The LH/hCG Axis in Endometrial Cancer: A New Target in the Treatment of Recurrent or Metastatic Disease

Endometrial cancer (EC) is a hormone-dependent cancer that currently represents the most frequent malignancy of the female reproductive tract. The involvement of steroid hormones in EC etiology and progression has been reported. More recently, gonadotropins, and, in particular LH/hCG, are emerging as novel regulators of tumor progression. In the present review, we discuss the role of the LH/hCG axis (i.e. LH/hCG and its receptors, LH/hCG-R) in both gonadal and nongonadal tissues, in physiological and neoplastic conditions. In cancer cells, LH/hCG mainly controls cell proliferation and apoptosis. In particular, in EC LH/hCG improves cell invasiveness, through a mechanism which involves the LH/hCG-R, which in turn activate protein kinase A and modulate integrin adhesion receptors. Indeed, the LH/hCG-R mRNA is expressed in primary ECs and this expression correlates with LH/hCG-induced cell invasiveness in vitro. These results lead to hypothesize that recurrent and metastatic ECs, which express LH/hCG-R, could benefit from therapies aimed at decreasing LH levels, through Gn-RH analogues. Hence, the LH/hCG axis could represent a prognostic factor and a new therapeutic target in EC

Cohabitation of settlements among crested porcupine (Hystrix cristata), red fox (Vulpes vulpes) and European badger (Meles meles)

In Italy, porcupines, badgers and red foxes share the same settlements. However, there is lack of informa-tion concerning their cohabitation. From 2012 to 2019, cohabitation by these three mammals was studied using camera-trapping and was found to occur only between porcupines and badgers, even in the presence of porcupettes. Cohabitation was associated with aggressive interaction between porcupines and badg-ers. Foxes were found to be scavengers of porcupine carcasses. Cohabitation among these semi-fossorial mammals and scavenging behaviour could play a role in disease transmission, including zoonotic diseases

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the graph and node/edge attributes change over time. In recent years, GNN-based models for temporal graphs have emerged as a promising area of research to extend the capabilities of GNNs. In this work, we provide the first comprehensive overview of the current stateof-the-art of temporal GNN, introducing a rigorous formalization of learning settings and tasks and a novel taxonomy categorizing existing approaches in terms of how the temporal aspect is represented and processed. We conclude the survey with a discussion of the most relevant open challenges for the field, from both research and application perspectives

An 8 bit current steering DAC for offset compensation purposes in sensor arrays

Abstract. An 8 bit segmented current steering DAC is presented for the compensation of mismatch of sensors with current output arranged in a large arrays. The DAC is implemented in a 1.8 V supply voltage 180 nm standard CMOS technology. Post layout simulations reveal that the design target concerning a sampling frequency of 2.6 MHz is exceeded, worst-case settling time equals 60.6 ns. The output current range is 0–10 μA, which translates into an LSB of 40 nA. Good linearity is achieved, INL < 0.5 LSB and DNL < 0.4 LSB, respectively. Static power consumption with the outputs operated at a voltage of 0.9 V is approximately 10 μW. Dynamic power, mainly consumed by switching activity of the digital circuit parts, amounts to 100 μW at 2.6 MHz operation frequency. Total area is 38.6 × 2933.0 μm2

Nonlinear imaging of damage in composite structures using sparse ultrasonic sensor arrays

In different engineering fields, there is a strong demand for diagnostic methods able to provide detailed information on material defects. Low velocity impact damage can considerably degrade the integrity of structural components and, if not detected, can result in catastrophic failures. This paper presents a nonlinear structural health monitoring imaging method, based on nonlinear elastic wave spectroscopy, for the detection and localisation of nonlinear signatures on a damaged composite structure. The proposed technique relies on the bispectral analysis of ultrasonic waveforms originated by a harmonic excitation and it allows for the evaluation of second order material nonlinearities due to the presence of cracks and delaminations. This nonlinear imaging technique was combined with a radial basis function approach in order to achieve an effective visualisation of the damage over the panel using only a limited number of acquisition points. The robustness of bispectral analysis was experimentally demonstrated on a damaged carbon fibre reinforced plastic (CFRP) composite panel, and the nonlinear source’s location was obtained with a high level of accuracy. Unlike other ultrasonic imaging methods for damage detection, this methodology does not require any baseline with the undamaged structure for the evaluation of the defect, nor a priori knowledge of the mechanical properties of the specimen

Nonlinear imaging of damage in composite structures using sparse ultrasonic sensor arrays

In different engineering fields, there is a strong demand for diagnostic methods able to provide detailed information on material defects. Low velocity impact damage can considerably degrade the integrity of structural components and, if not detected, can result in catastrophic failures. This paper presents a nonlinear structural health monitoring imaging method, based on nonlinear elastic wave spectroscopy, for the detection and localisation of nonlinear signatures on a damaged composite structure. The proposed technique relies on the bispectral analysis of ultrasonic waveforms originated by a harmonic excitation and it allows for the evaluation of second order material nonlinearities due to the presence of cracks and delaminations. This nonlinear imaging technique was combined with a radial basis function approach in order to achieve an effective visualisation of the damage over the panel using only a limited number of acquisition points. The robustness of bispectral analysis was experimentally demonstrated on a damaged carbon fibre reinforced plastic (CFRP) composite panel, and the nonlinear source’s location was obtained with a high level of accuracy. Unlike other ultrasonic imaging methods for damage detection, this methodology does not require any baseline with the undamaged structure for the evaluation of the defect, nor a priori knowledge of the mechanical properties of the specimen

Low energy actuation technique of bistable composites for aircraft morphing

Morphing structures for lightweight and energy-efficient aircraft mobile surfaces have been investigated for several years. This paper presents a novel lightweight, passive and low-energy morphing surface concept based on the "lever effect" of a bistable composite plate that can be integrated in aircraft moving surfaces. By using appropriate boundary conditions, it is demonstrated that the magnitude of the activation force on the bistable composite can be tailored to match the differential pressure on the aircraft's airfoil. As a consequence, the bistable laminate can be used as a passive morphing surface. Both numerical simulations and experimental testing are used to prove this concept on a NACA 2412 airfoil structure. The results show that, by choosing proper configuration of constraints, lay-up and aspect ratio of the bistable composite, it is possible to tailor and activate the snap-through mechanism in a passive manner. The proposed concept would save significant weight when compared to an active morphing concept