A Quantum-like Model of Interdependence for Embodied Human-Machine Teams: Reviewing the Path to Autonomy Facing Complexity and Uncertainty.

In this review, our goal is to design and test quantum-like algorithms for Artificial Intelligence (AI) in open systems to structure a human-machine team to be able to reach its maximum performance. Unlike the laboratory, in open systems, teams face complexity, uncertainty and conflict. All task domains have complexity levels-some low, and others high. Complexity in this new domain is affected by the environment and the task, which are both affected by uncertainty and conflict. We contrast individual and interdependence approaches to teams. The traditional and individual approach focuses on building teams and systems by aggregating the best available information for individuals, their thoughts, behaviors and skills. Its concepts are characterized chiefly by one-to-one relations between mind and body, a summation of disembodied individual mental and physical attributes, and degrees of freedom corresponding to the number of members in a team; however, this approach is characterized by the many researchers who have invested in it for almost a century with few results that can be generalized to human-machine interactions; by the replication crisis of today (e.g., the invalid scale for self-esteem); and by its many disembodied concepts. In contrast, our approach is based on the quantum-like nature of interdependence. It allows us theorization about the bistability of mind and body, but it poses a measurement problem and a non-factorable nature. Bistability addresses team structure and performance; the measurement problem solves the replication crisis; and the non-factorable aspect of teams reduces the degrees of freedom and the information derivable from teammates to match findings by the National Academies of Science. We review the science of teams and human-machine team research in the laboratory versus in the open field; justifications for rejecting traditional social science while supporting our approach; a fuller understanding of the complexity of teams and tasks; the mathematics involved; a review of results from our quantum-like model in the open field (e.g., tradeoffs between team structure and performance); and the path forward to advance the science of interdependence and autonomy.

Automated SSHHPS Analysis Predicts a Potential Host Protein Target Common to Several Neuroinvasive (+)ssRNA Viruses.

Within the viral genome, short stretches of homologous host pathogen sequences (SSHHPS) span the protease cleavage sites. To identify host proteins that may be cleaved during infection, we searched the human proteome for viral protease cleavage sites (~20 amino acids). We developed a sequence-to-symptom tool, automating the search and pairing process. We used the viral protein sequence, PHI-BLAST, and UniProt database for gene ontologies and disease relationships. We applied the tool to nine neuroinvasive viruses: Venezuelan and Eastern Equine encephalitis virus (VEEV, EEEV); severe acute respiratory syndrome (SARS, SARS-CoV-2); Middle East respiratory syndrome (MERS); EV-71; Japanese encephalitis virus (JEV); West Nile (WNV); and Zika (ZIKV). A comparison of the hits identified a protein common to all nine viruses called ADGRA2 (GPR124). ADGRA2 was a predicted hit of the 3CL main protease and papain-like protease (PLpro) of SARS-CoV-2. ADGRA2 is an adhesion G protein-coupled receptor and a key endothelial regulator of brain-specific angiogenesis. It is a Wnt7A/Wnt7B specific coactivator of beta-catenin signaling and is essential for blood-brain barrier (BBB) integrity in central nervous system (CNS) diseases. We show the cleavage of the predicted sequences in MYOM1, VWF by the SARS-CoV-2 PLpro; DNAH8 (dynein) by the MERS PLpro; ADGRA2 by the alphaviral VEEV nsP2 protease; and POT1 by the SARS-CoV-2 and MERS PLpro.

The SARS-CoV-2 SSHHPS Recognized by the Papain-like Protease.

Viral proteases are highly specific and recognize conserved cleavage site sequences of ∼6-8 amino acids. Short stretches of homologous host-pathogen sequences (SSHHPS) can be found spanning the viral protease cleavage sites. We hypothesized that these sequences corresponded to specific host protein targets since >40 host proteins have been shown to be cleaved by Group IV viral proteases and one Group VI viral protease. Using PHI-BLAST and the viral protease cleavage site sequences, we searched the human proteome for host targets and analyzed the hit results. Although the polyprotein and host proteins related to the suppression of the innate immune responses may be the primary targets of these viral proteases, we identified other cleavable host proteins. These proteins appear to be related to the virus-induced phenotype associated with Group IV viruses, suggesting that information about viral pathogenesis may be extractable directly from the viral genome sequence. Here we identify sequences cleaved by the SARS-CoV-2 papain-like protease (PLpro) in vitro within human MYH7 and MYH6 (two cardiac myosins linked to several cardiomyopathies), FOXP3 (an X-linked Treg cell transcription factor), ErbB4 (HER4), and vitamin-K-dependent plasma protein S (PROS1), an anticoagulation protein that prevents blood clots. Zinc inhibited the cleavage of these host sequences in vitro. Other patterns emerged from multispecies sequence alignments of the cleavage sites, which may have implications for the selection of animal models and zoonosis. SSHHPS/nsP is an example of a sequence-specific post-translational silencing mechanism.

Wavelength dependence of the bidirectional reflectance distribution function (BRDF) of beach sands.

The wavelength dependence of the dominant directional reflective properties of beach sands was demonstrated using principal component analysis and the related correlation matrix. In general, we found that the hyperspectral bidirectional reflectance distribution function (BRDF) of beach sands has weak wavelength dependence. Its BRDF varies slightly in three broad wavelength regions. The variations are more evident in surfaces of greater visual roughness than in smooth surfaces. The weak wavelength dependence of the BRDF of beach sand can be captured using three broad wavelength regions instead of hundreds of individual wavelengths.