Children grow up surrounded by opportunities to learn (the language of their community, the movements of their body, other people\'s preferences and mental lives, games, social norms, etc.). Here, we find that toddlers (N = 36; age range 2.3-3.2 years) rely on a logical reasoning strategy, Disjunctive Inference (i.e., A OR B, A is ruled out, THEREFORE, B), across a variety of situations, all before they have any formal education or extensive experience with words for expressing logical meanings. In learning new words, learning new facts about a person, and finding the winner of a race, toddlers systematically consider and reject competitors before deciding who must be the winner. This suggests that toddlers may have a general-purpose logical reasoning tool that they can use in any situation.

Analyzing the evolutionary features and internal logic of the one-vote veto system in China over the past two decades is highly significant when considering reform and standardization. In order to conduct this analysis, the Nvivo 12 software was used to examine policy texts related to the one-vote veto issued by Fujian, Hubei, and Gansu provinces. Through a comparative analysis of keyword frequency statistics, policy text form, and content characteristics across the three provinces, it was discovered that governmental departments have experienced fundamental changes in their utilization of the one-vote veto system after 20 years of development. These changes are primarily seen in the refinement of the description of the one-vote veto in policy texts, the gradual reduction in the withdrawal mechanism of the one-vote veto, and an expanded application field for the one-vote veto.

Spintronics, utilizing both the charge and spin of electrons, benefits from the nonvolatility, low switching energy, and collective behavior of magnetization. These properties allow the development of magnetoresistive random access memories, with magnetic tunnel junctions (MTJs) playing a central role. Various spin logic concepts are also extensively explored. Among these, spin logic devices based on the motion of magnetic domain walls (DWs) enable the implementation of compact and energy-efficient logic circuits. In these devices, DW motion within a magnetic track enables spin information processing, while MTJs at the input and output serve as electrical writing and reading elements. DW logic holds promise for simplifying logic circuit complexity by performing multiple functions within a single device. Nevertheless, the demonstration of DW logic circuits with electrical writing and reading at the nanoscale is still needed to unveil their practical application potential. In this review, we discuss material advancements for high-speed DW motion, progress in DW logic devices, groundbreaking demonstrations of current-driven DW logic, and its potential for practical applications. Additionally, we discuss alternative approaches for current-free information propagation, along with challenges and prospects for the development of DW logic.

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BACKGROUND: Socialism with Chinese characteristics has entered a new stage. The principal social contradiction is the uneven development of urban and rural areas. The rural revitalization strategy has emerged as time has required. The realization of rural revitalization not only requires development to lay the foundation of the countryside but also requires governance to lead the development of the countryside. Development and governance are two indispensable aspects of rural revitalization. However, China\'s rural areas have long been in a state of development without governance, and this situation must change. Therefore, systematically exploring the relationship between development and governance is the key to solving the current shortcomings in rural areas.
METHODS: Based on the data from the statistical yearbook, the study constructed a set of evaluation indicators for rural development governance and revitalization and verified the model\'s effectiveness.The entropy method and the assessment model were used to calculate the comprehensive score of rural development, governance, and revitalization. The relationship between rural development and governance was analyzed using a coupled coordination model. The regression analysis model was used to explore the relationship between the coupling results of rural development, governance, and rural revitalization.
RESULTS: From the comprehensive results, both development and governance show an upward trend, but the upward trend of development is better. From the analysis of coupling coordination between development and governance, the C value is in good condition, the T value fluctuates wildly, and the D fluctuates with the fluctuation of T. Judging from the comprehensive score of rural revitalization, it also shows an upward trend year by year. Judging from the regression analysis results of coupling coordination degree and rural revitalization comprehensive score, coupling coordination degree will significantly impact the rural revitalization evaluation value.
CONCLUSIONS: The study found that current rural development and governance present a spiral coupling coordination relationship, and the degree of coupling coordination significantly correlates with rural revitalization. Based on the research conclusions, the study further proposes three paths to promote the coupling and coordination of development and governance. The first is an organizational isomorphism, which builds a coupled coordination system for rural development and governance. The second is to tilt resources and improve the supply of connected and coordinated factors for rural development and governance. The third is the operating mechanism to optimize rural development and governance\'s coupling and coordination path.

Information, as the most elusive subject, is central to all forms of thought, governance, economic structure, science, and society. Regulation of information, especially within the healthcare field, is proving to be a difficult task globally, given the lack of a qualitative framework and understanding of the concept and properties of information (or data) itself. The presentation of the overall qualitative framework, comprising a qualitative analysis of information, data, and knowledge, will be valuable and of great assistance in delineating regulatory, ethical, and strategic trajectories. In addition, this framework provides insights (and answers) regarding (1) data privacy and protection; (2) delineations between information, data, and knowledge based on the important notion of trust; (3) a structured approach to establishing the necessary conditions for an open society and system, and the maintenance of said openness, based on the work of Karl Popper and Georg Wilhelm Friedrich Hegel; (4) an active agent approach that promotes autonomy and freedom and protects the open society; and (5) a data governance mechanism based on the work of Friedrich Hayek, which structures the current legal-ethical-financial and social society. This is insightful for questions relating to the extent of rights and duties, the extent of biological bodies and freedom, and the structure of relations in distributed networked systems. There is great value offered in this framework; furthermore, it provides critical insights and thoughts about (and uncovers the interplay between) academic culture, politics, science, society, and societal decay. Note that, in line with the ideas expressed in this manuscript, such as incorporation of personal experience (thereby mending the Kantian and Cartesian gap), a first-person perspective will be used, where relevant.

Boolean networks provide an effective mechanism for describing interactions and dynamics of gene regulatory networks (GRNs). Deriving accurate Boolean descriptions of GRNs is a challenging task. The number of experiments is usually much smaller than the number of genes. In addition, binarization leads to a loss of information and inconsistencies arise in binarized time-series data. The inference of Boolean networks from binarized time-series data alone often leads to complex and overfitted models. To obtain relevant Boolean models of gene regulatory networks, inference methods could incorporate data from multiple sources and prior knowledge in terms of general network structure and/or exact interactions. We propose the Boolean network inference method SAILoR (Structure-Aware Inference of Logic Rules). SAILoR incorporates time-series gene expression data in combination with provided reference networks to infer accurate Boolean models. SAILoR automatically extracts topological properties from reference networks. These can describe a more general structure of the GRN or can be more precise and describe specific interactions. SAILoR infers a Boolean network by learning from both continuous and binarized time-series data. It navigates between two main objectives, topological similarity to reference networks and correspondence with gene expression data. By incorporating the NSGA-II multi-objective genetic algorithm, SAILoR relies on the wisdom of crowds. Our results indicate that SAILoR can infer accurate and biologically relevant Boolean descriptions of GRNs from both a static and a dynamic perspective. We show that SAILoR improves the static accuracy of the inferred network compared to the network inference method dynGENIE3. Furthermore, we compared the performance of SAILoR with other Boolean network inference approaches including Best-Fit, REVEAL, MIBNI, GABNI, ATEN, and LogBTF. We have shown that by incorporating prior knowledge about the overall network structure, SAILoR can improve the structural correctness of the inferred Boolean networks while maintaining dynamic accuracy. To demonstrate the applicability of SAILoR, we inferred context-specific Boolean subnetworks of female Drosophila melanogaster before and after mating.

The development of machine learning (ML) tools in many different medical settings is largely increasing. However, the use of the resulting algorithms in daily medical practice is still an unsolved challenge. We propose an epistemological approach (i.e., based on logical principles) to the application of computational tools in clinical practice. We rely on the classification of scientific inference into deductive, inductive, and abductive comparing the characteristics of ML tools with those derived from evidence-based medicine [EBM] and experience-based medicine, as paradigms of well-known methods for generation of knowledge. While we illustrate our arguments using liver transplantation as an example, this approach can be applied to other aspects of the specialty. Regarding EBM, it generates general knowledge that clinicians apply deductively, but the certainty of its conclusions is not guaranteed. In contrast, automatic algorithms primarily rely on inductive reasoning. Their design enables the integration of vast datasets and mitigates the emotional biases inherent in human induction. However, its poor capacity for abductive inference (a logical mechanism inherent to human clinical experience) constrains its performance in clinical settings characterized by uncertainty, where data are heterogeneous, results are highly influenced by context, or where prognostic factors can change rapidly.

The revolutionary technology of CRISPR/Cas has reshaped the landscape of molecular biology and molecular engineering. This tool is of interest to researchers in multiple fields, including molecular diagnostics, molecular biochemistry circuits, and information storage. As CRISPR/Cas spreads to more niche areas, new application scenarios and requirements emerge. Developing programmability and compatibility of CRISPR/Cas becomes a critical issue in the new phase. Here, we report a redundancy-based modular CRISPR/Cas12a synergistic activation platform (MCSAP). The position, length, and concentration of the redundancy in the split DNA activators can finely regulate the activity of Cas12a. With the redundant structure as an interface, MCSAP serves as a modular plug-in to seamlessly integrate with the upstream molecular network. MCSAP successfully performs three different tasks: nucleic acid detection, enzyme detection, and logic operation. MCSAP can work as an effector for different molecular networks because of its compatibility and programmability. Our platform provides powerful yet easy-to-use tools and strategies for the fields of DNA nanotechnology, molecular engineering, and molecular biology.

MicroRNA (miRNA) and apurinic/apyrimidinic endonuclease 1 (APE1) have been reported to be closely associated with cancers, making them potential crucial biomarkers and therapeutic targets. However, focusing on the detection of a single target is not conducive to the diagnosis and prognosis assessment of diseases. In this study, an AND logic-gate-based dual-locking hairpin-mediated catalytic hairpin assembly (DL-CHA) was developed for sensitive and specific detection of microRNA and APE1. By addition of a lock to each of the hairpins, with APE1 and microRNA serving as keys, fluorescence signals could only be detected in the presence of simultaneous stimulation by APE1 and miRNA-224. This indicated that the biosensor could operate as an AND logic gate. DL-CHA exhibited advantages such as a low background, rapid response, and high logic capability. Therefore, the biosensor serves as a novel approach to cancer diagnosis with significant potential applications.