Contextual-Dependent Attention Effect on Crowded Orientation Signals in Human Visual Cortex

A target becomes hard to identify with nearby visual stimuli. This phenomenon, known as crowding, places a fundamental limit on conscious perception and object recognition. To understand the neural representation of crowded stimuli, we used fMRI and a forward encoding model to reconstruct the target-specific feature from multivoxel activation patterns evoked by orientation patches. Orientation-selective response profiles were constructed in V1–V4 for a target embedded in different contexts. Subjects of both sexes either directed their attention over all the orientation patches or selectively to the target. In the context with a weak crowding effect, attending to the target enhanced the orientation selectivity of the response profile; such effect increased along the visual pathway. In the context with a strong crowding effect, attending to the target enhanced the orientation selectivity of the response profile in the earlier visual area, but not in V4. The increase and decrease of orientation selectivity along the visual hierarchy demonstrate a contextual-dependent attention effect on crowded orientation signals: in the context with a weak crowding effect, selective attention gradually resolves the target from nearby distractors along the hierarchy; in the context with a strong crowding effect, while selective attention maintains the target feature in the earlier visual area, its effect decreases in the downstream area. Our findings reveal how the human visual system represents the target-specific feature at multiple stages under the limit of attention selection in a cluttered scene

MiR-130a inhibition protects rat cardiac myocytes from hypoxia-triggered apoptosis by targeting Smad4

Background: Cardiomyocyte death facilitates the pathological process underlying ischaemic heart diseases, such as myocardial infarction. Emerging evidence suggests that microRNAs play a critical role in the pathological process underlying myocardial infarction by regulating cardiomyocyte apoptosis. However, the relevance of miR-130a in regulating cardiomyocyte apoptosis and the underlying mechanism are still uncertain. Aim: We sought to explore the regulatory effect of miR-130a on hypoxic cardiomyocyte apoptosis. Methods: The expression of miR-130a was measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Cell survival was determined by the MTT assay. The lactate dehydrogenase (LDH) assay was performed to deter­mine the severity of hypoxia-induced cell injury. Apoptosis was assessed via caspase-3 analysis. Protein expression level was determined by Western blotting. The genes targeted by miR-130a were predicted using bioinformatics and were validated via the dual-luciferase reporter assay system. Results: We found that miR-130a expression was greatly increased in hypoxic cardiac myocytes, and that the downregulation of miR-130a effectively shielded cardiac myocytes from hypoxia-triggered apoptosis. In bioinformatic analysis the Smad4 gene was predicted to be the target of miR-130a. This finding was validated through the Western blot assay, dual-luciferase reporter gene assay, and qRT-PCR. MiR-130a inhibition significantly promoted the activation of Smad4 in hypoxic cardiomyocytes. Inter­estingly, knockdown of Smad4 markedly reversed the protective effects induced by miR-130a inhibition. Moreover, we found that the inhibition of miR-130a promoted the activation of transforming growth factor-b1 signalling. Blocking of Smad4 signal­ling significantly abrogated the protective effects of miR-130a inhibition. Conclusions: The findings indicate that inhibition of miR-130a, which targets the Smad4 gene, shields cardiac myocytes from hypoxic apoptosis. This study offers a novel perspective on the molecular basis of hypoxia-induced cardiomyocyte apoptosis and suggests a possible drug target for the treatment of myocardial infarction

A common variant near TGFBR3 is associated with primary open angle glaucoma

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Primary open angle glaucoma (POAG), a major cause of blindness worldwide, is a complex disease with a significant genetic contribution.We performed Exome Array (Illumina) analysis on 3504 POAG cases and 9746 controls with replication of the most significant findings in 9173 POAG cases and 26 780 controls across 18 collections of Asian, African and European descent. Apart from confirming strong evidence of association at CDKN2B-AS1 (rs2157719 [G], odds ratio [OR] = 0.71, P = 2.81 × 10−33), we observed one SNP showing significant association to POAG (CDC7–TGFBR3 rs1192415, ORG-allele = 1.13, Pmeta = 1.60 × 10−8). This particular SNP has previously been shown to be strongly associated with optic disc area and vertical cup-to-disc ratio, which are regarded as glaucoma-related quantitative traits. Our study now extends this by directly implicating it in POAG disease pathogenesis

A common variant near TGFBR3 is associated with primary open angle glaucoma

Primary open angle glaucoma (POAG), a major cause of blindness worldwide, is a complex disease with a significant genetic contribution. We performed Exome Array (Illumina) analysis on 3504 POAG cases and 9746 controls with replication of the most significant findings in 9173 POAG cases and 26 780 controls across 18 collections of Asian, African and European descent. Apart from confirming strong evidence of association at CDKN2B-AS1 (rs2157719 [G], odds ratio [OR] = 0.71, P = 2.81 × 10−33), we observed one SNP showing significant association to POAG (CDC7–TGFBR3 rs1192415, ORG-allele = 1.13, Pmeta = 1.60 × 10−8). This particular SNP has previously been shown to be strongly associated with optic disc area and vertical cup-to-disc ratio, which are regarded as glaucoma-related quantitative traits. Our study now extends this by directly implicating it in POAG disease pathogenesis

TMS learning

<div>Four files in mat format</div><div>PreLearning_Contra --> Fig 2 AB</div><div>PreLearning_Ipsi --> Fig 2 CD</div><div>PostLearning_Contra --> Fig 3 AB</div><div>PostLearning_Ipsi --> Fig 3 CD</div

An Improved Collaborative Filtering Recommendation Algorithm Based on Retroactive Inhibition Theory

Collaborative filtering (CF) is the most classical and widely used recommendation algorithm, which is mainly used to predict user preferences by mining the user&rsquo;s historical data. CF algorithms can be divided into two main categories: user-based CF and item-based CF, which recommend items based on rating information from similar user profiles (user-based) or recommend items based on the similarity between items (item-based). However, since user&rsquo;s preferences are not static, it is vital to take into account the changing preferences of users when making recommendations to achieve more accurate recommendations. In recent years, there have been studies using memory as a factor to measure changes in preference and exploring the retention of preference based on the relationship between the forgetting mechanism and time. Nevertheless, according to the theory of memory inhibition, the main factors that cause forgetting are retroactive inhibition and proactive inhibition, not mere evolutions over time. Therefore, our work proposed a method that combines the theory of retroactive inhibition and the traditional item-based CF algorithm (namely, RICF) to accurately explore the evolution of user preferences. Meanwhile, embedding training is introduced to represent the features better and alleviate the problem of data sparsity, and then the item embeddings are clustered to represent the preference points to measure the preference inhibition between different items. Moreover, we conducted experiments on real-world datasets to demonstrate the practicability of the proposed RICF. The experiments show that the RICF algorithm performs better and is more interpretable than the traditional item-based collaborative filtering algorithm, as well as the state-of-art sequential models such as LSTM and GRU