The Effectiveness of Cyrene as a Solvent in Exfoliating 2D TMDs Nanosheets.

The pursuit of environmentally friendly solvents has become an essential research topic in sustainable chemistry and nanomaterial science. With the need to substitute toxic solvents in nanofabrication processes becoming more pressing, the search for alternative solvents has taken on a crucial role in this field. Additionally, the use of toxic, non-economical organic solvents, such as N-methyl-2 pyrrolidone and dimethylformamide, is not suitable for all biomedical applications, even though these solvents are often considered as the best exfoliating agents for nanomaterial fabrication. In this context, the success of producing two-dimensional transition metal dichalcogenides (2D TMDs), such as MoS2 and WS2, with excellent captivating properties is due to the ease of synthesis based on environment-friendly, benign methods with fewer toxic chemicals involved. Herein, we report for the first time on the use of cyrene as an exfoliating agent to fabricate monolayer and few-layered 2D TMDs with a versatile, less time-consuming liquid-phase exfoliation technique. This bio-derived, aprotic, green and eco-friendly solvent produced a stable, surfactant-free, concentrated 2D TMD dispersion with very interesting features, as characterized by UV-visible and Raman spectroscopies. The surface charge and morphology of the fabricated nanoflakes were analyzed using ς-potential and scanning electron microscopy. The study demonstrates that cyrene is a promising green solvent for the exfoliation of 2D TMD nanosheets with potential advantages over traditional organic solvents. The ability to produce smaller-sized-especially in the case of WS2 as compared to MoS2-and mono/few-layered nanostructures with higher negative surface charge values makes cyrene a promising candidate for various biomedical and electronic applications. Overall, the study contributes to the development of sustainable and environmentally friendly methods for the production of 2D nanomaterials for various applications.

Combating Actions of Green 2D-Materials on Gram Positive and Negative Bacteria and Enveloped Viruses.

Interactions of novel bi-dimensional nanomaterials and live matter such as bacteria and viruses represent an extremely hot topic due to the unique properties of the innovative nanomaterials, capable in some cases to exhibit bactericide and antiviral actions. The interactions between bacteria and viruses and two dimensional nanosheets are here investigated. We extensively studied the interaction between a gram-negative bacterium, Escherichia coli, and a gram-positive bacterium, Staphylococcus aureus, with two different types of 2D nanoflakes such as MoS2, belonging to the Transition Metal Dichalcogenides family, and Graphene Oxide. The same two types of nanomaterials were employed to study their antiviral action toward the Herpes simplex virus type-1, (HSV-1). The experimental results showed different bactericide impacts as well as different antiviral power between the two nanomaterials. The experimental findings were interpreted in bacteria on the base of the Derjaguin-Landau-Verwey-Overbeek theory. A simple kinetic model of bacterial growth in the presence of the interacting nanosheets is also elaborated, to explain the observed results. The experimental results in viruses are really novel and somewhat surprising, evidencing a stronger antiviral action of Graphene Oxide as compared to MoS2. Results in viruses are complicated to quantitatively interpret due to the complexity of the system under study, constituted by virus/host cell and nanoflake, and due to the lack of a well assessed theoretical context to refer to. Thus, these results are interpreted in terms of qualitative arguments based on the chemical properties of the interactors in the given solvent medium.

Assessing co-regulation of directly linked genes in biological networks using microarray time series analysis.

Differential expression of genes detected with the analysis of high throughput genomic experiments is a commonly used intermediate step for the identification of signaling pathways involved in the response to different biological conditions. The impact analysis was the first approach for the analysis of signaling pathways involved in a certain biological process that was able to take into account not only the magnitude of the expression change of the genes but also the topology of signaling pathways including the type of each interactions between the genes. In the impact analysis, signaling pathways are represented as weighted directed graphs with genes as nodes and the interactions between genes as edges. Edges weights are represented by a ß factor, the regulatory efficiency, which is assumed to be equal to 1 in inductive interactions between genes and equal to -1 in repressive interactions. This study presents a similarity analysis between gene expression time series aimed to find correspondences with the regulatory efficiency, i.e. the ß factor as found in a widely used pathway database. Here, we focused on correlations among genes directly connected in signaling pathways, assuming that the expression variations of upstream genes impact immediately downstream genes in a short time interval and without significant influences by the interactions with other genes. Time series were processed using three different similarity metrics. The first metric is based on the bit string matching; the second one is a specific application of the Dynamic Time Warping to detect similarities even in presence of stretching and delays; the third one is a quantitative comparative analysis resulting by an evaluation of frequency domain representation of time series: the similarity metric is the correlation between dominant spectral components. These three approaches are tested on real data and pathways, and a comparison is performed using Information Retrieval benchmark tools, indicating the frequency approach as the best similarity metric among the three, for its ability to detect the correlation based on the correspondence of the most significant frequency components.

Genomic comparison using data mining techniques based on a possibilistic fuzzy sets model.

Current copiousness of genomic information stored in biological databases [Mar Albà, M., Lee, M., Pearl, D., Shepherd, F.M.G., Martin, A.J., Orengo, N., Kellam, C.A., 2001. P. VIDA: a virus database system for the organisation of virus genome open reading frames. Nuleic Acids Res. 133-136] makes ultimately feasible the proposal for an application of knowledge management aimed to discover general rules in subcellular phenomena. The goal of this work is primarily to discover relationships between genes by microarray analysis. The tools exploited come from clustering techniques and are mainly based on Knowledge Discovery in Databases (KDD) concepts [Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., 1996. From data mining to knowledge discovery in databases. AI Magazine 17(3), 37-54]. Starting from a data set, each element can be represented by a characteristic matrix, which sums up all data attributes. In this case data mining is oriented to perform a Pattern Recognition of related sequences, hidden in databases [Hand, D.J., Nicholas, A., 2005. Heard finding groups in gene expression data. J. Biomed. Biotechnol. 215-225]. Following a bottom up approach, the next refinement is to compare retrieved data to gather similar features, by dedicated clustering algorithms [Kaufman, L., Rousseeuw, P.J., 1990. Finding groups in data. An Introduction to Cluster Analysis. John Wiley & Sons, New York; Forman, G., Zhang, B., 2000. Distributed Data clustering can be efficient and exact HP. Laboratories Palo Alto HPL-2000, p. 158], driven by fuzzy logic, allowing us to perceive by intuition a common denominator for various genomic families and to anticipate likely future developments.