Dedicated Software Tool Research Articles

High-content CRISPR screening

CRISPR screens are a powerful source of biological discovery, enabling the unbiased interrogation of gene function in a wide range of applications and species. In pooled CRISPR screens, various genetically encoded perturbations are introduced into pools of cells. The targeted cells proliferate under a biological challenge such as cell competition, drug treatment or viral infection. Subsequently, the perturbation-induced effects are evaluated by sequencing-based counting of the guide RNAs that specify each perturbation. The typical results of such screens are ranked lists of genes that confer sensitivity or resistance to the biological challenge of interest. Contributing to the broad utility of CRISPR screens, adaptations of the core CRISPR technology make it possible to activate, silence or otherwise manipulate the target genes. Moreover, high-content read-outs such as single-cell RNA sequencing and spatial imaging help characterize screened cells with unprecedented detail. Dedicated software tools facilitate bioinformatic analysis and enhance reproducibility. CRISPR screening has unravelled various molecular mechanisms in basic biology, medical genetics, cancer research, immunology, infectious diseases, microbiology and other fields. This Primer describes the basic and advanced concepts of CRISPR screening and its application as a flexible and reliable method for biological discovery, biomedical research and drug development — with a special emphasis on high-content methods that make it possible to obtain detailed biological insights directly as part of the screen. CRISPR screening is a high-throughput approach for identifying genes, pathways and mechanisms involved in a given phenotype or biological process. High-content read-outs of these screens, such as imaging and single-cell sequencing techniques, have further broadened its applicability. This Primer by Bock et al. describes the main concepts of CRISPR screening and gives examples of its application as a method for biological discovery, with a focus on the use of high-content read-outs.

High-content CRISPR screening.

CRISPR screens are a powerful source of biological discovery, enabling the unbiased interrogation of gene function in a wide range of applications and species. In pooled CRISPR screens, various genetically encoded perturbations are introduced into pools of cells. The targeted cells proliferate under a biological challenge such as cell competition, drug treatment or viral infection. Subsequently, the perturbation-induced effects are evaluated by sequencing-based counting of the guide RNAs that specify each perturbation. The typical results of such screens are ranked lists of genes that confer sensitivity or resistance to the biological challenge of interest. Contributing to the broad utility of CRISPR screens, adaptations of the core CRISPR technology make it possible to activate, silence or otherwise manipulate the target genes. Moreover, high-content read-outs such as single-cell RNA sequencing and spatial imaging help characterize screened cells with unprecedented detail. Dedicated software tools facilitate bioinformatic analysis and enhance reproducibility. CRISPR screening has unravelled various molecular mechanisms in basic biology, medical genetics, cancer research, immunology, infectious diseases, microbiology and other fields. This Primer describes the basic and advanced concepts of CRISPR screening and its application as a flexible and reliable method for biological discovery, biomedical research and drug development - with a special emphasis on high-content methods that make it possible to obtain detailed biological insights directly as part of the screen.

Life cycle assessment of hydrogen-powered city buses in the High V.LO-City project: integrating vehicle operation and refuelling infrastructure

During the project High V.LO-City, which ended in December 2019, 14 hydrogen fuel cell buses were operated in four European cities. This paper aims at presenting total emissions through the lifetime of fuel cell buses with different hydrogen production options, including the refuelling stations. The environmental assessment of such bus system is carried out using the life cycle assessment methodology. Three hydrogen production pathways are investigated: water electrolysis, chlor-alkali electrolysis and steam methane reforming. Fuel economy during bus operation is around 10.25 KgH2/100 km, and the refuelling station energy demand ranges between 7 and 9 KWh/KgH2. To support the inventory stage, dedicated software tools were developed for collecting and processing a huge amount of bus data and refuelling station performance, for automating data entry and for impacts calculation. The results show that hydrogen-powered buses, compared to a diesel bus, have the potential to reduce emissions during the use phase, if renewables resources are used. On the other hand, impacts from the vehicle production, including battery pack and fuel cell stack, still dominate environmental load. Consequently, improving the emission profile of fuel cell bus system requires to promote clean electricity sources to supply a low-carbon hydrogen and to sharpen policy focus regarding life cycle management and to counter potential setbacks, in particular those related to problem shifting and to grid improvement. For hazardous emissions and resource use, the high energy intensity of mining and refining activities still poses challenges on how to further enhance the environmental advantages of fuel cells and battery packs.

Analytical strategies in venomics

Snakebite envenoming is a major issue in certain developing parts of the world, with tens of thousands of deaths and a multitude of lingering permanent medical conditions on a yearly basis as a result. Venoms consist of a plethora of biologically active compounds, mainly peptides and proteins, that can induce these effects. Snake venom characterisation, in terms of identifying and quantifying the venom toxin proteins in crude venoms, can aid in pinpointing the venom toxins responsible for pathologies observed in snakebite victims. It can also assist in developing a new generation of efficient antivenoms. To facilitate this characterisation process, several approaches have been developed, of which venomics, toxicovenomics and antivenomics are the most important ones and aim to map venom toxin composition, their toxic effects, and antivenom efficacy, respectively. Most venomics approaches include peptide-centric analyses of protein digests in combination with advanced separation and detection hardware and involve the use of generic and dedicated software tools for data processing. However, a shift towards protein-level, and even proteoform-level, analysis can be observed over the last few years. A variety of hyphenated intact protein separation techniques, including gel-eluted liquid fraction entrapment electrophoresis, OFFGEL, gel filtration and several (ultra)high pressure and nanoscale chromatographic separations can be named, which allow retention of intact or native states of toxin proteins with ever increasing resolutions. Similarly, rapid detection hardware advances on the mass spectrometry side, and labelling techniques, allow for increasingly sensitive and quantitative venom analysis and characterisation. Most venomics research includes several complementary analytical techniques performed in parallel, with each technique often leading to exclusive protein identifications but also showing significant overlap. The original venomics approach, developed by the Calvete group and termed “Snake venomics”, is used most frequently and is the most profound methodology that has been advanced over the years, thereby resulting in sequentially further identification of venom toxin proteins based on previous results using increasingly in-depth characterisation approaches. The latest advances in this regard nowadays allow for quantitative analysis of all venom toxins in crude venoms, named “Absolute venomics”. This review will focus on the analytical aspects of venomics for which sample preparation, separation, detection and quantitation techniques are discussed. Finally, a comparison between recently published venomics studies is made.

Analyzing Aircraft Maintenance Findings with Natural Language Processing

Aircraft maintenance at the technical base includes both scheduled and unscheduled repairs. When performing repairs, there are detailed inspections of aircraft, which are ordered in advance by the customer. During these inspections, it is possible to detect other unsatisfactory condition of the aircraft in terms of continuing airworthiness, which needs to be corrected. These unplanned failures are findings and are entered into the maintenance organization's system, which is required to keep records of activities performed on the aircraft. Finding descriptions are created by technicians and are usually stored as unstructured text that does not allow subsequent analysis with a database of such records. The presented work performs automatic text analysis of findings using a dedicated software tool. For the purpose of the analysis, a lexicon was created, with components and failures appearing in maintenance records. The lexicon was used in automatic text analysis, where the output was a statistic of the most frequent components and failures in the stored maintenance data. This statistic can be used for maintenance planning optimization and better prediction of unscheduled repairs.

MigraR: An open-source, R-based application for analysis and quantification of cell migration parameters

Background and objective: Cell migration is essential for many biological phenomena with direct impact on human health and disease. One conventional approach to study cell migration involves the quantitative analysis of individual cell trajectories recorded by time-lapse video microscopy. Dedicated software tools exist to assist the automated or semi-automated tracking of cells and translate these into coordinate positions along time. However, cell biologists usually bump into the difficulty of plotting and computing these data sets into biologically meaningful figures and metrics. Methods: This report describes MigraR, an intuitive graphical user interface executed from the RStudioTM (via the R package Shiny), which greatly simplifies the task of translating coordinate positions of moving cells into measurable parameters of cell migration (velocity, straightness, and direction of movement), as well as of plotting cell trajectories and migration metrics. One innovative function of this interface is that it allows users to refine their data sets by setting limits based on time, velocity and straightness. Results: MigraR was tested on different data to assess its applicability. Intended users of MigraR are cell biologists with no prior knowledge of data analysis, seeking to accelerate the quantification and visualization of cell migration data sets delivered in the format of Excel files by available cell-tracking software. Conclusions: Through the graphics it provides, MigraR is an useful tool for the analysis of migration parameters and cellular trajectories. Since its source code is open, it can be subject of refinement by expert users to best suit the needs of other researchers. It is available at GitHub and can be easily reproduced.

Concept of a design activity supporting tool in the design and development process of cyber physical system

ABSTRACT Cyber-Physical Systems (CPS) are systems that link cyberspace with the physical world by means of a network of interrelated elements (sensors and actuators) and computational engines. These different assets make it difficult to design properly and effectively with them all. Additionally, the designing of CPS requires multi-disciplinary project teams and the investigation of all activities which CPS should perform. The designers have to share their knowledge, experience and identify the assets and activities which are necessary for proper CPS functioning. The proper identification process of CPS activities allows improvement of the design process through more precise and problem-activity-dedicated knowledge and activity-design models management. This process can also be supported by dedicated software tools which enable easy access to the acquired and accumulated knowledge.

ARTIFICIAL INTELLIGENCE AND PATHOLOGY

The microscopic diagnosis of cancer remains challenging. Recent data from our group within the French (nationwide) LymphoPath network shows that 20% of diagnoses are inaccurate, with direct impact on patient care. Indeed, molecular techniques, that are not affordable for all pathology departments, have become critical for the final diagnosis. Currently, automated solutions that could help pathologists with diagnostic decisions or histological grading are lacking, and it is not reasonable to expect pathologists to be experts on rare tumours if they only see a few cases per year. Digital microscopy offers unique features which are not available in conventional optical microscopy. Assisted by dedicated software tools, digital microscopy enables dynamic and prompt access to any detail of the stained slides at several microscopic magnifications. Furthermore, the calibrated qualities and the number of discrete pixels in a digital slide allow automated image analysis and quantification by using computer vision and, in particular, deep learning approaches. Image feature extraction methods based on pixel detection or less often on object segmentation has brought much hope in improving the accuracy of the human eye. Automatic analysis of cancer whole-slide images has recently been performed to allow experts for predicting tumour classification, gene mutations and survival outcomes. However, the positioning of such technologies in routine practice is limited by the trained networks which frequently do not meet industrial constraints required for a general application such as certification, qualification and explainability (black box effect) of algorithms. This presentation will describe different approaches of machine learning aiming at classifying cancer subtypes through the analysis of histopathologic features. Advantages and drawbacks of these techniques will be discussed with a special emphasis on the risk of biased performance assessment of deep learning systems. The critical role of data sets quality will be discussed and different strategies will be envisaged to broaden the use deep neural networks. Providing that these limitations are taken into account and circumvented, it seems that artificial intelligence solutions dedicated to cancer histopathology should pave the road to precision medicine through data integration into a holistic patient dashboard for oncology care teams. Keywords: Bioinformatics; Computational and Systems Biology, Genomics, Epigenomics, and Other -Omics, Tumor Biology and Heterogeneity Conflicts of interests pertinent to the abstract P. Brousset Consultant or advisory role: Roche, MSB, Janssen Cilag laboratory Research funding: Roche, Pierre Fabre

Episode-level reflux characteristics: How experienced reviewers differentiate true reflux from artifact on pH-impedance studies.

Accurate reflux episode identification is crucial for pH-impedance interpretation. Individual reflux episode characteristics associated with inter-reviewer concordance are incompletely understood. Ambulatory pH-impedance studies from 19 GERD patients (median age 52years, 78.9% F) were analyzed by 5 reviewers. Metadata from pH-impedance studies were exported to a dedicated software tool designed to compare episode-by-episode identification between reviewers within a ±7.5s window. Patient position, acidic vs. nonacidic episodes, acid clearance time (ACT), bolus clearance time (BCT), and proximal extent of reflux episodes were compared between episodes identified by all reviewers against those identified by automated analysis, and one to four reviewers, respectively. Automated analysis identified 1644 episodes (median 78 episodes per patient, IQR 64-108), of which 84.9% were identified by ≥3 reviewers and 57.1% by all reviewers; 339 unique episodes were added by at least 1 reviewer. Characteristics defining 5 reviewer concordance included acid reflux episodes (88.9%), upright episodes (88.4%), high proximal extent (median 17cm, IQR 15-17cm), and longer acid clearance times (67.0s, IQR 29.0-146.0s) (P<0.001 compared to 1-4 reviewer concordance for each). In contrast, 1 reviewer-identified episodes were 69.8% acidic, 76.9% upright, and limited to the distal esophagus. Using 5-reviewer concordance, designation of GERD evidence changed from automated analysis in 16%-19% of patients. Acidic episodes with high proximal extent in the upright position and longer acid clearance times on pH-impedance studies have the highest concordance for identification by expert reviewers. Reflux episode identification may be influenced by reviewer opinion despite availability of established criteria.

Near real-time magnetic particle imaging for visual assessment of vascular stenosis in a phantom model.

This study aimed to investigate the potential of magnetic particle imaging (MPI) to quantify artificial stenoses in vessel phantoms in near real-time. Custom-made stenosis phantoms with different degrees of stenosis (0%, 25%, 50%, 75%, and 100%; length 40mm, inner diameter 8mm, Polyoxymethylene) were filled with diluted Ferucarbotran (superparamagnetic iron-oxide nanoparticle (SPION) tracer agent, 500mmol (Fe)/l). A traveling wave MPI scanner (spatial resolution~2mm, gradient strength~1.5T/m, field of view: 65mm length and 29mm diameter, frequencies f1=1050Hz and f2=12150Hz) was used to acquire images of the phantoms (200ms total acquisition time per image, 10 averages). Standardized grey scaling was used for comparability. All measured stenoses (n=80) were graded manually using a dedicated software tool. MPI allowed for accurate visualization of stenoses at a frame rate of 5frames per second. Less severe stenoses were detected more precisely than higher-grade stenoses and came with smaller standard deviations. In particular, the 0%, 25%, 50%, 75%, and 100% stenosis phantom were measured as 3.7±2.7% (mean±standarddeviation), 18.6±1.8%, 52.8±3.7%, 77.8±14.8% and 100±0%. Geometrical distortions occurred around the center of the high-grade stenosis and led to higher standard deviations compared to lower grade stenoses. In the frame of this study the MPI signal depended linearly on the SPION concentration down to 0.05mmol (Fe)/l. Near real-time MPI accurately visualized and quantified different stenosis grades in vascular phantoms.

Quiet Sleep Organization of Very Preterm Infants Is Correlated With Postnatal Maturation.

Background: Sleep is an important determinant of brain development in preterm infants. Its temporal organization varies with gestational age (GA) and post-menstrual age (PMA) but little is known about how sleep develops in very preterm infants. The objective was to study the correlation between the temporal organization of quiet sleep (QS) and maturation in premature infants without severe complications during their neonatal hospitalization.Methods: Percentage of time spent in QS and average duration of time intervals (ADI) spent in QS were analyzed from a cohort of newborns with no severe complications included in the Digi-NewB prospective, multicentric, observational study in 2017–19. Three groups were analyzed according to GA: Group 1 (27–30 weeks), Group 2 (33–37 weeks), Group 3 (>39 weeks). Two 8-h video recordings were acquired in groups 1 and 2: after birth (T1) and before discharge from hospital (T2). The annotation of the QS phases was performed by analyzing video recordings together with heart rate and respiratory traces thanks to a dedicated software tool of visualization and annotation of multimodal long-time recordings, with a double expert reading. Results are expressed as median (interquartile range, IQR). Correlations were analyzed using a linear mixed model.Results: Five newborns were studied in each group (160 h of recording). Median time spent in QS increased from 13.0% [IQR: 13–20] to 28.8% [IQR: 27–30] and from 17.0% [IQR: 15–21] to 29.6% [IQR: 29.5–31.5] in Group 1 and 2, respectively. Median ADI increased from 54 [IQR: 53–54] to 288 s [IQR: 279–428] and from 90 [IQR: 84–96] to 258 s [IQR: 168–312] in Group 1 and 2. Both groups reach values similar to that of group 3, respectively 28.2% [IQR: 24.5–31.3] and 270 s [IQR: 210–402]. The correlation between PMA and time spent in QS or ADI were, respectively 0.73 (p < 10−4) and 0.46 (p = 0.06). Multilinear analysis using temporal organization of QS gave an accurate estimate of PMA (r2 = 0.87, p < 0.001).Conclusion: The temporal organization of QS is correlated with PMA in newborns without severe complication. An automated standardized continuous behavioral quantification of QS could be interesting to monitor during the hospitalization stay in neonatal units.

Data normalization strategies in metabolomics: Current challenges, approaches, and tools.

Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from - either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.

A Camera Sensors-Based System to Study Drug Effects On In Vitro Motility: The Case of PC-3 Prostate Cancer Cells.

Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.

Characterization of a newly developed readout electronics system for strip detectors

The aim of the present paper was to investigate the performance of a newly developed general purpose strip detector system, and its energy resolution improvement provided by custom characterization and calibration procedures described in this paper. The system contains one 128-channel silicon strip detector, four 32-channel Front-End ASICs (FE-ASICs), FPGA-based control circuit, and signal conditioning and power regulation modules, providing energy measurements up to 3.2 MeV per channel. Additionally, a dedicated software tool was developed to control and configure the system, as well as, to automatically characterize and equalize the channels aiming a more uniform response and hence, better energy resolution. It is worth noting that a sigma of 1.6 keV has been achieved for all 128 channels within the lower range of the ASIC (up to 800 keV), while the ASICs were not optimized to work with the specific detector capacitance. This energy resolution is comparable to state of the art Si hybrid-pixel detector Timepix3 [1]. Using a conservative 6-sigma distance to the electronic noise level it was still obtained a system threshold level of 3 keV . This work provides valuable information for particular applications in which a general purpose strip detector-based system could be used efficiently to replace hybrid pixel detectors.

Clean energy production by PEM fuel cells on tourist ships: A time-dependent analysis

This manuscript presents a clean energy solution for marine applications, investigating both the choice of the most promising production and storage technologies and, in a second step, the best operational management in order to satisfy a ship's energy demand. The case study subject of the analysis is a ferry, with a capacity up to 200 people, operating on the artificial lake of Itaipu hydroelectric plant (on the border between Brazil and Paraguay). In the framework of new international rules, aimed at clean and sustainable solutions, in particular in Emission Control Areas, PEM fuel cells are a promising technology for onboard power generation. In this paper a PEM fuel cell system is investigated in detail, analyzing the best operative strategy in terms of energy efficiency, CO2 emissions and costs, in comparison with the state-of-the-art solution for ships (fuel oil ICEs). The analysis is performed with two dedicated software tools, both developed at the University of Genoa: the first is a tool modeled to support the preliminary design and the choice of the most promising solutions for maritime applications; the second is a software program for thermo-economic analysis of energy systems in time-dependent conditions, aimed at determination of the best operative strategy, minimizing operative costs.The proposed approach has general validity thus it can also be applied to different kinds of ships, even considering different technologies for energy generation and storage.

Interactive optimization of the resin transfer molding using a general-purpose tool: a case study

Simulation tools for liquid composite molding processes are a key to predict and solve manufacturing issues of composite materials. Numerical processes are commonly used to analyse and predict mould filling, considering also resin cure and exothermic reactions. These evaluations are usually performed through dedicated software tools that require highly specialized operators and purchasing costs. The present study relates to a multi-objective optimization approach for evaluating the effect of different process parameters of the resin transfer molding (RTM) process using a multi-purpose tool. Starting from a simple case, useful for analysing the effect of mesh type and size on the simulations, and then increasing the complexity of the models, virtual simulations have been validated through real tests. Afterward, this approach has been used for the optimization of the RTM process for the manufacturing of an automotive component. Gate positions, injection pressure and resin temperature have been optimized using finite-volume analysis with a multi-objective genetic algorithm. Finally, the parameters have been used in real experiments in order to validate the efficiency and the reliability of multi-purpose tool in simulating RTM processes.

CREATING AND MAINTAINING IFC–CITYGML CONVERSION RULES

Abstract. We employ a triple graph grammar to enable configurable conversion from IFC to CityGML. In this paper, we present the mathematical framework behind the graph transformation approach as well as an application to create, store and maintain transformation rules implementing this framework. Particular emphasis is put on how the approach enables graphical representation and static analysis of rules and rulesets, both in the theoretical framework and prototypical implementation. Even if various publications and tools for general graph transformation do already exist, we hope that the BIM–GIS community will benefit from a domain-specific introduction to the theory and dedicated software tools.

RBD Model-Based Approach for Reliability Assessment in Complex Systems

Nowadays, system reliability performance represents a key issue in any advanced technology application in order to guarantee ambient, personnel, and system safety. The core of this paper is the reliability block diagram (RBD) generation with the aim of providing project engineers a reliability prediction in the early stages of industrial product development. This study is focused on gas turbine auxiliary systems; these systems include both mechanical items (in particular hydraulic devices such as valves, pumps, and filters) and electronic ones (e.g., sensors, instruments, control logic). Such complex structures are decomposed in single blocks and interconnections to establish their mutual relationship and achieve reliability performance of the whole system. The case study is one of the most important gas turbine auxiliary systems, the mineral lube oil console. The aim of this paper is to introduce a new approach to assess system reliability prediction in presence of redundant and stand-by architectures; redundancy is widely used in industrial applications since it is one of the best techniques to achieve fault tolerance. The proposed method led to the development of a dedicated software tool RBDesigner that semiautomatically generates a RBD starting from the sketch of thermal-hydraulic systems and provides the most important reliability parameters. The use of the proposed tool allows project engineers to reduce time delivery, reduce time for improvements, achieve reliability targets, and guarantee availability performance to the customers. The strengths of RBDesigner were finally validated by a comparison with other commercial software solutions.

Black holes and rotation: Internal kinematics of star clusters with MUSE

AbstractOver the last decade, our abilities to observe the internal kinematics of star clusters have drastically increased. Where a few years back only small numbers of bright stars with radial velocity measurements were available, we can nowadays study the three-dimensional motions of large stellar samples, thanks to the combined datasets gathered by state-of-art spectrographs and astrometric satellites. In this work, I summarise the contribution of integral-field spectrographs, in particular MUSE, to this paradigm change. Using dedicated software tools, we were able to overcome a fundamental limitation of spectroscopy and advance to the crowded cluster centres. This allowed us to study the central kinematics in unprecedented detail and to start uncovering the populations of black holes that reside in massive star clusters.

Techno-economic analysis for the assessment of heat pump integration in a real poly-generative energy district

The present research study aims at analysing technical and economic feasible solutions for heat pumps integration in energy districts for polygeneration purpose and particularly to store excess of electricity via Power-to-heat schemes considering that, from previous researchers’ works, thermal storage has been identified as the most remunerative and easy to handle storage technology to maximise self-consumption in polygeneration grids. This technology is already developed and currently employed for conditioning of residential, commercial and industrial buildings. However, studies regarding the analysis of heat pumps’ integration in energy districts for distributed generation are still limited. The potential advantages of its employment in this context are fuel savings, a lower emission level and the possibility to couple it with local renewable energy sources (i.e. solar panels, wind turbines) and traditional generators (i.e. engines, micro gas turbines) in order to increase flexibility in operational terms. In this paper, a performance analysis of the poly-generation energy district installed at the University of Genoa Campus, located in Savona, is analysed throughout a whole year: the model is implemented using a dedicated software tool, developed by Thermochemical Power Group. Different solutions for the integration of the heat pump, including size optimization, are investigated, considering the real data related to the University of Genoa Campus: the final aim of the analysis is to determinate the best operational strategy, minimizing variable costs (i.e. fuel) and evaluating the economic feasibility of heat pump installation in the energy district. This work has been also redacted as a preliminary analysis for solar-coupled HP integration (from a optimized management point of view) to be performed in the demonstration campaign of H2020 ENVISION Project*, where both RINA Consulting and University of Genova collaborate.