Level Of Detail Research Articles

Finite Element Modeling Aspects of Buried Large Diameter Steel Pipe–Butterfly Valve Interaction

Buried flexible pipes are allowed to deflect up to 2–5% of the pipe diameter, which can become problematic for the connected direct-bury, large-diameter butterfly valves. The complex behavior of the pipe–valve–soil system makes it difficult to predict the deflection of the pipe/valve system. In the absence of field/experimental studies, the application of finite element analysis (FEA) seems necessary to predict deflection and stresses and to avoid potential downtime associated with disruption of service. This paper described the FEA of a large-diameter pipe–valve system, with different backfills under gravity, overburden, and internal pressure loads. The effects of modeling different components of the system (e.g., flanges, bearing housing, gate disc, etc.) were described and investigated. The goal of this study was to provide insight into the design and installation of direct-bury pipe–valve systems and evaluate current installation methods in the absence of guidelines. In addition, the level of modeling details required for FEA to yield accurate results was discussed.

Digitally-assisted iconology: A method for the analysis of digital media

Exploring medium-to-large datasets of social media imagery can be challenging. This paper describes a digitally-assisted iconology, a hybrid methodology that includes machine learning and data analytics for sorting through medium-sized datasets of images that lack metadata to describe their pictorial content. The method plays to the strengths of current digital technologies. Using machine learning, pictures are first clustered in a preliminary stage based upon basic formal presentational characteristics. Thematic analysis follows this preliminary stage, based upon an expansion of Aby Warburg’s “pre-coined expressive values”, which are frequently found in pictures displaying high levels of user reception. Once clustered via these two separate stages, the researcher can then drill down using familiar forms of visual analysis to explore how similar concepts have been rendered in different ways. The analysis may be augmented by exploring the commentary appended to these pictures, which adds a further level of detail providing insight into end-user interpretations. The approach – including its drawbacks – is demonstrated via a consequential dataset of pictures shared on Twitter in 2015, after a Syrian child was found drowned off the Turkish shore. Derivative imagery based upon the original photographs referenced longstanding iconographic themes.

Quantifying uncertainties in direct numerical simulations of a turbulent channel flow

Direct numerical simulation (DNS) provides unrivalled levels of detail and accuracy for simulating turbulent flows. However, like all numerical methods, DNS is subject to uncertainties arising from the numerical scheme and input parameters (e.g. mesh resolution). While uncertainty quantification (UQ) techniques are being employed more and more to provide a systematic analysis of uncertainty for lower-fidelity models, their application to DNS is still relatively rare. In light of this, the aim of this work is to apply UQ and sensitivity analysis to the DNS of a canonical wall-bounded turbulent channel flow at low Reynolds number (Reτ=180). To compute the DNS, Incompact3d – a highly scalable open-source framework based on high-order compact finite differences and a spectral Poisson solver – is used as a black-box solver. Stochastic collocation is used to propagate the input uncertainties through Incompact3d to the output quantities of interest (QOIs). To facilitate the non-intrusive forward UQ analysis, the open-source EasyVVUQ package is used to provide integrated capability for sampling, pre-processing, execution, post-processing, and analysis of the computational campaign. Three separate UQ campaigns are conducted. The first two examine the effect of domain size and the numerical parameters (e.g. mesh resolution, time step, sample time), respectively, and adopt Gaussian quadrature rules combined via tensor products to sample the multi-dimensional input space. Finally, the third campaign investigates the performance of a dimension-adaptive sampling strategy that significantly reduces the computational cost compared to the full tensor product approach. The analysis focuses on the cross-channel statistical moments of the QOIs, as well as local and global sensitivity analyses to assess the sensitivity of each QOI with respect to each individual input. This enables an assessment of the robustness and sensitivity of DNS to the user-defined numerical parameters for wall-bounded turbulent flows, and provides an indication of suitable ranges for defining the values of these parameters.

ДЕКОРАТИВНІ ЕЛЕМЕНТИ ПОСТМОДЕРНІСТСЬКИХ БУДИНКІВ ЛЬВІВСЬКОЇ АРХІТЕКТУРНОЇ ШКОЛИ

This study summarizes the results of the analysis of three dozen architectural objects in Lviv based on which presents architectural school in Lviv and its transition towards postmodern stylistics. The article considers the analysis of the phenomenon of postmodern architecture, in particular one of the key issues of the discussion with modernism - the possibility and nature of decorating the facades of buildings. Firstly, the role of geometric-decorative ornamentation as an indicator of the emergence of the so-called "Carpathian style," considered a proto-postmodernism, into a full-fledged postmodern architectural language was identified. Subsequently, the gradual disappearance of this ornamentation served as an indicator of the return to modernist paradigms, albeit in a significantly distinct form from the late Soviet neoclassicism. Additionally, the presence of such decorative elements in the Lviv context suggests that the same fundamental factors contributing to the development of postmodernism in North America and Europe were at play in this region. Secondly, the artistic and compositional peculiarities of postmodern decorative motifs in the Lviv architectural school were investigated. It was found that the architects from Lviv occupied a complex and ambivalent position concerning their search for alternatives to modernism. While their fundamental design principles and concepts displayed clear contrasts, on the level of details, particularly in decorative elements, they actively drew from modernist culture, specifically abstract art. Finally, potential directions for further research on the phenomenon of postmodernism in Lviv were outlined, with a specific focus on evaluating the decorative language of buildings from that era. The study proposes the hypothesis that during the 1990s to the early 2010s, a distinct system of geometric-decorative ornamentation might have begun to take shape within the Lviv architectural school. However, this development did not progress further due to the shift in design approaches and the adoption of neo-modernist architectural language. Further exploration of this aspect may shed light on the unique architectural developments in Lviv during that period.

A decomposition scheme for continuous Level of Detail, streaming and lossy compression of unordered point clouds

Modern laser scanners, depth sensor devices and Dense Image Matching techniques allow for capturing of extensive point cloud datasets. While capturing has become more user-friendly, the size of registered point clouds results in large datasets which pose challenges for processing, storage and visualization. This paper presents a decomposition scheme using oriented KD trees and the wavelet transform for unordered point clouds. Taking inspiration from image pyramids, the decomposition scheme comes with a Level of Detail representation where higher-levels are progressively reconstructed from lower ones, thus making it suitable for streaming and continuous Level of Detail. Furthermore, the decomposed representation allows common compression techniques to achieve higher compression ratios by modifying the underlying frequency data at the cost of geometric accuracy and therefore allows for flexible lossy compression. After introducing this novel decomposition scheme, results are discussed to show how it deals with data captured from different sources.

Flame Structure at Elevated Pressure Values and Reduced Reaction Mechanisms for the Combustion of CH4/H2 Mixtures

Understanding and controlling the combustion of clean and efficient fuel blends, like methane + hydrogen, is essential for optimizing energy production processes and minimizing environmental impacts. To extend the available experimental database on CH4 + H2 flame speciation, this paper reports novel measurement data on the chemical structure of laminar premixed burner-stabilized CH4/H2/O2/Ar flames. The experiments cover various equivalence ratios (φ = 0.8 and φ = 1.2), hydrogen content amounts in the CH4/H2 blend (XH2 = 25%, 50% and 75%), and different pressures (1, 3 and 5 atm). The flame-sampling molecular-beam mass spectrometry (MBMS) technique was used to detect reactants, major products, and several combustion intermediates, including major flame radicals. Starting with the detailed model AramcoMech 2.0, two reduced kinetic mechanisms with different levels of detail for the combustion of CH4/H2 blends are reported: RMech1 (30 species and 70 reactions) and RMech2 (21 species and 31 reactions). Validated against the literature data for laminar burning velocity and ignition delays, these mechanisms were demonstrated to reasonably predict the effect of pressure and hydrogen content in the mixture on the peak mole fractions of intermediates and adequately describe the new data for the structure of fuel-lean flames, which are relevant to gas turbine conditions.

Breaking the ESG rating divergence: An open geospatial framework for environmental scores

Information about a company's environmental, social and governance (ESG) performance has become increasingly important in the decision-making process of financial institutions. The financial implications of environmental challenges (e.g. water stress), negative social impacts (e.g. health impacts in local communities) or poor corporate governance (e.g. breaching legislation) all continue to increase. Accordingly, there is a need for financial institutions to incorporate information on ESG risks, opportunities and impacts in decisions that relate to risk management, investments, credit, strategy, and reporting. ESG information is typically disseminated through ESG ratings, which combine the three constituents into a single rating, or ascribe them separate scores. The compilation of ESG ratings and the identification of appropriate data sources is an inherently complex process; as such, there is no single standard for data collection or reporting. This has led to a divergence in the underlying data sources used by different rating providers, as well as in the determination of factors that are deemed worthy of measurement in the first place. For example, when assessing a company's environmental impact, one rating provider may rely on company-provided data, while another may incorporate independent third-party assessments. Unfortunately, there is currently no clear mechanism for effectively resolving such disagreements to establish a standardised approach to ESG rating assessments. However, geospatial data and analyses offer several key advantages for ESG assessments, including consistency, the potential for enhanced accuracy, and the ability to identify and assess environmental impacts at a detailed physical asset level, in addition to evaluating the broader spatial context. By incorporating geospatial information (obtained through manually processing remotely sensed data, or by using existing products) rating methodologies can be improved, and disparities can be addressed more effectively. This would enable a more comprehensive understanding of the environmental considerations of ESG assessments, promoting a more informed and precise decision-making process. Within this context, a few institutions (e.g. the University of Oxford, the WWF, and a few others) are pioneering thought leadership around spatial finance, including the assessment of ESG issues utilising geospatial intelligence, but there are no consistent frameworks for incorporating geospatial data into ESG ratings and analysis. This paper explores the opportunity for such a geospatial environmental scoring framework, defining a variety of methods in which open data with broad geographic coverage could be incorporated into ESG analysis, generalisable to a range of assets and sectors. The proposed framework is organised into two categories: localised effects, which directly impact the immediate vicinity of an asset, and delocalised effects, which contribute to global climate change and atmospheric pollution. Sub-scores are defined within these categories, which capture both the localised effects on land use, biodiversity, soils, and hydrology, and the global impacts resulting from atmospheric emissions. The approaches for handling geospatial data to generate both these sub-scores and the final E-score are presented, including a test case, and the complete methodology is made available in open repositories.

Abstract 13256: Efficient and Precise Phenotyping of Murine Fetal Hearts by Means of Synchrotron-Based Phase-Contrast Micro-Computed Tomography

Mouse models are routinely utilized to investigate the role of genes in congenital heart disease. Cardiovascular phenotyping is, however, quite challenging because of the small size of fetal mouse hearts. The most used method is histological assessment, which typically provides only two-dimensional information and thus might overlook important structural defects. Three-dimensional (3D) methods with sufficient resolution are either time-consuming, require specific sample preparation and/or selective staining, or cause destruction of the sample. Here, synchrotron radiation-based phase-contrast micro-computed tomography (SRμCT) was evaluated for fast and precise phenotyping of unstained, intact murine fetal hearts in a non-destructive way. Embryonic day (E) 18.5 hearts from control mice and mice that lack the secreted protease ADAMTS6 were formalin-fixed, paraffin-embedded and then scanned with SRμCT at the TOMCAT beamline (Swiss Light Source) with a 1.63 μm effective pixel size. Following SRμCT, the hearts were sectioned and processed for standard hematoxylin and eosin staining (H&E), and for specific immunofluorescence staining. SRμCT enabled evaluation of the hearts with a high level of detail. A full E18.5 mouse heart was scanned in less than 3 minutes, followed by virtual sectioning along arbitrary slicing planes and 3D rendering for morphological assessment. The resolution proved sufficient to trace minute features like the coronary arteries. In the Adamts6 knockouts, malformations such as a ventricular septal defect and double outlet right ventricle could be readily diagnosed. We also explored the potential of merging stained sections with SRμCT. In summary, SRμCT has the potential to become a powerful tool for fast phenotyping of mutant mouse models. The combination of 3D imaging with subsequent sectioning and methods like histology, immunohistochemistry and in situ hybridization will uncover pathophysiological mechanisms in 3D space.

Identifying energy model fingerprints in mitigation scenarios

Energy models are used to study emissions mitigation pathways, such as those compatible with the Paris Agreement goals. These models vary in structure, objectives, parameterization and level of detail, yielding differences in the computed energy and climate policy scenarios. To study model differences, diagnostic indicators are common practice in many academic fields, for example, in the physical climate sciences. However, they have not yet been applied systematically in mitigation literature, beyond addressing individual model dimensions. Here we address this gap by quantifying energy model typology along five dimensions: responsiveness, mitigation strategies, energy supply, energy demand and mitigation costs and effort, each expressed through several diagnostic indicators. The framework is applied to a diagnostic experiment with eight energy models in which we explore ten scenarios focusing on Europe. Comparing indicators to the ensemble yields comprehensive ‘energy model fingerprints’, which describe systematic model behaviour and contextualize model differences for future multi-model comparison studies.

Use of photogrammetry to survey Iron Age rock art motifs in the Côa Valley: the Vermelhosa Rock 3 case study (Vila Nova de Foz Côa, Portugal)

The Côa Valley, listed as a World Heritage site since 1998, presents over 1200 open-air engraved rock panels. TheArchaeological Park of the Côa Valley has meticulously documented these rock art motifs, employing various techniques including direct tracing processes on the rocks, using both natural and artificial lighting. However, this intensive work is highly demanding, especially considering that many of the rocks are not easily accessible. In the context of the "Open Access Rock Art Repository" (RARAA) project, this paper presents a methodology for the three-dimensional (3D) survey of rocks with rock art motifs, as well as the subsequent production of orthophotos from the resulting 3D models, accomplished through photogrammetry. These orthophotos serve as the foundation for the vector drawing of the motifs. Remarkably, the level of detail captured in these records has shown that most of the motifs are visible and can be accurately represented through the orthophotos. This has significantly reduced the time required for field surveys. However, in certain cases where specific small areas of the panel are affected by challenging lighting conditions, further fieldwork is still necessary, analogous to the direct tracing process. Additionally, this study introduces an information system designed to integrate the vector graphics and the motifs characterisation data; this supports enhanced research in the area and promotes improved open access for potential reuse in new interpretations or integration into future projects. By creating highly detailed 3D models, the authors complement the two-dimensional drawings of the surfaces and ensure the digital preservation of both the rocks and the associated iconography. These records serve as highly detailed digital surrogates that facilitate the monitoring efforts of the rocks and motifs; they also guarantee the availability of valuable resources for future research and analysis, even if natural or deliberate changes occur.

Expert system based fault diagnosis for railway point machines

To meet the increasing demands for availability at reasonable cost, operators and maintainers of railway point machines are constantly looking for innovative techniques for switch condition monitoring and prediction. This includes automated fault root cause diagnosis based on measurement data (such as motor current curves) and other information. However, large, comprehensive sets of labeled data suitable for standard machine learning are not yet available. Existing data-driven approaches focus only on the differentiation of a few major fault categories at the level of the measurement data (i.e., the “fault symptoms”). There is great potential in hybrid models that use expert knowledge in combination with multiple sources of information to automatically identify failure causes at a much more detailed level. This paper discusses a Bayesian network diagnostic model for determining the root causes of faults in point machines, based on expert knowledge and few labeled data examples from the Netherlands. Human-interpretable current curve features and other information sources (e.g., past maintenance actions) are used as evidence. The result of the model is a ranking of the most likely failure causes with associated probabilities in terms of fuzzy multi-label classification, which is directly aimed at providing decision support to maintenance engineers. The validity and limitations of the model are demonstrated by a scenario-based evaluation and a brief analysis using information theoretic measures. We present the information sources used, the detailed development process and the analysis methodology. This article is intended to be a guide to developing similar models for various complex technical assets.

Remote sensing of urban greenspace exposure and equality: Scaling effects from greenspace and population mapping

Satellite observations are increasingly used to characterize greenspace coverage, exposure, and equality assessment for environmental and health studies. Given the difference in spatial resolutions (namely, scale effect), different satellite datasets capture distinct levels of landscape details in urban green environments. However, existing studies on measuring scale effects are limited to the greenness mapping in a few sampled cities regardless of the scale effects from population mapping and the associated controls from greenspace landscape configurations. To close this knowledge gap, we conducted a comprehensive inventory of the scale effects, using widely used satellite-based greenness (i.e., 10-m Sentinel-2, 30-m Landsat-8, and 500-m MODIS) and population (i.e., 30-m HRPD, 100-m WorldPop, and 1-km GPW) mapping datasets over 679 major cities (urban area > 50 km2) in the United States. Results show that (1) compared with high-resolution Sentinel-2, Landsat-8 and MODIS overestimate greenspace coverage and human exposure but underestimate the inequality of human exposure to greenspace; (2) the differences in greenspace coverage and exposure across satellite sensors are linearly correlated with the greenspace provision magnitude; (3) landscape configuration explains the greenspace coverage differences across different satellite sensors. Aggregated and fragmented landscape metrics correlate positively and negatively with greenspace coverage differences, respectively; and (4) the spatial resolution of greenspace mapping shows a decreasing control while population data has tiny impacts on the inequality measurement of human exposure to greenspace. These findings answer how varying-scale satellite datasets cause a discrepancy in the measurement of greenspace coverage, human exposure, and inequality assessment. We advocate that researchers should select appropriate satellite-based greenness datasets by accounting for trade-offs between specific research benefits and costs to better position future greenspace-related environment and health outcome studies.

Development of a Machine Learning Framework Based on Occupant-Related Parameters to Predict Residential Electricity Consumption in the Hot and Humid Climate

Occupant-related variables constitute one of the most significant groups of factors influencing residential building energy consumption. However, prediction methods often oversimplify these parameters, leading to substantial discrepancies between predicted and actual consumption. To address this issue, the present study aims to develop a machine learning framework for predicting electricity consumption in residential buildings based on occupant-related factors. The study incorporates twenty-six inputs, including occupant characteristics such as demographics, occupancy, behavior, and behavioral efficiency, two time-related factors, and three extra parameters related to equipment (refrigerator age, hot water source, and type of electricity meter) for training and testing the Random Forest (RF) algorithm in both regression and classification forms. The results indicate that the trained RF regressor exhibits well performance (R2Train = 0.989, R2Test = 0.916, MAETrain = 0.81, MAETest = 2.21, RMSETrain = 1.27, and RMSETest = 3.45). Furthermore, feature importance analysis reveals that the most significant parameter is the time of year, representing weather conditions, followed by the number of occupants, neighborhood, indoor set-point range, mean age of occupants, window opening, and cooling system mode. Even after removing the least impactful factors, the model maintains strong performance with the 16 most important variables (R2Train = 0.986, R2Test = 0.910, MAETrain = 0.83, MAETest = 2.25, RMSETrain = 1.31, and RMSETest = 3.49). Additionally, the RF classifier is designed for problems with 2, 4, 6, and 8 classes based on energy consumption ranges. The results of this model demonstrate that the 2-class model achieves the highest performance (AccuracyTest = 0.963, MAETest = 0.04, and RMSETest = 0.19). However, it lacks detailed categorization of homes based on electricity consumption. On the other hand, the 4-class and 6-class models strike a good balance between prediction performance and the level of detail. In conclusion, the proposed method can accurately predict residential electricity consumption and can serve as a valuable reference for researchers and utility managers when formulating energy reduction policies and comparing the effectiveness of different strategies.

Early childhood memories of individuals convicted of sexual offences

ABSTRACT This study used innovative transdisciplinary methods to describe the nature and extent of early childhood memories recalled by 84 adults convicted of sexual offences. The timing of the memories, level of detail recalled and way memories were recollected were largely consistent with extant memory research. One important finding, however, was that more than 30% of our participants recalled particularly traumatic and distressing childhood experiences – a much higher proportion than previously observed in nonoffending samples. The extent to which these memories laid the foundation for subsequent emotional content and feature in the evolution of cognitive schemata is not yet well understood. With that in mind, we consider the implications of our findings for the event centrality in self-narratives. We recommend the inclusion of treatment modalities that maximise as yet unrecognised and undervalued narrative inclinations and story-telling abilities of a complicated population of individuals with rich lived experience that stands to benefit greatly from such approaches.

Disaggregation of the Copernicus Land Use/Land Cover (LULC) and Population Density Data to Fit Mesoscale Flood Risk Assessment Requirements in Partially Urbanized Catchments in Croatia

Flood risk assessment at the mesoscale requires data that are spatially and thematically detailed enough to provide reliable estimates at the catchment level. However, data availability and suitability are often contradictory: available data are rarely suitable at the required level of detail. To overcome this problem, numerous disaggregation methods have been proposed in recent decades, often based on somewhat generalised imperviousness characteristics derived from the available urban land use/land cover (LULC) nomenclature. To reduce generalisation, we propose a new disaggregation approach using a spatially distributed imperviousness density (IMD) layer at a very detailed spatial resolution of 10 m as ancillary data to improve the thematic detail of the urban classes of the available LULC datasets (Coastal Zones, Natura 2000) and the dasymetric mapping of the census data. The nomenclature of the urban classes and the impervious density thresholds were taken from the detailed Urban Atlas dataset. The disaggregation of the census data is then built on the resulting geometry of thematically improved residential classes. Assuming that IMD values indicate a built-up density, the proposed weighting scheme is IMD-dependent: it accounts for variability in the built-up density and, hence, variability in population. The approach was tested in three catchments in Croatia, each with a different degree of urbanisation. The resulting statistics (mean square error and percentage error) indicate that residential areas and population density depend on IMD. Using IMD as additional data therefore greatly improves the assessment of elements that are exposed to flooding and, consequently, the damage and flood risk assessment.

Large-Scale Post-Transplant TCR Deep Sequencing Reveals a Major T Cell Diversity Bottleneck with Post-Transplant Cyclophosphamide with Implications for Both Efficacy and Toxicity: Results of the BMT CTN 1801 Study

Background: BMT CTN 1703 was a Phase 3 trial comparing Tac/MTX (n = 217) to post-transplant cyclophosphamide (PT-Cy)/Tac/MMF (n = 214) GVHD prophylaxis after reduced intensity conditioning unrelated donor hematopoietic cell transplant (HCT). PT-Cy significantly improved GVHD-free Relapse-Free Survival (Bolanos-Meade et al NEJM 2023). However overall survival was not significantly different and the # of Grade 2 infections was higher with PT-Cy. To investigate its biologic underpinnings 1703 was linked to a mechanistic study, BMT CTN 1801, which co-enrolled 324 pts (159 CNI/MTX 165 PT-Cy). The resulting dataset enabled an analysis of post-HCT T cell reconstitution at an unprecedented level of detail. Methods: Samples were collected from the HCT infusion and on D+7, 14, 28, 63, 98, 180, 270, 1- and 2-yr. We extracted DNA (2,225 samples) and performed Adaptive Biotech β-strand T Cell Receptor (TCR) Immunosequencing (TCR-Seq). We profiled 44,077 (median) TCRs/sample resulting in 215,155,719 T cells analyzed. We evaluated multiple TCR diversity measures longitudinally and before/after cGVHD and infections. Each measure evaluates a specific aspect of TCR diversity, including Simpsons Clonality (focuses on the most expanded clones) Diversity Slope (mid-range clones) Richness (both mid-range and less-expanded clones) and Singletons (the TCR-Seq equivalent of naïve T cells). This analysis was robust to the full range (1000-50,000) of down-sampled TCRs sequenced. Results: We identified major differences in TCR reconstitution in Tac/MTX vs PT-Cy that begin early and persist through 2 yr post-HCT. These resulted in a significantly less diverse TCR repertoire with PT-Cy with substantial implications for its efficacy and toxicity. The most salient differences are: (1) PT-Cy caused significant in-vivo T cell depletion early post-HCT (% T cells/total WBC at D+28: 8.2% (Tac/MTX) vs 2.2% (PT-Cy) p=2.2x10 -23). (2) The remaining donor T cells in PT-Cy undergo substantial clonal expansion ( Fig 1) resulting in significantly constrained TCR diversity measured by Clonality, Slope, Richness, Singletons (not shown) and Singletons/Richness ( Fig 2) which began early (D+14) and persisted for 2 yr. (3) Linked T cell depletion and clonal expansion resulted in a TCR diversity bottleneck after PT-Cy ( Fig 1). Due to the bottleneck, while the fraction of T cells originating from the transplant infusion at =/> 1 yr was similar between arms (18% vs 24%, p=0.7) there were significantly fewer Singleton TCRs in PT-Cy, suggesting a reduction in naïve T cells ( Fig 2), an outcome that has critical consequences for both cGVHD and infection. Thus while cGVHD onset with Tac/MTX was linked to a significant drop in TCR diversity due to singleton clonal expansion (median log-2 fold change in singletons with Tac/MTX = -0.25 p = 0.01) this drop was not observed with PT-Cy (log-2 fold change = -0.05, p = 0.1). Along with the significantly decreased # singletons at all time-points in PT-Cy ( Fig 2) this suggests that while cGVHD after Tac/MTX is linked to substantial naïve T cell expansion, the same is not true with PT-Cy, identifying a novel mechanism for cGVHD control with PT-Cy. The substantially fewer singletons in PT-Cy vs Tac/MTX may also increase the risk of Grade 2+ infections. T cell responses to new infections rely on antigen recognition/effector maturation of naïve (singleton TCR) T cells. After infection, the TCR repertoire became less diverse for both Tac/MTX and PT-Cy (log-2-fold singleton change -0.17 and -1.2 p = 0.0006 and 0.01) indicating that singletons in both cohorts were capable of expanding to pathogens. However the markedly decreased # singletons in PT-Cy at all timepoints was associated with significantly fewer singletons prior to infection (mean 3979 at 5K read-depth for Tac/MTX vs 2820 for PT-Cy p<0.001). This singleton deficit would place PT-Cy patients at higher risk of missing the critical mass of TCRs required to respond effectively to infections, consistent with their increased rate of Gr 2 infections. Conclusions: We performed longitudinal TCR-Seq on >200 million T cells from 324 HCT patients, representing the largest post-HCT TCR-Seq analysis in the history of the field, and enabling novel insights into TCR dynamics. These data identify a previously unappreciated global TCR diversity bottleneck with PT-Cy that simultaneously protects patients from cGVHD and places them at higher risk of infectious complications.

Impact of model detail on daylighting metrics in residential buildings

Daylight metrics that shall guide performance-driven design must be applicable to models that reflect the limited knowledge at the time of planning decisions. In particular in residential buildings, the furnishing is beyond the control of such decisions and therefore unknown in the planning phase. The impact of model detail on illuminance- and luminance-based metrics is tested on a residential unit in Singapore. At two elevations, representing a work-plane and the eye level of a standing inhabitant, the metrics are computed from a) a detailed model incorporating furniture and shading devices, b) a model with only shading devices, and c) a minimalist model representing only the design decisions of the architect. As a potential approximation to the detailed model, d) a variant of the minimalist model with wall reflectance reduced to 0.20 is tested. The results for the three levels of detail are compared and indicate the significant impact of detail on all assessed daylight metrics.

A framework for considering prior information in network-based approaches to omics data analysis.

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Model simplification of geometry and facilities, for energy and indoor environment towards more reliable energy labelling

Reliable building energy and indoor climate performance assessment require adequate and often time-consuming modelling. Currently, energy performance certification (EPC) is carried out in Member States (MS) using simple steady-state tools. These tools simplify not only building geometry but also HVAC systems, boundary conditions, and building loads. These simplifications can cause the so-called “performance gap”, which is the difference between modelled prediction and actual operation. This creates a lack of trust in the EPCs and scepticism. This paper is scouting toward shifting to dynamic models of different detail levels, considering the zoning, heating and ventilation facilitie’s complexity. In the scope of this dynamic thermal simulation study, a residential multi-apartment building located in Denmark is investigated. The thermal zone simplification has 5 levels of complexity, from modelling each room as a thermal zone to the whole staircase as one thermal zone. The heating system was modelled as: i) ideal loads, ii) electrical radiators, iii) water radiators. Ventilation was modelled as i) zone ventilation, and ii) airflow network. Modelling results are evaluated for heat demand, thermal and atmospheric comfort.

Subthalamic nucleus shows opposite functional connectivity pattern in Huntington's and Parkinson's disease.

Huntington's and Parkinson's disease are two movement disorders representing mainly opposite states of the basal ganglia inhibitory function. Despite being an integral part of the cortico-subcortico-cortical circuitry, the subthalamic nucleus function has been studied at the level of detail required to isolate its signal only through invasive studies in Huntington's and Parkinson's disease. Here, we tested whether the subthalamic nucleus exhibited opposite functional signatures in early Huntington's and Parkinson's disease. We included both movement disorders in the same whole-brain imaging study, and leveraged ultra-high-field 7T MRI to achieve the very fine resolution needed to investigate the smallest of the basal ganglia nuclei. Eleven of the 12 Huntington's disease carriers were recruited at a premanifest stage, while 16 of the 18 Parkinson's disease patients only exhibited unilateral motor symptoms (15 were at Stage I of Hoehn and Yahr off medication). Our group comparison interaction analyses, including 24 healthy controls, revealed a differential effect of Huntington's and Parkinson's disease on the functional connectivity at rest of the subthalamic nucleus within the sensorimotor network, i.e. an opposite effect compared with their respective age-matched healthy control groups. This differential impact in the subthalamic nucleus included an area precisely corresponding to the deep brain stimulation 'sweet spot'-the area with maximum overall efficacy-in Parkinson's disease. Importantly, the severity of deviation away from controls' resting-state values in the subthalamic nucleus was associated with the severity of motor and cognitive symptoms in both diseases, despite functional connectivity going in opposite directions in each disorder. We also observed an altered, opposite impact of Huntington's and Parkinson's disease on functional connectivity within the sensorimotor cortex, once again with relevant associations with clinical symptoms. The high resolution offered by the 7T scanner has thus made it possible to explore the complex interplay between the disease effects and their contribution on the subthalamic nucleus, and sensorimotor cortex. Taken altogether, these findings reveal for the first time non-invasively in humans a differential, clinically meaningful impact of the pathophysiological process of these two movement disorders on the overall sensorimotor functional connection of the subthalamic nucleus and sensorimotor cortex.