Elman neural networks and time integration for object recognition

We consider a system based on an Elman network for a categorization task. Four objects are investigated by an automa walking around in circles. The shapes are derived from four version of a cross: square, thick cross, critical cross and thin cross. Therefore, the input of the system is represented by the distance-wave relieved by the sensor at each step. We let several parameters vary: starting point and speed of the automa walk, radius of the circle and size of the shape. The system is trained using a back-propagation algorithm. We describe the complete setup of the parameters and noises, which the automa will have to face for the prediction/categorization task

First constraints of dense molecular gas at z~7.5 from the quasar P\=oniu\=a'ena

We report the detection of CO(6-5) and CO(7-6) and their underlying continua from the host galaxy of quasar J100758.264+211529.207 (P\=oniu\=a'ena) at z=7.5419, obtained with the NOrthern Extended Millimeter Array (NOEMA). P\=oniu\=a'ena belongs to the HYPerluminous quasars at the Epoch of ReionizatION (HYPERION) sample of 17 $z>6$ quasars selected to be powered by supermassive black holes (SMBH) which experienced the fastest mass growth in the first Gyr of the Universe. The one reported here is the highest-redshift measurement of the cold and dense molecular gas to date. The host galaxy is unresolved and the line luminosity implies a molecular reservoir of $\rm M(H_2)=(2.2\pm0.2)\times 10^{10}$ $\rm M_\odot$, assuming a CO spectral line energy distribution typical of high-redshift quasars and a conversion factor $\alpha=0.8$ $\rm M_{\odot} (K\,km \, s^{-1} \,pc^{2})^{-1}$. We model the cold dust spectral energy distribution (SED) to derive a dust mass of M$_{\rm dust} =(2.1\pm 0.7)\times 10^8$ $\rm M_\odot$, and thus a gas to dust ratio $\sim100$. Both the gas and dust mass are not dissimilar from the reservoir found for luminous quasars at $z\sim6$. We use the CO detection to derive an estimate of the cosmic mass density of $\rm H_2$, $\Omega_{H_2} \simeq 1.31 \times 10^{-5}$. This value is in line with the general trend suggested by literature estimates at $z < 7$ and agrees fairly well with the latest theoretical expectations of non-equilibrium molecular-chemistry cosmological simulations of cold gas at early times.Comment: Submitted to ApJ Letter

Accurate dust temperature and star formation rate in the most luminous z>6z>6 quasar in the HYPerluminous quasars at the Epoch of ReionizatION (HYPERION) sample

We present ALMA Band 9 continuum observation of the ultraluminous quasi-stellar object (QSO) SDSS J0100+2802, providing a $\sim 10\sigma$ detection at $\sim 670$ GHz. SDSS J0100+2802 is the brightest QSO with the most massive super massive black hole (SMBH) known at $z>6$, and we study its dust spectral energy distribution in order to determine the dust properties and the star formation rate (SFR) of its host-galaxy. We obtain the most accurate estimate so far of the temperature, mass and emissivity index of the dust, having $T_{\rm dust}=48.4\pm2.3$ K, $M_{\rm dust}=(2.29\pm0.83)\times 10^7$ M$_\odot$, $\beta=2.63\pm 0.23$. This allows us to measure the SFR with the smallest statistical error for this QSO, SFR$=265\pm 32\ \rm M_\odot yr^{-1}$. Our results enable us to evaluate the relative growth of the SMBH and host galaxy of J0100+2802, finding that the SMBH is dominating the process of BH-galaxy growth in this QSO at $z=6.327$, when the Universe was $865$ Myr old. Such unprecedented constraints on the host galaxy SFR and dust temperature can only be obtained through high frequency observations, and highlight the importance of ALMA Band 9 to obtain a robust overview of the build-up of the first quasars' host galaxies at $z>6$.Comment: 10 pages, 4 figures, 1 table. Accepted for publication in ApJ

Interrupting the nitrosative stress fuels tumor-specific cytotoxic T lymphocytes in pancreatic cancer

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors owing to its robust desmoplasia, low immunogenicity, and recruitment of cancer-conditioned, immunoregulatory myeloid cells. These features strongly limit the success of immunotherapy as a single agent, thereby suggesting the need for the development of a multitargeted approach. The goal is to foster T lymphocyte infiltration within the tumor landscape and neutralize cancer-triggered immune suppression, to enhance the therapeutic effectiveness of immune-based treatments, such as anticancer adoptive cell therapy (ACT). METHODS: We examined the contribution of immunosuppressive myeloid cells expressing arginase 1 and nitric oxide synthase 2 in building up a reactive nitrogen species (RNS)-dependent chemical barrier and shaping the PDAC immune landscape. We examined the impact of pharmacological RNS interference on overcoming the recruitment and immunosuppressive activity of tumor-expanded myeloid cells, which render pancreatic cancers resistant to immunotherapy. RESULTS: PDAC progression is marked by a stepwise infiltration of myeloid cells, which enforces a highly immunosuppressive microenvironment through the uncontrolled metabolism of L-arginine by arginase 1 and inducible nitric oxide synthase activity, resulting in the production of large amounts of reactive oxygen and nitrogen species. The extensive accumulation of myeloid suppressing cells and nitrated tyrosines (nitrotyrosine, N-Ty) establishes an RNS-dependent chemical barrier that impairs tumor infiltration by T lymphocytes and restricts the efficacy of adoptive immunotherapy. A pharmacological treatment with AT38 ([3-(aminocarbonyl)furoxan-4-yl]methyl salicylate) reprograms the tumor microenvironment from protumoral to antitumoral, which supports T lymphocyte entrance within the tumor core and aids the efficacy of ACT with telomerase-specific cytotoxic T lymphocytes. CONCLUSIONS: Tumor microenvironment reprogramming by ablating aberrant RNS production bypasses the current limits of immunotherapy in PDAC by overcoming immune resistance

ELMAN NEURAL NETWORKS AND TIME INTEGRATION FOR OBJECT RECOGNITION

ABSTRACT. We consider a system based on an Elman network for a categorization task. Four objects are investigated by an automa walking around in circles. The shapes are derived from four version of a cross: square, thick cross, critical cross and thin cross. Therefore, the input of the system is represented by the distance-wave relieved by the sensor at each step. We let several parameters vary: starting point and speed of the automa walk, radius of the circle and size of the shape. The system is trained using a back-propagation algorithm. We describe the complete setup of the parameters and noises, which the automa will have to face for the prediction/categorization task

Clima e dintorni. Giustizia ambientale e lotta al cambiamento climatico

Il volume curato dal Presidente dell’ISPRA Stefano Laporta, coadiuvato da Gianfranco G. Nucera, Giulietta Rak e Francesca Zappacosta, raccoglie i contributi scientifici presentati nel contesto di un corso universitario presso la Sapienza Università di Roma nella Facoltà di Scienze politiche, nell’anno accademico 2020/2021. Il titolo del volume: “Clima e dintorni. Giustizia ambientale e lotta al cambiamento climatico”, rappresenta con efficace sintesi i contenuti scientifici offerti da autori di varia formazione scientifica ed esperienza professionale, che hanno affrontato con passione civile e competenza tecnica la tematica del climate change e prospettato la necessità di interventi incisivi e consapevoli della complessità ed interazione dei fenomeni da affrontare

General differential Hebbian learning: Capturing temporal relations between events in neural networks and the brain.

Learning in biologically relevant neural-network models usually relies on Hebb learning rules. The typical implementations of these rules change the synaptic strength on the basis of the co-occurrence of the neural events taking place at a certain time in the pre- and post-synaptic neurons. Differential Hebbian learning (DHL) rules, instead, are able to update the synapse by taking into account the temporal relation, captured with derivatives, between the neural events happening in the recent past. The few DHL rules proposed so far can update the synaptic weights only in few ways: this is a limitation for the study of dynamical neurons and neural-network models. Moreover, empirical evidence on brain spike-timing-dependent plasticity (STDP) shows that different neurons express a surprisingly rich repertoire of different learning processes going far beyond existing DHL rules. This opens up a second problem of how capturing such processes with DHL rules. Here we propose a general DHL (G-DHL) rule generating the existing rules and many others. The rule has a high expressiveness as it combines in different ways the pre- and post-synaptic neuron signals and derivatives. The rule flexibility is shown by applying it to various signals of artificial neurons and by fitting several different STDP experimental data sets. To these purposes, we propose techniques to pre-process the neural signals and capture the temporal relations between the neural events of interest. We also propose a procedure to automatically identify the rule components and parameters that best fit different STDP data sets, and show how the identified components might be used to heuristically guide the search of the biophysical mechanisms underlying STDP. Overall, the results show that the G-DHL rule represents a useful means to study time-sensitive learning processes in both artificial neural networks and brain

Helicobacter pylori Induces Apoptosis of Human Monocytes but Not Monocyte-Derived Dendritic Cells: Role of the cag Pathogenicity Island

Monocytes are circulating precursors of the dendritic cell subset, professional antigen-presenting cells with a unique ability to initiate the innate and adaptive immune response. In this study, we have investigated the effects of wild-type Helicobacter pylori strains and their isogenic mutants with mutations in known bacterial virulence factors on monocytes and monocyte-derived dendritic cells. We show that H. pylori strains induce apoptosis of human monocytes by a mechanism that is dependent on the expression of a functional cag pathogenicity island. This effect requires an intact injection organelle for direct contact between monocytes and the bacteria but also requires a still-unidentified effector that is different from VacA or CagA. The exposure of in vitro-generated monocyte-derived dendritic cells to H. pylori stimulates the release of inflammatory cytokines by a similar mechanism. Of note is that dendritic cells are resistant to H. pylori-induced apoptosis. These phenomena may play a critical role in the evasion of the immune response by H. pylori, contributing to the persistence of the infection