Differential effects of intrinsic properties of natural scenes and interference mechanisms on recognition processes in long-term visual memory.

Humans display remarkable long-term visual memory (LTVM) processes. Even though images may be intrinsically memorable, the fidelity of their visual representations, and consequently the likelihood of successfully retrieving them, hinges on their similarity when concurrently held in LTVM. In this debate, it is still unclear whether intrinsic features of images (perceptual and semantic) may be mediated by mechanisms of interference generated at encoding, or during retrieval, and how these factors impinge on recognition processes. In the current study, participants (32) studied a stream of 120 natural scenes from 8 semantic categories, which varied in frequencies (4, 8, 16 or 32 exemplars per category) to generate different levels of category interference, in preparation for a recognition test. Then they were asked to indicate which of two images, presented side by side (i.e. two-alternative forced-choice), they remembered. The two images belonged to the same semantic category but varied in their perceptual similarity (similar or dissimilar). Participants also expressed their confidence (sure/not sure) about their recognition response, enabling us to tap into their metacognitive efficacy (meta-d'). Additionally, we extracted the activation of perceptual and semantic features in images (i.e. their informational richness) through deep neural network modelling and examined their impact on recognition processes. Corroborating previous literature, we found that category interference and perceptual similarity negatively impact recognition processes, as well as response times and metacognitive efficacy. Moreover, images semantically rich were less likely remembered, an effect that trumped a positive memorability boost coming from perceptual information. Critically, we did not observe any significant interaction between intrinsic features of images and interference generated either at encoding or during retrieval. All in all, our study calls for a more integrative understanding of the representational dynamics during encoding and recognition enabling us to form, maintain and access visual information.

Lithiation of phosphorus at the nanoscale: a computational study of LinPm clusters.

Systematic structure prediction of LinPm nanoclusters was performed for a wide range of compositions (0 ≤ n ≤ 10, 0 ≤ m ≤ 20) using the evolutionary global optimization algorithm USPEX coupled with density functional calculations. With increasing Li concentration, the number of P-P bonds in the cluster reduces and the phosphorus backbone undergoes the following transformations: elongated tubular → multi-fragment (with mainly P5 rings and P7 cages) → cyclic topology → branched topology → P-P dumbbells → isolated P ions. By applying several stability criteria, we determined the most favorable LinPm clusters and found that they are located in the compositional area between m ≈ n/3 and m ≈ n/3 + 6. For instance, the Li3P7 cluster has the highest stability and is known to be the structural basis of the corresponding bulk crystal. The obtained results provide valuable insights into the lithiation mechanism of nanoscale phosphorus which is of interest for development of novel phosphorus-based anode materials.

Plasma microRNA Profiling in Type 2 Diabetes Mellitus: A Pilot Study.

Type 2 diabetes mellitus (T2D) is a chronic metabolic disease characterized by insulin resistance and ß-cell dysfunction and leading to many micro- and macrovascular complications. In this study we analyzed the circulating miRNA expression profiles in plasma samples from 44 patients with T2D and 22 healthy individuals using next generation sequencing and detected 229 differentially expressed miRNAs. An increased level of miR-5588-5p, miR-125b-2-3p, miR-1284, and a reduced level of miR-496 in T2D patients was verified. We also compared the expression landscapes in the same group of patients depending on body mass index and identified differential expression of miR-144-3p and miR-99a-5p in obese individuals. Identification and functional analysis of putative target genes was performed for miR-5588-5p, miR-125b-2-3p, miR-1284, and miR-496, showing chromatin modifying enzymes and apoptotic genes being among the significantly enriched pathways.

Ultralow reaction barriers for CO oxidation in Cu-Au nanoclusters.

Systematic structure prediction of CunAum nanoclusters was carried out for a wide compositional area (n + m ≤ 15) using the evolutionary algorithm USPEX and DFT calculations. The obtained structural data allowed us to assess the local stability of clusters and their suitability for catalysis of CO oxidation. Using these two criteria, we selected several most promising clusters for an accurate study of their catalytic properties. The adsorption energies of reagents, reaction paths, and activation energies were calculated. We found several cases with low activation energies and explained these cases using the patterns of structural change at the moment of CO2 desorption. The unique case is the Cu7Au6 cluster, which has extremely low activation energies for all transition states (below 0.05 eV). We thus showed that higher flexibility due to the binary nature of nanoclusters makes it possible to achieve the maximum catalytic activity. Considering the lower price of copper, Cu-Au nanoparticles are a promising new family of catalysts.

Phosphorus nanoclusters and insight into the formation of phosphorus allotropes.

Elemental phosphorus has a striking variety of allotropes, which we analyze by looking at stable phosphorus clusters. We determine the ground-state structures of Pn clusters in a wide range of compositions (n = 2-50) using density functional calculations and global optimization techniques. We explain why the high-energy white phosphorus is so easily formed, compared to the much more stable allotropes - the tetrahedral P4 cluster is so much more stable than nearby compositions that only by increasing the size to P10 one can get a more stable non-P4-based structure. Starting from 17 atoms, phosphorus clusters have a single-stranded structure, consisting of a set of well-resolved structural units connected by P2 linking fragments. The investigation of relative stability has revealed even-odd alternations and structural magic numbers. The former are caused by the higher stability of clusters with even numbers of atoms due to closed electronic shells. The structural magic numbers are associated with the presence of particular stable structural units and lead to enhanced stability of P18+12k (k = 0, 1, 2) clusters. We also compare the energies of the obtained ground-state structures with clusters of different phosphorus allotropes. Clusters of fibrous phosphorus are energetically the closest to the ground states, white phosphorus clusters are found to be less stable, and the least stable allotrope at the nanocluster scale is black phosphorene.

Overview of Transcriptomic Research on Type 2 Diabetes: Challenges and Perspectives.

Type 2 diabetes (T2D) is a common chronic disease whose etiology is known to have a strong genetic component. Standard genetic approaches, although allowing for the detection of a number of gene variants associated with the disease as well as differentially expressed genes, cannot fully explain the hereditary factor in T2D. The explosive growth in the genomic sequencing technologies over the last decades provided an exceptional impetus for transcriptomic studies and new approaches to gene expression measurement, such as RNA-sequencing (RNA-seq) and single-cell technologies. The transcriptomic analysis has the potential to find new biomarkers to identify risk groups for developing T2D and its microvascular and macrovascular complications, which will significantly affect the strategies for early diagnosis, treatment, and preventing the development of complications. In this article, we focused on transcriptomic studies conducted using expression arrays, RNA-seq, and single-cell sequencing to highlight recent findings related to T2D and challenges associated with transcriptome experiments.

Genetic and Phenotypic Factors Affecting Glycemic Response to Metformin Therapy in Patients with Type 2 Diabetes Mellitus.

Metformin is an oral hypoglycemic agent widely used in clinical practice for treatment of patients with type 2 diabetes mellitus (T2DM). The wide interindividual variability of response to metformin therapy was shown, and recently the impact of several genetic variants was reported. To assess the independent and combined effect of the genetic polymorphism on glycemic response to metformin, we performed an association analysis of the variants in ATM, SLC22A1, SLC47A1, and SLC2A2 genes with metformin response in 299 patients with T2DM. Likewise, the distribution of allele and genotype frequencies of the studied gene variants was analyzed in an extended group of patients with T2DM (n = 464) and a population group (n = 129). According to our results, one variant, rs12208357 in the SLC22A1 gene, had a significant impact on response to metformin in T2DM patients. Carriers of TT genotype and T allele had a lower response to metformin compared to carriers of CC/CT genotypes and C allele (p-value = 0.0246, p-value = 0.0059, respectively). To identify the parameters that had the greatest importance for the prediction of the therapy response to metformin, we next built a set of machine learning models, based on the various combinations of genetic and phenotypic characteristics. The model based on a set of four parameters, including gender, rs12208357 genotype, familial T2DM background, and waist-hip ratio (WHR) showed the highest prediction accuracy for the response to metformin therapy in patients with T2DM (AUC = 0.62 in cross-validation). Further pharmacogenetic studies may aid in the discovery of the fundamental mechanisms of type 2 diabetes, the identification of new drug targets, and finally, it could advance the development of personalized treatment.

Eye-movements reveal semantic interference effects during the encoding of naturalistic scenes in long-term memory.

Similarity-based semantic interference (SI) hinders memory recognition. Within long-term visual memory paradigms, the more scenes (or objects) from the same semantic category are viewed, the harder it is to recognize each individual instance. A growing body of evidence shows that overt attention is intimately linked to memory. However, it is yet to be understood whether SI mediates overt attention during scene encoding, and so explain its detrimental impact on recognition memory. In the current experiment, participants watched 372 photographs belonging to different semantic categories (e.g., a kitchen) with different frequency (4, 20, 40 or 60 images), while being eye-tracked. After 10 minutes, they were presented with the same 372 photographs plus 372 new photographs and asked whether they recognized (or not) each photo (i.e., old/new paradigm). We found that the more the SI, the poorer the recognition performance, especially for old scenes of which memory representations existed. Scenes more widely explored were better recognized, but for increasing SI, participants focused on more local regions of the scene in search for its potentially distinctive details. Attending to the centre of the display, or to scene regions rich in low-level saliency was detrimental to recognition accuracy, and as SI increased participants were more likely to rely on visual saliency. The complexity of maintaining faithful memory representations for increasing SI also manifested in longer fixation durations; in fact, a more successful encoding was also associated with shorter fixations. Our study highlights the interdependence between attention and memory during high-level processing of semantic information.

Pharmacogenetics of Type 2 Diabetes-Progress and Prospects.

Type 2 diabetes mellitus (T2D) is a chronic metabolic disease resulting from insulin resistance and progressively reduced insulin secretion, which leads to impaired glucose utilization, dyslipidemia and hyperinsulinemia and progressive pancreatic beta cell dysfunction. The incidence of type 2 diabetes mellitus is increasing worldwide and nowadays T2D already became a global epidemic. The well-known interindividual variability of T2D drug actions such as biguanides, sulfonylureas/meglitinides, DPP-4 inhibitors/GLP1R agonists and SGLT-2 inhibitors may be caused, among other things, by genetic factors. Pharmacogenetic findings may aid in identifying new drug targets and obtaining in-depth knowledge of the causes of disease and its physiological processes, thereby, providing an opportunity to elaborate an algorithm for tailor or precision treatment. The aim of this article is to summarize recent progress and discoveries for T2D pharmacogenetics and to discuss the factors which limit the furthering accumulation of genetic variability knowledge in patient response to therapy that will allow improvement the personalized treatment of T2D.