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  1. Abstract

    Proteoglycans are core proteins associated with carbohydrate/sugar moieties that are highly variable in disaccharide composition, which dictates their function. These carbohydrates are named glycosaminoglycans, and they can be attached to proteoglycans or found free in tissues or on cell surfaces. Glycosaminoglycans such as hyaluronan, chondroitin sulfate, dermatan sulfate, keratan sulfate, and heparin/heparan sulfate have multiple functions including involvement in inflammation, immunity and connective tissue structure, and integrity. Heparan sulfate is a highly sulfated polysaccharide that is abundant in the periodontium including alveolar bone. Recent evidence supports the contention that heparan sulfate is an important player in modulating interactions between damage associated molecular patterns and inflammatory receptors expressed by various cell types. The structure of heparan sulfate is reported to dictate its function, thus, the utilization of a homogenous and structurally defined heparan sulfate polysaccharide for modulation of cell function offers therapeutic potential. Recently, a chemoenzymatic approach was developed to allow production of many structurally defined heparan sulfate carbohydrates. These oligosaccharides have been studied in various pathological inflammatory conditions to better understand their function and their potential application in promoting tissue homeostasis. We have observed that specific size and sulfation patterns can modulate inflammation and promote tissue maintenance including an anabolic effect in alveolar bone. Thus, new evidence provides a strong impetus to explore heparan sulfate as a potential novel therapeutic agent to treat periodontitis, support alveolar bone maintenance, and promote bone formation.

     
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    Free, publicly-accessible full text available August 24, 2024
  2. Robles, A. (Ed.)
    Although various navigation apps are available, people who are blind or have low vision (PVIB) still face challenges to locate store entrances due to missing geospatial information in existing map services. Previously, we have developed a crowdsourcing platform to collect storefront accessibility and localization data to address the above challenges. In this paper, we have significantly improved the efficiency of data collection and user engagement in our new AI-enabled Smart DoorFront platform by designing and developing multiple important features, including a gamified credit ranking system, a volunteer contribution estimator, an AI-based pre-labeling function, and an image gallery feature. For achieving these, we integrate a specially designed deep learning model called MultiCLU into the Smart DoorFront. We also introduce an online machine learning mechanism to iteratively train the MultiCLU model, by using newly labeled storefront accessibility objects and their locations in images. Our new DoorFront platform not only significantly improves the efficiency of storefront accessibility data collection, but optimizes user experience. We have conducted interviews with six adults who are blind to better understand their daily travel challenges and their feedback indicated that the storefront accessibility data collected via the DoorFront platform would be very beneficial for them. 
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    Free, publicly-accessible full text available June 1, 2024
  3. Free, publicly-accessible full text available June 1, 2024
  4. Mobile and embedded devices are becoming ubiquitous. Applications such as rescue with autonomous robots and event analysis on traffic cameras rely on devices with limited power supply and computational sources. Thus, the demand for efficient computer vision algorithms increases. Since 2015, we have organized the IEEE Low-Power Computer Vision Challenge to advance the state of the art in low-power computer vision. We describe the competition organizing details including the challenge design, the reference solution, the dataset, the referee system, and the evolution of the solutions from two winning teams. We examine the winning teams’ development patterns and design decisions, focusing on their techniques to balance power consumption and accuracy. We conclude that a successful competition needs a well-designed reference solution and automated referee system, and a solution with modularized components is more likely to win. We hope this paper provides guidelines for future organizers and contestants of computer vision competitions. 
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    Free, publicly-accessible full text available July 1, 2024
  5. Free, publicly-accessible full text available June 5, 2024
  6. Free, publicly-accessible full text available June 1, 2024
  7. Abstract

    Accurate nuclear reaction rates for26P(p,γ)27S are pivotal for a comprehensive understanding of therp-process nucleosynthesis path in the region of proton-rich sulfur and phosphorus isotopes. However, large uncertainties still exist in the current rate of26P(p,γ)27S because of the lack of nuclear mass and energy level structure information for27S. We reevaluate this reaction rate using the experimentally constrained27S mass, together with the shell model predicted level structure. It is found that the26P(p,γ)27S reaction rate is dominated by a direct capture reaction mechanism despite the presence of three resonances atE= 1.104, 1.597, and 1.777 MeV above the proton threshold in27S. The new rate is overall smaller than the other previous rates from the Hauser–Feshbach statistical model by at least 1 order of magnitude in the temperature range of X-ray burst interest. In addition, we consistently update the photodisintegration rate using the new27S mass. The influence of new rates of forward and reverse reaction in the abundances of isotopes produced in therp-process is explored by postprocessing nucleosynthesis calculations. The final abundance ratio of27S/26P obtained using the new rates is only 10% of that from the old rate. The abundance flow calculations show that the reaction path26P(p,γ)27S(β+,ν)27P is not as important as previously thought for producing27P. The adoption of the new reaction rates for26P(p,γ)27S only reduces the final production of aluminum by 7.1% and has no discernible impact on the yield of other elements.

     
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  8. Bansal, M (Ed.)
    Predicting the secondary structure of RNA is an important problem in molecular biology, providing insights into the function of non-coding Rn As and with broad applications in understanding disease, the development of new drugs, among others. Combinatorial algorithms for predicting RNA foldings can generate an exponentially large number of equally optimal foldings with respect to a given optimization criterion, making it difficult to determine how well any single folding represents the entire space. We provide efficient new algorithms for providing insights into this large space of optimal RNA foldings and a research software tool, toRNAdo, that implements these algorithms. 
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