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Referring Image Segmentation (RIS) has been extensively studied over the past decade, leading to the development of advanced algorithms. However, there has been a lack of research investigating how existing algorithms should be benchmarked with complex language queries, which include more informative descriptions of surrounding objects and backgrounds (e.g., the black car vs. the black car is parking on the road and beside the bus). Given the significant improvement in the semantic understanding capability of large pre-trained models, it is crucial to take a step further in RIS by incorporating complex language that resembles real-world applications. To close this gap, building upon the existing RefCOCO and Visual Genome datasets, we propose a new RIS benchmark with complex queries, namely RIS-CQ. The RIS-CQ dataset is of high quality and large scale, which challenges the existing RIS with enriched, specific, and informative queries, and enables a more realistic scenario of RIS research. Besides, we present a niche targeting method to better task the RIS-CQ, called Dual-Modality Graph Alignment (DuMoGa) model, which outperforms a series of RIS methods. To provide a valuable foundation for future advancements in the field of RIS with complex queries, we release the datasets, pre-processing and synthetic scripts, and the algorithm implementations at https://github.com/lili0415/DuMoGa. [ABSTRACT FROM AUTHOR]

To efficiently utilize the complementary attributes in RGBT images, we proposes an object tracking algorithm called Isomeric Feature Encoding Network (IFENet). Based on the different characteristics of RGBT images, IFENet employs the global-memory enhancement (GME) in the early stage of image feature encoding to explore detailed information (such as texture and color) in the RGB modality. It also utilizes the border-region salience enhancement (BRE) to improve the saliency difference between the object region and the background. Furthermore, an interest region sampling is introduced to reduce computational consumption and improve the operational efficiency. Validation results on the open-source datasets demonstrate the effectiveness of IFENet. Compared to current mainstream RGBT tracking algorithms, IFENet achieves better tracking accuracy and robustness. It can effectively handle challenging scenarios such as fast-moving objects, large-scale deformations, and camera motion. Moreover, IFENet achieves an average tracking speed of 62FPS, meeting real-time tracking requirements. [ABSTRACT FROM AUTHOR]

Aiming at the limitations of visible images in object detection, this paper proposes a dual-branch network object detection algorithm based on dual-modality fusion of visible and infrared images. Based on YOLOv7-s, the algorithm firstly introduces a spatial attention module to enhance the model’s ability of capturing key features; secondly, to resolve the problem of inconsistent object sizes, a visible multi-scale feature fusion module is proposed, meanwhile, the structure of the SimCSPSPPF module (an improved spatial pyramid pooling module) from YOLOv6 is adopted to construct an infrared multi-scale feature fusion module to efficiently extract multi-scale features from infrared images; finally, a cross-modal feature fusion module is proposed to fuse corresponding scale features from visible and infrared images. The proposed algorithm is tested on KAIST, FLIR, and GIR datasets, experimental results show that the proposed algorithm has better performance, compared with the YOLOv7-s algorithm to detect visible and infrared images separately on the KAIST dataset, the detection accuracy is improved by 18.0 and 5.1%, respectively, and detection speed is 51.8 FPS; on FLIR and GIR datasets, the proposed algorithm also demonstrates significant advantages. Furthermore, the proposed algorithm can detect objects on individual visible or infrared images while maintaining high detection accuracy. [ABSTRACT FROM AUTHOR]

Ocular aberrometry with a wide dynamic range for assessing vision performance and anterior segment imaging that provides anatomical details of the eye are both essential for vision research and clinical applications. Defocus error is a major limitation of digital wavefront aberrometry (DWA), as the blurring of the detected point spread function (PSF) significantly reduces the signal-to-noise ratio (SNR) beyond the ±3 D range. With the aid of Badal-like precompensation of defocus, the dynamic defocus range of the captured aberrated PSFs can be effectively extended. We demonstrate a dual-modality MHz VCSEL-based swept-source OCT (SS-OCT) system with easy switching between DWA and OCT imaging modes. The system is capable of measuring aberrations with defocus dynamic range of 20 D as well as providing fast anatomical imaging of the anterior segment at an A-scan rate of 1.6 MHz. [ABSTRACT FROM AUTHOR]

Continuous monitoring of bio/physiological signals will play an essential role in personalized, patient‐centric medicine in the future healthcare industry. One of the challenges involves providing a sustainable, energy‐efficient operation for long‐term monitoring devices. The current technology based on power sources requires periodic replacement. Here, a hybrid film composed of a layer of piezoelectric nanowire (PVDFHFP) and a layer of microgel (pNIPAM‐APBA) is presented that continuously monitors glucose, does not rely on enzymes, is self‐charging, and decouples changes in read‐out signals due to glucose detection from the force is applied to activate self‐charging. By characterizing self‐charging/discharging properties, the operation and sensing mechanism for the nanowire/microgel hybrid film are proposed. A reasonable sensitivity of 70 mV mm−1 with a high linearity value of R2 = 0.9956 is observed. This work demonstrates the potential for use in a self‐charging wearable platform that enables continuous monitoring of bio/physiological signals. [ABSTRACT FROM AUTHOR]

RGB and Thermal (RGBT) tracking is an important supplement of visual object tracking for its' unique practical and research value. However, due to the limitations of the RGBT camera, extra interferences are introduced in the data synchronously. Removing or alleviating these interferences is a crucial direction to improve the performance of the RGBT tracking task. For this problem, we propose the multi-stage matching guidance and context integration network (M2GCI) for RGBT tracking. M2GCI reorganizes the feature-encoding pipeline into two context-integrating stages that are responsible for processing primary and senior information respectively. Firstly, the primary features encoded from primary encoders are integrated by the spatially adaptive fusion strategy. Next, the senior encoders cooperating with axial external-attention redistribution strategy further extract senior features which are more reliable for target prediction. Extensive experiments on the RGBT234 and GTOT provide that the proposed M2GCI can achieve more precision and robust tracking under some difficult scenarios compared with the excellent methods. [ABSTRACT FROM AUTHOR]

SignificanceOral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings.AimTo develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection.ApproachThe dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3  MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images.ResultsWe investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300  ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists.ConclusionsOur study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.

TMP is gradually becoming a fundamental element for quality assurance and control in ionizing and non-ionizing radiation imaging modalities as well as in the development of different techniques. This study aimed to evaluate and obtain polyvinyl chloride tissue mimicking material for dual-modality breast phantoms in mammography and ultrasound. Breast tissue equivalence was evaluated based on X-ray attenuation properties, speed of sound, attenuation, and acoustic impedance. There are six samples of PVC-plasticizer material with variations of PVC concentration and additives. The evaluation of X-ray attenuation was carried out using mammography from 23 to 35 kV, while the acoustic properties were assessed with mode A ultrasound and a transducer frequency of 5 MHz. A breast phantom was created from TMP material with tissue equivalence and was then evaluated using mammography as well as ultrasound to analyze its image quality. The results showed that samples A (PVC 5%, DOP 95%), B (PVC 7%, DOP 93%), C (PVC 10%, DOP 90%), E (PVC 7%, DOP 90%, graphite 3%), and F (PVC 7%, DOP 90%, silicone oil 3%) have the closest equivalent to the ACR breast phantom material with a different range of 0.01–1.39 in the 23–35 kV range. Based on the evaluation of the acoustic properties of ultrasound, A had high similarity to fat tissue with a difference of 0.03 (dB cm− 1 MHz− 1) and 0.07 (106 kg m− 2 s− 1), while B was close to the glandular tissue with a difference of 9.2 m s− 1. Multilayer breast phantom images' results showed gray levels in mammography and ultrasound modalities. Therefore, this study succeeded in establishing TMP material for mammography and ultrasound. It can also be used for simple quality assurance and control programs. [ABSTRACT FROM AUTHOR]

Oncotheranostic nanoagents are powerful artificial tools that significantly outperform traditional antitumor therapies. Among the wide variety of nanoagents, poly-(lactic-co-glycolic acid) (PLGA) nanoparticles have proven to be one of the most successful candidates for translation into clinical practice. Here, we present PLGA nanoparticles for HER2-targeted diagnostic and photodynamic therapy with proven efficacy in vivo. Namely, we developed an oncotheranostic platform that is based on PLGA nanoparticles and possesses red/green light dual-activated diagnostic/therapeutic properties for HER2-targeted photodynamic therapy of aggressive breast cancer. PLGA nanoparticles were loaded with a red light-activated dye Nile Blue with pronounced solvatochromic properties for diagnostic applications and with a green light-activated photodynamic sensitizer Rose Bengal for photodynamic therapy. Targeted delivery was ensured by the noncovalent decoration of nanoparticles with anti-HER2 antibodies, which can be readily adapted for large-scale biotechnological production. In vitro and in vivo studies proved the effectiveness of red light-mediated HER2-specific imaging and green light-induced cytotoxicity of anti-HER2 PLGA. Interestingly, these particles fluoresced only after cellular internalization, which minimizes background signals inside the organism, thus facilitating real-time diagnostics. The particles allowed efficient and selective visualization of the HER2-positive primary tumor node and metastases spread and led to complete remission in BALB/c Nu/Nu mice with the HER2-positive xenografts after a single session of photodynamic therapy. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 has brought drastic changes to English teaching as it has shifted from the offline mode before the pandemic to the online mode during the pandemic. However, in the post-pandemic era, there are still many problems in the effective implementation of the process of English teaching, leading to the inability of achieving better results in the quality and efficiency of English teaching and effective cultivation of students' practical application ability. In recent years, English speaking has attracted the attention of experts and scholars. Therefore, this study constructs an interactive English-speaking practice scene based on a virtual character. A dual-modality emotion recognition method is proposed that mainly recognizes and analyzes facial expressions and physiological signals of students and the virtual character in each scene. Thereafter, the system adjusts the difficulty of the conversation according to the current state of students, toward making the conversation more conducive to the students' understanding and gradually improving their English-speaking ability. The simulation compares nine facial expressions based on the eNTERFACE05 and CAS-PEAL datasets, which shows that the emotion recognition method proposed in this manuscript can effectively recognize students' emotions in interactive English-speaking practice and reduce the recognition time to a great extent. The recognition accuracy of the nine facial expressions was close to 90% for the dual-modality emotion recognition method in the eNTERFACE05 dataset, and the recognition accuracy of the dual-modality emotion recognition method was significantly improved with an average improvement of approximately 5%. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 has brought drastic changes to English teaching as it has shifted from the offline mode before the pandemic to the online mode during the pandemic. However, in the post-pandemic era, there are still many problems in the effective implementation of the process of English teaching, leading to the inability of achieving better results in the quality and efficiency of English teaching and effective cultivation of students’ practical application ability. In recent years, English speaking has attracted the attention of experts and scholars. Therefore, this study constructs an interactive English-speaking practice scene based on a virtual character. A dual-modality emotion recognition method is proposed that mainly recognizes and analyzes facial expressions and physiological signals of students and the virtual character in each scene. Thereafter, the system adjusts the difficulty of the conversation according to the current state of students, toward making the conversation more conducive to the students’ understanding and gradually improving their English-speaking ability. The simulation compares nine facial expressions based on the eNTERFACE05 and CAS-PEAL datasets, which shows that the emotion recognition method proposed in this manuscript can effectively recognize students’ emotions in interactive English-speaking practice and reduce the recognition time to a great extent. The recognition accuracy of the nine facial expressions was close to 90% for the dual-modality emotion recognition method in the eNTERFACE05 dataset, and the recognition accuracy of the dual-modality emotion recognition method was significantly improved with an average improvement of approximately 5%.

Emotion recognition has a significant impact on people’s health and life quality. Recent studies have shown that emotion recognition can be achieved by analyzing audio-visual emotion channels and physiological signals. However, the main challenges are: 1. the audio-visual techniques can be easily fooled by fake facial expressions; 2. the contact physiological signal monitoring introduces additional stress and is usually unsuitable for long-term monitoring; 3. The non-contact physiological signal monitoring methods are easily affected by complex environmental conditions. In this paper, a non-contact dual-modality emotion recognition system is proposed. First, the respiratory and heartbeat signals are measured by radar and camera simultaneously. Then, a hybrid signal optimization approach is proposed to remove the influence of body motion and light conditions on the physiological signal. It includes a light-intensity-based scheme selecting proper heartbeat signal for different light conditions, and an optical-flow-based algorithm pruning signals with significant radial body motion. Finally, the features extracted from the optimized physiological signals are fused to train an emotion recognition system. As shown in the experimental results, the proposed system could achieve high classification accuracy of 89.6% for 10-fold cross-validation at sample level, and 71.0 % for cross-validation at subject level. The respiratory and heartbeat signals by non-contact approaches are demonstrated to be reliable in emotion recognition. [ABSTRACT FROM AUTHOR]

Yu Zhang1,2, Yixin Zhong1,2, Man Ye1, Jie Xu1, Jia Liu1, Jun Zhou1, Shike Wang1, Dajing Guo1, Zhigang Wang2, Haitao Ran2 1Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 2Chongqing Key Laboratory of Ultrasound Molecular Imaging, Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of ChinaCorrespondence: Haitao RanDepartment of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Road, Yuzhong District, Chongqing 400010, People’s Republic of ChinaEmail rht66@163.comDajing GuoDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, No. 74 Linjiang Road, Yuzhong District, Chongqing 400010, People’s Republic of ChinaTel +86 236 369 3537Email guodaj@hospital.cqmu.edu.cnBackground: Platelet activation and subsequent aggregation are the initial stages of thrombosis. A molecular probe that specifically targets activated platelets and remains retained under high shear stress in vivo can enhance the imaging effect to achieve early and accurate diagnosis.Methods and materials: In this study, we constructed nanoparticles (NPs) using polydopamine to carry two peptides that simultaneously bind integrin αIIbβ3 and P-selectin on activated platelets to enhance the targeting of NPs to thrombus.Results: The targeting specificity and binding stability of the NPs on red and white thrombi were demonstrated in vitro using a simulated circulatory device and the targeting effect of the NPs on mixed thrombus was studied by magnetic resonance (MR)/photoacoustic (PA) dual-modality imaging in vivo. NPs that were surface modified with both peptides have higher selectivity and retention to red and white thrombi in vitro than NPs with a single or no peptide, and the targeting effect was closely related to the number and distribution of activated platelets as well as the structure and type of thrombus. The NPs also have MR/PA dual-modality imaging functionality, significantly enhancing the imaging of mixed thrombus in vivo.Conclusion: These dual-targeted NPs have improved targeting specificity and binding stability to different thrombi under high shear stress and are beneficial for the early diagnosis of thrombosis.Keywords: dual-modality, dual-ligand, targeting effect, thrombus, magnetic resonance imaging, photoacoustic imaging

Quality of language comprehension determines performance in all kinds of activities including academics. Processing of words initially develops as auditory, and gradually extends to visual as children learn to read. School failure is highly related to listening and reading comprehension problems. In this study we analyzed sex-differences in comprehension of texts in Spanish (standardized reading test PROLEC-R) in three modalities (visual, auditory, and both simultaneously: dual-modality) presented to 12–14-years old students, native in Spanish. We controlled relevant cognitive variables such as attention (d2), phonological and semantic fluency (FAS) and speed of processing (WISC subtest Coding). Girls' comprehension was similar in the three modalities of presentation, however boys were importantly benefited by dual-modality as compared to boys exposed only to visual or auditory text presentation. With respect to the relation of text comprehension and school performance, students with low grades in Spanish showed low auditory comprehension. Interestingly, visual and dual modalities preserved comprehension levels in these low skilled students. Our results suggest that the use of visual-text support during auditory language presentation could be beneficial for low school performance students, especially boys, and encourage future research to evaluate the implementation in classes of the rapidly developing technology of simultaneous speech transcription, that could be, in addition, beneficial to non-native students, especially those recently incorporated into school or newly arrived in a country from abroad. [ABSTRACT FROM AUTHOR]

Biothiols, such as cysteine (Cys) and homocysteine (Hcy), play vital roles in biological homeostasis and are closely related to various pathological and physiological processes in the living systems. Therefore, the in vivo detection of biothiols is of great importance for early diagnosis of diseases and assessment of disease progression. In this work, we developed a near-infrared (NIR) fluorescence and photoacoustic dual-modal molecular probe (NIR-S) that can be specifically activated by Cys or Hcy. The aryl-thioether substituted cyanine probe can undergo nucleophilic substitution and Smiles rearrangement reaction, resulting in specific turn-on NIR fluorescence and ratiometric photoacoustic responses for Hcy/Cys. Thus, NIR-S not only realizes the specific NIR fluorescence and photoacoustic dual mode imaging to detect Hcy/Cys in solution, but also can be applied to living cells and mice to detect Hcy/Cys. This work provided a practical tool to detect Hcy/Cys levels in vivo, which would be beneficial for the early diagnosis and progress of diseases. [ABSTRACT FROM AUTHOR]

Aptamers which are promising and effective molecular probes, can deliver either fluorescent materials or radionuclides to tumors. This study aimed to develop a novel both fluorescent and radionuclide dual-modality probe based on a truncated aptamer and evaluate its stability and binding affinities in vitro. The aptamer JHIT2 with binding specifically to HepG2 cells was previously generated by Cell-SELEX. Using mfold and RNAstructure software to predict the secondary structure folded by a middle random sequence to truncate the primer sequences at both ends of the aptamer JHIT2 to yield the aptamer JHIT2e, with a similar secondary structure to JHIT2 and the same specificity and affinity as JHIT2. Attaching carboxyfluorescein (FAM) readily to the aptamer JHIT2e and then attaching iodine-131 to the FAM moiety which has multiple sites for iodine labeling to develop a novel both fluorescent and radionuclide dual-modality probe, termed 131I-FAM-JHIT2e. Cell uptake and fluorescence imaging assays in vitro confirmed that 131I-FAM-JHIT2e had both FAM fluorescence signal and radio-activity signal and maintained specific binding ability to the human hepatoma cell line HepG2. This work formed a basis for aptamer-based, dual-modality imaging probe that contains both fluorescent and radionuclide tags, which also is potential for theranostics. Image 1 • Truncating the primer sequences at both ends of aptamer JHIT2 to yield JHIT2e. • JHIT2e has the same specificity and affinity as JHIT2. • 131I-FAM-JHIT2e was prepared by attaching iodine-131 to FAM of FAM-JHIT2e. • 131I-FAM-JHIT2e has both FAM fluorescence and radio-activity signal. • 131I-FAM-JHIT2e has specific binding ability to HepG2 cells. [ABSTRACT FROM AUTHOR]

Gastric precancerous lesions (GPL) significantly elevate the risk of gastric cancer, and precise diagnosis and timely intervention are critical for patient survival. Due to the elusive pathological features of precancerous lesions, the early detection rate is less than 10%, which hinders lesion localization and diagnosis. In this paper, we provide a GPL pathological dataset and propose a novel method for improving the segmentation accuracy on a limited-scale dataset, namely RGB and Hyperspectral dual-modal pathological image Cross-attention U-Net (CrossU-Net). Specifically, we present a self-supervised pre-training model for hyperspectral images to serve downstream segmentation tasks. Secondly, we design a dual-stream U-Net-based network to extract features from different modal images. To promote information exchange between spatial information in RGB images and spectral information in hyperspectral images, we customize the cross-attention mechanism between the two networks. Furthermore, we use an intermediate agent in this mechanism to improve computational efficiency. Finally, we add a distillation loss to align predicted results for both branches, improving network generalization. Experimental results show that our CrossU-Net achieves accuracy and Dice of 96.53% and 91.62%, respectively, for GPL lesion segmentation, providing a promising spectral research approach for the localization and subsequent quantitative analysis of pathological features in early diagnosis. • This paper presented a dual-modality CrossU-Net for lesion segmentation of gastric precancerous from RGB and HSI images. • Developed a Spectral-wise Siamese (SpeSiam) self-supervised pre-training module that focuses on spectral analysis. • Designed a dual-stream Cross-attention U-Net network to improve lesion tissue identification. • Demonstrated the effectiveness of our approach through comparisons with single-modal methods for pathological diagnosis. [ABSTRACT FROM AUTHOR]

Abstract Given the increasing exploration of fluorescent tracers in the field of nuclear medicine, a need has risen for practical development guidelines that can help improve the translation aspects of fluorescent tracers. This editorial discusses the does and don’ts in developing fluorescence tracers. It has been put forward by the European Association of Nuclear Medicine (EANM) Translational Molecular Imaging & Therapy committee and has been approved by the EANM board.

Foot-and-mouth disease virus (FMDV) is a highly contagious disease that affects cloven-hoofed animals. The traditional diagnostic methods for FMDV have several drawbacks such as cross-reactivity, low sensitivity, and low selectivity. To overcome these drawbacks, we present an optical and electrochemical dual-modal approach for the specific detection of FMDV serotypes O and A by utilizing a magnetic nanoparticle labeling technique with resorufin β-d-glucopyranoside (res-β-glc) and β-glucosidase (β-glc), without the use of typical lateral flow assay or polymerase chain reaction. FMDV serotypes O and A were reacted with pan-FMDV antibodies that recognize all seven FMDV serotypes (O, A, C, Asia 1, SAT 1, SAT 2, and SAT 3). The antigen–antibody complex was then immobilized on magnetic nanoparticles and reacted with β-glc-conjugated FMDV type O or type A antibodies. Subsequently, the addition of res-β-glc resulted in the release of fluorescent resorufin and glucose owing to catalytic hydrolysis by β-glc. The detection limit of fluorescent signals using a fluorescence spectrophotometer was estimated to be log(6.7) and log(5.9) copies/mL for FMDV type O and A, respectively, while that of electrochemical signals using a glucometer was estimated to be log(6.9) and log(6.1) copies/mL for FMDV type O and A, respectively. Compared with a commercially available lateral flow assay diagnostic kit for immunochromatographic detection of FMDV type O and A, this dual-modal detection platform offers approximately four-fold greater sensitivity. This highly sensitive and accurate dual-modal detection method can be used for effective disease diagnosis and treatment, and will find application in the early-stage diagnosis of viral diseases and next-generation diagnostic platforms.

Abstract In this work, we report a simple strategy by mixing a coumarin appended tetraphenylethylene derivative (TPEC) and γ-cydodextrin(γ-CD) in aqueous solution, based on the host-guest interaction between TPEC and γ-CD to construct a dual-modality supramolecular hyperbranched polymer. The host-guest interaction between γ-CD and TPEC efficiently restricted the intramolecular rotation and the non-radiative relaxation channel, minimizing energy loss, thereby resulting in the strong emission of TPEC in dilute solution. Moreover, we achieved the conversion between non-covalent supramolecular hyperbranched polymers and its corresponding covalent supramolecular hyperbranched polymers under the irradiation of UV light, which can be reversibly controlled through the irradiation at the wavelengths of 254 nm and 365 nm. Furthermore, both non-covalent supramolecular hyperbranched polymers and covalent supramolecular hyperbranched polymers can self-assemble into spherical structures. This methodology provides a new idea for exploring a new type of supramolecular hyperbranched polymers with aggregation-induced emission. Graphical abstract We have prepared a dual-modality supramolecular polymer with aggregation-induced emission in aqueous solution by taking advantage of host−guest interactions between γ-CD and coumarin moieties. Image 1 Highlights • Supramolecular polymers were constructed through host-guest interactions between γ-CD and coumarin. • Supramolecular polymers exhibited aggregation-induced emission. • Non-covalent and covalent supramolecular polymers can be reversibly controlled through the irradiation of UV light. • Supramolecular polymers self-assembled into spherical structures. [ABSTRACT FROM AUTHOR]

Graphical abstract Tumor targeting near-infrared fluorescence and magnetic resonance dual-modality imaging probes are prepared by connecting functional molecules on thioether-bridged mesoporous organosilica nanoparticles via click reaction. Abstract In this work, near-infrared fluorescence (NIRF) and magnetic resonance (MR) dual-modality imaging probes are prepared by conjugating maleimide derivative cyanine dye (Mal-Cy5.5), gadolinium‐diethylenetriamine pentaacetic acid (Gd-DTPA), and RGD peptide (Mal-PEG2-RGD) on thioether-bridged mesoporous organosilica nanoparticles (MONs) via click reaction. Fourier transform infrared (FT-IR) spectra, zeta potentials, UV–vis spectra, and energy dispersive X-ray (EDX) spectrum confirm the successful modifications of the functional molecules on the MONs. The prepared MON-Gd-Cy5.5-RGD probes shows excellent NIRF and MR imaging properties, and the relaxivity rate (r1) is measured up to 2.85 mM−1 s −1. In addition, the MON-Gd-Cy5.5-RGD probes show excellent in vitro and in vivo biocompatibility. Confocal laser scanning microscopy and flow cytometry demonstrate that the MON-Gd-Cy5.5-RGD can efficiently target to MDA-MB-231 tumor cells. Additionally, ex vivo NIFR and in vivo MR imaging demonstrate that the MON-Gd-Cy5.5-RGD probes can accumulate in tumor and improve the signals of tumor. [ABSTRACT FROM AUTHOR]

Di(2-ethylhexyl)phthalate (DEHP), as an environmental endocrine disruptor, has adverse effects on eco-environments and health. Thus, it is crucial to highly sensitive on-site detect DEHP. Herein, a double-enzyme active MnO 2 @BSA mediated dual-modality photoelectrochemical (PEC)/colorimetric aptasensing platform with the cascaded sensitization structures of ZnIn 2 S 4 and TiO 2 as signal generators was engineered for rapid and ultrasensitive detection of DEHP using an all-in-one lab-on-paper analytical device. Benefitting from cascaded sensitization effect, the ZnIn 2 S 4 /TiO 2 photosensitive structures-assembled polypyrrole paper electrode gave an enhanced photocurrent signal. The MnO 2 @BSA nanoparticles (NPs) with peroxidase-mimic and oxidase-mimic double-enzymatic activity induced multiple signal quenching effects and catalyzed color development. Specifically, the MnO 2 @BSA NPs acted as peroxidase mimetics to generate catalytic precipitates, which not only obstructed interfacial electron transfer but also served as electron acceptors to accept photogenerated electrons. Besides, the steric hindrance effect from MnO 2 @BSA NPs-loaded branchy polymeric DNA duplex structures further decreased photocurrent signal. The target recycling reaction caused the detachment of MnO 2 @BSA NPs to increase PEC signal, realizing the ultrasensitive detection of DEHP with a low detection limit of 27 fM. Ingeniously, the freed MnO 2 @BSA NPs flowed to colorimetric zone with the aid of fluid channels and acted as oxidase mimetics to induce color intensity enhancement, resulting in the rapid visual detection of DEHP. This work provided a prospective paradigm to develop field-based paper analytical tool for DEHP detection in aqueous environment. [Display omitted] • The cascaded sensitization structure of ZnIn 2 S 4 /TiO 2 was in situ grown on polypyrrole paper electrode. • The MnO 2 @BSA NPs exhibited peroxidase- and oxidase-mimic dual-enzymatic activity. • The dual-enzyme active MnO 2 @BSA NPs mediated the PEC/colorimetric detection of DEHP. • An all-in-one lab-on-paper dual-modality analytical device was assembled. [ABSTRACT FROM AUTHOR]

• This paper proposed a new method, DuFF-Net, for GPL classification from RGB and hyperspectral dual-modality images. • Presented an S-SimSiam self-supervised pre-training module to utilize the spectral and spatial features of HSIs. • Customized a dual-stream ResNet-based model to achieve information complementarity. • Increased the screening accuracy to 96.15 % while saving approximately 50 % labeling workload for classification tasks. Gastric precancerous conditions are closely linked to the development of gastric cancer. However, the detection of gastric precancerous lesions (GPL) is limited by the indistinct symptoms and the low detection rate of microscope images. This paper proposes an RGB and Hyperspectral Dual-modality imaging Feature Fusion Network (DuFF-Net) to improve the classification accuracy of GPL. To fully exploit information of different modality images, we customize a dual-stream ResNet-based model for feature sharing and fusion. Skip-Connections are added between inter-path of networks to achieve information interaction. In the decision step, we adopt the SE-based attention module and Pearson Correlation to highlight and select effective features. Experimental results show that the DuFF-Net increases the screening accuracy to 96.15 % for two types of gastric precancerous tissues with high morphological similarity. Furthermore, our approach reduces the labeling workload for classification tasks by approximately 50 %. These findings provide valuable guidance for the screening and subsequent lesion segmentation of GPLs. [ABSTRACT FROM AUTHOR]

In the oil industry, during the production of oil and gas, barium sulfate (BaSO 4) scale may occur on the inner walls of the pipelines leading to the reduction of the internal diameter, making the fluids' passage difficult and complicating the calculation of the fluids volume fraction. This paper presents a methodology to predict volume fraction of fluids and BaSO 4 scale thickness from obtaining spectra of two NaI(Tl) detectors that record the transmitted and scattered beams of gamma-rays. Theoretical models for a multiphase annular flow regime (gas-saltwater-oil-scale) were developed using MCNP6 code, which is a mathematical code based on the Monte Carlo method. The simulated data was used to train a deep neural network (DNN) to predict the volume fraction of gas, saltwater and oil, and the concentric scale thickness. A Python optimization library called Optuna was used for the hyperparameters search to design the DNN architecture. The methodology presented great results, especially for scale thickness prediction. Although the results for saltwater did not reach the same level, it was still possible to predict approximately 70% of the patterns up to 10% relative error. This achievement indicates the possibility to calculate the volume fraction of fluids and the concentric scale thickness in the offshore oil industry using gamma densitometry and deep learning models. • Prediction of the volume fraction of fluids and scale in an annular multiphase system. • Methodology based on transmitted and scattered beams of gamma-rays (dual-modality). • Application of deep neural network using Python hyperparameters library. • Dataset was developed using the MCNP6 code. [ABSTRACT FROM AUTHOR]

A current controversy in the area of implicit statistical learning (ISL) is whether this process consists of a single, central mechanism or multiple modality-specific ones. To provide insight into this question, the current study involved three ISL experiments to explore whether multimodal input sources are processed separately in each modality or are integrated together across modalities. In Experiment 1, visual and auditory ISL were measured under unimodal conditions, with the results providing a baseline level of learning for subsequent experiments. Visual and auditory sequences were presented separately, and the underlying grammar used for both modalities was the same. In Experiment 2, visual and auditory sequences were presented simultaneously with each modality using the same artificial grammar to investigate whether redundant multisensory information would result in a facilitative effect (i.e., increased learning) compared to the baseline. In Experiment 3, visual and auditory sequences were again presented simultaneously but this time with each modality employing different artificial grammars to investigate whether an interference effect (i.e., decreased learning) would be observed compared to the baseline. Results showed that there was neither a facilitative learning effect in Experiment 2 nor an interference effect in Experiment 3. These findings suggest that participants were able to track simultaneously and independently two sets of sequential regularities under dual-modality conditions. These findings are consistent with the theories that posit the existence of multiple, modality-specific ISL mechanisms rather than a single central one.

Electrical capacitance tomography has been widely used in flame monitoring. In this paper, a dual-modality electrical tomography system was developed for imaging of flame admittivity distribution, including conductivity and permittivity distributions. An admittance measuring circuit was developed to obtain both resistance and capacitance of the flame impedance, and a Calderon method-based reconstruction algorithm was developed, which could be used to directly reconstruct the cross-sectional images of both conductivity and permittivity in the flame. The 2-D and 3-D phantoms were modeled in simulation to validate the feasibility of the developed system and algorithm. Experiments were carried out to monitor a Bunsen burner flame with varied air flow rates. Both simulation and experimental results were confirmed that the developed technique has not only reconstructed the cross-sectional distribution of admittivity in the flame but also revealed variations in flame admittivity with height. In addition, it was discovered that the conductivity and permittivity distributions in the flame were affected by the air flow rates. Therefore, the system can be used to monitor the conductivity and permittivity distributions in the flame and, hence, can further help study the combustion mechanism. [ABSTRACT FROM AUTHOR]

Magnetic Fe3O4 nanoparticles with narrow size distribution were synthesized by simple and high yielded co-precipitation technique using ferrous salts with a molar ratio of Fe3+/Fe2+=2. After coating of the nanoparticles with Stöber silica (SiO2@Fe3O4), nanoparticles were functionalized by amine groups. Then chelator molecules diethylenetriaminepentaacetic (DTPA) and tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) were coupled to the APTS-SiO2@Fe3O4 to chelating Y-90 radiometal that makes these nanoparticles a suitable agent for therapeutic application as dual-modality PET/MRI imaging. The results show the coupling of DTPA takes place better than DOTA. Synthesis of magnetic nanoparticles (MNPs) was followed by structure identification using XRD, SEM, TGA and IR techniques. In order to trace MNPs biodistribution, the radiolabeled MNPs-DTPA were prepared using 90Y (production of 90Y/90Sr generator) with a good labeling efficiency (about 92%, RTLS method). The biodistribution of the radiolabeled MNPs was checked in normal male rats up to 24 h compared to free Y3+. The data shows that the tracer accumulation is in reticuloendothelial tissue while the stability of the complex is highly retained. [ABSTRACT FROM AUTHOR]

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A current controversy in the area of implicit statistical learning (ISL) is whether this process consists of a single, central mechanism or multiple modality-specific ones. To provide insight into this question, the current study involved three ISL experiments to explore whether multimodal input sources are processed separately in each modality or are integrated together across modalities. In Experiment 1, visual and auditory ISL were measured under unimodal conditions, with the results providing a baseline level of learning for subsequent experiments. Visual and auditory sequences were presented separately, and the underlying grammar used for both modalities was the same. In Experiment 2, visual and auditory sequences were presented simultaneously with each modality using the same artificial grammar to investigate whether redundant multisensory information would result in a facilitative effect (i.e., increased learning) compared to the baseline. In Experiment 3, visual and auditory sequences were again presented simultaneously but this time with each modality employing different artificial grammars to investigate whether an interference effect (i.e., decreased learning) would be observed compared to the baseline. Results showed that there was neither a facilitative learning effect in Experiment 2 nor an interference effect in Experiment 3. These findings suggest that participants were able to track simultaneously and independently two sets of sequential regularities under dual-modality conditions. These findings are consistent with the theories that posit the existence of multiple, modality-specific ISL mechanisms rather than a single central one. [ABSTRACT FROM AUTHOR]

• The first generation of single-detector dual-modality imaging flow cytometry (dIFC) for simultaneous brightfield and light scattering imaging was developed. • The special optical design with a knife-edge right-angle (KERA) prism makes our dIFC compact and convenient. • Compared with current IFC, our dIFC is demonstrated with attractive capability for label-free analysis of single micrometer particles and cells, especially when the light scattering image modality is adopted. A dual-modality imaging flow cytometer (dIFC) that obtains both brightfield and light scattering images of single cells simultaneously on one detector was developed. The unique optical layout of our system with a knife-edge right-angle (KERA) prism is key for single-detector dual-modality imaging. By using our dIFC, label-free brightfield and light scattering images of single micrometer particles and cells (ovarian cancer cell lines A2780 and Hey) were obtained. Automatic classification of ovarian cell subtypes was achieved with an accuracy of 89.52% for light scattering imaging, and 70.25% for brightfield imaging by integration of dIFC with machine learning. Compared with the obtaining of brightfield and fluorescence images in conventional IFC, our dIFC is promising for label-free cellular analysis especially by adopting the light scattering image modality. [ABSTRACT FROM AUTHOR]

Given the increasing exploration of fluorescent tracers in the field of nuclear medicine, a need has risen for practical development guidelines that can help improve the translation aspects of fluorescent tracers. This editorial discusses the does and don'ts in developing fluorescence tracers. It has been put forward by the European Association of Nuclear Medicine (EANM) Translational Molecular Imaging & Therapy committee and has been approved by the EANM board. [ABSTRACT FROM AUTHOR]

Multifunctional manganese oxide nanoparticles (NPs) with impressive enhanced T1 contrast ability show great promise in biomedical diagnosis. Herein, we developed a dual-modality imaging agent system based on polyethylene glycol (PEG)-coated manganese oxide NPs conjugated with organic dye (Cy7.5), which functions as a fluorescence imaging (FI) agent as well as a magnetic resonance imaging (MRI) imaging agent. The formed Mn3O4@PEG-Cy7.5 NPs with the size of ~10 nm exhibit good colloidal stability in different physiological media. Serial FI and MRI studies that non-invasively assessed the bio-distribution pattern and the feasibility for in vivo dual-modality imaging-guided lymph node mapping have been investigated. In addition, histological and biochemical analyses exhibited low toxicity even at a dose of 20 mg/kg in vivo. Since Mn3O4@PEG-Cy7.5 NPs exhibited desirable properties as imaging agents and good biocompatibility, this work offers a robust, safe, and accurate diagnostic platform based on manganese oxide NPs for tumor metastasis diagnosis. [ABSTRACT FROM AUTHOR]

A new class of uniform biodegradable yolk-shell Fe 3 O 4 @PFH@PMAA-DOX microspheres was developed as ultrasound/MR dual-modality imaging contrast agents and drug delivery system. PMAA shells exhibit excellent drug loading capability and efficiency, the DOX-loading capacity is 15.4 wt% and the drug loading efficiency is 91%. In vivo ultrasound and MRI experiments indicate that the as-prepared Fe 3 O 4 @PFH@PMAA-DOX microspheres successfully reach tumor tissues and possess robust dual-modality imaging capability, the r 2 value of MRI is 71.82 mM −1 s −1 . In vitro cytotoxicity and in vivo tissues biopsies show that the loaded DOX can be control-released under pH, redox and ultrasound multi-stimuli and kill cancer cells effectively. Furthermore, disulfide-crosslinked PMAA shells and PGA-stabilized Fe 3 O 4 cores can be respectively degraded into short hydrophilic polymer chains and iron ions, reducing their cumulative toxicity in organisms. Thus, the multifunctional biodegradable microspheres are safer for normal tissues and more beneficial in actual clinical applications. [ABSTRACT FROM AUTHOR]

We report a novel aptasensor for the simultaneous colorimetric and electrochemical detection of mercury (Hg2+). This device consists of a paper-based microfluidic component (μ-PAD) incorporated into a miniaturized three-electrode system fabricated through printed circuit board (PCB) technology. This biosensor is portable, rapid, versatile, and can detect Hg2+ down to 0.01 ppm based on 3σ of the blank/slope criteria. Moreover, it is highly selective against As2+, Cu2+, Zn2+, Pb2+, Cd2+, Mg2+, and Fe2+, reaching up to 13 times more of the input signal than the other heavy metals. The colorimetric detection mechanism uses aptamer functionalized polystyrene (PS)-AgNPs and Ps-AuNPs microparticles' specific aggregation. The Ps-AuNPs-based system allows qualitative detection (LOD 5 ppm) and stability over seven days (up to 97.59% signal retention). For the Ps-AgNPs-based system, the detection limit is 0.5 ppm with a linear range from 0.5 to 20 ppm (adjusted R2= 0.986) and stability over 30 days (up to 94.95% signal retention). The electrochemical component measures changes in charge transfer resistance upon target-aptamer hybridization using a [Ru (NH 3) 6 ]3+Cl 3 ] redox probe. The latest component presents a linear range from 0.01 to 1 ppm (adjusted R2= 0.935) with a LOD of 0.01 ppm and performance stability over seven days (up to 102.52 ± 11.7 signal retention). This device offers a universal dual detection platform with multiplexing, multi-replication, quantitative color analysis, and minimization of false results. Furthermore, detection results in river samples showed recoveries up to 91.12% (RSD 0.85) and 105.61% (RSD 1.62) for the electrochemical and colorimetric components, respectively. The proposed system is highly selective with no false-positive or false-negative results in an overall wide linear range and can safeguard the accuracy of detection results in aptasensing platforms in general. [Display omitted] [ABSTRACT FROM AUTHOR]

This work demonstrates the combination of optical coherence tomography (OCT) and hyperspectral imaging (HSI) using a double-clad optical fiber coupler. The single-mode core of the fiber is used for OCT imaging, while the inner cladding of the double-clad fiber provides an efficient way to capture the reflectance spectrum of the sample. The combination of both methods enables three-dimensional acquisition of the sample morphology with OCT, enhanced with complementary molecular information contained in the hyperspectral image. The HSI data can be used to highlight the presence of specific molecules with characteristic absorption peaks or to produce true color images overlaid on the OCT volume for improved tissue identification by the clinician. Such a system could be implemented in a number of clinical endoscopic applications and could improve the current practice in tissue characterization, diagnosis, and surgical guidance. [ABSTRACT FROM AUTHOR]

A ratiometric fluorescence and colorimetric assay for ALDS based on the NH 2 -MIL-101(Fe) peroxidase nanozyme. [Display omitted] • A dual-modality sensing platform is developed for ratiometric fluorescence and colorimetric determination of alendronate sodium (ALDS). • The Two-dimensional correlation spectroscopy (2D-COS) was applied to study the catalytic and sensing mechanism of nanozyme as NH 2 – MIL-101(Fe). • This technique was helpful to understand interaction of substrates on nanozyme and develop more sensitive sensors for assaying. We developed a dual-modality sensing platform for ratiometric fluorescence and colorimetric determination of alendronate sodium (ALDS). This platform was performed by using a NH 2 – MIL-101(Fe) as a peroxidase mimic. Since preferential complexing between Fe3+ (active site for peroxidase) and ALDS, the production of 2,3-diaminophenazine (DAP, oxidized product of OPD) has been inhibited in the presence of H 2 O 2. As a result, the ratiometric fluorescence value of F 556 / F 456 and absorbance at 450 nm exhibited significant changes, which could be used as the dual-modality sensing platform. In addition, Two-dimensional correlation spectroscopy (2D-COS) analysis on Fourier-transform infrared (FTIR), ultraviolet visible and ratiometric fluorescence spectra were applied to investigate the binding features. Synchronous and asynchronous maps of these spectra confirmed our above hypothesis, in which Fe3+-ALDS complex was the critical factor that regulated dual-modality signals. To our knowledge, the 2D-COS method was applied to study the catalytic and sensing mechanism of nanozyme as NH 2 – MIL-101(Fe) for the first time. This technique was helpful to understand interaction of substrates on nanozyme and develop more sensitive sensors for assaying. [ABSTRACT FROM AUTHOR]

Phase-contrast optical microscopy that converts phase modulation of transparent samples into intensity modulation plays an indispensable role in biomedical research. In this study, we propose a dual-modality quantitative phase contrast microscopic (DQPCM) platform that integrates quantitative differential interference phase-contrast microscopy (qDIC-PCM) and quantitative Zernike phase-contrast microscopy (qZ-PCM) in the same microscopy. Both imaging modalities are realized through spatial light modulator (SLM) based pupil phase modulation, and switching between the two imaging modes is performed by rotating two polarizers. Both imaging modalities have the merits of high stability and low coherent noise due to their common path interference structure and partially coherent illumination. The experimental results indicate that qDIC-PCM is superior to qZ-PCM in quantitative phase imaging of samples with certain scattering, while qZ-PCM has higher spatial resolution than qDIC-PCM. The proposed technique will be very helpful for imaging the dynamic processes of biological samples with high spatiotemporal resolution. [ABSTRACT FROM AUTHOR]