Seeing the invisible: The scope and limits of unconscious processing in binocular rivalry

When an image is presented to one eye and a very different image is presented to the corresponding location of the other eye, they compete for conscious representation, such that only one image is visible at a time while the other is suppressed. Called binocular rivalry, this phenomenon and its deviants have been extensively exploited to study the mechanism and neural correlates of consciousness. In this paper, we propose a framework, the unconscious binding hypothesis, to distinguish unconscious processing from conscious processing. According to this framework, the unconscious mind not only encodes individual features but also temporally binds distributed features to give rise to cortical representation, but unlike conscious binding, such unconscious binding is fragile. Under this framework, we review evidence from psychophysical and neuroimaging studies, which suggests that: (1) for invisible low level features, prolonged exposure to visual pattern and simple translational motion can alter the appearance of subsequent visible features (i.e. adaptation); for invisible high level features, although complex spiral motion cannot produce adaptation, nor can objects/words enhance subsequent processing of related stimuli (i.e. priming), images of tools can nevertheless activate the dorsal pathway; and (2) although invisible central cues cannot orient attention, invisible erotic pictures in the periphery can nevertheless guide attention, likely through emotional arousal; reciprocally, the processing of invisible information can be modulated by attention at perceptual and neural levels

Mixture of easy trials enables transient and sustained perceptual improvements through priming and perceptual learning.

The sense of vision allows us to discriminate fine details across a wide range of tasks. How to improve this perceptual skill, particularly within a short training session, is of substantial interest. Emerging evidence suggests that mixing easy trials can quickly improve performance in hard trials, but it is equivocal whether the improvement is short-lived or long-lasting, and additionally what accounts for this improvement. Here, by tracking objective performance (accuracy) and subjective experience (ratings of target visibility and choice confidence) over time and in a large sample of participants, we demonstrate the coexistence of transient and sustained effects of mixing easy trials, which differ markedly in their timescales, in their effects on subjective awareness, and in individual differences. In particular, whereas the transient effect was found to be ubiquitous and manifested similarly across objective and subjective measures, the sustained effect was limited to a subset of participants with weak convergence from objective and subjective measures. These results indicate that mixture of easy trials enables two distinct, co-existing forms of rapid perceptual improvements in hard trials, as mediated by robust priming and fragile learning. Placing constraints on theory of brain plasticity, this finding may also have implications for alleviating visual deficits

Generating Synthetic Data for Neural Keyword-to-Question Models

Search typically relies on keyword queries, but these are often semantically ambiguous. We propose to overcome this by offering users natural language questions, based on their keyword queries, to disambiguate their intent. This keyword-to-question task may be addressed using neural machine translation techniques. Neural translation models, however, require massive amounts of training data (keyword-question pairs), which is unavailable for this task. The main idea of this paper is to generate large amounts of synthetic training data from a small seed set of hand-labeled keyword-question pairs. Since natural language questions are available in large quantities, we develop models to automatically generate the corresponding keyword queries. Further, we introduce various filtering mechanisms to ensure that synthetic training data is of high quality. We demonstrate the feasibility of our approach using both automatic and manual evaluation. This is an extended version of the article published with the same title in the Proceedings of ICTIR'18.Comment: Extended version of ICTIR'18 full paper, 11 page

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine

AZI23'UTR Is a New SLC6A3 Downregulator Associated with an Epistatic Protection Against Substance Use Disorders

Regulated activity of SLC6A3, which encodes the human dopamine transporter (DAT), contributes to diseases such as substance abuse disorders (SUDs); however, the exact transcription mechanism remains poorly understood. Here, we used a common genetic variant of the gene, intron 1 DNP1B sequence, as bait to screen and clone a new transcriptional activity, AZI23'UTR, for SLC6A3. AZI23'UTR is a 3' untranslated region (3'UTR) of the human 5-Azacytidine Induced 2 gene (AZI2) but appeared to be transcribed independently of AZI2. Found to be present in both human cell nuclei and dopamine neurons, this RNA was shown to downregulate promoter activity through a variant-dependent mechanism in vitro. Both reduced RNA density ratio of AZI23'UTR/AZI2 and increased DAT mRNA levels were found in ethanol-naive alcohol-preferring rats. Secondary analysis of dbGaP GWAS datasets (Genome-Wide Association Studies based on the database of Genotypes and Phenotypes) revealed significant interactions between regions upstream of AZI23'UTR and SLC6A3 in SUDs. Jointly, our data suggest that AZI23'UTR confers variant-dependent transcriptional regulation of SLC6A3, a potential risk factor for SUDs

Hierarchical TiO2 spheres assisted with graphene for a high performance lithium–sulfur battery

In this study, we report hierarchical TiO2 sphere–sulfur frameworks assisted with graphene as a cathode material for high performance lithium–sulfur batteries. With this strategy, the volume expansion and aggregation of sulfur nanoparticles can be effectively mitigated, thus enabling high sulfur utilization and improving the specific capacity and cycling stability of the electrode. Modification of the TiO2–S nanocomposites with graphene can trap the polysulfides via chemisorption and increase the electronic connection among various components. The graphene-assisted TiO2–S composite electrodes exhibit high specific capacity of 660 mA h g−1 at 5C with a capacity loss of only 0.04% per cycle in the prolonged charge–discharge processes at 1C

Supercharging academic writing with generative AI: framework, techniques, and caveats

Academic writing is an indispensable yet laborious part of the research enterprise. This Perspective maps out principles and methods for using generative artificial intelligence (AI), specifically large language models (LLMs), to elevate the quality and efficiency of academic writing. We introduce a human-AI collaborative framework that delineates the rationale (why), process (how), and nature (what) of AI engagement in writing. The framework pinpoints both short-term and long-term reasons for engagement and their underlying mechanisms (e.g., cognitive offloading and imaginative stimulation). It reveals the role of AI throughout the writing process, conceptualized through a two-stage model for human-AI collaborative writing, and the nature of AI assistance in writing, represented through a model of writing-assistance types and levels. Building on this framework, we describe effective prompting techniques for incorporating AI into the writing routine (outlining, drafting, and editing) as well as strategies for maintaining rigorous scholarship, adhering to varied journal policies, and avoiding overreliance on AI. Ultimately, the prudent integration of AI into academic writing can ease the communication burden, empower authors, accelerate discovery, and promote diversity in science.Comment: 14 pages, 2 figures, 1 table, 1 bo

Lovastatin enhances adenovirus-mediated TRAIL induced apoptosis by depleting cholesterol of lipid rafts and affecting CAR and death receptor expression of prostate cancer cells

Oncolytic adenovirus and apoptosis inducer TRAIL are promising cancer therapies. Their antitumor efficacy, when used as single agents, is limited. Oncolytic adenoviruses have low infection activity, and cancer cells develop resistance to TRAIL-induced apoptosis. Here, we explored combining prostate-restricted replication competent adenovirus-mediated TRAIL (PRRA-TRAIL) with lovastatin, a commonly used cholesterol-lowering drug, as a potential therapy for advanced prostate cancer (PCa). Lovastatin significantly enhanced the efficacy of PRRA-TRAIL by promoting the in vivo tumor suppression, and the in vitro cell killing and apoptosis induction, via integration of multiple molecular mechanisms. Lovastatin enhanced PRRA replication and virus-delivered transgene expression by increasing the expression levels of CAR and integrins, which are critical for adenovirus 5 binding and internalization. Lovastatin enhanced TRAIL-induced apoptosis by increasing death receptor DR4 expression. These multiple effects of lovastatin on CAR, integrins and DR4 expression were closely associated with cholesterol-depletion in lipid rafts. These studies, for the first time, show correlations between cholesterol/lipid rafts, oncolytic adenovirus infection efficiency and the antitumor efficacy of TRAIL at the cellular level. This work enhances our understanding of the molecular mechanisms that support use of lovastatin, in combination with PRRA-TRAIL, as a candidate strategy to treat human refractory prostate cancer in the future