The urban sprawl dynamics: does a neural network understand the spatial logic better than a cellular automata?

Cellular Automata are usually considered the most efficient technology to understand the spatial logic of urban dynamics: they are inherently spatial, they are simple and computationally efficient and are able to represent a wide range of pattern and situations. Nevertheless the implementation of a CA requires the formulation of explicit spatial rules which represents the greatest limit of this approach. Whatever rich and complex the rules are, they don`t are able to capture satisfactorily the variety of the real processes. Recent developments in natural algorithms, and particularly in Artificial Neural Networks (ANN), allow to reverse the approach by learning the rules and the behaviours in urban land use dynamics directly from the Data Base, following a bottom-up process. The basic problem is to discover how and in to what extent the land use change of each cell i at time t+1 is determined by the neighbouring conditions (CA assumptions) or by other social, environmental, territorial features (i.e. political maps, planning rules) which where holding at the previous time t. Once the NN has learned the rules, it is able to predict the changes at time t+2 and following. In this paper we show and discuss the prediction capability of different architectures of supervised and unsupervised ANN. The Case study and Data Base concern the land use dynamics, between two temporal thresholds, in the South metropolitan area of Milan. The records have been randomly split in two sets which have been alternatively used in Training and in Testing phase in each ANN. The different ANNs performances have been evaluated with Statistical Functions. Finally, for the prediction, we have used the average of the prediction values of the 10 ANNs, and tested the results through the usual Statistical Functions.

A plant 3'-phosphoesterase involved in the repair of DNA strand breaks generated by oxidative damage.

Two novel, structurally and functionally distinct phosphatases have been identified through the functional complementation, by maize cDNAs, of an Escherichia coli diphosphonucleoside phosphatase mutant strain. The first, ZmDP1, is a classical Mg(2+)-dependent and Li(+)-sensitive diphosphonucleoside phosphatase that dephosphorylates both 3'-phosphoadenosine 5'-phosphate (3'-PAP) and 2'-PAP without any discrimination between the 3'- and 2'-positions. The other, ZmDP2, is a distinct phosphatase that also catalyzes diphosphonucleoside dephosphorylation, but with a 12-fold lower Li(+) sensitivity, a strong preference for 3'-PAP, and the unique ability to utilize double-stranded DNA molecules with 3'-phosphate- or 3'-phosphoglycolate-blocking groups as substrates. Importantly, ZmDP2, but not ZmDP1, conferred resistance to a DNA repairdeficient E. coli strain against oxidative DNA-damaging agents generating 3'-phosphate- or 3'-phosphoglycolate-blocked single strand breaks. ZmDP2 shares a partial amino acid sequence similarity with a recently identified human polynucleotide kinase 3'-phosphatase that is thought to be involved in DNA repair, but is devoid of 5'-kinase activity. ZmDP2 is the first DNA 3'-phosphoesterase thus far identified in plants capable of converting 3'-blocked termini into priming sites for reparative DNA polymerization

The urban sprawl dynamics: does a neural network understand the spatial logic better than a cellular automata?

Cellular Automata are usually considered the most efficient technology to understand the spatial logic of urban dynamics: they are inherently spatial, they are simple and computationally efficient and are able to represent a wide range of pattern and situations. Nevertheless the implementation of a CA requires the formulation of explicit spatial rules which represents the greatest limit of this approach. Whatever rich and complex the rules are, they don`t are able to capture satisfactorily the variety of the real processes. Recent developments in natural algorithms, and particularly in Artificial Neural Networks (ANN), allow to reverse the approach by learning the rules and the behaviours in urban land use dynamics directly from the Data Base, following a bottom-up process. The basic problem is to discover how and in to what extent the land use change of each cell i at time t+1 is determined by the neighbouring conditions (CA assumptions) or by other social, environmental, territorial features (i.e. political maps, planning rules) which where holding at the previous time t. Once the NN has learned the rules, it is able to predict the changes at time t+2 and following. In this paper we show and discuss the prediction capability of different architectures of supervised and unsupervised ANN. The Case study and Data Base concern the land use dynamics, between two temporal thresholds, in the South metropolitan area of Milan. The records have been randomly split in two sets which have been alternatively used in Training and in Testing phase in each ANN. The different ANNs performances have been evaluated with Statistical Functions. Finally, for the prediction, we have used the average of the prediction values of the 10 ANNs, and tested the results through the usual Statistical Functions

A selective alpha1D-adrenoreceptor antagonist inhibits human prostate cancer cell proliferation and motility "in vitro"

The progression of prostate cancer (PC) to a metastatic hormone refractory disease is the major contributor to the overall cancer mortality in men, mainly because the conventional therapies are generally ineffective at this stage. Thus, other therapeutic options are needed as alternatives or in addition to the classic approaches to prevent or delay tumor progression. Catecholamines participate to the control of prostate cell functions by the activation of alpha1-adrenoreceptors (alpha1-AR) and increased sympathetic activity has been linked to PC development and evolution. Molecular and pharmacological studies identified three alpha1-AR subtypes (A, B and D), which differ in tissue distribution, cell signaling, pharmacology and physiological role. Within the prostate, alpha1A-ARs mainly control stromal cell functions, while alpha1B- and alpha1D- subtypes seem to modulate glandular epithelial cell growth. The possible direct contribution of alpha1D-ARs in tumor biology is supported by their overexpression in PC. The studies here presented investigate the "in vitro" antitumor action of A175, a selective alpha1D-AR antagonist we have recently obtained by modifying the potent, but not subtype-selective alpha1-AR antagonist (S)-WB4101, in the hormone-refractory PC3 and DU145 PC cell lines. The results indicate that A175 has an alpha1D-AR-mediated significant and dose-dependent antiproliferative action that possibly involves the induction of G0/G1 cell cycle arrest, but not apoptosis. In addition, A175 reduces cell migration and adhesiveness to culture plates. In conclusion, our work clarified some cellular aspects promoted by alpha1D-AR activity modulation and supports a further pharmacological approach in the cure of hormone-refractory PC, by targeting specifically this AR subtype

Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor

An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein integrated with machine learning features was developed. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed to SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles, the analytical protocol involving a single-step sample incubation. Immunosensor performance was assessed by validation carried out in viral transfer medium, which is commonly used for de-sorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis: different support vector machine classifiers were evaluated proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, ML algorithm can be easily integrated into the developed cloud-based portable Wi-Fi device. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection

Soft Perfusable Device to Culture Skeletal Muscle 3D Constructs in Air

Devices for in vitro culture of three-dimensional (3D) skeletal muscle tissues have multiple applications, including tissue engineering and muscle-powered biorobotics. In both cases, it is crucial to recreate a biomimetic environment by using tailored scaffolds at multiple length scales and to administer prodifferentiative biophysical stimuli (e.g., mechanical loading). On the contrary, there is an increasing need to develop flexible biohybrid robotic devices capable of maintaining their functionality beyond laboratory settings. In this study, we describe a stretchable and perfusable device to sustain cell culture and maintenance in a 3D scaffold. The device mimics the structure of a muscle connected to two tendons: Tendon−Muscle−Tendon (TMT). The TMT device is composed of a soft (E ∼ 6 kPa) porous (pore diameter: ∼650 μm) polyurethane scaffold, encased within a compliant silicone membrane to prevent medium evaporation. Two tendon-like hollow channels interface the scaffold with a fluidic circuit and a stretching device. We report an optimized protocol to sustain C2C12 adhesion by coating the scaffold with polydopamine and fibronectin. Then, we show the procedure for the soft scaffold inclusion in the TMT device, demonstrating the device’s ability to bear multiple cycles of elongations, simulating a protocol for cell mechanical stimulation. By using computational fluid dynamic simulations, we show that a flow rate of 0.62 mL/min ensures a wall shear stress value safe for cells (<2 Pa) and 50% of scaffold coverage by an optimal fluid velocity. Finally, we demonstrate the effectiveness of the TMT device to sustain cell viability under perfusion for 24 h outside of the CO2 incubator. We believe that the proposed TMT device can be considered an interesting platform to combine several biophysical stimuli, aimed at boosting skeletal muscle tissue differentiation in vitro, opening chances for the development of muscle-powered biohybrid soft robots with long-term operability in real-world environments

Small molecule ligands for α9 * and α7 nicotinic receptors: A survey and an update, respectively

The α9- and α7-containing nicotinic acetylcholine receptors (nAChRs) mediate numerous physiological and pathological processes by complex mechanisms that are currently the subject of intensive study and debate. In this regard, selective ligands serve as invaluable investigative tools and, in many cases, potential therapeutics for the treatment of various CNS disfunctions and diseases, neuropathic pain, inflammation, and cancer. However, the present scenario differs significantly between the two aforementioned nicotinic subtypes. Over the past few decades, a large number of selective α7-nAChR ligands, including full, partial and silent agonists, antagonists, and allosteric modulators, have been described and reviewed. Conversely, reports on selective α9-containing nAChR ligands are relatively scarce, also due to a more recent characterization of this receptor subtype, and hardly any focusing on small molecules. In this review, we focus on the latter, providing a comprehensive overview, while providing only an update over the last five years for α7-nAChR ligands