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Developing precise tumor cell-specific mitochondrial ferroptosis-related inhibition miRNA imaging methods holds enormous potential for anticancer drug screening and cancer treatment. Nevertheless, traditional amplification methods still tolerated the limited tumor specificity because of the “off-tumor” signal leakage resulting from their “always-active” sensing mode. To overcome this limitation, we herein developed a dual (exogenous 808 nm NIR light and endogenous APE1) activated nanoladder for precise imaging of mitochondrial ferroptosis-related miRNA with tumor cell specificity and improved imaging resolution. Exogenous NIR light-activation can regulate the ferroptosis-related inhibition miRNA imaging signals within mitochondria, and endogenous enzyme-activation can confine signals to tumor cells. Based on this dual activation design, off-tumor signals were greatly reduced and tumor-to-background contrast was enhanced with an improved tumor/normal discrimination ratio, realizing tumor cell-specific precise imaging of mitochondrial ferroptosis-related inhibition miRNA.

The detection of anions using carbon dots (CDs) has received less attention compared to cations. Therefore, the present study aimed to develop a fluorescence sensor based on carbon dots (CDs) capable of detecting S2−in real water samples. The CDs were successfully prepared from the residues of a traditional Chinese herb, Gardenia, which emitted green photoluminescence (PL) under ultraviolet light irradiation. The as-prepared CDs were quasi-spherical in shape and ranged in size from 10 to 30 nm. Different detailed analyses proved that the CDs had good morphology, various functional groups, high water solubility, great optical features, and excellent stability under diverse environmental conditions. The ion detection showed that only Ag+had the strongest fluorescence quenching effect on the CDs, however, the addition of S2−could recover their fluorescence. Based on these results, an “off–on” fluorescence sensor was achieved to selectively detect the concentration of S2−in real water samples with a limit of detection (LOD) of 39 μM, which further expanded the application of residues from traditional Chinese herbal medicine.

Programming ultrasensitive and stimuli-responsive DNAzyme-based probes that contain logic gate biocomputation hold great potential for precise molecular imaging. In this work, a DNA computation-mediated DNAzyme platform that can be activated by 808 nm NIR light and target c-MYC was designed for spatiotemporally controlled ultrasensitive AND-gated molecular imaging. Particularly, the sensing and recognition function of the traditional DNAzyme platform was inhibited by introducing a blocking sequence containing a photo-cleavable linker (PC-linker) that can be indirectly cleaved by 808 nm NIR light and thus enables the AND-gated molecular imaging. According to the responses toward three designed SDz, nPC-SDz, and m-SDz DNAzyme probes, the fluorescence recovery in diverse cell lines (MCF-7, HeLa, and L02) and inhibitor-treated cells was investigated to confirm the AND-gated sensing mechanism. It is worth noting that thanks to the strand displacement amplification and the ability of gold nanopyramids (Au NBPs) to enhance fluorescence, the fluorescence intensity increased by ∼7.9 times and the detection limit decreased by nearly 40.5 times. Moreover, false positive signals can be also excluded due to such AND-gated design. Furthermore, such a designed “AND-gate” sensing manner can also be applied to spatiotemporally controlled ultrasensitive in vivo molecular imaging, indicating its promising potential in precise biological molecular imaging.

Discrimination of malignancy from thyroid nodules poses challenges in clinical practice. We aimed to identify the plasma metabolomic biomarkers in discriminating papillary thyroid cancer (PTC) from benign thyroid nodule (BTN). Metabolomics profiling of plasma was performed in two independent cohorts of 651 subjects of PTC (n= 215), BTN (n= 230), and healthy controls (n= 206). In addition, 132 patients with thyroid micronodules (<1 cm) and 44 patients with BTN suspected malignancy by ultrasound were used for biomarker validation. Recursive feature elimination algorithm was used for metabolic biomarkers selecting. Significant differential metabolites were demonstrated in patients with thyroid nodules (PTC and BTN) from healthy controls (P= 0.0001). A metabolic biomarker panel (17 differential metabolites) was identified to discriminate PTC from BTN with an AUC of 97.03% (95% CI: 95.28–98.79%), 91.89% sensitivity, and 92.63% specificity in discovery cohort. The panel had an AUC of 92.72% (95% CI: 87.46–97.99%), 86.57% sensitivity, and 92.50% specificity in validation cohort. The metabolic biomarker signature could correctly identify 84.09% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. Moreover, high accuracy of 87.88% for diagnosis of papillary thyroid microcarcinoma was displayed by this panel and showed significant improvement in accuracy, AUC and specificity when compared with ultrasound. We identified a novel metabolic biomarker signature to discriminate PTC from BTN. The clinical use of this biomarker panel would have improved diagnosis stratification of thyroid microcarcinoma in comparison to ultrasound.

Signal amplification strategies with spatiotemporally high sensitivity can provide more accurate information and hold great promise for improving the accuracy of disease diagnosis. Herein, a 808 nm near-infrared (NIR) light-activated plasmon enhanced fluorescence-catalytic hairpin assembly (PEF-CHA) signal amplification strategy was proposed for spatiotemporally controllable precise imaging of miRNA in vitro and in vivo with ultrasensitivity. The proposed 808 nm NIR light-activated PEF-CHA signal amplification strategy is constructed through combining up-conversion photocontrol and PEF technologies with CHA. It is worth noting that the laser irradiation-induced overheating effect could be effectively alleviated by using Nd3+-sensitized upconversion nanoparticles (UCNPs) to convert 808 nm NIR light to ultraviolet (UV) light, which is almost nondestructive to cells or tissues. In addition, nonspecific activation as well as false positive signals can be effectively avoided. Moreover, the detection limit can be reduced by approximate 38 times thanks to the high sensitivity of the proposed strategy. Furthermore, we demonstrate that the 808 nm NIR light-activated PEF-CHA signal amplification strategy can be expanded to sensitive and activatable imaging of intratumoral miRNAs in living mice, showing feasible prospects for precise biological and medical analysis.

Rechargeable aqueous zinc-ion batteries (AZIBs) have attracted significant attention in large-scale energy storage systems due to their unique merits, such as intrinsic safety, low cost, and relatively high theoretical energy density. However, the dilemma of the uncontrollable Zn dendrites, severe hydrogen evolution reaction (HER), and side reactions that occur on the Zn anodes have hindered their commercialization. Herein, a state-of-the-art review of the rational design of highly reversible Zn anodes for high-performance AZIBs is provided. Firstly, the fundamental understanding of Zn deposition, with regard to the nucleation, electro-crystallization, and growth of the Zn nucleus is systematically clarified. Subsequently, a comprehensive survey of the critical factors influencing Zn plating together with the current main challenges is presented. Accordingly, the rational strategies emphasizing structural design, interface engineering, and electrolyte optimization have been summarized and analyzed in detail. Finally, future perspectives on the remaining challenges are recommended, and this review is expected to shed light on the future development of stable Zn anodes toward high-performance AZIBs.

A kind of D-π-A mechanoresponsive luminescent molecule 2- (4- (1H-phenanthro[9,10-d] imidazol) benzylidene) malononitrile (ANPPI) was synthesized and characterized by UV absorption, photoluminescence, and other techniques. The obvious mechanoresponsive properties of ANPPIwere reversible upon grinding and fuming, and were demonstrated by X-ray diffraction, scanning electron microscopy, and differential scanning calorimetry.Graphical AbstractIn this paper, a D-π-A fluorescent molecule with reversible mechanical response characteristics was synthesized and verified by X-ray diffraction, scanning electron microscopy and differential scanning calorimetry.

Most of the advanced first-order logic automated theorem proving (ATP) systems adopt binary resolution methods as the core inference mechanism, where only two clauses are involved and a complementary pair of literals are eliminated during each deduction step. Recently, a novel multi-clause inference rule is introduced along with its soundness and completeness, which is called as standard contradiction separation rule (in short, S-CS rule) and allows multiple (two or more) clauses to be involved in each deduction step. This paper introduces and evaluates the application of S-CS rule in first-order logic ATP. Firstly, it analyzes several deduction methods of S-CS rule. It is then focused on how this multi-clause deduction theory can be achieved through forming a specific and effective algorithm, and finally how it can be applied in the top ATP systems in order to improve their performances. Concretely, two novel multi-clause S-CS dynamic deduction algorithms are proposed based on optimized proof search, including related heuristic strategy, then the application method applied in the state of the art ATP system Eprover (the version of Eprover 2.3) is introduced. Eprover with the proposed multi-clause deduction algorithms are evaluated through the FOF division of the CASC-J9 (in 2018) ATP system competition. Experimental results show that Eprover with the proposed multi-clause deduction algorithms outperform the plain Eprover itself to a certain extent.

Due to the need of the logical foundation for uncertain information processing, development of efficient automated reasoning system based on non-classical logics is always an active research area. The present paper focuses on the resolution-based automated reasoning theory in a many-valued logic with truth-values defined in a lattice-ordered many-valued algebraic structure - lattice implication algebras (LIA). Specifically, as a continuation and extension of the established work on binary resolution at a certain truth-value level α(called α-resolution), a non-clausal multi-ary α-generalized resolution calculus is introduced for a lattice-valued propositional logic LP(X) based on LIA, which is essentially a non-clausal generalized resolution avoiding reduction to normal clausal form. The new resolution calculus in LP(X) is then proved to be sound and complete. The concepts and theoretical results are further extended and established in the corresponding lattice-valued first-order logic LF(X) based on LIA.

Decision heuristic strategy can be viewed as one of the most central features of state-of-the-art conflict-driven clause-learning SAT solvers. Variable state independent decaying sum (VSIDS) still is the dominant branching heuristics because of its low cost. VSIDS consists of a rewarding mechanism for variables participating in the conflict. This paper proposes a new rewarding mechanism for branching strategy, rewarding variables differently depended on information provided by the conflict analysis process, that is to say, the literal block distance value of the learnt clause and the size of the backtrack level decided by the learnt clause. We implement it as part of the Glucose 3.0 solver and MapleCOMSPS solver. Compared with Glucose 3.0, the number of solved instances of the improved Glucose_LBD + BTL is enhanced by 6.0%; compared with MapleCOMSPS, the number of solved instances of MapleCOMSPS_LBD + BTL is added by 3.4%. These empirical results further shed light on the proposed heuristic having the advantage of solving Application benchmark from the SAT Competitions 2015–2017.

Current vehicle navigation systems usually rely on global real-time traffic information. They suggest the best paths to travelers through an independent and selfish-routing mechanism. Existing studies show that such systems have a great potential to cause traffic congestions oscillating among several alternative corridors, i.e., iteratively leading exceeding traffic into light traffic corridors and making congestion on one and then another within a short time period. To address this issue, this research proposes a local information provision strategy, given travelers are still guided by an independent and selfish-routing mechanism. The coverage of the real-time information provision is identified by the minimum bounding rectangle, and the ellipse method respectively. The benefits of the proposed strategy in system level are investigated by the simulation studies built upon the Borman expressway network. The simulation results indicate that as the local travel time information provision strategy is used, it may save 10–25% of the system travel time resulting from global information provision strategy under medium or high network traffic loads. Moreover, the sensitivity analyses indicate that the merits of the local travel time information provision strategy are more significant as the penetration of smart vehicles increases over the network. Thus, the proposed local information provision strategy outperforms global information provision strategy in system level network performance.

Seed predation by insect herbivores reduces crop production worldwide. Foraging on seeds at pre-dispersal generally means that females need to find the suitable host plant within a relatively short timeframe in order to synchronize larval development with seed production. The mechanistic understanding of host finding by seed pests can be harnessed for more sustainable pest management strategies. We here studied the chemical communication between the bean bug Riptortus pedestris, a major pest of legumes, and several crop species and cultivars in the Fabaceae. Via a comparative chemical analysis, we found that 1-octen-3-ol is the principal constituent of the floral scents of most species tested in the subfamily Faboideae, including soybean and faba bean. With field trapping and laboratory bioassays, including electroantennography, we further revealed that this compound can be perceived, and stimulate attraction responses, by R. pedestrisnymphs and adults. The addition of 1-octen-3-ol to pheromone traps might therefore improve trapping efficacy for controlling populations of this important granivore pest on legumes.

This study proposed a coordinated online in-vehicle routing mechanism for smart vehicles with real-time information exchange and portable computation capabilities. The proposed coordinated routing mechanism was modeled as a pure-strategy atomic routing game and was implemented by a sequentially updating distributed algorithm. This study showed the existence of an equilibrium joint routing decision for the routing game and the convergence of the distributed algorithm to an equilibrium; this equilibrium was based on the assumption that individual smart vehicles were selfish players who tried to minimize their own travel time and shared their route choices with other smart vehicles directly or indirectly. Numerical experiments based on the Sioux Falls, South Dakota, network indicated that the proposed distributed algorithm converged very quickly; thus the algorithm has great potential for online applications. Moreover, the proposed coordinate routing mechanism outperformed traditional independent routing mechanisms under various traffic regimes. The mechanism reduced travel time for both the overall system and the majority of individual vehicles; this achievement was the core idea of intelligent transportation systems. These positive effects were more significant in congested traffic condition than in free traffic. The experiments also indicated that even with imperfect information provision, the proposed approach could work well for convergence as well as travel time reduction in systems and individual vehicles.

This study proposed a coordinated online in-vehicle routing mechanism for smart vehicles with real-time information exchange and portable computation capabilities. The proposed coordinated routing mechanism was modeled as a pure-strategy atomic routing game and was implemented by a sequentially updating distributed algorithm. This study showed the existence of an equilibrium joint routing decision for the routing game and the convergence of the distributed algorithm to an equilibrium; this equilibrium was based on the assumption that individual smart vehicles were selfish players who tried to minimize their own travel time and shared their route choices with other smart vehicles directly or indirectly. Numerical experiments based on the Sioux Falls, South Dakota, network indicated that the proposed distributed algorithm converged very quickly; thus the algorithm has great potential for online applications. Moreover, the proposed coordinate routing mechanism outperformed traditional independent routing mechanisms under various traffic regimes. The mechanism reduced travel time for both the overall system and the majority of individual vehicles; this achievement was the core idea of intelligent transportation systems. These positive effects were more significant in congested traffic condition than in free traffic. The experiments also indicated that even with imperfect information provision, the proposed approach could work well for convergence as well as travel time reduction in systems and individual vehicles.

Based on the general form of a-resolution principle for a lattice-valued logic with truth-values defined in a lattice-valued logical algebra structure - lattice implication algebra, the further extended a-resolution method in this lattice-valued logic is discussed in the present paper in order to increase the efficiency of the resolution method. Firstly, a-quasi-lock semantic resolution method in lattice-valued propositional logic LP(X) is established by combining the lock and semantic resolution simultaneously, and its theorems of soundness and conditional completeness are proved. Secondly, this a-quasi-lock semantic resolution method is extended into the corresponding lattice-valued first-order logic LF(X), and its soundness and conditional completeness are also established. This extended resolution method will provide a theoretical basis for automated soft theorem proving and program verification based on lattice-valued logic.

AbstractBased on the general form of α-resolution principle for a lattice-valued logic with truth-values defined in a lattice-valued logical algebra structure - lattice implication algebra, the further extended α-resolution method in this lattice-valued logic is discussed in the present paper in order to increase the efficiency of the resolution method. Firstly, α-quasi-lock semantic resolution method in lattice-valued propositional logic LP(X) is established by combining the lock and semantic resolution simultaneously, and its theorems of soundness and conditional completeness are proved. Secondly, this α-quasi-lock semantic resolution method is extended into the corresponding lattice-valued first-order logic LF(X), and its soundness and conditional completeness are also established. This extended resolution method will provide a theoretical basis for automated soft theorem proving and program verification based on lattice-valued logic.

Abstract

AbstractThis paper puts forward a new method of alphanumeric character recognition based on BP neural network classification and combined features. This method firstly establishes three BP networks respectively for three categories of characters which are classified according to their Euler numbers, with the combination of grid feature and projection feature as the input of each BP network. When recognizing a character, its combined features are fed into the three BP networks simultaneously without the necessity for judging its Euler number. The final recognition result is elaborated by synthetically analyzing the outputs of three BP networks. Experimental results show that the proposed method can effectively improve the recognition ability and efficiency, and has a good property of fault tolerance and robustness. Furthermore, the weight coefficients of combined features for each BP network are optimized, which can further improve the recognition rate.

This paper puts forward a new method of alphanumeric character recognition based on BP neural network classification and combined features. This method firstly establishes three BP networks respectively for three categories of characters which are classified according to their Euler numbers, with the combination of grid feature and projection feature as the input of each BP network. When recognizing a character, its combined features are fed into the three BP networks simultaneously without the necessity for judging its Euler number. The final recognition result is elaborated by synthetically analyzing the outputs of three BP networks. Experimental results show that the proposed method can effectively improve the recognition ability and efficiency, and has a good property of fault tolerance and robustness. Furthermore, the weight coefficients of combined features for each BP network are optimized, which can further improve the recognition rate.

This paper focuses on efficient non-clausal resolution-based automated reasoning methods and algorithms for a lattice-ordered linguistic truth-valued logic, which corresponds to extensions of α-lock resolution. Firstly, α-generalized lock resolution is proposed for lattice-valued propositional logic and first order logic, respectively, along with their concepts, soundness and completeness. Then, α-generalized lock resolution for first order linguistic truth-valued lattice-valued logic LV(n×2)F(X)is equivalently transformed into that for propositional logic LnP(X), which can greatly reduce the complexity of the resolution procedure. Finally, α-generalized linear semi-lock resolution is discussed, and its general algorithm is also contrived. This work provides more efficient and natural resolution automated reasoning scheme in linguistic truth-valued logic based on lattice implication algebra with the aim at establishing formal tools for symbolic natural language processing.

AbstractThis paper focuses on effcient non-clausal resolution-based automated reasoning methods and algorithms for a lattice-ordered linguistic truth-valued logic, which corresponds to extensions of α-lock resolution. Firstly, α-generalized lock resolution is proposed for lattice-valued propositional logic and first order logic, respectively, along with their concepts, soundness and completeness. Then, α-generalized lock resolution for first order linguistic truth-valued lattice-valued logic LV(n×2)F(X) is equivalently transformed into that for propositional logic LnP(X), which can greatly reduce the complexity of the resolution procedure. Finally, α-generalized linear semi-lock resolution is discussed, and its general algorithm is also contrived. This work provides more efficient and natural resolution automated reasoning scheme in linguistic truth-valued logic based on lattice implication algebra with the aim at establishing formal tools for symbolic natural language processing.

We present an experimental investigation of how the different operating parameters affect the random telegraph signal (RTS) noise in surface acoustic wave (SAW) devices. The altered parameters include: SAW power and frequency, phase difference between two counterpropagating SAW beams, and asymmetrical voltage applied to the two side gates of the quantum point contact. The results indicate that proper tuning of the relevant parameters can reduce the RTS noise, which would be beneficial for metrological applications. Building on the experimental results, physical models have been proposed to explain the reason why the effective reduction of the noise can be achieved.

Catalytically inactive, latent Ru–carboxylate complex 2, which was recently serendipitously isolated, can be activated in situ efficiently in ionic liquids ILs with the aid of imidazolium halides or tetrabutylammonium halides. The activity of the in situ activated catalyst was largely dependent on IL anions, imidazolium halides, and terabutylammonium halides. Under the optimal condition of 1.0 mol of [bmim][Br] in CH2Cl2[bmim][SbF6] 1:1, latent Ru complex 2was activated in situ to catalyze the ringclosing metathesis of various dienes with conversions of up to >99 . © WileyVCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2009

Nanogap antennas with strong electromagnetic fields of the “hot spot” in the gap region of two adjacent particles that can significantly improve the optical properties of fluorophores hold great potential for ultrasensitive bioanalysis. Herein, a DNA computation-mediated self-assembly of Au NBP dimer-based plasmonic nanogap antennas was designed for imaging of intracellular correlated dual disease biomarkers. It is worth noting that with the benefit from the electromagnetic fields of the “hot spot” in the gap region and strand displacement amplification, the fluorescence intensity can be enhanced ∼14.7-fold by Au NBP dimer-based plasmonic nanogap antennas. In addition, the AND-gate sensing mechanism was confirmed through monitoring the response of three designed nAP-PH1, m-PH1, and PH1 probes, the fluorescence recovery in different cell lines (Hela and L02), and inhibitor-treated cells, respectively. Furthermore, thanks to the “dual keys” activation design, such an “AND-gate” sensing manner can be used for ultrasensitive correlated multiplexed molecular imaging, demonstrating its feasible prospect in correlated multiplexed molecular imaging.