Exploring CNNs: an application study on nuclei recognition task in colon cancer histology images

In this work we explore the recent advances in the field of Convolutional Neural Network (CNN), with particular interest to the task of image classification. Moreover, we explore a new neural network algorithm, called ladder network, which enables the semi-supervised framework on pre-existing neural networks. These techniques were applied to a task of nuclei classification in routine colon cancer histology images. Specifically, starting from an existing CNN developed for this purpose, we improve its performances utilizing a better data augmentation, a more efficient initialization of the network and adding the batch normalization layer. These improvements were made to achieve a state-of-the-art architecture which could be compatible with the ladder network algorithm. A specific custom version of the ladder network algorithm was implemented in our CNN in order to use the amount of data without a label presented with the used database. However we observed a deterioration of the performances using the unlabeled examples of this database, probably due to a distribution bias in them compared to the labeled ones. Even without using of the semi-supervised framework, the ladder algorithm allows to obtain a better representation in the CNN which leads to a dramatic performance improvement of the starting CNN algorithm. We reach this result only with a little increase in complexity of the final model, working specifically on the training process of the algorithm

Preliminary mission profile of Hera's Milani CubeSat

CubeSats offer a flexible and low-cost option to increase the scientific and technological return of small-body exploration missions. ESA's Hera mission, the European component of the Asteroid Impact and Deflection Assessment (AIDA) international collaboration, plans on deploying two CubeSats in the proximity of binary system 65803 Didymos, after arrival in 2027. In this work, we discuss the feasibility and preliminary mission profile of Hera's Milani CubeSat. The CubeSat mission is designed to achieve both scientific and technological objectives. We identify the design challenges and discuss design criteria to find suitable solutions in terms of mission analysis, operational trajectories, and Guidance, Navigation, & Control (GNC) design. We present initial trajectories and GNC baseline, as a result of trade-off analyses. We assess the feasibility of the Milani CubeSat mission and provide a preliminary solution to cover the operational mission profile of Milani in the close-proximity of Didymos system.Comment: Accepted on Advances in Space Researc

Tunable spin and orbital Edelstein effect at (111) LaAlO3_3/SrTiO3_3 interface

Converting charge current into spin current is one of the main mechanisms exploited in spintronics. One prominent example is the Edelstein effect, namely the generation of a magnetization in response to an external electric field, which can be realized in systems with lack of inversion symmetry. If a system has electrons with an orbital angular momentum character, an orbital magnetization can be generated by the applied electric field giving rise to the so-called orbital Edelstein effect. Oxide heterostructures are the ideal platform for these effects due to the strong spin-orbit coupling and the lack of inversion symmetries. Beyond a gate-tunable spin Edelstein effect, we predict an orbital Edelstein effect an order of magnitude larger then the spin one at the (111) LaAlO$_3$/SrTiO$_3$ interface. We model the material as a bilayer of $t_{2g}$ orbitals using a tight-binding approach, while transport properties are obtained in the Boltzmann approach. We give an effective model at low filling which explains the non-trivial behaviour of the Edelstein response, showing that the hybridization between the electronic bands crucially impacts the Edelstein susceptibility.Comment: 12 pages, 7 figure

Pentraxin-3 in late-preterm newborns with hypoxic respiratory failure.

The aim of this study was: echocardiographical assessment of cardiac alterations in late-preterm newborns with hypoxic respiratory failure (HRF), and, study serum pentraxin-3 (PTX-3) in relation to the severity of respiratory impairment and to some echocardiographic parameters (i.e. ejection fraction (EF), stroke volume (SV) and cardiac output (CO). We enrolled in this study 40 newborn infants whose 22 (group I) with moderate HRF and 18 (group II) with severe HRF. In group I the mean values of EF, SV and CO were significantly higher than in the group II. Our results showed a significant increase of PTX-3 in group II patients at 24h of life when compared to group I. Taking patients all together (n=40), we found a significant (R=-73) reverse correlation between EF and serum values of PTX-3. PTX-3 in our patients with HRF is affected by the severity of the hypoxic insult and correlate with the cardio-vascular impairment

Combined HW/SW Drift and Variability Mitigation for PCM-based Analog In-memory Computing for Neural Network Applications

Matrix-Vector Multiplications (MVMs) represent a heavy workload for both training and inference in Deep Neural Networks (DNNs) applications. Analog In-memory Computing (AIMC) systems based on Phase Change Memory (PCM) has been shown to be a valid competitor to enhance the energy efficiency of DNN accelerators. Although DNNs are quite resilient to computation inaccuracies, PCM non-idealities could strongly affect MVM operations precision, and thus the accuracy of DNNs. In this paper, a combined hardware and software solution to mitigate the impact of PCM non-idealities is presented. The drift of PCM cells conductance is compensated at the circuit level through the introduction of a conductance ratio at the core of the MVM computation. A model of the behaviour of PCM cells is employed to develop a device-aware training for DNNs and the accuracy is estimated in a CIFAR-10 classification task. This work is supported by a PCM-based AIMC prototype, designed in a 90-nm STMicroelectronics technology, and conceived to perform Multiply-and-Accumulate (MAC) computations, which are the kernel of MVMs. Results show that the MAC computation accuracy is around 95% even under the effect of cells drift. The use of a device-aware DNN training makes the networks less sensitive to weight variability, with a 15% increase in classification accuracy over a conventionally-trained Lenet-5 DNN, and a 36% gain when drift compensation is applied

Choroid plexus enlargement is associated with neuroinflammation and reduction of blood brain barrier permeability in depression.

BACKGROUND: Recent studies have shown that choroid plexuses (CP) may be involved in the neuro-immune axes, playing a role in the interaction between the central and peripheral inflammation. Here we aimed to investigate CP volume alterations in depression and their associations with inflammation. METHODS: 51 depressed participants (HDRS score > 13) and 25 age- and sex-matched healthy controls (HCs) from the Wellcome Trust NIMA consortium were re-analysed for the study. All the participants underwent full peripheral cytokine profiling and simultaneous [11C]PK11195 PET/structural MRI imaging for measuring neuroinflammation and CP volume respectively. RESULTS: We found a significantly greater CP volume in depressed subjects compared to HCs (t(76) = +2.17) that was positively correlated with [11C]PK11195 PET binding in the anterior cingulate cortex (r = 0.28, p = 0.02), prefrontal cortex (r = 0.24, p = 0.04), and insular cortex (r = 0.24, p = 0.04), but not with the peripheral inflammatory markers: CRP levels (r = 0.07, p = 0.53), IL-6 (r = -0.08, p = 0.61), and TNF-α (r = -0.06, p = 0.70). The CP volume correlated with the [11C]PK11195 PET binding in CP (r = 0.34, p = 0.005). Integration of transcriptomic data from the Allen Human Brain Atlas with the brain map depicting the correlations between CP volume and PET imaging found significant gene enrichment for several pathways involved in neuroinflammatory response. CONCLUSION: This result supports the hypothesis that changes in brain barriers may cause reduction in solute exchanges between blood and CSF, disturbing the brain homeostasis and ultimately contributing to inflammation in depression. Given that CP anomalies have been recently detected in other brain disorders, these results may not be specific to depression and might extend to other conditions with a peripheral inflammatory component

Decoding Algorithms and HW Strategies to Mitigate Uncertainties in a PCM-Based Analog Encoder for Compressed Sensing

Analog In-Memory computing (AIMC) is a novel paradigm looking for solutions to prevent the unnecessary transfer of data by distributing computation within memory elements. One such operation is matrix-vector multiplication (MVM), a workhorse of many fields ranging from linear regression to Deep Learning. The same concept can be readily applied to the encoding stage in Compressed Sensing (CS) systems, where an MVM operation maps input signals into compressed measurements. With a focus on an encoder built on top of a Phase-Change Memory (PCM) AIMC platform, the effects of device non-idealities, namely programming spread and drift over time, are observed in terms of the reconstruction quality obtained for synthetic signals, sparse in the Discrete Cosine Transform (DCT) domain. PCM devices are simulated using statistical models summarizing the properties experimentally observed in an AIMC prototype, designed in a 90 nm STMicroelectronics technology. Different families of decoders are tested, and tradeoffs in terms of encoding energy are analyzed. Furthermore, the benefits of a hardware drift compensation strategy are also observed, highlighting its necessity to prevent the need for a complete reprogramming of the entire analog array. The results show >30 dB average reconstruction quality for mid-range conductances and a suitably selected decoder right after programming. Additionally, the hardware drift compensation strategy enables robust performance even when different drift conditions are tested

Prolonged complete hematologic response in relapsed/refractory T-large granular lymphocyte leukemia after bendamustine treatment

T-large granular lymphocyte leukemia (T-LGLL) is a chronic clonal proliferation of effector memory cytotoxic CD3+CD57+CD56- T cells and the current guidelines suggest immunosuppressive therapy as first-line therapy, but the treatment of refractory/relapsed patients is still challenging due to the lack of prospective studies. We describe a series of two refractory/relapsed T-LGLL patients successfully treated with bendamustine, a chemotherapeutic agent largely used for B-cell neoplasms, but poorly investigated for the treatment of T-cell diseases. Complete remission (CR) was achieved in 3 and 6 months, respectively, and maintained for at least 20 months. One patient relapsed after a 20-month CR, but she was responsive to bendamustine therapy again, obtaining a further prolonged CR. Bendamustine as single agent or in combination could be a feasible therapeutic option in refractory/relapsed T-LGLL, especially for elderly patients because of its safety profile

Generation and in vivo validation of an IL-12 fusion protein based on a novel anti-human FAP monoclonal antibody

BACKGROUND In this study, we describe the generation of a fully human monoclonal antibody (named '7NP2') targeting human fibroblast activation protein (FAP), an antigen expressed in the microenvironment of different types of solid neoplasms. METHODS 7NP2 was isolated from a synthetic antibody phage display library and was improved by one round of mutagenesis-based affinity maturation. The tumor recognition properties of the antibody were validated by immunofluorescence procedures performed on cancer biopsies from human patients. A fusion protein consisting of the 7NP2 antibody linked to interleukin (IL)-12 was generated and the anticancer activity of the murine surrogate product (named mIL12-7NP2) was evaluated in mouse models. Furthermore, the safety of the fully human product (named IL12-7NP2) was evaluated in Cynomolgus monkeys. RESULTS Biodistribution analysis in tumor-bearing mice confirmed the ability of the product to selectively localize to solid tumors while sparing healthy organs. Encouraged by these results, therapy studies were conducted in vivo, showing a potent antitumor activity in immunocompetent and immunodeficient mouse models of cancer, both as single agent and in combination with immune checkpoint inhibitors. The fully human product was tolerated when administered to non-human primates. CONCLUSIONS The results obtained in this work provided a rationale for future clinical translation activities using IL12-7NP2