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Drug–target interaction is crucial in the discovery of new drugs. Computational methods can be used to identify new drug–target interactions at low costs and with reasonable accuracy. Recent studies pay more attention to machine-learning methods, ranging from matrix factorization to deep learning, in the DTI prediction. Since the interaction matrix is often extremely sparse, DTI prediction performance is significantly decreased with matrix factorization-based methods. Therefore, some matrix factorization methods utilize side information to address both the sparsity issue of the interaction matrix and the cold-start issue. By combining matrix factorization and autoencoders, we propose a hybrid DTI prediction model that simultaneously learn the hidden factors of drugs and targets from their side information and interaction matrix. The proposed method is composed of two steps: the pre-processing of the interaction matrix, and the hybrid model. We leverage the similarity matrices of both drugs and targets to address the sparsity problem of the interaction matrix. The comparison of our approach against other algorithms on the same reference datasets has shown good results regarding area under receiver operating characteristic curve and the area under precision–recall curve. More specifically, experimental results achieve high accuracy on golden standard datasets (e.g., Nuclear Receptors, GPCRs, Ion Channels, and Enzymes) when performed with five repetitions of tenfold cross-validation. Graphical abstract: [Figure not available: see fulltext.]Display graphical of the hybrid model of Matrix Factorization with Denoising Autoencoderswith the help side information of drugs and targets for Prediction of Drug-Target Interactions.

Polyvinylidene fluoride (PVDF), polyamide (Nylon) and anodic aluminum oxide (AAO) filter membranes were coated with in-situ synthesized silver nanoparticle/reduced graphene oxide (AgNP/rGO) nanocomposite by vacuum filtration, and surface-enhanced Raman scattering (SERS) performance of the prepared substrates was investigated using Rhodamine 6G (R6G) as a probe molecule. Analyte solutions were applied by drop casting and penetrated into the flexible PVDF and Nylon-based substrates within minutes, enabling very fast SERS measurements; however, this step took up to 2 hrs in the rigid AAO-based substrate. Scanning electron microscopy and Energy-dispersive X-ray spectroscopy mapping results showed that filter membrane surfaces were completely covered with AgNP/rGO nanocomposite at which AgNPs were uniformly distributed on rGO flakes; however, some large, partially reduced or unreduced GO flakes decorated with AgNP agglomerates (AgNP/GO) were also observed. Higher SERS signals were generally obtained from these AgNP/GO flakes compared to the AgNP/rGO coated regions, since they contain higher amount of oxygen-rich functional groups which enabled a higher chemical and electromagnetic enhancement in SERS signals. All the substrates could detect 10−7 M R6G even after being stored in a vacuum desiccator for 3 months, indicating that AgNP/rGO coated filter membranes are promising as stable SERS substrates thanks to their long shelf life.

Human dopamine transporter gene (SLC6A3) is one of the neurotransmission genes that plays an important role in controlling the behavior and psychological reactions toward various physical activities. The aim of the present study is to evaluate the associations between SLC6A3 3’-UTR VNTR (3’VNTR) genotypes and eight personality traits in Egyptian athletes. One hundred athletes (50 males and 50 females) and one hundred non-athletes participated in this investigation either in individual or team games. The Freiburg Personality inventory-Revised questionnaire was applied to athletes to evaluate personality traits according to the scale, at a scheduled time, using descriptive analysis. Genotyping for SLC6A3 3’-UTR VNTR polymorphism in athletes was realized to detect the different genotypes by PCR amplifications using desired primers. Three different genotypes of the 3’VNTR polymorphism; namely (9/9), (9/10), (10/10) were identified. The 9/10 and 10/10 genotypes of SLC6A3 gene were significantly higher in players than those in the control group. There were no statistically significant differences in personality traits values among males and females. The Cronbach’s alpha stability coefficient for four items is higher than 0.7 suggesting that these items (Neuroticism, Aggressiveness, Depression and Excitability) have a good reliability and support the used personality dimensions for this investigation. Our findings revealed a significant association between the genotypes of 3’VNTR and the personality traits in Egyptian athletes. These data shed light on the role of SLC6A3 gene in athletes’ behavioral performance and could serve as the basis for further characterization in the large number of Egyptian athletes.

Relative populations of the four energy-lowest isolated-pentagon-rule (IPR) isomers of Y@C82 under the high-temperature synthetic conditions are computed using the Gibbs energy based on characteristics from the density functional theory calculations (B3LYP/6–31G*∼SDD entropy, B3LYP/6–31G*∼SDD and B2PLYPD/6–31 + G*∼SDD energetics). Two major species are predicted, Y@C2v;9-C82 and Y@Cs(c);6-C82, their calculated equilibrium relative populations agreeing with observations.