Quelques considérations sur la structure électronique du naphtalène

Theπ electronic structure of naphtalene is studied, using a semi-empirical approximation for coulombic and resonance integrals; forH andS (H, hamiltonian;S, overlap) the complete matrices are adopted, together withKohlrausch's nuclear effective charges. A non uniform charge distribution is obtained, with charge accumulation at the tertiary carbons. Bond orders between non neighboring atoms indicate contributions to the fundamental state from other structures besides the classical ones. A comparative study of the obtained interatomic distances is carried. It is shown that [H, S]=0 is not a sufficient condition for the identity of the bond orders for the fundamental state calculated with and without overlap.     

Beyond generative models: superfast traversal,optimization, novelty,exploration and discovery (STONED) algorithm for molecules using SELFIES

Inverse design allows the generation of molecules with desirable physical quantities using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. First, we achieve non-trivial performance on typical benchmarks for generative models without any training. Additionally, we demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. Overall, we anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wider adoption.

Interpolation and exploration within the chemical space for inverse design.     

Capillary gas chromatographic separation of C10–C12 secondary phenylalkanes on modified cyclodextrin stationary phases

The separation of isomers and enantiomers of branched C₁₀-C₁₂ phenylalkanes by gas chromatography on fused silica capillary columns coated with some modified β- and γ-cyclodextrins was studied. It was shown that the separation of positional isomers of C₁₀-C₁₂ phynylalkanes on modified cyclodextrin capillary columns is not better than that on a column coated with modified polyethylene glycol. Differences were found in the enantioselectivity of modified β- and γ-cyclodextrins for the separation of C₁₀-C₁₂ secondary phenylalkane enantiomers. While alkylderivatives of β-CDs resolve enantiomers of 3-phenylalkanes, alkyl derivatives of γ-CD resolve enantiomers of 2-phenylalkanes. Since shape selectivity factors of modified cyclodextrins have indicated no inclusion of the considered solutes in cyclodextrin cavities, enantioselective interactions most probably occur on the outer sphere of cyclodextrins.     

Time evolution of infinite classical systems with singular,long range,two body interactions

Existence of dynamics for infinitely many hard-spheres inv dimensions is proven in a set of full equilibrium measure.Singular unbounded perturbations are considered with pair potentials diverging as (x – a)^–, >2 anda is the hard-core diameter. Long range forces are allowed with potentials decreasing at infinity asx , <v. The result corrects and generalizes a proof given in a previous paper by the same authors.Research partially supported by a CNR fellowship Posit. 204530.Research partially supported by a CNR fellowship.     

An Extensive Assessment of Network Embedding in PPI Network Alignment

Network alignment is a fundamental task in network analysis. In the biological field, where the protein–protein interaction (PPI) is represented as a graph, network alignment allowed the discovery of underlying biological knowledge such as conserved evolutionary pathways and functionally conserved proteins throughout different species. A recent trend in network science concerns network embedding, i.e., the modelling of nodes in a network as a low-dimensional feature vector. In this survey, we present an overview of current PPI network embedding alignment methods, a comparison among them, and a comparison to classical PPI network alignment algorithms. The results of this comparison highlight that: (i) only five network embeddings for network alignment algorithms have been applied in the biological context, whereas the literature presents several classical network alignment algorithms; (ii) there is a need for developing an evaluation framework that may enable a unified comparison between different algorithms; (iii) the majority of the proposed algorithms perform network embedding through matrix factorization-based techniques; (iv) three out of five algorithms leverage external biological resources, while the remaining two are designed for domain agnostic network alignment and tested on PPI networks; (v) two algorithms out of three are stated to perform multi-network alignment, while the remaining perform pairwise network alignment.     

Inhibition of Bacterial Adhesion and Biofilm Formation by Seed-Derived Ethanol Extracts from Persea americana Mill

The increase in antibiotic resistance demands innovative strategies to combat microorganisms. The current study evaluated the antibacterial and antivirulence effects of ethanol extracts from Persea americana seeds obtained by the Soxhlet (SE) and maceration (MaE) methods. The UHPLC-DAD-QTOF analysis showed mainly the presence of polyphenols and neolignan. Ethanol extracts were not cytotoxic to mammalian cells (CC₅₀ > 500 µg/mL) and displayed a moderate antibacterial activity against Pseudomonas aeruginosa (IC₅₀ = 87 and 187 µg/mL) and Staphylococcus aureus (IC₅₀ = 144 and 159 µg/mL). Interestingly, no antibacterial activity was found against Escherichia coli. SE and MaE extracts were also able to significantly reduce the bacterial adhesion to A549 lung epithelial cells. Additionally, both extracts inhibited the biofilm growth at 24 h and facilitated the release of internal cell components in P. aeruginosa, which might be associated with cell membrane destabilization. Real-time PCR and agarose electrophoresis gel analysis indicated that avocado seed ethanol extracts (64 µg/mL) downregulated virulence-related factors such as mexT and lasA genes. Our results support the potential of bioproducts from P. americana seeds as anti-adhesive and anti-biofilm agents.     

Copper Binding and Oligomerization Studies of the Metal Resistance Determinant CrdA from Helicobacter pylori

Within this research, the CrdA protein from Helicobacter pylori (HpCrdA), a putative copper-binding protein important for the survival of bacterium, was biophysically characterized in a solution, and its binding affinity toward copper was experimentally determined. Incubation of HpCrdA with Cu(II) ions favors the formation of the monomeric species in the solution. The modeled HpCrdA structure shows a conserved methionine-rich region, a potential binding site for Cu(I), as in the structures of similar copper-binding proteins, CopC and PcoC, from Pseudomonas syringae and from Escherichia coli, respectively. Within the conserved amino acid motif, HpCrdA contains two additional methionines and two glutamic acid residues (MMXEMPGMXXMXEM) in comparison to CopC and PcoC but lacks the canonical Cu(II) binding site (two His) since the sequence has no His residues. The methionine-rich site is in a flexible loop and can adopt different geometries for the two copper oxidation states. It could bind copper in both oxidation states (I and II), but with different binding affinities, micromolar was found for Cu(II), and less than nanomolar is proposed for Cu(I). Considering that CrdA is a periplasmic protein involved in chaperoning copper export and delivery in the H. pylori cell and that the affinity of the interaction corresponds to a middle or strong metal–protein interaction depending on the copper oxidation state, we conclude that the interaction also occurs in vivo and is physiologically relevant for H. pylori.     

Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Estimation

On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, Rt has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an Rt estimation by adjusting the data produced by the output layer of the NN on the related Rt estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the Rt as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the Rt. Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively.     

New aspects of gold nanorod formation via seed-mediated method

Gold nanorods (AuNRs) were obtained via a wet chemistry technique, in aqueous medium, employing crystallisation seeds. The kinetics of formation, the aspect ratio, and the selectivity of the particles were evaluated according to the parameters of synthesis: the growth-driving agent, seed, and gold precursor concentrations. In 2–4 h, the rod particles attained the expected size and shape under kinetic control, and were stable for at least 2 days. In order to obtain good quality AuNRs in good yields, without enrichment, we suggest keeping the growth-driving agent/gold molar ratio, the Au^I/seed ratio, and the concentration of the reagents in the final solution within specific ranges. For example, even if good molar ratios between the reagents are maintained, relatively highly concentrated reaction solutions lead to AuNRs with lower aspect ratios. The main properties of the prepared colloidal systems and the nanoparticles were evaluated by UV–vis spectroscopy and transmission electron microscopy, respectively.