Newest and Lowest Rated Papers
New insights into the pathogenicity of leptospires: evasion of host defences.
Marina Cinco
The new microbiologica·2010
Major progress has been made in the basic research of leptospirosis a global zoonotic disease. Recent knowledge on the genome of L. interrogans and the emergence of new genetic tools for comparative genetic studies have further developed research into the genetic pathogenesis of this illness. Many of these studies have compared the putative pathogenicity factors found in L. interrogans, with representative strains of saprophytic leptospires. Leptospires display a rich repertoire of adhesins endowed with multifunctional biological activities such as adhesion to host tissue components, plasminogen activation, resistance to complement. These adhesins are proteins or liproteins located on the outer membrane. Some of them (LenA) escape innate defence such as complement killing and some escape phagocytosis. Much work has to be done to elucidate many other aspects of Leptospira pathogenic factors such as those switched on in chronic infection.
No ratings yet
View paper →dentities, Experience, and Change in Early Mexican Villages
Diehl, Richard A., Hepp, Guy David, Santasilia, Catharina E.
muse.jhu.edu·2022
No abstract available
No ratings yet
View paper →Contemporary Symbolic Regression Methods and their Relative Performance
La Cava, William, Orzechowski, Patryk, Burlacu, Bogdan, de Franca, Fabricio, Virgolin, Marco, Jin, Ying, Kommenda, Michael, Moore, Jason
datasets-benchmarks-proceedings.neurips.cc
No abstract available
No ratings yet
View paper →Building machines that learn and think like people
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman
Cambridge Core·2016
Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.
No ratings yet
View paper →Blockade of Chronic Type I Interferon Signaling to Control Persistent LCMV Infection
Elizabeth B. Wilson, Douglas H. Yamada, Heidi Elsaesser, Jonathan Herskovitz, Jane Deng, Genhong Cheng, Bruce J. Aronow, Christopher L. Karp, David G. Brooks
Science·2013·642 citations
<jats:title>INTERFER(ON)ing Persistence</jats:title>
<jats:p>
During persistent viral infections, a dysregulated immune response fails to control the infection.
<jats:bold>
Wilson
<jats:italic>et al.</jats:italic>
</jats:bold>
(p.
<jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6129" page="202" related-article-type="in-this-issue" vol="340" xlink:href="10.1126/science.1235208">202</jats:related-article>
) and
<jats:bold>
Teijaro
<jats:italic>et al.</jats:italic>
</jats:bold>
(p.
<jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6129" page="207" related-article-type="in-this-issue" vol="340" xlink:href="10.1126/science.1235214">207</jats:related-article>
; see the Perspective by
<jats:bold>
<jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6129" page="155" related-article-type="in-this-issue" vol="340" xlink:href="10.1126/science.1237568">Odorizzi and Wherry</jats:related-article>
</jats:bold>
) show this occurs because type I interferons (IFN I), critical for early responses to viral infection, contribute to the altered immunity seen during persistent infection. Antibody blockade of IFN I signaling during chronic lymphocytic choriomeningitis virus (LCMV) in mice resulted in reduced viral titers at later stages of infection, reduced expression of inhibitory immune molecules and prevented the disruptions to secondary lymphoid organs typically observed during persistent infection with LCMV. Whether type I IFNs are also detrimental to persistent viral infection humans, such as HIV and hepatitis C virus, remains to be determined.
</jats:p>
No ratings yet
View paper →Rebalancing viral and immune damage versus repair prevents death from lethal influenza infection
Hiroshi Ichise, Emily Speranza, Federica La Russa, Tibor Z. Veres, Colin J. Chu, Anita Gola, Beatrice H. Clark, Ronald N. Germain
Science·2025
<jats:p>
Maintaining tissue function while eliminating infected cells is fundamental, and inflammatory damage plays a major contribution to lethality after lung infection. We tested 50 immunomodulatory regimes to determine their ability to protect mice from lethal infection. Only neutrophil depletion soon after infection prevented death from influenza. This result suggests that the infected host passed an early tipping point after which limiting innate damage alone could not rescue lung function. We investigated treatments that could have efficacy when administered later in infection. We found that partial limitation of viral spread together with enhancement of epithelial repair, by interferon blockade or limiting CD8
<jats:sup>+</jats:sup>
T cell–mediated killing of epithelial cells, reduced lethality. This finding highlights the importance of rebalancing repair and damage processes in the survival of pulmonary infections.
</jats:p>
No ratings yet
View paper →TIME, WORK-DISCIPLINE, AND INDUSTRIAL CAPITALISM
E. P. Thompson
Past and Present·1967·2192 citations
No abstract available
No ratings yet
View paper →Inductive biases for deep learning of higher-level cognition
Anirudh Goyal, Yoshua Bengio
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences·2022·140 citations
<jats:p>A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behaviour of complex systems like brains, and substantial computation might be needed to simulate human-like intelligence. This hypothesis would suggest that studying the kind of inductive biases that humans and animals exploit could help both clarify these principles and provide inspiration for AI research and neuroscience theories. Deep learning already exploits several key inductive biases, and this work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans’ abilities in terms of flexible out-of-distribution and systematic generalization, which is currently an area where a large gap exists between state-of-the-art machine learning and human intelligence.</jats:p>
No ratings yet
View paper →Neural Networks and Neuroscience-Inspired Computer Vision
David Daniel Cox, Thomas Dean
Current Biology·2014·168 citations
No abstract available
No ratings yet
View paper →Generating meaning: active inference and the scope and limits of passive AI
Giovanni Pezzulo, Thomas Parr, Paul Cisek, Andy Clark, Karl Friston
Trends in Cognitive Sciences·2024·43 citations
No abstract available
No ratings yet
View paper →Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network
Ikhwan Jeon, Taegon Kim
Frontiers in Computational Neuroscience·2023·12 citations
<jats:p>Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.</jats:p>
No ratings yet
View paper →