Multihierarchical Regulation To Achieve Quantum Dot Nanospheres with a Photoluminescence Quantum Yield Close to 100.

Quantum dots (QDs) exhibit superior brightness and photochemical stability, making them the preferred option for highly sensitive single-molecule detection compared with fluorescent dyes or proteins. Nevertheless, their high surface energy leads to nonspecific adsorption and poor colloidal stability. In the past decades, we have found that QD-based fluorescent nanoparticles (FNs) can not only address these limitations but also enhance detection sensitivity. However, the photoluminescence quantum yield (PLQY) of FNs is significantly lower compared with that of original QDs. It is urgent to develop a strategy to solve the issue, aiming to further enhance detection sensitivity. In this study, we found that the decrease of PLQY of FNs prepared by free radical polymerization was attributed to two factors: (1) generation of defects that can cause nonradiative transitions resulting from QD-ligands desorption and QD-shell oxidation induced by free radicals; (2) self-absorption resulting from aggregation caused by incompatibility of QDs with polymers. Based on these, we proposed a multihierarchical regulation strategy that includes: (1) regulating QD-ligands; (2) precisely controlling free radical concentration; and (3) constructing cross-linked structures of polymer to improve compatibility and to reduce the formation of surface defects. It is crucial to emphasize that the simultaneous coordination of multiple factors is essential. Consequently, a world-record PLQY of 97.6% for FNs was achieved, breaking through the current bottleneck at 65%. The flexible application of this regulatory concept paves the way for the large-scale production of high-brightness QD-polymer complexes, enhancing their potential applications in sensitive biomedical detection.

[Effect of Integrated Landscape Characteristics Around Chaohu Lake on River Water Quality Based on Watershed Units].

Considering the impact of differences in watershed characteristics on river water quality, with the Chaohu Lake Basin as the research object, based on the data of water quality, meteorology, topography, soil, and remote sensing images of the river monitoring points from October 2019 to September 2020, the watershed unit at each monitoring point was divided through digital terrain analysis, and the comprehensive landscape characteristics based on the watershed unit were explored through the comprehensive use of correlation analysis, redundancy analysis, and multiple regression analysis to investigate the influence of comprehensive landscape characteristics based on watershed units (including land use, climate, topography, soil, etc.) on the water quality of rivers around Chaohu Lake. The results showed that:① the water quality of rivers around Chaohu Lake had large spatial differences, with the main pollutants being total nitrogen and ammonia nitrogen. Most of the rivers had total nitrogen concentrations exceeding the Class V water quality standards, and the areas with serious nitrogen and phosphorus pollution were concentrated in the urban area of Hefei and the surrounding rivers, as well as in the middle and lower reaches of the Fengle and Hangbu Rivers. ② The comprehensive landscape characteristics of the watershed unit had a significant impact on the river water quality. Among them, the proportion of built-up land, the density of patches, the dispersion and juxtaposition index, and the Shannon diversity index were positively correlated with the water quality indicators, whereas the proportion of forest and grassland and the spreading index were negatively correlated with the water quality indicators. ③ In different seasons, the effect of the integrated landscape characteristics of the watershed unit on river water quality was stronger in the wet season than in the dry season, which was mainly caused by the difference in precipitation in the dry and wet seasons.

IremulbNet: Rethinking the inverted residual architecture for image recognition.

An increasing need of running Convolutional Neural Network (CNN) models on mobile devices encourages the studies on efficient and lightweight neural network model. In this paper, an Inverse Residual Multi-Branch Network named IremulbNet is proposed to solve the problem of insufficient classification accuracy in existing lightweight network models. The core module of this model is to reconstruct an inverse residual structure, in which a special feature fusion method, multi-branch feature extraction, and depthwise separable convolution techniques are used to improve the classification accuracy. The performance of model is tested using image databases. Experimental results show that for the fine-grained image dataset Imagenet-woof, IremulbNet achieved 10.9%, 12.2%, and 15.3% higher accuracy than that of MobileNet V3, ShuffleNet V2, and PeleeNet, respectively. Moreover, it can reduce inference time (GPU) about 42.09% and 75.56% compared to classic ResNet50 and DenseNet121.

Enhancing Circularly Polarized Luminescence in Quantum Dots through Chiral Coordination-Mediated Growth at the Liquid/Liquid Interface.

Here, we develop a novel methodology for synthesizing chiral CdSe@ZnS quantum dots (QDs) with enhanced circularly polarized luminescence (CPL) by incorporating l-/d-histidine (l-/d-His) ligands during ZnS shell growth at the water/oil interface. The resulting chiral QDs exhibit exceptional absolute photoluminescence quantum yield of up to 67.2%, surpassing the reported limits of 40.0% for chiral inorganic QDs, along with absorption dissymmetry factor (|gabs|) and luminescence dissymmetry factor (|glum|) values of 10-2, exceeding the range of 10-5-10-3 and 10-4-10-2, respectively. Detailed investigations of the synthetic pathway reveal that the interface, as a binary synthetic environment, facilitates the coordinated ligand exchange and shell growth mediated by chiral His-Zn2+ coordination complexes, leading to a maximum fluorescent brightness and chiroptical activities. The growth process, regulated by the His-Zn2+ coordination complex, not only reduces trap states on the CdSe surface, thereby enhancing the fluorescence intensity, but also significantly promotes the orbital hybridization between QDs and chiral ligands, effectively overcoming the shielding effect of the wide bandgap shell and imparting pronounced chirality. The proposed growth pathway elucidates the origin of chirality and provides insights into the regulation of the CPL intensity in chiral QDs. Furthermore, the application of CPL QDs in multilevel anticounterfeiting systems overcomes the limitations of replication in achiral fluorescence materials and enhances the system's resistance to counterfeiting, thus opening new opportunities for chiral QDs in optical anticounterfeiting and intelligent information encryption.

Acid-Resistant Near-Infrared II Ag2Se Quantum Dots for Gastrointestinal Imaging.

With the development of near-infrared II (NIR-II) fluorescence imaging, Ag2Se quantum dots (QDs) have become promising label candidates due to their negligible toxicity and narrow band gap. Despite their potential for gastrointestinal (GI) imaging, the application of Ag2Se QDs still presents significant challenges due to issues such as fluorescence extinction or poor stability in the complex digestive microenvironment. Herein, we have proposed a novel approach to the continuous production of Se precursors using glutathione (GSH) as the reductant under acidic conditions, realizing the continuous growth of water-dispersible Ag2Se QDs. The Ag2Se QDs emitting at 600-1100 nm have been successfully synthesized. Meanwhile, the silver-rich surface of the synthesized NIR-II Ag2Se QDs has been passivated well with the dense GSH, resulting in exceptional colloidal stability and photostability and endowing them with acid resistance. As a result, the obtained NIR-II Ag2Se QDs have exhibited remarkable stability in gastric acid, thus enabling their utilization for long-term real-time monitoring of GI peristalsis via NIR-II fluorescence imaging. Moreover, in contrast to conventional barium meal-based X-ray imaging, NIR-II fluorescence imaging with as-prepared NIR-II Ag2Se QDs can offer clearer visualization of fine intestinal structures, with a width as small as 1.07 mm. The developed strategy has offered a new opportunity for the synthesis of acid-resistant nanocrystals, and the acid-resistant, low-toxicity, and biocompatible NIR-II Ag2Se QDs synthesized in this work show a great promise for GI imaging and diagnosis of GI diseases in vivo.

Deep reinforcement learning framework for thoracic diseases classification via prior knowledge guidance.

The chest X-ray is commonly employed in the diagnosis of thoracic diseases. Over the years, numerous approaches have been proposed to address the issue of automatic diagnosis based on chest X-rays. However, the limited availability of labeled data for related diseases remains a significant challenge in achieving accurate diagnoses. This paper focuses on the diagnostic problem of thorax diseases and presents a novel deep reinforcement learning framework. This framework incorporates prior knowledge to guide the learning process of diagnostic agents, and the model parameters can be continually updated as more data becomes available, mimicking a person's learning process. Specifically, our approach offers two key contributions: (1) prior knowledge can be acquired from pre-trained models using old data or similar data from other domains, effectively reducing the dependence on target domain data; and (2) the reinforcement learning framework enables the diagnostic agent to be as exploratory as a human, leading to improved diagnostic accuracy through continuous exploration. Moreover, this method effectively addresses the challenge of learning models with limited data, enhancing the model's generalization capability. We evaluate the performance of our approach using the well-known NIH ChestX-ray 14 and CheXpert datasets, and achieve competitive results. More importantly, in clinical application, we make considerable progress. The source code for our approach can be accessed at the following URL: https://github.com/NeaseZ/MARL.

Capture of small clusters by ligand-solvent interaction.

Clusters are considered to become increasingly significant for elaborating the nanocrystal's formation mechanism. However, capturing the clusters with high chemical potential is challenging because of the lack of effective strategies. In this work, the key role of ligand-solvent interaction has been revealed for the stabilization of clusters in silver telluride synthesis. The Flory interaction coefficient that comprehensively regards the temperature and dispersion, polarity, and hydrogen bonding of the solvent has been used to evaluate the ligand-solvent interaction and thus assist in the design of synthetic systems. Small silver telluride clusters have been successfully captured, and the composition of the smallest cluster is determined as Ag7Te8(SCy)2 (SCy represents the ligand). This work provides new insights into the design of cluster/nanocrystal synthesis systems and paves the way to revealing the mechanism of precursor-cluster-nanocrystal conversion.

Identifying Liver Metastasis-Related Genes Through a Coexpression Network to Construct a 5-Gene Model for Predicting Pancreatic Ductal Adenocarcinoma Patient Prognosis.

OBJECTIVES: This study aimed to develop a liver metastasis-related gene prognostic index (LMPI) for pancreatic ductal adenocarcinoma prognosis and therapy. METHODS: The Cancer Genome Atlas data set was used to identify liver metastasis-related hub genes via weighted gene coexpression network analysis. The core genes were identified to construct an LMPI by using the Cox regression method. An immune cell abundance identifier was applied to determine the immune cell abundance. RESULTS: A total of 78 hub liver metastasis-related genes in the black module were significantly enriched in complement and coagulation cascades, fat digestion and absorption, and the PPAR signaling pathway. Then, an LMPI was constructed on the basis of the 5 prognostic genes (MOGAT3, ASGR1, TRPM8, SGSM1, and LOC101927851). Patients with higher LMPI scores had poor overall survival, more co-occurring or mutually exclusive pairs of driver gene mutations, and less benefit from immunotherapy than patients with lower LMPI scores. In addition, a high correlation was also found between LMPI scores and immune infiltration, such as CD4 naive, CD8 T, cytotoxic T, T helper 2, follicular helper T, and natural killer cells. CONCLUSIONS: The core genes of the LMPI developed may be independent factors for predicting prognosis, immune characteristics, and immunotherapy efficacy in pancreatic ductal adenocarcinoma.

Diarylheptanoid 35d overcomes EGFR TKI resistance by inducing hsp70-mediated lysosomal degradation of EGFR in EGFR-mutant lung adenocarcinoma.

Epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma (LUAD) patients often respond to EGFR tyrosine kinase inhibitors (TKIs) initially but eventually develop resistance to TKIs. The switch of EGFR downstream signaling from TKI-sensitive to TKI-insensitive is a critical mechanism-driving resistance to TKIs. Identification of potential therapies to target EGFR effectively is a potential strategy to treat TKI-resistant LUADs. In this study, we developed a small molecule diarylheptanoid 35d, a curcumin derivative, that effectively suppressed EGFR protein expression, killed multiple TKI-resistant LUAD cells in vitro, and suppressed tumor growth of EGFR-mutant LUAD xenografts with variant TKI-resistant mechanisms including EGFR C797S mutations in vivo. Mechanically, 35d triggers heat shock protein 70-mediated lysosomal pathway through transcriptional activation of several components in the pathway, such as HSPA1B, to induce EGFR protein degradation. Interestingly, higher HSPA1B expression in LUAD tumors associated with longer survival of EGFR-mutant, TKI-treated patients, suggesting the role of HSPA1B on retarding TKI resistance and providing a rationale for combining 35d with EGFR TKIs. Our data showed that combination of 35d significantly inhibits tumor reprogression on osimertinib and prolongs mice survival. Overall, our results suggest 35d as a promising lead compound to suppress EGFR expression and provide important insights into the development of combination therapies for TKI-resistant LUADs, which could have translational potential for the treatment of this deadly disease.

Redox Imbalance Triggered Intratumoral Cascade Reaction for Tumor "turn on" Imaging and Synergistic Therapy.

The redox homeostasis in tumors enhances their antioxidant defense ability, limiting reactive oxygen species mediated tumor therapy efficacy. The development of strategies for specific and continuous disruption of the redox homeostasis in tumor cells facilitates the improvement of the cancer therapeutic effect by promoting the apoptosis of tumor cells. Herein, a responsively biodegradable targeting multifunctional integrated nanosphere (HDMn-QDs/PEG-FA) is designed to enhance the anti-tumor efficacy by triggering intratumoral cascade reactions to effectively disrupt intracellular redox homeostasis. Once HDMn-QDs/PEG-FA enters tumor cells, manganese dioxide (MnO2 ) shell on the surface of nanosphere consumes glutathione (GSH) to produce Mn2+ , enabling enhanced chemodynamic therapy (CDT) via a Fenton-like reaction and T1 -weighted magnetic resonance imaging. Meanwhile, the degradation of MnO2 can also cause the fluorescence recovery of quantum dots conjugated on the surface of the shell, realizing "turn-on" fluorescence imaging. In addition, the doxorubicin is released because of the cleavage of the embedded SS bond in the hybrid core framework by GSH. A superior synergistic therapeutic efficiency combined CDT and chemotherapy is shown by HDMn-QDs/PEG-FA in vivo. The tumor-inhibition rate reaches to 94.8% and does not cause normal tissue damage due to the good targeting and tumor microenvironment-specific response.

PLAGL1 is associated with prognosis and cell proliferation in pancreatic adenocarcinoma.

BACKGROUND: Emerging evidence has shown the crucial roles of pleomorphic adenoma gene (PLAG) family genes in multiple cancers. However, their functions and mechanisms in pancreatic adenocarcinoma (PAAD) remain poorly understood. METHODS: We analyzed the expression levels of PLAG family genes in both The Cancer Genome Atlas (TCGA) database and a Gene Expression Omnibus (GEO) database, and confirmed the results in our three independent cohorts of 382 PAAD tissues and 362 adjacent nontumor pancreatic tissues. Integrated analyses were carried out to explore the function, mechanism and prognostic value of the selected PLAG family gene in PAAD patients. RESULTS: By analyzing the TCGA and GEO databases, PLAGL1 was identified to be downregulated in PAAD tissues, and its decreasing levels of both mRNA and protein were verified in our three independent PAAD cohorts. PLAGL1 expression was inversely correlated with clinicopathological factors including the Ki67+ cell rate and pathologic stage. Further GSEA of the TCGA-PAAD cohort demonstrated that multiple signaling pathways implicated in cell proliferation were enriched in the lower PLAGL1 expressing PAAD group. Moreover, we demonstrated that PLAGL1 expression was obviously negatively associated with patients' overall survival outcome in both the TCGA-PAAD cohort and our verification cohorts. Additionally, through MTS and BrdU assays, we further demonstrated in vitro that PLAGL1 had the impact of preventing the proliferation of pancreatic cancer cells. CONCLUSIONS: Our present study suggested that downregulated PLAGL1 might act as a biomarker in predicts poor prognosis and one of important factors in increasing cell proliferation in PAAD. This study provides us with a novel prognostic marker and therapeutic strategy for PAAD, which deserves further study.

Fluorescent Covalent Organic Frameworks: A Promising Material Platform for Explosive Sensing.

Covalent organic frameworks (COFs) are a novel class of porous crystalline organic materials with organic small molecule units connected by strong covalent bonds and extending in two- or three-dimension in an ordered mode. The tunability, porosity, and crystallinity have endowed covalent organic frameworks the capability of multi-faceted functionality. Introduction of fluorophores into their backbones or side-chains creates emissive covalent organic frameworks. Compared with common fluorescent organic solid materials, COFs possess several intrinsic advantages being as a type of irreplaceable fluorescence materials mainly because its highly developed pore structures can accommodate various types of guest analytes by specific or non-specific chemical bonding and non-bonding interaction. Developments in fluorescent COFs have provided opportunities to enhance sensing performance. Moreover, due to its inherent rigidified structures and fixed conformations, the intramolecular rotation, vibration, and motion occurred in common organic small molecules, and organic solid systems can be greatly inhibited. This inhibition decreases the decay of excited-state energy as heat and blocks the non-radiative quenching channel. Thus, fluorescent COFs can be designed, synthesized, and precisely tuned to exhibit optimal luminescence properties in comparison with common homogeneous dissolved organic small molecule dyes and can even compete with the currently mainstream organic solid semiconductor-based luminescence materials. This mini-review discusses the major design principle and the state-of-the-art paragon examples of fluorescent COFs and their typical applications in the detection and monitoring of some key explosive chemicals by fluorescence analysis. The challenges and the future direction of fluorescent COFs are also covered in detail in the concluding section.

Artificially regulated synthesis of nanocrystals in live cells.

Live cells, as reservoirs of biochemical reactions, can serve as amazing integrated chemical plants where precursor formation, nucleation and growth of nanocrystals, and functional assembly, can be carried out accurately following an artificial program. It is crucial but challenging to deliberately direct intracellular pathways to synthesize desired nanocrystals that cannot be produced naturally in cells, because the relevant reactions exist in different spatiotemporal dimensions and will never encounter each other spontaneously. This article summarizes the progress in the introduction of inorganic functional nanocrystals into live cells via the 'artificially regulated space-time-coupled live-cell synthesis' strategy. We also describe ingenious bio-applications of nanocrystal-cell systems, and quasi-biosynthesis strategies expanded from live-cell synthesis. Artificially regulated live-cell synthesis-which involves the interdisciplinary application of biology, chemistry, nanoscience and medicine-will enable researchers to better exploit the unanticipated potentialities of live cells and open up new directions in synthetic biology.

A Temporal-Aware Relation and Attention Network for Temporal Action Localization.

Temporal action localization is currently an active research topic in computer vision and machine learning due to its usage in smart surveillance. It is a challenging problem since the categories of the actions must be classified in untrimmed videos and the start and end of the actions need to be accurately found. Although many temporal action localization methods have been proposed, they require substantial amounts of computational resources for the training and inference processes. To solve these issues, in this work, a novel temporal-aware relation and attention network (abbreviated as TRA) is proposed for the temporal action localization task. TRA has an anchor-free and end-to-end architecture that fully uses temporal-aware information. Specifically, a temporal self-attention module is first designed to determine the relationship between different temporal positions, and more weight is given to features within the actions. Then, a multiple temporal aggregation module is constructed to aggregate the temporal domain information. Finally, a graph relation module is designed to obtain the aggregated graph features, which are used to refine the boundaries and classification results. Most importantly, these three modules are jointly explored in a unified framework, and temporal awareness is always fully used. Extensive experiments demonstrate that the proposed method can outperform all state-of-the-art methods on the THUMOS14 dataset with an average mAP that reaches 67.6% and obtain a comparable result on the ActivityNet1.3 dataset with an average mAP that reaches 34.4%. Compared with A2Net (TIP20), PCG-TAL (TIP21), and AFSD (CVPR21) TRA can achieve improvements of 11.7%, 4.4%, and 1.8%, respectively on the THUMOS14 dataset.

Immunoprofiling of Severity and Stage of Bacterial Infectious Diseases by Ultrabright Fluorescent Nanosphere-Based Dyad Test Strips.

Bacterial infectious diseases are common clinical diseases that seriously threaten human health, especially in countries and regions with poor environmental hygiene. Due to the lack of characteristic clinical symptoms and signs, it is a challenge to distinguish a bacterial infection from other infections, leading to misdiagnosis and antibiotic overuse. Therefore, there is an urgent need to develop a specific method for detection of bacterial infections. Herein, utilizing ultrabright fluorescent nanospheres (FNs) as reporters, immunochromatographic dyad test strips are developed for the early detection of bacterial infections and distinction of different stages of bacterial infectious diseases in clinical samples. C-reactive protein (CRP) and heparin-binding protein (HBP) are quantified and assayed because their levels in plasma are varied dynamically and asynchronously during the progression of the disease. The detection limits of CRP and HBP can reach as low as 0.51 and 0.65 ng/mL, respectively, due to the superior fluorescence intensity of each FN, which is 570 times stronger than that of a single quantum dot. The assay procedure can be achieved in 22 min, fully meeting the needs of rapid and ultrasensitive detection in the field. This constructed strip has been successfully used to profile the stage and severity of bacterial infections by monitoring the levels of CRP and HBP in human plasma samples, showing great potential as a point-of-care biosensor for clinical diagnosis. In addition to bacterial infections, the developed ultrabright FN-based point-of-care testing can be readily expanded for rapid, quantitative, and ultrasensitive detection of other trace substances in complex systems.

In Situ Self-Sorting Peptide Assemblies in Living Cells for Simultaneous Organelle Targeting.

Self-sorting is a common phenomenon in eukaryotic cells and represents one of the versatile strategies for the formation of advanced functional materials; however, developing artificial self-sorting assemblies within living cells remains challenging. Here, we report on the GSH-responsive in situ self-sorting peptide assemblies within cancer cells for simultaneous organelle targeting to promote combinatorial organelle dysfunction and thereby cell death. The self-sorting system was created via the design of two peptides E3C16E6 and EVMSeO derived from lipid-inspired peptide interdigitating amphiphiles and peptide bola-amphiphiles, respectively. The distinct organization patterns of the two peptides facilitate their GSH-induced self-sorting into isolated nanofibrils as a result of cleavage of disulfide-connected hydrophilic domains or reduction of selenoxide groups. The GSH-responsive in situ self-sorting in the peptide assemblies within HeLa cells was directly characterized by super-resolution structured illumination microscopy. Incorporation of the thiol and ER-targeting groups into the self-sorted assemblies endows their simultaneous targeting of endoplasmic reticulum and Golgi apparatus, thus leading to combinatorial organelle dysfunction and cell death. Our results demonstrate the establishment of the in situ self-sorting peptide assemblies within living cells, thus providing a unique platform for drug targeting delivery and an alternative strategy for modulating biological processes in the future.

In-Sequence High-Specificity Dual-Reporter Unlocking of Fluorescent Probe Enables the Precise Identification of Atherosclerotic Plaques.

The formation of atherosclerotic plaques is the root cause of various cardiovascular diseases (CVDs). Effective CVD interventions thus call for precise identification of the plaques to aid clinical assessment, diagnosis, and treatment of such diseases. In this study, we introduce a dual-target sequentially activated fluorescence reporting system, termed in-sequence high-specificity dual-reporter unlocking (iSHERLOCK), to precisely identify the atherosclerotic plaques in vivo and ex vivo. ISHERLOCK was achieved by creating a three-in-one fluorescent probe that permits highly specific and sensitive detection of lipid droplets and hypochlorous acid via "off-on" and ratiometric readouts, respectively. Based on this format, the upregulated lipid accumulation and oxidative stress-the two hallmarks of atherosclerosis (AS)-were specifically measured in the atherosclerotic plaques, breaking through the barrier of precise tissue biopsy of AS and thus aiding effective CVD stewardship.

Investigation of citrinin and monacolin K gene clusters variation among pigment producer Monascus species.

The filamentous fungi Monascus spp. have been widely used in the production of food colorants. However, the presence of mycotoxin citrinin and the antihypercholestrolemia agent monacolin K in Monascus-fermented products (MFPs) has raised food safety concerns. Here we de novo-sequenced the genomes of 26 Monascus species and proposed an unprecedented classification system, consist of sections A, B and C, according to the biosynthetic gene clusters (BGCs) distribution and phylogeny results. Based on the absence of citrinin gene cluster, section B species were genetically incapable of synthesizing citrinin. A distinguished section A strain named Monascus sanguineus was believed to be a promising food-pigment-producer particularly owing to the simultaneous inactivation of citrinin and monacolin K clusters. Interestingly, gene losses within Monascus secondary metabolism gene clusters were broadly discovered, which may convey a selective advantage in nutrients and energy competition to support the strong pigment-producing ability. Overall, our sectional delimitation system will reshape the industrial strategies for this economically important fungus.

Domain-Adversarial-Guided Siamese Network for Unsupervised Cross-Domain 3-D Object Retrieval.

Recent advances in 3-D sensors and 3-D modeling have led to the availability of massive amounts of 3-D data. It is too onerous and time consuming to manually label a plentiful of 3-D objects in real applications. In this article, we address this issue by transferring the knowledge from the existing labeled data (e.g., the annotated 2-D images or 3-D objects) to the unlabeled 3-D objects. Specifically, we propose a domain-adversarial guided siamese network (DAGSN) for unsupervised cross-domain 3-D object retrieval (CD3DOR). It is mainly composed of three key modules: 1) siamese network-based visual feature learning; 2) mutual information (MI)-based feature enhancement; and 3) conditional domain classifier-based feature adaptation. First, we design a siamese network to encode both 3-D objects and 2-D images from two domains because of its balanced accuracy and efficiency. Besides, it can guarantee the same transformation applied to both domains, which is crucial for the positive domain shift. The core issue for the retrieval task is to improve the capability of feature abstraction, but the previous CD3DOR approaches merely focus on how to eliminate the domain shift. We solve this problem by maximizing the MI between the input 3-D object or 2-D image data and the high-level feature in the second module. To eliminate the domain shift, we design a conditional domain classifier, which can exploit multiplicative interactions between the features and predictive labels, to enforce the joint alignment in both feature level and category level. Consequently, the network can generate domain-invariant yet discriminative features for both domains, which is essential for CD3DOR. Extensive experiments on two protocols, including the cross-dataset 3-D object retrieval protocol (3-D to 3-D) on PSB/NTU, and the cross-modal 3-D object retrieval protocol (2-D to 3-D) on MI3DOR-2, demonstrate that the proposed DAGSN can significantly outperform state-of-the-art CD3DOR methods.