When neuro-robots go wrong: A review.

Neuro-robots are a class of autonomous machines that, in their architecture, mimic aspects of the human brain and cognition. As such, they represent unique artifacts created by humans based on human understanding of healthy human brains. European Union's Convention on Roboethics 2025 states that the design of all robots (including neuro-robots) must include provisions for the complete traceability of the robots' actions, analogous to an aircraft's flight data recorder. At the same time, one can anticipate rising instances of neuro-robotic failure, as they operate on imperfect data in real environments, and the underlying AI behind such neuro-robots has yet to achieve explainability. This paper reviews the trajectory of the technology used in neuro-robots and accompanying failures. The failures demand an explanation. While drawing on existing explainable AI research, we argue explainability in AI limits the same in neuro-robots. In order to make robots more explainable, we suggest potential pathways for future research.

Explainable AI: A Neurally-Inspired Decision Stack Framework.

European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect data. This paper puts forward a neurally inspired theoretical framework called "decision stacks" that can provide a way forward in research to develop Explainable Artificial Intelligence (X-AI). By leveraging findings from the finest memory systems in biological brains, the decision stack framework operationalizes the definition of explainability. It then proposes a test that can potentially reveal how a given AI decision was made.

Nicotinic receptor targeting in physiological and environmental vulnerability: A whole of biosphere perspective.

We propose a biosphere model of convergent interactions between nicotine and neonicotinoids (neonics) within a related framework of nicotinic receptor targeting agents (NrTA) across the globe. We explore how rising global trends in the use nicotine as well as neonics impacts vulnerability, within and across species, and posit that evolutionary conservation at the nicotinic acetylcholine receptor (nAChR) provides an operational strategy map for pathogens and disease. Furthermore, we examine the effects of NrTA exposure on balance within extant and developing ecological niches, food chains, and human societies. We advocate for a global strategy for biomonitoring across agriculture, wildlife, and human centers. Such a strategy would relate emergent pathogenic and infectious diseases, amongst others, along a tractable biological stress pathway. This new framework aims to better prepare society in the face of emergent pandemics through 1. identifying primary chemical drivers that can impact emergent diseases; 2. outlining data-driven strategy options for health and environmental policy decision makers.

Does COVID19 Infect the Brain? If So, Smokers Might Be at a Higher Risk.

COVID19 is a devastating global pandemic with epicenters in China, Italy, Spain, and now the United States. While the majority of infected cases appear mild, in some cases, individuals present serious cardiorespiratory complications with possible long-term lung damage. Infected individuals report a range of symptoms from headaches to shortness of breath to taste and smell loss. To that end, less is known about how the virus may impact different organ systems. The SARS-CoV2 virus, which is responsible for COVID19, is highly similar to SARS-CoV. Both viruses have evolved an ability to enter host cells through direct interaction with the angiotensin converting enzyme (ACE) 2 protein at the surface of many cells. Published findings indicate that SARS-CoV can enter the human nervous system with evidence from both postmortem brains and detection in cerebrospinal fluid of infected individuals. Here, we consider the ability of SARS-CoV2 to enter and infect the human nervous system based on the strong expression of the ACE2 target throughout the brain. Moreover, we predict that nicotine exposure through various kinds of smoking (cigarettes, electronic cigarettes, or vape) can increase the risk for COVID19 neuroinfection based on known functional interactions between the nicotinic receptor and ACE2. We advocate for higher surveillance and analysis of neurocomplications in infected cases. SIGNIFICANCE STATEMENT: The COVID19 epidemic has spurred a global public health crisis. While many of the cases requiring hospitalization and intensive medical care center on cardiorespiratory treatment, a growing number of cases present neurological symptoms. Viral entry into the brain now appears a strong possibility with deleterious consequences and an urgent need for addressing.

Is nicotine exposure linked to cardiopulmonary vulnerability to COVID-19 in the general population?

The recent emergence of COVID-19 has resulted in a worldwide crisis, with large populations locked down and transportation links severed. While approximately 80% of infected individuals have minimal symptoms, around 15-20% need to be hospitalized, greatly stressing global healthcare systems. As of March 10, the death rate appears to be about 3.4%, although this number is highly stratified among different populations. Here, we focus on those individuals who have been exposed to nicotine prior to their exposure to the virus. We predict that these individuals are 'primed' to be at higher risk because nicotine can directly impact the putative receptor for the virus (ACE2) and lead to deleterious signaling in lung epithelial cells.

Microbial resolution of whole genome shotgun and 16S amplicon metagenomic sequencing using publicly available NEON data.

Microorganisms are ubiquitous in the biosphere, playing a crucial role in both biogeochemistry of the planet and human health. However, identifying these microorganisms and defining their function are challenging. Widely used approaches in comparative metagenomics, 16S amplicon sequencing and whole genome shotgun sequencing (WGS), have provided access to DNA sequencing analysis to identify microorganisms and evaluate diversity and abundance in various environments. However, advances in parallel high-throughput DNA sequencing in the past decade have introduced major hurdles, namely standardization of methods, data storage, reproducible interoperability of results, and data sharing. The National Ecological Observatory Network (NEON), established by the National Science Foundation, enables all researchers to address queries on a regional to continental scale around a variety of environmental challenges and provide high-quality, integrated, and standardized data from field sites across the U.S. As the amount of metagenomic data continues to grow, standardized procedures that allow results across projects to be assessed and compared is becoming increasingly important in the field of metagenomics. We demonstrate the feasibility of using publicly available NEON soil metagenomic sequencing datasets in combination with open access Metagenomics Rapid Annotation using the Subsystem Technology (MG-RAST) server to illustrate advantages of WGS compared to 16S amplicon sequencing. Four WGS and four 16S amplicon sequence datasets, from surface soil samples prepared by NEON investigators, were selected for comparison, using standardized protocols collected at the same locations in Colorado between April-July 2014. The dominant bacterial phyla detected across samples agreed between sequencing methodologies. However, WGS yielded greater microbial resolution, increased accuracy, and allowed identification of more genera of bacteria, archaea, viruses, and eukaryota, and putative functional genes that would have gone undetected using 16S amplicon sequencing. NEON open data will be useful for future studies characterizing and quantifying complex ecological processes associated with changing aquatic and terrestrial ecosystems.

Biofixation of atmospheric nitrogen in the context of world staple crop production: Policy perspectives.

The extensive use of nitrogen (N) fertilizers implicates a paradox: while fertilizers ensure the supply of a large amount of food, they cause negative environmental externalities, including reduced biodiversity, and eutrophic streams and lakes. Moreover, such fertilizers may also result in a major public health hazard: increased antibiotic resistance. This article discusses the critical implications of perturbations in N cycle caused by excessive use of fertilizers and resulting policy implications as they relate to ecosystem services. While there are solutions such as cover crops, these solutions are expensive and inconvenient for farmers. We advocate the use of biological fixation (BF) for staple crops-microbiome mediated natural supply of fixed N. This would involve engineering a microbiome that can be grown cheaply and at industrial scale. Fertilizers resulting from such innovation are termed as "biofertilizers" in this article. Following a qualitative cost-benefit analysis broken down by key stakeholders and a quick exploration of policy frameworks as they relate to the advancement of biofertilizers, we propose a practical pathway of where and how research investments should be directed to make such a solution feasible. We make five policy recommendations for decision-makers to facilitate a successful trajectory for this solution: (1) Future agricultural science should seek to understand how BF might be employed as a practical and efficient strategy. This effort would require that industry and the government partner to establish a pre-competitive research laboratory equipped with the latest state-of-the-art technologies that conduct metagenomic experiments to reveal signature microbiomes and form novel symbiotic connections. (2) To have a smooth ride in the market, ag-bio companies should: (i) create awareness among farmers; (ii) impart skills to farmers in testing and using biofertilizers, and (iii) conduct extensive field tests and more research in studying the scalability potential of such fertilizers. (3)The United States Department of Agriculture (USDA) and state governments should provide research and development (R&D) tax credits to biotech companies specifically geared towards R&D investments aimed at increasing the viability of BF and microbiome engineering. (4) To control agricultural pollution in the biosphere, federal governments should consider passing a Clean Agriculture Act (CAA), including a specific clause that regulate the use of chemical fertilizers. (5) Governments and the UN Food and Agriculture Organization (FAO) should coordinate Biological Advanced Research in Agriculture (BARA)-a global agricultural innovation initiative for investments and research in biological fixation and ethical, legal, and social implications of such innovation. While biological fixation will be central in BARA, we envision it to conduct research around other agricultural innovations as well, such as increasing photosynthetic efficiency.

Making BREAD: Biomimetic Strategies for Artificial Intelligence Now and in the Future.

The Artificial Intelligence (AI) revolution foretold of during the 1960s is well underway in the second decade of the twenty first century. Its period of phenomenal growth likely lies ahead. AI-operated machines and technologies will extend the reach of Homo sapiens far beyond the biological constraints imposed by evolution: outwards further into deep space, as well as inwards into the nano-world of DNA sequences and relevant medical applications. And yet, we believe, there are crucial lessons that biology can offer that will enable a prosperous future for AI. For machines in general, and for AI's especially, operating over extended periods or in extreme environments will require energy usage orders of magnitudes more efficient than exists today. In many operational environments, energy sources will be constrained. The AI's design and function may be dependent upon the type of energy source, as well as its availability and accessibility. Any plans for AI devices operating in a challenging environment must begin with the question of how they are powered, where fuel is located, how energy is stored and made available to the machine, and how long the machine can operate on specific energy units. While one of the key advantages of AI use is to reduce the dimensionality of a complex problem, the fact remains that some energy is required for functionality. Hence, the materials and technologies that provide the needed energy represent a critical challenge toward future use scenarios of AI and should be integrated into their design. Here we look to the brain and other aspects of biology as inspiration for Biomimetic Research for Energy-efficient AI Designs (BREAD).

For an international decade of the mind.

The International Decade of the Mind Project seeks to harness science across multiple disciplines to discover how human "mind" emerges from the biological activity of human brains. Given the complexity of the human brain, with approximately 10(11) neurons each with 10(4) connections, the effort will be daunting and require resources and expertise from many nations. The Decade of the Mind Project began as a United States initiative in 2007 and expanded to Europe in 2009 and then Asia in 2010. Here we advocate for a team-based approach to the Decade of the Mind initiative, where each nation contributes to the overall scientific effort with its own indigenous scientific expertise.

Synchronized changes to relative neuron populations in postnatal human neocortical development.

UNLABELLED: Mammalian prenatal neocortical development is dominated by the synchronized formation of the laminae and migration of neurons. Postnatal development likewise contains "sensitive periods" during which functions such as ocular dominance emerge. Here we introduce a novel neuroinformatics approach to identify and study these periods of active development. Although many aspects of the approach can be used in other studies, some specific techniques were chosen because of a legacy dataset of human histological data (Conel in The postnatal development of the human cerebral cortex, vol 1-8. Harvard University Press, Cambridge, 1939-1967). Our method calculates normalized change vectors from the raw histological data, and then employs k-means cluster analysis of the change vectors to explore the population dynamics of neurons from 37 neocortical areas across eight postnatal developmental stages from birth to 72 months in 54 subjects. We show that the cortical "address" (Brodmann area/sub-area and layer) provides the necessary resolution to segregate neuron population changes into seven correlated "k-clusters" in k-means cluster analysis. The members in each k-cluster share a single change interval where the relative share of the cortex by the members undergoes its maximum change. The maximum change occurs in a different change interval for each k-cluster. Each k-cluster has at least one totally connected maximal "clique" which appears to correspond to cortical function. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11571-010-9103-3) contains supplementary material, which is available to authorized users.

Theta phase precession emerges from a hybrid computational model of a CA3 place cell.

The origins and functional significance of theta phase precession in the hippocampus remain obscure, in part, because of the difficulty of reproducing hippocampal place cell firing in experimental settings where the biophysical underpinnings can be examined in detail. The present study concerns a neurobiologically based computational model of the emergence of theta phase precession in which the responses of a single model CA3 pyramidal cell are examined in the context of stimulation by realistic afferent spike trains including those of place cells in entorhinal cortex, dentate gyrus, and other CA3 pyramidal cells. Spike-timing dependent plasticity in the model CA3 pyramidal cell leads to a spatially correlated associational synaptic drive that subsequently creates a spatially asymmetric expansion of the model cell's place field. Following an initial training period, theta phase precession can be seen in the firing patterns of the model CA3 pyramidal cell. Through selective manipulations of the model it is possible to decompose theta phase precession in CA3 into the separate contributing factors of inheritance from upstream afferents in the dentate gyrus and entorhinal cortex, the interaction of synaptically controlled increasing afferent drive with phasic inhibition, and the theta phase difference between dentate gyrus granule cell and CA3 pyramidal cell activity. In the context of a single CA3 pyramidal cell, the model shows that each of these factors plays a role in theta phase precession within CA3 and suggests that no one single factor offers a complete explanation of the phenomenon. The model also shows parallels between theta phase encoding and pattern completion within the CA3 autoassociative network.

Intellectual property conundrum for the biological sciences.

Policy regarding academically generated biomedical intellectual property (IP) has been shaped by two important events: the Vannevar Bush report to then President Roosevelt in 1945 and the Bayh-Dole Act of 1980. This policy, which vests the intellectual property produced from federally funded biomedical research from the government to the academic institution, was designed to promote technology transfer and thus promote the health of the U.S. economy. However, the policy has led to significant challenges, particularly in implementation. Here it is argued that the difficulties are due to differences in the structure of motivations between biomedical scientists, institutional officials, and private sector entrepreneurs. Understanding these differences may lead to a review of policy with the goal of enhancing technology transfer for the future.

Partial reinforcement across trials impairs escape performance but spares place learning in the water maze.

We studied the effects of partial reinforcement on escape performance and place learning in the water maze. Rats given 50% reinforcement across trials (i.e. the escape platform was present only on odd trials) were compared to controls given 100% reinforcement (platform present on all trials). Control groups either received 8 or 4 trials per day, which was equal to either the total number of trials (100%-8) or reinforced escapes (100%-4) of the 50% group. Analysis of escape performance (latency) revealed that the 50% group was impaired relative to the 100%-8 group, but not the 100%-4 group, during the first 5 days of acquisition. The 50% group was impaired relative to both control groups on days 6-10 of overtraining. However, analyses of within-trial behavior (target annulus preference and thigmotaxis) on nonreinforced trials suggest that the 50% group did learn the location of the hidden platform (place information), in addition to a wall-based thigmotactic response. By dividing the 60s nonreinforced trials into three 20-s time bins, we were able to detect a significant preference for the target annulus early in the trial (bin 1 of trial 40 and bins 1-2 of trial 80). Further, there was a significant increase in time spent in the periphery of the pool, near the wall, in the last time bin of trial 40. Because the platform was in the middle zone, this behavior competed with a place response. We conclude that across-trial partial reinforcement procedures may promote response competition and mask evidence of place learning in addition to weakening escape performance late in training.