Polymerization of styrene to isotactic polymer with MAO-Ni(acac)2. Examination of the factors that influence activity and stereospecificity

The soluble catalyst system methylaluminoxane (MAO)-Ni(acac), (acac: acetylacetonate) gives polystyrene consisting of an amorphous (aPS) and a crystalline isotactic (ips) fraction. Al(CH₃)₃, which is always present in commercial samples of MAO, decreases both polymer yield and stereospecificity. The polymer yield increases with increasing the MAO/Ni ratio but, at the same time, the iPS/aPS ratio decreases. Addition of N(C₂H₅)₃ (mole ratio N/Ni = 1) increases the proportion of the isotactic fraction, while it decreases the polymer yield. A tentative interpretation of the stereospecificity is reported.     

Multicategory classification via discrete support vector machines

Discrete support vector machines (DSVM), originally proposed for binary classification problems, have been shown to outperform other competing approaches on well-known benchmark datasets. Here we address their extension to multicategory classification, by developing three different methods. Two of them are based respectively on one-against-all and round-robin classification schemes, in which a number of binary discrimination problems are solved by means of a variant of DSVM. The third method directly addresses the multicategory classification task, by building a decision tree in which an optimal split to separate classes is derived at each node by a new extended formulation of DSVM. Computational tests on publicly available datasets are then conducted to compare the three multicategory classifiers based on DSVM with other methods, indicating that the proposed techniques achieve significantly higher accuracies. This research was partially supported by PRIN grant 2004132117.     

K18- and CAG-hACE2 Transgenic Mouse Models and SARS-CoV-2: Implications for Neurodegeneration Research

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global pandemic that might lead to very serious consequences. Notably, mental status change, brain confusion, and smell and taste disorders along with neurological complaints have been reported in patients infected with SARS-CoV-2. Furthermore, human brain tissue autopsies from COVID-19 patients show the presence of SARS-CoV-2 neuroinvasion, which correlates with the manifestation of meningitis, encephalitis, leukocyte infiltration, and neuronal damage. The olfactory mucosa has been suggested as a way of entry into the brain. SARS-CoV-2 infection is also known to provoke a hyper-inflammatory reaction with an exponential increase in the production of pro-inflammatory cytokines leading to systemic responses, even in the absence of direct infection of brain cells. Angiotensin-converting enzyme 2 (ACE2), the entry receptor of SARS-CoV-2, has been extensively demonstrated to be present in the periphery, neurons, and glial cells in different brain regions. To dissect the details of neurological complications and develop therapies helping COVID-19 survivors regain pre-infection quality of life, the development of robust clinical models is highly warranted. Several human angiotensin-converting enzyme 2 (hACE2) transgenic mouse models have been developed and used for antiviral drug screening and vaccine development, as well as for better understanding of the molecular pathogenetic mechanisms of SARS-CoV-2 infection. In this review, we summarize recent results from the studies involving two such mouse models, namely K18- and CAG-hACE2 transgenics, to evaluate the direct and indirect impact of SARS-CoV-2 infection on the central nervous system.     

A Silver Monochrome “Concetto spaziale” by Lucio Fontana: A Spectroscopic Non- and Micro-Invasive Investigation of Materials

In several of his artworks, for instance the Venezie cycle, Fontana employed metallic paints; previous investigations on such materials highlighted the use of different synthetic binders and of thick paint layers below the metal one, having different colours to change the visual perception of the metallic surface. In the present work, a monochrome silver “Concetto spaziale” by the Italo–Argentine artist belonging to a private collection recently gifted to the museum of the Church of San Fedele in Milano, Italy, was investigated to deepen the knowledge of this particular group of Fontana’s paintings. The artwork was initially visually inspected in visible and ultraviolet (UV) light. Subsequently, a non-invasive spectroscopic investigation was performed by X-ray fluorescence (XRF), reflection Fourier-transform infrared (FTIR) and Raman spectroscopy. A minute fragment of silver-coloured paint was taken from the reverse of the painting, near the cut edge, and examined by scanning electron microscopy coupled with energy dispersive X-ray analysis (SEM-EDX) and micro-Fourier-transform Raman (FT-Raman) spectroscopy. The analytical data made it possible to identify the composition of the metallic paint layer and of the underlying dark one, both from the point of view of the pigments and of the binders used, also highlighting the potential of the non-invasive and micro-invasive methods adopted in the investigation.     

Gemini pyridinium surfactants: synthesis and conductometric study of a novel class of amphiphiles1

A new series of pyridinium cationic gemini surfactants was prepared by quaternization of the 2,2'-(alpha,omega-alkanediyl)bispyridines with N-alkylating agents, whose reactivity is briefly discussed. Particularly useful was the use of long-chain alkyl triflates (trifluoromethanesulfonates) for both overcoming the sterical hindrance in the pyridines and obtaining higher synthetic yields. Well-known 4,4'-(alpha,omega-alkanediyl)bis(1-alkylpyridinium) structures showed narrow temperature ranges for practical applications, due to their high Krafft points, while the new 2,2'-(alpha,omega-alkanediyl)bis(1-alkylpyridinium) series, accounted for good surface active properties. Due to the Krafft points below 0 degrees C, they could be exploited as solutions in water at any temperature. The characterization of the behavior of the series was performed by conductivity measurements. Some of the proposed structures exhibited unusual surface active behavior, which was interpreted in terms of particular conformational arrangements.     

4‐Naphthylmethyl Proline Forms a Channel Structure

Proline and proline derivatives are useful tools to control the structural properties of peptides and proteins, and thereby modulate numerous processes. Here, we show that proline derivatives can have unique structural properties in the solid state by presenting the crystal structure of zwitterionic (2S,4S)/(2S,4R)‐4‐[(naphthalen‐2‐yl)methyl]proline (H‐Nap‐OH). This amphiphilic proline derivative forms a columnar structure around large hydrophilic and small hydrophobic channels with diameters of 9 Å and 4 Å, respectively. We show that this architecture, which is unprecedented for amino acids, results from the combination of a hydrogen bond network between the ammonium and carboxylate moieties and π–π as well as CH–π interactions between the aromatic moieties.     

Charge renormalization in planar and spherical charged lipidic aqueous interfaces

The charge renormalization in planar and spherical charged lipidic aqueous interfaces has been investigated by means of thermodynamic and electrokinetic measurements. We analyzed the behavior of mixed DOTAP/DOPE monolayers at the air-electrolyte solution interface and DOTAP/DOPE liposomes 100 nm in size dispersed in an aqueous phase of varying ionic strength. For the two systems, we have compared the "effective" surface charge derived from the measurements of surface potential and zeta-potential to the "bare" charge based on the stoichiometry of the lipid mixture investigated. The results confirm that a strong charge renormalization occurs, whose strength depends on the geometry of the mesoscopic system. The dependence of the "effective" charge on the "bare" charge is discussed in light of an analytical approximation based on the Poisson-Boltzmann equation recently proposed.     

Thermodynamic properties of the aqueous solution of potassium salts of some 4-((alkylcarbonyl)amino)-2-hydroxybenzoic acids at 298 and 313 K

To understand the aggregation behavior of surface-active ligands with a salycilic polar head, we undertook a systematic study of some classes of anionic surfactants where the presence and the position of the -OH and the carboxylic group differ. This paper reports the dilution heats at 298 and 313 K of aqueous solutions of potassium 4-((alkylcarbonyl)amino)-2-hydroxybenzoate (KPAS-C(n) where n stands for the number of carbon atoms in the chain) in KOH at 0.1 m, measured as a function of concentration. From the experimental data, apparent and partial molar enthalpies vs concentration were obtained. By using a pseudo-phase-transition approach, the enthalpy changes upon micelle formation (DeltaH(m)) and assuming that in the restricted range of temperature examined heat capacities are constant, the heat capacity changes have been obtained. Micelle formation enthalpies are seen to be additive with a group contribution for the methylene group of -1.5+/-0.1 kJ mol(-1) per group at 298 K and -2.3+/-0.1 kJ mol(-1) per group at 313 K, comparable with that obtained for similar anionic compounds in the same experimental conditions and for N-alkylnicotinamide chlorides (cationic surfactants). The -CH(2)- group contribution to the micelle formation heat capacities is -53+/-1 J K(-1) mol(-1).     

Accurately learning from few examples with a polyhedral classifier

In the context of learning theory many efforts have been devoted to developing classification algorithms able to scale up with massive data problems. In this paper the complementary issue is addressed, aimed at deriving powerful classification rules by accurately learning from few data. This task is accomplished by solving a new mixed integer programming model that extends the notion of discrete support vector machines, in order to derive an optimal set of separating hyperplanes for binary classification problems. According to the cardinality of the set of hyperplanes, the classification region may take the form of a convex polyhedron or a polytope in the original space where the examples are defined. Computational tests on benchmark datasets highlight the effectiveness of the proposed model, that yields the greatest accuracy when compared to other classification approaches. This research was partially supported by PRIN grant 2004132117.