The Frequent Complete Subgraphs in the Human Connectome

While it is still not possible to describe the neural-level connections of the human brain, we can map the human connectome with several hundred vertices, by the application of diffusion-MRI based techniques. In these graphs, the nodes correspond to anatomically identified gray matter areas of the brain, while the edges correspond to the axonal fibers, connecting these areas. In our previous contributions, we have described numerous graph-theoretical phenomena of the human connectomes. Here we map the frequent complete subgraphs of the human brain networks: in these subgraphs, every pair of vertices is connected by an edge. We also examine sex differences in the results. The mapping of the frequent subgraphs gives robust substructures in the graph: if a subgraph is present in the 80% of the graphs, then, most probably, it could not be an artifact of the measurement or the data processing workflow. We list here the frequent complete subgraphs of the human braingraphs of 414 subjects, each with 463 nodes, with a frequency threshold of 80%, and identify 812 complete subgraphs, which are more frequent in male and 224 complete subgraphs, which are more frequent in female connectomes

Building Protein Domain Based Composite Biobricks for Mammalian Expression Systems

The purpose of this RFC is to describe a method that allows the design of protein domain based parts, starting with gene centered information and translate these informations into BBF RFC 25 compatible part. The method is designed to be used in mammalian expression systems

Internal wettability investigation of mesoporous silica materials by ellipsometric porosimetry

Silica-based mesoporous films have been widely applied in the fabrication of advanced functional materials, such as anti-reflective coatings, bio-, and chemical sensing devices, due to their unique properties, e.g., high surface area, controlled porosity, and the ease and tailorability of their synthesis. Precise knowledge of their pore architecture is crucial, highlighting the need for accurate characterization tools. In this sense, ellipsometric porosimetry represents a powerful and versatile characterization platform, providing access to reliable information about total porosity, pore size, pore size dispersity, mechanical properties (Young's modulus) and surface area of a great variety of mesoporous thin films. While the underlying framework of modeling capillary condensation via the Kelvin equation is well established, one descriptor, the internal wettability of mesoporous architectures remains a challenging variable for reliable material characterization. Wetting on the nanoscale cannot be observed via the traditional drop-shape method, while approximating internal wetting by the macroscopic property can be inaccurate as the two wetting behaviors do not necessarily correlate. Herein, we present a method based on vacuum ellipsometric porosimetry for the determination of the internal contact angle of functionalized mesoporous silica thin films. Tuning of the surface energy for a known mesoporous architecture by methyl-functionalization enabled us to relate differences in the pore filling for various adsorptives (water, methanol, toluene, cyclohexane) to their internal contact angles. Our study serves as a guide for generalized internal contact angle determination suitable for a wide range of organic adsorptives and mesoporous sorbent materials

Taxonomy based on science is necessary for global conservation

Peer reviewe

Flowification: Everything is a normalizing flow

The two key characteristics of a normalizing flow is that it is invertible (in particular, dimension preserving) and that it monitors the amount by which it changes the likelihood of data points as samples are propagated along the network. Recently, multiple generalizations of normalizing flows have been introduced that relax these two conditions. On the other hand, neural networks only perform a forward pass on the input, there is neither a notion of an inverse of a neural network nor is there one of its likelihood contribution. In this paper we argue that certain neural network architectures can be enriched with a stochastic inverse pass and that their likelihood contribution can be monitored in a way that they fall under the generalized notion of a normalizing flow mentioned above. We term this enrichment flowification. We prove that neural networks only containing linear layers, convolutional layers and invertible activations such as LeakyReLU can be flowified and evaluate them in the generative setting on image datasets.Comment: NeurIPS 202