Group sparse-based Taylor expansion method for liver pharmacokinetic parameters imaging of dynamic fluorescence molecular tomography.

OBJECTIVE: Pharmacokinetic parametric images obtained through dynamic fluorescence molecular tomography (DFMT) has ability of capturing dynamic changes in fluorescence concentration, thereby providing three-dimensional metabolic information for applications in biological research and drug development. However, data processing of DFMT is time-consuming, involves a vast amount of data, and the problem itself is ill-posed, which significantly limits the application of pharmacokinetic parametric images reconstruction. In this study, group sparse-based Taylor expansion (GSTE) method is proposed to address these problems. APPROACH: Firstly, Taylor expansion framework is introduced to reduce time and computational cost. Secondly, group sparsity based on structural prior is introduced to improve reconstruction accuracy. Thirdly, alternating iterative solution based on accelerated gradient descent (AGD) algorithm is introduced to solve the problem. MAIN RESULTS: Numerical simulation and in vivo experimental results demonstrate that, in comparison to existing methods, the proposed approach significantly enhances reconstruction speed without a degradation of quality, particularly when confronted with background fluorescence interference from other organs. SIGNIFICANCE: Our research greatly reduces time and computational cost, providing strong support for real-time monitoring of liver metabolism.

Soil microbiome of shiro reveals the symbiotic relationship between Tricholoma bakamatsutake and Quercus mongolica.

Tricholoma bakamatsutake is a delicious and nutritious ectomycorrhizal fungus. However, its cultivation is hindered owing to limited studies on its symbiotic relationships. The symbiotic relationship between T. bakamatsutake and its host is closely related to the shiro, a complex network composed of mycelium, mycorrhizal roots, and surrounding soil. To explore the symbiotic relationship between T. bakamatsutake and its host, soil samples were collected from T. bakamatsutake shiro (Tb) and corresponding Q. mongolica rhizosphere (CK) in four cities in Liaoning Province, China. The physicochemical properties of all the soil samples were then analyzed, along with the composition and function of the fungal and bacterial communities. The results revealed a significant increase in total potassium, available nitrogen, and sand in Tb soil compared to those in CK soil, while there was a significant decrease in pH, total nitrogen, total phosphorus, available phosphorus, and silt. The fungal community diversity in shiro was diminished, and T. bakamatsutake altered the community structure of its shiro by suppressing other fungi, such as Russula (ectomycorrhizal fungus) and Penicillium (phytopathogenic fungus). The bacterial community diversity in shiro increased, with the aggregation of mycorrhizal-helper bacteria, such as Paenibacillus and Bacillus, and plant growth-promoting bacteria, such as Solirubrobacter and Streptomyces, facilitated by T. bakamatsutake. Microbial functional predictions revealed a significant increase in pathways associated with sugar and fat catabolism within the fungal and bacterial communities of shiro. The relative genetic abundance of carboxylesterase and gibberellin 2-beta-dioxygenase in the fungal community was significantly increased, which suggested a potential symbiotic relationship between T. bakamatsutake and Q. mongolica. These findings elucidate the microbial community and relevant symbiotic environment to better understand the relationship between T. bakamatsutake and Q. mongolica.

Protecting the non-operative lobe/s of the operative lung can reduce the pneumonia incidence after thoracoscopic lobectomy: a randomised controlled trial.

Lung isolation usually refers to the isolation of the operative from the non-operative lung without isolating the non-operative lobe(s) of the operative lung. We aimed to evaluate whether protecting the non-operative lobe of the operative lung using a double-bronchial blocker (DBB) with continuous positive airway pressure (CPAP) could reduce the incidence of postoperative pneumonia. Eighty patients were randomly divided into two groups (n = 40 each): the DBB with CPAP (Group DBB) and routine bronchial blocker (Group BB) groups. In Group DBB, a 7-Fr BB was placed in the middle bronchus of the right lung for right lung surgery and in the inferior lobar bronchus of the left lung for left lung surgery. Further, a 9-Fr BB was placed in the main bronchus of the operative lung. In Group BB, routine BB placement was performed on the main bronchus on the surgical side. The primary endpoint was the postoperative pneumonia incidence. Compared with Group BB, Group DBB had a significantly lower postoperative pneumonia incidence in the operative (27.5% vs 5%, P = 0.013) and non-operative lung (40% vs 15%) on postoperative day 1. Compared with routine BB use for thoracoscopic lobectomy, using the DBB technique to isolate the operative lobe from the non-operative lobe(s) of the operative lung and providing CPAP to the non-operative lobe(s) through a BB can reduce the incidence of postoperative pneumonia in the operative and non-operative lungs.

Enhanced model iteration algorithm with graph neural network for diffuse optical tomography.

Diffuse optical tomography (DOT) employs near-infrared light to reveal the optical parameters of biological tissues. Due to the strong scattering of photons in tissues and the limited surface measurements, DOT reconstruction is severely ill-posed. The Levenberg-Marquardt (LM) is a popular iteration method for DOT, however, it is computationally expensive and its reconstruction accuracy needs improvement. In this study, we propose a neural model based iteration algorithm which combines the graph neural network with Levenberg-Marquardt (GNNLM), which utilizes a graph data structure to represent the finite element mesh. In order to verify the performance of the graph neural network, two GNN variants, namely graph convolutional neural network (GCN) and graph attention neural network (GAT) were employed in the experiments. The results showed that GCNLM performs best in the simulation experiments within the training data distribution. However, GATLM exhibits superior performance in the simulation experiments outside the training data distribution and real experiments with breast-like phantoms. It demonstrated that the GATLM trained with simulation data can generalize well to situations outside the training data distribution without transfer training. This offers the possibility to provide more accurate absorption coefficient distributions in clinical practice.

Histone H3K9 Lactylation Confers Temozolomide Resistance in Glioblastoma via LUC7L2-Mediated MLH1 Intron Retention.

Temozolomide (TMZ) resistance remains the major obstacle in the treatment of glioblastoma (GBM). Lactylation is a novel post-translational modification that is involved in various tumors. However, whether lactylation plays a role in GBM TMZ resistance remains unclear. Here it is found that histone H3K9 lactylation (H3K9la) confers TMZ resistance in GBM via LUC7L2-mediated intron 7 retention of MLH1. Mechanistically, lactylation is upregulated in recurrent GBM tissues and TMZ-resistant cells, and is mainly concentrated in histone H3K9. Combined multi-omics analysis, including CUT&Tag, SLAM-seq, and RNA-seq, reveals that H3K9 lactylation is significantly enriched in the LUC7L2 promoter and activates LUC7L2 transcription to promote its expression. LUC7L2 mediates intron 7 retention of MLH1 to reduce MLH1 expression, and thereby inhibit mismatch repair (MMR), ultimately leading to GBM TMZ resistance. Of note, it is identified that a clinical anti-epileptic drug, stiripentol, which can cross the blood-brain barrier and inhibit lactate dehydrogenase A/B (LDHA/B) activity, acts as a lactylation inhibitor and renders GBM cells more sensitive to TMZ in vitro and in vivo. These findings not only shed light on the mechanism of lactylation in GBM TMZ resistance but also provide a potential combined therapeutic strategy for clinical GBM treatment.

FSMN-Net: a free space matching network based on manifold convolution for optical molecular tomography.

Optical molecular tomography (OMT) can monitor glioblastomas in small animals non-invasively. Although deep learning (DL) methods have made remarkable achievements in this field, improving its generalization against diverse reconstruction systems remains a formidable challenge. In this Letter, a free space matching network (FSMN-Net) was presented to overcome the parameter mismatch problem in different reconstruction systems. Specifically, a novel, to the best of our knowledge, manifold convolution operator was designed by considering the mathematical model of OMT as a space matching process. Based on the dynamic domain expansion concept, an end-to-end fully convolutional codec further integrates this operator to realize robust reconstruction with voxel-level accuracy. The results of numerical simulations and in vivo experiments demonstrate that the FSMN-Net can stably generate high-resolution reconstruction volumetric images under different reconstruction systems.

Distinct latitudinal patterns and drivers of topsoil nitrogen and phosphorus across urban forests in eastern China.

Nitrogen (N) and phosphorus (P) are the two most important macronutrients supporting forest growth. Unprecedented urbanization has created growing areas of urban forests that provide key ecosystem services for city dwellers. However, the large-scale patterns of soil N and P content remain poorly understood in urban forests. Based on a systematic soil survey in urban forests from nine large cities across eastern China, we examined the spatial patterns and key drivers of topsoil (0-20 cm) total N content, total P content, and N:P ratio. Topsoil total N content was found to change significantly with latitude in the form of an inverted parabolic curve, while total P content showed an opposite latitudinal pattern. Variance partition analysis indicated that regional-scale patterns of topsoil total N and P contents were dominated by climatic drivers and partially regulated by time and pedogenic drivers. Conditional regression analyses showed a significant increase in topsoil total N content with lower mean annual temperature (MAT) and higher mean annual precipitation (MAP), while topsoil total P content decreased significantly with higher MAP. Topsoil total N content also increased significantly with the age of urban park and varied with pre-urban soil type, while no such effects were found for topsoil total P content. Moreover, topsoil N:P ratio showed a latitudinal pattern similar to that of topsoil total N content and also increased significantly with lower MAT and higher MAP. Our findings demonstrate distinct latitudinal trends of topsoil N and P contents and highlight a dominant role of climatic drivers in shaping the large-scale patterns of topsoil nutrients in urban forests.

Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor.

Dynamic fluorescence molecular tomography (DFMT), as a noninvasive optical imaging method, can quantify metabolic parameters of living animal organs and assist in the diagnosis of metabolic diseases. However, existing DFMT methods do not have a high capacity to reconstruct abnormal metabolic regions, and require additional prior information and complicated solution methods. This paper introduces a problem decomposition and prior refactor (PDPR) method. The PDPR decomposes the metabolic parameters into two kinds of problems depending on their temporal coupling, which are solved using regularization and parameter fitting. Moreover, PDPR introduces the idea of divide-and-conquer to refactor prior information to ensure discrimination between metabolic abnormal regions and normal tissues. Experimental results show that PDPR is capable of separating abnormal metabolic regions of the liver and has the potential to quantify metabolic parameters and diagnose liver metabolic diseases in small animals.

Molecular determinants within the C-termini of L-HDAg that regulate hepatitis D virus replication and assembly.

Background & Aims: Hepatitis D virus (HDV) is the causative agent of chronic hepatitis delta, the most severe form of viral hepatitis. HDV encodes one protein, hepatitis delta antigen (HDAg), in two isoforms: S- and L-HDAg. They are identical in sequence except that L-HDAg contains an additional 19-20 amino acids at its C-terminus, which confer regulatory roles that are distinct from those of S-HDAg. Notably, these residues are divergent between different genotypes. We aimed to elucidate the molecular determinants within the C-termini that are essential for the regulatory role of L-HDAg in HDV replication and assembly. Methods: Northern blot, reverse-transcription quantitative PCR, and a newly established HDV trans-complementary system were used in this study. Results: C-termini of L-HDAg, albeit with high sequence variation among different genotypes, are interchangeable with respect to the trans-inhibitory function of L-HDAg and HDV assembly. The C-terminus of L-HDAg features a conserved prenylation CXXQ motif and is enriched with proline and hydrophobic residues. Abolishment of the CXXQ motif attenuated the inhibitory effect of L-HDAg on HDV replication. In contrast, the enrichment of proline and hydrophobic residues per se does not modify the trans-inhibitory function of L-HDAg. Nevertheless, these residues are essential for HDV assembly. Mechanistically, prolines and hydrophobic residues contribute to HDV assembly via a mode of action independent of the prenylated CXXQ motif. Conclusions: Within the C-terminus of L-HDAg, the CXXQ motif and the enrichment of proline and hydrophobic residues are all essential determinants of L-HDAg's regulatory roles in HDV replication and assembly. This intrinsic viral regulatory mechanism we elucidated deepens our understanding of the unique life cycle of HDV. Impact and implications: Hepatitis D virus (HDV) encodes one protein, hepatitis delta antigen (HDAg), in two isoforms: S- and L-HDAg. They are identical in sequence except that L-HDAg contains an additional 19-20 amino acids at its C-terminus. This C-terminal extension in L-HDAg confers regulatory roles in the HDV life cycle that are distinct from those of S-HDAg. Herein, we found that C-termini of L-HDAg, although with high sequence variation, are interchangeable among different HDV genotypes. Within the C-terminus of L-HDAg, the prenylation motif, and the enrichment of proline and hydrophobic residues are all essential determinants of L-HDAg's regulatory roles in HDV replication and assembly.

Global distribution of surface soil organic carbon in urban greenspaces.

Urban greenspaces continue to grow with global urbanization. The global distribution and stock of soil organic carbon (SOC) in urban greenspaces remain largely undescribed and missing in global carbon (C) budgets. Here, we synthesize data of 420 observations from 257 cities in 52 countries to evaluate the global pattern of surface SOC density (0-20 cm depth) in urban greenspaces. Surface SOC density in urban greenspaces increases significantly at higher latitudes and decreases significantly with higher mean annual temperature, stronger temperature and precipitation seasonality, as well as lower urban greenness index. By mapping surface SOC density using a random forest model, we estimate an average SOC density of 55.2 (51.9-58.6) Mg C ha-1 and a SOC stock of 1.46 (1.37-1.54) Pg C in global urban greenspaces. Our findings present a comprehensive assessment of SOC in global urban greenspaces and provide a baseline for future urban soil C assessment under continuing urbanization.

miR-26a exerts broad-spectrum antiviral effects via the enhancement of RIG-I-mediated type I interferon response by targeting USP15.

IMPORTANCE: miR-26a serves as a potent positive regulator of type I interferon (IFN) responses. By inhibiting USP15 expression, miR-26a promotes RIG-I K63-ubiquitination to enhance type I IFN responses, resulting in an active antiviral state against viruses. Being an intricate regulatory network, the activation of type I IFN responses could in turn suppress miR-26a expression to avoid the disordered activation that might result in the so-called "type I interferonopathy." The knowledge gained would be essential for the development of novel antiviral strategies against viral infection.

Drug repurposing screen identifies vidofludimus calcium and pyrazofurin as novel chemical entities for the development of hepatitis E interventions.

Hepatitis E virus (HEV) infection can cause severe complications and high mortality, particularly in pregnant women, organ transplant recipients, individuals with pre-existing liver disease and immunosuppressed patients. However, there are still unmet needs for treating chronic HEV infections. Herein, we screened a best-in-class drug repurposing library consisting of 262 drugs/compounds. Upon screening, we identified vidofludimus calcium and pyrazofurin as novel anti-HEV entities. Vidofludimus calcium is the next-generation dihydroorotate dehydrogenase (DHODH) inhibitor in the phase 3 pipeline to treat autoimmune diseases or SARS-CoV-2 infection. Pyrazofurin selectively targets uridine monophosphate synthetase (UMPS). Their anti-HEV effects were further investigated in a range of cell culture models and human liver organoids models with wild type HEV strains and ribavirin treatment failure-associated HEV strains. Encouragingly, both drugs exhibited a sizeable therapeutic window against HEV. For instance, the IC50 value of vidofludimus calcium is 4.6-7.6-fold lower than the current therapeutic doses in patients. Mechanistically, their anti-HEV mode of action depends on the blockage of pyrimidine synthesis. Notably, two drugs robustly inhibited ribavirin treatment failure-associated HEV mutants (Y1320H, G1634R). Their combination with IFN-α resulted in synergistic antiviral activity. In conclusion, we identified vidofludimus calcium and pyrazofurin as potent candidates for the treatment of HEV infections. Based on their antiviral potency, and also the favorable safety profile identified in clinical studies, our study supports the initiation of clinical studies to repurpose these drugs for treating chronic hepatitis E.

Rapid migration of Mongolian oak into the southern Asian boreal forest.

The migration of trees induced by climatic warming has been observed at many alpine treelines and boreal-tundra ecotones, but the migration of temperate trees into southern boreal forest remains less well documented. We conducted a field investigation across an ecotone of temperate and boreal forests in northern Greater Khingan Mountains of northeast China. Our analysis demonstrates that Mongolian oak (Quercus mongolica), an important temperate tree species, has migrated rapidly into southern boreal forest in synchrony with significant climatic warming over the past century. The average rate of migration is estimated to be 12.0 ± 1.0 km decade-1 , being slightly slower than the movement of isotherms (14.7 ± 6.4 km decade-1 ). The migration rate of Mongolian oak is the highest observed among migratory temperate trees (average rate 4.0 ± 1.0 km decade-1 ) and significantly higher than the rates of tree migration at boreal-tundra ecotones (0.9 ± 0.4 km decade-1 ) and alpine treelines (0.004 ± 0.003 km decade-1 ). Compared with the coexisting dominant boreal tree species, Dahurian larch (Larix gmelinii), temperate Mongolian oak is observed to have significantly lower capacity for light acquisition, comparable water-use efficiency but stronger capacity to utilize nutrients especially the most limiting nutrient, nitrogen. In the context of climatic warming, and in addition to a high seed dispersal capacity and potential thermal niche differences, the advantage of nutrient utilization, reflected by foliar elementomes and stable nitrogen isotope ratios, is also likely a key mechanism for Mongolian oak to coexist with Dahurian larch and facilitate its migration toward boreal forest. These findings highlight a rapid deborealization of southern Asian boreal forest in response to climatic warming.

Extreme precipitation causes divergent responses of root respiration to nitrogen enrichment in an alpine meadow.

Grassland roots are fundamental to obtain the most limiting soil water and nitrogen (N) resources. However, this natural pattern could be significantly changed by recent co-occurrence of N deposition and extreme precipitations, likely with complex interactions on grassland root production and respiration. Despite this nonlinearity, we still know little about how extreme precipitation change nonlinearly regulates the responses of root respiration to N enrichment. Here, we conducted a 6-year experiment of N addition in an alpine meadow, coincidently experiencing extreme precipitations among experimental years. Our results demonstrated that root respiration showed divergent responses to N addition along with extreme precipitation changes among years. Under normal rainfall year, root respiration was significantly stimulated by N addition, whereas it was depressed under high or low water. Moreover, we revealed that both root biomass and traits (i.e. specific root length) were critical mechanisms in affecting root respiration response, but their relative importance changed with water condition. For example, specific root length and specific root respiration were more dominant than root biomass in determining root respiration response under low water, or vice versa. Overall, this study comprehensively reveals the nonlinearity of root respiration responses to the interactions of N enrichment and extreme water change. These new findings help to reconcile previously conflicting results that obtain in a specific episode of water gradient, with important implications for understanding grassland belowground carbon dynamics in facing combined N deposition and extreme precipitation events.

The effect of aperiodic components in distinguishing Alzheimer's disease from frontotemporal dementia.

Distinguishing between Alzheimer's disease (AD) and frontotemporal dementia (FTD) presents a clinical challenge. Inexpensive and accessible techniques such as electroencephalography (EEG) are increasingly being used to address this challenge. In particular, the potential relevance between aperiodic components of EEG activity and these disorders has gained interest as our understanding evolves. This study aims to determine the differences in aperiodic activity between AD and FTD and evaluate its potential for distinguishing between the two disorders. A total of 88 participants, including 36 patients with AD, 23 patients with FTD, and 29 healthy controls (CN) underwent cognitive assessment and scalp EEG acquisition. Neuronal power spectra were parameterized to decompose the EEG spectrum, enabling comparison of group differences in different components. A support vector machine was employed to assess the impact of aperiodic parameters on the differential diagnosis. Compared with the CN group, both the AD and FTD groups showed varying degrees of increased alpha power (both periodic and raw power) and theta alpha power ratio. At the channel level, theta power (both periodic and raw power) in the frontal regions was higher in the AD group compared to the FTD group, and aperiodic parameters (both exponents and offsets) in the frontal, temporal, central, and parietal regions were higher in the AD group than in the FTD group. Importantly, the inclusion of aperiodic parameters led to improved performance in distinguishing between the two disorders. These findings highlight the significance of aperiodic components in discriminating dementia-related diseases.

Structure-fused deep 3D hierarchical network: A bioluminescence tomography scheme for different imaging objects.

Monte Carlo eXtreme (MCX) method has a unique advantage for deep neural network based bioluminescence tomography (BLT) reconstruction. However, this method ignores the distribution of sources energy and relies on the determined tissue structure. In this paper, a deep 3D hierarchical reconstruction network for BLT was proposed where the inputs were divided into two parts -- bioluminescence image (BLI) and anatomy of the imaged object by CT. Firstly, a parallel encoder is used to feature the original BLI & CT slices and integrate their features to distinguish the different tissue structure of imaging objects; Secondly, GRU is used to fit the spatial information of different slices and convert it into 3D features; Finally, the 3D features are decoded to the spacial and energy information of source by a symmetrical decoding structure. Our research suggested that this method can effectively compute the radiation intensity and the spatial distribution of the source for different imaging object.

Adaptive Sparsity Orthogonal Least Square with Neighbor Strategy for Fluorescence Molecular Tomography.

Fluorescence molecular tomography (FMT) is a highly sensitive and noninvasive optical imaging technique which has been widely applied to disease diagnosis and drug discovery. However, FMT reconstruction is a highly ill-posed problem. In this work, L0-norm regularization is employed to construct the mathematical model of the inverse problem of FMT. And an adaptive sparsity orthogonal least square with a neighbor strategy (ASOLS-NS) is proposed to solve this model. This algorithm can provide an adaptive sparsity and can establish the candidate sets by a novel neighbor expansion strategy for the orthogonal least square (OLS) algorithm. Numerical simulation experiments have shown that the ASOLS-NS improves the reconstruction of images, especially for the double targets reconstruction.Clinical relevance- The purpose of this work is to improve the reconstruction results of FMT. Current experiments are focused on simulation experiments, and the proposed algorithm will be applied to the clinical tumor detection in the future.

One-dimensional convolutional neural network for Jacobian in Diffuse Optical Tomography.

Non-linear least square minimization algorithms are often employed to solve Diffuse Optical Tomography (DOT) inverse problem. However, it is time-consuming to calculate the Jacobian matrix. This work has proposed a data-driven neural network method to improve computational efficiency. The singular value decomposition is employed to compute the updated Jacobian and a mapping from boundary measurements to the singular values based on a convolutional neural network (CNN) is learned to obtain the singular values. The method is validated with 3D numerical simulation data. We have demonstrated that the approach can save computation time compared to Adjoint method, and reconstructed absorption coefficient close to Adjoint method.Clinical Relevance- These results are not focused on clinical relevance currently, but in the future may be helpful to accelerant DOT reconstruction in clinic.

Tyrosine catabolism enhances genotoxic chemotherapy by suppressing translesion DNA synthesis in epithelial ovarian cancer.

Amino acid metabolism has been actively investigated as a potential target for antitumor therapy, but how it may alter the response to genotoxic chemotherapy remains largely unknown. Here, we report that the depletion of fumarylacetoacetate hydrolase (FAH), an enzyme that catalyzes the final step of tyrosine catabolism, reduced chemosensitivity in epithelial ovarian cancer (EOC). The expression level of FAH correlated significantly with chemotherapy efficacy in patients with EOC. Mechanistically, under genotoxic chemotherapy, FAH is oxidized at Met308 and translocates to the nucleus, where FAH-mediated tyrosine catabolism predominantly supplies fumarate. FAH-produced fumarate binds directly to REV1, resulting in the suppression of translesion DNA synthesis (TLS) and improved chemosensitivity. Furthermore, in vivo tyrosine supplementation improves sensitivity to genotoxic chemotherapeutics and reduces the occurrence of therapy resistance. Our findings reveal a unique role for tyrosine-derived fumarate in the regulation of TLS and may be exploited to improve genotoxic chemotherapy through dietary tyrosine supplementation.

Liver injury monitoring using dynamic fluorescence molecular tomography based on a time-energy difference strategy.

Dynamic fluorescence molecular tomography (DFMT) is a promising molecular imaging technique that offers the potential to monitor fast kinetic behaviors within small animals in three dimensions. Early monitoring of liver disease requires the ability to distinguish and analyze normal and injured liver tissues. However, the inherent ill-posed nature of the problem and energy signal interference between the normal and injured liver regions limit the practical application of liver injury monitoring. In this study, we propose a novel strategy based on time and energy, leveraging the temporal correlation in fluorescence molecular imaging (FMI) sequences and the metabolic differences between normal and injured liver tissue. Additionally, considering fluorescence signal distribution disparity between the injured and normal regions, we designed a universal Golden Ratio Primal-Dual Algorithm (GRPDA) to reconstruct both the normal and injured liver regions. Numerical simulation and in vivo experiment results demonstrate that the proposed strategy can effectively avoid signal interference between liver and liver injury energy and lead to significant improvements in morphology recovery and positioning accuracy compared to existing approaches. Our research presents a new perspective on distinguishing normal and injured liver tissues for early liver injury monitoring.