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.

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.

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.

Reservoir computing as digital twins for nonlinear dynamical systems.

We articulate the design imperatives for machine learning based digital twins for nonlinear dynamical systems, which can be used to monitor the "health" of the system and anticipate future collapse. The fundamental requirement for digital twins of nonlinear dynamical systems is dynamical evolution: the digital twin must be able to evolve its dynamical state at the present time to the next time step without further state input-a requirement that reservoir computing naturally meets. We conduct extensive tests using prototypical systems from optics, ecology, and climate, where the respective specific examples are a chaotic CO2 laser system, a model of phytoplankton subject to seasonality, and the Lorenz-96 climate network. We demonstrate that, with a single or parallel reservoir computer, the digital twins are capable of a variety of challenging forecasting and monitoring tasks. Our digital twin has the following capabilities: (1) extrapolating the dynamics of the target system to predict how it may respond to a changing dynamical environment, e.g., a driving signal that it has never experienced before, (2) making continual forecasting and monitoring with sparse real-time updates under non-stationary external driving, (3) inferring hidden variables in the target system and accurately reproducing/predicting their dynamical evolution, (4) adapting to external driving of different waveform, and (5) extrapolating the global bifurcation behaviors to network systems of different sizes. These features make our digital twins appealing in applications, such as monitoring the health of critical systems and forecasting their potential collapse induced by environmental changes or perturbations. Such systems can be an infrastructure, an ecosystem, or a regional climate system.

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.

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.

Activation of microglial GPR109A alleviates thermal hyperalgesia in female lupus mice by suppressing IL-18 and glutamatergic synaptic activity.

Many patients with systemic lupus erythematosus (SLE) live with chronic pain despite advances in medical management in reducing mortality related to SLE. Few animal studies have addressed mechanisms and treatment for chronic pain caused by SLE. In this study, we provide the first evidence for the analgesic effects of a GPR109A specific agonist (MK1903) and its action mechanisms in thermal hyperalgesia in female MRL/lpr mice, an SLE mouse model. Specifically, we show that MRL/lpr mice had a higher sensitivity to thermal stimuli at age 11-16 weeks, which was accompanied with significantly microglial and astrocytic activation, increases in p38 MAPK and glutamatergic synaptic activities in the spinal dorsal horn. We demonstrate that thermal hyperalgesia in MRL/lpr mice was significantly attenuated by intrathecal injection of MK1903. GPR109A was expressed in spinal microglia but not astrocytes or neurons. Its expression was significantly increased in MRL/lpr mice with thermal hyperalgesia. Activation of GPR109A receptors in microglia attenuated glutamatergic synaptic activity via suppressing production of interleukin-18 (IL-18). We provide evidence that activation of GPR109A attenuated thermal hyperalgesia in the SLE animal model via suppressing p38 MAPK activity and production of IL-18. Our study suggests that targeting the microglial GPR109A is a potent approach for reversing spinal neuroinflammation, abnormal excitatory synaptic activity, and management of thermal hyperalgesia caused by SLE.

Phytohormone abscisic acid ameliorates neuropathic pain via regulating LANCL2 protein abundance and glial activation at the spinal cord.

Spinal neuroinflammation plays a critical role in the genesis of neuropathic pain. Accumulating data suggest that abscisic acid (ABA), a phytohormone, regulates inflammatory processes in mammals. In this study, we found that reduction of the LANCL2 receptor protein but not the agonist ABA in the spinal cord is associated with the genesis of neuropathic pain. Systemic or intrathecal administration of ABA ameliorates the development and pre-existence of mechanical allodynia and heat hyperalgesia in animals with partial sciatic nerve ligation (pSNL). LANCL2 is expressed only in microglia in the spinal dorsal horn. Pre-emptive treatment with ABA attenuates activation of microglia and astrocytes, ERK activity, and TNFα protein abundance in the dorsal horn in rats with pSNL. These are accompanied by restoration of spinal LANCL2 protein abundance. Spinal knockdown of LANCL2 gene with siRNA recapitulates the behavioral and spinal molecular changes induced by pSNL. Activation of spinal toll-like receptor 4 (TLR4) with lipopolysaccharide leads to activation of microglia, and over production of TNFα, which are concurrently accompanied by suppression of protein levels of LANCL2 and peroxisome proliferator activated-receptor γ. These changes are ameliorated when ABA is added with LPS. The anti-inflammatory effects induced by ABA do not requires Gi protein activity. Our study reveals that the ABA/LANCL2 system is a powerful endogenous system regulating spinal neuroinflammation and nociceptive processing, suggesting the potential utility of ABA as the management of neuropathic pain.

Gas-solid interfacial charge transfer in volatile organic compound detection by CuCrO2nanoparticles.

Nanostructured metal oxide semiconductors have received great attention used as the chemiresistive layer of gas sensor to detect the volatile organic compound recently. As indispensable complementary parts for dominative n-type semiconductors, the p-type metal oxides based gas sensors fail to be studied sufficiently, which hampers their practical applications. In this work, the p-type delafossite CuCrO2nanoparticles were synthesized, characterized, and tested for gas sensing, followed by the first principles calculations to simulate the generation of chemiresistive signal. The hydrothermal synthesis time of CuCrO2nanoparticles is optimized as 24 h with a higher proportion of oxygen vacancies but a smaller size, which is confirmed by the microscopy and spectrum characterization and allows for a prevailing gas sensitivity. Meanwhile, this CuCrO2gas sensor is proven to perform a higher selectivity to n-propanol and a low detection limit of 1 ppm. The adsorption sites and charge variations of dehydrogenation at the gas-solid interface predicted by the theoretical analysis are claimed to be crucial to such selectivity. It is an innovative approach to understand the chemiresistive gas sensing by evaluating the preference of charge transfer between the sensor and target gaseous molecule, which provides a new route to precisely design and develop the advanced sensing devices for the diverse applications.

Effects of bentonite on the growth process of submerged macrophytes and sediment microenvironment.

The effects of clay mineral bentonite on the growth process of submerged macrophyte V. spiralis and sediment microenvironment were investigated in the study for the first time, aiming to determine whether it is suitable for application in the field of ecological restoration. The growth index, and physiological and biochemical index of V. spiralis in the experiments were measured once a month, and the changes of rhizosphere microorganisms and physicochemical properties of sediments were also studied at the same time. The results demonstrated that bentonite can effectively promote the growth of V. spiralis. The treatment groups of RB1/1 and MB1/5 (the mass ratios of bentonite to sediment were 1/1 and 1/5, respectively.) showed the best V. spiralis growth promotion rates which were 18.78%, and 11.79%, respectively. The highest microbial diversity and abundance existed in group of RB10 (the mass ratio of sediment to bentonite was 10/1), in which the OTUs, Shannon, Chao and Ace were 1521.0, 5.20, 1712.26, and 1686.31, respectively. Bentonite was conducive to the propagation of rhizosphere microorganisms, and further changed the physical and chemical properties of the sediment microenvironment. The nutrient elements dissolved from bentonite may be one of the main reasons that promoted the growth of V. spiralis. The purpose of this result is to prove that bentonite can be further applied as sediment improver and growing media in ecological restoration projects in eutrophic shallow lakes.

Algal Accumulation Decreases Sediment Nitrogen Removal by Uncoupling Nitrification-Denitrification in Shallow Eutrophic Lakes.

In eutrophic lakes, the decay of settled algal biomass generates organic carbon and consumes oxygen, favoring sediment nitrogen loss via denitrification. However, persistent winds can cause algae to accumulate into dense mats, with uncertain impacts on sediment nitrogen removal. In this study, we investigated the effects of algal accumulation on sediment nitrogen removal in a shallow and eutrophic Chinese lake, Taihu. We found that experimental treatments of increased algal accumulation were associated with decreased sediment nitrogen losses, indicating the potential for a break in coupled nitrification-denitrification. Likewise, field measurements indicated similar decreases in sediment nitrogen losses when algal accumulation occurred. It is possibly caused by the decay of excess algal biomass, which likely depleted dissolved oxygen, and could have inhibited nitrification and thereby denitrification in sediments. We estimate that if such algal accumulations occurred over 20% or 10% of lake area in Taihu, sediment nitrogen removal rates decreased from 835.6 to 167.2 and 77.2 µmol N m-2h-1, respectively, during algal accumulation period. While nitrogen removal may recover later, the apparent nitrogen removal decrease may create a window for algal proliferation and intensification. This study advances our knowledge on the impacts of algal blooms on nitrogen removal in shallow eutrophic lakes.

Dimalononitrile-containing probe based on aggregation-enhanced emission features for the multi-mode fluorescence detection of volatile amines.

A novel multi-mode probe consisting of a hexaphenyl-1,3-butadiene derivative, 2,2'-((((1Z,3Z)-1,2,3,4-tetraphenylbuta-1,3-diene-1,4-diyl)bis(4,1-phenylene))bis(methanylylidene))dimalononitrile (ZZ-HPB-CN), with typical aggregation-enhanced emission (AEE) features was easily prepared for the highly sensitive and rapid detection of amine vapors. The ZZ-HPB-CN sensor, which was prepared by simply depositing ZZ-HPB-CN on filter paper, could detect low concentration vapors of volatile amines using fluorescence, ultraviolet and naked-eye detection. The limit of detection of the sensor was as low as 1 ppb for the fluorescence detection. The color change of the sensor caused by 1-10 ppm amine vapors could be observed under UV light or with the naked eye. The high sensitivity, quick response and easy operation of the probe give it great potential for real-life applications.

Interleukin-24 Concentrations Not Related with Age, but Affect Pro- and Anti-Inflammatory Cytokines Differently in Healthy Donors.

BACKGROUND: Interleukin 24 (IL-24) is expressed at different levels in a variety of tumor tissues and matched normal tissues and is regarded as a potential tumor biomarker as its expression levels in tumor tissues are associated with tumor patient prognosis. At present, the expression level of IL-24 in healthy human peripheral blood is unknown. METHODS: In this study, 1940 blood samples were collected using different processing methods from healthy donors. ELISA was used to detect IL-24 concentrations. RESULTS: The results showed that processing methods had the greatest influence on test results, with the highest IL24 concentration in EDTA plasma and the lowest in sodium citrate plasma. Lengths of storage time at 4°C had no obvious effect on IL-24 test results, and IL-24 in peripheral blood was stable for 15 days. IL-24 concentration in the sera of healthy donors showed no associations with age, blood glucose, hemoglobin, total cholesterol, carcinoembryonic antigen, absolute lymphocyte counts, alpha fetoprotein, white blood cells, thyroid stimulating hormone, or cereal third transaminase. We also confirmed that IL-24 expression level in the blood of healthy subjects was positively correlated with pro-inflammatory cytokines, tumor necrosis factor-alpha (TNF-α), and interleukin 6 (IL-6), but negatively correlated with anti-inflammatory cytokine, IL-10. CONCLUSIONS: We observed that sample processing methods influence the detection of IL-24 levels as EDTA plasma had the highest IL-24 concentration, and citric acid sodium, the lowest. Age, gender, and physical and chemical indicators were not related to IL-24 concentrations. We confirmed the IL-24 concentration was positively related to IL-6 and TNF-α and negatively to IL-10.

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.

Network pharmacology, molecular docking, and molecular dynamics simulations shed light on the mechanism behind Gynostemma pentaphyllum's efficacy against osteosarcoma.

Osteosarcoma (OS) is the most common type of malignant bone tumor, that poses a serious threat to the lives and health of children and adolescents. Traditional Chinese medicines (TCM) have gained attention for treating OS because of their potent anti-cancer effects and fewer side effects. It is commonly understood that Gynostemma pentaphyllum (Thunb.) Makino (GP) exhibits inhibitory effects on most tumors. However, the knowledge of the systematic mechanisms involved is limited. In this study, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was searched to screen the effective ingredients and corresponding target genes of GP, and disease target databases were searched to identify relevant targets for OS. Venn analysis was used to visualize overlapping genes, which were further extracted using the protein-protein interaction network. R software was used to conduct gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analysis, molecular docking and molecular dynamics simulation further validate the binding efficacy of potential therapeutic targets to compound molecules. In total, 161 and 1981 proteins were identified as target genes of GP and OS, respectively, and 104 overlapping genes were identified. Through analysis of the core subnetwork, 12 hub genes were identified, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed that the PI3K/Akt signaling pathway was the most significant. Molecular docking and molecular dynamics simulations show that a high affinity between quercetin and these targets, especially with the combination of TNF free energy (Δ Gbind) minimum, MM/PBSA and MM/GBSA is 42.85 kcal/mol, respectively, and 45.29 kcal/mol. The active ingredients Rhamnazin and Quercetin in Gypenoylum play a therapeutic role in OS through several key targets and pathways. This study provides ideas and references for further research on drug development.

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.

The Emerging Role of Herbal Medicines in Cancer by Interfering with Posttranslational Modifications.

Significance: Herbal medicines have a long history of comprehensive cancer treatment through various posttranslational modifications (PTMs). Recently, emerging evidence revealed that dysregulation of reactive oxygen species (ROS) and ROS-regulated signaling pathways influence cancer initiation, growth, and progression in a paradoxical role with either low levels or increasing levels of basal ROS. However, ROS-triggered modifications of target proteins in the face of ROS-mediated signal transduction are not fully understood in the anticancer therapies of herbal medicines. In this review, we briefly introduce the PTM-dependent regulations of herbal medicines, and then focus on the current ideals that targeting ROS-dependent PTMs via antioxidant and redox signaling pathways can provide a promising strategy in herbal-based anticancer effects. Recent Advances: Advanced development in highly sensitive mass spectrometry-based techniques has helped utilize ROS-triggered protein modifications in numerous cancers. Accumulating evidence has been achieved in laboratory to extensively ascertain the biological mechanism of herbal medicines targeting ROS in cancer therapy. Two general mechanisms underlining ROS-induced cell signaling include redox state and oxidative modification of target protein, indicating a new perspective to comprehend the intricate dialogues between herbal medicines and cancer cellular contexts. Critical Issues: Complex components of herbal medicines limit the benefits of herbal-based cancer therapies. In this review, we address that ROS-dependent PTMs add a layer of proteomic complexity to the cancer through altering the protein structure, expression, function, and localization. Elaborating ROS-triggered PTMs implicated in cell signaling, apoptosis, and transcriptional regulation function, and the possible cellular signaling, has provided important information about the contribution of many ROS targeting herbal therapies in anticancer effects. Continued optimization of proteomic strategies for PTM analysis in herbal medicines is also briefly discussed. Future Directions: Rigorous evaluations of herbal medicines and proteomic strategies are necessary to explore the aberrant regulation of ROS-triggered antioxidant and redox signaling contributing to the novel protein targets and herbal-associated pharmacological issues. These efforts will eventually help develop more herbal drugs as modern therapeutic agents.

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.

Oriented regulation of earthworm production and vermicompost quality by carbon bioavailability management.

Vermicomposting is an efficient bioconversion technology for recycling nutrients from organic waste materials. The biodegradability of raw materials has a significant impact on the earthworm transformation product. However, the management of carbon bioavailability is often overlooked during the vermicomposting process due to the varying degradability of C-rich source in different organic waste. This research aims to investigate the impact of different bioavailable carbon compositions on vermicomposting and to develop a strategy for efficient carbon management. The study involved systematic vermicomposting using four different biodegradable carbon sources (pineapple peels, rice straw, tomato straw, and sawdust) with varying carbon­nitrogen ratios (ranging from 24 to 42). The earthworm production and vermicompost quality were comprehensively evaluated, along with the influence of carbon components on microbial community structure. The results indicated that the optimal vermicomposting treatments were achieved at PCM24, RCM30, TCM30, and MCM30 treatments. Maintaining an approximate ratio of 1:(0.5-1.3) between available and recalcitrant carbon components based on the optimal carbon­nitrogen ratio was found to be optimal for regulating vermicomposting products. Increasing the proportion of available carbon enhanced the quality of vermicompost fertilizer, while a higher proportion of recalcitrant carbon could improve earthworm biomass production efficiency. Labile carbon proportion I (LCP1) and available carbon component (ACC) were identified as key indicators in influencing the formation of microbial community structure. Different carbon compositions led to the specific development and formation of microbial communities, further resulting in significant variations in vermicompost quality under the mediation of microbes. This study, for the first time, clarifies the impact of vermicomposting performance and microbial community from the perspective of carbon bioavailability, which is of great significance for the oriented regulation the vermicomposting efficiency and product in practice.