Enhancer infestation drives tumorigenic activation of inactive B compartment in Epstein-Barr virus-positive nasopharyngeal carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus (EBV)-associated malignant epithelial tumor endemic to Southern China and Southeast Asia. While previous studies have revealed a low frequency of gene mutations in NPC, its epigenomic aberrations are not fully elucidated apart from DNA hypermethylation. Epigenomic rewiring and enhancer dysregulation, such as enhancer hijacking due to genomic structural changes or extrachromosomal DNA, drive cancer progression. METHODS: We conducted Hi-C, 4C-seq, ChIP-seq, and RNA-seq analyses to comprehensively elucidate the epigenome and interactome of NPC using C666-1 EBV(+)-NPC cell lines, NP69T immortalized nasopharyngeal epithelial cells, clinical NPC biopsy samples, and in vitro EBV infection in HK1 and NPC-TW01 EBV(-) cell lines. FINDINGS: In C666-1, the EBV genome significantly interacted with inactive B compartments of host cells; the significant association of EBV-interacting regions (EBVIRs) with B compartment was confirmed using clinical NPC and in vitro EBV infection model. EBVIRs in C666-1 showed significantly higher levels of active histone modifications compared with NP69T. Aberrant activation of EBVIRs after EBV infection was validated using in vitro EBV infection models. Within the EBVIR-overlapping topologically associating domains, 14 H3K4me3(+) genes were significantly upregulated in C666-1. Target genes of EBVIRs including PLA2G4A, PTGS2 and CITED2, interacted with the enhancers activated in EBVIRs and were highly expressed in NPC, and their knockdown significantly reduced cell proliferation. INTERPRETATION: The EBV genome contributes to NPC tumorigenesis through "enhancer infestation" by interacting with the inactive B compartments of the host genome and aberrantly activating enhancers. FUNDING: The funds are listed in the Acknowledgements section.

Design and validation of an open-sourced automation system for vertical farming.

The Modular Automated Crop Array Online System (MACARONS) is a scalable, customisable and open-sourced platform designed for plant care, monitoring, and transportation. It offers specific dosing for individual plants, automated data logging of temperature, humidity, and images, and custom behaviours programmable in Python. Monitoring and control of the system is achieved through a web-interface. The system was validated by autonomously caring for five lettuce plants over a five-week period. This was done indoors under artificial lighting and uncontrolled ambient conditions. The system is estimated to perform the tasks required 30% faster than a human operator and can handle payloads of up to 5 kg with a maximum footprint of 750 mm × 500 mm. The validated system supports 12 payloads and can be easily scaled to accommodate more. The designs are released and meets the requirements of CERN-OSH-W, which includes step-by-step graphical build instructions and can be built at a cost of GBP 2241.72 (USD 2793.82). The system aims to provide cost-effective automation to reduce labour costs and provide precise control of irrigation and nutrients. The current system is limited by the dosing time and the space-use efficiency. We provided future directions and modifications that can be made to address this.

Experimental and numerical data of thermal response tests executed in groups of energy piles connected in series.

The use of energy piles as heat exchangers for Ground Source Heat Pump (GSHP) systems, providing heating and cooling, is a well researched application worldwide [1]. However, a broader implementation in practice still faces resistance, mainly because of the lack of accessible, easy to implement design methods and uncertainty regarding the thermo-mechanical effects. These issues need to be addressed to close the gap between research and practice. This work presents data of a full-scale thermal response test (TRT) undertaken in a group of eight energy screw piles connected in series, that are part of an operational GSHP system of a building located in Melbourne, Australia. The temperature was measured in the inlet and outlet of the pipe circuit (circulating water temperature) and at the bottom of each pile (external pipe wall temperature). Besides providing insights regarding the thermal performance of short energy pile groups, the test was used to validate a finite element numerical model (FEM). The model was then used to expand the database of thermal performance of energy pile groups by simulating several long thermal response tests, considering different energy pile group geometries, configurations and material properties. The experimental data presented can be used for analyses and validation of thermal modelling methodologies that consider the group effect of energy piles, given the lack of TRTs performed in groups of energy piles reported in literature. Moreover, the extensive set of simulated data can be analysed to understand the thermal behaviour of energy pile groups and evaluate how alternative simpler heat transfer models, feasibly applied in industry practice, perform in a range of scenarios that could be encountered in daily practice.

Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks.

Ventilation plays a noteworthy role in maintaining a healthy, comfortable and energy-efficient indoor environment and mitigating the risk of aerosol transmission and disease infection (e.g., SARS-COV-2). In most commercial and office buildings, demand-controlled ventilation (DCV) systems are widely utilized to conserve energy based on occupancy. However, as the presence of occupants is often inherently stochastic, accurate occupancy prediction is challenging. This study, therefore, proposes an autoencoder Bayesian Long Short-term Memory neural network (LSTM) model for probabilistic occupancy prediction, taking account of model misspecification, epistemic uncertainty, and aleatoric uncertainty. Performances of the proposed models are evaluated using real data in an educational building at the University of Cambridge, UK. The models trained on data of one open-plan space are used to predict occupant numbers for other spaces (with similar layout and function) in the same building. The probabilistic occupant profiles are then used for estimating optimal ventilation rates for two scenarios (i.e., normal DCV mode for energy conservation and anti-infection mode for virus transmission prevention). Results show that, during the test period, for the 1-h ahead prediction, the proposed model achieved better performance with up to 5.8% mean absolute percentage error reduction than the traditional LSTM model. More flexible alternatives for ventilation can be offered by the proposed risk-aware decision-making schemes serving different purposes under real operation. The findings from this study provide new occupancy forecasting solutions and explore the potential of probabilistic decision making for building ventilation optimization.

Energy transition pathways amongst low-income urban households: A mixed method clustering approach.

Studies on clean energy transition amongst low-income urban households in the Global South use an array of qualitative and quantitative methods. However, attempts to combine qualitative and quantitative methods are rare and there are a lack of systematic approaches to this. This paper demonstrates a two stage approach using clustering methods to analyse a mixed dataset containing quantitative household survey data and qualitative interview data. By clustering the quantitative and qualitative data separately, latent groups with common characteristics and narratives arising from each of the two analyses are identified. A second stage of clustering identifies links between these qualitative and quantitative clusters and enables inference of energy transition pathways followed by low-income urban households defined by both quantitative characteristics and qualitative narratives. This approach can support interdisciplinary collaboration in energy research, providing a systematic approach to comparing and identifying links between quantitative and qualitative findings.•A mixed dataset comprising of quantitative survey data and qualitative interview data on low-income household energy use is analysed using hierarchical clustering to detect communities within each dataset.•Interviewees are matched to quantitative survey clusters and a second stage of clustering is performed using cluster membership as variables.•Second stage clusters identify common pairs of survey and interview clusters which define energy transition pathways based on socio-economic characteristics, energy use patterns, and narratives for decision making and practices.

Profiling of 3D Genome Organization in Nasopharyngeal Cancer Needle Biopsy Patient Samples by a Modified Hi-C Approach.

Nasopharyngeal cancer (NPC), a cancer derived from epithelial cells in the nasopharynx, is a cancer common in China, Southeast Asia, and Africa. The three-dimensional (3D) genome organization of nasopharyngeal cancer is poorly understood. A major challenge in understanding the 3D genome organization of cancer samples is the lack of a method for the characterization of chromatin interactions in solid cancer needle biopsy samples. Here, we developed Biop-C, a modified in situ Hi-C method using solid cancer needle biopsy samples. We applied Biop-C to characterize three nasopharyngeal cancer solid cancer needle biopsy patient samples. We identified topologically associated domains (TADs), chromatin interaction loops, and frequently interacting regions (FIREs) at key oncogenes in nasopharyngeal cancer from the Biop-C heatmaps. We observed that the genomic features are shared at some important oncogenes, but the patients also display extensive heterogeneity at certain genomic loci. On analyzing the super enhancer landscape in nasopharyngeal cancer cell lines, we found that the super enhancers are associated with FIREs and can be linked to distal genes via chromatin loops in NPC. Taken together, our results demonstrate the utility of our Biop-C method in investigating 3D genome organization in solid cancers.

Impacts of underground climate change on urban geothermal potential: Lessons learnt from a case study in London.

While urban underground is being increasingly used for various purposes, two concerns should be addressed with respect to the urban underground climate change: i) how much energy has been stored in urban subsurface due to the heat rejection from underground heated spaces (such as tunnels and basements) and ii) how much of the thermal demand of a city or district can be supplied by harvesting this accumulative thermal energy in the ground. However, our understanding of the temperature rise in the ground and of the geothermal potential of urban subsurface is still limited. This paper quantifies the geothermal potential for a 12 km2 densely populated borough in central London by considering the spatio-temporal temperature variation in the ground owing to continuous rejection of heat into the ground, coupled with the effect of geothermal extraction capacity. A large-scale transient semi-3D geothermal subsurface model of the site is developed, and the thermal interaction between underground heated spaces, geothermal energy extraction systems and the ground and groundwater are simulated. The concurrent heat rejection and extraction processes in the subsurface are computed so that the most influencing parameters of the subsurface on its geothermal potential are identified. Results show that up to 50% of the borough's total heat demand can be supplied via geothermal installations leading to around 33% reduction in CO2 emission. The geothermal extraction efficiency in sand and gravel primarily depends on the ground conditions such as the thickness of the permeable layer and the groundwater flow regime. In impermeable ground such as clay, however, the underground built environment such as heated spaces have shown to have a significant impact on improving the geothermal extraction efficiency.

Multi-fidelity approach to Bayesian parameter estimation in subsurface heat and fluid transport models.

The increased use of the urban subsurface for competing purposes, such as anthropogenic infrastructures and geothermal energy applications, leads to an urgent need for large-scale sophisticated modelling approaches for coupled mass and heat transfer. However, such models are subject to large uncertainties in model parameters, the physical model itself and in available measured data, which is often rare. Thus, the robustness and reliability of the computer model and its outcomes largely depend on successful parameter estimation and model calibration, which are hampered by the computational burden of large-scale coupled models. To tackle this problem, we develop a novel Bayesian approach for parameter estimation, which allows us to account for different sources of uncertainty, is capable of dealing with sparse field data and makes optimal use of the output data from expensive numerical model runs. This is achieved by combining output data from different models that represent the same physical problem, but at different levels of fidelity, e.g. reflected by different spatial resolution. By applying this new approach to a 1D analytical heat transfer model and a large-scale semi-3D numerical model while using synthetic data, we show that the accuracy and precision of parameter estimation by this multi-fidelity framework by far exceeds the standard single-fidelity results. The consideration of different error terms in the Bayesian framework also allows assessment of the model bias and the discrepancy between the different fidelity levels. These are emulated by Gaussian Process models, which facilitate re-iteration of the parameter estimation without additional model runs.

Large-scale urban underground hydro-thermal modelling - A case study of the Royal Borough of Kensington and Chelsea, London.

The shallow subsurface of dense cities is increasingly exploited for various purposes due to the significant rise in urban populations. Past research has shown that underground activities have a significant impact on local subsurface temperatures. However, the resulting spatial variability of ground temperature elevations on a city-scale is not well understood due to the lack of sufficient information and modelling complexity at such large scales. Resilient and sustainable planning of underground developments and geothermal exploitation in the short and long-term necessitate more detailed, more reliable knowledge of subsurface thermal status. This paper investigates the impact of some common underground heat sources such as train tunnels and residential basements on subsurface temperature elevation on a large scale and highlights the influence of local geology, hydrogeology, density, and type and arrangement of the heat sources on ground thermal disturbance. To tackle the size issues and computational expenses of such a large-scale problem, a semi-3D hydro-thermal numerical approach is presented to capture the combined influence of underground built environment characteristics coupled with ground properties on ground temperature elevation within the Royals Borough of Kensington and Chelsea (RBKC), London. Numerical results show that the extent of ground thermal disturbance is mostly affected by geological and hydrogeological characteristics in permeable ground (River Terrace Deposits). Density and spatial distribution of heat sources, however, are critical parameters in ground temperature evaluation in highly impermeable ground such as London Clay Formation. The locality of temperature rise and potential ground energy within immediate impermeable ground surrounding heat sources versus significantly large extent of ground thermal disturbance in permeable ground, highlights the significant dependency of ground thermal state and geothermal potential at the studied site to the ground and underground built environment characteristics and necessitates a better understanding of shallow subsurface thermal state for a sustainable and resilient urban underground development.

Unit-related factors that affect nursing time with patients: spatial analysis of the time and motion study.

OBJECTIVE: The primary goal of this study was to test the hypothesis that nurses adopt distinct movement strategies based on features of unit topology and nurse assignments. The secondary goal was to identify aspects of unit layout or organization that influence the amount of time nurses spend in the patient room. BACKGROUND: Previous research has demonstrated a link between nursing hours and patient outcomes. Unit layout may affect direct patient care time by determining aspects of nurse behavior, such as the amount of time nurses spend walking. The recent nurses' Time and Motion study employed multiple technologies to track the movements and activities of 767 medical-surgical nurses. With regard to unit layout, initial analysis of the data set did not detect differences between types of units and time spent in the patient room. The analysis reported here applies novel techniques to this data set to examine the relationship between unit layout and nurse behavior. METHODS: Techniques of spatial analysis, borrowed from the architectural theory of spatial syntax, were applied to the Time and Motion data set. Motion data from radio-frequency identification tracking of nurses was combined with architectural drawings of the study units and clinical information such as nurse-patient assignment. Spatial analytic techniques were used to determine the average integration or centrality of nurse assignments for each shift. RESULTS: Nurse assignments with greater average centrality to all assigned rooms were associated with a higher number of entries to patient rooms, as well as to the nurse station. Number of entries to patient rooms was negatively correlated with average time per visit, but positively correlated with total time spent in patient rooms. The data describe two overall strategies of nurse mobility patterns: fewer, longer visits versus more frequent, shorter visits. CONCLUSIONS: Results suggest that the spatial qualities of nurse assignments and unit layout affect nurse strategies for moving through units and affect how frequently nurses enter patient rooms and the nurse station.