Neoantigen-Specific T-Cell Immune Responses: The Paradigm of NPM1-Mutated Acute Myeloid Leukemia

The C-terminal aminoacidic sequence from NPM1-mutated protein, absent in normal human tissues, may serve as a leukemia-specific antigen and can be considered an ideal target for NPM1-mutated acute myeloid leukemia (AML) immunotherapy. Different in silico instruments and in vitro/ex vivo immunological platforms have identified the most immunogenic epitopes from NPM1-mutated protein. Spontaneous development of endogenous NPM1-mutated-specific cytotoxic T cells has been observed in patients, potentially contributing to remission maintenance and prolonged survival. Genetically engineered T cells, namely CAR-T or TCR-transduced T cells, directed against NPM1-mutated peptides bound to HLA could prospectively represent a promising therapeutic approach. Although either adoptive or vaccine-based immunotherapies are unlikely to be highly effective in patients with full-blown leukemia, these strategies, potentially in combination with immune-checkpoint inhibitors, could be promising in maintaining remission or preemptively eradicating persistent measurable residual disease, mainly in patients ineligible for allogeneic hematopoietic stem cell transplant (HSCT). Alternatively, neoantigen-specific donor lymphocyte infusion derived from healthy donors and targeting NPM1-mutated protein to selectively elicit graft-versus-leukemia effect may represent an attractive option in subjects experiencing post-HSCT relapse. Future studies are warranted to further investigate dynamics of NPM1-mutated-specific immunity and explore whether novel individualized immunotherapies may have potential clinical utility in NPM1-mutated AML patients.     

Building kernels from binary strings for image matching.

In the statistical learning framework, the use of appropriate kernels may be the key for substantial improvement in solving a given problem. In essence, a kernel is a similarity measure between input points satisfying some mathematical requirements and possibly capturing the domain knowledge. In this paper, we focus on kernels for images: we represent the image information content with binary strings and discuss various bitwise manipulations obtained using logical operators and convolution with nonbinary stencils. In the theoretical contribution of our work, we show that histogram intersection is a Mercer's kernel and we determine the modifications under which a similarity measure based on the notion of Hausdorff distance is also a Mercer's kernel. In both cases, we determine explicitly the mapping from input to feature space. The presented experimental results support the relevance of our analysis for developing effective trainable systems.     

Promoting the integration of technology in teaching: An analysis of the factors that increase the intention to use technologies among Italian teachers

Background: The rapid spread of COVID-19 forced many countries to adopt severe containment measures, transferring all didactic activities into virtual environments. However, the integration of technology in teaching may present difficulties, especially in some countries, such as Italy. Objectives: The present study analyzed how the two main factors of the Technology Acceptance Model (TAM), namely perceived usefulness and perceived ease of use, combined with online teaching self-efficacy, were associated with the intention to use technology to teach. We posited a moderated moderation model in which perceived usefulness represented the main predictor, with self-efficacy and perceived ease of use as moderators of intention to use technology to teach. Method: The model was tested through multiple regressions, using the PROCESS macro on SPSS 26 with a sample of 178 upper secondary school teachers in Italy. Results and conclusions: Regressions showed that each variable significantly predicted the intention to use technology. In addition, a moderation effect of self-efficacy on the perceived usefulness of using technology was found for medium and high-level of perceived ease of use of technology. Implications: The present study provides targeted implications for distance education policy and practice to promote its adoption (or the blended modality) in Italian upper schools.     

Nanowire Assisted Mechanotyping of Cellular Metastatic Potential

Nanotechnology has provided tools for next generation biomedical devices which rely on nanostructure interfaces with living cells. In vitro biomimetic structures have enabled observation of cell response to various mechanical and chemical cues, and there is a growing interest in isolating and harnessing the specific cues that 3D microenvironments can provide without the requirement for such culture and the experimental drawbacks associated with it. Here, a randomly oriented gold coated Si nanowire substrate with patterned hydrophobic–hydrophilic areas for the differentiation of isogenic breast cancer cells of varying metastatic potential is reported. When considering synthetic surfaces for the study of cell-nanotopography interfaces, randomly oriented nanowires more closely resemble the isotropic architecture of a natural extracellular matrix. In the study reported here, the authors show that primary cancer cells preferably attach to the hydrophilic region of randomly oriented nanowire substrate while secondary cancer cells do not adhere. Using machine learning analysis of fluorescence images, cells are found to spread and elongate on the nanowire substrates as compared to a flat substrate, where they mostly remain round. Such platforms can not only be used for developing bioassays but also as stepping stones for tissue printing technologies where cells can be selectively patterned at desired locations.     

Electrokinetic removal of 2,6-dichlorophenol and diuron from kaolinite and humic acid-clay system

This paper presents the results of a study on the electrokinetic treatment of kaolinite and humic acid kaolinite complexes spiked with 2,6-dichlorophenol or 3-(3,4-dichlorophenyl)-1,1-dimethylurea (diuron). In particular, the attention was paid to the interaction between solid surface and dissolved organics: the effects of contaminant sorption as well as the physicochemical reactions on the efficiency of electrokinetic remediation were investigated. Using a 3V/cm voltage gradient, approximately 90% of diuron was removed from kaolinite after one water pore volume was collected in the cathode reservoir, but much lower efficiency was obtained in the electrokinetic removal of this compound from humic acid-coated kaolinite. The results also showed that partial degradation of the contaminant occurred during electrokinetic treatment of kaolinite clay spiked with 2,6-dichlorophenol: the contamination in the clay could be remediated by the combination of electrokinetic extraction and electrochemical reactions.     

Push &; Pull: autonomous deployment of mobile sensors for a complete coverage

Mobile sensor networks are important for several strategic applications devoted to monitoring critical areas. In such hostile scenarios, sensors cannot be deployed manually and are either sent from a safe location or dropped from an aircraft. Mobile devices permit a dynamic deployment reconfiguration that improves the coverage in terms of completeness and uniformity. In this paper we propose a distributed algorithm for the autonomous deployment of mobile sensors called Push & Pull. According to our proposal, movement decisions are made by each sensor on the basis of locally available information and do not require any prior knowledge of the operating conditions or any manual tuning of key parameters. We formally prove that, when a sufficient number of sensors are available, our approach guarantees a complete and uniform coverage. Furthermore, we demonstrate that the algorithm execution always terminates preventing movement oscillations. Numerous simulations show that our algorithm reaches a complete coverage within reasonable time with moderate energy consumption, even when the target area has irregular shapes. Performance comparisons between Push & Pull and one of the most acknowledged algorithms show how the former one can efficiently reach a more uniform and complete coverage under a wide range of working scenarios.     

Identification of new epidemiological molecular markers by comparative proteomics of serogroup A meningococcal isolates from three pandemic waves

We previously described the first reference map for the proteome of one strain of serogroup A Neisseria meningitidis (MenA), a major cause of epidemic meningitis in humans. As a preliminary finding, in that work we noted that 2‐DE protein maps of closely related MenA isolates from different epidemics spreads could be easily compared to detect minor differences and that 2‐DE phenotypes attributable to the well‐known epidemiological marker tbpB agreed with the genoclouds model of MenA epidemiological variation during pandemic waves. We explored here the possibility that an extended comparative study of 2‐DE maps of isolates representative of the nine genoclouds described by Achtman and collaborators could be used to discriminate between strains otherwise undistinguishable. We showed the example of 14 proteins with different 2‐DE spot patterns in different genoclouds that could be considered as putative tracers for alike‐strains discrimination. We introduce the novel concept that comparative proteomics can be useful in identifying new epidemiological markers for N. meningitidis.     

Effect of uncertainties on substructure coupling: Modelling and reduction strategies

Different strategies for modelling and reducing the effect of uncertainties on the dynamic behaviour of coupled structures are discussed in this paper. In assembled structures, each component satisfies specifications about material properties and dimensional tolerances. Variability of design specifications within the tolerance field may affect the dynamic behaviour of the assembled structure more than that of any individual component substructure. Among modelling strategies, non-predictive techniques such as post-processing of data from a set of randomly assembled structures and analysis of sensitivities to uncertain variables are considered. However, emphasis is placed on a less trivial yet simple strategy such as the use of design of experiments to fit a regression model. Different reduction strategies are considered according to whether small design changes (even revised tolerance allocation) are still possible or not. In the latter case, selective assembly is extended to problems where the performance requirement is not the clearance between two parts, but a prescribed dynamic behaviour.     

Environmental data processing by clustering methods for energy forecast and planning

This paper presents a statistical approach based on the k-means clustering technique to manage environmental sampled data to evaluate and to forecast of the energy deliverable by different renewable sources in a given site. In particular, wind speed and solar irradiance sampled data are studied in association to the energy capability of a wind generator and a photovoltaic (PV) plant, respectively. The proposed method allows the sub-sets of useful data, describing the energy capability of a site, to be extracted from a set of experimental observations belonging the considered site. The data collection is performed in Sicily, in the south of Italy, as case study. As far as the wind generation is concerned, a suitable generator, matching the wind profile of the studied sites, has been selected for the evaluation of the producible energy. With respect to the photovoltaic generation, the irradiance data have been taken from the acquisition system of an actual installation. It is demonstrated, in both cases, that the use of the k-means clustering method allows data that do not contribute to the produced energy to be grouped into a cluster, moreover it simplifies the problem of the energy assessment since it permits to obtain the desired information on energy capability by managing a reduced amount of experimental samples. In the studied cases, the proposed method permitted a reduction of the 50% of the data with a maximum discrepancy of 10% in energy estimation compared to the classical statistical approach. Therefore, the adopted k-means clustering technique represents an useful tool for an appropriate and less demanding energy forecast and planning in distributed generation systems.