The evolutionary features and internal logic of the one-vote veto system: A Nvivo analysis based on the policy texts of the three provinces in the east, middle and west.

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.

Ultrasensitive Photoelectrochemical Biosensor for Dual-miRNAs Detection Based on Molecular Logic Gates and Methylene Blue Sensitized ZnO@CdS@Au Nanorods.

The occurrence of cancer is often closely related to multiple tumor markers, so it is important to develop multitarget detection methods. By the proper design of the input signals and logical operations of DNA logic gates, detection and diagnosis of cancer at different stages can be achieved. For example, in the early stages, specific input signals can be designed to correspond to early specific tumor markers, thereby achieving early cancer detection. In the late stage, logic gates for multitarget detection can be designed to simultaneously detect multiple biomarkers to improve diagnostic accuracy and comprehensiveness. In this work, we constructed a dual-target-triggered DNA logic gate for anchoring DNA tetrahedra, where methylene blue was embedded in the DNA tetrahedra to sensitize ZnO@CdS@Au, achieving ultrasensitive detection of the target substance. We tested the response of AND and OR logic gates to the platform. For AND logic gates, the sensing platform only responds when both miRNAs are present. In the concentration range of 10 aM to 10 nM, the photoelectric signal gradually increases with an increase of the target concentration. Subsequently, we used OR logic gates for miRNA detection. Even if only one target exists, the sensing platform exhibits excellent performance. Similarly, within the concentration range of 10 aM to 10 nM, the photoelectric signal gradually increases with an increase of the target concentration. The minimum detection limit is 1.10 aM. Whether it is the need to detect multiple targets simultaneously or only one of them, we can achieve it by selecting the appropriate logic gate. This strategy holds promising application prospects in fields such as biosensing, medical diagnosis, and environmental monitoring.

Surface Cues Explain the Logic-Liking Effect in Disjunctions.

The finding that people tend to prefer logically valid conclusions over invalid ones is known in the literature as the logic-liking effect and has traditionally been interpreted as evidence for the notion of so-called logical intuitions. Results of more recent empirical studies investigating conditional and categorical syllogisms suggest, however, that previous instances of the logic-liking effect can be accounted for by a confound in terms of surface-feature atmosphere. But the true nature of this atmosphere effect has so far remained largely elusive. Here, we address this issue and introduce two variants of disjunctive syllogisms that enable us to deconfound validity, possibility of the conclusion, and surface-feature atmosphere, which has been impossible with simple disjunctive syllogisms used in earlier studies. Three experiments, in which participants were asked to provide liking and logic ratings for these arguments, revealed that the logic-liking effect in disjunctive syllogisms can be explained by an atmosphere confound in combination with implied demand to consider logicality when judging likability. We also observed a strong atmosphere effect in logic ratings over and above an effect of logical validity per se. Furthermore, atmosphere effects appear to be induced only by specific surface features, namely those that are ecologically valid, if fallible, predictors for logicality. We conclude that acquired atmosphere heuristics provide proxies for logical validity that reasoners often take at face value. A comparison of the present results with previous findings from experiments that focused on conditional and categorical syllogisms additionally indicates that these atmosphere heuristics are used irrespective of an argument's complexity.

SAILoR: Structure-Aware Inference of Logic Rules.

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.

Modular CRISPR/Cas12a synergistic activation platform for detection and logic operations.

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.

The realization logic of rural revitalization: Coupled coordination analysis of development and governance.

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.

AND Logic-Gate-Based Dual-Locking Probe System for the Sensitive Detection of microRNA and APE1.

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.

Photonic crystal-enhanced fluorescence biosensor with logic gate operation based on one-pot cascade amplification DNA circuit for enzyme-free and ultrasensitive analysis of two microRNAs.

The development of sensitive and efficient analytical methods for multiple biomarkers is crucial for cancer screening at early stage. MicroRNAs (miRNAs) are a kind of biomarkers with diagnostic potential for cancer. However, the ultrasensitive and logical analysis of multiple miRNAs with simple operation still faces some challenges. Herein, a photonic crystal (PC)-enhanced fluorescence biosensor with logic gate operation based on one-pot cascade amplification DNA circuit was developed for enzyme-free and ultrasensitive analysis of two cancer-related miRNAs. The fluorescence biosensor was performed by biochemical recognition amplification module (BCRAM) and physical enhancement module (PEM) to achieve logical and sensitive detection. In the BCRAM, one-pot cascade amplification circuit consisted of the upstream parallel entropy-driven circuit (EDC) and the downstream shared catalytic hairpin assembly (CHA). The input of target miRNA would trigger each corresponding EDC, and the parallel EDCs released the same R strand for triggering subsequent CHA; thus, the OR logic gate was obtained with minimization of design and operation. In the PEM, photonic crystal (PC) array was prepared easily for specifically enhancing the fluorescence output from BCRAM by the optical modulation capabilities; meanwhile, the high-throughput signal readout was achieved by microplate analyzer. Benefiting from the integrated advantages of two modules, the proposed biosensor achieved ultrasensitive detection of two miRNAs with easy logic gate operation, obtaining the LODs of 8.6 fM and 6.7 fM under isothermal and enzyme-free conditions. Hence, the biosensor has the advantages of high sensitivity, easy operation, multiplex and high-throughput analysis, showing great potential for cancer screening at early stage.

Learning the Meanings of Function Words From Grounded Language Using a Visual Question Answering Model.

Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non-symbolic general statistical learning algorithms, without any prior knowledge of linguistic meaning.

Genies, lawyers, and smart-asses: Extending proxy failures to intentional misunderstandings.

We propose that the logic of a genie - an agent that exploits an ambiguous request to intentionally misunderstand a stated goal - underlies a common and consequential phenomenon, well within what is currently called proxy failures. We argue that such intentional misunderstandings are not covered by the current proposed framework for proxy failures, and suggest to expand it.

Deep learning assisted logic gates for real-time identification of natural tetracycline antibiotics.

The overuse and misuse of tetracycline (TCs) antibiotics, including tetracycline (TTC), oxytetracycline (OTC), doxycycline (DC), and chlortetracycline (CTC), pose a serious threat to human health. However, current rapid sensing platforms for tetracyclines can only quantify the total amount of TCs mixture, lacking real-time identification of individual components. To address this challenge, we integrated a deep learning strategy with fluorescence and colorimetry-based multi-mode logic gates in our self-designed smartphone-integrated toolbox for the real-time identification of natural TCs. Our ratiometric fluorescent probe (CD-Au NCs@ZIF-8) encapsulated carbon dots and Au NCs in ZIF-8 to prevent false negative or positive results. Additionally, our independently developed WeChat app enabled linear quantification of the four natural TCs using the fluorescence channels. The colorimetric channels were also utilized as outputs of logic gates to achieve real-time identification of the four individual natural tetracyclines. We anticipate this strategy could provide a new perspective for effective control of antibiotics.

Genetically encoded Boolean logic operators to sense and integrate phenylpropanoid metabolite levels in plants.

Synthetic biology has the potential to revolutionize biotechnology, public health, and agriculture. Recent studies have shown the enormous potential of plants as chassis for synthetic biology applications. However, tools to precisely manipulate metabolic pathways for bioproduction in plants are still needed. We used bacterial allosteric transcription factors (aTFs) that control gene expression in a ligand-specific manner and tested their ability to repress semi-synthetic promoters in plants. We also tested the modulation of their repression activity in response to specific plant metabolites, especially phenylpropanoid-related molecules. Using these aTFs, we also designed synthetic genetic circuits capable of computing Boolean logic operations. Three aTFs, CouR, FapR, and TtgR, achieved c. 95% repression of their respective target promoters. For TtgR, a sixfold de-repression could be triggered by inducing its ligand accumulation, showing its use as biosensor. Moreover, we designed synthetic genetic circuits that use AND, NAND, IMPLY, and NIMPLY Boolean logic operations and integrate metabolite levels as input to the circuit. We showed that biosensors can be implemented in plants to detect phenylpropanoid-related metabolites and activate a genetic circuit that follows a predefined logic, demonstrating their potential as tools for exerting control over plant metabolic pathways and facilitating the bioproduction of natural products.

Erasable and Field Programmable DNA Circuits Based on Configurable Logic Blocks.

DNA is commonly employed as a substrate for the building of artificial logic networks due to its excellent biocompatibility and programmability. Till now, DNA logic circuits are rapidly evolving to accomplish advanced operations. Nonetheless, nowadays, most DNA circuits remain to be disposable and lack of field programmability and thereby limits their practicability. Herein, inspired by the Configurable Logic Block (CLB), the CLB-based erasable field-programmable DNA circuit that uses clip strands as its operation-controlling signals is presented. It enables users to realize diverse functions with limited hardware. CLB-based basic logic gates (OR and AND) are first constructed and demonstrated their erasability and field programmability. Furthermore, by adding the appropriate operation-controlling strands, multiple rounds of programming are achieved among five different logic operations on a two-layer circuit. Subsequently, a circuit is successfully built to implement two fundamental binary calculators: half-adder and half-subtractor, proving that the design can imitate silicon-based binary circuits. Finally, a comprehensive CLB-based circuit is built that enables multiple rounds of switch among seven different logic operations including half-adding and half-subtracting. Overall, the CLB-based erasable field-programmable circuit immensely enhances their practicability. It is believed that design can be widely used in DNA logic networks due to its efficiency and convenience.

How does communication affect patient safety? Protocol for a systematic review and logic model.

INTRODUCTION: One in 10 patients are harmed in healthcare, more than three million deaths occur annually worldwide due to patient safety incidents, and the economic burden of patient safety incidents accounts for 15% of hospital expenditure. Poor communication between patients and practitioners is a significant contributor to patient safety incidents. This study aims to evaluate the extent to which patient safety is affected by communication and to provide a logic model that illustrates how communication impacts patient safety. METHODS AND ANALYSIS: We will conduct a systematic review of randomised and non-randomised studies, reported in any language, that quantify the effects of practitioner and patient communication on patient safety. We will search MEDLINE, CINAHL, APA PsychINfo, CENTRAL, Scopus and ProQuest theses and dissertations from 2013 to 7 February 2024. We will also hand-search references of included studies. Screening, data extraction and risk of bias assessment will be conducted by two independent reviewers. Risk of bias will be assessed using the Cochrane Risk of Bias in Non-Randomised Studies of Interventions (ROBINS-I) for non-randomised studies, and the Cochrane Risk of Bias V.2 (RoB2) for randomised controlled trials. If appropriate, results will be pooled with summary estimates and 95% confidence intervals (CIs); otherwise, we will conduct a narrative synthesis. We will organise our findings by healthcare discipline, type of communication and type of patient safety incident. We will produce a logic model to illustrate how communication impacts patient safety. ETHICS AND DISSEMINATION: This systematic review does not require formal ethics approval. Findings will be disseminated through international conferences, news and peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42024507578.

Causal inference in ethnographic research: Refining explanations with abductive logic, strength of evidence assessments, and graphical models.

In their classic accounts, anthropological ethnographers developed causal arguments for how specific sociocultural structures and processes shaped human thought, behavior, and experience in particular settings. Despite this history, many contemporary ethnographers avoid establishing in their work direct causal relationships between key variables in the way that, for example, quantitative research relying on experimental or longitudinal data might. As a result, ethnographers in anthropology and other fields have not advanced understandings of how to derive causal explanations from their data, which contrasts with a vibrant "causal revolution" unfolding in the broader social and behavioral sciences. Given this gap in understanding, we aim in the current article to clarify the potential ethnography has for illuminating causal processes related to the cultural influence on human knowledge and practice. We do so by drawing on our ongoing mixed methods ethnographic study of games, play, and avatar identities. In our ethnographic illustrations, we clarify points often left unsaid in both classic anthropological ethnographies and in more contemporary interdisciplinary theorizing on qualitative research methodologies. More specifically, we argue that for ethnographic studies to illuminate causal processes, it is helpful, first, to state the implicit strengths and logic of ethnography and, second, to connect ethnographic practice more fully to now well-developed interdisciplinary approaches to causal inference. In relation to the first point, we highlight the abductive inferential logic of ethnography. Regarding the second point, we connect the ethnographic logic of abduction to what Judea Pearl has called the ladder of causality, where moving from association to intervention to what he calls counterfactual reasoning produces stronger evidence for causal processes. Further, we show how graphical modeling approaches to causal explanation can help ethnographers clarify their thinking. Overall, we offer an alternative vision of ethnography, which contrasts, but nevertheless remains consistent with, currently more dominant interpretive approaches.

Leveraging logical definitions and lexical features to detect missing IS-A relations in biomedical terminologies.

Biomedical terminologies play a vital role in managing biomedical data. Missing IS-A relations in a biomedical terminology could be detrimental to its downstream usages. In this paper, we investigate an approach combining logical definitions and lexical features to discover missing IS-A relations in two biomedical terminologies: SNOMED CT and the National Cancer Institute (NCI) thesaurus. The method is applied to unrelated concept-pairs within non-lattice subgraphs: graph fragments within a terminology likely to contain various inconsistencies. Our approach first compares whether the logical definition of a concept is more general than  that of the other concept. Then, we check whether the lexical features of the concept are contained in those of the other concept. If both constraints are satisfied, we suggest a potentially missing IS-A relation between the two concepts. The method identified 982 potential missing IS-A relations for SNOMED CT and 100 for NCI thesaurus. In order to assess the efficacy of our approach, a random sample of results belonging to the "Clinical Findings" and "Procedure" subhierarchies of SNOMED CT and results belonging to the "Drug, Food, Chemical or Biomedical Material" subhierarchy of the NCI thesaurus were evaluated by domain experts. The evaluation results revealed that 118 out of 150 suggestions are valid for SNOMED CT and 17 out of 20 are valid for NCI thesaurus.

Light-triggered AND logic tetrapeptide dynamic covalent assembly.

We demonstrate light-triggered dynamic covalent assembly of a linear short tetrapeptide containing two terminal cysteine residues in an AND logic manner. A photobase generator is introduced to accomplish light-mediated pH regulation to increase the reduction potential of thiols in the tetrapeptide, which activates its oxidative polymerization through disulfide bonds. Interestingly, it is elucidated that under light irradiation, mere co-existence of photobase generator and the oxidizing agent permits the polymerization performance of this tetrapeptide. Hence, a light-triggered AND logic dynamic covalent assembly of a tetrapeptide is achieved. Further, upon redox response, the reversible aggregation and disaggregation can be transformed for numerous times due to the dynamic covalent feature of disulfide bond. As a comparison, no assembly occurs for a short peptide containing one terminal cysteine residue under the same stimuli condition. This work offers a new approach to remotely control programmable molecular assembly of short linear peptides based on dynamic covalent bond, holding great potential in wide bioapplications.

LollipopE: Bi-centered lollipop embedding for complex logic query on knowledge graph.

Answering complex First-Order Logic (FOL) query plays a vital role in multi-hop knowledge graph (KG) reasoning. Geometric methods have emerged as a promising category of approaches in this context. However, existing best-performing geometric query embedding (QE) model is still up against three-fold potential problems: (i) underutilization of embedding space, (ii) overreliance on angle information, (iii) uncaptured hierarchy structure. To bridge the gap, we propose a lollipop-like bi-centered query embedding method named LollipopE. To fully utilize embedding space, LollipopE employs learnable centroid positions to represent multiple entities distributed along the same axis. To address the potential overreliance on angular metrics, we design an angular-based and centroid-based metric. This involves calculating both an angular distance and a centroid-based geodesic distance, which empowers the model to make more informed selections of relevant answers from a wider perspective. To effectively capture the hierarchical relationships among entities within the KG, we incorporate dynamic moduli, which allows for the representation of the hierarchical structure among entities. Extensive experiments demonstrate that LollipopE surpasses the state-of-the-art geometric methods. Especially, on more hierarchical datasets, LollipopE achieves the most significant improvement.

Eu(III) functionalized ZnMOF based efficient dual-emission sensor integrated with self-calibrating logic gate for intelligent detection of epinephrine.

The rapid detection of epinephrine (EPI) in serum holds immense importance in the early disease diagnosis and regular monitoring. On the basis of the coordination post-synthetic modification (PSM) strategy, a Eu3+ functionalized ZnMOF (Eu3+@ZnMOF) was fabricated by anchoring the Eu3+ ions within the microchannels of ZnMOF as secondary luminescent centers. Benefiting from two independent luminescent centers, the prepared Eu3+@ZnMOF shows great potential as a multi-signal self-calibrating luminescent sensor in visually and efficiently detecting serum EPI levels, with high reliability, fast response time, excellentrecycleability, and low detection limits of 17.8 ng/mL. Additionally, an intelligent sensing system was designed in accurately and reliably detecting serum EPI levels, based on the designed self-calibrating logic gates. Furthermore, the possible sensing mechanisms were elucidated through theoretical calculations as well as spectral overlaps. This work provides an effective and promising strategy for developing MOFs-based self-calibrating intelligent sensing platforms to detect bioactive molecules in bodily fluids.