A simple regulatory architecture allows learning the statistical structure of a changing environment

Bacteria live in environments that are continuously fluctuating and changing. Exploiting any predictability of such fluctuations can lead to an increased fitness. On longer timescales, bacteria can ‘learn’ the structure of these fluctuations through evolution. However, on shorter timescales, inferring the statistics of the environment and acting upon this information would need to be accomplished by physiological mechanisms. Here, we use a model of metabolism to show that a simple generalization of a common regulatory motif (end-product inhibition) is sufficient both for learning continuous-valued features of the statistical structure of the environment and for translating this information into predictive behavior; moreover, it accomplishes these tasks near-optimally. We discuss plausible genetic circuits that could instantiate the mechanism we describe, including one similar to the architecture of two-component signaling, and argue that the key ingredients required for such predictive behavior are readily accessible to bacteria.

Multi-Scale Modeling of Mechanobiological Behavior of Bone

The simulation and theoretical or numerical predictive modeling of the development and growth of biological tissues mainly in the case of bone is a complicated task. As a result, many and various knowledge tools required (experimental, theoretical and numerical) are not yet mastered and even discovered. We will cite here some techniques and methods as well as results specific to the multi-scale numerical modeling methodology, and multiphysics using finite element coupling with neural network computation of biological tissues applied to the predictive behavior of cortical bone based of the microstructure of their local constituents and their reconstruction according to local mechanobiology. It follows that additional work is necessary to give more precision on the different models, the considered approaches show their potential utility to understand this behavior in terms of biological evolutions as well as the subsequent use in medical applications.

Improve Safety at Highway Crossings Through Predictive Behavior and Innovative Technologies

The science and technology used in highway crossings in the United States and around the world have come a long way from a single flagman sitting in a booth, equipped with a red flag or lantern in his hand, to clear tracks and stop pedestrians, horses, and or a motor coaches for an approaching train to a fully automatic warning system requiring limited monthly testing. Today’s highway crossings are monitored by railways and municipalities such that, any changes in railway or roadway traffic conditions can be scrutinized. These changes, an increase in train or vehicle traffic, may trigger the need for additional protection devices to be implemented to make highway crossings safer for all; passing trains, motorists and pedestrians. But, are these requirements enough to eliminate accidents. Historically speaking, these accidents range from; failures with the activation of the warning system; distracted motorists or motorists not willing to comply to the warning; pedestrians rushing to beat the train while underestimating the trains speed or; fully knowing and willing to not stop at the flashing lights and gates and willing to take the risk and go around flashing gates. This paper will investigate the current and future technologies that are being tested and implemented on highway crossings as well as look into the predictive behavior of motorists and pedestrians as they approach crossings and how changes can be implemented to maximize the effectiveness of a highway crossing. Key elements from various studies will be included that have been suggested through analyzing driver’s behavior at highway crossings, as well as the additional technologies that have and can be implemented to provide additional warnings to alert motorist of trains approaching.

Variables de riesgo y protección relacionadas con la tentativa de suicidio

Risk and protection variables related to suicidal attempt. Suicide is an important public health problem, being the suicidal attempt the most predictive behavior of completed suicide. The aim of this study was to detect if there are differences in psychosocial and emotional variables in people with and without suicidal ideation and attempt. The sample consisted of 166 participants (86.36% women), aged between 20 and 77 years (M= 36, SD= 14.12) with and without suicide attempts, which was in turn divided into three groups through the Scale of Suicidal Ideation. The results show that there are important differences between the three groups in the psychological variables measured. We discuss the role of psychosocial variables, which are at the base of the increased risk or protection towards the ideation or suicidal attempt, to promote public suicide prevention policies more focused on those clinical subpopulations with specific risk profiles.Resumen: El suicidio es un importante problema de salud pública, siendo la tentativa de suicidio la conducta más predictiva del suicidio consumado. Mediante el presente estudio se pretende detectar si existen diferencias en variables psicosociales y emocionales en personas con y sin ideación y tentativa suicida. La muestra estuvo constituida por 166 participantes (86.36 % mujeres), con edades comprendidas entre los 20 y 77 años (M= 36; DT= 14.12) con y sin tentativas suicidas, que fue a su vez dividida en tres grupos a través de la Escala de Ideación Suicida. Los resultados muestran que existen importantes diferencias entre los tres grupos en las variables psicológicas medidas. Se discute el papel de las variables psicosociales, que están en la base del incremento del riesgo o protección hacia la ideación o tentativa suicida, para propiciar políticas públicas de prevención del suicidio más centradas en aquellas subpoblaciones clínicas con perfiles de riesgo concretos.

Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks

Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.