The emerging role of herbal medicines in cancer by interfering with post-translational modification.

SIGNIFICANCE: Herbal medicines demonstrate clinical promise for cancer treatment. Protein post translational modifications (PTMs) regulate tumorigenesis and cancer progression. While PTMs contributing to cancer are well-studied, the precise mechanisms and defined targets of herbal medicines on PTM-associated carcinogenesis remain unclear. Hence, comprehensively understanding how PTMs regulate cancer hallmarks is crucial to elucidate the pharmacological mechanisms of herbal medicines for cancer treatment. RECENT ADVANCES: Advanced development in highly sensitive mass spectrometry (MS)-based techniques has helped utilize PTM-focused studies on cancers. Accumulating evidence has been achieved in laboratory to ascertain the biological mechanism of herbal medicines in cancer therapy. Implication of the strong association between cancer and PTM makes new perspective to comprehend the intricate dialogues between herbal medicines and cellular contexts. CRITICAL ISSUES: Complex components of herbal medicines limit the benefits of herbal-based cancer therapies. In this review, we address that PTMs add a layer of proteomic complexity to the cancer through altering the protein structure, expression, function, and localization. Elaborating PTM implicated in cell signaling, apoptosis and transcriptional regulation function, and the possible cellular signaling, have provided important information about the mechanism of many herbal therapies. Continued optimization of proteomic strategies for PTM analysis in herbal medicines are also discussed. FUTURE DIRECTIONS: Rigorous evaluations of herbal medicines and the chemoproteomic strategies are necessary to explore the aberrant regulation of PTM dynamics contributed to the cancer development and herbal associated pharmacological issues. These efforts will eventually help develop more herbal drugs as modern therapeutic agents.

Reservoir-computing based associative memory and itinerancy for complex dynamical attractors.

Traditional neural network models of associative memories were used to store and retrieve static patterns. We develop reservoir-computing based memories for complex dynamical attractors, under two common recalling scenarios in neuropsychology: location-addressable with an index channel and content-addressable without such a channel. We demonstrate that, for location-addressable retrieval, a single reservoir computing machine can memorize a large number of periodic and chaotic attractors, each retrievable with a specific index value. We articulate control strategies to achieve successful switching among the attractors, unveil the mechanism behind failed switching, and uncover various scaling behaviors between the number of stored attractors and the reservoir network size. For content-addressable retrieval, we exploit multistability with cue signals, where the stored attractors coexist in the high-dimensional phase space of the reservoir network. As the length of the cue signal increases through a critical value, a high success rate can be achieved. The work provides foundational insights into developing long-term memories and itinerancy for complex dynamical patterns.

A partial siphon operational strategy strengthens nitrogen removal performance in partially saturated vertical flow constructed wetlands.

The carbon‒oxygen balance has always been problematic in constructed wetlands (CWs), putting pressure on stable and efficient nitrogen removal. In this study, a novel partial siphon operational strategy was developed to further optimize the carbon and oxygen distributions of a partially saturated vertical flow CW (SVFCW) to enhance nitrogen removal. The removal performances of the partial siphon SVFCW (S-SVFCW) were monitored and compared with those of the SVFCWs at different partial siphon depths (15 cm, 25 cm and 35 cm) in both the warm and cold seasons. The results showed that the partial siphon operating strategy significantly facilitated the removal of ammonia and total nitrogen (TN) in both the warm and cold seasons. When the partial siphon depth was 25 cm, the S-SVFCWs had the highest TN removal efficiency in both the warm (71%) and cold (56%) seasons, with an average improvement of 46% and 52%, respectively, compared with those of the SVFCWs. The oxidation‒reduction potential (ORP) results indicated that richer OPR environments and longer hydraulic detention times were obtained in the S-SVFCWs, which enriched the denitrification bacteria. Microbial analysis revealed greater nitrification and denitrification potentials in the unsaturated zone with enriched functional genes (e.g., amo_AOA, amo_AOB, nxrA and nirK), which are related to nitrification and denitrification processes. Moreover, the strengthening mechanism was the intensified oxygen supply and carbon utilization efficiency based on the cyclic nitrogen profile analysis. This study provides a novel partial siphon operational strategy for enhancing the nitrogen removal capacity of SVFCWs without additional energy or land requirements.

Deep-learning reconstruction of complex dynamical networks from incomplete data.

Reconstructing complex networks and predicting the dynamics are particularly challenging in real-world applications because the available information and data are incomplete. We develop a unified collaborative deep-learning framework consisting of three modules: network inference, state estimation, and dynamical learning. The complete network structure is first inferred and the states of the unobserved nodes are estimated, based on which the dynamical learning module is activated to determine the dynamical evolution rules. An alternating parameter updating strategy is deployed to improve the inference and prediction accuracy. Our framework outperforms baseline methods for synthetic and empirical networks hosting a variety of dynamical processes. A reciprocity emerges between network inference and dynamical prediction: better inference of network structure improves the accuracy of dynamical prediction, and vice versa. We demonstrate the superior performance of our framework on an influenza dataset consisting of 37 US States and a PM2.5 dataset covering 184 cities in China.

Reassessing the greenhouse effect of biogenic carbon emissions in constructed wetlands.

Biogenic carbon emissions, including carbon dioxide (CO2) and methane (CH4), have emerged as a major concern during organic pollutant degradation within constructed wetlands (CWs). Since these organic compounds primarily originate from the photosynthetic fixation of atmospheric CO2, it potentially introduces uncertainty when assessing the greenhouse effect of biogenic carbon emissions in CWs based on direct field observations. To objectively assessing this effect, this study proposed a new strategy by quantifying CO2-equivalent (CO2-eq) changes as carbon passes through CWs and tested it in various types of CWs based on 64 literature records. The findings reveal that CWs can contribute to CO2-eq additions, yet are only responsible for 15.6% derived from direct field observations. The type of CWs plays a crucial role in these CO2-eq additions, with vertical flow CWs causing the lowest levels (6.8%), followed by surface flow CWs (14.2%). In contrast, horizontal flow CWs are associated with the strongest CO2-eq addition (25.7%). The findings provide new insights for the objective assessment of the greenhouse effect of biogenic carbon emissions in CWs, which will be beneficial for future life cycle assessment.

Downstream STING pathways IRF3 and NF-κB differentially regulate CCL22 in response to cytosolic dsDNA.

Double-stranded DNA (dsDNA) in the cytoplasm of eukaryotic cells is abnormal and typically indicates the presence of pathogens or mislocalized self-DNA. Multiple sensors detect cytosolic dsDNA and trigger robust immune responses via activation of type I interferons. Several cancer immunotherapy treatments also activate cytosolic nucleic acid sensing pathways, including oncolytic viruses, nucleic acid-based cancer vaccines, and pharmacological agonists. We report here that cytosolic dsDNA introduced into malignant cells can robustly upregulate expression of CCL22, a chemokine responsible for the recruitment of regulatory T cells (Tregs). Tregs in the tumor microenvironment are thought to repress anti-tumor immune responses and contribute to tumor immune evasion. Surprisingly, we found that CCL22 upregulation by dsDNA was mediated primarily by interferon regulatory factor 3 (IRF3), a key transcription factor that activates type I interferons. This finding was unexpected given previous reports that type I interferon alpha (IFN-α) inhibits CCL22 and that IRF3 is associated with strong anti-tumor immune responses, not Treg recruitment. We also found that CCL22 upregulation by dsDNA occurred concurrently with type I interferon beta (IFN-ß) upregulation. IRF3 is one of two transcription factors downstream of the STimulator of INterferon Genes (STING), a hub adaptor protein through which multiple dsDNA sensors transmit their signals. The other transcription factor downstream of STING, NF-κB, has been reported to regulate CCL22 expression in other contexts, and NF-κB has also been associated with multiple pro-tumor functions, including Treg recruitment. However, we found that NF-κB in the context of activation by cytosolic dsDNA contributed minimally to CCL22 upregulation compared with IRF3. Lastly, we observed that two strains of the same cell line differed profoundly in their capacity to upregulate CCL22 and IFN-ß in response to dsDNA, despite apparent STING activation in both cell lines. This finding suggests that during tumor evolution, cells can acquire, or lose, the ability to upregulate CCL22. This study adds to our understanding of factors that may modulate immune activation in response to cytosolic DNA and has implications for immunotherapy strategies that activate DNA sensing pathways in cancer cells.

Maltitol-Derived Sacrificial Agent for Enhancing the Compatibility between PCE and Cement Paste.

At present, it is known that when there is clay in concrete, polycarboxylates (PCE) will preferably adsorb in the clay, so that PCE cannot be fully combined with cement particles, which reduces the workability of the cement slurry. In this paper, a new type of maltitol-ammonium salt cationic (KN-lm) sacrificial agent (SA) has been successfully developed via a simple method, which makes PCE easier to bond with cement particles in the cement slurry containing clay. The effect of KN-lm on the fluidity of clay-containing cement paste is studied, and the experimental results show that KN-lm, as an efficient SA of cement slurry, makes PCE more compatible with clay-containing cement slurry, and increases the initial fluidity of cement slurry by about 19%. Further investigations of TOC, XRD, and zeta potential measurements reveal that a KN-lm ion is only preferably adsorbed into clay compared to PCE through electrostatic adsorption but without having any crystal structure change, thus resulting in good dispersion of cement particles. The addition of KN-lm plays an important role in hindering the hydration expansion of the clay by preferential electrostatic adsorption, which means PCE cannot easily insert into the interlayer of the clay.

Network pharmacology-based research on the effect of Radix Astragali on osteosarcoma and the underlying mechanism.

To explore the anti-tumor effects of Radix Astragali on osteosarcoma and its mechanism. We analyzed the PPI network of Radix Astragali's potential targets for treating osteosarcoma and got the hub targets. We used KM curves to screen hub targets that could prolong sarcoma patients' survival time. We performed GO and KEGG enrichment analysis of Radix Astragali's potential targets and predicted Radix Astragali's molecular mechanism and function in treating osteosarcoma. The binding process between the hub targets, which could prolong sarcoma patients' survival time, and Radix Astragali was simulated through molecular docking. PPI network analysis of potential therapeutic targets discriminated 25 hub targets. The KM curves of the hub targets showed there were 13 hub targets that were effective in improving the 5-year survival rate of sarcoma patients. GO and KEGG enrichment demonstrated that Radix Astragali regulates multiple signaling pathways of osteosarcoma. Molecular docking results indicated that Radix Astragali could bind freely to the hub target, which could prolong the sarcoma patient's survival time. Radix Astragali act on osteosarcoma by regulating a signaling network formed by hub targets connecting multiple signaling pathways. Radix Astragali has the potential to become a drug for treating osteosarcoma and prolonging the sarcoma patient's survival time.

Model-free tracking control of complex dynamical trajectories with machine learning.

Nonlinear tracking control enabling a dynamical system to track a desired trajectory is fundamental to robotics, serving a wide range of civil and defense applications. In control engineering, designing tracking control requires complete knowledge of the system model and equations. We develop a model-free, machine-learning framework to control a two-arm robotic manipulator using only partially observed states, where the controller is realized by reservoir computing. Stochastic input is exploited for training, which consists of the observed partial state vector as the first and its immediate future as the second component so that the neural machine regards the latter as the future state of the former. In the testing (deployment) phase, the immediate-future component is replaced by the desired observational vector from the reference trajectory. We demonstrate the effectiveness of the control framework using a variety of periodic and chaotic signals, and establish its robustness against measurement noise, disturbances, and uncertainties.

Deficient AMPK activity contributes to hyperexcitability in peripheral nociceptive sensory neurons and thermal hyperalgesia in lupus mice.

Patients with systemic lupus erythematosus (SLE) often suffer from chronic pain. Little is known about the peripheral mechanisms underlying the genesis of chronic pain induced by SLE. The aim of this study was to investigate whether and how membrane properties in nociceptive neurons in the dorsal root ganglions (DRGs) are altered by SLE. We found elevation of resting membrane potentials, smaller capacitances, lower action potential thresholds and rheobases in nociceptive neurons in the DRGs from MRL/lpr mice (an SLE mouse model) with thermal hyperalgesia. DRGs from MRL/lpr mice had increased protein expressions in TNFα, IL-1ß, and phosphorylated ERK but suppressed AMPK activity, and no changes in sodium channel 1.7 protein expression. We showed that intraplantar injection of Compound C (an AMPK inhibitor) induced thermal hyperalgesia in normal mice while intraplantar injection of AICAR (an AMPK activator) reduced thermal hyperalgesia in MRL/Lpr mice. Upon inhibition of AMPK membrane properties in nociceptive neurons from normal control mice could be rapidly switched to those found in SLE mice with thermal hyperalgesia. Our study indicates that increased excitability in peripheral nociceptive sensory neurons contributes to the genesis of thermal hyperalgesia in mice with SLE, and AMPK regulates membrane properties in nociceptive sensory neurons as well as thermal hyperalgesia in mice with SLE. Our study provides a basis for targeting signaling pathways regulating membrane properties of peripheral nociceptive neurons as a means for conquering chronic pain caused by SLE.

Effects of growth feedback on gene circuits: A dynamical understanding.

The successful integration of engineered gene circuits into host cells remains a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback, where the circuit influences cell growth and vice versa. Understanding the dynamics of circuit failures and identifying topologies resilient to growth feedback are crucial for both fundamental and applied research. Utilizing transcriptional regulation circuits with adaptation as a paradigm, we systematically study 435 distinct topological structures and uncover six categories of failures. Three dynamical mechanisms of circuit failures are identified: continuous deformation of the response curve, strengthened or induced oscillations, and sudden switching to coexisting attractors. Our extensive computations also uncover a scaling law between a circuit robustness measure and the strength of growth feedback. Despite the negative effects of growth feedback on the majority of circuit topologies, we identify a few circuits that maintain optimal performance as designed, a feature important for applications.

Short-lived chimera states.

In the classic Kuramoto system of coupled two-dimensional rotators, chimera states characterized by the coexistence of synchronous and asynchronous groups of oscillators are long-lived because the average lifetime of these states increases exponentially with the system size. Recently, it was discovered that, when the rotators in the Kuramoto model are three-dimensional, the chimera states become short-lived in the sense that their lifetime scales with only the logarithm of the dimension-augmenting perturbation. We introduce transverse-stability analysis to understand the short-lived chimera states. In particular, on the unit sphere representing three-dimensional (3D) rotations, the long-lived chimera states in the classic Kuramoto system occur on the equator, to which latitudinal perturbations that make the rotations 3D are transverse. We demonstrate that the largest transverse Lyapunov exponent calculated with respect to these long-lived chimera states is typically positive, making them short-lived. The transverse-stability analysis turns the previous numerical scaling law of the transient lifetime into an exact formula: the "free" proportional constant in the original scaling law can now be precisely determined in terms of the largest transverse Lyapunov exponent. Our analysis reinforces the speculation that in physical systems, chimera states can be short-lived as they are vulnerable to any perturbations that have a component transverse to the invariant subspace in which they live.

Outstanding Humidity Chemiresistors Based on Imine-Linked Covalent Organic Framework Films for Human Respiration Monitoring.

Human metabolite moisture detection is important in health monitoring and non-invasive diagnosis. However, ultra-sensitive quantitative extraction of respiration information in real-time remains a great challenge. Herein, chemiresistors based on imine-linked covalent organic framework (COF) films with dual-active sites are fabricated to address this issue, which demonstrates an amplified humidity-sensing signal performance. By regulation of monomers and functional groups, these COF films can be pre-engineered to achieve high response, wide detection range, fast response, and recovery time. Under the condition of relative humidity ranging from 13 to 98%, the COFTAPB-DHTA film-based humidity sensor exhibits outstanding humidity sensing performance with an expanded response value of 390 times. Furthermore, the response values of the COF film-based sensor are highly linear to the relative humidity in the range below 60%, reflecting a quantitative sensing mechanism at the molecular level. Based on the dual-site adsorption of the (-C=N-) and (C-N) stretching vibrations, the reversible tautomerism induced by hydrogen bonding with water molecules is demonstrated to be the main intrinsic mechanism for this effective humidity detection. In addition, the synthesized COF films can be further exploited to effectively detect human nasal and oral breathing as well as fabric permeability, which will inspire novel designs for effective humidity-detection devices.

CD44 expression is correlated with osteosarcoma cell progression and immune infiltration and affects the Wnt/ß-catenin signaling pathway.

CD44 is associated with a variety of human diseases and plays a potential role in tumorigenesis, however, the mechanism of its role in osteosarcoma remains unclear. We analyzed the expression of CD44 in the Cancer Genome Atlas (TCGA) and genotype-tissue expression pan-cancer data and found that it was highly expressed in most tumors, including sarcoma. The expression of CD44 in osteosarcoma cell lines was higher than that in human osteoblast cell line in the results of the Western blot and Immunohistochemical staining assay. The results of colony formation assay and CCK 8 showed that CD44 improved the proliferation capacity of osteosarcoma cells, transwell assay and wound healing assay showed that CD44 improved the migration capacity of osteosarcoma cells. Further studies revealed that CD44 exerts its influence on the biological behavior of osteosarcoma cells through the Wnt/ß-catenin signaling pathway. Since CD44 may be involved in the immune response, we analyzed the correlation between CD44 expression and immune cell infiltration in TCGA database using the previous cluster analyzer R software package, TIMER2.0 database and, GEPIA2 database, and found its involvement in the immune infiltration of osteosarcoma. Therefore, we believe that CD44 could be a potential target for the treatment of osteosarcoma patients and may be a candidate biomarker for immune infiltration-related prognosis.

Network pharmacology-based research on the effect of angelicin on osteosarcoma and the underlying mechanism.

To explore the antitumor effects of angelicin on osteosarcoma and the underlying mechanism. We aimed to elucidate the mechanism by network pharmacology, molecular docking, and in vitro experiments. We analyzed a PPI network of potential angelicin targets in the treatment of osteosarcoma and identified hub targets. We systematically performed GO and KEGG enrichment analyses of the potential targets of angelicin, and we predicted it function in osteosarcoma treatment and the underlying molecular mechanism. Through molecular docking, the interactions between hub targets and angelicin were simulated, and then, the hub targets of angelicin were identified. Based on these results, we validated the effects of angelicin on osteosarcoma cells by conducting in vitro experiments. The PPI network analysis of potential therapeutic targets identified four apoptosis-related hub targets, namely, BCL-2, Casp9, BAX and BIRC 2. GO and KEGG enrichment analyses demonstrated that angelicin regulates osteosarcoma cell apoptosis. Molecular docking results indicated that angelicin can freely bind to the hub targets listed above. In vitro experiments showed that angelicin promoted osteosarcoma cell apoptosis in a dose-dependent manner and inhibited osteosarcoma cell migration and proliferation in a time- and dose-dependent manner. The RT-PCR results showed that angelicin simultaneously promoted the mRNA expression of Bcl-2 and Casp9 and inhibited the mRNA expression of BAX and BIRC 2. Angelicin promotes osteosarcoma cell apoptosis and inhibits osteosarcoma cell proliferation and migration by activating a signaling network that is composed of hub targets that link multiple signaling pathways. Angelicin could become an alternative drug for the treatment of osteosarcoma.

Forest Ecosystem Service Trade-Offs/Synergies and System Function Optimization in Karst Desertification Control.

Karst desertification control forests are essential for ecosystem multi functionality, but the trade-offs/synergies are unclear for forest ecosystem services. In order to clarify the trade-offs/synergies, this study was conducted on eight forest communities in a karst desertification control area and was based on vegetation surveys and structural and functional monitoring. It analyzes water holding capacity, species diversity, soil conservation, and carbon storage characteristics and their trade-off/synergies. The results indicate the following: (1) The Cladrastis platycarpa + Cotinus coggygria community (H1) had the highest water holding capacity and species diversity with values of 252.21 t·hm-2 and 2.56, respectively. Soil conservation was highest in the Zanthoxylum bungeanum + Glycine max community (H6), with an index value of 1.56. Carbon storage was the greatest in the Tectona grandis community (H8), at 103.93 t·hm-2. The results of these studies have shown that there are significant differences in different types of forest community ecosystem services. (2) Water holding capacity, species diversity, soil conservation, and carbon storage, all have synergistic relationships, suggesting a trend towards synergistic enhancement between the services. (3) The species diversity of the forest ecosystems was shown to be in a trade-off with carbon storage and soil conservation, which suggests that the services are in competition with each other. To further improve the service capacity of forest ecosystems, the trade-offs between the regulation of forest community structure and function and the improvement of services should be optimized.

Thin boundary layer model underestimates greenhouse gas diffusion from inland waterways.

Inland waters are significant sources of atmospheric greenhouse gas (GHG) emissions. The thin boundary layer (TBL) model is often employed as a means of estimating GHG diffusion in inland waters based on gas transfer velocity (k) at the air-water interface, with k being subject to regulation by near-surface turbulence that is primarily driven by wind speed in many cases. This wind speed-based estimation of k (wind-k), however, can introduce substantial uncertainty for turbulent waterways where wind speed does not accurately represent overall turbulence. In this study, GHG diffusion in the Beijing-Hangzhou Grand Canal (China), the first and longest man-made canal in the world, was estimated using the TBL model, revealing that this model substantially underestimated GHG diffusion when relying on wind-k. Strikingly, the carbon dioxide, methane, and nitrous oxide diffusions were respectively underestimated by 159%, 162%, and 124% when using this model. These findings are significant for developing more reliable approaches to evaluate GHG emissions from inland waterways.

Total removal of a large esophageal schwannoma by submucosal tunneling endoscopic resection: A case report and review of literature.

BACKGROUND: Primary schwannoma is a rare submucosal tumor of the esophagus, which is most often benign, and surgery is the only effective treatment. So far, only a few cases have been reported. Herein, we reported a single case diagnosed with primary esophageal schwannoma that was totally removed by submucosal tunneling endoscopic resection (STER). CASE SUMMARY: A 62-year-old man presented to the hospital with a history of resection of a malignant gastric tumor and mild dysphagia. Endoscopic examination revealed a large submucosal elevated lesion in the esophagus 25-30 cm from the incisors. Endoscopic ultrasonography detected a 45 mm × 35 mm × 31 mm hypoechoic lesion; chest computed tomography showed a mass of approximately 55 mm × 35 mm × 29 mm. A preliminary examination showed features suggestive of a stromal tumor. Pathological findings indicated esophageal schwannoma. Next, STER alone was performed to completely resect the mass, and the patient recovered well post-surgery. Afterward, the patient was discharged and showed no tumor recurrence at 33 mo of follow-up. CONCLUSION: Endoscopic resection is still an effective treatment for large esophageal schwannomas (> 30 mm) under meticulous morphological evaluation.

Clinical observation of Porcine Collagen Membrane + artificial Bovine Bone Granules Guided tissue regeneration combined with Autologous CGF in the treatment of severe periodontitis bone defect.

Objective: To investigate and analyze the clinical observation of porcine collagen membrane + artificial bovine bone granules guided tissue regeneration (GTR) combined with autologous concentration of growth factors (CGF) in the treatment of severe periodontitis bone defect. Methods: A total of 94 patients with severe periodontitis bone defects admitted to Shanxi Bethune Hospital from January 2019 to January 2022 were included. They were divided into two groups by simple randomization method. Patients in the control group were treated with porcine collagen membrane + artificial bovine bone granules GTR, while those in the observation group were treated with autologous CGF on the basis of the control group. Before and after treatment, periodontal clinical indicators [sulcus bleeding index (SBI), gingival retreat index (GR), probing depth (PD), clinical attachment loss (CAL), alveolar bone height (AH)] and bone resorption markers [Osteoprotegerin (OPG), bone gla protein (BGP), Type-1 collagen N-terminal peptide (NTX)] were compared between the two groups, and the incidence of postoperative complications in the two groups was recorded. Results: The total efficacy of observation group was significantly higher than that of control group (p<0.05). Three months after surgery, the observation group had lower levels of SBI, PD, CAL and NTX while higher levels of GR, AH, OPG and BGP than the control group (p<0.05). There was no significant difference in complication rate between the two groups (p>0.05). Conclusion: Porcine collagen membrane + artificial bovine bone granules GTR combined with autologous CGF boasts various benefits in the treatment of severe periodontitis bone defects, such as improvement of clinical outcomes, amelioration of periodontal tissue and inhibition of bone resorption.

Absolute and Relative Depth-Induced Network for RGB-D Salient Object Detection.

Detecting salient objects in complicated scenarios is a challenging problem. Except for semantic features from the RGB image, spatial information from the depth image also provides sufficient cues about the object. Therefore, it is crucial to rationally integrate RGB and depth features for the RGB-D salient object detection task. Most existing RGB-D saliency detectors modulate RGB semantic features with absolution depth values. However, they ignore the appearance contrast and structure knowledge indicated by relative depth values between pixels. In this work, we propose a depth-induced network (DIN) for RGB-D salient object detection, to take full advantage of both absolute and relative depth information, and further, enforce the in-depth fusion of the RGB-D cross-modalities. Specifically, an absolute depth-induced module (ADIM) is proposed, to hierarchically integrate absolute depth values and RGB features, to allow the interaction between the appearance and structural information in the encoding stage. A relative depth-induced module (RDIM) is designed, to capture detailed saliency cues, by exploring contrastive and structural information from relative depth values in the decoding stage. By combining the ADIM and RDIM, we can accurately locate salient objects with clear boundaries, even from complex scenes. The proposed DIN is a lightweight network, and the model size is much smaller than that of state-of-the-art algorithms. Extensive experiments on six challenging benchmarks, show that our method outperforms most existing RGB-D salient object detection models.