Contribution of a time-dependent metric on the dynamics of an interface between two immiscible electro-magnetically controllable Fluids

We consider the case of a deformable material interface between two immiscible moving media, both of them being magnetiable. The time dependence of the metric at the interface introduces a non linear term, proportional to the mean curvature, in the surface dynamical equations of mass momentum and angular momentum. We take into account the effects of that term also in the singular magnetic and electric fields inside the interface which lead to the existence of currents and charges densities through the interface, from the derivation of the Maxwell equations inside both bulks and the interface. Also, we give the expression for the entropy production and of the different thermo-dynamical fluxes. Our results enlarge previous results from other theories where the specific role of the time dependent surface metric was insufficiently stressed.Comment: 25 page

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anticancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anticancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anticancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties

Towards AI-driven longevity research : an overview

202405 bcrcVersion of RecordPublishedC

Lack of consensus on an aging biology paradigm? A global survey reveals an agreement to disagree, and the need for an interdisciplinary framework

At a recent symposium on aging biology, a debate was held as to whether or not we know what biological aging is. Most of the participants were struck not only by the lack of consensus on this core question, but also on many basic tenets of the field. Accordingly, we undertook a systematic survey of our 71 participants on key questions that were raised during the debate and symposium, eliciting 37 responses. The results confirmed the impression from the symposium: there is marked disagreement on the most fundamental questions in the field, and little consensus on anything other than the heterogeneous nature of aging processes. Areas of major disagreement included what participants viewed as the essence of aging, when it begins, whether aging is programmed or not, whether we currently have a good understanding of aging mechanisms, whether aging is or will be quantifiable, whether aging will be treatable, and whether many non-aging species exist. These disagreements lay bare the urgent need for a more unified and cross-disciplinary paradigm in the biology of aging that will clarify both areas of agreement and disagreement, allowing research to proceed more efficiently. We suggest directions to encourage the emergence of such a paradigm