A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data

Correlations in neural activity have been demonstrated to have profound consequences for sensory encoding. To understand how neural populations represent stimulus information, it is therefore necessary to model how pairwise and higher-order spiking correlations between neurons contribute to the collective structure of population-wide spiking patterns. Maximum entropy models are an increasingly popular method for capturing collective neural activity by including successively higher-order interaction terms. However, incorporating higher-order interactions in these models is difficult in practice due to two factors. First, the number of parameters exponentially increases as higher orders are added. Second, because triplet (and higher) spiking events occur infrequently, estimates of higher-order statistics may be contaminated by sampling noise. To address this, we extend previous work on the Reliable Interaction class of models to develop a normalized variant that adaptively identifies the specific pairwise and higher-order moments that can be estimated from a given dataset for a specified confidence level. The resulting “Reliable Moment” model is able to capture cortical-like distributions of population spiking patterns. Finally, we show that, compared with the Reliable Interaction model, the Reliable Moment model infers fewer strong spurious higher-order interactions and is better able to predict the frequencies of previously unobserved spiking patterns

Large Language Models for Granularized Barrett's Esophagus Diagnosis Classification

Diagnostic codes for Barrett's esophagus (BE), a precursor to esophageal cancer, lack granularity and precision for many research or clinical use cases. Laborious manual chart review is required to extract key diagnostic phenotypes from BE pathology reports. We developed a generalizable transformer-based method to automate data extraction. Using pathology reports from Columbia University Irving Medical Center with gastroenterologist-annotated targets, we performed binary dysplasia classification as well as granularized multi-class BE-related diagnosis classification. We utilized two clinically pre-trained large language models, with best model performance comparable to a highly tailored rule-based system developed using the same data. Binary dysplasia extraction achieves 0.964 F1-score, while the multi-class model achieves 0.911 F1-score. Our method is generalizable and faster to implement as compared to a tailored rule-based approach

Immune reconstitution disease associated with parasitic infections following antiretroviral treatment

HIV-associated immune reconstitution disease (IRD) is the clinical presentation or deterioration of opportunistic infections that results from enhancement of pathogen-specific immune responses among patients responding to antiretroviral treatment (ART). The vast majority of reported cases of IRD have been associated with mycobacterial, chronic viral and invasive fungal infections; such cases result from dysregulated augmentation of cell-mediated type 1 cytokine-secreting host immune responses. However, the spectrum of infections now recognized as associated with IRD is expanding and includes a number of parasitic infections, which may be mediated by different immunopathological mechanisms. These include leishmaniasis (visceral, cutaneous, mucosal and post kala azar dermal leishmaniasis), schistosomiasis and strongyloidiasis. Since the major burden of HIV lies in resource-limited countries where access to ART is now rapidly expanding, increased awareness and knowledge of these phenomena is important. Here we review the clinical spectrum and pathogenesis of IRD associated with parasitic infections

Knotty-Centrality: Finding the Connective Core of a Complex Network

A network measure called knotty-centrality is defined that quantifies the extent to which a given subset of a graph’s nodes constitutes a densely intra-connected topologically central connective core. Using this measure, the knotty centre of a network is defined as a sub-graph with maximal knotty-centrality. A heuristic algorithm for finding subsets of a network with high knotty-centrality is presented, and this is applied to previously published brain structural connectivity data for the cat and the human, as well as to a number of other networks. The cognitive implications of possessing a connective core with high knotty-centrality are briefly discussed

The brain's connective core and its role in animal cognition

This paper addresses the question of how the brain of an animal achieves cognitive integration—that is to say how it manages to bring its fullest resources to bear on an ongoing situation. To fully exploit its cognitive resources, whether inherited or acquired through experience, it must be possible for unanticipated coalitions of brain processes to form. This facilitates the novel recombination of the elements of an existing behavioural repertoire, and thereby enables innovation. But in a system comprising massively many anatomically distributed assemblies of neurons, it is far from clear how such open-ended coalition formation is possible. The present paper draws on contemporary findings in brain connectivity and neurodynamics, as well as the literature of artificial intelligence, to outline a possible answer in terms of the brain's most richly connected and topologically central structures, its so-called connective core

What lies between market and hierarchy? Insights from internalization theory and global value chain theory

In this paper, we suggest that internalization theory might be extended by incorporating complementary insights from GVC theory. More specifically, we argue that internalization theory can explain why lead firms might wish to externalize selected activities, but that it is largely silent on the mechanisms by which those lead firms might exercise control over the resultant externalized relationships with their GVC partners. We advance an explanation linking the choice of control mechanism to two factors: power asymmetries between the lead firms and their GVC partners, and the degree of codifiability of the information to be exchanged in the relationship

Prevalence and genetic diversity of Avipoxvirus in house sparrows in Spain

Avipoxvirus (APV) is a fairly common virus affecting birds that causes morbidity and mortality in wild and captive birds. We studied the prevalence of pox-like lesions and genetic diversity of APV in house sparrows (Passer domesticus) in natural, agricultural and urban areas in southern Spain in 2013 and 2014 and in central Spain for 8 months (2012±2013). Overall, 3.2% of 2,341 house sparrows visually examined in southern Spain had cutaneous lesions consistent with avian pox. A similar prevalence (3%) was found in 338 birds from central Spain. Prevalence was higher in hatch-year birds than in adults. We did not detect any clear spatial or temporal patterns of APV distribution. Molecular analyses of poxvirus-like lesions revealed that 63% of the samples were positive. Molecular and phylogenetic analyses of 29 DNA sequences from the fpv167 gene, detected two strains belonging to the canarypox clade (subclades B1 and B2) previously found in Spain. One of them appears predominant in Iberia and North Africa and shares 70% similarity to fowlpox and canarypox virus. This APV strain has been identified in a limited number of species in the Iberian Peninsula, Morocco and Hungary. The second one has a global distribution and has been found in numerous wild bird species around the world. To our knowledge, this represents the largest study of avian poxvirus disease in the broadly distributed house sparrow and strongly supports the findings that Avipox prevalence in this species in South and central Spain is moderate and the genetic diversity low.This study was funded by the Spanish Ministry of Science and Innovation (Project CGL2010-15734/BOS), the Spanish Ministry of Economy and Competitiveness (Project CGL2013-41642-P/BOS) and the Innovation and Development Agency of Andalusia (Spain) (P11-RNM-7038). Grants were awarded to JMP (Juan de la Cierva- JCI-2012-11868) and MAJM (FPIBES-2011-047609), Spanish Ministry of Economy and Competitiveness; RAJW (CEI-PICATA2012), CEI Campus of International Excellence; MM (FPU12/0568), Spanish Ministry of Education, Culture and Sports. RAJW was supported by the Craaford Foundation (grant 20160971) during the writing of this publication. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript