Application of the Catecholaminergic Neuron Electron Transport (CNET) Physical Substrate for Consciousness and Action Selection to Integrated Information Theory

A newly discovered physical mechanism involving incoherent electron tunneling in layers of the protein ferritin that are found in catecholaminergic neurons (catecholaminergic neuron electron transport or CNET) is hypothesized to support communication between neurons. Recent tests further confirm that these ferritin layers can also perform a switching function (in addition to providing an electron tunneling mechanism) that could be associated with action selection in those neurons, consistent with earlier predictions based on CNET. While further testing would be needed to confirm the hypothesis that CNET allows groups of neurons to communicate and act as a switch for selecting one of the neurons in the group to assist in reaching action potential, this paper explains how that hypothesized behavior would be consistent with Integrated Information Theory (IIT), one of a number of consciousness theories (CTs). While the sheer number of CTs suggest that any one of them alone is not sufficient to explain consciousness, this paper demonstrates that CNET can provide a physical substrate and action selection mechanism that is consistent with IIT and which can also be applied to other CTs, such as to conform them into a single explanation of consciousness.

Interpreting Duns Scotus

John Duns Scotus is commonly recognized as one of the most original thinkers of medieval philosophy. His influence on subsequent philosophers and theologians is enormous and extends well beyond the limits of the Middle Ages. His thought, however, might be intimidating for the non-initiated, because of the sheer number of topics he touched on and the difficulty of his style. The eleven essays collected here, especially written for this volume by some of the leading scholars in the field, take the reader through various topics, including Duns Scotus's intellectual environment, his argument for the existence of God, and his conceptions of modality, order, causality, freedom, and human nature. This volume provides a reliable point of entrance to the thought of Duns Scotus while giving a snapshot of some of the best research that is now being done on this difficult but intellectually rewarding thinker.

Segmental error patterns in Finnish-accented English

Research suggests that explicit pronunciation teaching improves second language speech production, but language teachers often lack the relevant knowledge to teach pronunciation. This study examined segmental error patterns in Finnish-accented English and the relationship between segmental errors and foreign accent ratings in two groups differing in amount of second language experience. Our study identified a number of common segmental error patterns in Finnish-accented English, which may guide formal pronunciation instruction. We further found that the sheer number of segmental errors in a sentence affected foreign accent ratings as did the number of vowel errors in a sentence. We speculate that the detrimental effect of vowel errors may be related to the finding that vowel errors resulted in non-English segments more often than consonant errors did. Finally, we found a facilitative effect of second language experience on foreign accent rating that cannot be reduced to number of segmental errors, despite the finding that number of consonant errors was reduced with increased second language experience.

Are Palmer’s Elm-Leaf Goldenrod and the Smooth Elm-Leaf Goldenrod Real? The Angiosperms353 Kit Provides Within-Species Signal in Solidago ulmifolia s. l.

Abstract— The genus Solidago represents a taxonomically challenging group due to its sheer number of species, putative hybridization, polyploidy, and shallow genetic divergence among species. Here we use a dataset obtained exclusively from herbarium specimens to evaluate the status of Solidago ulmifolia var. palmeri, a morphologically subtle taxon potentially confined to Alabama, Arkansas, Mississippi, and Missouri. A multivariate analysis of both discrete and continuous morphological data revealed no clear distinction between S. ulmifolia var. palmeri and Solidago ulmifolia var. ulmifolia. Solidago ulmifolia var. palmeri’s status was also assessed with a phylogenomic and SNP clustering analysis of data generated with the “Angiosperms353” probe kit. Neither analysis supported Solidago ulmifolia var. palmeri as a distinct taxon, and we suggest that this name should be discarded. The status of Solidago delicatula (formerly known as Solidago ulmifolia var. microphylla) was also assessed. Both morphological and phylogenetic analyses supported the species status of S. delicatula and we suggest maintaining this species at its current rank. These results highlight the utility of the Angiosperms353 probe kit, both with herbarium tissue and at lower taxonomic levels. Indeed, this is the first study to utilize this kit to identify genetic groups within a species.

Sharing taxonomic expertise between natural history collections using image recognition

Natural history collections play a vital role in biodiversity research and conservation by providing a window to the past. The usefulness of the vast amount of historical data depends on their quality, with correct taxonomic identifications being the most critical. The identification of many of the objects of natural history collections, however, is wanting, doubtful or outdated. Providing correct identifications is difficult given the sheer number of objects and the scarcity of expertise. Here we outline the construction of an ecosystem for the collaborative development and exchange of image recognition algorithms designed to support the identification of objects. Such an ecosystem will facilitate sharing taxonomic expertise among institutions by offering image datasets that are correctly identified by their in-house taxonomic experts. Together with openly accessible machine learning algorithms and easy to use workbenches, this will allow other institutes to train image recognition algorithms and thereby compensate for the lacking expertise.

Structural Insights of SARS-CoV-2 Spike Protein from Delta and Omicron Variants

Given the continuing heavy toll of the COVID-19 pandemic and the emergence of the Delta (B.1.617.2) and Omicron (B.1.1.529) variants, the WHO declared both as variants of concern (VOC). There are valid concerns that the latest Omicron variant might have increased infectivity and pathogenicity. In addition, the sheer number of S protein mutations in the Omicron variant raise concerns of potential immune evasion and resistance to therapeutics such as monoclonal antibodies. However, structural insights that underpin the potential increased pathogenicity are unknown. Here we adopted an artificial intelligence (AI)-based approach to predict the structural changes induced by mutations of the Delta and Omicron variants in the spike (S) protein using Alphafold. This was followed by docking the human angiotensin-converting enzyme 2 (ACE2) with the predicted S proteins for Wuhan-Hu-1, Delta, and Omicron variants. Our in-silico structural analysis indicates that S protein for Omicron variant has a higher binding affinity to ACE-2 receptor, compared to Wuhan-Hu-1 and Delta variants. In addition, the recognition sites of the receptor binding domains for Delta and Omicron variants showed lower electronegativity compared to Wuhan-Hu-1. Importantly, further molecular insights revealed significant changes induced at fusion protein (FP) site, which may mediate enhanced viral entry. These results represent the first computational analysis of structural changes associated with Omicron variant using Alphafold, Collectively, our results highlight potential structural basis for enhanced pathogenicity of the Omicron variant, however further validation using X-ray crystallography and cryo-EM are warranted.

The Effectiveness of Adaptation Methods in Improving User Engagement and Privacy Protection on Social Network Sites

Research finds that the users of Social Networking Sites (SNSs) often fail to comprehensively engage with the plethora of available privacy features— arguably due to their sheer number and the fact that they are often hidden from sight. As different users are likely interested in engaging with different subsets of privacy features, an SNS could improve privacy management practices by adapting its interface in a way that proactively assists, guides, or prompts users to engage with the subset of privacy features they are most likely to benefit from. Whereas recent work presents algorithmic implementations of such privacy adaptation methods, this study investigates the optimal user interface mechanism to present such adaptations. In particular, we tested three proposed “adaptation methods” (automation, suggestions, highlights) in an online between-subjects user experiment in which 406 participants used a carefully controlled SNS prototype. We systematically evaluate the effect of these adaptation methods on participants’ engagement with the privacy features, their tendency to set stricter settings (protection), and their subjective evaluation of the assigned adaptation method. We find that the automation of privacy features afforded users the most privacy protection, while giving privacy suggestions caused the highest level of engagement with the features and the highest subjective ratings (as long as awkward suggestions are avoided). We discuss the practical implications of these findings in the effectiveness of adaptations improving user awareness of, and engagement with, privacy features on social media.

Application of the Catecholaminergic Neuron Electron Transport (CNET) Physical Substrate for Consciousness and Action Selection to Integrated Information Theory

A newly-discovered physical mechanism involving electron tunneling in layers of the protein ferritin that are found in catecholaminergic neurons (catecholaminergic neuron electron transport or CNET), is hypothesized to support communication between neurons. Recent tests further confirm that these ferritin layers can also perform a switching function (in addition to providing an electron tunneling mechanism) that could be associated with action selection in those neurons, consistent with earlier predictions based on CNET. While further testing would be needed to confirm the hypothesis that CNET allows groups of neurons to communicate and act as a switch for selecting one of the neurons in the group to assist in reaching action potential, this paper explains how that hypothesized behavior would be consistent with Integrated Information Theory (IIT), one of a number of consciousness theories (CTs). While the sheer number of CTs suggest that any one of them is not sufficient to explain consciousness, this paper demonstrates that CNET can provide a physical substrate that is consistent with IIT and which can also be applied to other CTs, such as to conform them into a single explanation of consciousness.

Principal indicators to monitor Sustainable Development Goals

Hundreds of indicators are available to monitor progress of countries and regions towards the Sustainable Development Goals (SDGs). However, the sheer number of indicators poses unprecedented challenges for data collection and compilation. Here we identify a subset of SDG indicators (principal indicators) that are relatively easy to collect data for and also are representative for all the indicators by considering the complex interrelationship among them. We find 147 principal indicators that can represent at least 90% of the annual variances of 351 SDG indicators in the past (2000-2017) and are expected to do so for the future (2018-2030) with the lowest difficulty of data collection. Our results can guide future investment in building the data infrastructure for SDG monitoring to give priorities to these principal indicators for global comparison.

Keynote: Abstract Wikipedia

Wikipedia’s vision is a world in which everyone can share in the sum of all knowledge. One of the largest hindrances is the sheer number of languages Wikipedia needs to cover in order to achieve that goal. We want to tackle this problem with a multilingual Wikipedia allowing for content to be shared between language editions. We separate this goal into two parts: creating and maintaining content in an abstract notation within a project called Abstract Wikipedia, and creating a new project called Wikifunctions that can translate this notation to natural language.