Applications of three-dimensional carbon nanotube

In this paper, we show that it is possible to synthesize carbon-based three-dimensional networks by adding sulfur, as growth enhancer, during the synthesis process. The obtained material is self-supporting and consists of curved and interconnected carbon nanotubes and to lesser extent of carbon fibers. Studies on the microstructure indicate that the assembly presents a marked variability in the tube external diameter and in the inner structure. We study the relationship between the observed microscopic properties and some potential applications. In particular, we show that the porous nature of the network is directly responsible for the hydrophobic and the lipophilic behavior. Moreover, we used a cut piece of the produced carbon material as working electrode in a standard electrochemical cell and, thus, demonstrating the capability of the system to respond to incident light in the visible and near-ultraviolet region and to generate a photocurrent

On the use of domain adaptation techniques for bridge damage detection in a changing environment

Structural Health Monitoring of civil infrastructures often suffers from the limited availability of damage labelled data. The work here seeks to overcome this issue by using Transfer Learning approaches, in the form of Domain Adaptation, for leveraging information from a source structure with determined health-state labels to make inferences on an unlabeled monitored structure. The idea is to exploit source data to train a Machine Learning algorithm and achieve improved early-stage damage detection capabilities across a population of bridges. To account for differences in the underlying distributions of each structure, Transfer Learning is seen as a strategy enabling population-level bridge SHM. In this paper, the natural frequencies obtained from multiple vibration measurements are extracted to characterise different domains during pristine and abnormal conditions. Such damage-sensitive features are aligned via Domain Adaptation and used to train a standard classifier within a shared feature space. The methodology is validated on the heterogeneous population composed of the Z24 and S101 bridges. The results prove the effectiveness to successfully exchange damage labels, thus increasing available information for health-state inference for SHM applications with sparce datasets

Central nervous system myeloma and unusual extramedullary localizations: real life practical guidance

Central nervous system localization of multiple myeloma (CNS-MM) accounts for about 1% of all MM during disease course or even rarer at diagnosis. A difference in the origin, i.e., osteodural or primary dural vs leptomeningeal/intraparenchymal, seems to define two distinct types of intracranial myeloma, with different clinical behavior. CNS-MM may occur also as a presentation of MM. Treatment is still unsatisfactory and many treatments have been reported: chemotherapy, intrathecal therapy, and radiotherapy, with dismal prognosis. Other sites of myeloma localization could be also of interest and deserve description. Because of the rarity and aggressiveness of the disease clinicians are often doubtful on how to treat it since there is no general agreement. Moreover, recent drugs such as the anti CD38 monoclonal antibody, immunomodulatory drugs, and proteasome inhibitors have changed the treatment of patients with MM with a significant improvement in overall response and survival. The role of novel agents in CNS MM management and unusual presentations will be discussed as well as the potential role of other new immunomodulatory drugs and proteasome inhibitors that seem to cross the blood-brain barrier. The purpose of this review is to increase awareness of the clinical unusual presentation and neuroradiological findings, give practical diagnostic advice and treatment options algorithm

An application of domain adaptation for population-based structural health monitoring

In the field of civil infrastructure, Structural Health Monitoring generally suffers from a scarcity of labelled damage-state data. To solve this issue, this work adopts a Transfer Learning approach for leveraging information from a source structure, characterised by a rich class of damage labels, to improve inferences on a target structure with limited knowledge. The goal is to train a machine learning algorithm on a bridge undergoing damage and to afterwards transfer the available labelled damage-state data across the members of the investigated population. Given possible differences exhibited by each structure, a domain adaptation technique in the field of statistic alignment, called Normal Condition Alignment (NCA), is applied to match different distributions in a shared feature space. The methodology is validated on a heterogeneous population composed of two numerical bridges of different geometry and materials, representing the Z24 and the S101 benchmark bridges. Finite Element Models are built to simulate healthy conditions and several damage cases. The natural frequencies describing such scenarios are considered as damage-sensitive features and thus employed to characterise the two domains and fed to a supervised learning-based classifier. The presented approach is deemed effective to provide mappings that allow the exchange of health-state information from source to target datasets, becoming a promising approach to be applied within a population of real bridges

Spectroscopic, Morphological and Mechanistic Investigation of the Solvent.Promoted Aggregation of Porphyrins Modified in meso-positions by Glucosylated steroids

Solvent-driven aggregation of a series of porphyrin derivatives was studied by UV/Vis and circular dichroism spectroscopy. The porphyrins are characterised by the presence in the meso positions of steroidal moieties further conjugated with glucosyl groups. The presence of these groups makes the investigated macrocycles amphiphilic and soluble in aqueous solvent, namely, dimethyl acetamide/ water. Aggregation of the macrocycles is triggered by a change in bulk solvent composition leading to formation of large architectures that express supramolecular chirality, steered by the presence of the stereogenic centres on the periphery of the macrocycles. The aggregation behaviour and chiroptical features of the aggregates are strongly dependent on the number of moieties decorating the periphery of the porphyrin framework. In particular, experimental evidence indicates that the structure of the steroid linker dictates the overall chirality of the supramolecular architectures. Moreover, the porphyrin concentration strongly affects the aggregation mechanism and the CD intensities of the spectra. Notably, AFM investigations reveal strong differences in aggregate morphology that are dependent on the nature of the appended functional groups, and closely in line with the changes in aggregation mechanism. The suprastructures formed at lower concentration show a network of long fibrous structures spanning over tens of micrometres, whereas the aggregates formed at higher concentration have smaller rodshaped structures that can be recognised as the result of coalescence of smaller globular structures. The fully steroid substituted derivative forms globular structures over the whole concentration range explored. Finally, a rationale for the aggregation phenomena was given by semiempirical calculations at the PM6 level

A global optimization approach applied to structural dynamic updating

In this paper, the application of stochastic global optimization tech- niques, in particular the GlobalSearch and MultiStart solvers from MatLab®, to improve the updating of a structural dynamic model, are presented. For com- parative purposes, the efficiency of these global methods relatively to the local search method previously used in a Finite Element Model Updating program is evaluated. The obtained solutions showed that the GlobalSearch and MultiStart solvers are able to achieve a better solution than the local solver previously used, in the updating of a structural dynamic model. The results show also that the GlobalSearch solver is more efficient than the MultiStart, since requires less computational effort to obtain the global solution.Fundação para a Ciência e a Tecnologia (FCT

Novel NPM1 exon 5 mutations and gene fusions leading to aberrant cytoplasmic nucleophosmin in AML

Nucleophosmin (NPM1) mutations in acute myeloid leukemia (AML) affect exon 12, but also sporadically affect exons 9 and 11, causing changes at the protein C-terminal end (tryptophan loss, nuclear export signal [NES] motif creation) that lead to aberrant cytoplasmic NPM1 (NPM1c+), detectable by immunohistochemistry. Combining immunohistochemistry and molecular analyses in 929 patients with AML, we found non–exon 12 NPM1 mutations in 5 (1.3%) of 387 NPM1c+ cases. Besides mutations in exons 9 (n = 1) and 11 (n = 1), novel exon 5 mutations were discovered (n = 3). Another exon 5 mutation was identified in an additional 141 patients with AML selected for wild-type NPM1 exon 12. Three NPM1 rearrangements (NPM1/RPP30, NPM1/SETBP1, NPM1/CCDC28A) were detected and characterized among 13 979 AML samples screened by cytogenetic/fluorescence in situ hybridization and RNA sequencing. Functional studies demonstrated that in AML cases, new NPM1 proteins harbored an efficient extra NES, either newly created or already present in the fusion partner, ensuring its cytoplasmic accumulation. Our findings support NPM1 cytoplasmic relocation as critical for leukemogenesis and reinforce the role of immunohistochemistry in predicting AML-associated NPM1 genetic lesions. This study highlights the need to develop new assays for molecular diagnosis and monitoring of NPM1-mutated AML