Roles of familiarity and novelty in visual preference judgments are segregated across object categories

Understanding preference decision making is a challenging problem because the underlying process is often implicit and dependent on context, including past experience. There is evidence for both familiarity and novelty as critical factors for preference in adults and infants. To resolve this puzzling contradiction, we examined the cumulative effects of visual exposure in different object categories, including faces, natural scenes, and geometric figures, in a two-alternative preference task. The results show a clear segregation of preference across object categories, with familiarity preference dominant in faces and novelty preference dominant in natural scenes. No strong bias was observed in geometric figures. The effects were replicated even when images were converted to line drawings, inverted, or presented only briefly, and also when spatial frequency and contour distribution were controlled. The effects of exposure were reset by a blank of 1 wk or 3 wk. Thus, the category-specific segregation of familiarity and novelty preferences is based on quick visual categorization and cannot be caused by the difference in low-level visual features between object categories. Instead, it could be due either to different biological significances/attractiveness criteria across these categories, or to some other factors, such as differences in within-category variance and adaptive tuning of the perceptual system

Gut-brain axis: gut dysbiosis and psychiatric disorders in Alzheimer’s and Parkinson’s disease

Gut dysbiosis and psychiatric symptoms are common early manifestations of Alzheimer’s disease (AD) and Parkinson’s disease (PD). These diseases, characterised by progressive neuron loss and pathological protein accumulation, impose debilitating effects on patients. Recently, these pathological proteins have been linked with gut dysbiosis and psychiatric disorders. The gut-brain axis links the enteric and central nervous systems, acting as a bidirectional communication pathway to influence brain function and behavior. The relationship triad between gut dysbiosis, psychiatric disorders, and neurodegeneration has been investigated in pairs; however, evidence suggests that they are all interrelated and a deeper understanding is required to unravel the nuances of neurodegenerative diseases. Therefore, this review aims to summarise the current literature on the roles of gut dysbiosis and psychiatric disorders in pathological protein-related neurodegenerative diseases. We discussed how changes in the gut environment can influence the development of psychiatric symptoms and the progression of neurodegeneration and how these features overlap in AD and PD. Moreover, research on the interplay between gut dysbiosis, psychiatric disorders, and neurodegeneration remains in its early phase. In this review, we highlighted potential therapeutic approaches aimed at mitigating gastrointestinal problems and psychiatric disorders to alter the rate of neurodegeneration. Further research to assess the molecular mechanisms underlying AD and PD pathogenesis remains crucial for developing more effective treatments and achieving earlier diagnoses. Moreover, exploring non-invasive, early preventive measures and interventions is a relatively unexplored but important avenue of research in neurodegenerative diseases

Whole-genome association studies of alcoholism with loci linked to schizophrenia susceptibility

BACKGROUND: Alcoholism is a complex disease. There have been many reports on significant comorbidity between alcoholism and schizophrenia. For the genetic study of complex diseases, association analysis has been recommended because of its higher power than that of the linkage analysis for detecting genes with modest effects on disease. RESULTS: To identify alcoholism susceptibility loci, we performed genome-wide single-nucleotide polymorphisms (SNP) association tests, which yielded 489 significant SNPs at the 1% significance level. The association tests showed that tsc0593964 (P-value 0.000013) on chromosome 7 was most significantly associated with alcoholism. From 489 SNPs, 74 genes were identified. Among these genes, GABRA1 is a member of the same gene family with GABRA2 that was recently reported as alcoholism susceptibility gene. CONCLUSION: By comparing 74 genes to the published results of various linkage studies of schizophrenia, we identified 13 alcoholism associated genes that were located in the regions reported to be linked to schizophrenia. These 13 identified genes can be important candidate genes to study the genetic mechanism of co-occurrence of both diseases

Unveiling the Direct Correlation between the CVD-Grown Graphene and the Growth Template

Chemical vapor deposition (CVD) is known to produce continuous, large-area graphene sheet with decent physical properties. In the CVD process, catalytic metal substrates are typically used as the growth template, and copper has been adopted as the representative material platform due to its low carbon solubility and resulting monolayer graphene growth capability. For the widespread industrial applications of graphene, achieving the high-quality is essential. Several factors affect the qualities of CVD-grown graphene, such as pressure, temperature, carbon precursors, or growth template. In this work, we provide detailed analysis on the direct relation between the metallic growth substrate (copper) and overall properties of the resulting CVD-grown graphene. The surface morphology of copper substrate was modulated via simple chemical treatments, and its effect on physical, optical, and electrical properties of graphene was analyzed. Based on these results, we propose a simple synthesis route to produce high-quality, continuous, monolayer graphene sheet, which can facilitate the commercialization of CVD graphene into realit

Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm

Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene × gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene × gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms

Assessment of infrasound signals recorded on seismic stations and infrasound arrays in the western United States using ground truth sources

The article of record as published may be located at https://doi.org/10.1093/gji/ggy042Funded by Naval Postgraduate SchoolGround truth sources in Utah during 2003–2013 are used to assess the contribution of temporal atmospheric conditions to infrasound detection and the predictive capabilities of atmospheric models. Ground truth sources consist of 28 long duration static rocket motor burn tests and 28 impulsive rocket body demolitions. Automated infrasound detections from a hybrid of regional seismometers and infrasound arrays use a combination of short-term time average/long-term time average ratios and spectral analyses. These detections are grouped into station triads using a Delaunay triangulation network and then associated to estimate phase velocity and azimuth to filter signals associated with a particular source location. The resulting range and azimuth distribution from sources to detecting stations varies seasonally and is consistent with predictions based on seasonal atmospheric models. Impulsive signals from rocket body detonations are observed at greater distances (>700 km) than the extended duration signals generated by the rocket burn test (up to 600 km). Infrasound energy attenuation associated with the two source types is quantified as a function of range and azimuth from infrasound amplitude measurements. Ray-tracing results using Ground-to-Space atmospheric specifica- tions are compared to these observations and illustrate the degree to which the time variations in characteristics of the observations can be predicted over a multiple year time period.The Naval Postgraduate School, under Grant No. N00244-14-1- 0002, funded this work.The Naval Postgraduate School, under Grant No. N00244-14-1- 0002, funded this work

MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation

We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-view HDR synthetic dataset, scene-level inverse rendering has mainly been studied using single-view image. We were able to successfully perform scene-level inverse rendering using multi-view images by expanding OpenRooms dataset and designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Our experiments show that the proposed method not only achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene. Also, our sophisticated 3D spatially-varying lighting volume allows for photorealistic object insertion in any 3D location.Comment: Accepted by CVPR 2023; Project Page is https://bring728.github.io/mair.project