Emerindanols A and B: Two Bipolyhydroindenol Sesterterpenes with 5/6-6/5 Coupled Ring System Discovered by Genome Mining.

Genome mining of Emericella sp. XL-029 achieved a new type E sesterterpene synthase, EmES, which affored a novel bipolyhydroindenol sesterterpene, emerindanol A. Heterologous coexpression with the upstream P450 oxidase revealed C-4 hydroxylated product, emerindanol B. Notably, emerindanols A and B represented the first sesterterpenes featuring a unique 5/6-6/5 coupled ring system. EmES was postulated to initiate through C1-IV-V pathway and convert the fused ring intermediate into the final coupled ring product through a spiro skeleton.

Automatic three-dimensional facial symmetry reference plane construction based on facial planar reflective symmetry net.

OBJECTIVES: Three-dimensional (3D) facial symmetry analysis is based on the 3D symmetry reference plane (SRP). Artificial intelligence is widely used in the dental and oral sciences. This study developed a novel deep learning model called the facial planar reflective symmetry net (FPRS-Net) to automatically construct an SRP and established a method for defining a 3D point-cloud region of interest (ROI) and high-dimensional feature computations suitable for this network model. METHODS: Overall, 240 patients were enrolled. The deep learning model was trained and predicted using 200 samples, and its clinical suitability was evaluated with 40 samples. Four FPRS-Net models were prepared, each using supervised and unsupervised learning approaches based on full facial and ROI data (FPRS-NetS, FPRS-NetSR, FPRS-NetU, and FPRS-NetUR). These models were trained on 160 3D facial datasets, validated on 20 cases, and tested on another 20 cases. The model predictions were evaluated using an additional 40 clinical 3D facial datasets by comparing the mean square error of the SRP between the parameters predicted by the four FPRS-Net models and the truth plane. The clinical suitability of FPRS-Net models was evaluated by measuring the angle error between the predicted and ground-truth planes; experts evaluated the predicted SRP of the four FPRS-Net models using the visual analogue scales (VAS) method. RESULTS: The FPRS-NetSR and FPRS-NetU models achieved an average angle error of 0.84° and 0.99° in predicting 3D facial SRP, respectively, with a VAS value of >8. Using the four FPRS-Net models to create an SRP in 40 cases of 3D facial data required <4 s. CONCLUSIONS: Our study demonstrated a new solution for automatically constructing oral clinical 3D facial SRPs. CLINICAL SIGNIFICANCE: This study proposes an innovative deep learning algorithm (FPRS-Net) to construct a symmetry reference plane that can reduce workload, shorten the time required for digital design, reduce dependence on expert experience, and improve therapeutic efficiency and effectiveness in dental clinics.

Magnetized bentonite modified rice straw biochar: qualitative and quantitative analysis of Cd(II) adsorption mechanism.

Industrialization has caused a significant global issue with cadmium (Cd) pollution. In this study, Biochar (Bc), generated through initial pyrolysis of rice straw, underwent thorough mixing with magnetized bentonite clay, followed by activation with KOH and subsequent pyrolysis. Consequently, a magnetized bentonite modified rice straw biochar (Fe3O4@B-Bc) was successfully synthesized for effective treatment and remediation of this problem. Fe3O4@B-Bc not only overcomes the challenges associated with the difficult separation of individual bentonite or biochar from water, but also exhibited a maximum adsorption capacity of Cd(II) up to 241.52 mg g-1. The characterization of Fe3O4@B-Bc revealed that its surface was rich in C, O and Fe functional groups, which enable efficient adsorption. The quantitative calculation of the contribution to the adsorption mechanism indicates that cation exchange and physical adsorption accounted for 65.87% of the total adsorption capacity. In conclusion, Fe3O4@B-Bc can be considered a low-cost and recyclable green adsorbent, with broad potential for treating cadmium-polluted water.

Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception.

The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.

Lightweight Knowledge Distillation-Based Transfer Learning Framework for Rolling Bearing Fault Diagnosis.

Compared to fault diagnosis across operating conditions, the differences in data distribution between devices are more pronounced and better aligned with practical application needs. However, current research on transfer learning inadequately addresses fault diagnosis issues across devices. To better balance the relationship between computational resources and diagnostic accuracy, a knowledge distillation-based lightweight transfer learning framework for rolling bearing diagnosis is proposed in this study. Specifically, a deep teacher-student model based on variable-scale residual networks is constructed to learn domain-invariant features relevant to fault classification within both the source and target domain data. Subsequently, a knowledge distillation framework incorporating a temperature factor is established to transfer fault features learned by the large teacher model in the source domain to the smaller student model, thereby reducing computational and parameter overhead. Finally, a multi-kernel domain adaptation method is employed to capture the feature probability distribution distance of fault characteristics between the source and target domains in Reproducing Kernel Hilbert Space (RKHS), and domain-invariant features are learned by minimizing the distribution distance between them. The effectiveness and applicability of the proposed method in situations of incomplete data across device types were validated through two engineering cases, spanning device models and transitioning from laboratory equipment to real-world operational devices.

A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication.

Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuromorphic computing. In this work, enlightened from time-varied synaptic rule, an in-memory asymmetric encryption as pre-authentication is utilized with subsequent convolutional neural network (ConvNet) for target recognition, achieving in-memory two-factor authentication (IM-2FA). The unipolar self-oscillated synaptic behavior is adopted to function as in-memory asymmetric encryption, which can greatly decrease the complexity of the peripheral circuit compared to bipolar stimulation. Results show that without passing the encryption process with suitable weights at the correct time, the ConvNet for target recognition will not work properly with an extremely low accuracy lower than 0.86%, thus effectively blocking out the potential risks of involuntary access. When a set of correct weights is evolved at a suitable time, a recognition rate as high as 99.82% can be implemented for target recognition, which verifies the effectiveness of the IM-2FA strategy.

An ultrasmall organic synapse for neuromorphic computing.

High-performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain-inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high-density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL10 with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as-fabricated PBFCL10 synapses can switch between 32 conductance states linearly with a high cycle-to-cycle uniformity of 98.89% and device-to-device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking-plasticity-related algorithm for decision-making tasks.

Approaching the Zero-Power Operating Limit in a Self-Coordinated Organic Protonic Synapse.

High-performance artificial synapse with nonvolatile memory and low power consumption is a perfect candidate for brainoid intelligence. Unfortunately, due to the energy barrier paradox between ultra-low power and nonvolatile modulation of device conductances, it is still a challenge at the moment to construct such ideal synapses. Herein, a proton-reservoir type 4,4',4″,4'''-(Porphine-5,10,15,20-tetrayl) tetrakis (benzenesulfonic acid) (TPPS) molecule and fabricated organic protonic memristors with device width of 10 µm to 100 nm is synthesized. The occurrence of sequential proton migration and interfacial self-coordinated doping will introduce new energy levels into the molecular bandgap, resulting in effective and nonvolatile modulation of device conductance over 64 continuous states with retention exceeding 30 min. The power consumptions of modulating and reading the device conductance approach the zero-power operating limits, which range from 16.25 pW to 2.06 nW and 6.5 fW to 0.83 pW, respectively. Finally, a robust artificial synapse is successfully demonstrated, showing spiking-rate-dependent plasticity (SRDP) and spiking-timing-dependent plasticity (STDP) characteristics with ultra-low power of 0.66 to 0.82 pW, as well as 100 long-term depression (LTD)/potentiation (LTP) cycles with 0.14%/0.30% weight variations.

Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications.

Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO4)]2/triphenylamine-containing polymer (BTPA-F) redox system. The device with bilayer structure organic materials as the resistive switching layer (RSL) exhibits memristive behaviors and excellent long-term synaptic plasticity. Additionally, the device's conductance states can be precisely modulated by consecutively applying voltage pulses between the top and bottom electrodes. A three-layer perception neural network with in situ computing enabled was then constructed utilizing the proposed memristor and trained on the basis of the device's synaptic plasticity characteristics and conductance modulation rules. Recognition accuracies of 97.3% and 90% were achieved, respectively, for the raw and 20% noisy handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, demonstrating the feasibility and applicability of implementing neuromorphic computing applications utilizing the proposed organic memristor.

Learning-Based Intrinsic Reflectional Symmetry Detection.

Reflectional symmetry is a ubiquitous pattern in nature. Previous works usually solve this problem by voting or sampling, suffering from high computational cost and randomness. In this article, we propose a learning-based approach to intrinsic reflectional symmetry detection. Instead of directly finding symmetric point pairs, we parametrize this self-isometry using a functional map matrix, which can be easily computed given the signs of Laplacian eigenfunctions under the symmetric mapping. Therefore, we manually label the eigenfunction signs for a variety of shapes and train a novel neural network to predict the sign of each eigenfunction under symmetry. Our network aims at learning the global property of functions and consequently converts the problem defined on the manifold to the functional domain. By disentangling the prediction of the matrix into separated bases, our method generalizes well to new shapes and is invariant under perturbation of eigenfunctions. Through extensive experiments, we demonstrate the robustness of our method in challenging cases, including different topology and incomplete shapes with holes. By avoiding random sampling, our learning-based algorithm is over 20 times faster than state-of-the-art methods, and meanwhile, is more robust, achieving higher correspondence accuracy in commonly used metrics.

Development of artesunate intelligent prodrug liposomes based on mitochondrial targeting strategy.

Breast cancer is the leading cause of cancer-related deaths in women and remains a formidable therapeutic challenge. Mitochondria participate in a myriad of essential cellular processes, such as metabolism, and are becoming an ideal target for cancer therapy. Artemisinin and its derivatives have demonstrated multiple activities in the context of various cancers. Mitochondrial autophagy(mitophagy) is one of the important anti-tumor mechanisms of artemisinin drugs. However, the lack of specific tumor targeting ability limits the anti-tumor efficacy of artemisinin drugs. In this study, a GSH-sensitive artesunate smart conjugate (TPP-SS-ATS) was synthesized and liposomes (TPP-SS-ATS-LS) that target tumor cells and mitochondria were further prepared. The advantages of TPP-SS-ATS-LS targeting to the breast tumor were verified by in vivo and in vitro evaluations. In our study, the cytotoxicity was obviously enhanced in vitro and tumor growth inhibition rate was increased from 37.7% to 56.4% at equivalent artesunate dosage in breast cancer orthotopic implanted mice. Meanwhile, mitochondrial dysfunction, suppression of ATP production and respiratory capacity were detected in breast cancer cells. We further discovered that TPP-SS-ATS-LS inhibited tumor cells proliferation through mitophagy by regulating PHB2 and PINK1 expression. These results provide new research strategies for the development of new artemisinin-based anti-tumor drugs.

Undescribed specialised metabolites from the endophytic fungus Emericella sp. XL029 and their antimicrobial activities.

The fungus Emericella sp. XL029 isolated from leaves of Panax notoginseng was investigated for agents with potential antibacterial and antifungal activities using a one strain-many compounds (OSMAC) strategy. Fifteen compounds, including seven undescribed structures, were obtained from this species. Their structures were confirmed by extensive spectroscopic data, single-crystal X-ray crystallography and quantum chemistry calculations. Emerlactam A exhibited better antibacterial activity against multidrug-resistant Enterococcus faecium and antifungal activity against Helminthosporium maydis, with an MIC value of 12.5 µg/mL. Quiannulatic acid displayed significant antibacterial activity against multidrug-resistant Enterococcus faecium and multidrug-resistant Enterococcus faecalis with MIC values of 1.56 µg/mL and 3.13 µg/mL, respectively. 5-alkenylresorcinol exhibited significant antifungal activity against all tested phytopathogenic fungi with MIC values ranging from 6.25 to 12.5 µg/mL.

Establishment and evaluation of glucose-modified nanocomposite liposomes for the treatment of cerebral malaria.

Cerebral malaria (CM) is a life-threatening neurological complication caused by Plasmodium falciparum. About 627,000 patients died of malaria in 2020. Currently, artemisinin and its derivatives are the front-line drugs used for the treatment of cerebral malaria. However, they cannot target the brain, which decreases their effectiveness. Therefore, increasing their ability to target the brain by the nano-delivery system with brain-targeted materials is of great significance for enhancing the effects of antimalarials and reducing CM mortality. This study used glucose transporter 1 (GLUT1) on the blood-brain barrier as a target for a synthesized cholesterol-undecanoic acid-glucose conjugate. The molecular dynamics simulation found that the structural fragment of glucose in the conjugate faced the outside the phospholipid bilayers, which was conducive to the recognition of brain-targeted liposomes by GLUT1. The fluorescence intensity of the brain-targeted liposomes (na-ATS/TMP@lipoBX) in the mouse brain was significantly higher than that of the non-targeted liposomes (na-ATS/TMP@lipo) in vivo (P < 0.001) after intranasal administration. The infection and recurrence rate of the mice receiving na-ATS/TMP@lipoBX treatment were significantly decreased, which had more advantages than those of other administration groups. The analysis of pharmacokinetic data showed that na-ATS/TMP@lipoBX could enter the brain in both systemic circulation and nasal-brain pathway to treat malaria. Taken together, these results in this study provide a new approach to the treatment of cerebral malaria.

[Research of preparation quality markers of Yulian Tang with anti-inflammatory activity].

The lipopolysaccharide(LPS)-indused RAW264.7 cells inflammation model was used as a carrier to investigated the effects of the preparation quality markers of Yulian Tang with anti-inflammatory activity in vitro. RAW264.7 cells were treated with LPS(50 ng·mL~(-1)) or/and different concentrations(low dose 0.1 µmol·L~(-1); medium dose 1 µmol·L~(-1); high dose 10 µmol·L~(-1)) of 18 chemical components in Yulian Tang for 24 h. Then the activity of RAW264.7 cell was detected using Cell Counting Kit-8(CCK-8) and the concentrations of inflammatory factors TNF-α and IL-6 in the supernatant of RAW264.7 cell were detected by ELISA assay. As the concentrations of chemical components in Yulian Tang increased, berberine, coptisine, magnoflorine, epiberberine, columbamine and costunolide had stronger inhibitory effects on TNF-α, whereas limonin, dehydroevodiamine, chlorogenic acid, neochlorogenic acid, groenlandicine, evodiamine, rutaecarpine and phellodendrine showed weakened inhibitory effects on TNF-α. The concentrations of palmatine, jatrorrhizine, dehydrocostus lactone and cryptochlorogenic acid had no significant effect on their inhibitory effect on TNF-α. Furthermore, dehydrorutaecarpine, chlorogenic acid, neochlorogenic acid, evodiamine, rutaecarpine, costunolide, phellodendrine and cryptochlorogenic acid showed stronger inhibitory effect on IL-6 as their concentrations increased; berberine, coptisine, magnoflorine, epiberberine, limonin, columbamine, groenlandicine and dehydrocostus lactone had no changes in their inhibitory effects on IL-6 as the concentrations increased. Palmatine and jatrorrhizine had the best inhibitory effect on IL-6. Combining the previous analysis of qualitative and quantitative preparation quality markers of Yulian Tang with the above result of dose-response relationship, we finally identified 15 preparation quality markers of Yulian Tang with anti-inflammatory activity, namely berberine, coptisine, palmatine, magnoflorine, epiberberine, limonin, columbamine, jatrorrhizine, neochlorogenic acid, chlorogenic acid, groenlandicine, evodiamine, rutaecarpine, dehydrocostus lactone and costunolide. In conclusion, our study provides a quick strategy for screening the qualitative preparation quality markers of Yulian Tang with anti-inflammatory activity. Moreover, it also provides an explicit route for the determination of preparation quality markers of Yulian Tang with other activities.

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network.

With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, and the global search ability is weak. Based on the above reasons, this paper proposes an algorithm based on genetic algorithm and neural network to build a prediction model of behavior recognition. Firstly, genetic algorithm is used to cluster the redundant data, so that the data are in fragment order, and then it is used to reduce the data redundancy of different behaviors and weaken the influence of dimension on behavior recognition. Then, the genetic algorithm clusters the data to form subgenetic particles with different dimensions and carries out coevolution and optimal location sharing for subgenetic particles with different dimensions. Through simulation test, the algorithm constructed in this paper is better than genetic algorithm and neural network algorithm in terms of calculation accuracy and convergence speed. Finally, the prediction model is constructed by setting the initial value and threshold to predict the behavior recognition, and the results show that the accuracy of the model constructed in this paper is improved in the analysis of behavior recognition.

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix.

The rapid development of social economy not only increases people's living pressure but also reduces people's health. Looking for a healthy development prediction model has become a domestic concern. Based on the analysis of the influencing factors of health development, this paper looks for a model to predict the development of public health, so as to improve the accuracy of health development prediction. In this paper, the linear sequential extreme learning machine algorithm can be used to evaluate the health status of a large number of data, analyze the differences of each evaluation index, and construct the analysis model of health status. Therefore, this paper introduces rough set theory into linear sequential extreme learning machine algorithm. Rough set can analyze the double analysis of evaluation scheme, predict the health development of different individuals, and improve the evaluation accuracy of mass health evaluation. The simulation results show that the improved line sequential extreme learning machine algorithm can accurately analyze the mass health and meet the needs of different individuals' health evaluation.

A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition.

The interference of noise will cause the degradation of image quality, which can have a negative impact on the subsequent image processing and visual effect. Although the existing image denoising algorithms are relatively perfect, their computational efficiency is restricted by the performance of the computer, and the computational process consumes a lot of energy. In this paper, we propose a method for image denoising and recognition based on multi-conductance states of memristor devices. By regulating the evolution of Pt/ZnO/Pt memristor wires, 26 continuous conductance states were obtained. The image feature preservation and noise reduction are realized via the mapping between the conductance state and the image pixel. Furthermore, weight quantization of convolutional neural network is realized based on multi-conductance states. The simulation results show the feasibility of CNN for image denoising and recognition based on multi-conductance states. This method has a certain guiding significance for the construction of high-performance image noise reduction hardware system.

Clustering-Based Noise Elimination Scheme for Data Pre-Processing for Deep Learning Classifier in Fingerprint Indoor Positioning System.

Wi-Fi-based indoor positioning systems have a simple layout and a low cost, and they have gradually become popular in both academia and industry. However, due to the poor stability of Wi-Fi signals, it is difficult to accurately decide the position based on a received signal strength indicator (RSSI) by using a traditional dataset and a deep learning classifier. To overcome this difficulty, we present a clustering-based noise elimination scheme (CNES) for RSSI-based datasets. The scheme facilitates the region-based clustering of RSSIs through density-based spatial clustering of applications with noise. In this scheme, the RSSI-based dataset is preprocessed and noise samples are removed by CNES. This experiment was carried out in a dynamic environment, and we evaluated the lab simulation results of CNES using deep learning classifiers. The results showed that applying CNES to the test database to eliminate noise will increase the success probability of fingerprint location. The lab simulation results show that after using CNES, the average positioning accuracy of margin-zero (zero-meter error), margin-one (two-meter error), and margin-two (four-meter error) in the database increased by 17.78%, 7.24%, and 4.75%, respectively. We evaluated the simulation results with a real time testing experiment, where the result showed that CNES improved the average positioning accuracy to 22.43%, 9.15%, and 5.21% for margin-zero, margin-one, and margin-two error, respectively.

[Screening of quantitative preparation quality markers of Yulian Decoction].

On the basis of the qualitative preparation quality markers of Yulian Decoction, we screened out the quantitative markers and explored a general strategy for analyzing the component migration in Chinese herbal pieces, preparations, and plasma. A method capable of simultaneously determining 28 chemical components in Yulian Decoction was established based on HPLC-MS/MS. This method was used to determine the migrated components in herbal pieces-lyophilized powder preparations-rat plasma after administration of Yulian Decoction. Liquid chromatography was performed under the following conditions: C_(18)-reversed phase chromatographic column(2.1 mm × 100 mm, 1.8 µm); acetonitrile-water(containing 0.1% formic acid) as the mobile phase for gradient elution; the flow rate of 0.2 mL·min~(-1). Electrospray ionization source was adopted for mass spectrometry detection, in which positive and negative ion modes and multiple reaction monitoring were applied. Confirmed by the methodological investigation in linear range, recovery(95.48%-103.4%), precision(RSD, 0.45%-3.8%), stability, and repeatability(RSD, 5.6%-14%), the established method was suitable for the detection and quantification of the components in Yulian Decoction. The results showed that in the lyophilized powder of Yulian Decoction, berberine was greater than 5% in mass fraction, magnoflorine, epiberberine, coptisine, palmatine, and limonin in the range of 1%-5%, and dehydroevodiamine, evodiamine, rutaecarpine, costunolide, and dehydrocostus lactone in the range of 0.002%-1%. Of the 28 components detected in pieces, 27 were found to migrate to the lyophilized powder, and 11 were detected in rat plasma. Fifteen components were preliminarily determined as quantitative preparation quality markers for Yulian Decoction, including berberine, epiberberine, coptisine, palmatine, evodiamine, rutaecarpine, limonin, costunolide, dehydrocostus lactone, magnoflorine, jatrorrhizine, columbamine, groenlandicine, chlorogenic acid, and neochlorogenic acid. In conclusion, the HPLC-MS/MS general strategy was established for analyzing the migration of multiple components in Chinese herbal pieces, preparations, and plasma, which can provide the basis for the screening of quantitative preparation quality markers and multi-index quality control of Yulian Decoction.

A highly sensitive chemosensor for rapid recognition of Cu2+ and HSO3- in 100% aqueous solution.

Dual-responsive chemosensors have garnered much research interests owing to the ability of recognizing two analytes simultaneously. Herein, the chemosensor BPIS composed of hemicyanine and 2, 2'-dipyridylamine (DPA) was facilely synthesized for sensitive and expeditious recognition of Cu2+ and HSO3- in 100% aqueous solution. By adding Cu2+, BPIS showed substantial spectral changes accompanied by a noticeable color change from pink to yellow under daylight. The absorbance and fluorescence intensity were linearly correlated to the Cu2+ concentration, enabling the quantitative recognition of Cu2+. The limit of detection (LOD) for Cu2+ was down to 4.02 × 10-9 M. The response time of BPIS towards Cu2+ was 10 s, imparting BPIS great potential in real-time detection of Cu2+. Meanwhile, BPIS manifested ratiometric fluorescence response by introducing HSO3- owing to the 1,4-addition between HSO3- and the unsaturated CC bond of BPIS. The color of the BPIS solution progressively faded from pink to colorless with increasing HSO3- concentration, and a LOD of 3.47 × 10-9 M was obtained. In addition, BPIS-coated test paper was found to be an efficient tool for fast, sensitive, portable detection of Cu2+ and HSO3- by naked eyes. More importantly, the precise detection of Cu2+ and HSO3- in real water and sugars were realized, respectively, by capitalizing on BPIS as the signal tool.