Communicative habitus and social distancing. Effects on the modes of sign production during the COVID-19 pandemic

In the year 2020, in the context of the SARS-CoV-2 pandemic, Mexico’s government implemented health policies of “social distancing”. In this essay, we make a theoretical reflection on this policies’ effects on signs’ production. The paper begins with our definition of communicational habitus and the semiotic relationship with social distancing to analyze systems’ evolutionary control from a systemic-semiotic approach.

Using a Human Interviewer or an Automatic Interviewer in the Evaluation of Patients with AD from Speech

Currently, there are more and more frequent studies focused on the evaluation of Alzheimer’s disease (AD) from the automatic analysis of the speech of patients, in order to detect the presence of the disease in an individual or for the evolutionary control of the disease. However, studies focused on analyzing the effect of the methodology used to generate the spontaneous speech of the speaker who undergoes this type of analysis are rare. The objective of this work is to study two different strategies to facilitate the generation of the spontaneous speech of a speaker for further analysis: the use of a human interviewer that promotes the generation of speech through an interview and the use of an automatic system (an automatic interviewer) that invites the speaker to describe certain visual stimuli. In this study, a database called Cross-Sectional Alzheimer Prognosis R2019 has been created, consisting of speech samples from speakers recorded using both methodologies. The speech recordings have been studied through a feature extraction based on five basic temporal measurements. This study demonstrates the discriminatory capacity between the speakers with AD and the control subjects independent of the strategy used in the generation of spontaneous speech. These results are promising and can serve as a basis for knowing the effectiveness and extension of automated interview processes, especially in telemedicine and telecare scenarios.

Cultural and Biological Evolution

Although there is no necessary relation between “big data” and “monism”—the program of reducing cultural and mental processes to computational and biological principles—both these programs reject a dualism between res extensa and res cogitans. Opposing this philosophy of science, I have argued in the above chapter that a second contingency of possible relations and expectations feeds back on the manifest relations. This second contingency cannot be studied from a natural-science or life-sciences perspective, but is the proper domain of the social sciences, where the focus is on what things mean as different from what they are. Next-order selection mechanisms can take evolutionary control. The complexity of the communication evolves against the arrow of time in terms of interacting codes, which generate redundancies and therefore new options. As human beings, we can follow the potentially unintended consequences of the communication dynamics reflexively. Both consciousness and communication are self-organizing and thus resilient against steering.

Eco-evolutionary control of pathogens

Control can alter the eco-evolutionary dynamics of a target pathogen in two ways, by changing its population size and by directed evolution of new functions. Here, we develop a payoff model of eco-evolutionary control based on strategies of evolution, regulation, and computational forecasting. We apply this model to pathogen control by molecular antibody–antigen binding with a tunable dosage of antibodies. By analytical solution, we obtain optimal dosage protocols and establish a phase diagram with an error threshold delineating parameter regimes of successful and compromised control. The solution identifies few independently measurable fitness parameters that predict the outcome of control. Our analysis shows how optimal control strategies depend on mutation rate and population size of the pathogen, and how monitoring and computational forecasting affect protocols and efficiency of control. We argue that these results carry over to more general systems and are elements of an emerging eco-evolutionary control theory.

Optimal evolutionary control for artificial selection on molecular phenotypes

Controlling an evolving population is an important task in modern molecular genetics, including in directed evolution to improve the activity of molecules and enzymes, breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens. An optimal intervention to direct evolution should be designed by considering its impact over an entire stochastic evolutionary trajectory that follows. As a result, a seemingly suboptimal intervention at a given time can be globally optimal as it can open opportunities for more desirable actions in the future. Here, we propose a feedback control formalism to devise globally optimal artificial selection protocol to direct the evolution of molecular phenotypes. We show that artificial selection should be designed to counter evolutionary tradeoffs among multi-variate phenotypes to avoid undesirable outcomes in one phenotype by imposing selection on another. Control by artificial selection is challenged by our ability to predict molecular evolution. We develop an information theoretical framework and show that molecular time-scales for evolution under natural selection can inform how to monitor a population to acquire sufficient predictive information for an effective intervention with artificial selection. Our formalism opens a new avenue for devising optimal artificial selection for directed evolution of molecular functions.

Eco-evolutionary control of pathogens

Control can alter the eco-evolutionary dynamics of a target pathogen in two ways, by changing its population size and by directed evolution of new functions. Here we develop a fitness model of eco-evolutionary control that specifies a minimum leverage for successful control against the intrinsic dynamics of the pathogen. We apply this model to pathogen control by molecular antibody-antigen binding with a tunable level of antibodies. By analytical solution, we obtain a phase diagram of optimal control and show that an error threshold separates regimes of successful and futile control. Our analysis identifies few, independently measurable fitness parameters that predict the outcome of control. We show that optimal control strategies depend on mutation rate and population size of the pathogen, and we discuss how monitoring and computational forecasting affect the efficiency of control. We argue that these results carry over to more general systems and are elements of an emerging eco-evolutionary control theory.