Sex-specific topology of the nociceptive circuit shapes dimorphic behavior in C. elegans

How sexually dimorphic behavior is encoded in the nervous system is poorly understood. Here, we characterize the dimorphic nociceptive behavior in C. elegans and study the underlying circuits, which are composed of the same neurons but are wired differently. We show that while sensory transduction is similar in the two sexes, the downstream network topology markedly shapes behavior. We fit a network model that replicates the observed dimorphic behavior in response to external stimuli, and use it to predict simple network rewirings that would switch the behavior between the sexes. We then show experimentally that these subtle synaptic rewirings indeed flip behavior. Strikingly, when presented with aversive cues, rewired males were compromised in finding mating partners, suggesting that network topologies that enable efficient avoidance of noxious cues have a reproductive "cost". Our results present a deconstruction of the design of a neural circuit that controls sexual behavior, and how to reprogram it.

Landscape of epithelial-mesenchymal plasticity as an emergent property of coordinated teams in regulatory networks

Elucidating the principles of cellular decision-making is of fundamental importance. These decisions are often orchestrated by underlying regulatory networks. While we understand the dynamics of simple network motifs, how do large networks lead to a limited number of phenotypes, despite their complexity, remains largely elusive. Here, we investigate five different networks governing epithelial-mesenchymal plasticity and identified a latent design principles in their topology that limits their phenotypic repertoire - the presence of two 'teams' of nodes engaging in a mutually inhibitory feedback loop, forming a toggle switch. These teams are specific to these networks and directly shape the phenotypic landscape and consequently the frequency and stability of terminal phenotypes vs. the intermediary ones. Our analysis reveals that network topology alone can contain information about phenotypic distributions it can lead to, thus obviating the need to simulate them. We unravel topological signatures that can drive canalization of cell-fates during diverse decision-making processes.

FISCHER, Frank y MILLING, Carsten. Easy Linavis. Simple Network Visualization for Literary Texts, 2017.

En este texto se reseña la herramienta digital online Easy Linavis (Ezlinavis), desarrollada principalmente para la extracción de datos sobre personajes de un texto dramático y para generar la visualización de los mismos en grafos o redes. Se analizarán sus distintas funcionalidades a través de la descripción de la herramienta. Finalmente, se presentarán unas conclusiones sobre la experiencia de uso y las posibilidades de Ezlinavis como recurso y como proyecto de Humanidades Digitales.

On the von Neumann entropy of language networks: Applications to cross-linguistic comparisons

Words are not isolated entities within a language. In this paper, we measure the number of choices transmitted in natural language by means of the von Neumann entropy of language networks. This quantity, introduced in Quantum Information accounts, provides a detailed characterization of network complexities. The simulations are based on a large parallel corpus of 362 languages across 55 linguistic families (focusing on the sub-sample of 85 languages from the Americas). With this, we constructed language networks as a simple way to describe word connectivity patterns for each language. We studied several aspects of the von Neumann entropy of language networks. First, we discovered large groups of languages with low average degree and high von Neumann entropy. The results suggested also that large von Neumann entropy is associated with word entropy (as a proxy for morphological complexity), and is inversely related to degree regularity. This means that there are pressures at play that keep a balance between word morphological complexity and patterns of connections between words. We suggested also a strong influence of functional words on low von Neumann entropy languages. Our approach is thus a simple network-based contribution to establish cross-linguistic language comparisons from textual data.

Comparison of Deep Neural Network Models and Effectiveness of EMG Signal Feature Value for Estimating Dorsiflexion

(1) Background: Robotic ankle–foot orthoses (AFO) are often used for gait rehabilitation. Our research focuses on the design and development of a robotic AFO with minimum number of sensor inputs. However, this leads to degradation of gait estimation accuracy. (2) Methods: To prevent degradation of accuracy, we compared a few neural network models in order to determine the best network when only two input channels are being used. Further, the EMG signal feature value of average rate of change was used as input. (3) Results: LSTM showed the highest accuracy. However, MLP with a small number of hidden layers showed results similar to LSTM. Moreover, the accuracy for all models, with the exception of LSTM for one subject (SD), increased with the addition of feature value (average rate of change) as input. (4) Conclusions: In conclusion, time-series networks work best with a small number of sensor inputs. However, depending on the optimizer being used, even a simple network can outrun a deep learning network. Furthermore, our results show that applying EMG signal feature value as an input tends to increase the estimation accuracy of the network.

Cryptography in Hierarchical Coded Caching: System Model and Cost Analysis

The idea behind network caching is to reduce network traffic during peak hours via transmitting frequently-requested content items to end users during off-peak hours. However, due to limited cache sizes and unpredictable access patterns, this might not totally eliminate the need for data transmission during peak hours. Coded caching was introduced to further reduce the peak hour traffic. The idea of coded caching is based on sending coded content which can be decoded in different ways by different users. This allows the server to service multiple requests by transmitting a single content item. Research works regarding coded caching traditionally adopt a simple network topology consisting of a single server, a single hub, a shared link connecting the server to the hub, and private links which connect the users to the hub. Building on the results of Sengupta et al. (IEEE Trans. Inf. Forensics Secur., 2015), we propose and evaluate a yet more complex system model that takes into consideration both throughput and security via combining the mentioned ideas. It is demonstrated that the achievable rates in the proposed model are within a constant multiplicative and additive gap with the minimum secure rates.

Explainable U-Net model forMedical Image Segmentation

In this nutshell, we propose a simple, efficient, and explainable deep learning-based U-Net algorithm for the MedAI challenge, focusing on precise segmentation of polyp and instrument and transparency on algorithms. We develop a straightforward encoder-decoder-based algorithm for the task above. We make an effort to make a simple network as much as possible. Specially, we focus on input resolution and width of the model to find the best optimal settings for the network. We perform ablation studies to cover this aspect.

Co-authorship network structure analysis

The analysis of networks of collaboration between scientists reveals features of academic communities that help in understanding the specifics of collaborative scientific work and identifying the notable researchers. In these networks, the set of nodes consists of authors and there exists a link between two authors if they have coauthored one or more papers. This article presents an analysis of the co-authorship network based on bibliometric data retrieved from the distributed economic database. Here we use the simple network model without taking into account the strength of collaborative ties. The data were analyzed using statistical techniques in order to get such parameters as the number of papers per author, the number of authors per paper, the average number of coauthors per author and collaboration indices. We show that the largest component occupies near 90 % of the network and the node degree distribution follows a power-law. The study of typical distances between nodes and the degree of clustering makes it possible to classify the network as a ‘small world’ network.

Object Detection Via Flexible Anchor Generation

This paper designs a method that can generate anchors of various shapes for the object detection framework. This method has the characteristics of novelty and flexibility. Different from the previous anchors generated by a pre-defined manner, our anchors are generated dynamically by an anchor generator. Specially, the anchor generator is not fixed but learned from the hand-designed anchors, which means that our anchor generator is able to work well in various scenes. In the inference time, the weights of anchor generator are estimated by a simple network where the input is some hand-designed anchor. In addition, in order to make the difference between the number of positive and negative samples smaller, we use an adaptive IOU threshold related to the object size to solve this problem. At the same time, we proved that our proposed method is effective and conducted a lot of experiments on the COCO dataset. Experimental results show that after replacing the anchor generation method in the previous object detectors (such as SSD, mask RCNN, and Retinanet) with our proposed method, the detection performance of the model has been greatly improved compared to before the replacement, which proves our method is effective.

Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features

With the rapid development of single-cell sequencing techniques, several large-scale cell atlas projects have been launched across the world. However, it is still challenging to integrate single-cell RNA-seq (scRNA-seq) datasets with diverse tissue sources, developmental stages and/or few overlaps, due to the ambiguity in determining the batch information, which is particularly important for current batch-effect correction methods. Here, we present SCORE, a simple network-based integration methodology, which incorporates curated molecular network features to infer cellular states and generate a unified workflow for integrating scRNA-seq datasets. Validating on real single-cell datasets, we showed that regardless of batch information, SCORE outperforms existing methods in accuracy, robustness, scalability and data integration.