Reversible Chemosensor for Bioimaging and Biosensing of Zn(II) and hpH in Cells, Larval Zebrafish, and Plants with Dual-Channel Fluorescence Signals.

Zinc/Zn(II) is an essential trace element for humans and acts as an important substance that maintains the normal growth, development, and metabolism of the body. Excess or deficient Zn(II) can cause abnormal metabolism in the human body, leading to a series of diseases. Moreover, biosystems have complex homeostasis systems, especially harsh pH (OH-) environments. Thus, investigating the variation in the levels of Zn(II) and OH- is extremely important in clinical, medical, and environmental testing. Nevertheless, the lack of practical and convenient fluorescence imaging tools limits the tracing of Zn(II) and OH- in biosystems. In this work, a well-designed dual-channel fluorescent signal response chemosensor (DACH-fhba) was assembled for selective sensing of Zn(II) and OH- in the biosystem using a fluorescence turn-on strategy. On encountering Zn(II), the chemosensor emitted a blue fluorescence signal (455 nm). Meanwhile, the bright green fluorescence signal (530 nm) increased with OH- addition simultaneously. With the blue/green dual fluorescence response of DACH-fhba, the sensor exhibited high stability and reversibility. Notably, the bioimaging revealed that DACH-fhba successfully tracked Zn(II) and OH- in live cells, larval zebrafish, and plants. Further results implied that DACH-fhba can be used to achieve visual detection of Zn(II) and OH- in organisms. Altogether, this work is conducive to the monitoring of Zn(II) and OH- in organisms and promotes the understanding of the function of Zn(II) and OH- in biosystems.

Overexpression of the Melatonin Synthesis-Related Gene SlCOMT1 Improves the Resistance of Tomato to Salt Stress.

Melatonin can increase plant resistance to stress, and exogenous melatonin has been reported to promote stress resistance in plants. In this study, a melatonin biosynthesis-related SlCOMT1 gene was cloned from tomato (Solanum lycopersicum Mill. cv. Ailsa Craig), which is highly expressed in fruits compared with other organs. The protein was found to locate in the cytoplasm. Melatonin content in SlCOMT1 overexpression transgenic tomato plants was significantly higher than that in wild-type plants. Under 800 mM NaCl stress, the transcript level of SlCOMT1 in tomato leaf was positively related to the melatonin contents. Furthermore, compared with that in wild-type plants, levels of superoxide and hydrogen peroxide were lower while the content of proline was higher in SlCOMT1 transgenic tomatoes. Therefore, SlCOMT1 was closely associated with melatonin biosynthesis confers the significant salt tolerance, providing a clue to cope with the growing global problem of salination in agricultural production.

SiC and N, S-doped carbon nanosheets and lignin-enhanced organohydrogel for low-temperature tolerant solid-state supercapacitors.

The rational design of porous carbon materials and hydrogel electrolytes with excellent mechanical properties and low-temperature tolerance are significance for the development of flexible solid-state supercapacitors. In this study, we introduce a novel methodology for synthesizing SiC/N, S-doped porous carbon nanosheets from bamboo pulp red liquor (RL). We leverage the SiO2 and the sodium salt in RL as templates and sodium lignosulfonate as sulfur dopants for the pyrolysis process and use NH4Cl as a nitrogen dopant. This innovative approach results in a material with a remarkable specific surface area of 1659.19 m2 g-1, a specific capacitance of 308 F g-1 at a current density of 1 A g-1 and excellent stability. Additionally, we harness alkali lignin extracted from RL to enhance a poly (vinyl alcohol) (PVA) matrix, creating a gel electrolyte with low-temperature tolerance and outstanding mechanical properties. A flexible solid-state supercapacitor, which incorporates our electrodes and gel electrolyte, demonstrates high energy density (5.2 W h kg-1 at 251 W kg-1 power density). Impressively, it maintains 82 % of its capacitance over 10,000 cycles of charge and discharge. This provides a new solution for the development of flexible solid-state supercapacitors.

Preparation and Characterization of Dopamine-Modified Carbon Fiber Paper Composites for Gas Diffusion Layers.

Carbon fibers (CFs) cannot be directly used for the preparation of CF paper because of their chemically inert nature. Herein, the surface of CFs was modified using the spontaneous oxidative self-polymerization of dopamine. By taking full advantage of the spontaneous oxidation and self-polymerization properties of PD to maintain the maximum strength of CFs, a polydopamine-modified CF paper (PDA-CFP) with excellent performance was prepared using PD-modified CFs (PDA-CFs). This increased the proportion of hydrophilic functional groups on the surface of carbon fibers, increased the O/C ratio on the CF surface by 6 times, and improved the bond strength between the modified CF and the adhesive by making full use of the interaction force between polydopamine and PVA fibers. In this way, the primary properties of the CF paper were improved. Overall, the results showed that the dispersion of CF was considerably improved with dopamine modification. In addition, the primary physical properties of PDA-CFP were better than those of virgin CF paper (CFP-0). PDA-CFP exhibited a maximum tensile strength of 2.04 kN·m-1, a minimum resistivity of 0.06055 Ω·cm-1, and a minimum porosity of 72.4%. The tightness was increased by up to 12.1%.

Pyrolysis Enzymolysis-Treated Pomelo Peel: Porous Carbon Materials with Fe-Nx Sites for High-Performance Supercapacitor and Efficient Oxygen Reduction Applications.

This paper proposes a different strategy for deriving carbon materials from biomass, abandoning traditional strong corrosive activators and using a top-down approach with a mild green enzyme targeted to degrade the pectin matrix in the inner layer of pomelo peel cotton wool, inducing a large number of nanopores on its surface. Meanwhile, the additional hydrophilic groups produced via an enzymatic treatment can be used to effectively anchor the metallic iron atoms and prepare porous carbon with uniformly dispersed Fe-Nx structures, in this case optimizing sample PPE-FeNPC-900's specific surface area by up to 1435 m2 g-1. PPE-FeNPC-900 is used as the electrode material in a 6 M KOH electrolyte; it manifests a decent specific capacitance of 400 F g-1. The assembled symmetrical supercapacitor exhibits a high energy density of 12.8 Wh kg-1 at a 300 W kg-1 power density and excellent cycle stability. As a catalyst, it also exhibits a half-wave potential of 0.850 V (vs. RHE) and a diffusion-limited current of 5.79 mA cm-2 at 0.3 V (vs. RHE). It has a higher electron transfer number and a lower hydrogen peroxide yield compared to commercial Pt/C catalysts. The green, simple, and efficient strategy designed in this study converts abundant, low-cost waste biomass into high-value multifunctional carbon materials, which are critical for achieving multifunctional applications.

Entropy-Mediated High-Entropy MXenes Nanotherapeutics: NIR-II-Enhanced Intrinsic Oxidase Mimic Activity to Combat Methicillin-Resistant Staphylococcus Aureus Infection.

Bacterial infections, such as bacterial keratitis (BK) and subcutaneous abscess, pose significant challenges to global healthcare. Innovative and new antibacterial agents and antibacterial strategies are in demand to control infections in this era of high drug resistance. Nanotechnology is gradually emerging as an economically feasible and effective anti-infection treatment. High-entropy MXenes (HE MXenes) are used to confer desirable properties with exposed active sites to high-entropy atomic layers, whose potential application in the field of biomedicine remains to be explored. Herein, monolayer HE MXenes are fabricated by implementing transition metals with high entropy and low Gibbs free energy to fill the gap in the biocatalytic performance of non-high-entropy MXenes. HE MXenes are endowed with extremely strong oxidase mimic activity (Km = 0.227 mm) and photothermal conversion efficiency (65.8%) in the second near-infrared (NIR-II) biowindow as entropy increases. Subsequently, HE MXenes realize NIR-II-enhanced intrinsic oxidase mimic activity for killing methicillin-resistant Staphylococcus aureus and rapidly removing the biofilm. Furthermore, HE MXenes can effectively treat BK and subcutaneous abscess infection induced by methicillin-resistant Staphylococcus aureus as nanotherapeutic agents with minuscule side effects. Overall, monolayer HE MXenes demonstrate promising clinical application potential in the treatment of drug-resistant bacterial infections and promote the healing of infected tissues.

A Real-Time Global Inference Network for One-Stage Referring Expression Comprehension.

Referring expression comprehension (REC) is an emerging research topic in computer vision, which refers to the detection of a target region in an image given a test description. Most existing REC methods follow a multistage pipeline, which is computationally expensive and greatly limits the applications of REC. In this article, we propose a one-stage model toward real-time REC, termed real-time global inference network (RealGIN). RealGIN addresses the issues of expression diversity and complexity of REC with two innovative designs: adaptive feature selection (AFS) and Global Attentive ReAsoNing (GARAN). Expression diversity concerns varying expression content, which includes information such as colors, attributes, locations, and fine-grained categories. To address this issue, AFS adaptively fuses features of different semantic levels to tackle the changes in expression content. In contrast, expression complexity concerns the complex relational conditions in expressions that are used to identify the referent. To this end, GARAN uses the textual feature as a pivot to collect expression-aware visual information from all regions and then diffuses this information back to each region, which provides sufficient context for modeling the relational conditions in expressions. On five benchmark datasets, i.e., RefCOCO, RefCOCO+, RefCOCOg, ReferIT, and Flickr30k, the proposed RealGIN outperforms most existing methods and achieves very competitive performances against the most advanced one, i.e., MAttNet. More importantly, under the same hardware, RealGIN can boost the processing speed by 10-20 times over the existing methods.

Towards Lightweight Transformer Via Group-Wise Transformation for Vision-and-Language Tasks.

Despite the exciting performance, Transformer is criticized for its excessive parameters and computation cost. However, compressing Transformer remains as an open problem due to its internal complexity of the layer designs, i.e., Multi-Head Attention (MHA) and Feed-Forward Network (FFN). To address this issue, we introduce Group-wise Transformation towards a universal yet lightweight Transformer for vision-and-language tasks, termed as LW-Transformer. LW-Transformer applies Group-wise Transformation to reduce both the parameters and computations of Transformer, while also preserving its two main properties, i.e., the efficient attention modeling on diverse subspaces of MHA, and the expanding-scaling feature transformation of FFN. We apply LW-Transformer to a set of Transformer-based networks, and quantitatively measure them on three vision-and-language tasks and six benchmark datasets. Experimental results show that while saving a large number of parameters and computations, LW-Transformer achieves very competitive performance against the original Transformer networks for vision-and-language tasks. To examine the generalization ability, we apply LW-Transformer to the task of image classification, and build its network based on a recently proposed image Transformer called Swin-Transformer, where the effectiveness can be also confirmed.

Knowing What to Learn: A Metric-Oriented Focal Mechanism for Image Captioning.

Despite considerable progress, image captioning still suffers from the huge difference in quality between easy and hard examples, which is left unexploited in existing methods. To address this issue, we explore the hard example mining in image captioning, and propose a simple yet effective mechanism to instruct the model to pay more attention to hard examples, thereby improving the performance in both general and complex scenarios. We first propose a novel learning strategy, termed Metric-oriented Focal Mechanism (MFM), for hard example mining in image captioning. Differing from the existing strategies for classification tasks, MFM can adopt the generative metrics of image captioning to measure the difficulties of examples, and then up-weight the rewards of hard examples during training. To make MFM applicable to different datasets without tedious parameter tuning, we further introduce an adaptive reward metric called Effective CIDEr (ECIDEr), which considers the data distribution of easy and hard examples during reward estimation. Extensive experiments are conducted on the MS COCO benchmark, and the results show that while maintaining the performance on simple examples, MFM can significantly improve the quality of captions for hard examples. The ECIDEr-based MFM is equipped on the current SOTA method, e.g., DLCT (Luo et al., 2021), which outperforms all existing methods and achieves new state-of-the-art performance on both the off-line and on- line testing, i.e., 134.3 CIDEr for the off-line testing and 136.1 for the on- line testing of MSCOCO. To validate the generalization ability of ECIDEr-based MFM, we also apply it to another dataset, namely Flickr30k, and superior performance gains can also be obtained.

Polydopamine, harness of the antibacterial potentials-A review.

Antibiotic resistance is one of the major causes of morbidity and mortality, triggered by the adhesion of microbes and to some extent the formation of biofilms. This condition has been quite challenging in the health and industrial sector. Conditions and processes required to foil these infectious and resistance are of much concern. The synthesis of PDA material, inspired by the Mytilus edulis foot protein (MEFP)5 possesses unique characteristics that allow for, adhesion, photothermal therapy, synergistic effects with other materials, biocompatibility process, etc. Therefore, their usage holds great potential for dealing with both the infectious nature and the antibiotic resistance processes. Hence, this review provides an overview of the mechanism involved in accomplishing and eradicating bacteria, the recently harnessed antibacterial effect of the PDA through other properties they possess, a way forward in tapping the benefit embedded in the PDA, and the future perspective.

Fast Class-Wise Updating for Online Hashing.

Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this end, most existing works exploit this problem under a supervised setting, i.e., using class labels to boost the hashing performance, which suffers from the defects in both adaptivity and efficiency: First, large amounts of training batches are required to learn up-to-date hash functions, which leads to poor online adaptivity. Second, the training is time-consuming, which contradicts with the core need of online learning. In this paper, a novel supervised online hashing scheme, termed Fast Class-wise Updating for Online Hashing (FCOH), is proposed to address the above two challenges by introducing a novel and efficient inner product operation. To achieve fast online adaptivity, a class-wise updating method is developed to decompose the binary code learning and alternatively renew the hash functions in a class-wise fashion, which well addresses the burden on large amounts of training batches. Quantitatively, such a decomposition further leads to at least 75 percent storage saving. To further achieve online efficiency, we propose a semi-relaxation optimization, which accelerates the online training by treating different binary constraints independently. Without additional constraints and variables, the time complexity is significantly reduced. Such a scheme is also quantitatively shown to well preserve past information during updating hashing functions. We have quantitatively demonstrated that the collective effort of class-wise updating and semi-relaxation optimization provides a superior performance comparing to various state-of-the-art methods, which is verified through extensive experiments on three widely-used datasets.

Plenty is Plague: Fine-Grained Learning for Visual Question Answering.

Visual Question Answering (VQA) has attracted extensive research focus recently. Along with the ever-increasing data scale and model complexity, the enormous training cost has become an emerging challenge for VQA. In this article, we show such a massive training cost is indeed plague. In contrast, a fine-grained design of the learning paradigm can be extremely beneficial in terms of both training efficiency and model accuracy. In particular, we argue that there exist two essential and unexplored issues in the existing VQA training paradigm that randomly samples data in each epoch, namely, the "difficulty diversity" and the "label redundancy". Concretely, "difficulty diversity" refers to the varying difficulty levels of different question types, while "label redundancy" refers to the redundant and noisy labels contained in individual question type. To tackle these two issues, in this article we propose a fine-grained VQA learning paradigm with an actor-critic based learning agent, termed FG-A1C. Instead of using all training data from scratch, FG-A1C includes a learning agent that adaptively and intelligently schedules the most difficult question types in each training epoch. Subsequently, two curriculum learning based schemes are further designed to identify the most useful data to be learned within each inidividual question type. We conduct extensive experiments on the VQA2.0 and VQA-CP v2 datasets, which demonstrate the significant benefits of our approach. For instance, on VQA-CP v2, with less than 75 percent of the training data, our learning paradigms can help the model achieves better performance than using the whole dataset. Meanwhile, we also shows the effectivenesss of our method in guiding data labeling. Finally, the proposed paradigm can be seamlessly integrated with any cutting-edge VQA models, without modifying their structures.

Identification of an Au(I) N-Heterocyclic Carbene Compound as a Bactericidal Agent Against Pseudomonas aeruginosa.

The opportunistic pathogen Pseudomonas aeruginosa (P. aeruginosa) causes infections that are difficult to treat, which is due to the bacterial resistance to antibiotics. We herein identify a gold(I) N-heterocyclic carbene compound as a highly potent antibacterial agent towards P. aeruginosa. The compound significantly attenuates P. aeruginosa virulence and leads to low tendency to develop bacterial resistance. The antibacterial mechanism studies show that the compound abrogates bacterial membrane integrity, exhibiting a high bactericidal activity toward P. aeruginosa. The relatively low cytotoxic compound has excellent therapeutic effects on both the eukaryotic cell co-culture and murine wound infection experiments, suggesting its potential application as a bactericidal agent to combat P. aeruginosa infection.

A Vehicle-Free Antimicrobial Polymer Hybrid Gold Nanoparticle as Synergistically Therapeutic Platforms for Staphylococcus aureus Infected Wound Healing.

Pathogenic bacteria infection is a serious threat to human public health due to the high morbidity and mortality rates. Nano delivery system for delivering antibiotics provides an alternative option to improve the efficiency compared to conventional therapeutic agents. In addition to the drug loading capacity of nanocarriers, which is typically around 10%, further lowers the drug dose that pathological bacteria are exposed to. Moreover, nanocarriers that are not eliminated from the body may cause side effects. These limitations have motivated the development of self-delivery systems that are formed by the self-assembly of different therapeutic agents. In this study, a vehicle-free antimicrobial polymer polyhexamethylene biguanide (PHMB, with bactericidal and anti-biofilm functions) hybrid gold nanoparticle (Au NPs, with photothermal therapy (PTT)) platform (PHMB@Au NPs) is developed. This platform exhibits an excellent synergistic effect to enhance the photothermal bactericidal effect for Staphylococcus aureus under near-infrared irradiation. Furthermore, the results showed that PHMB@Au NPs inhibit the formation of biofilms, quickly remove bacteria to promote wound healing through PTT in infection model in vivo, and even mediate the transition of macrophages from M1 to M2 type, and accelerate tissue angiogenesis. PHMB@Au NPs will have promising value as highly effective antimicrobial agents for patient management.

The SUMO E3 Ligase MdSIZ1 Sumoylates a Cell Number Regulator MdCNR8 to Control Organ Size.

Plant growth and organ size putatively associated with crop yield are regulated by a complex network of genes including ones for controlling cell proliferation. The gene fw2.2 was first identified in tomatoes and reported to govern fruit size variation through controlling cell division. In this study, we isolated a putative ortholog of the tomato fw2.2 gene from apple, Cell Number Regulator 8 (MdCNR8). Our functional analysis showed that MdCNR8 may control fruit size and root growth. MdCNR8 was mediated by the SUMO E3 ligase MdSIZ1, and SUMOylation of MdCNR8 at residue-Lys39 promoted the translocation of MdCNR8 from plasma membrane to the nucleus. The effect of MdCNR8 in inhibiting root elongation could be completely counteracted by the coexpression of MdSIZ1. Moreover, the lower cell proliferation of apple calli due to silencing MdSIZ1 could be rescued by silencing MdCNR8. Collectively, our results showed that the MdSIZ1-mediated SUMOylation is required for the fulfillment of MdCNR8 in regulating cell proliferation to control plant organ size. This regulatory interaction between MdSIZ1 and MdCNR8 will facilitate understanding the mechanism underlying the regulation of organ size.

A reaction-based colorimetric and ratiometric chemosensor for imaging identification of HClO in live cells, mung bean sprouts, and paper strips.

Hypochlorous acid (HClO) as well as its ionic form (ClO-), representative of reactive oxygen species (ROS), are essential players in all sorts of biological processes. The abnormal level of each can lead to the onset of various diseases. Besides, Sodium hypochlorite, a commonly-used bleaching agent in our daily lives, could also result in breathing and skin problems when overexposed. Therefore, developing a molecular chemosensor for sensing HClO is of biological and environmental importance. Though many such chemosensors have been reported, new HClO chemosensors with different sensing performances may still come in handy in certain situations. In this work, we have developed a new coumarin-based chemosensor, CM-hbt, for realizing both ratiometric and colorimetric imaging detection of HClO in live cells. Notably, we further explored its application in sensing HClO in plant mung beans as well as fabricated an easy-to-use paper strip apparatus for facilitating its quick detection, which is seldomly seen in other HClO chemosensors. All the analysis results confirmed the high sensitivity and selectivity of this novel chemosensor. DFT calculations were used to decipher the underlying sensing mechanism of CM-hbt. Overall, this work presents a novel chemosensor, CM-hbt, as a colorimetric and ratiometric chemosensor for realizing imaging detection of HClO in a variety of different model systems, which highlights its broad spectrum of application potentials.

Detecting High Frequency Oscillations for Stereoelectroencephalography in Epilepsy via Hypergraph Learning.

Successful epilepsy surgeries depend highly on pre-operative localization of epileptogenic zones. Stereoelectroencephalography (SEEG) records interictal and ictal activities of the epilepsy in order to precisely find and localize epileptogenic zones in clinical practice. While it is difficult to find distinct ictal onset patterns generated the seizure onset zone from SEEG recordings in a confined region, high frequency oscillations are commonly considered as putative biomarkers for the identification of epileptogenic zones. Therefore, automatic and accurate detection of high frequency oscillations in SEEG signals is crucial for timely clinical evaluation. This work formulates the detection of high frequency oscillations as a signal segment classification problem and develops a hypergraph-based detector to automatically detect high frequency oscillations such that human experts can visually review SEEG signals. We evaluated our method on 4,000 signal segments from clinical SEEG recordings that contain both ictal and interictal data obtained from 19 patients who suffer from refractory focal epilepsy. The experimental results demonstrate the effectiveness of the proposed detector that can successfully localize interictal high frequency oscillations and outperforms multiple peer machine learning methods. In particular, the proposed detector achieved 90.7% in accuracy, 80.9% in sensitivity, and 96.9% in specificity.

Reaction-based chemosensor as dual-channel indicator for visualizing and bioimaging of exogenous hypochlorite concentrations in living cells, Pseudomonas aeruginosa, and zebrafish.

Tracking and quantifying hypochlorite (ClO-) in biological systems and environments remain challenging tasks, and many efforts have been made to improve ClO- recognition performance by modifying the sensor structure. In this study, a pre-designed coumarin/furanohydrazide-based sensor (CMFH) with the coumarin moiety as the building block (fluorogen) was rationally prepared as a ratiometric and colorimetric chemosensor for ClO- recognition. As expected, CMFH demonstrated excellent sensitivity and selectivity for ClO- detection. The fluorescence signal ratio (F466/F556) showed strong ClO- dependency, and the sensor exhibited ultrafast detection (within 60 s) and a low detection limit of 563 nM. Due to its low cytotoxicity and good tissue permeability, CMFH was demonstrated as a dual-channel sensor for ClO- bioimaging and visualization in cells, zebrafish, and even bacteria. Furthermore, CMFH-loaded paper strips were successfully applied to the colorimetric and fluorescent visualization of ClO-. The results demonstrate that CMFH has potential application value for tracking ClO- in various biosystems and environments.

Evolving Fully Automated Machine Learning via Life-Long Knowledge Anchors.

Automated machine learning (AutoML) has achieved remarkable progress on various tasks, which is attributed to its minimal involvement of manual feature and model designs. However, most of existing AutoML pipelines only touch parts of the full machine learning pipeline, e.g., neural architecture search or optimizer selection. This leaves potentially important components such as data cleaning and model ensemble out of the optimization, and still results in considerable human involvement and suboptimal performance. The main challenges lie in the huge search space assembling all possibilities over all components, as well as the generalization ability over different tasks like image, text, and tabular etc. In this paper, we present a first-of-its-kind fully AutoML pipeline, to comprehensively automate data preprocessing, feature engineering, model generation/selection/training and ensemble for an arbitrary dataset and evaluation metric. Our innovation lies in the comprehensive scope of a learning pipeline, with a novel "life-long" knowledge anchor design to fundamentally accelerate the search over the full search space. Such knowledge anchors record detailed information of pipelines and integrates them with an evolutionary algorithm for joint optimization across components. Experiments demonstrate that the result pipeline achieves state-of-the-art performance on multiple datasets and modalities. Specifically, the proposed framework was extensively evaluated in the NeurIPS 2019 AutoDL challenge, and won the only champion with a significant gap against other approaches, on all the image, video, speech, text and tabular tracks.

Similarity-Preserving Linkage Hashing for Online Image Retrieval.

Online image hashing aims to update hash functions on-the-fly along with newly arriving data streams, which has found broad applications in computer vision and beyond. To this end, most existing methods update hash functions simply using discrete labels or pairwise similarity to explore intra-class relationships, which, however, often deteriorates search performance when facing a domain gap or semantic shift. One reason is that they ignore the particular semantic relationships among different classes, which should be taken into account in updating hash functions. Besides, the common characteristics between the label vectors (can be regarded as a sort of binary codes) and to-be-learned binary hash codes have left unexploited. In this paper, we present a novel online hashing method, termed Similarity Preserving Linkage Hashing (SPLH), which not only utilizes pairwise similarity to learn the intra-class relationships, but also fully exploits a latent linkage space to capture the inter-class relationships and the common characteristics between label vectors and to-be-learned hash codes. Specifically, SPLH first maps the independent discrete label vectors and binary hash codes into a linkage space, through which the relative semantic distance between data points can be assessed precisely. As a result, the pairwise similarities within the newly arriving data stream are exploited to learn the latent semantic space to benefit binary code learning. To learn the model parameters effectively, we further propose an alternating optimization algorithm. Extensive experiments conducted on three widely-used datasets demonstrate the superior performance of SPLH over several state-of-the-art online hashing methods.