Project description:Cognitive functions rely on the extensive use of information stored in the brain, and the searching for the relevant information for solving some problem is a very complex task. Human cognition largely uses biological search engines, and we assume that to study cognitive function we need to understand the way these brain search engines work. The approach we favor is to study multi-modular network models, able to solve particular problems that involve searching for information. The building blocks of these multimodular networks are the context dependent memory models we have been using for almost 20 years. These models work by associating an output to the Kronecker product of an input and a context. Input, context and output are vectors that represent cognitive variables. Our models constitute a natural extension of the traditional linear associator. We show that coding the information in vectors that are processed through association matrices, allows for a direct contact between these memory models and some procedures that are now classical in the Information Retrieval field. One essential feature of context-dependent models is that they are based on the thematic packing of information, whereby each context points to a particular set of related concepts. The thematic packing can be extended to multimodular networks involving input-output contexts, in order to accomplish more complex tasks. Contexts act as passwords that elicit the appropriate memory to deal with a query. We also show toy versions of several 'neuromimetic' devices that solve cognitive tasks as diverse as decision making or word sense disambiguation. The functioning of these multimodular networks can be described as dynamical systems at the level of cognitive variables.
Project description:BACKGROUND:The Four-Dimensional Symptom Questionnaire (4DSQ) is a self-report questionnaire designed to measure distress, depression, anxiety, and somatization. Prior to computing scale scores from the item scores, the three highest response alternatives ('Regularly', 'Often', and 'Very often or constantly present') are usually collapsed into one category to reduce the influence of extreme responding on item- and scale scores. In this study, we evaluate the usefulness of this transformation for the distress scale based on a variety of criteria. METHODS:Specifically, by using the Graded Response Model, we investigated the effect of this transformation on model fit, local measurement precision, and various indicators of the scale's validity to get an indication on whether the current practice of recoding should be advocated or not. In particular, the effect on the convergent- (operationalized by the General Health Questionnaire and the Maastricht Questionnaire), divergent- (operationalized by the Neuroticism scale of the NEO-FFI), and predictive validity (operationalized as obtrusion with daily chores and activities, the Biographical Problem list and the Utrecht Burnout Scale) of the distress scale was investigated. RESULTS:Results indicate that recoding leads to (i) better model fit as indicated by lower mean probabilities of exact test statistics assessing item fit, (ii) small (<.02) losses in the sizes of various validity coefficients, and (iii) a decrease (DIFF (SE's) = .10-.25) in measurement precision for medium and high levels of distress. CONCLUSIONS:For clinical applications and applications in longitudinal research, the current practice of recoding should be avoided because recoding decreases measurement precision for medium and high levels of distress. It would be interesting to see whether this advice also holds for the three other domains of the 4DSQ.
Project description:Transplantation with donor corneas is the mainstay for treating corneal blindness, but a severe worldwide shortage necessitates the development of other treatment options. Corneal perforation from infection or inflammation is sealed with cyanoacrylate glue. However, the resulting cytotoxicity requires transplantation. LiQD Cornea is an alternative to conventional corneal transplantation and sealants. It is a cell-free, liquid hydrogel matrix for corneal regeneration, comprising short collagen-like peptides conjugated with polyethylene glycol and mixed with fibrinogen to promote adhesion within tissue defects. Gelation occurs spontaneously at body temperature within 5 min. Light exposure is not required-particularly advantageous because patients with corneal inflammation are typically photophobic. The self-assembling, fully defined, synthetic collagen analog is much less costly than human recombinant collagen and reduces the risk of immune rejection associated with xenogeneic materials. In situ gelation potentially allows for clinical application in outpatient clinics instead of operating theaters, maximizing practicality, and minimizing health care costs.
Project description:Forest regeneration is a key element in achieving sustainable forest management. Partial harvest methods have been used extensively in temperate broadleaf and mixedwood ecosystems to promote regeneration on poorly stocked sites and to maintain forest composition and productivity. However, their effectiveness in promoting conifer establishment has yet to be demonstrated in unmanaged boreal forests, especially those dominated by black spruce (Picea mariana (Mill.) BSP) where constraints for regeneration differ from those found in more meridional regions. We aimed to evaluate conifer seedling density and dimensions, 10 years after the onset of a gradient of silvicultural treatments varying in harvesting intensities, and to identify the critical factors driving the regeneration process. Study blocks of even-aged black spruce stands in the eastern Canadian boreal forest were submitted to three variants of shelterwood harvesting: a seed-tree harvest, a clear-cut and an untreated control. Shelterwood and seed-tree harvesting were combined with spot scarification to promote regeneration. Shelterwood and seed-tree harvesting produced a density of conifer regeneration sufficient to maintain forest productivity, but they did not promote seedling growth. Black spruce was the predominant species in terms of regeneration density, with proportions 3-5× higher than that for balsam fir (Abies balsamea (L.) Mill.). Ten years after treatment, seed-origin black spruce seedlings were abundant in skidding trails, while layers dominated the residual strips. Balsam fir density was not influenced by treatment nor by tree position relative to skidding trails. Balsam fir and black spruce had different responses to treatment in terms of height and diameter, the former exhibiting a better growth performance and larger diameter in the residual strips. Spot scarification created micro-sites that had a significant impact on the regeneration process. Overall, our results support that shelterwood and seed-tree harvesting combined with scarification enable adequate regeneration in black spruce stands, confirming these treatments as viable silvicultural alternatives to clear-cutting when required by sustainable forest management objectives.
Project description:We have identified a sine oculis gene in the planarian Girardia tigrina (Platyhelminthes; Turbellaria; Tricladida). The planarian sine oculis gene (Gtso) encodes a protein with a sine oculis (Six) domain and a homeodomain that shares significant sequence similarity with so proteins assigned to the Six-2 gene family. Gtso is expressed as a single transcript in both regenerating and fully developed eyes. Whole-mount in situ hybridization studies show exclusive expression in photoreceptor cells. Loss of function of Gtso by RNA interference during planarian regeneration inhibits eye regeneration completely. Gtso is also essential for maintenance of the differentiated state of photoreceptor cells. These results, combined with the previously demonstrated expression of Pax-6 in planarian eyes, suggest that the same basic gene regulatory circuit required for eye development in Drosophila and mouse is used in the prototypic eye spots of platyhelminthes and, therefore, is truly conserved during evolution.
Project description:Disorders of gut-brain interactions (DGBIs) are heterogeneous in nature and intertwine with diverse pathophysiological mechanisms. Regular functioning of the gut requires complex coordinated interplay between a variety of gastrointestinal (GI) cell types and their functions are regulated by multiple mechanisms at the transcriptional, post-transcriptional, translational, and post-translational levels. MicroRNAs (miRNAs) are small non-coding RNA molecules that post-transcriptionally regulate gene expression by binding to specific mRNA targets to repress their translation and/or promote the target mRNA degradation. Dysregulation of miRNAs might impair gut physiological functions leading to DGBIs and gut motility disorders. Studies have shown miRNAs regulate gut functions such as visceral sensation, gut immune response, GI barrier function, enteric neuronal development, and GI motility. These biological processes are highly relevant to the gut where neuroimmune interactions are key contributors in controlling gut homeostasis and functional defects lead to DGBIs. Although extensive research has explored the pathophysiology of DGBIs, further research is warranted to bolster the molecular mechanisms behind these disorders. The therapeutic targeting of miRNAs represents an attractive approach for the treatment of DGBIs because they offer new insights into disease mechanisms and have great potential to be used in the clinic as diagnostic markers and therapeutic targets. Here, we review recent advances regarding the regulation of miRNAs in GI pacemaking cells, immune cells, and enteric neurons modulating pathophysiological mechanisms of DGBIs. This review aims to assess the impacts of miRNAs on the pathophysiological mechanisms of DGBIs, including GI dysmotility, impaired intestinal barrier function, gut immune dysfunction, and visceral hypersensitivity. We also summarize the therapeutic alternatives for gut microbial dysbiosis in DGBIs, highlighting the clinical insights and areas for further exploration. We further discuss the challenges in miRNA therapeutics and promising emerging approaches.
Project description:Resection of brain tumors frequently causes injury to the surrounding brain tissue that exacerbates cerebral edema by activating an inflammatory cascade. Although corticosteroids are often utilized peri-operatively to alleviate the symptoms associated with brain edema, they increase operative morbidities and suppress the efficacy of immunotherapy. Thus, novel approaches to minimize cerebral edema caused by neurosurgical procedures will have significant utility in the management of patients with brain tumors. We have studied the role of the receptor for advanced glycation end products (RAGE) and its ligands on inflammatory responses to neurosurgical injury in mice and humans. Blood-brain barrier (BBB) integrity and neuroinflammation were characterized by Nanostring, flow cytometry, qPCR, and immunoblotting of WT and RAGE knockout mice brains subjected to surgical brain injury (SBI). Human tumor tissue and fluid collected from the resection cavity of patients undergoing craniotomy were also analyzed by single-cell RNA sequencing and ELISA. Genetic ablation of RAGE significantly abrogated neuroinflammation and BBB disruption in the murine SBI model. The inflammatory responses to SBI were associated with infiltration of S100A9-expressing myeloid-derived cells into the brain. Local release of pro-inflammatory S100A9 was confirmed in patients following tumor resection. RAGE and S100A9 inhibitors were as effective as dexamethasone in attenuating neuroinflammation. However, unlike dexamethasone and S100A9 inhibitor, RAGE inhibition did not diminish the efficacy of anti-PD-1 immunotherapy in glioma-bearing mice. These observations confirm the role of the RAGE axis in surgically induced neuroinflammation and provide an alternative therapeutic option to dexamethasone in managing post-operative cerebral edema.
Project description:In this paper we argue that for the (probabilistic) interpretation of generic sentences of the form "Gs are f," three types of alternatives play a role: (i) alternative features of f, (ii) alternative groups, or kinds, of G, and (iii) alternative causal background factors. In the first part of this paper we argue for the relevance of these alternatives. In the second part, we describe the results of some experiments that empirically tested in particular the second use of alternatives.
Project description:Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular structure of many natural networks, including brain functional connectivity networks. Sparsification procedures are often applied to remove the weakest edges, which are the most affected by experimental noise, and to reduce the density of the graph, thus making it theoretically and computationally more tractable. However, weak links may also contain significant structural information, and procedures to identify the optimal tradeoff are the subject of active research. Here, we explore the use of percolation analysis, a method grounded in statistical physics, to identify the optimal sparsification threshold for community detection in brain connectivity networks. By using synthetic networks endowed with a ground-truth modular structure and realistic topological features typical of human brain functional connectivity networks, we show that percolation analysis can be applied to identify the optimal sparsification threshold that maximizes information on the networks' community structure. We validate this approach using three different community detection methods widely applied to the analysis of brain connectivity networks: Newman's modularity, InfoMap and Asymptotical Surprise. Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold. This data-driven method should prove particularly useful in the analysis of the community structure of brain networks in populations characterized by different connectivity strengths, such as patients and controls.
Project description:The extent to which brain functions are localized or distributed is a foundational question in neuroscience. In the human brain, common fMRI methods such as cluster correction, atlas parcellation, and anatomical searchlight are biased by design toward finding localized representations. Here we introduce the functional searchlight approach as an alternative to anatomical searchlight analysis, the most commonly used exploratory multivariate fMRI technique. Functional searchlight removes any anatomical bias by grouping voxels based only on functional similarity and ignoring anatomical proximity. We report evidence that visual and auditory features from deep neural networks and semantic features from a natural language processing model, as well as object representations, are more widely distributed across the brain than previously acknowledged and that functional searchlight can improve model-based similarity and decoding accuracy. This approach provides a new way to evaluate and constrain computational models with brain activity and pushes our understanding of human brain function further along the spectrum from strict modularity toward distributed representation.