ONE-DIMENSIONAL MODELING OF OXY-FUEL FLUIDIZED BED COMBUSTION FOR CO2 CAPTURE

A one-dimensional model has been developed with the aim of investigating oxyfuel circulating fluidized bed (CFB) combustion for CO2 capture with the main focus of assessing the heat balance of the CFB loop. For different oxygen concentrations in the gas flow fed to the furnace, the model calculates the external solids recirculation and the heat load in the furnace and in external particle coolers

HMD-NeMo: Online 3D Avatar Motion Generation From Sparse Observations

Generating both plausible and accurate full body avatar motion is the key to the quality of immersive experiences in mixed reality scenarios. Head-Mounted Devices (HMDs) typically only provide a few input signals, such as head and hands 6-DoF. Recently, different approaches achieved impressive performance in generating full body motion given only head and hands signal. However, to the best of our knowledge, all existing approaches rely on full hand visibility. While this is the case when, e.g., using motion controllers, a considerable proportion of mixed reality experiences do not involve motion controllers and instead rely on egocentric hand tracking. This introduces the challenge of partial hand visibility owing to the restricted field of view of the HMD. In this paper, we propose the first unified approach, HMD-NeMo, that addresses plausible and accurate full body motion generation even when the hands may be only partially visible. HMD-NeMo is a lightweight neural network that predicts the full body motion in an online and real-time fashion. At the heart of HMD-NeMo is the spatio-temporal encoder with novel temporally adaptable mask tokens that encourage plausible motion in the absence of hand observations. We perform extensive analysis of the impact of different components in HMD-NeMo and introduce a new state-of-the-art on AMASS dataset through our evaluation.Comment: Accepted at ICCV 202

Efficient non-degenerate two-photon excitation for fluorescence microscopy

Non-degenerate two-photon excitation (ND-TPE) has been explored in two-photon excitation microscopy. However, a systematic study of the efficiency of ND-TPE to guide the selection of fluorophore excitation wavelengths is missing. We measured the relative non-degenerate two-photon absorption cross-section (ND-TPACS) of several commonly used fluorophores (two fluorescent proteins and three small-molecule dyes) and generated 2-dimensional ND-TPACS spectra. We observed that the shape of a ND-TPACS spectrum follows that of the corresponding degenerate two-photon absorption cross-section (D-TPACS) spectrum, but is higher in magnitude. We found that the observed enhancements are higher than theoretical predictions.Published versio

Role of peptidylarginine deiminase 2 (PAD2) in mammary carcinoma cell migration

BACKGROUND: Penetration of the mammary gland basement membrane by cancer cells is a crucial first step in tumor invasion. Using a mouse model of ductal carcinoma in situ, we previously found that inhibition of peptidylarginine deiminase 2 (PAD2, aka PADI2) activity appears to maintain basement membrane integrity in xenograft tumors. The goal of this investigation was to gain insight into the mechanisms by which PAD2 mediates this process. METHODS: For our study, we modulated PAD2 activity in mammary ductal carcinoma cells by lentiviral shRNA-mediated depletion, lentiviral-mediated PAD2 overexpression, or PAD inhibition and explored the effects of these treatments on changes in cell migration and cell morphology. We also used these PAD2-modulated cells to test whether PAD2 may be required for EGF-induced cell migration. To determine how PAD2 might promote tumor cell migration in vivo, we tested the effects of PAD2 inhibition on the expression of several cell migration mediators in MCF10DCIS.com xenograft tumors. In addition, we tested the effect of PAD2 inhibition on EGF-induced ductal invasion and elongation in primary mouse mammary organoids. Lastly, using a transgenic mouse model, we investigated the effects of PAD2 overexpression on mammary gland development. RESULTS: Our results indicate that PAD2 depletion or inhibition suppresses cell migration and alters the morphology of MCF10DCIS.com cells. In addition, we found that PAD2 depletion suppresses the expression of the cytoskeletal regulatory proteins RhoA, Rac1, and Cdc42 and also promotes a mesenchymal to epithelial-like transition in tumor cells with an associated increase in the cell adhesion marker, E-cadherin. Our mammary gland organoid study found that inhibition of PAD2 activity suppresses EGF-induced ductal invasion. In vivo, we found that PAD2 overexpression causes hyperbranching in the developing mammary gland. CONCLUSION: Together, these results suggest that PAD2 plays a critical role in breast cancer cell migration. Our findings that EGF treatment increases protein citrullination and that PAD2 inhibition blocks EGF-induced cell migration suggest that PAD2 likely functions within the EGF signaling pathway to mediate cell migration

Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning

Background: Dental caries is a prevalent, complex, chronic illness that is avoidable. Better dental health outcomes are achieved as a result of accurate and early caries risk prediction in children, which also helps to avoid additional expenses and repercussions. In recent years, artificial intelligence (AI) has been employed in the medical field to aid in the diagnosis and treatment of medical diseases. This technology is a critical tool for the early prediction of the risk of developing caries. Aim: Through the development of computational models and the use of machine learning classification techniques, we investigated the potential for dental caries factors and lifestyle among children under the age of five. Design: A total of 780 parents and their children under the age of five made up the sample. To build a classification model with high accuracy to predict caries risk in 0–5-year-old children, ten different machine learning modelling techniques (DT, XGBoost, KNN, LR, MLP, RF, SVM (linear, rbf, poly, sigmoid)) and two assessment methods (Leave-One-Out and K-fold) were utilised. The best classification model for caries risk prediction was chosen by analysing each classification model’s accuracy, specificity, and sensitivity. Results: Machine learning helped with the creation of computer algorithms that could take a variety of parameters into account, as well as the identification of risk factors for childhood caries. The performance of the classifier is almost unbiased, making it generalizable. Among all applied machine learning algorithms, Multilayer Perceptron and Random Forest had the best accuracy, with 97.4%. Support Vector Machine with RBF Kernel (with an accuracy of 97.4%) was better than Extreme Gradient Boosting (with 94.9% accuracy). Conclusion: The outcomes of this study show the potential of regular screening of children for caries risk by experts and finding the risk scores of dental caries for any individual. Therefore, in order to avoid dental caries, it is possible to concentrate on each individual by utilizing machine learning modelling

Nonlinear Dynamics of 3D Massive Gravity

We explore the nonlinear classical dynamics of the three-dimensional theory of "New Massive Gravity" proposed by Bergshoeff, Hohm and Townsend. We find that the theory passes remarkably highly nontrivial consistency checks at the nonlinear level. In particular, we show that: (1) In the decoupling limit of the theory, the interactions of the helicity-0 mode are described by a single cubic term -- the so-called cubic Galileon -- previously found in the context of the DGP model and in certain 4D massive gravities. (2) The conformal mode of the metric coincides with the helicity-0 mode in the decoupling limit. Away from this limit the nonlinear dynamics of the former is described by a certain generalization of Galileon interactions, which like the Galileons themselves have a well-posed Cauchy problem. (3) We give a non-perturbative argument based on the presence of additional symmetries that the full theory does not lead to any extra degrees of freedom, suggesting that a 3D analog of the 4D Boulware-Deser ghost is not present in this theory. Last but not least, we generalize "New Massive Gravity" and construct a class of 3D cubic order massive models that retain the above properties.Comment: 21 page

Forest structural complexity tool—an open source, fully-automated tool for measuring forest point clouds

Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed forest measurements rapidly and cheaply, however, they have been held back from large-scale implementation due to the complex and time-consuming workflows required to utilize them. This work is focused on describing and evaluating an approach to create a robust, sensor-agnostic and fully automated forest point cloud measurement tool called the Forest Structural Complexity Tool (FSCT). The performance of FSCT is evaluated using 49 forest plots of terrestrial laser scanned (TLS) point clouds and 7022 destructively sampled manual diameter measurements of the stems. FSCT was able to match 5141 of the reference diameter measurements fully automatically with mean, median and root mean squared errors (RMSE) of 0.032 m, 0.02 m, and 0.103 m respectively. A video demonstration is also provided to qualitatively demonstrate the diversity of point cloud datasets that the tool is capable of measuring. FSCT is provided as open source, with the goal of enabling plot scale remote sensing techniques to replace most structural forest mensuration in research and industry. Future work on this project will seek to make incremental improvements to this methodology to further improve the reliability and accuracy of this tool in most high-resolution forest point clouds

The ER Aminopeptidases, ERAP1 and ERAP2, synergize to self-modulate their respective activities

IntroductionCritical steps in Major Histocompatibility Complex Class I (MHC-I) antigen presentation occur in the endoplasmic reticulum (ER). In general, peptides that enter the ER are longer than the optimal length for MHC-I binding. The final trimming of MHC-I epitopes is performed by two related aminopeptidases, ERAP1 and ERAP2 in humans that possess unique and complementary substrate trimming specificities. While ERAP1 efficiently trims peptides longer than 9 residues, ERAP2 preferentially trims peptides shorter than 9 residues.Materials and MethodsUsing a combination of biochemical and proteomic studies followed by biological verification.ResultsWe demonstrate that the optimal ligands for either enzyme act as inhibitors of the other enzyme. Specifically, the presence of octamers reduced the trimming of long peptides by ERAP1, while peptides longer than nonomers inhibit ERAP2 activity.DiscussionWe propose a mechanism for how ERAP1 and ERAP2 synergize to modulate their respective activities and shape the MHC-I peptidome by generating optimal peptides for presentation

High-potency ligands for DREADD imaging and activation in rodents and monkeys.

Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) are a popular chemogenetic technology for manipulation of neuronal activity in uninstrumented awake animals with potential for human applications as well. The prototypical DREADD agonist clozapine N-oxide (CNO) lacks brain entry and converts to clozapine, making it difficult to apply in basic and translational applications. Here we report the development of two novel DREADD agonists, JHU37152 and JHU37160, and the first dedicated 18F positron emission tomography (PET) DREADD radiotracer, [18F]JHU37107. We show that JHU37152 and JHU37160 exhibit high in vivo DREADD potency. [18F]JHU37107 combined with PET allows for DREADD detection in locally-targeted neurons, and at their long-range projections, enabling noninvasive and longitudinal neuronal projection mapping