Proprioception and Control of a Soft Pneumatic Actuator Made of a Self-Healable Hydrogel.

The current evolutionary trends in soft robotics try to exploit the capacities of smart materials to achieve compact robotics designs with embodied intelligence. In this way, the number of elements that compose the soft robot can be reduced, as the smart material can cover different aspects (e.g., structure and sensorization) all in one. This work follows this tendency and presents a custom-designed hydrogel that exhibits two smart features, self-healing and ionic conductivity, used to build a pneumatic actuator. The self-healing capability provides the actuator's structure with the ability to self-repair from damages (e.g., punctures or cuts), an important quality to prolong the life cycle of the actuator. The ionic conductivity enables the actuator's proprioception: the structure itself serves as a curvature sensor. The behavior of this proprioceptive curvature sensor is analyzed in this work, studying its linearity, stability, and performance after a self-healing process. This sensor is also proposed as feedback in a closed-loop scheme to automatically control the actuator's curvature. A proportional-integral-derivative controller is designed based on an empirical model of the actuator's dynamics, and then validated in experimental tests, proving the proprioceptive sensor as proper feedback. These control tests are performed over undamaged and self-healed actuators, thus demonstrating all the capabilities of our soft material.

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W(k)(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation).

Current topics on software use in medicinal chemistry: intellectual property, taxes, and regulatory issues.

In recent times, there has been an increased use of software and computational models in Medicinal Chemistry, both for the prediction of effects such as drug-target interactions, as well as for the development of (Quantitative) Structure-Activity Relationships ((Q)SAR). Whilst the ultimate goal of Medicinal Chemistry research is for the discovery of new drug candidates, a secondary yet important outcome that results is in the creation of new computational tools. The adoption of computational tools by medicinal chemists is sadly, and all too often accompanied, by a lack of understanding of the legal aspects related to software and model use, that is, the copyright protection of new medicinal chemistry software and software-mediated discovered products. This article aims to provide a reference to the various legal avenues that are available for the protection of software, and the acceptance and legal treatment of scientific results and techniques derived from such software. An overview of relevant international tax issues is also presented. We have considered cases of patents protecting software, models, and/or new compounds discovered using methods such as molecular modeling or QSAR. This paper has been written and compiled by the authors as a review of current topics and trends on the legal issues in certain fields of Medicinal Chemistry and as such is not intended to be exhaustive.