The role of initial geometry in experimental models of wound closing

Wound healing assays are commonly used to study how populations of cells, initialised on a two-dimensional surface, act to close an artificial wound space. While real wounds have different shapes, standard wound healing assays often deal with just one simple wound shape, and it is unclear whether varying the wound shape might impact how we interpret results from these experiments. In this work, we describe a new kind of wound healing assay, called a sticker assay, that allows us to examine the role of wound shape in a series of wound healing assays performed with fibroblast cells. In particular, we show how to use the sticker assay to examine wound healing with square, circular and triangular shaped wounds. We take a standard approach and report measurements of the size of the wound as a function of time. This shows that the rate of wound closure depends on the initial wound shape. This result is interesting because the only aspect of the assay that we change is the initial wound shape, and the reason for the different rate of wound closure is unclear. To provide more insight into the experimental observations we describe our results quantitatively by calibrating a mathematical model, describing the relevant transport phenomena, to match our experimental data. Overall, our results suggest that the rates of cell motility and cell proliferation from different initial wound shapes are approximately the same, implying that the differences we observe in the wound closure rate are consistent with a fairly typical mathematical model of wound healing. Our results imply that parameter estimates obtained from an experiment performed with one particular wound shape could be used to describe an experiment performed with a different shape. This fundamental result is important because this assumption is often invoked, but never tested

Tooth Development: Learn from "The Normal" and "The Abnormal".

For the past decades, tooth development has been extensively studied as a model for understanding organogenesis of ectoderm-derived structures. Much has been learned from the morphological patterning and molecular signaling of normal tooth development in model organisms, mainly rodents. On the other hand, human inherited dental anomalies also provide a valuable source for studying tooth development. Discerning the genetic etiology of these developmental defects not only enhances our understanding of normal tooth development but also provides a fundamental basis for developing potential therapeutic strategies for these disorders. Familial tooth agenesis (FTA) and amelogenesis imperfecta (AI) are the two most prevalent inherited dental defects in humans, characterized by failed tooth development and dental enamel malformations respectively. In this dissertation research, we described 7 FTA and 12 AI families and aimed to define the genetic etiology of the diseases through mutational analyses. By using target gene approaches and whole exome analyses, we successfully identified the disease-causing mutations in 2 FTA families and in all the AI cases. The results not only expanded the mutation spectrum of known disease-associated genes but also established novel candidate genes, revealing critical players in tooth and enamel development. Nevertheless, although human genetic studies of inherited dental and enamel defects have revealed many genes associated with the diseases, the functions of many of these genes and their roles in normal development and pathological conditions remain to be elucidated. Recently, mutations in FAM83H (family with sequence similarity 83, member H) were identified to cause autosomal dominant hypocalcified amelogenesis imperfecta (ADHCAI). Although many disease-causing FAM83H mutations have been reported, the cellular functions of this gene and the pathogenesis of its associated enamel defects are completely unknown. In this study, we used a biochemical approach to study FAM83H protein-protein interactions and identified CK1 (casein kinase 1) and SEC16A as FAM83H interacting proteins, which indicated a potential cellular function of FAM83H in vesicle trafficking and protein transport. The results also provided an explanation for the high mutation homogeneity of FAM83H disease-causing mutations revealed by human genetic studies and suggested a potential pathological mechanism for FAM83H-associated enamel defects.PhDOral Health SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107122/1/shihkaiw_1.pd

Refrigerant- Lubricant Mixture Properties Influencing Bubble Dynamic Parameters and Heat Transfer Coefficient in Nucleate Pool Boiling

We have been successfully developed a model regarding lubricant effect on individual processes of bubble nucleation, growth and departure period for nucleate pool boiling heat transfer. In this study, three type POE refrigeration lubricants with different refrigerant miscibility (POEA/POEB/POEC), two viscosity grades (ISO68 & 170), three kind of refrigerants (R-134a/R-1234ze/R-134yf), and three different saturated temperatures (10℃/0℃/10℃) are taken into calculation under different heat flux ranging from 10 KW/m2 to 80 KW/m2. Based on this model, a knowledge of chemical structures and physical properties of lubricant and refrigerant is sufficient to get bubble dynamic parameters and predict the boiling performance near metal surface. According to calculating results, several key factors play an important role in pool boiling heat transfer and show drastic influence on bubble parameters and HTC, such as refrigerant type, saturated temperature, heat flux and lubricant concentration. Regarding lubricant chemical structure effect on heat transfer performance, it will be direct related to OCR and following influence on HTC in real evaporator environment. But if keeping same lubricant concentration, different results will appear. Various lubricant structures may provide different volume size, adsorption energy on metal surface and interaction force between refrigerant and lubricant, but these factors sometimes offset each other and lead to only a slight difference in bubble size, contact angle, surface coverage concentration, and HTC. The calculation indicates that the presence of lubricant imposes a negative effect on HTC during waiting period of bubble formation and departure period, but a positive effect on HTC may prevail in bubble growth period. Such two effects compete during the boiling process and could lead increase or impair heat transfer performance at a low lubricant concentration

3D Theory of Microscopic Instabilities Driven by Space-Charge Forces

Microscopic, or short-wavelength, instabilities are known for drastic reduction of the beam quality and strong amplification of the noise in a beam. Space charge and coherent synchrotron radiation are known to be the leading causes for such instabilities. In this paper we present rigorous 3D theory of such instabilities driven by the space-charge forces. We define the condition when our theory is applicable for an arbitrary accelerator system with 3D coupling. Finally, we derive a linear integral equation describing such instability and identify conditions when it can be reduced to an ordinary second order differential equation.Comment: 38 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:2101.0410

Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense

Visual commonsense understanding requires Vision Language (VL) models to not only understand image and text but also cross-reference in-between to fully integrate and achieve comprehension of the visual scene described. Recently, various approaches have been developed and have achieved high performance on visual commonsense benchmarks. However, it is unclear whether the models really understand the visual scene and underlying commonsense knowledge due to limited evaluation data resources. To provide an in-depth analysis, we present a Multimodal Evaluation (ME) pipeline to automatically generate question-answer pairs to test models' understanding of the visual scene, text, and related knowledge. We then take a step further to show that training with the ME data boosts the model's performance in standard VCR evaluation. Lastly, our in-depth analysis and comparison reveal interesting findings: (1) semantically low-level information can assist the learning of high-level information but not the opposite; (2) visual information is generally under utilization compared with text.Comment: Accepted to EMNLP 2022 Long Pape

The Effect of Refrigeration Lubricant Properties on Nucleate Pool Boiling Heat Transfer Performance

Refrigeration lubricant plays a key role in lubricating and sealing during vapor compression processes. However, it may migrate to the evaporator to influence the heat transfer characteristics, either enhancement or degradation. The aim of this study is to fundamentally understand the effect of lubricant properties and bubble parameters on heat transfer performance. To clarify parameters affecting the heat transfer coefficient, several experiments were conducted on a horizontal flat surface, and pool-boiling phenomenon was recording by high-speed camera. Comparisons of heat transfer measurements for different refrigerant/lubricant mixtures were made, including two different refrigerants (R-134a & R-1234ze) and eight POE lubricants with different miscibility, ISO68 to ISO170 viscosity range. This study shows that improvements over pure refrigerant heat transfer can be obtained for refrigerant /lubricant mixtures with small lubricant mass fraction, high lubricant viscosity, and a low critical solution temperature (CST). The presence of lubricant will decrease the departure bubble diameter and may deteriorate heat transfer performance when the lubricant mass fraction is higher than 3%. A mechanistic explanation was provided for the observed refrigerant/lubricant boiling phenomenon, and we were successfully in creating a new model to quantify the effect of lubricant properties on the heat transfer performance. This model was developed based on cavity boiling theory, interfacial energy calculation between metal-liquid surface, and liquid-bubble interface. According to the model, the presence of lubricant layer on metal surface and surrounding the bubble will significantly alter waiting time of boiling, bubble departure time, activity site density of boiling incipience and superheat on heating surface

IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models

The field of vision-and-language (VL) understanding has made unprecedented progress with end-to-end large pre-trained VL models (VLMs). However, they still fall short in zero-shot reasoning tasks that require multi-step inferencing. To achieve this goal, previous works resort to a divide-and-conquer pipeline. In this paper, we argue that previous efforts have several inherent shortcomings: 1) They rely on domain-specific sub-question decomposing models. 2) They force models to predict the final answer even if the sub-questions or sub-answers provide insufficient information. We address these limitations via IdealGPT, a framework that iteratively decomposes VL reasoning using large language models (LLMs). Specifically, IdealGPT utilizes an LLM to generate sub-questions, a VLM to provide corresponding sub-answers, and another LLM to reason to achieve the final answer. These three modules perform the divide-and-conquer procedure iteratively until the model is confident about the final answer to the main question. We evaluate IdealGPT on multiple challenging VL reasoning tasks under a zero-shot setting. In particular, our IdealGPT outperforms the best existing GPT-4-like models by an absolute 10% on VCR and 15% on SNLI-VE. Code is available at https://github.com/Hxyou/IdealGPTComment: 13 pages, 5 figure