Reassessing the role of urban green space in air pollution control.

The assumption that vegetation improves air quality is prevalent in scientific, popular, and political discourse. However, experimental and modeling studies show the effect of green space on air pollutant concentrations in urban settings is highly variable and context specific. We revisited the link between vegetation and air quality using satellite-derived changes of urban green space and air pollutant concentrations from 2,615 established monitoring stations over Europe and the United States. Between 2010 and 2019, stations recorded declines in ambient NO2, (particulate matter) PM10, and PM2.5 (average of -3.14% y-1), but not O3 (+0.5% y-1), pointing to the general success of recent policy interventions to restrict anthropogenic emissions. The effect size of total green space on air pollution was weak and highly variable, particularly at the street scale (15 to 60 m radius) where vegetation can restrict ventilation. However, when isolating changes in tree cover, we found a negative association with air pollution at borough to city scales (120 to 16,000 m) particularly for O3 and PM. The effect of green space was smaller than the pollutant deposition and dispersion effects of meteorological drivers including precipitation, humidity, and wind speed. When averaged across spatial scales, a one SD increase in green space resulted in a 0.8% (95% CI: -3.5 to 2%) decline in air pollution. Our findings suggest that while urban greening may improve air quality at the borough-to-city scale, the impact is moderate and may have detrimental street-level effects depending on aerodynamic factors like vegetation type and urban form.

A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications.

Background: The University of Southampton, in collaboration with the University Hospital Southampton (UHS) NHS Foundation Trust and industrial partners, has been at the forefront of developing three-dimensional (3D) imaging workflows using X-ray microfocus computed tomography (µCT) -based technology. This article presents the outcomes of these endeavours and highlights the distinctive characteristics of a µCT facility tailored explicitly for 3D X-ray Histology, with a primary focus on applications in biomedical research and preclinical and clinical studies. Methods: The UHS houses a unique 3D X-ray Histology (XRH) facility, offering a range of services to national and international clients. The facility employs specialised µCT equipment explicitly designed for histology applications, allowing whole-block XRH imaging of formalin-fixed and paraffin-embedded tissue specimens. It also enables correlative imaging by combining µCT imaging with other microscopy techniques, such as immunohistochemistry (IHC) and serial block-face scanning electron microscopy, as well as data visualisation, image quantification, and bespoke analysis. Results: Over the past seven years, the XRH facility has successfully completed over 120 projects in collaboration with researchers from 60 affiliations, resulting in numerous published manuscripts and conference proceedings. The facility has streamlined the µCT imaging process, improving productivity and enabling efficient acquisition of 3D datasets. Discussion & Conclusions: The 3D X-ray Histology (XRH) facility at UHS is a pioneering platform in the field of histology and biomedical imaging. To the best of our knowledge, it stands out as the world's first dedicated XRH facility, encompassing every aspect of the imaging process, from user support to data generation, analysis, training, archiving, and metadata generation. This article serves as a comprehensive guide for establishing similar XRH facilities, covering key aspects of facility setup and operation. Researchers and institutions interested in developing state-of-the-art histology and imaging facilities can utilise this resource to explore new frontiers in their research and discoveries.

Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning.

Conventional monitoring systems for air quality, such as reference stations, provide reliable pollution data in urban settings but only at relatively low spatial density. This study explores the potential of low-cost sensor systems (LCSs) deployed at homes of residents to enhance the monitoring of urban air pollution caused by residential wood burning. We established a network of 28 Airly (Airly-GSM-1, SP. Z o.o., Poland) LCSs in Kristiansand, Norway, over two winters (2021-2022). To assess performance, a gravimetric Kleinfiltergerät measured the fine particle mass concentration (PM2.5) in the garden of one participant's house for 4 weeks. Results showed a sensor-to-reference correlation equal to 0.86 for raw PM2.5 measurements at daily resolution (bias/RMSE: 9.45/11.65 µg m-3). High-resolution air quality maps at a 100 m resolution were produced by combining the output of an air quality model (uEMEP) using data assimilation techniques with the network data that were corrected and calibrated by using a proposed five-step network data processing scheme. Leave-one-out cross-validation demonstrated that data assimilation reduced the model's RMSE, MAE, and bias by 44-56, 38-48, and 41-52%, respectively.

Estimation of self-exciting point processes from time-censored data.

Self-exciting point processes, widely used to model arrival phenomena in nature and society, are often difficult to identify. The estimation becomes even more challenging when arrivals are recorded only as bin counts on a finite partition of the observation interval. In this paper, we propose the recursive identification with sample correction (RISC) algorithm for the estimation of process parameters from time-censored data. In every iteration, a synthetic sample path is generated and corrected to match the observed bin counts. Then the process parameters update and a unique iteration is performed to successively approximate the stochastic characteristics of the observed process. In terms of finite-sample approximation error, the proposed RISC framework performs favorably over extant methods, as well as compared to a naïve locally uniform sample redistribution. The results of an extensive numerical experiment indicate that the reconstruction of an intrabin history based on the conditional intensity of the process is crucial for attaining superior performance in terms of estimation error.

The effectiveness of moderating harmful online content.

In 2022, the European Union introduced the Digital Services Act (DSA), a new legislation to report and moderate harmful content from online social networks. Trusted flaggers are mandated to identify harmful content, which platforms must remove within a set delay (currently 24 h). Here, we analyze the likely effectiveness of EU-mandated mechanisms for regulating highly viral online content with short half-lives. We deploy self-exciting point processes to determine the relationship between the regulated moderation delay and the likely harm reduction achieved. We find that harm reduction is achievable for the most harmful content, even for fast-paced platforms such as Twitter. Our method estimates moderation effectiveness for a given platform and provides a rule of thumb for selecting content for investigation and flagging, managing flaggers' workload.

SensEURCity: A multi-city air quality dataset collected for 2020/2021 using open low-cost sensor systems.

Low-cost air quality sensor systems can be deployed at high density, making them a significant candidate of complementary tools for improved air quality assessment. However, they still suffer from poor or unknown data quality. In this paper, we report on a unique dataset including the raw sensor data of quality-controlled sensor networks along with co-located reference data sets. Sensor data are collected using the AirSensEUR sensor system, including sensors to monitor NO, NO2, O3, CO, PM2.5, PM10, PM1, CO2 and meteorological parameters. In total, 85 sensor systems were deployed throughout a year in three European cities (Antwerp, Oslo and Zagreb), resulting in a dataset comprising different meteorological and ambient conditions. The main data collection included two co-location campaigns in different seasons at an Air Quality Monitoring Station (AQMS) in each city and a deployment at various locations in each city (also including locations at other AQMSs). The dataset consists of data files with sensor and reference data, and metadata files with description of locations, deployment dates and description of sensors and reference instruments.

Survival-Adjusted FEV1 and BMI Percentiles for Patients with Cystic Fibrosis before the Era of Triple CFTR Modulator Therapy in Germany.

BACKGROUND: Pulmonary disease is the major cause for morbidity and mortality in cystic fibrosis (CF). In CF, forced expiratory volume in 1 s (FEV1) referenced against a healthy population (FEV1%predicted) and body mass index (BMI) do not allow for the comparison of disease severity across age and gender. OBJECTIVES: We aimed to determine updated FEV1 and BMI percentiles for patients with CF and to study their dependence on mortality attrition. METHODS: Age- and height-adjusted FEV1 and BMI percentiles for CF patients aged 6-50 years were calculated from 4,947 patients of the German CF Registry for the period 2016-2019 utilizing quantile regression and a Generalized Additive Model for Location, Scale and Shape (GAMLSS). Further, survival-adjusted percentiles were estimated. RESULTS: In patients with CF, FEV1 increased throughout childhood until maximal median values at 16 years in females (2.46 L) and 18 years in males (3.27 L). During adulthood, FEV1 decreased substantially. At 17 years of age, the 25th BMI percentile of patients with CF (females 18.50 and males 18.15 kg/m2) was below the 10th BMI percentile of the German reference cohort. From the age of 20 years, survival (96.3%) decreased tremendously. At 50 years of age (survival 15.0%), the 50th CF-specific FEV1 or BMI percentile among the survivors corresponded to the 92.5th percentile among the total CF birth cohort. CONCLUSIONS: Continuously updated disease-specific FEV1 and BMI percentiles with correction for survival may serve as age-independent measure of disease severity in CF (accessible via https://cfpercentiles.statup.solutions).

Modified skulls but conservative brains? The palaeoneurology and endocranial anatomy of baryonychine dinosaurs (Theropoda: Spinosauridae).

The digital reconstruction of neurocranial endocasts has elucidated the gross brain structure and potential ecological attributes of many fossil taxa, including Irritator, a spinosaurine spinosaurid from the "mid" Cretaceous (Aptian) of Brazil. With unexceptional hearing capabilities, this taxon was inferred to integrate rapid and controlled pitch-down movements of the head that perhaps aided in the predation of small and agile prey such as fish. However, the neuroanatomy of baryonychine spinosaurids remains to be described, and potentially informs on the condition of early spinosaurids. Using micro-computed tomographic scanning (µCT), we reconstruct the braincase endocasts of Baryonyx walkeri and Ceratosuchops inferodios from the Wealden Supergroup (Lower Cretaceous) of England. We show that the gross endocranial morphology is similar to other non-maniraptoriform theropods, and corroborates previous observations of overall endocranial conservatism amongst more basal theropods. Several differences of unknown taxonomic utility are noted between the pair. Baryonychine neurosensory capabilities include low-frequency hearing and unexceptional olfaction, whilst the differing morphology of the floccular lobe tentatively suggests less developed gaze stabilisation mechanisms relative to spinosaurines. Given the morphological similarities observed with other basal tetanurans, baryonychines likely possessed comparable behavioural sophistication, suggesting that the transition from terrestrial hypercarnivorous ancestors to semi-aquatic "generalists" during the evolution of Spinosauridae did not require substantial modification of the brain and sensory systems.

StrainDesign: a comprehensive Python package for computational design of metabolic networks.

SUMMARY: Various constraint-based optimization approaches have been developed for the computational analysis and design of metabolic networks. Herein, we present StrainDesign, a comprehensive Python package that builds upon the COBRApy toolbox and integrates the most popular metabolic design algorithms, including nested strain optimization methods such as OptKnock, RobustKnock and OptCouple as well as the more general minimal cut sets approach. The optimization approaches are embedded in individual modules, which can also be combined for setting up more elaborate strain design problems. Advanced features, such as the efficient integration of GPR rules and the possibility to consider gene and reaction additions or regulatory interventions, have been generalized and are available for all modules. The package uses state-of-the-art preprocessing methods, supports multiple solvers and provides a number of enhanced tools for analyzing computed intervention strategies including 2D and 3D plots of user-selected metabolic fluxes or yields. Furthermore, a user-friendly interface for the StrainDesign package has been implemented in the GUI-based metabolic modeling software CNApy. StrainDesign provides thus a unique and rich framework for computational strain design in Python, uniting many algorithmic developments in the field and allowing modular extension in the future. AVAILABILITY AND IMPLEMENTATION: The StrainDesign package can be retrieved from PyPi, Anaconda and GitHub (https://github.com/klamt-lab/straindesign) and is also part of the latest CNApy package.

Ontogenetic changes in cortical bone vascular microstructure in the domestic duck (Anas platyrhynchos) and ring-necked pheasant (Phasianus colchicus).

Age-related changes in bone microstructure can inform our understanding the biology of both extant and fossil birds, but to date, histological work in birds, and particularly work using high-resolution 3D imaging, has largely been restricted to limited growth stages. We used minimally destructive synchrotron radiation-based X-ray computed tomography to visualise and measure key morphological and histological traits in 3D across development in the domestic duck and ring-necked pheasant. We use these measurements to build on the database of key reference material for interpreting bone histology. We found that growth patterns differed between the two species, with the ducks showing rapid growth in their lower limbs and early lower limb maturation, while pheasants grew more slowly, reflecting their later age at maturity. In the pheasant, both walking and flight occur early and their upper and lower limbs grew at similar rates. In the duck, flight and wing development are delayed until the bird is almost at full body mass. Through juvenile development, the second moment of area for the duck wing was low but increased rapidly towards the age of flight, at which point it became significantly greater than that of the lower limb, or the pheasant. On a microstructural level, both cortical porosity and canal diameter were related to cortical bone deposition rate. In terms of orientation, vascular canals in the bone cortex were more laminar in the humerus and femur compared with the tibiotarsus, and laminarity increased through juvenile development in the humerus, but not the tibiotarsus, possibly reflecting torsional vs compressive loading. These age-related changes in cortical bone vascular microstructure of the domestic duck and pheasant will help understanding the biology of both extant and fossil birds, including age estimation, growth rate and growth patterns, and limb function.

Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores.

Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.

Intraspecific Diversity in Aquatic Ecosystems: Comparison between Spirodela polyrhiza and Lemna minor in Natural Populations of Duckweed.

Samples of two duckweed species, Spirodela polyrhiza and Lemna minor, were collected around small ponds and investigated concerning the question of whether natural populations of duckweeds constitute a single clone, or whether clonal diversity exists. Amplified fragment length polymorphism was used as a molecular method to distinguish clones of the same species. Possible intraspecific diversity was evaluated by average-linkage clustering. The main criterion to distinguish one clone from another was the 95% significance level of the Jaccard dissimilarity index for replicated samples. Within natural populations of L. minor, significant intraspecific genetic differences were detected. In each of the three small ponds harbouring populations of L. minor, based on twelve samples, between four and nine distinct clones were detected. Natural populations of L. minor consist of a mixture of several clones representing intraspecific biodiversity in an aquatic ecosystem. Moreover, identical distinct clones were discovered in more than one pond, located at a distance of 1 km and 2.4 km from each other. Evidently, fronds of L. minor were transported between these different ponds. The genetic differences for S. polyrhiza, however, were below the error-threshold of the method within a pond to detect distinct clones, but were pronounced between samples of two different ponds.

CNApy: a CellNetAnalyzer GUI in Python for analyzing and designing metabolic networks.

SUMMARY: Constraint-based reconstruction and analysis (COBRA) is a widely used modeling framework for analyzing and designing metabolic networks. Here, we present CNApy, an open-source cross-platform desktop application written in Python, which offers a state-of-the-art graphical front-end for the intuitive analysis of metabolic networks with COBRA methods. While the basic look-and-feel of CNApy is similar to the user interface of the MATLAB toolbox CellNetAnalyzer, it provides various enhanced features by using components of the powerful Qt library. CNApy supports a number of standard and advanced COBRA techniques and further functionalities can be easily embedded in its GUI facilitating modular extension in the future. AVAILABILITY AND IMPLEMENTATION: CNApy can be installed via conda and its source code is freely available at https://github.com/cnapy-org/CNApy under the Apache 2 license.

Calibration of CO, NO2, and O3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring.

During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that has a unit cost five times less expensive than the state-of-the-art solutions (approximately €1000). The evaluated platform can integrate a wide variety of sensors capable of measuring up to 12 parameters, including the regulatory pollutants defined in the European Directive. In this work, we developed new calibration models (multivariate linear regression and random forest) and evaluated their effectiveness in meeting the data quality objective (DQO) for the following parameters: carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). The experimental results show that the proposed calibration managed an improvement of 12% for the CO and O3 gases and a similar accuracy for the NO2 gas compared to similar state-of-the-art studies. The evaluated parameters had different calibration accuracies due to the non-identical levels of gas concentration at which the sensors were exposed during the model's training phase. After the calibration algorithms were applied to the evaluated platform, its performance met the DQO criteria despite the overall low price level of the platform.

Immunofluorescence-guided segmentation of three-dimensional features in micro-computed tomography datasets of human lung tissue.

Micro-computed tomography (µCT) provides non-destructive three-dimensional (3D) imaging of soft tissue microstructures. Specific features in µCT images can be identified using correlated two-dimensional (2D) histology images allowing manual segmentation. However, this is very time-consuming and requires specialist knowledge of the tissue and imaging modalities involved. Using a custom-designed µCT system optimized for imaging unstained formalin-fixed paraffin-embedded soft tissues, we imaged human lung tissue at isotropic voxel sizes less than 10 µm. Tissue sections were stained with haematoxylin and eosin or cytokeratin 18 in columnar airway epithelial cells using immunofluorescence (IF), as an exemplar of this workflow. Novel utilization of tissue autofluorescence allowed automatic alignment of 2D microscopy images to the 3D µCT data using scripted co-registration and automated image warping algorithms. Warped IF images, which were accurately aligned with the µCT datasets, allowed 3D segmentation of immunoreactive tissue microstructures in the human lung. Blood vessels were segmented semi-automatically using the co-registered µCT datasets. Correlating 2D IF and 3D µCT data enables accurate identification, localization and segmentation of features in fixed soft lung tissue. Our novel correlative imaging workflow provides faster and more automated 3D segmentation of µCT datasets. This is applicable to the huge range of formalin-fixed paraffin-embedded tissues held in biobanks and archives.

A robust, semi-automated approach for counting cementum increments imaged with synchrotron X-ray computed tomography.

Cementum, the tissue attaching mammal tooth roots to the periodontal ligament, grows appositionally throughout life, displaying a series of circum-annual incremental features. These have been studied for decades as a direct record of chronological lifespan. The majority of previous studies on cementum have used traditional thin-section histological methods to image and analyse increments. However, several caveats have been raised in terms of studying cementum increments in thin-sections. Firstly, the limited number of thin-sections and the two-dimensional perspective they impart provide an incomplete interpretation of cementum structure, and studies often struggle or fail to overcome complications in increment patterns that complicate or inhibit increment counting. Increments have been repeatedly shown to both split and coalesce, creating accessory increments that can bias increment counts. Secondly, identification and counting of cementum increments using human vision is subjective, and it has led to inaccurate readings in several experiments studying individuals of known age. Here, we have attempted to optimise a recently introduced imaging modality for cementum imaging; X-ray propagation-based phase-contrast imaging (PPCI). X-ray PPCI was performed for a sample of rhesus macaque (Macaca mulatta) lower first molars (n = 10) from a laboratory population of known age. PPCI allowed the qualitative identification of primary/annual versus intermittent secondary increments formed by splitting/coalescence. A new method for semi-automatic increment counting was then integrated into a purpose-built software package for studying cementum increments, to count increments in regions with minimal complications. Qualitative comparison with data from conventional cementochronology, based on histological examination of tissue thin-sections, confirmed that X-ray PPCI reliably and non-destructively records cementum increments (given the appropriate preparation of specimens prior to X-ray imaging). Validation of the increment counting algorithm suggests that it is robust and provides accurate estimates of increment counts. In summary, we show that our new increment counting method has the potential to overcome caveats of conventional cementochronology approaches, when used to analyse three-dimensional images provided by X-ray PPCI.

Characterization of the Developing Lacunocanalicular Network During Fracture Repair.

Fracture repair is a normal physiological response to bone injury. During the process of bony callus formation, a lacunocanalicular network (LCN) is formed de novo that evolves with callus remodeling. Our aim was the longitudinal assessment of the development and evolution of the LCN during fracture repair. To this end, 45 adult wild-type C57BL/6 mice underwent closed tibial fracture surgery. Fractured and intact contralateral tibias were harvested after 2, 3, and 6 weeks of bone healing (n = 15/group). High-resolution micro-computed tomography (µCT) and deconvolution microscopy (DV) approaches were applied to quantify lacunar number density from the calluses and intact bone. On histological sections, Goldner's trichrome staining was used to assess lacunar occupancy, fluorescein isothiocyanate staining to visualize the canalicular network, and terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate-biotin nick end labeling (TUNEL) staining to examine osteocyte apoptosis. Analysis of µCT scans showed progressive decreases in mean lacuna volume over time (-27% 2-3 weeks; -13% 3-6 weeks). Lacunar number density increased considerably between 2 and 3 weeks (+156%). Correlation analysis was performed, showing a positive linear relationship between canalicular number density and trabecular thickness (R 2 = 0.56, p < 0.001) and an inverse relationship between mean lacuna volume and trabecular thickness (R 2 = 0.57, p < 0.001). Histology showed increases in canalicular number density over time (+22% 2-3 weeks, +51% 3-6 weeks). Lacunar occupancy in new bone of the callus was high (>90%), but the old cortical bone within the fracture site appeared necrotic as it underwent resorption. In conclusion, our data shows a progressive increase in the complexity of the LCN over time during fracture healing and demonstrates that this network is initiated during the early stages of repair. Further studies are needed to address the functional importance of osteocytes in bone healing, particularly in detecting and translating the effects of micromotion in the fracture. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

New spinosaurids from the Wessex Formation (Early Cretaceous, UK) and the European origins of Spinosauridae.

Spinosaurids are among the most distinctive and yet poorly-known of large-bodied theropod dinosaurs, a situation exacerbated by their mostly fragmentary fossil record and competing views regarding their palaeobiology. Here, we report two new Early Cretaceous spinosaurid specimens from the Wessex Formation (Barremian) of the Isle of Wight. Large-scale phylogenetic analyses using parsimony and Bayesian techniques recover the pair in a new clade within Baryonychinae that also includes the hypodigm of the African spinosaurid Suchomimus. Both specimens represent distinct and novel taxa, herein named Ceratosuchops inferodios gen. et sp. nov. and Riparovenator milnerae gen. et sp. nov. A palaeogeographic reconstruction suggests a European origin for Spinosauridae, with at least two dispersal events into Africa. These new finds provide welcome information on poorly sampled areas of spinosaurid anatomy, suggest that sympatry was present and potentially common in baryonychines and spinosaurids as a whole, and contribute to updated palaeobiogeographic reconstructions for the clade.

Systematizing the different notions of growth-coupled product synthesis and a single framework for computing corresponding strain designs.

A widely used design principle for metabolic engineering of microorganisms aims to introduce interventions that enforce growth-coupled product synthesis such that the product of interest becomes a (mandatory) by-product of growth. However, different variants and partially contradicting notions of growth-coupled production (GCP) exist. Herein, we propose an ontology for the different degrees of GCP and clarify their relationships. Ordered by coupling degree, we distinguish four major classes: potentially, weakly, and directionally growth-coupled production (pGCP, wGCP, dGCP) as well as substrate-uptake coupled production (SUCP). We then extend the framework of Minimal Cut Sets (MCS), previously used to compute dGCP and SUCP strain designs, to allow inclusion of implicit optimality constraints, a feature required to compute pGCP and wGCP designs. This extension closes the gap between MCS-based and bilevel-based strain design approaches and enables computation (and comparison) of designs for all GCP classes within a single framework. By computing GCP strain designs for a range of products, we illustrate the hierarchical relationships between the different coupling degrees. We find that feasibility of coupling is not affected by the chosen GCP degree and that strongest coupling (SUCP) requires often only one or two more interventions than wGCP and dGCP. Finally, we show that the principle of coupling can be generalized to couple product synthesis with other cellular functions than growth, for example, with net ATP formation. This work provides important theoretical results and algorithmic developments and a unified terminology for computational strain design based on GCP.