Point to Pipe: Automatic Reconstruction and Classification of Pipes Using Lasergrammetry and Thermogrammetry for Building Information Modeling (BIM)

Existing buildings account for 40% of global energy consumption, and two-thirds of them will be still be operational in 2050. As most of these buildings lack the needed documentation for energy upgrades, it is essential to understand and represent the current conditions of their envelopes and mechanical systems. This project proposed a skeleton-based application for reconstructing and classifying pipes in existing buildings using point clouds from laser scanners and thermal images for Building Information Modeling (BIM) applications. MATLAB and Dynamo were used to process and model this information in Revit. Initial results indicate that the application is robust to identifying pipes and connections, and that thermal images can be used to create sematic-rich models. These results can contribute to improving the capabilities of some of the commercially available software for pipe reconstruction in BIM and to expediting the digital reconstruction processes in existing buildings

Dissecting the development of plasmacytoid dendritic cells

Plasmacytoid dendritic cells (pDCs) are an immune subset specialized in the production of Type I Interferons (IFNs). Conventional dendritic cells (cDCs) originate mostly from a common dendritic cell progenitor (CDP), whereas pDCs have been shown to develop from both CDPs as well as common lymphoid progenitors (CLPs). In contrast to the current literature, we here show that pDCs mostly differentiate from an IL-7R expressing lymphoid progenitor. IL-7R+ progenitors can be subdivided into three distinct subsets based on the expression of SiglecH and Ly6D: double negative (DN), Ly6D+ single positive (SP) and double positive (DP) progenitors. Each of these subsets identifies a specific developmental stage along the pDC lineage, where commitment by IL-7R+ progenitors is achieved upon expression of Ly6D and SiglecH (DP pre-pDCs). Further, RNA sequencing analysis of IL-7R+ lymphoid progenitor subsets revealed the transcriptional landscape of pDC development along the lymphoid branch, where high expression of the transcription factor IRF8 marks pDC commitment and anticipates the increase of TCF4 levels. The transcriptional signature of DP pre-pDCs correlates with the lineage potential assessed in vitro, in which DP pre-pDCs are fully committed to the pDC lineage. Moreover, single cell RNA sequencing on bone marrow and splenic pDCs revealed pDC heterogeneity in both tissues and further supported the dual origin of pDC from myeloid and lymphoid precursors. While all pDCs have the potential to secrete Type I IFNs and have high expression levels of pDC-specific transcript, only myeloid-derived pDCs share with cDCs the capacity to process and present antigen, suggesting that functional specification is directly linked to developmental origin

Long-Term Dagum-PVF Frailty Regression Model: Application in Health Studies

Survival models incorporating cure fractions, commonly known as cure fraction models or long-term survival models, are widely employed in epidemiological studies to account for both immune and susceptible patients in relation to the failure event of interest under investigation. In such studies, there is also a need to estimate the unobservable heterogeneity caused by prognostic factors that cannot be observed. Moreover, the hazard function may exhibit a non-monotonic form, specifically, an unimodal hazard function. In this article, we propose a long-term survival model based on the defective version of the Dagum distribution, with a power variance function (PVF) frailty term introduced in the hazard function to control for unobservable heterogeneity in patient populations, which is useful for accommodating survival data in the presence of a cure fraction and with a non-monotone hazard function. The distribution is conveniently reparameterized in terms of the cure fraction, and then associated with the covariates via a logit link function, enabling direct interpretation of the covariate effects on the cure fraction, which is not usual in the defective approach. It is also proven a result that generates defective models induced by PVF frailty distribution. We discuss maximum likelihood estimation for model parameters and evaluate its performance through Monte Carlo simulation studies. Finally, the practicality and benefits of our model are demonstrated through two health-related datasets, focusing on severe cases of COVID-19 in pregnant and postpartum women and on patients with malignant skin neoplasms

A comparison of substance use stigma and health stigma in a population of veterans with co-occurring mental health and substance use disorders

OBJECTIVE: This pilot study examined whether substance use or mental illness was more stigmatizing among individuals with co-occurring mental health and substance abuse problems. METHODS: This study included 48 individuals with co-occurring substance use and mental health problems enrolled in a Substance Abuse and Mental Health Services funded treatment program. Subjects received a baseline assessment that included addiction, mental health, and stigma measures. RESULTS: The sample consisted primarily of White males with an average age of 38 years. Substance abuse was found to be more stigmatizing than mental illness, F(1, 47) = 14.213, p < .001, and stigma varied across four different levels of stigma (Aware, Agree, Apply, and Harm), F(2.099, 98.675) = 117.883, p < .001. The interaction between type and level of stigma was also significant, F(2.41, 113.284) = 20.250, p < .001, indicating that differences in reported stigma between types varied across levels of stigma. Post hoc tests found a significant difference between all levels of stigma except for the comparison between Apply and Harm. Reported stigma was significantly higher for substance abuse than mental illness at the Aware and Agree levels. In addition, pairwise comparisons found significant differences between all levels of stigma with the exception of the comparison between Apply and Harm, indicating a pattern whereby reported stigma generally decreased from the first level (Aware stage) to subsequent levels. CONCLUSIONS: These results have important implications for treatment, suggesting the need to incorporate anti-stigma interventions for individuals with co-occurring disorders with a greater focus on substance abuse

Accurate shellcode recognition from network traffic data using artificial neural nets

This paper presents an approach to shellcode recognition directly from network traffic data using a multi-layer perceptron with back-propagation learning algorithm. Using raw network data composed of a mixture of shellcode, image files, and DLL-Dynamic Link Library files, our proposed design was able to classify the three types of data with high accuracy and high precision with neither false positives nor false negatives. The proposed method comprises simple and fast pre-processing of raw data of a fixed length for each network data package and yields perfect results with 100\% accuracy for the three data types considered. The research is significant in the context of network security and intrusion detection systems. Work is under way for real time recognition and fine-tuning the differentiation between various shellcodes

Comparative genome-centric analysis reveals seasonal variation in the function of coral reef microbiomes

Microbially mediated processes contribute to coral reef resilience yet, despite extensive characterisation of microbial community variation following environmental perturbation, the effect on microbiome function is poorly understood. We undertook metagenomic sequencing of sponge, macroalgae and seawater microbiomes from a macroalgae-dominated inshore coral reef to define their functional potential and evaluate seasonal shifts in microbially mediated processes. In total, 125 high-quality metagenome-assembled genomes were reconstructed, spanning 15 bacterial and 3 archaeal phyla. Multivariate analysis of the genomes relative abundance revealed changes in the functional potential of reef microbiomes in relation to seasonal environmental fluctuations (e.g. macroalgae biomass, temperature). For example, a shift from Alphaproteobacteria to Bacteroidota-dominated seawater microbiomes occurred during summer, resulting in an increased genomic potential to degrade macroalgal-derived polysaccharides. An 85% reduction of Chloroflexota was observed in the sponge microbiome during summer, with potential consequences for nutrition, waste product removal, and detoxification in the sponge holobiont. A shift in the Firmicutes:Bacteroidota ratio was detected on macroalgae over summer with potential implications for polysaccharide degradation in macroalgal microbiomes. These results highlight that seasonal shifts in the dominant microbial taxa alter the functional repertoire of host-associated and seawater microbiomes, and highlight how environmental perturbation can affect microbially mediated processes in coral reef ecosystems.Australian Government Department of Industry, Innovation and Science; Advance Queensland PhD Scholarship Great Barrier Reef Marine Park Authority Management Award National Environmental Science Program (NESP)info:eu-repo/semantics/publishedVersio