Abstract: Green infrastructure (GI) is already known to be a suitable way to enhance air quality in urban environments. Living wall systems (LWS) can be implemented in locations where other forms of GI, such as trees or hedges, are not suitable. However, much debate remains about the variables that influence their particulate matter (PM) accumulation efficiency. This study attempts to clarify which plant species are relatively the most efficient in capturing PM and which traits are decisive when it comes to the implementation of a LWS. We investigated 11 plant species commonly used on living walls, located close to train tracks and roads. PM accumulation on leaves was quantified by magnetic analysis (Saturation Isothermal Remanent Magnetization (SIRM)). Several leaf morphological variables that could potentially influence PM capture were assessed, as well as the Wall Leaf Area Index. A wide range in SIRM values (2.74–417 μA) was found between all species. Differences in SIRM could be attributed to one of the morphological parameters, namely SLA (specific leaf area). This suggest that by just assessing SLA, one can estimate the PM capture efficiency of a plant species, which is extremely interesting for urban greeners. Regarding temporal variation, some species accumulated PM over the growing season, while others actually decreased in PM levels. This decrease can be attributed to rapid leaf expansion and variations in meteorology. Correct assessment of leaf age is important here; we suggest individual labeling of leaves for further studies. Highest SIRM values were found close to ground level. This suggests that, when traffic is the main pollution source, it is most effective when LWS are applied at ground level. We conclude that LWS can act as local sinks for PM, provided that species are selected correctly and systems are applied according to the state of the art.

Abstract: Background: Currently, nanoliposomal irinotecan (nal-IRI) + 5-fluorouracil/folinic acid (5-FU/ LV) is the only approved second-line treatment for patients suffering from metastatic pancreatic ductal adenocarcinoma (mPDAC). However, also other chemotherapeutic regimens are used in this setting and due to the lack of clear real-world data on the efficacy of the different regimens, there is no consensus on the optimal treatment sequence for mPDAC patients. Objectives: To provide information on the safe and efficacious use of nal-IRI + 5-FU/LV in clinical practice in Belgium, which is needed for healthcare professionals to estimate the risk-benefit ratio of the intervention. Methods: Medical data of adult patients with mPDAC who were treated with nal-IRI + 5-FU/ LV in one of the participating Belgian hospitals were retrospectively collected. Kaplan-Meier analysis was performed to obtain survival curves to estimate the median overall survival (OS) and progression-free survival (PFS). All other results were presented descriptively. Results: A total of 56 patients [median age at diagnosis: 69 years (range 43 years), 57.1% male] were included. Patients received a median of 5 (range 49 cycles) nal-IRI + 5-FU/LV cycles, extended over 10 weeks (range 130.8 weeks). The median start dose for nal-IRI was 70 mg/ m(2) (range 49.24 mg/m(2)) and chemotherapy dose reduction and delay occurred in, respectively, 42.8% and 37.5% of the patients. The median OS was 6.8 months (95% CI: 5.6-8.4 months) with a 6-month survival rate of 57.4% and a 1-year survival rate of 27.8% in the overall study population. The median OS for patients treated with nal-IRI as second-line therapy or as laterline treatment was, respectively, 6.8 months (95% CI: 5.9-7.0 months) and 5.6 months (95% CI: 4.2-no upper limit). In the overall study population, a median PFS of 3.1 months (95% CI: 2.4-4.6 months) and a disease control rate of 48.3%, comprising 30.4% stable disease, 16.1% partial and 1.8% complete response, was observed. The median PFS for patients treated with nal-IRI as second-line therapy was 3.9 months (95% CI: 2.8-4.8 months) while this was 2.4 months (95% CI: 1.9-9.1 months) for those that received nal-IRI in a later-line treatment. In terms of safety, gastrointestinal problems occurred most (64.3% of the patients) and from all reported treatment emergent adverse events, 39.2% were grade 3 or 4. Conclusion: Nal-IRI + 5-FU/LV is a valuable, effective, and safe sequential treatment option following gemcitabine-based therapy in patients with mPDAC.

Abstract: Purple non-sulfur bacteria (PNSB) show great potential for environmental and industrial biotechnology, producing microbial protein, biohydrogen, polyhydroxyalkanoates (PHAs), pigments, etc. When grown photoheterotrophically, the carbon source is typically more reduced than the PNSB biomass, which leads to a redox imbalance. To mitigate the excess of electrons, PNSB can exhibit several ‘electron sinking’ strategies, such as CO2 fixation, N2 fixation, and H2 and PHA production. The lack of a comprehensive (over)view of these redox strategies is hindering the implementation of PNSB for biotechnology applications. This review aims to present the state of the art of redox homeostasis in phototrophically grown PNSB, presenting known and theoretically expected strategies, and discussing them from stoichiometric, thermodynamic, metabolic, and economic points of view.

Abstract: Quantitative structure determination is needed in order to study and understand nanomaterials at the atomic scale. Materials characterisation resulting in precise structural information is a crucial point to understand the structure–property relation of materials. Counting the number of atoms and retrieving the 3D atomic structure of nanoparticles plays an important role here. In this paper, an overview will be given of the atom-counting methodology and its applications over the past decade. The procedure to count the number of atoms will be discussed in detail and it will be shown how the performance of the method can be further improved. Furthermore, advances toward mixed element nanostructures, 3D atomic modelling based on the atom-counting results, and quantifying the nanoparticle dynamics will be highlighted.

Abstract: Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM) images in terms
of composition or thickness often relies on probe-position integrated scattering cross sections (PPISCS). In
order to compare experimental PPISCS with theoretically predicted ones, expensive simulations are needed for
a given specimen, zone axis orientation, and a variety of microscope settings. The computation time of such
simulations can be in the order of hours using a single GPU card. ADF STEM simulations can be efficiently
parallelized using multiple GPUs, as the calculation of each pixel is independent of other pixels. However, most
research groups do not have the necessary hardware, and, in the best-case scenario, the simulation time will
only be reduced proportionally to the number of GPUs used. In this manuscript, we use a learning approach and
present a densely connected neural network that is able to perform real-time ADF STEM PPISCS predictions as
a function of atomic column thickness for most common face-centered cubic (fcc) crystals (i.e., Al, Cu, Pd, Ag,
Pt, Au and Pb) along [100] and [111] zone axis orientations, root-mean-square displacements, and microscope
parameters. The proposed architecture is parameter efficient and yields accurate predictions for the PPISCS
values for a wide range of input parameters that are commonly used for aberration-corrected transmission
electron microscopes.

Abstract: In this paper convexity constraints are derived for a background model of electron energy loss spectra (EELS) that is linear in the fitting parameters. The model outperforms a power-law both on experimental and simulated backgrounds, especially for wide energy ranges, and thus improves elemental quantification results. Owing to the model’s linearity, the constraints can be imposed through fitting by quadratic programming. This has important advantages over conventional nonlinear power-law fitting such as high speed and a guaranteed unique solution without need for initial parameters. As such, the need for user input is significantly reduced, which is essential for unsupervised treatment of large datasets. This is demonstrated on a demanding spectrum image of a semiconductor device sample with a high number of elements over a wide energy range.

Abstract: Incorporating natural spaces within urban areas has been shown to have multiple benefits. However, despite greening and adaptation strategies at different levels of government, progress remains slow with a lack of easy to use and comprehensive tools identified as key to overcoming this. This paper presents a co-designed tool with academic and local authority partners to demonstrate the ecosystem service benefits of small-scale urban green infrastructure projects. Through the tool, users can readily assess the impact of green infrastructure investments on the delivery of a selection of ecosystem services in the early stages of a project. Furthermore, the tool provides a standardised assessment of cultural ecosystem services' contributions, as well as offering a method to score spatial designs on the impact on habitat for biodiversity. Use of the tool is demonstrated using a pilot study in Kapelle, the Netherlands. The results set out an overview of the impacts of the spatial design on estimated ecosystem service delivery. They also show the tool's potential to add value in early project stages and as a planning and design tool, helping to maximise the benefits that can be achieved through green infrastructure design. Complementing these arguments with ball-park estimations on green infrastructure costs, the Nature Smart Cities Business Model aims to offer public sector officers the means to create a business case for green infrastructure measures, facilitating the translation from strategies to actual plans, thus benefitting green infrastructure implementation in the public realm.