On the reactivity of nanoparticulate elemental sulfur : experimentation and field observations

Indiana University-Purdue University Indianapolis (IUPUI)The reaction between elemental sulfur and sulfide is a lynchpin in the biotic and abiotic cycling of sulfur. This dissertation is focused on the reactivity of elemental sulfur nanoparticles (S8weimarn, S8raffo) among other forms of elemental sulfur (S8aq, S8aq-surfactant, α-S8), and how the variation of their surface area, character and coatings reflect on the analytical, physical-chemical and geochemical processes involving sulfur cycling. A comprehensive electrochemical investigation utilizing mercury-surface electrodes showed that elemental sulfur compounds are represented by three main voltammetric signals, corresponding to potentials at -1.2V, -0.8V, and -0.6V in the absence of organics at circumneutral pH. Dissolved S8aq-surfactant signals can be found from -0.3V up to -1.0V, depending on the surfactant in the system. Variations in current response resulted from differences in electron transfer efficiency among the forms of S8, due to their molecular structural variability. Based on this observation a new reaction pathway between S8 and Hg-surface electrodes is proposed, involving an amalgam-forming intermediate step. The kinetics of the nucleophilic dissolution of S8nano by sulfide, forming polysulfides, were investigated under varying surface area, surface character and presence or absence of surfactant coatings on S8nano. Hydrophobic S8weimarn and hydrophilic S8raffo show kinetic rate laws of 8 = 10−11.33 ( −700.65 ) (Molar(S8)/second/dm-1) and8 = 10−4.11 −0.35 ( −615.77 ) (Molar(S8)/second), respectively. The presence of surfactant molecules can influence the reaction pathways by dissolving S8nano and releasing S8aqsurfactant, evolving the rate-limiting step as a function of the degree of the solubilization of S8nano. The reaction rate of S8biological can be compared with those of S8raffo and S8weimarn in circumneutral pH values and T=50oC, making the forms of S8nano successful abiotic analogue models of microbially produced S8biological. Field observations and geochemical kinetic modeling in the geothermal features of Yellowstone indicate that the nucleophilic dissolution reaction appears to be a key abiotic pathway for the cycling of sulfur species and the enhancement of elemental sulfur bioavailability. Furthermore, in situ and ex situ voltammetry in the same geothermal waters disclosed chaotic variability in chemical gradients of sulfide (observed over small temporal and spatial scales) which can be considered as an ecological stressor capable of influencing single cell physiology and microbial community adaptation

Three-dimensional positional changes of teeth adjacent to posterior edentulous spaces in relation to age at time of tooth loss and elapsed time

The purpose of this study was to study the stability of teeth adjacent to posterior edentulous spaces and correlate it with patient age and time lapse since tooth loss. Dental casts, panoramic radiographs, and questionnaires of patients treated in a University setting were employed. Teeth adjacent and opposing posterior edentulous spaces were examined for the following parameters: Supraeruption, rotation, space closure, and axial inclination. One hundred twenty three patients with 229 edentulous spaces were analyzed. Statistical analysis showed that the effects of "jaw", "gender", and "age group at the time of tooth loss" were not significant for any of the variables tested. The effect of time lapse since tooth loss was significant regarding the "amount of distal tooth inclination" (P<0.001), the "amount of distal tooth rotation" (P=0.004), and "space closure" (P=0.038). Post-hoc analysis of the "amount of distal tooth inclination" revealed a marked increase in inclination 5 years after tooth loss. Within the limitations of this study, it was concluded that in the group of patients studied, minor positional changes in teeth opposing or adjacent to posterior edentulous spaces had occurred. The greatest changes in position were recorded for mandibular teeth distal to edentulous spaces

Design and application of dispersion entropy algorithms for physiological time-series analysis

Changes in the variability of recorded physiological time-series have been connected with transitions in the state of the monitored physiological system. The two primary paradigms describing this connection are the Critical Slow Down (CSD) and the Loss of Complexity (LoC) paradigms. The CSD paradigm considers that during frail or pathological states, a slowing down is observed in the capacity of the system to recover from external stressors resulting in increased output complexity for certain regulated variables. The LoC paradigm suggests that when the equilibrium of a system is disrupted, certain effector variables that displayed multi-scale complexity produce output measurements of reduced variability indicating a loss in the system’s flexibility and capacity to adapt in the presence of external stressors. For this purpose, entropy has emerged as a prominent nonlinear metric capable of assessing the non-linear dynamics and variability of time-series. Consequently, multiple entropy quantification algorithms have been developed for the analysis of time-series. These algorithms are based on Shannon Entropy such as the Permutation Entropy and Dispersion Entropy (DisEn) algorithms; and on Conditional Entropy such as the Sample Entropy and Fuzzy Entropy algorithms. Within the scope of this study, the univariate and multivariate DisEn algorithms, first introduced in 2016 and in 2019 respectively, are used as the foundation and benchmark for the introduction of novel algorithmic variations. The selection of the DisEn algorithms is made due to their capability of producing features with significant discrimination capacity taking into consideration amplitude-based information while maintaining a linear computational complexity and having a functional multivariate variation capable of quantifying cross-channel dynamics. To initially ensure the effective quantification of DisEn during univariate physiological timeseries analysis, the effect of missing and outlier samples, which are common occurrence in physiological recordings, is studied and quantified. To improve algorithmic robustness, novel variations of the univariate DisEn algorithm are introduced for the analysis of low recording quality time-series. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate variability, electroencephalogram, and respiratory impedance time-series. The analysis indicates that missing samples have a reduced effect on the output DisEn and the error percentage can be maintained at values lower than 8% with the introduction of a variation that skips invalid values. Contrary to missing samples, outliers have a major disruptive effect with error percentages in the range of 57% to 73% for the original DisEn algorithm that is limited in values lower than 22% with the introduction of respective variations. To expand the study from univariate to multivariate analysis, the multivariate DisEn algorithm is applied to physiological network segments formulated from multi-channel recordings of synchronized electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The effect of outliers, present across different channels, is quantified for both univariate and multivariate DisEn features. The sensitivity of DisEn features to outliers is utilized for the detection of artifactual network segments using logistic regression classifiers. Two variations of the classifier are deployed in several experimental setups, with the first utilizing solely univariate and the second both univariate and multivariate DisEn features. Noteworthy performance is achieved, with the percentage of correct network segment classifications surpassing 95% in a number of experimental setups, for both configurations. Finally, to improve DisEn quantification during the analysis of multivariate systems for physiological monitoring applications, the framework of Stratified Entropy is introduced. Based on the framework, a set of strata with a clear hierarchy of prioritization are defined. Each channel of an input multi-channel time-series is allocated to a stratum and their contribution to the output DisEn value is determined by their allocation. Three novel Stratified DisEn algorithms are presented, as implementations of the framework, allowing multivariate analysis with controllable contribution from each channel to the output DisEn value. The original algorithm and the novel variations are implemented on synthetic time-series consisting of 1/f and white Gaussian noise, waveform physiological time-series and derived physiological data. The introduced Stratified DisEn variations operate as expected and correctly prioritize the channels allocated to the primary stratum of the hierarchy across all synthetic time-series setups. The results of waveform physiological time-series indicate that certain of the novel features extracted through Stratified DisEn achieve effect size increases in the range of 0.2 to 1.4 when separating between states of healthy sleep and sleep with obstructive sleep apnea. The derived physiological data results further highlight the increased discrimination capacity of the novel features with increases in the range of 5% to 30% in the mean absolute difference between values extracted during steady versus stressful physiological states. Furthermore, an example of decrease in the output DisEn values when moving from a steady to a stressful physiological state is highlighted during the prioritization of the heart rate channel, in alignment with LoC, providing an example of how Stratified Entropy could be used to test hypothesis based on the CSD and LoC paradigms. By making steps towards addressing the challenge of low data quality and providing a new framework of analysis, this thesis aims to improve the process of assessing and measuring the variability of physiological time-series, leading to the consequent extraction of viable physiological information

Assessment of Outliers and Detection of Artifactual Network Segments using Univariate and Multivariate Dispersion Entropy on Physiological Signals

Network physiology has emerged as a promising paradigm for the extraction of clinically relevant information from physiological signals by moving from univariate to multivariate analysis, allowing for the inspection of interdependencies between organ systems. However, for its successful implementation, the disruptive effects of artifactual outliers, which are a common occurrence in physiological recordings, have to be studied, quantified, and addressed. Within the scope of this study, we utilize Dispersion Entropy (DisEn) to initially quantify the capacity of outlier samples to disrupt the values of univariate and multivariate features extracted with DisEn from physiological network segments consisting of synchronised, electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The DisEn algorithm is selected due to its efficient computation and good performance in the detection of changes in signals for both univariate and multivariate time-series. The extracted features are then utilised for the training and testing of a logistic regression classifier in univariate and multivariate configurations in an effort to partially automate the detection of artifactual network segments. Our results indicate that outlier samples cause significant disruption in the values of extracted features with multivariate features displaying a certain level of robustness based on the number of signals formulating the network segments from which they are extracted. Furthermore, the deployed classifiers achieve noteworthy performance, where the percentage of correct network segment classification surpasses 95% in a number of experimental setups, with the effectiveness of each configuration being affected by the signal in which outliers are located. Finally, due to the increase in the number of features extracted within the framework of network physiology and the observed impact of artifactual samples in the accuracy of their values, the implementation of algorithmic steps capable of effective feature selection is highlighted as an important area for future research

Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis

Multivariate Entropy quantification algorithms are becoming a prominent tool for the extraction of information from multi-channel physiological time-series. However, in the analysis of physiological signals from heterogeneous organ systems, certain channels may overshadow the patterns of others, resulting in information loss. Here, we introduce the framework of Stratified Entropy to prioritize each channels' dynamics based on their allocation to respective strata, leading to a richer description of the multi-channel time-series. As an implementation of the framework, three algorithmic variations of the Stratified Multivariate Multiscale Dispersion Entropy are introduced. These variations and the original algorithm are applied to synthetic time-series, waveform physiological time-series, and derivative physiological data . Based on the synthetic time-series experiments, the variations successfully prioritize channels following their strata allocation while maintaining the low computation time of the original algorithm. In experiments on waveform physiological time-series and derivative physiological data, increased discrimination capacity was noted for multiple strata allocations in the variations when benchmarked to the original algorithm. This suggests improved physiological state monitoring by the variations. Furthermore, our variations can be modified to utilize a priori knowledge for the stratification of channels. Thus, our research provides a novel approach for the extraction of previously inaccessible information from multi-channel time series acquired from heterogeneous systems

Transient topographies: a photographic languaje for impermanence

The Glass/Wood photographic project started as a research initiative by architectural photographer Erieta Attali under the auspices of Melbourne RMIT’s Doctorate program. This paper examines the discussion that led to the project’s conception, the process behind it and finally the outcome: a case-study monograph by the same name. The aim is to demonstrate how a photographic narrative can articulate an architectural argument and feed back into the design discourse, transgressing the limits between documentation and interpretation of architecture. Attali photographed Glass/Wood in New Canaan, Connecticut, a guesthouse extension by Kengo Kuma in 2010 to an existing modernist pavilion buried deep within a deciduous forest, in the course of two years. As the passage of eight seasons narrates the continuity between landscape and architecture, both architect’s and photographer’s ambitions become apparent: architecture that dissolves into the landscape and photography as a medium that communicates shifting relations instead of static, iconic images. The challenge we faced was how to communicate this synergy and the reciprocal processes that feed it. The paper has a tripartite structure. The first section examines Attali’s photographic language formulated in landscapes and archaeological sites, its translation into architecture and the narrative that it builds around Glass/Wood. The second section approaches the architectural subject of the series by dissecting some of the theoretical foundations of Kuma’s practice, especially its ambiguous relation to image representation in general and photography in particular. The third and final section recounts the creation of the Glass/Wood book through a multidisciplinary effort towards the articulation of a consistent visual argument in the form of a hybrid architectural/photographic monograph

Sulfur and oxygen isotope insights into sulfur cycling in shallow-sea hydrothermal vents, Milos, Greece

Shallow-sea (5 m depth) hydrothermal venting off Milos Island provides an ideal opportunity to target transitions between igneous abiogenic sulfide inputs and biogenic sulfide production during microbial sulfate reduction. Seafloor vent features include large (>1 m2) white patches containing hydrothermal minerals (elemental sulfur and orange/yellow patches of arsenic-sulfides) and cells of sulfur oxidizing and reducing microorganisms. Sulfide-sensitive film deployed in the vent and non-vent sediments captured strong geochemical spatial patterns that varied from advective to diffusive sulfide transport from the subsurface. Despite clear visual evidence for the close association of vent organisms and hydrothermalism, the sulfur and oxygen isotope composition of pore fluids did not permit delineation of a biotic signal separate from an abiotic signal. Hydrogen sulfide (H2S) in the free gas had uniform δ34S values (2.5 ± 0.28‰, n = 4) that were nearly identical to pore water H2S (2.7 ± 0.36‰, n = 21). In pore water sulfate, there were no paired increases in δ34SSO4 and δ18OSO4 as expected of microbial sulfate reduction. Instead, pore water δ34SSO4 values decreased (from approximately 21‰ to 17‰) as temperature increased (up to 97.4°C) across each hydrothermal feature. We interpret the inverse relationship between temperature and δ34SSO4 as a mixing process between oxic seawater and 34S-depleted hydrothermal inputs that are oxidized during seawater entrainment. An isotope mass balance model suggests secondary sulfate from sulfide oxidation provides at least 15% of the bulk sulfate pool. Coincident with this trend in δ34SSO4, the oxygen isotope composition of sulfate tended to be 18O-enriched in low pH (75°C) pore waters. The shift toward high δ18OSO4 is consistent with equilibrium isotope exchange under acidic and high temperature conditions. The source of H2S contained in hydrothermal fluids could not be determined with the present dataset; however, the end-member δ34S value of H2S discharged to the seafloor is consistent with equilibrium isotope exchange with subsurface anhydrite veins at a temperature of ~300°C. Any biological sulfur cycling within these hydrothermal systems is masked by abiotic chemical reactions driven by mixing between low-sulfate, H2S-rich hydrothermal fluids and oxic, sulfate-rich seawater

Age-associated alterations in corpus callosum white matter integrity in bipolar disorder assessed using probabilistic tractography

OBJECTIVES: Atypical age-associated changes in white matter integrity may play a role in the neurobiology of bipolar disorder, but no studies have examined the major white matter tracts using nonlinear statistical modeling across a wide age range in this disorder. The goal of this study was to identify possible deviations in the typical pattern of age-associated changes in white matter integrity in patients with bipolar disorder across the age range of 9-62 years. METHODS: Diffusion tensor imaging was performed in 57 (20 male and 37 female) patients with a diagnosis of bipolar disorder and 57 (20 male and 37 female) age- and sex-matched healthy volunteers. Mean diffusivity and fractional anisotropy were computed for the genu and splenium of the corpus callosum, two projection tracts, and five association tracts using probabilistic tractography. RESULTS: Overall, patients had lower fractional anisotropy and higher mean diffusivity compared to healthy volunteers across all tracts (while controlling for the effects of age and age2 ). In addition, there were greater age-associated increases in mean diffusivity in patients compared to healthy volunteers within the genu and splenium of the corpus callosum beginning in the second and third decades of life. CONCLUSIONS: Our findings provide evidence for alterations in the typical pattern of white matter development in patients with bipolar disorder compared to healthy volunteers. Changes in white matter development within the corpus callosum may lead to altered inter-hemispheric communication that is considered integral to the neurobiology of the disorder