In this paper, we introduce PADTUN system, a working prototype of a DSS in tunnelling domain using semantic technologies. Ontologies are developed and used to capture tacit knowledge from tunnel experts. Tunnel inspection data are annotated with ontologies to take advantage of inferring capabilities offered by semantic technologies. In addition, an intelligent mechanism is developed to exploit abstraction and inference capabilities to identify ROI. PADTUN is developed in real-world settings offered by the NeTTUN EU Project and is applied in a tunnel diagnosis use case with Société Nationale des Chemins de Fer Français (SNCF), France. We show how the use of semantic technologies allows addressing the complex issues of pathology and ROI inferencing and matching experts’ expectations of decision support

Composite-pulse magnetometry with a solid-state quantum sensor

The sensitivity of quantum magnetometers is challenged by control errors and, especially in the solid-state, by their short coherence times. Refocusing techniques can overcome these limitations and improve the sensitivity to periodic fields, but they come at the cost of reduced bandwidth and cannot be applied to sense static (DC) or aperiodic fields. Here we experimentally demonstrate that continuous driving of the sensor spin by a composite pulse known as rotary-echo (RE) yields a flexible magnetometry scheme, mitigating both driving power imperfections and decoherence. A suitable choice of RE parameters compensates for different scenarios of noise strength and origin. The method can be applied to nanoscale sensing in variable environments or to realize noise spectroscopy. In a room-temperature implementation based on a single electronic spin in diamond, composite-pulse magnetometry provides a tunable trade-off between sensitivities in the microT/sqrt(Hz) range, comparable to those obtained with Ramsey spectroscopy, and coherence times approaching T1

A robust, scanning quantum system for nanoscale sensing and imaging

Controllable atomic-scale quantum systems hold great potential as sensitive tools for nanoscale imaging and metrology. Possible applications range from nanoscale electric and magnetic field sensing to single photon microscopy, quantum information processing, and bioimaging. At the heart of such schemes is the ability to scan and accurately position a robust sensor within a few nanometers of a sample of interest, while preserving the sensor's quantum coherence and readout fidelity. These combined requirements remain a challenge for all existing approaches that rely on direct grafting of individual solid state quantum systems or single molecules onto scanning-probe tips. Here, we demonstrate the fabrication and room temperature operation of a robust and isolated atomic-scale quantum sensor for scanning probe microscopy. Specifically, we employ a high-purity, single-crystalline diamond nanopillar probe containing a single Nitrogen-Vacancy (NV) color center. We illustrate the versatility and performance of our scanning NV sensor by conducting quantitative nanoscale magnetic field imaging and near-field single-photon fluorescence quenching microscopy. In both cases, we obtain imaging resolution in the range of 20 nm and sensitivity unprecedented in scanning quantum probe microscopy

Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses

Background: The explosive growth of biological data provides opportunities for new statistical and comparative analyses of large information sets, such as alignments comprising tens of thousands of sequences. In such studies, sequence annotations frequently play an essential role, and reliable results depend on metadata quality. However, the semantic heterogeneity and annotation inconsistencies in biological databases greatly increase the complexity of aggregating and cleaning metadata. Manual curation of datasets, traditionally favoured by life scientists, is impractical for studies involving thousands of records. In this study, we investigate quality issues that affect major public databases, and quantify the effectiveness of an automated metadata extraction approach that combines structural and semantic rules. We applied this approach to more than 90,000 influenza A records, to annotate sequences with protein name, virus subtype, isolate, host, geographic origin, and year of isolation. Results: Over 40,000 annotated Influenza A protein sequences were collected by combining information from more than 90,000 documents from NCBI public databases. Metadata values were automatically extracted, aggregated and reconciled from several document fields by applying user-defined structural rules. For each property, values were recovered from ≥88.8% of records, with accuracy exceeding 96% in most cases. Because of semantic heterogeneity, each property required up to six different structural rules to be combined. Significant quality differences between databases were found: GenBank documents yield values more reliably than documents extracted from GenPept. Using a simple set of semantic rules and a reasoner, we reconstructed relationships between sequences from the same isolate, thus identifying 7640 isolates. Validation of isolate metadata against a simple ontology highlighted more than 400 inconsistencies, leading to over 3,000 property value corrections. Conclusion: To overcome the quality issues inherent in public databases, automated knowledge aggregation with embedded intelligence is needed for large-scale analyses. Our results show that user-controlled intuitive approaches, based on combination of simple rules, can reliably automate various curation tasks, reducing the need for manual corrections to approximately 5% of the records. Emerging semantic technologies possess desirable features to support today's knowledge aggregation tasks, with a potential to bring immediate benefits to this field

Specific mechanisms of chromosomal instability indicate therapeutic sensitivities in high-grade serous ovarian carcinoma.

Chromosomal instability (CIN) comprises continual gain and loss of chromosomes or parts of chromosomes and occurs in the majority of cancers, often conferring poor prognosis. Due to a scarcity of functional studies and poor understanding of how genetic or gene expression landscapes connect to specific CIN mechanisms, causes of CIN in most cancer types remain unknown. High-grade serous ovarian carcinoma (HGSC), the most common subtype of ovarian cancer, is the major cause of death due to gynaecological malignancy in the Western world, with chemotherapy resistance developing in almost all patients. HGSC exhibits high rates of chromosomal aberrations and knowledge of causative mechanisms would represent an important step towards combating this disease. Here we perform the first in-depth functional characterization of mechanisms driving CIN in HGSC in seven cell lines that accurately recapitulate HGSC genetics. Multiple mechanisms co-existed to drive CIN in HGSC, including elevated microtubule dynamics and DNA replication stress that can be partially rescued to reduce CIN by low doses of paclitaxel and nucleoside supplementation, respectively. Distinct CIN mechanisms indicated relationships with HGSC-relevant therapy including Poly (ADP-Ribose) Polymerase (PARP) inhibition and microtubule-targeting agents. Comprehensive genomic and transcriptomic profiling revealed deregulation of various genes involved in genome stability but were not directly predictive of specific CIN mechanisms, underscoring the importance of functional characterization to identify causes of CIN. Overall, we show that HGSC CIN is complex and suggest that specific CIN mechanisms could be used as functional biomarkers to indicate appropriate therapy

Uncoordinated Loss of Chromatid Cohesion Is a Common Outcome of Extended Metaphase Arrest

Chromosome segregation requires coordinated separation of sister chromatids following biorientation of all chromosomes on the mitotic spindle. Chromatid separation at the metaphase-to-anaphase transition is accomplished by cleavage of the cohesin complex that holds chromatids together. Here we show using live-cell imaging that extending the metaphase bioriented state using five independent perturbations (expression of non-degradable Cyclin B, expression of a Spindly point mutant that prevents spindle checkpoint silencing, depletion of the anaphase inducer Cdc20, treatment with a proteasome inhibitor, or treatment with an inhibitor of the mitotic kinesin CENP-E) leads to eventual scattering of chromosomes on the spindle. This scattering phenotype is characterized by uncoordinated loss of cohesion between some, but not all sister chromatids and subsequent spindle defects that include centriole separation. Cells with scattered chromosomes persist long-term in a mitotic state and eventually die or exit. Partial cohesion loss-associated scattering is observed in both transformed cells and in karyotypically normal human cells, albeit at lower penetrance. Suppressing microtubule dynamics reduces scattering, suggesting that cohesion at centromeres is unable to resist dynamic microtubule-dependent pulling forces on the kinetochores. Consistent with this view, strengthening cohesion by inhibiting the two pathways responsible for its removal significantly inhibits scattering. These results establish that chromosome scattering due to uncoordinated partial loss of chromatid cohesion is a common outcome following extended arrest with bioriented chromosomes in human cells. These findings have important implications for analysis of mitotic phenotypes in human cells and for development of anti-mitotic chemotherapeutic approaches in the treatment of cancer

High spatial and temporal resolution wide-field imaging of neuron activity using quantum NV-diamond

A quantitative understanding of the dynamics of biological neural networks is fundamental to gaining insight into information processing in the brain. While techniques exist to measure spatial or temporal properties of these networks, it remains a significant challenge to resolve the neural dynamics with subcellular spatial resolution. In this work we consider a fundamentally new form of wide-field imaging for neuronal networks based on the nanoscale magnetic field sensing properties of optically active spins in a diamond substrate. We analyse the sensitivity of the system to the magnetic field generated by an axon transmembrane potential and confirm these predictions experimentally using electronically-generated neuron signals. By numerical simulation of the time dependent transmembrane potential of a morphologically reconstructed hippocampal CA1 pyramidal neuron, we show that the imaging system is capable of imaging planar neuron activity non-invasively at millisecond temporal resolution and micron spatial resolution over wide-fields