Total Synthesis of (–)-Anaferine: A Further Ramification in a Diversity-Oriented Approach

The piperidine ring is a widespread motif in several natural bioactive alkaloids of both vegetal and marine origin. In the last years, a diversity-oriented synthetic (DOS) approach, aimed at the generation of a library of piperidine-based derivatives, was developed in our research group, employing commercially available 2-piperidine ethanol as a versatile precursor. Here, we report the exploration of another ramification of our DOS approach, that led us to the stereoselective total synthesis of (\u2013)-anaferine, a bis-piperidine alkaloid present in Withania somnifera extract. This natural product was obtained in 9% overall yield over 13 steps, starting from a key homoallylic alcohol previously synthesised in our laboratory. Therefore, the collection of piperidine-derivatives accessible from 2-piperidine ethanol was enriched with a new, diverse scaffold

Intercomparison Exercise for Heavy Metals in PM10

The Joint Research Centre (JRC) has carried out an Intercomparison Exercise (IE) for the determination of heavy metals in particulate matter (PM10). The IE focussed on Lead (Pb), Arsenic (As), Nickel (Ni) and Cadmium (Cd), the heavy metals regulated by the 1st and 4th Daughter Directives for Air Pollution. Copper (Cu), Chromium (Cr) and Zinc (Zn), the elements included in the EMEP programme together with Aluminium (Al), Cobalt (Co), Iron (Fe), Manganese (Mn) and Vanadium (V) were also tested. Fourteen Laboratories, generally members of the Network of Air Quality Reference Laboratories (AQUILA), participated in the IE. The participants mainly used microwave digestion with nitric acid and hydrogen peroxide and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) or Graphite Furnace Atomic Absorption Spectrometry (GF-AAS) for analysis as recommended in the reference method (EN 14902). However, a few participants used other methods: Energy Dispersive X-ray Fluorescence (EDXRF), Atomic Emission Spectrometry (ICP-AES) and Voltammetry for analysis and vaporisation on hot plate before microwave digestion, Soxhlet extraction, high pressure or cold Hydrogen Fluoride methods for digestion. Each participant received 5 samples to be analysed: a liquid sample prepared by dilution of a Certified Reference Material (CRM), a solution of a dust CRM sample digested by the JRC13F, a sub-sample of a dust CRM that each participating laboratory had to digest and analyse, a solution prepared by JRC after digestion of an exposed filter and a pair of filters (one blank filter and one exposed filter) to be digested and analysed by each participant. For 89 % of all types of samples, the DQOs of the 1st and 4th European Directives (uncertainty of 25 % for Pb and 40 % for As, Cd and Ni) were met. All together, this is a very good score. The best results were obtained for the liquid CRM, dust CRM digested by JRC, dust CRM and filter digested by JRC with 92, 90, 96 and 93 % of DQOs being met, respectively. It was found that the DQOs were not met if the difference of acidity between test samples and participant calibration standards was high. Conversely, only 76 % of DQOs were met for the filter to be digested by each participant with (about 85 % for Cd and Ni, 73/64 % for Pb and As, the most difficult element to determine). The worst results were associated with special events: explosion in microwave oven during digestion for two participants, a wrong dilution factor used by one participant and a huge contamination in the blank filter for another participant. Among the two explosions, one of them was probably the effect of a lack of temperature control in the digestion vessel. For the other explosion, the microwave digestion and the digestion program advised by EN 14902 is to be questioned. Moreover, satisfactory results were obtained using Soxhlet extraction, high pressure method and cold Hydrogen Fluoride digestion methods which are not presented in EN 14902. The DQOs of As and Cd could not be met with EDXRF whose limit of detection was too high for these two elements and for Cd using Voltammetry which suffered a strong interference for this element. Regarding the methods of analysis, apart the points mentioned just before about EDXRF and Voltammetry, good results were observed using ICP-OES for Cd, Ni and Pb. A few discrepancies were also registered for GF-AAS and ICP-MS but they were created by the special events or acidity problem mentioned before. This shows that even though GF-AAS and ICP-MS are found suitable, the implementation by each participant may be responsible for important mistakes.JRC.H.4-Transport and air qualit

Range Restriction to Harden CNNs Against Hardware Faults: A Broad Empirical Analysis

Due to the increasing use of Deep Learning in mission/safety-critical application contexts, in the recent past several techniques have been designed to harden the system to guarantee its correct behavior even in presence of faults affecting the hardware. Often, such new techniques are evaluated on a reduced set of Convolutional Neural Network (CNN) models and/or data sets, such that their generality and robustness could actually be limited. This paper presents a broad and systematic experimental evaluation of a state-of-the-art range restriction technique presented in literature, i) by applying it to a large set of CNNs, implementing different functional tasks, and ii) by using multiple datasets. The obtained results demonstrate that the effectiveness of the technique highly depends on the complexity of the considered task; in particular, classification CNNs benefit the most, while regression, image segmentation, and object detection are subject to different levels of benefits

Requirement of a Membrane Potential for the Posttranslational Transfer of Proteins into Mitochondsria

Posttranslational transfer of most precursor proteins into mitochondria is dependent on energization of the mitochondria. Experiments were carried out to determine whether the membrane potential or the intramitochondrial ATP is the immediate energy source. Transfer in vitro of precursors to the ADP/ATP carrier and to ATPase subunit 9 into isolated Neurospora mitochondria was investigated. Under conditions where the level of intramitochondrial ATP was high and the membrane potential was dissipated, import and processing of these precursor proteins did not take place. On the other hand, precursors were taken up and processed when the intramitochondrial ATP level was low, but the membrane potential was not dissipated. We conclude that a membrane potential is involved in the import of those mitochondrial precursor proteins which require energy for intracellular translocatio

Stereoselective Synthetic Strategies to (-)-Cannabidiol

(-)-Cannabidiol (CBD) is a non-psychoactive compound that has already found many medical applications, from the treatment of epilepsy to other neurological disorders. (-)-CBD is usually extracted from Cannabis Sativa, but unfortunately, its isolation among many other structurally related cannabinoids can be challenging. This, along with the increased demand for (-)-CBD, prompted chemists to come up with synthetic strategies that could afford this cannabinoid in good yield and high enantiopurity. Herein, we aim to review the fundamental strategies employed in the stereoselective synthesis of (-)-cannabidiol, spanning from classic approaches to automated ones, highlighting the challenges encountered in the total synthesis of this cannabinoid

Retargeted adenoviruses for radiation-guided gene delivery

The combination of radiation with radiosensitizing gene delivery or oncolytic viruses promises to provide an advantage that could improve the therapeutic results for glioblastoma. X-rays can induce significant molecular changes in cancer cells. We isolated the GIRLRG peptide that binds to radiation-inducible 78 kDa glucose-regulated protein (GRP78), which is overexpressed on the plasma membranes of irradiated cancer cells and tumor-associated microvascular endothelial cells. The goal of our study was to improve tumor-specific adenovirus-mediated gene delivery by selectively targeting the adenovirus binding to this radiation-inducible protein. We employed an adenoviral fiber replacement approach to conduct a study of the targeting utility of GRP78-binding peptide. We have developed fiber-modified adenoviruses encoding the GRP78-binding peptide inserted into the fiber-fibritin. We have evaluated the reporter gene expression of fiber-modified adenoviruses in vitro using a panel of glioma cells and a human D54MG tumor xenograft model. The obtained results demonstrated that employment of the GRP78-binding peptide resulted in increased gene expression in irradiated tumors following infection with fiber-modified adenoviruses, compared with untreated tumor cells. These studies demonstrate the feasibility of adenoviral retargeting using the GRP78-binding peptide that selectively recognizes tumor cells responding to radiation treatment

Stimulus-responsive liposomes for biomedical applications

Liposomes are amphipathic lipidic supramolecular aggregates that are able to encapsulate and carry molecules of both hydrophilic and hydrophobic nature. They have been widely used as in vivo drug delivery systems for some time because they offer features such as synthetic flexibility, biodegradability, biocompatibility, low immunogenicity, and negligible toxicity. In recent years, the chemical modification of liposomes has paved the way to the development of smart liposome-based drug delivery systems, which are characterized by even more tunable and disease-directed features. In this review, we highlight the different types of chemical modification introduced to date, with a particular focus on internal stimuli-responsive liposomes and prodrug activation

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Extending OpenStack Monasca for Predictive Elasticity Control

Traditional auto-scaling approaches are conceived as reactive automations, typically triggered when predefined thresholds are breached by resource consumption metrics. Managing such rules at scale is cumbersome, especially when resources require non-negligible time to be instantiated. This paper introduces an architecture for predictive cloud operations, which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the system. In this way, they can anticipate load peaks and trigger appropriate scaling actions in advance, such that new resources are available when needed. The proposed architecture is implemented in OpenStack, extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard metrics. We use our architecture to implement predictive scaling policies leveraging on linear regression, autoregressive integrated moving average, feed-forward, and recurrent neural networks (RNN). Then, we evaluate their performance on a synthetic workload, comparing them to those of a traditional policy. To assess the ability of the different models to generalize to unseen patterns, we also evaluate them on traces from a real content delivery network (CDN) workload. In particular, the RNN model exhibites the best overall performance in terms of prediction error, observed client-side response latency, and forecasting overhead. The implementation of our architecture is open-source

Predictive auto-scaling with OpenStack Monasca

Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a predefined threshold. Tuning these rules becomes particularly cumbersome when scaling-up a cluster involves non-negligible times to bootstrap new instances, as it happens frequently in production cloud services. To deal with this problem, we propose an architecture for auto-scaling cloud services based on the status in which the system is expected to evolve in the near future. Our approach leverages on time-series forecasting techniques, like those based on machine learning and artificial neural networks, to predict the future dynamics of key metrics, e.g., resource consumption metrics, and apply a threshold-based scaling policy on them. The result is a predictive automation policy that is able, for instance, to automatically anticipate peaks in the load of a cloud application and trigger ahead of time appropriate scaling actions to accommodate the expected increase in traffic. We prototyped our approach as an open-source OpenStack component, which relies on, and extends, the monitoring capabilities offered by Monasca, resulting in the addition of predictive metrics that can be leveraged by orchestration components like Heat or Senlin. We show experimental results using a recurrent neural network and a multi-layer perceptron as predictor, which are compared with a simple linear regression and a traditional non-predictive auto-scaling policy. However, the proposed framework allows for the easy customization of the prediction policy as needed