1,021 research outputs found
Imaging of tumor specific antigens and microenvironment
The events that drive tumor progression and metastatization initiate within the cancer cell itself, that accumulates mutations and de-differentiate until reaching a very unstable stage. But these events alone are not sufficient to maintain sustained metastasis development and growth. Support from the host is required in order to promote angiogenesis and inhibit immune response that would kill cancer cells. The sum of these events is the result of the interaction between the tumor and cellular or non-cellular components that surround the cancer cells. This concept gained more and more importance during the recent years due to the possibility to develop a wide range of therapeutic strategies to target those components and pathways involved in the establishment of tumor microenvironment. However, it has been reported that cancer lesions, even within the same patient, may express different markers, thus, needing two different therapeutic approaches. Therefore, there is the need of a non-invasive tool to characterize each lesion before planning the most appropriate therapy. This will allow to save money, time and reduce the side-effects as much as possible. In this scenario, nuclear medicine offers a wide set of potential radiopharmaceuticals to image markers expressed by both cancer cells and microenvironment allowing to plan the most appropriate therapy in each patient. In this thesis we present four different strategies to image tumor specific antigens and microenvironment focusing on cancer, immune system and angiogenesis
Market expansion and the co-opetition of criminal organizations in Italy
A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and EconomicsA mafia modernization process will be addressed. That is the process by which the expansion growth into new territories coincided with a shift of objectives and interests of the mafia itself, which turned from being "traditional" to "entrepreneurial." Beside that we will examine the strategies adopted by the criminal organizations in order to successfully face the legal market and maintain at the same time a deep control over their home-regions
Online Sensitivity Optimization in Differentially Private Learning
Training differentially private machine learning models requires constraining
an individual's contribution to the optimization process. This is achieved by
clipping the -norm of their gradient at a predetermined threshold prior to
averaging and batch sanitization. This selection adversely influences
optimization in two opposing ways: it either exacerbates the bias due to
excessive clipping at lower values, or augments sanitization noise at higher
values. The choice significantly hinges on factors such as the dataset, model
architecture, and even varies within the same optimization, demanding
meticulous tuning usually accomplished through a grid search. In order to
circumvent the privacy expenses incurred in hyperparameter tuning, we present a
novel approach to dynamically optimize the clipping threshold. We treat this
threshold as an additional learnable parameter, establishing a clean
relationship between the threshold and the cost function. This allows us to
optimize the former with gradient descent, with minimal repercussions on the
overall privacy analysis. Our method is thoroughly assessed against alternative
fixed and adaptive strategies across diverse datasets, tasks, model dimensions,
and privacy levels. Our results indicate that it performs comparably or better
in the evaluated scenarios, given the same privacy requirements
Immunoscintigraphy for therapy decision making and follow-up of biological therapies
With the availability of new biological therapies there is the need of more accurate diagnostic tools to noninvasively
assess the presence of their targets. In this scenario nuclear medicine offers many radiopharmaceuticals for
SPECT or PET imaging of many pathological conditions. The availability of monoclonal antibodies provides tools to
target specific antigens involved in angiogenesis, cell cycle or modulation of the immune systems. The radiolabelling of
such therapeutic mAbs is a promising method to evaluate the antigenic status of each cancer lesion or inflamed sites
before starting the therapy. It may also allow to perform follow-up of such biological therapies. In the present review we
provide an overview of the most studied radiolabelled antibodies for therapy decision making and follow-up of patients
affected by cancer and other pathological conditions
Path Integral Monte Carlo study confirms a highly ordered snowball in 4He nanodroplets doped with an Ar+ ion
By means of the exact Path Integral Monte Carlo method we have performed a
detailed microscopic study of He nanodroplets doped with an argon ion,
Ar, at K. We have computed density profiles, energies, dissociation
energies and characterized the local order around the ion for nanodroplets with
a number of 4He atoms ranging from 10 to 64 and also 128. We have found the
formation of a stable solid structure around the ion, a "snowball", consisting
of 3 concentric shells in which the 4He atoms are placed on at the vertices of
platonic solids: the first inner shell is an icosahedron (12 atoms); the second
one is a dodecahedron with 20 atoms placed on the faces of the icosahedron of
the first shell; the third shell is again an icosahedron composed of 12 atoms
placed on the faces of the dodecahedron of the second shell. The "magic
numbers" implied by this structure, 12, 32 and 44 helium atoms, have been
observed in a recent experimental study [Bartl et al, J. Phys. Chem. A 118,
2014] of these complexes; the dissociation energy curve computed in the present
work shows jumps in correspondence with those found in the nanodroplets
abundance distribution measured in that experiment, strengthening the agreement
between theory and experiment. The same structures were predicted in Ref.
[Galli et al, J. Phys. Chem. A 115, 2011] in a study regarding
Na@He when n>30; a comparison between Ar@He and
Na@He complexes is also presented.Comment: 10 pages, 4 figure
Un ricordo di Francesco Palla
Lo scorso Gennaio è improvvisamente mancato Francesco Palla, direttore dell’Osservatorio Astrofisico di Arcetri fino al 2011, molto conosciuto a Firenze anche per le sua attività di comunicazione della scienza, e tra i fondatori di questa rivista. Ne ricordiamo la figura di scienziato e di divulgatore appassionato.Francesco Palla, Director of the Arcetri Astrophysical Observatory up to 2011, died suddenly last January. He was also well known in Florence for his commitment to spreading knowledge of science and was one of the founders of this journal. Here we should like to remember Francesco in his role of eclectic scientist and passionate communicator
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
Federated learning (FL) is a framework for training machine learning models
in a distributed and collaborative manner. During training, a set of
participating clients process their data stored locally, sharing only the model
updates obtained by minimizing a cost function over their local inputs. FL was
proposed as a stepping-stone towards privacy-preserving machine learning, but
it has been shown vulnerable to issues such as leakage of private information,
lack of personalization of the model, and the possibility of having a trained
model that is fairer to some groups than to others. In this paper, we address
the triadic interaction among personalization, privacy guarantees, and fairness
attained by models trained within the FL framework. Differential privacy and
its variants have been studied and applied as cutting-edge standards for
providing formal privacy guarantees. However, clients in FL often hold very
diverse datasets representing heterogeneous communities, making it important to
protect their sensitive information while still ensuring that the trained model
upholds the aspect of fairness for the users. To attain this objective, a
method is put forth that introduces group privacy assurances through the
utilization of -privacy (aka metric privacy). -privacy represents a
localized form of differential privacy that relies on a metric-oriented
obfuscation approach to maintain the original data's topological distribution.
This method, besides enabling personalized model training in a federated
approach and providing formal privacy guarantees, possesses significantly
better group fairness measured under a variety of standard metrics than a
global model trained within a classical FL template. Theoretical justifications
for the applicability are provided, as well as experimental validation on
real-world datasets to illustrate the working of the proposed method
Optimum VM Placement for NFV Infrastructures
This paper shows how to use a Linux-based operating system as a real-time processing platform for low-latency and predictable packet processing in cloudified radio-access network (cRAN) scenarios. This use-case exhibits challenging end-to-end processing latencies, in the order of milliseconds for the most time-critical layers of the stack. A significant portion of the variability and instability in the observed end-to-end performance in this domain is due to the power saving capabilities of modern CPUs, often in contrast with the low-latency and high-performance requirements of this type of applications. We discuss how to properly configure the system for this scenario, and evaluate the proposed configuration on a synthetic application designed to mimic the behavior and computational requirements of typical software components implementing baseband processing in production environments
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