1,721,006 research outputs found
Increasing particle therapy biological effectiveness by nuclear reaction-driven binary strategies
Full text linkRational design of gold nanoparticles functionalized with carboranes for application in Boron Neutron Capture Therapy
No full textIn this paper we propose a bottom-up approach to obtain new boron carriers built with ortho-carborane functionalized gold nanoparticles (GNPs) for applications in Boron Neutron Capture Therapy.
The interaction between carboranes and the gold surface was assured by one or two SH-groups directly linked to the boron atoms of the B10C2 cage. This allowed obtaining stable, non toxic systems, though optimal biological performance was hampered by low solubility in aqueous media. To improve cell uptake, the hydrophilic character of carborane functionalized GNPs was enhanced by further coverage with an appropriately tailored diblock copolymer (PEO-b-PCL). This polymer also contained pendant carboranes to provide anchoring to the pre-functionalized GNPs. In vitro tests, carried out on osteosarcoma cells, showed that the final vectors possessed excellent biocompatibility joint to the capacity of concentrating boron atoms in the target, which is encouraging evidenced to pursue applications in vivo
Modeling radiation-induced cell death: role of different levels of DNA damage clustering
No full textSome open questions on the mechanisms underlying radiation-induced cell death were addressed by a biophysical model, focusing on DNA damage clustering and its consequences. DNA "cluster lesions" (CLs) were assumed to produce independent chromosome fragments that, if created within a micrometer-scale threshold distance (d), can lead to chromosome aberrations following mis-rejoining; in turn, certain aberrations (dicentrics, rings and large deletions) were assumed to lead to clonogenic cell death. The CL yield and d were the only adjustable parameters. The model, implemented as a Monte Carlo code called BIophysical ANalysis of Cell death and chromosome Aberrations (BIANCA), provided simulated survival curves that were directly compared with experimental data on human and hamster cells exposed to photons, protons, α-particles and heavier ions including carbon and iron. d = 5 μm, independent of radiation quality, and CL yields in the range ~2-20 CLs Gy(-1) cell(-1), depending on particle type and energy, led to good agreement between simulations and data. This supports the hypothesis of a pivotal role of DNA cluster damage at sub-micrometric scale, modulated by chromosome fragment mis-rejoining at micrometric scale. To investigate the features of such critical damage, the CL yields were compared with experimental or theoretical yields of DNA fragments of different sizes, focusing on the base-pair scale (related to the so-called local clustering), the kbp scale ("regional clustering") and the Mbp scale, corresponding to chromatin loops. Interestingly, the CL yields showed better agreement with kbp fragments rather than bp fragments or Mbp fragments; this suggests that also regional clustering, in addition to other clustering levels, may play an important role, possibly due to its relationship with nucleosome organization in the chromatin fiber
Microdosimetric measurements in the thermal neutron irradiation facility of LENA reactor
No full textA twin TEPC with electric-field guard tubes has been constructed to be used to characterize the
BNCT field of the irradiation facility of LENA reactor. One of the two mini TEPC was doped with
50 ppm of 10B in order to simulate the BNC events occurring in BNCT. By properly processing the
two microdosimetric spectra, the gamma, neutron and BNC spectral components can be derived with
good precision (~6%). However, direct measurements of 10B in some doped plastic samples, which
were used for constructing the cathode walls, point out the scarce accuracy of the nominal 10B
concentration value. The influence of the Boral® door, which closes the irradiation channel, has been
measured. The gamma dose increases significantly (+ 51%) when the Boral® door is closed. The
crypt-cell-regeneration weighting function has been used to measure the quality, namely the RBEμ
value, of the radiation field in different conditions. The measured RBEμ values are only partially
consistent with the RBE values of other BNCT facilities
Machine learning for screening and predicting the best surface modifiers for a rational optimization of efficient perovskite solar cells
No full textThe key to keep the rising slope of perovskite solar cell performances is to reduce non-radiative losses by minimizing defect density. To this end, a large variety of strategies have been adopted spanning from the use of interfacial layers, surface modifiers, to interface engineering. Although winning concepts have been demonstrated, they result from a mere trial and error approach, which is time consuming and operator-dependent. To face this challenge, in this work, we propose the use of a machine learning approach for an educated and rational material screening with optimal characteristics in terms of surface passivation. In particular, we applied Shapley additive explanation to extract the specific chemical features of the passivator, which directly impact the device parameters, specifically the open circuit voltage (Voc). By monitoring the different material parameters as input, we were able to list the most promising passivators and directly test them in working solar cells. By comparing the device performances with the results of the modeling and with additional optical and morphological characterization, we retrieved the most significant material properties linked to the highest efficiency, which are (i) the presence of chlorine and its strong binding capacity to positively charged defects on perovskite surface, reducing the non-radiative recombination and (ii) an increased flexibility of the molecule, resulting in better coverage of the surface. Finally, we tested the predictive power of the ML algorithm proposing a new passivator, which, implemented in a working device, leads to the predicted high Voc confirming the results of the modeling
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Multi-input Deep Learning Model to Classify COVID-19 Pneumonia Severity from Imaging and Clinical Data
No full textThe study of multi-input models that can process heterogeneous data is a challenge at the frontier of machine learning. We implemented a multi-modal approach aiming at exploiting both imaging and clinical information of patients to predict the severity of their outcome. As a specific use case, we developed a fully automated algorithm to predict the outcome of COVID-19 patients based on chest X-Ray images and clinical data, provided by the AI4COVID Challenge (1589 subjects). The system can distinguish between severe cases, those who needed intensive care or died, and mild ones. The system is composed of three Convolutional Neural Networks (CNN) for pre-processing, lung segmentation (U-Net architecture), and outcome classification. The first CNN is devoted to recognize the gray-level encoding needed to standardize the images. The U-Net for lung segmentation has been trained using two datasets collected for Tuberculosis screening. We achieved a Dice Similarity Coefficient (DSC) equal to 0.96 ± 0.03. This was needed to focus the final classifier on evaluating features within the lung. Without the careful selection of the lung, in fact, the prediction strongly depended on features outside the lung district (e.g. ECG cables, respiratory masks). The outcome classifier is a multi-input CNN made of two branches joined at the bottom. The first branch is a ResNet that takes the segmented images as input, while the second branch is a Multi-Layer Perceptron (MLP) that takes in the preprocessed clinical parameters. We obtained an AUC equal to 0.84 and an accuracy equal to 76%. We also computed the saliency maps with the gradCAM and the feature importance to obtain a reasonable explanation of the classifier. This method based on data aggregation and on merging clinical and imaging information can be applied also to domains different from COVID-19 patients
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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