Consequences of polar form coherence for fMRI responses in human visual cortex

AbstractRelevant features in the visual image are often spatially extensive and have complex orientation structure. Our perceptual sensitivity to such spatial form is demonstrated by polar Glass patterns, in which an array of randomly-positioned dot pairs that are each aligned with a particular polar displacement (rotation, for example) yield a salient impression of spatial structure. Such patterns are typically considered to be processed in two main stages: local spatial filtering in low-level visual cortex followed by spatial pooling and complex form selectivity in mid-level visual cortex. However, it remains unclear both whether reciprocal interactions within the cortical hierarchy are involved in polar Glass pattern processing and which mid-level areas identify and communicate polar Glass pattern structure. Here, we used functional magnetic resonance imaging (fMRI) at 7T to infer the magnitude of neural response within human low-level and mid-level visual cortex to polar Glass patterns of varying coherence (proportion of signal elements). The activity within low-level visual areas V1 and V2 was not significantly modulated by polar Glass pattern coherence, while the low-level area V3, dorsal and ventral mid-level areas, and the human MT complex each showed a positive linear coherence response functions. The cortical processing of polar Glass patterns thus appears to involve primarily feedforward communication of local signals from V1 and V2, with initial polar form selectivity reached in V3 and distributed to multiple pathways in mid-level visual cortex

Bootstrapped learning of novel objects

Recognition of familiar objects in cluttered backgrounds is a challenging computational problem. Camouflage provides a particularly striking case, where an object is difficult to detect, recognize, and segment even when in "plain view." Current computational approaches combine low-level features with high-level models to recognize objects. But what if the object is unfamiliar? A novel camouflaged object poses a paradox: A visual system would seem to require a model of an object's shape in order to detect, recognize, and segment it when camouflaged. But, how is the visual system to build such a model of the object without easily segmentable samples? One possibility is that learning to identify and segment is opportunistic in the sense that learning of novel objects takes place only when distinctive clues permit object segmentation from background, such as when target color or motion enables segmentation on single presentations. We tested this idea and discovered that, on the contrary, human observers can learn to identify and segment a novel target shape, even when for any given training image the target object is camouflaged. Further, perfect recognition can be achieved without accurate segmentation. We call the ability to build a shape model from high-ambiguity presentations bootstrapped learning

Why the visual recognition system might encode the effects of illumination

A key problem in recognition is that the image of an object depends on the lighting conditions. We investigated whether recognition is sensitive to illumination using 3-D objects that were lit from either the left or right, varying both the shading and the cast shadows. In experiments 1 and 2 participants judged whether two sequentially presented objects were the same regardless of illumination. Experiment 1 used six objects that were easily discriminated and that were rendered with cast shadows. While no cost was found in sensitivity, there was a response time cost over a change in lighting direction. Experiment 2 included six additional objects that were similar to the original six objects making recognition more difficult. The objects were rendered with cast shadows, no shadows, and as a control, white shadows. With normal shadows a change in lighting direction produced costs in both sensitivity and response times. With white shadows there was a much larger cost in sensitivity and a comparable cost in response times. Without cast shadows there was no cost in either measure, but the overall performance was poorer. Experiment 3 used a naming task in which names were assigned to six objects rendered with cast shadows. Participants practised identifying the objects in two viewpoints lit from a single lighting direction. Viewpoint and illumination invariance were then tested over new viewpoints and illuminations. Costs in both sensitivity and response time were found for naming the familiar objects in unfamiliar lighting directions regardless of whether the viewpoint was familiar or unfamiliar. Together these results suggest that illumination effects such as shadow edges: (1) affect visual memory; (2) serve the function of making unambigous the three-dimensional shape

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process

How Haptic Size Sensations Improve Distance Perception

Determining distances to objects is one of the most ubiquitous perceptual tasks in everyday life. Nevertheless, it is challenging because the information from a single image confounds object size and distance. Though our brains frequently judge distances accurately, the underlying computations employed by the brain are not well understood. Our work illuminates these computions by formulating a family of probabilistic models that encompass a variety of distinct hypotheses about distance and size perception. We compare these models' predictions to a set of human distance judgments in an interception experiment and use Bayesian analysis tools to quantitatively select the best hypothesis on the basis of its explanatory power and robustness over experimental data. The central question is: whether, and how, human distance perception incorporates size cues to improve accuracy. Our conclusions are: 1) humans incorporate haptic object size sensations for distance perception, 2) the incorporation of haptic sensations is suboptimal given their reliability, 3) humans use environmentally accurate size and distance priors, 4) distance judgments are produced by perceptual “posterior sampling”. In addition, we compared our model's estimated sensory and motor noise parameters with previously reported measurements in the perceptual literature and found good correspondence between them. Taken together, these results represent a major step forward in establishing the computational underpinnings of human distance perception and the role of size information.National Institutes of Health (U.S.) (NIH grant R01EY015261)University of Minnesota (UMN Graduate School Fellowship)National Science Foundation (U.S.) (Graduate Research Fellowship)University of Minnesota (UMN Doctoral Dissertation Fellowship)National Institutes of Health (U.S.) (NIH NRSA grant F32EY019228-02)Ruth L. Kirschstein National Research Service Awar

Synergistic use of glycomics and single-molecule molecular inversion probes for identification of congenital disorders of glycosylation type-1

Congenital disorders of glycosylation type 1 (CDG-I) comprise a group of 27 genetic defects with heterogeneous multisystem phenotype, mostly presenting with nonspecific neurological symptoms. The biochemical hallmark of CDG-I is a partial absence of complete N-glycans on transferrin. However, recent findings of a diagnostic N-tetrasaccharide for ALG1-CDG and increased high-mannose N-glycans for a few other CDG suggested the potential of glycan structural analysis for CDG-I gene discovery. We analyzed the relative abundance of total plasma N-glycans by high resolution quadrupole time-of-flight mass spectrometry in a large cohort of 111 CDG-I patients with known (n = 75) or unsolved (n = 36) genetic cause. We designed single-molecule molecular inversion probes (smMIPs) for sequencing of CDG-I candidate genes on the basis of specific N-glycan signatures. Glycomics profiling in patients with known defects revealed novel features such as the N-tetrasaccharide in ALG2-CDG patients and a novel fucosylated N-pentasaccharide as specific glycomarker for ALG1-CDG. Moreover, group-specific high-mannose N-glycan signatures were found in ALG3-, ALG9-, ALG11-, ALG12-, RFT1-, SRD5A3-, DOLK-, DPM1-, DPM3-, MPDU1-, ALG13-CDG, and hereditary fructose intolerance. Further differential analysis revealed high-mannose profiles, characteristic for ALG12- and ALG9-CDG. Prediction of candidate genes by glycomics profiling in 36 patients with thus far unsolved CDG-I and subsequent smMIPs sequencing led to a yield of solved cases of 78% (28/36). Combined plasma glycomics profiling and targeted smMIPs sequencing of candidate genes is a powerful approach to identify causative mutations in CDG-I patient cohorts

Omics analyses and biochemical study of Phlebiopsis gigantea elucidate its degradation strategy of wood extractives

14 páginas.- 6 figuras. 1 tabla.- 50 referencias.- Supplementary Information Te online version contains supplementary material available at https://doi.org/10.1038/s41598-021-91756-5Wood extractives, solvent-soluble fractions of woody biomass, are considered to be a factor impeding or excluding fungal colonization on the freshly harvested conifers. Among wood decay fungi, the basidiomycete Phlebiopsis gigantea has evolved a unique enzyme system to efficiently transform or degrade conifer extractives but little is known about the mechanism(s). In this study, to clarify the mechanism(s) of softwood degradation, we examined the transcriptome, proteome, and metabolome of P. gigantea when grown on defined media containing microcrystalline cellulose and pine sapwood extractives. Beyond the conventional enzymes often associated with cellulose, hemicellulose and lignin degradation, an array of enzymes implicated in the metabolism of softwood lipophilic extractives such as fatty and resin acids, steroids and glycerides was significantly up-regulated. Among these, a highly expressed and inducible lipase is likely responsible for lipophilic extractive degradation, based on its extracellular location and our characterization of the recombinant enzyme. Our results provide insight into physiological roles of extractives in the interaction between wood and fungi. © 2021, The Author(s).The work partly conducted by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, was supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Research was also supported by NSF Grants 1457695 and 1457721 to J.M.B. and D.C., respectively, by CSIC project 201740E071 to A.G. and by JSPS Grant-in-Aid for Scientific Research 19K15881, JST-ACTX PJ2519A059 and 2017 Feasibility Study Program of the Frontier Chemistry Center, Faculty of Engineering, Hokkaido University to C.H.Peer reviewe

Triglyceride-rich lipoproteins and their remnants : metabolic insights, role in atherosclerotic cardiovascular disease, and emerging therapeutic strategies-a consensus statement from the European Atherosclerosis Society

Recent advances in human genetics, together with a large body of epidemiologic, preclinical, and clinical trial results, provide strong support for a causal association between triglycerides (TG), TG-rich lipoproteins (TRL), and TRL remnants, and increased risk of myocardial infarction, ischaemic stroke, and aortic valve stenosis. These data also indicate that TRL and their remnants may contribute significantly to residual cardiovascular risk in patients on optimized low-density lipoprotein (LDL)-lowering therapy. This statement critically appraises current understanding of the structure, function, and metabolism of TRL, and their pathophysiological role in atherosclerotic cardiovascular disease (ASCVD). Key points are (i) a working definition of normo- and hypertriglyceridaemic states and their relation to risk of ASCVD, (ii) a conceptual framework for the generation of remnants due to dysregulation of TRL production, lipolysis, and remodelling, as well as clearance of remnant lipoproteins from the circulation, (iii) the pleiotropic proatherogenic actions of TRL and remnants at the arterial wall, (iv) challenges in defining, quantitating, and assessing the atherogenic properties of remnant particles, and (v) exploration of the relative atherogenicity of TRL and remnants compared to LDL. Assessment of these issues provides a foundation for evaluating approaches to effectively reduce levels of TRL and remnants by targeting either production, lipolysis, or hepatic clearance, or a combination of these mechanisms. This consensus statement updates current understanding in an integrated manner, thereby providing a platform for new therapeutic paradigms targeting TRL and their remnants, with the aim of reducing the risk of ASCVD. [GRAPHICS] .Peer reviewe

VLDL Hydrolysis by Hepatic Lipase Regulates PPARδ Transcriptional Responses

PPARs (α,γ,δ) are a family of ligand-activated transcription factors that regulate energy balance, including lipid metabolism. Despite these critical functions, the integration between specific pathways of lipid metabolism and distinct PPAR responses remains obscure. Previous work has revealed that lipolytic pathways can activate PPARs. Whether hepatic lipase (HL), an enzyme that regulates VLDL and HDL catabolism, participates in PPAR responses is unknown.Using PPAR ligand binding domain transactivation assays, we found that HL interacted with triglyceride-rich VLDL (>HDL≫LDL, IDL) to activate PPARδ preferentially over PPARα or PPARγ, an effect dependent on HL catalytic activity. In cell free ligand displacement assays, VLDL hydrolysis by HL activated PPARδ in a VLDL-concentration dependent manner. Extended further, VLDL stimulation of HL-expressing HUVECs and FAO hepatoma cells increased mRNA expression of canonical PPARδ target genes, including adipocyte differentiation related protein (ADRP), angiopoietin like protein 4 and pyruvate dehydrogenase kinase-4. HL/VLDL regulated ADRP through a PPRE in the promoter region of this gene. In vivo, adenoviral-mediated hepatic HL expression in C57BL/6 mice increased hepatic ADRP mRNA levels by 30%. In ob/ob mice, a model with higher triglycerides than C57BL/6 mice, HL overexpression increased ADRP expression by 70%, demonstrating the importance of triglyceride substrate for HL-mediated PPARδ activation. Global metabolite profiling identified HL/VLDL released fatty acids including oleic acid and palmitoleic acid that were capable of recapitulating PPARδ activation and ADRP gene regulation in vitro.These data define a novel pathway involving HL hydrolysis of VLDL that activates PPARδ through generation of specific monounsaturated fatty acids. These data also demonstrate how integrating cell biology with metabolomic approaches provides insight into specific lipid mediators and pathways of lipid metabolism that regulate transcription