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Non-Destructive Viability Testing of Cotton Seeds Using Raman Spectroscopy and Optical Coherence Tomography
No full textIn recent years, the field of plant breeding has been quick to adopt new technologies for the analysis of field and crop systems, with optimistic results and massive quantities of data generated. In this thesis two such optical technologies, Raman spectroscopy and Optical Coherence Tomography (OCT), are used to produce quantifiable characteristics of cotton seed that may be used for analysis of viability and determination of the features that correlate to survivability. It was found that spatial variability of the Raman spectrum, as well as the occurrence or absence of specific delineations in the OCT tomograms correlate with seed viability.
Raman spectroscopy has proven to be an invaluable tool in the nondestructive analysis of biological materials. As it was previously found that fatty acid and carbohydrate content could be linked to seed viability, Raman signatures of these compounds are of particular interest. The ability of OCT to provide three-dimensional morphological structural details non-invasively, suggests its potential use for the analysis of internal seed structures. The features of interest include damage to the seed coat as well as clear demarcations in the seed interior, or subsurface features that could impact the germination of the seed. A comparison between the features documented by both technologies on each seed paired with a germination test allows correlations about the impacts that each feature and their combination have on the survivability of seeds.
We predict seed viability using the correlation of the observations we make in the Raman and OCT measurements with seed viability. With both cotton varieties (Tamcot 73 and G11) we find that when we select only seeds predicted to be viable, the germination rate is higher than for randomly selected control seeds. While the Tamcot 73 seeds we predicted to be viable germinated slightly better than the control seeds of the same variety, the overall low germination rate of these seeds indicates that other factors not measured by our techniques can have a large remaining impact. For the most recently harvested Tamcot G11 seeds, the germination rate of the control seeds was about 85%, leaving not much room for improvement, and thus a lower level of significance. The level of significance was highest for G11 seeds harvested in 2019, indicating that combined OCT and Raman measurements of seeds can be used to predict seed viability and to improve agronomic outcomes by selecting seeds with a higher expected chance to be germinating
Effect of Sample Size on Linear Elastic Fracture Toughness of FFF-Processed PLA Compact Tension Specimens
No full textThe "Effect of Sample Size on Linear Elastic Fracture Toughness of FFF-Processed PLA Compact Tension Specimens" investigates the impact of sample size on the fracture toughness of 3D printed Polylactic Acid (PLA) using Fused Filament Fabrication (FFF) technology. ASTM D5045 standard is widely recognized and employed as a benchmark for evaluating the fracture toughness of materials within the linear-elastic fracture mechanics (LEFM) domain. This research aims to assess the applicability of this standard for evaluating fracture toughness in additive manufacturing thermoplastic materials focusing on the influence of specimen size, among other parameters, on the material's mechanical properties. Through a comprehensive experimental setup involving various sample sizes, infill sizes, and layout patterns, the study provides a detailed analysis of fracture toughness behavior through numerous tests. The findings reveal significant dependencies of fracture toughness on the sample size, indicating a deviation from true plane strain conditions and highlighting the necessity of reporting conditional stress intensity factors (����_����) for this particular case. The research contributes valuable insights into the mechanical characterization of 3D-printed thermoplastic materials and suggests practical implications for improving additive manufacturing testing and evaluation practices
Electro-Chemo-Mechanics of Several Pure Metal Anodes for Rechargeable Batteries
No full textBy enabling constant access to electronic devices and seamless communication capabilities, energy storage devices have greatly improved our lives. With advances in technology over time, the demand for larger capacity and longer lifetime energy storage devices continues to increase. Still, technological hurdles, such as safety issues, kinetic limitations, and material degradation over time, remain that must be addressed to enable the next generation of high-capacity batteries. While battery research primarily has focused on improvements in electrochemistry, higher capacity technologies have necessitated a shift to the focus towards more multidisciplinary approaches in research.
This dissertation explores battery materials from a mechanical and coupled electro-chemo-mechanical perspective by evaluating various materials��� intrinsic mechanical properties and analyzing their mechanical behaviors during electrochemical cycling. Fracture behavior of sodium was investigated, highlighting its extreme ductility and flaw-insensitivity at room temperature. Furthermore, calcium was mechanically assessed at various length scales, which unveiled calcium���s relatively limited ���indentation size effects��� as compared to counterparts of Li and Na metal, likely due to its relatively high melting point and relatively low dislocation spacing. Additionally, various lithiated compositions and phases of Li���Mg alloy anode were mechanically evaluated via nanoindentation. XRD patterns on post-mortem lithiated Mg samples revealed that Li���Mg alloy anodes undergo a single-phase transformation electrochemically, from the Mg-rich �� phase (HCP) to Li-rich �� phase (BCC). Furthermore, the nanoindentation hardness and elastic modulus of Li���Mg was found to significantly decrease with increasing lithium concentration. This dissertation also investigates the mechanical behavior of pure lithium metal anodes under various electrochemical conditions. These tests uncovered a marked current-dependent mechanical stress response of lithium. Keeping in mind that the processes of electrodeposition of lithium and the growth of Li dendrites, while distinct, share similar underlying mechanisms, these measurements provide important insight into understanding the growth of Li dendrites.
Overall, this dissertation investigates the mechanics of various battery anode materials that exhibit an intimate interplay between mechanics and electrochemistry; their overall performance in practice is thus highly dependent on their mechanical behavior. As such, this dissertation provides general insight into understanding energy storage systems in terms of the intricate interactions between mechanics and electrochemistry
SMC-M1 Connectivity and Motor Sequence Learning: A TMS Study
No full textWe experience various types of motor learning throughout our lives. As children, we learned to walk unconsciously. As we grow older, we continue to learn skills like sports or musical instruments. In this study, the primary motor cortex (M1) and the supplementary motor complex (SMC), which are brain regions involved in motor sequence learning but where many aspects still remain unclear, were mainly investigated, and the connectivity of these two regions was measured using the paired-pulse transcranial magnetic stimulation (ppTMS) technique. In Experiment I, sixty-three right-handed undergraduate students participated. Conditioning stimulus (CS) was administered at SMC (i.e., 4 cm anterior to Cz), and test stimulus (TS) was applied at M1. As a result, consistent with the previous ppTMS studies, a facilitatory influence was observed between SMC and M1. In Experiment II, fifty-one right-handed undergraduate students participated. After measuring baseline SMC-M1 connectivity in the same way as in Experiment I, individuals practiced one of three motor sequential tasks (i.e., implicit, explicit, or random sequence task). After practice, ppTMS as post-training stimulation was administered in the same way as the baseline stimulation to investigate the changes in the connectivity between SMC and M1. As a result, it was found that the facilitatory influence decreased after the explicit sequence task that involved motor chunking. This may be because SMC plays a role in motor chunking. In Experiment III, instead of a motor task, intermittent theta-burst stimulation (iTBS) was administered at SMC between two ppTMS sessions, and the connectivity changes were investigated. Similar to the influence of the explicit sequence task in Experiment II, the facilitatory influence decreased in the iTBS group. Although the mechanisms are different from each other, iTBS appears to induce post-synaptic plasticity in the cortico-basal ganglia-thalamic network. After post-stimulation of ppTMS, participants performed the explicit sequence task used in Experiment II twice (i.e., practice and retention test) to reveal the effect of iTBS on motor sequence learning. In the iTBS group, the offline improvement was disturbed at concatenation points compared to the Rest group. In the future, follow-up research that dissociates motor chunking conditions using neuroimaging techniques seems necessary
Discovering Global False Negatives on the Fly for Self-supervised Contrastive Learning
No full textIn self-supervised contrastive learning, negative pairs are typically constructed using an anchor image and a sample drawn from the entire dataset, excluding the anchor. However, this approach can result in the creation of negative pairs with similar semantics, referred to as ���false negatives���, leading to their embeddings being falsely pushed apart. To address this issue, we introduce GLOFND, an optimization-based approach that automatically learns on the fly the threshold for each anchor data to identify its false negatives during training. In contrast to previous methods for false negative discovery, our approach globally detects false negatives across the entire dataset rather than locally within the mini-batch. Moreover, the per-iteration computation cost of our approach remains independent of the dataset size. Experimental results on image and image-text data demonstrate the effectiveness of the proposed method
Structure-Guided Strategies to Combat Antibiotic Resistance
No full textThe PhD research focuses on structure-guided strategies to combat antibiotic resistance. It includes three sub-projects: 1). SEQ-9 overcomes Mtb ribosome methylation and inhibits ribosomal activities. Antibiotics are implemented to cue tuberculosis caused by Mycobacterium to inhibit Mtb ribosomes and prevent downstream cellular activities. However, Mtb cells evolve to escape antibiotic pressure. One strategy Mtb implemented is methylation in certain adenosines, which helps Mtb with antibiotic resistance. Our studies found that a naturally derived molecule, SEQ-9, effectively inhibits the methylated Mtb ribosome. By determining the structures of SEQ-9-bound ribosomes, we concluded that SEQ-9 would undergo conformational changes to accommodate the methylation and still be able to inhibit the ribosome. Our results were part of research supported by multiple labs and a pharmaceutical company, Sanofi R&D, and are published in Cell. 2). An antibody derived from AP205 against Acinetobacter genomospecies 16 cells. This research aims to study the organizational pattern of AP205 and the phage-host relationship. Using Cryo-EM, we obtained high-resolution structures of AP205, the host acceptor, and the AP205-host receptor complex. We designed an antibody-like protein based on the structures, and the protein successfully targeted the host receptor. A collaborator is working on the antibody to illustrate its effects against the host. 3). An ongoing project studying Mtb ClpXP protease complex. Mtb ClpXP protease complex plays an essential role in cellular proteohomeostasis. The complex recognizes unfolded/misfolded proteins and degrades them to prevent abnormal activities. Dysregulation of ClpXP functions can cause detrimental effects on cells. To understand the function of the ClpXP complex, we performed structural analysis on the ClpXP complex and yielded a high-resolution structure. We also observed a structure that has yet to be discovered
Spectroscopic Studies of Stars and Black Holes Across Cosmic Time
No full textIn this work, I present my spectroscopic studies of galaxy evolution across a large majority of cosmic time (0 ��� z ��� 9). My career to this point has two distinct phases: pre-JWST, when I utilized spectroscopy from HST to study star formation, dust attenuation, and accreting black holes out to the peak of cosmic star formation rate and active supermassive black hole density (cosmic noon; z ��� 2), and post-JWST, where my focus shifted to this new observatory to push the study of galaxy evolution to some of the earliest known galaxies at z ��� 8.
In the pre-JWST era, I used HST grism spectroscopy to study star formation rates and histories as well as dust attenuation by using the near-IR Paschen lines of hydrogen. Emission from Paschen lines indicates near-instantaneous star formation, while probing regions inaccessible to UV or optical star formation tracers due to dust attenuation.
I also used HST grism spectroscopy to probe galaxies around cosmic noon which exhibit the extremely-high-ionization [Ne V] line. This spectral feature is a strong tracer of an ionizing spectrum powered by an accreting black hole, at least in this epoch of cosmic time.
These studies serve as optimal pilot campaigns for the JWST era, where JWST spectroscopy can probe these features out to much earlier epochs of cosmic time. I discuss the implications of JWST studies of Paschen-line star formation rates and dust attenuation out to cosmic noon, and studies of high-ionization emission lines in the epoch of Reionization.
Using early JWST spectroscopy in conjunction with HST spectroscopy and photoionization models, I develop a novel diagnostic to trace ionization from different sources at all epochs of cosmic time. This is designed specifically with elusive objects in the early Universe in mind, including Population III stars and accreting intermediate-mass black holes, in an effort to study the earliest stages of star formation and black hole growth.
We stand at the beginning of a new era in the study of galaxy evolution. With the incredible capabilities of JWST, we can study the physical mechanisms governing galaxies across cosmic time like never before
Geomicrobiology of Seafloor Basalts and Viral Metagenomes of Seafloor Habitats in the Pacific Ocean
No full textSeafloor basalts, inactive sulfides, and seamounts represent broadly distributed biospheres on the seafloor. Here, the microbial community composition, functional potential, and extracellular enzyme activity rates were assessed within these seafloor habitats, with an emphasis on seafloor basalt samples. Extracellular enzyme activity assays were performed using seafloor basalt from the East Pacific Rise (EPR) 9��50'N and Davidson Seamount, to understand their role in carbon, nitrogen, and phosphorus acquisition. Metagenomics was also used to evaluate the differences in the microbial community composition and functional potential of seafloor basalts from two different age groups represented by two separate eruptions at EPR 9��50'N. Viruses from metagenomes of seafloor basalts, inactive sulfides, and ferromanganese crust from the EPR 9��50'N, Southern Mariana Trough, PACManus, and Takuyo-Daigo Seamount were also analyzed to characterize the overall viral community composition, auxiliary metabolic gene function, and virus-host linkages. The analyses conducted here offer an insightful understanding of the resident microbial community within seafloor habitats, shedding light on their nutrient acquisition mechanisms via extracellular enzyme activity, and their genetic potential to uptake and utilize nutrients in their environment. In addition, these analyses explore the previously overlooked role of viruses in the survival of the prokaryotic community present. The abundance and importance of these seafloor habitats in the ocean emphasizes the need to understand the ecology of the resident microbial life, and their potentially significant role in marine nutrient cycling
Markov Duality and Asymptotics of Interacting Particle Systems
No full textWe propose novel algebraic methods to Markov dualities. The first method produces orthogonal polynomial dualities from the ������bialgebra structure of Drinfeld���Jimbo quantum groups. The ������structure allows for the construction of certain unitary symmetries, which imply the orthogonality of the duality functions. In the case of the quantum group Uq(gln+1), the result is a nested multivariate q���Krawtchouk duality for the n���species ASEP(q, ��). The method also applies to other quantized simple Lie algebras (e.g. so2n) and to stochastic vertex models. The second method constructs duality functions for integrable dynamic models. This method will be implemented on dynamic stochastic higher spin vertex models, where we prove the duality functions between the dynamic stochastic higher spin vertex models and non dynamic stochastic higher spin vertex models are the 3��2 functions. A degeneration of these duality functions is dual q���Krawtchouk polynomials, which agree with orthogonal polynomial dualities between ASEP and dynamic ASEP of Groenevelt���Wagenaar [1]. The method involves using the universal twister of Uq(sl2), viewed as a quasi���triangular, quasi���������Hopf algebra. The algebraic method is presented very generally and is expected to produce duality functions for other dynamic integrable models.
As probabilistic applications of the duality relations found, we provide the explicit contour integral formula of the q���shifted factorial moments (namely the q-analogue of the Pochhammer symbol) for the two���species q���TAZRP (totally asymmetric zero range process). We use those contour integral formulas to find the asymptotics of the two���point correlations. In addition, we also focus on the open symmetric exclusion process with parameter m (open SEP(m/2)). Based on duality relations, we prove that the hydrodynamic limit of the density profile for a d���dimensional open SEP(m/2) solves the (d + 1)���dimensional heat equation with certain initial condition and boundary condition. Last, as an application of the duality of dynamic six vertex model, we prove ii that the asymptotic fluctuations of the dynamic stochastic six vertex model with step initial conditions are governed by the Tracy���Widom distribution
Improving the In Vitro-to-In Vivo Extrapolation to Predict Environmental Chemicals Toxicokinetics and Hazard by Construction of New In Silico Models with In Vitro Testing Data
No full textIn light of the vast number of chemicals present in diverse environmental media, necessitating exposure assessments to protect human health, in vivo studies have traditionally been considered gold standards for providing hazard and kinetic data for risk assessments. However, their limited throughput and higher costs mean only a fraction of chemicals possess sufficient toxicity data for assessing health risks. To address this crucial data gap, New Approach Methodologies (NAM), particularly the application of in vitro-to-in vivo extrapolation (IVIVE) with in vitro studies and in silico computational models, have been developed. The demand for assessing risks for numerous chemicals necessitates effective and high-throughput methodologies. Nonetheless, IVIVE faces challenges, including the accuracy of in silico predictions on toxicokinetic (TK) properties based on in vitro datasets and linking in vitro adverse outcomes to whole-organ level phenotypes.
We here hypothesized that that innovative computational approaches integrated with in vitro studies will enhance TK predictions and hazard identification for environmental chemicals while advancing high-throughput screening methodologies. To test this hypothesis, three specific aims were proposed: (1) developing a physiologically-based gut absorption model for probabilistic prediction of environmental chemical bioavailability based on in vitro permeability measurements, (2) improving the prediction of renal clearance for per- and polyfluoroalkyl substances by integrating in vitro toxicokinetic datasets from different assays with physiologically-based kidney model to implement IVIVE, and (3) identifying the proarrhythmic potential of environmental chemicals by integrating an in vitro human model with Bayesian population modeling.
The achievement of these aims will demonstrate how novel in silico models and in vitro datasets can enhance the prediction of hazards and toxicokinetics for environmental chemicals. Additionally, these studies will facilitate the application of in vitro datasets in high-throughput screening of chemicals for risk assessment. Ultimately, we anticipate that these endeavors will contribute to filling critical data gaps related to TK properties and hazards for environmental chemicals, aligning with the long-term goal of protecting human health from exposures to hazardous environmental chemicals