Computer vision in microstructural analysis

The following is a laboratory experiment designed to be performed by advanced-high school and beginning-college students. It is hoped that this experiment will create an interest in and further understanding of materials science. The objective of this experiment is to demonstrate that the microstructure of engineered materials is affected by the processing conditions in manufacture, and that it is possible to characterize the microstructure using image analysis with a computer. The principle of computer vision will first be introduced followed by the description of the system developed at Texas A&M University. This in turn will be followed by the description of the experiment to obtain differences in microstructure and the characterization of the microstructure using computer vision

A preliminary analysis in the preparation and obturation of oval canals

Objective: 1) to observe the appearance of uninstrumented recesses in oval canals after instrumentation using circumferential filing. 2) To determine the quality of obturation both with cold lateral compaction and thermo plasticized gutta-percha technique in these recesses Study design: Twenty non-carious, freshly extracted human mandibular incisors with single canals were divided into group I (n=10) and group II (n=10) according to the two different obturation methods. The sectioned roots were prepared with flexofiles (Dentsply, Maillefer) ranging from #15 to #40 by using circumferential filing. The obturated specimens were horizontally sectioned at 5 mm from the apex. Each section was observed under the stereomicroscope at x40 magnification. The quality of obturation in the recesses was evaluated by using 3-point scoring system; the results were subjected to statistical analysis. Results and Conclusion: The results showed that, uninstrumented recesses might be left in many oval canals, even after circumferential filing with smaller size files. In addition, the thermoplasticized obturation technique was better in filling the recesses as compared to lateral compaction but the results were statistically insignifican

The M2a macrophage phenotype accompanies pulmonary granuloma resolution in Mmp12 knock-out mice instilled with multiwall carbon nanotubes

Sarcoidosis is a chronic disease with unknown etiology and pathophysiology, characterized by granuloma formation. Matrix Metalloproteinase-12 (MMP12) is an elastase implicated in active granulomatous sarcoidosis. Previously, we reported that oropharyngeal instillation of multiwall carbon nanotubes (MWCNT) into C57Bl/6 mice induced sarcoid-like granulomas and upregulation of MMP12. When Mmp12 knock-out (KO) mice were instilled with MWCNT, granuloma formation occurred 10 days post-instillation but subsequently resolved at 60 days. Thus, we concluded that MMP12 was essential to granuloma persistence. The aim of the current study was to identify potential mechanisms of granuloma resolution in Mmp12KO mice. Strikingly, an M2 macrophage phenotype was present in Mmp12KO but not in C57Bl/6 mice. Between 10 and 60 days, macrophage populations in MWCNT-instilled Mmp12KO mice demonstrated an M2c to M2a phenotypic shift, with elevations in levels of IL-13, an M2 subtype-regulating factor. Furthermore, the M2 inducer, Apolipoprotein E (ApoE), and Matrix Metalloproteinase-14 (MMP14), a promoter of collagen degradation, were upregulated in 60-day MWCNT-instilled Mmp12KO mice. In conclusion, alveolar macrophages express two M2 phenotypes in Mmp12KO mice: M2c at 10 days when granulomas form, and M2a at 60 days when granulomas are resolving. Findings suggest that granuloma resolution in 60-day Mmp12KO mice requires an M2a macrophage phenotype.ECU Open Access Publishing Support Fun

Alveolar macrophages of GM-CSF knockout mice exhibit mixed M1 and M2 phenotypes

Background Activin A is a pleiotrophic regulatory cytokine, the ablation of which is neonatal lethal. Healthy human alveolar macrophages (AMs) constitutively express activin A, but AMs of patients with pulmonary alveolar proteinosis (PAP) are deficient in activin A. PAP is an autoimmune lung disease characterized by neutralizing autoantibodies to Granulocyte-Macrophage Colony Stimulating Factor (GM-CSF). Activin A can be stimulated, however, by GM-CSF treatment of AMs in vitro. To further explore pulmonary activin A regulation, we examined AMs in bronchoalveolar lavage (BAL) from wild-type C57BL/6 compared to GM-CSF knockout mice which exhibit a PAP-like histopathology. Both human PAP and mouse GM-CSF knockout AMs are deficient in the transcription factor, peroxisome proliferator activated receptor gamma (PPARγ). Results In sharp contrast to human PAP, activin A mRNA was elevated in mouse GM-CSF knockout AMs, and activin A protein was increased in BAL fluid. Investigation of potential causative factors for activin A upregulation revealed intrinsic overexpression of IFNγ, a potent inducer of the M1 macrophage phenotype, in GM-CSF knockout BAL cells. IFNγ mRNA was not elevated in PAP BAL cells. In vitro studies confirmed that IFNγ stimulated activin A in wild-type AMs while antibody to IFNγ reduced activin A in GM-CSF knockout AMs. Both IFNγ and Activin A were also reduced in GM-CSF knockout mice in vivo after intratracheal instillation of lentivirus-PPARγ compared to control lentivirus vector. Examination of other M1 markers in GM-CSF knockout mice indicated intrinsic elevation of the IFNγ-regulated gene, inducible Nitrogen Oxide Synthetase (iNOS), CCL5, and interleukin (IL)-6 compared to wild-type. The M2 markers, IL-10 and CCL2 were also intrinsically elevated. Conclusions Data point to IFNγ as the primary upregulator of activin A in GM-CSF knockout mice which in addition, exhibit a unique mix of M1-M2 macrophage phenotypes

Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms

Forests are one of the major carbon sinks that significantly contribute towards achieving targets of the Kyoto Protocol, and its successors, in reducing greenhouse (GHG) emissions. In order to contribute to regular National Inventory Reporting, and as part of the on-going development of the Irish national GHG reporting system (CARBWARE), improvements in characterisation of changes in forest carbon stocks have been recommended to provide a comprehensive information flow into CARBWARE. The Irish National Forest Inventory (NFI) is updated once every six years, thus there is a need for an enhanced forest monitoring system to obtain annual forest updates to support government agencies and forest management companies in their strategic decision making and to comply with international GHG reporting standards. Sustainable forest management is imperative to promote net carbon absorption from forests. Based on the NFI data, Irish forests have removed or sequestered an average of 3.8 Mt of atmospheric CO2 per year between 2007 and 2016. However, unmanaged and degraded forests become a net emitter of carbon. Disturbances from human induced activities such as clear felling, thinning and deforestation results in carbon emissions back into the atmosphere. Funded by the Department of Agriculture, Food and the Marine (DAFM, Ireland), this PhD study focuses on exploring the potential of data from L-band Synthetic Aperture Radar (SAR) satellite based sensors for monitoring changes in the small stand forests of Ireland. Historic data from ALOS PALSAR in the late 2000s and more recent data from ALOS-2 PALSAR-2 sensors have been used to map forest areas and characterise the different disturbances observed within three different regions of Ireland. Forest mapping and disturbance characterisation was achieved by combining the machine learning supervised Random Forests (RF) and unsupervised Iterative Self-Organizing Data Analysis (ISODATA) classification techniques. The lack of availability of ground truth data supported use of this unsupervised approach which forms natural clusters based on their multi-temporal signatures, with divergence statistics used to select the optimal number of clusters to represent different forest classes. This approach to forest monitoring using SAR imagery has not been reported in the peer-review literature and is particularly beneficial where there is a dearth of ground-based information. When applied to the forests, mapped with an accuracy of up to 97% by RF, the ISODATA technique successfully identified the unique multi-temporal pattern associated with clear-fells which exhibited a decrease of 4 to 5 decibels (dB) between the images acquired before and after the event. The clustering algorithm effectively highlighted the occurrence of other disturbance events within forests with a decrease of 2±0.5dB between two consecutive years, as well as areas of tree growth and afforestation. A highlight of the work is the successful transferability of the algorithm, developed using ALOS PALSAR, to ALOS-2 PALSAR-2 data thereby demonstrating the potential continuity of annual forest monitoring. The higher spatial and radiometric resolutions of ALOS-2 PALSAR-2 data have shown improvements in forest mapping compared to ALOS PALSAR data. From mapping a minimum forest size of 1.8 ha with ALOS PALSAR, a minimum area of 1.1 ha was achieved with the ALOS-2 PALSAR-2 images. Moreover, even with some different backscatter characteristics of images acquired in different seasons, similar signature patterns between the sensors were retrieved that helped to define the cluster groups, thus demonstrating the robustness of the algorithm and its successful transferability. Having proven the potential to monitor forest disturbances, the results from both the sensors were used to detect deforestation over the time period 2007-2016. Permanent land-use changes pertaining to conversion of forests to agricultural lands and windfarms were identified which are important with respect to forest monitoring and carbon reporting in Ireland. Overall, this work has presented a viable approach to support forest monitoring operations in Ireland. By providing disturbance information from SAR, it can supplement projects working with optical images which are generally limited by cloud cover, particularly in parts of northern, western and upland Ireland. This approach adds value to ground based forest monitoring by mapping distinct forests over large areas on an annual basis. This study has demonstrated the ability to apply the algorithm to three different study areas, with a vision to operationalise the algorithm on a national scale. The main limitations experienced in this study were the lack of L-band SAR data availability and reference datasets. With typically only one image acquired per year, and discrepancies and omissions existing within reference datasets, understanding the behaviour of certain cluster groups representing disturbances was challenging. However, this approach has addressed some issues within the reference datasets, for example locating areas for which a felling licence was granted but where trees were never cut, by providing detailed systematic mapping of forests. Future satellites such as Tandem-L, SAOCOM-2A and 2B, P-band BIOMASS mission and ALOS-4 PALSAR-3 may overcome the issue of limited SAR image acquisitions provided more images per year are available, especially during the summer months

Model-Theoretic Logic for Mathematical Theory of Semantic Information and Communication

In this paper, we propose an advancement to Tarskian model-theoretic semantics, leading to a unified quantitative theory of semantic information and communication. We start with description of inductive logic and probabilities, which serve as notable tools in development of the proposed theory. Then, we identify two disparate kinds of uncertainty in semantic communication, that of physical and content, present refined interpretations of semantic information measures, and conclude with proposing a new measure for semantic content-information and entropy. Our proposition standardizes semantic information across different universes and systems, hence bringing measurability and comparability into semantic communication. We then proceed with introducing conditional and mutual semantic cont-information measures and point out to their utility in formulating practical and optimizable lossless and lossy semantic compression objectives. Finally, we experimentally demonstrate the value of our theoretical propositions