How the Covid-19 crisis shaped research collaboration behaviour

Scientometrics - The paper explores the impact of Covid-19 on scientists' collaboration behaviour in the 14 countries with the largest research output. The approach is bibliometric, taking the...     

The geographic proximity effect on domestic cross-sector vis-à-vis intra-sector research collaborations

Scientometrics - Geographic proximity is acknowledged to be a key factor in research collaborations. Specifically, it can work as a possible substitute for institutional proximity. The present...     

Predicting the future success of scientific publications through social network and semantic analysis

Scientometrics - Studying research fronts enables researchers to understand how their academic fields emerged, how they are currently developing and their changes over time. While topic modelling...     

In situ 4D tomography image analysis framework to follow sintering within 3D-printed glass scaffolds

We propose a novel image analysis framework to automate analysis of X-ray microtomography images of sintering ceramics and glasses, using open-source toolkits and machine learning. Additive manufacturing (AM) of glasses and ceramics usually requires sintering of green bodies. Sintering causes shrinkage, which presents a challenge for controlling the metrology of the final architecture. Therefore, being able to monitor sintering in 3D over time (termed 4D) is important when developing new porous ceramics or glasses. Synchrotron X-ray tomographic imaging allows in situ, real-time capture of the sintering process at both micro and macro scales using a furnace rig, facilitating 4D quantitative analysis of the process. The proposed image analysis framework is capable of tracking and quantifying the densification of glass or ceramic particles within multiple volumes of interest (VOIs) along with structural changes over time using 4D image data. The framework is demonstrated by 4D quantitative analysis of bioactive glass ICIE16 within a 3D-printed scaffold. Here, densification of glass particles within 3 VOIs were tracked and quantified along with diameter change of struts and interstrut pore size over the 3D image series, delivering new insights on the sintering mechanism of ICIE16 bioactive glass particles in both micro and macro scales.     

Cracking the Breast Cancer Glyco-Code through Glycan-Lectin Interactions: Targeting Immunosuppressive Macrophages

The immune microenvironment of breast cancer (BC) is composed by high macrophage infiltrates, correlated with the most aggressive subtypes. Tumour-associated macrophages (TAM) within the BC microenvironment are key regulators of immune suppression and BC progression. Nevertheless, several key questions regarding TAM polarisation by BC are still not fully understood. Recently, the modulation of the immune microenvironment has been described via the recognition of abnormal glycosylation patterns at BC cell surface. These patterns rise as a resource to identify potential targets on TAM in the BC context, leading to the development of novel immunotherapies. Herein, we will summarize recent studies describing advances in identifying altered glycan structures in BC cells. We will focus on BC-specific glycosylation patterns known to modulate the phenotype and function of macrophages recruited to the tumour site, such as structures with sialylated or N-acetylgalactosamine epitopes. Moreover, the lectins present at the surface of macrophages reported to bind to such antigens, inducing tumour-prone TAM phenotypes, will also be highlighted. Finally, we will discuss and give our view on the potential and current challenges of targeting these glycan-lectin interactions to reshape the immunosuppressive landscape of BC.     

Dimensionality reduction based on binary encoding for hyperspectral data

Binary encoding is an approach that aims at summarizing the information contained in various spectral bands into a single image that stores the meaningful information of the bands. In this paper, it is introduced a feature extraction approach to reduce the dimensionality of hyperspectral data with binary encoding for classification purposes. Different options to reduce the radiometric information of the pixels are introduced, such as using a single threshold or multiple thresholds. After the dimensionality reduction, the separation of the spectral classes was analysed and the thematic classification of the reduced data was performed. In order to evaluate the performance of the proposed approach, experiments on AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) image, ROSIS (Reflection Optics System Imaging Spectrometer) hyperspectral image and HYDICE (Hyperspectral Digital Imagery Collection Experiment) hyperspectral image are presented. In the experiments, neighbouring spectral bands are grouped and coded and the results of the classification are compared. The results show that the use of binary encoding based on three thresholds by spectral region is more efficient than with the use of one threshold. The thematic mapping of the hyperspectral data with reduced dimension confirms the competitiveness of the binary encoding method compared with other dimension reduction methods, such as the Principal Component Analysis (PCA), the Principal Component Analysis – Fisher’s Linear Discriminant Analysis (PCA-LDA), the Discriminant Analysis Feature Extraction (DAFE) and the Non-parametric Weighted Feature Extraction (NWFE). In this context, the present methodology shows to be promising, because it reduces the computational complexity and improves performance.     

Chemical fingerprinting identifies Echium vulgare,Eupatorium cannabinum and Senecio spp. as plant species mainly responsible for pyrrolizidine alkaloids in bee-collected pollen

Various studies have shown that bee-collected pollen sold as nutritional supplements may contain toxic pyrrolizidine alkaloids (PAs) and, thus, pose a potential health risk for consumers. The level of contamination may vary according to its geographical and botanical origin. Here, the PA content of pollen produced in Switzerland was studied and 32 commercially available bee-collected pollen supplements produced between 2010 and 2014 were analysed. In addition, at what time period bees collect PA-containing pollen was investigated. Hence, this study looked into the occurrence of PAs in pollen samples collected daily during two-to-three consecutive seasons. Furthermore, the PA spectrum in pollen was compared to the spectrum found in flower heads of PA-plants to unambiguously identify plants responsible for PA contamination of pollen. The PA concentration of commercial and daily collected pollen was determined by target analysis using an HPLC-MS/MS system, allowing the detection of 18 different PAs and PA N-oxides found in the genera Echium, Eupatorium and Senecio, while the comparison of the PA spectrum in pollen and flower heads was performed by LC-HR-MS, allowing the detection of all PA types in a sample, including saturated, non-carcinogenic PAs. Of the commercially available pollen, 31% contained PAs with a mean concentration of 319 ng/g, mainly Echium- and Eupatorium-type PAs, while the PA concentrations were below the limit of quantitation (LOQ) in 69% of the pollen samples. Bees collected pollen containing Echium-type PAs mainly in June and July, while they gathered pollen containing Eupatorium-type PAs from mid-July to August. Senecio-type PAs appeared from June to September. Comparison of the PA array in pollen and plants identified E. vulgare and E. cannabinum as the main plants responsible for PA contamination of Swiss bee-collected pollen, and to a lesser extent also identified plants belonging to the genus Senecio.     

Pt on SAS-CeO2 nanopowder as catalyst for the CO-WGS reaction

In this paper, we present a study on the preparation of ceria nanopowder by Supercritical Antisolvent technique and its use as catalysts support for water gas shift reaction. The effect of the concentration of ceria precursor and the solution flow rate was evaluated on particle size and granulometric distribution. The increase of concentration led to an increase in the average particle size, whereas the solution flow rate had a negligible effect. The platinum/ceria catalyst was prepared by wet impregnation and fully characterized; SEM and TEM-EDX showed a mean particle size of around 50 nm and a good dispersion of the active component. The results of the activity tests highlighted a good performance of the SAS-derived catalyst, that showed higher CO conversion with respect to a catalyst obtained from commercial ceria nanopowder.     

Occurrence of deoxynivalenol in wheat flour,instant noodle and biscuits commercialised in Brazil

A total of 134 samples, consisting of 58 wheat flour, 40 instant noodle and 36 biscuits, were analysed for the presence of deoxynivalenol (DON). The samples were obtained from retail markets of the city of São Paulo during the period 2010–2014. DON was determined by high performance liquid chromatography with ultraviolet detection and immunoaffinity sample clean-up. Method validation followed international guidelines. The LOD and LOQ were 60 and 200 µg kg^?1, respectively, considering the three different types of samples analysed. The lowest recovery found in this study was 91.8% with RSD 4.5% for instant noodles. DON was detected in 91.4%, 97.5% and 97.2% of samples wheat flour, instant noodles and biscuits, respectively, resulting in a total of 94.8% with levels ranging from LOD to 1720.0 µg kg^?1.