Two-dimensional Fourier domain Ronchi ruling measurement using Talbot-based crossing point modeling

We propose a direct two-dimensional Fourier domain fitting-free method to determine the period of a Ronchi ruling. A precise method to measure a spatial frequency target's quality and fidelity is highly desired as the pattern period directly affects every aspect of a spatial frequency target-based metrology, including the accuracy and precision of the measurement or evaluations. A standard Talbot experimental apparatus and the Talbot effect are used to obtain and model our data. To determine the period of the ruling directly, only a common digital camera, with a protective glass and an air gap in front of the sensor array, and a Ronchi ruling of chrome deposited on a glass substrate are required. The Talbot effect-based crossing point modeling technique requires no calibration or a priori information but simply the pixel size of the digital camera and a precise means of measuring the spatial frequency from a Talbot image. For a Ronchi ruling with a period specification of 0.1 mm, the nanometric measurement was found to be 0.100010 mm with an error level of 5 nm. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A Level Set Kalman Filter Approach to Estimate the Circadian Phase and its Uncertainty from Wearable Data

Daily (~24hr) rhythms of behavior and physiology such as sleep and hormone secretion are coordinated by an endogenous timer, the circadian clock. The accurate estimation of the clock state (i.e., the circadian phase) outside of the laboratory has enormous potential for precision medicine. Several methods that predict the phase from measurements collected with wearables (e.g., Apple Watch) have been recently developed. However, computation of the uncertainty in the estimation remains an open problem. The uncertainty analysis is necessary because the estimation accuracy can largely change even by a small perturbation of daily routine. Here, we present a method to account for the uncertainty and estimate the circadian phase using a new extension of Kalman filtering named the level set Kalman filter. Using the newly proposed method, we study the relationship between phase uncertainty and process noise from various sources. This allows the identification of the magnitude of the noise in the circadian system, which is impossible with previous methods. Moreover, our study reveals how much the uncertainty of the phase estimate of the central clock that is inaccessibly located in the brain can be reduced when measurements of the peripheral clock phase are given from wearables. We also show that our method has a performance improvement over the previous methods. Finally, we apply our method to real-world data to further identify its usefulness. These results set the stage for systematically understanding the circadian dynamics in the real world

Modelling of plant circadian clock for characterizing hypocotyl growth under different light quality conditions

To meet the ever-increasing global food demand, the food production rate needs to be increased significantly in the near future. Speed breeding is considered as a promising agricultural technology solution to achieve the zero-hunger vision as specified in the United Nations Sustainable Development Goal 2. In speed breeding, the photoperiod of the artificial light has been manipulated to enhance crop productivity. In particular, regulating the photoperiod of different light qualities rather than solely white light can further improve speed breading. However, identifying the optimal light quality and the associated photoperiod simultaneously remains a challenging open problem due to complex interactions between multiple photoreceptors and proteins controlling plant growth. To tackle this, we develop a first comprehensive model describing the profound effect of multiple light qualities with different photoperiods on plant growth (i.e. hypocotyl growth). The model predicts that hypocotyls elongated more under red light compared to both red and blue light. Drawing similar findings from previous related studies, we propose that this might result from the competitive binding of red and blue light receptors, primarily Phytochrome B (phyB) and Cryptochrome 1 (cry1) for the core photomorphogenic regulator, CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1). This prediction is validated through an experimental study on Arabidopsis thaliana. Our work proposes a potential molecular mechanism underlying plant growth under different light qualities and ultimately suggests an optimal breeding protocol that takes into account light quality

Natural Language Dataset Generation Framework for Visualizations Powered by Large Language Models

We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and diverse NL datasets using only Vega-Lite specifications as input, thereby streamlining the development of Natural Language Interfaces (NLIs) for data visualization. To synthesize relevant chart semantics accurately and enhance syntactic diversity in each NL dataset, we leverage 1) a guided discovery incorporated into prompting so that LLMs can steer themselves to create faithful NL datasets in a self-directed manner; 2) a score-based paraphrasing to augment NL syntax along with four language axes. We also present a new collection of 1,981 real-world Vega-Lite specifications that have increased diversity and complexity than existing chart collections. When tested on our chart collection, VL2NL extracted chart semantics and generated L1/L2 captions with 89.4% and 76.0% accuracy, respectively. It also demonstrated generating and paraphrasing utterances and questions with greater diversity compared to the benchmarks. Last, we discuss how our NL datasets and framework can be utilized in real-world scenarios. The codes and chart collection are available at https://github.com/hyungkwonko/chart-llm.Comment: 22 pages, 5 figure

Sex- and Age-Related Changes in Connexin 43 Expression in Normal Rat Bladder

Purpose Gap junctions are intercellular channels to facilitate electrical and metabolic communication between adjacent cells. Connexin 43 is the most predominant type of connexin expressed on rat detrusor muscle cells. We investigated the connexin 43 expressions in various age groups of either sex in normal rats. Methods Eighty Sprague-Dawley rats were used for analysis. Each group was quantified by 8 rats at 1 week, 2 weeks, 1 month, 3 months, and 6 months of age in either sex. In each animal, bladder was removed without any kind of intervention and fresh-frozen in liquid nitrogen. Total RNA extraction was done with easy-BLUE total RNA extraction kit. Reverse transcription polymerase chain reaction was done for connexin 43 and glyceraldehyde-3-phosphate dehydrogenase as an internal control using ImProm-II Reverse Transcription System. Results In female rats, no age-related change was detected in connexin 43 expressions. In male rats, connexin expression at 3 months of age showed significant decrease compared with 1 week, 2 weeks, and 6 months of age (P<0.05). When connexin expression at the same age in male and female were compared, only 3 months group in male showed significant decrease than the same age group in female. Conclusions Our data suggest that the expressions of connexin 43 mRNA in normal detrusor muscle cell showed age-related changes especially in male rats. Although it is difficult to interpret these findings at this stage, age should be considered as a possible compounding factor affecting connexin 43 expressions in male rats

Antitumor activity of sorafenib-incorporated nanoparticles of dextran/poly(dl-lactide-co-glycolide) block copolymer

Sorafenib-incoporated nanoparticles were prepared using a block copolymer that is composed of dextran and poly(DL-lactide-co-glycolide) [DexbLG] for antitumor drug delivery. Sorafenib-incorporated nanoparticles were prepared by a nanoprecipitation-dialysis method. Sorafenib-incorporated DexbLG nanoparticles were uniformly distributed in an aqueous solution regardless of the content of sorafenib. Transmission electron microscopy of the sorafenib-incorporated DexbLG nanoparticles revealed a spherical shape with a diameter < 300 nm. Sorafenib-incorporated DexbLG nanoparticles at a polymer/drug weight ratio of 40:5 showed a relatively uniform size and morphology. Higher initial drug feeding was associated with increased drug content in nanoparticles and in nanoparticle size. A drug release study revealed a decreased drug release rate with increasing drug content. In an in vitro anti-proliferation assay using human cholangiocarcinoma cells, sorafenib-incorporated DexbLG nanoparticles showed a similar antitumor activity as sorafenib. Sorafenib-incorporated DexbLG nanoparticles are promising candidates as vehicles for antitumor drug targeting

Process optimization for polishing large aspheric mirrors

ABSTRACT Large telescope mirrors have stringent requirements for surface irregularity on all spatial scales. Large scale errors, typically represented with Zernike polynomials, are relatively easy to control. Errors with smaller spatial scale can be more difficult because the specifications are tighter. Small scale errors are controlled with a combination of natural smoothing from large tools and directed figuring with precisely controlled small tools. The optimization of the complete process builds on the quantitative understanding of natural smoothing, convergence of small tool polishing, and confidence in the surface measurements. This paper provides parametric models for smoothing and directed figuring that can be used to optimize the manufacturing process