The novel coronavirus disease 2019 (COVID-19) has infected more than 100 million people globally within the first year of the pandemic. With a death toll surpassing 500,000 in the United States ...alone, containing the pandemic is predicated on achieving herd immunity on a global scale. This implies that at least 70-80 % of the population must achieve active immunity against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), either as a result of a previous COVID-19 infection or by vaccination against SARS-CoV-2. In December 2020, the first two vaccines were approved by the FDA through emergency use authorization in the United States. These vaccines are based on the mRNA vaccine platform and were developed by Pfizer/BioNTech and Moderna. Published safety and efficacy trials reported high efficacy rates of 94-95 % after two interval doses, in conjunction with limited side effects and a low rate of adverse reactions. The rapid pace of vaccine development and the uncertainty of potential long-term adverse effects raised some level of hesitation against mRNA vaccines in the global community. A successful vaccination campaign is contingent on widespread access to the vaccine under appropriate storage conditions, deployment of a sufficient number of vaccinators, and the willingness of the population to be vaccinated. Thus, it is important to clarify the objective data related to vaccine safety, including known side effects and potential adverse reactions. The present review was designed to provide an update on the current state of science related to the safety and efficacy of SARS-CoV-2 mRNA vaccines.
Majorana bosons, that is, tight bosonic analogs of the Majorana fermionic quasiparticles of condensed-matter physics, are forbidden for gapped free bosonic matter within a standard Hamiltonian ...scenario. We show how the interplay between dynamical metastability and nontrivial bulk topology makes their emergence possible in noninteracting bosonic chains undergoing Markovian dissipation. This leads to a distinctive form of topological metastability, whereby a conserved Majorana boson localized on one edge is paired, in general, with a symmetry generator localized on the opposite edge. We argue that Majorana bosons are robust against disorder and identifiable by signatures in the zero-frequency steady-state power spectrum. Our results suggest that symmetry-protected topological phases for free bosons may arise in transient metastable regimes, which persist over practical timescales.
Molecular automata are mixtures of molecules that undergo precisely defined structural changes in response to sequential interactions with inputs. Previously studied nucleic acid-based automata ...include game-playing molecular devices (MAYA automata) and finite-state automata for the analysis of nucleic acids, with the latter inspiring circuits for the analysis of RNA species inside cells. Here, we describe automata based on strand-displacement cascades directed by antibodies that can analyse cells by using their surface markers as inputs. The final output of a molecular automaton that successfully completes its analysis is the presence of a unique molecular tag on the cell surface of a specific subpopulation of lymphocytes within human blood cells.
This article reviews studies demonstrating enhancement with transcranial direct current stimulation (tDCS) of attention, learning, and memory processes in healthy adults. Given that these are ...fundamental cognitive functions, they may also mediate stimulation effects on other higher-order processes such as decision-making and problem solving. Although tDCS research is still young, there have been a variety of methods used and cognitive processes tested. While these different methods have resulted in seemingly contradictory results among studies, many consistent and noteworthy effects of tDCS on attention, learning, and memory have been reported. The literature suggests that although tDCS as typically applied may not be as useful for localization of function in the brain as some other methods of brain stimulation, tDCS may be particularly well-suited for practical applications involving the enhancement of attention, learning, and memory, in both healthy subjects and in clinical populations.
•Neuroenhancement with tDCS is reviewed.•A variety of tDCS methods produce similar cognitive effects.•Beneficial effects on attention, learning, and memory have been found.•tDCS may be particularly well-suited for neuroenhancement.
The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different ...explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
Study of the interactions established between the viral glycoproteins and their host receptors is of critical importance for a better understanding of virus entry into cells. The novel coronavirus ...SARS-CoV-2 entry into host cells is mediated by its spike glycoprotein (S-glycoprotein), and the angiotensin-converting enzyme 2 (ACE2) has been identified as a cellular receptor. Here, we use atomic force microscopy to investigate the mechanisms by which the S-glycoprotein binds to the ACE2 receptor. We demonstrate, both on model surfaces and on living cells, that the receptor binding domain (RBD) serves as the binding interface within the S-glycoprotein with the ACE2 receptor and extract the kinetic and thermodynamic properties of this binding pocket. Altogether, these results provide a picture of the established interaction on living cells. Finally, we test several binding inhibitor peptides targeting the virus early attachment stages, offering new perspectives in the treatment of the SARS-CoV-2 infection.
In this article, we survey various non-contact, non-destructive testing methods by way of terahertz (THz) spectroscopy and imaging designed for use in various industrial sectors. A brief overview of ...the working principles of THz spectroscopy and imaging is provided, followed by a survey of selected applications from three industries-the building and construction industry, the energy and power industry, and the manufacturing industry. Material characterization, thickness measurement, and defect/corrosion assessment are demonstrated through the examples presented. The article concludes with a discussion of novel spectroscopy and imaging devices and techniques that are expected to accelerate industry adoption of THz systems.
Predicting crop yield based on the environmental, soil, water and crop parameters has been a potential research topic. Deep-learning-based models are broadly used to extract significant crop features ...for prediction. Though these methods could resolve the yield prediction problem there exist the following inadequacies: Unable to create a direct non-linear or linear mapping between the raw data and crop yield values; and the performance of those models highly relies on the quality of the extracted features. Deep reinforcement learning provides direction and motivation for the aforementioned shortcomings. Combining the intelligence of reinforcement learning and deep learning, deep reinforcement learning builds a complete crop yield prediction framework that can map the raw data to the crop prediction values. The proposed work constructs a Deep Recurrent Q-Network model which is a Recurrent Neural Network deep learning algorithm over the Q-Learning reinforcement learning algorithm to forecast the crop yield. The sequentially stacked layers of Recurrent Neural network is fed by the data parameters. The Q- learning network constructs a crop yield prediction environment based on the input parameters. A linear layer maps the Recurrent Neural Network output values to the Q-values. The reinforcement learning agent incorporates a combination of parametric features with the threshold that assist in predicting crop yield. Finally, the agent receives an aggregate score for the actions performed by minimizing the error and maximizing the forecast accuracy. The proposed model efficiently predicts the crop yield outperforming existing models by preserving the original data distribution with an accuracy of 93.7%.
The use of multivalent carbohydrate compounds to block cell-surface lectin receptors is a promising strategy to inhibit the entry of pathogens into cells and could lead to the discovery of novel ...antiviral agents. One of the main problems with this approach, however, is that it is difficult to make compounds of an adequate size and multivalency to mimic natural systems such as viruses. Hexakis adducts of 60fullerene are useful building blocks in this regard because they maintain a globular shape at the same time as allowing control over the size and multivalency. Here we report water-soluble tridecafullerenes decorated with 120 peripheral carbohydrate subunits, so-called 'superballs', that can be synthesized efficiently from hexakis adducts of 60fullerene in one step by using copper-catalysed azide–alkyne cycloaddition click chemistry. Infection assays show that these superballs are potent inhibitors of cell infection by an artificial Ebola virus with half-maximum inhibitory concentrations in the subnanomolar range.
Highlights • Codon optimization is a gene engineering approach to increase protein production. • Assumptions underlying codon optimization approaches may be invalid. • Codon optimization for nucleic ...acid therapies presents potentially unique hazards. • Hazards can arise by disrupting or introducing overlapping functions in mRNAs.
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