OGG1: An emerging multifunctional therapeutic target for the treatment of diseases caused by oxidative DNA damage.

Oxidative DNA damage-related diseases, such as incurable inflammation, malignant tumors, and age-related disorders, present significant challenges in modern medicine due to their complex molecular mechanisms and limitations in identifying effective treatment targets. Recently, 8-oxoguanine DNA glycosylase 1 (OGG1) has emerged as a promising multifunctional therapeutic target for the treatment of these challenging diseases. In this review, we systematically summarize the multiple functions and mechanisms of OGG1, including pro-inflammatory, tumorigenic, and aging regulatory mechanisms. We also highlight the potential of OGG1 inhibitors and activators as potent therapeutic agents for the aforementioned life-limiting diseases. We conclude that OGG1 serves as a multifunctional hub; the inhibition of OGG1 may provide a novel approach for preventing and treating inflammation and cancer, and the activation of OGG1 could be a strategy for preventing age-related disorders. Furthermore, we provide an extensive overview of successful applications of OGG1 regulation in treating inflammatory, cancerous, and aging-related diseases. Finally, we discuss the current challenges and future directions of OGG1 as an emerging multifunctional therapeutic marker for the aforementioned challenging diseases. The aim of this review is to provide a robust reference for scientific researchers and clinical drug developers in the development of novel clinical targeted drugs for life-limiting diseases, especially for incurable inflammation, malignant tumors, and age-related disorders.

Exploring Carbonization Temperature to Create Closed Pores for Hard Carbon as High-Performance Sodium-Ion Battery Anodes.

Biomass-derived porous carbon materials are meaningful to employ as a hard carbon precursor for anode materials of sodium-ion batteries (SIBs) from a sustainability perspective. Here, a straightforward approach is proposed to develop rich closed pores in pinenut-derived carbon, with the aim of improving Na+ plateau storage by adjusting the pyrolysis temperature. The optimized sample, namely the pinenut-derived carbon at 1300 °C, demonstrates remarkable reversible specific capacity of 278 mAh g-1, along with a high initial Coulomb efficiency of 85% and robust cycling stability (with a capacity retention of 89% after 800 cycles at 0.2 A g-1). In situ and ex situ analyses unveil that the developed closed pores play a significant role in enhancing the plateau capacity, providing compelling evidence for the "adsorption-filling" mechanism. Moreover, the corresponding full-cell achieves a high energy density of 245.7 Wh kg-1 (based on the total weight of both electrode active materials) and exhibits outstanding rate capability (191.4 mAh g-1 at 3 A g-1).

Optimization of Mechanical and Thermoelectric Properties of SnTe-Based Semiconductors by Mn Alloying Modulated Precipitation Evolution.

Multiscale defects engineering offers a promising strategy for synergistically enhancing the thermoelectric and mechanical properties of thermoelectric semiconductors. However, the specific impact of individual defects, in particular precipitation, on mechanical properties remains ambiguous. In this work, the mechanical and thermoelectric properties of Sn1.03- xMnxTe (x = 0-0.30) semiconductors are systematically studied. Mn-alloying induces dense dislocations and Mn nano-precipitates, resulting in an enhanced compressive strength with x increased to 0.15. Quantitative calculations are performed to assess the strengthening contributions including grain boundary, solid solution, dislocation, and precipitation strengthening. Due to the dominant contribution of precipitation strengthening, the yield strength of the x = 0.10 sample is improved by ≈74.5% in comparison to the Mn-free Sn1.03Te. For x ≥ 0.15, numerous MnTe precipitates lead to a synergistic enhancement of strength-ductility. In addition, multiscale defects induced by Mn alloying can scatter phonons over a wide frequency spectrum. The peak figure of merit ZT of ≈1.3 and an ultralow lattice thermal conductivity of ≈0.35 Wm-1 K-1 are obtained at 873 K for x = 0.10 and x = 0.30 samples respectively. This work reveals tha precipitation evolution optimizes the mechanical and thermoelectric properties of Sn1.03- xMnxTe semiconductors, which may hold potential implications for other thermoelectric systems.

Modulation of Buried Interface by 1-(3-aminopropyl)-Imidazole for Efficient Inverted Formamidinium-Cesium Perovskite Solar Cells.

Considering the direct influence of substrate surface nature on perovskite (PVK) film growth, buried interfacial engineering is crucial to obtain ideal perovskite solar cells (PSCs). Herein, 1-(3-aminopropyl)-imidazole (API) is introduced at polytriarylamine (PTAA)/PVK interface to modulate the bottom property of PVK. First, the introduction of API improves the growth of PVK grains and reduces the Pb2+ defects and residual PbI2 present at the bottom of the film, contributing to the acquisition of high-quality PVK film. Besides, the presence of API can optimize the energy structure between PVK and PTAA, which facilitates the interfacial charge transfer. Density functional theory (DFT) reveals that the electron donor unit (R-C ═ N) of the API prefers to bind with Pb2+ traps at the PVK interface, while the formation of hydrogen bonds between the R-NH2 of API and I- strengthens the above binding ability. Consequently, the optimum API-treated inverted formamidinium-cesium (FA/Cs) PSCs yields a champion power conversion efficiency (PCE) of 22.02% and exhibited favorable stability.

Bridging-BPs: a novel approach to predict potential drug-target interactions based on a bridging heterogeneous graph and BPs2vec.

Predicting drug-target interactions (DTIs) is a convenient strategy for drug discovery. Although various computational methods have been put forward in recent years, DTIs prediction is still a challenging task. In this paper, based on indirect prior information (we term them as mediators), we proposed a new model, called Bridging-BPs (bridging paths), for DTIs prediction. Specifically, we regarded linkage process between mediators and DTs (drugs and proteins) as 'bridging' and source (drug)-mediators-destination (protein) as bridging paths. By integrating various bridging paths, we constructed a bridging heterogeneous graph for DTIs. After that, an improved graph-embedding algorithm-BPs2vec-was designed to capture deep topological features underlying the bridging graph, thereby obtaining the low-dimensional node vector representations. Then, the vector representations were fed into a Random Forest classifier to train and score the probability, outputting the final classification results for potential DTIs. Under 5-fold cross validation, our method obtained AUPR of 88.97% and AUC of 88.63%, suggesting that Bridging-BPs could effectively mine the link relationships hidden in indirect prior information and it significantly improved the accuracy and robustness of DTIs prediction without direct prior information. Finally, we confirmed the practical prediction ability of Bridging-BPs by case studies.

Preclinical monitoring of radiation-induced brain injury via GluCEST MRI and resting-state fMRI at 7 T: an exploratory study on MRI-guided OAR avoidance.

PURPOSE: To assess the value of glutamate chemical exchange saturation transfer (GluCEST) after whole-brain radiotherapy (WBRT) as an imaging marker of radiation-induced brain injury (RBI) and to preliminarily show the feasibility of multiparametric MRI-guided organ at risk (OAR) avoidance. METHODS: Rats were divided into two groups: the control (CTRL) group (n = 9) and the RBI group (n = 9). The rats in the RBI group were irradiated with an X­ray radiator and then subjected to a water maze experiment 4 weeks later. In combination with high-performance liquid chromatography (HPLC), we evaluated the value of GluCEST applied to glutamate changes for RBI and investigated the effect of such changes on glutamatergic neuronal function. RESULTS: The average GluCEST values were markedly lower in the hippocampus and cerebral cortex. Positive correlations were observed between GluCEST values and regional homogeneity (ReHo) values in both the hippocampus and the cerebral cortex. HPLC showed a positive correlation with GluCEST values in the hippocampus. GluCEST values were positively correlated with spatial memory. CONCLUSION: GluCEST MRI provides a visual assessment of glutamate changes in RBI rats for monitoring OAR cognitive toxicity reactions and may be used as a biomarker of OAR avoidance as well as metabolism to facilitate monitoring and intervention in radiation damage that occurs after radiotherapy.

UTP11 promotes the growth of hepatocellular carcinoma by enhancing the mRNA stability of Oct4.

BACKGROUND: Several publications suggest that UTP11 may be a promising gene engaged for involvement of hepatocellular carcinoma (HCC) pathology. However, there are extremely limited biological, mechanistic and clinical studies of UTP11 in HCC. METHODS: To anayze the UTP11 mRNA expression in HCC and normal clinical samples and further investigate the correlation between UTP11 expression and pathology and clinical prognosis via the Cancer Tissue Gene Atlas (TCGA) database. The protein levels of UTP11 were checked using the Human Protein Atlas (HPA) database. GO-KEGG enrichment was performed from Cancer Cell Line Encyclopedia (CCLE) database and TCGA dataset. The levels of UTP11 were tested with qRT-PCR and western blotting assays. Cell viability, immunofluorescence and flow cytometry assays and animal models were used to explore the potential involvement of UTP11 in regulating HCC growth in vitro and in vivo. The correlation of UTP11 and tumor stemness scores and stemness-associated proteins from TCGA database. The mRNA stability was treated with Actinomycin D, followed by testing the mRNA expression using qRT-PCR assay. RESULTS: UTP11 was highly expressed in HCC samples compared to normal tissues from TCGA database. Similarly, UTP11 protein expression levels were obviously elevated in HCC tissue samples from HPA database. Furthermore, UTP11 levels were correlated with poor prognosis in HCC patient samples in TCGA dataset. In addition, the UTP11 mRNA levels was notably enhanced in different HCC cell lines than in normal liver cells and knocking down UTP11 was obviously reduced the viability and cell death of HCC cells. UTP11 knockdown suppressed the tumor growth of HCC in vivo experiment and extended the mice survival time. GO-KEEG analysis from CCLE and TCGA database suggested that UTP11 might involve in RNA splicing and the stability of mRNA. Further, UTP11 was positively correlated with tumor stemness scores and stemness-associated proteins from TCGA database. Knockdown of UTP11 was reduced the expression of stem cell-related genes and regulated the mRNA stability of Oct4. CONCLUSIONS: UTP11 is potentially a diagnostic molecule and a therapeutic candidate for treatment of HCC.

A Mine Water Source Prediction Model Based on LIF Technology and BWO-ELM.

The traditional methods for identifying water sources in coal mines lack the ability to quickly detect water sources and are prone to causing secondary pollution of samples. In contrast, laser induced fluorescence (LIF) technology has been introduced for the identification of coal mine water sources due to its high sensitivity and real-time performance. However, extreme learning machine (ELM) have shortcomings in randomly selecting weights and biases. The Beluga Whale Optimization (BWO) algorithm has efficient optimization capability, global search capability, adaptability and parallelism, and can find the optimal weights and biases in a short time. The combination of LIF technology and BWO-ELM model can be applied to quickly identify the welling water source in coal mine. Select sandstone water and old goaf water from the Huainan mining area as experimental samples, and mix them in different proportions to prepare 7 mixed water samples for testing. Utilize LIF technology to obtain spectral curve images, preprocess them with polynomial smoothing algorithm (SG) and spectral multiple scattering correction (MSC), and perform dimensionality reduction using factor analysis (FA) and linear discriminant analysis (LDA) methods. Finally, construct ELM models, Long Short Term Memory (LSTM) models, BWO-ELM models, and Particle Swarm Optimization Extreme Learning Machine(PSO-ELM) models for the dimensionality reduced data. In order to improve the reliability and accuracy of the results, the experimental results were kept to 5 decimal places. From the experimental results, it can be seen that SG-LDA-BWO-ELM has the best fitting effect, with a fitting coefficient of 0.99990, a root mean square error of 0.00041, a mean square error approaching 0, and an average absolute error of 0.00021. It has the best convergence and the smallest absolute error among all models, making it the most suitable for identifying mine water inrush. It is of great significance for preventing and controlling mine water disasters and ensuring coal mine production safety.

Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks.

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.

Discovery of a potent and selective covalent threonine tyrosine kinase (TTK) inhibitor.

Threonine tyrosine kinase (TTK) is a critical component of the spindle assembly checkpoint and plays a pivotal role in mitosis. TTK has been identified as a potential therapeutic target for human cancers. Here, we describe our design, synthesis and evaluation of a class of covalent TTK inhibitors, exemplified by 16 (SYL1073). Compound 16 potently inhibits TTK kinase with an IC50 of 0.016 µM and displays improved selectivity in a panel of kinases. Mass spectrometry analysis reveals that 16 covalently binds to the C604 cysteine residue in the hinge region of the TTK kinase domain. Furthermore, 16 achieves strong potency in inhibiting the growth of various human cancer cell lines, outperforming its relative reversible inhibitor, and eliciting robust downstream effects. Taken together, compound 16 provides a valuable lead compound for further optimization toward the development of drug for treatment of human cancers.

Repurposing sodium stibogluconate as an uracil DNA glycosylase inhibitor against prostate cancer using a time-resolved oligonucleotide-based drug screening platform.

Repurposing drugs can significantly reduce the time and costs associated with drug discovery and development. However, many drug compounds possess intrinsic fluorescence, resulting in aberrations such as auto-fluorescence, scattering and quenching, in fluorescent high-throughput screening assays. To overcome these drawbacks, time-resolved technologies have received increasing attention. In this study, we have developed a rapid and efficient screening platform based on time-resolved emission spectroscopy in order to screen for inhibitors of the DNA repair enzyme, uracil-DNA glycosylase (UDG). From a database of 1456 FDA/EMA-approved drugs, sodium stibogluconate was discovered as a potent UDG inhibitor. This compound showed synergistic cytotoxicity against 5-fluorouracil-resistant cancer cells. This work provides a promising future for time-resolved technologies for high-throughput screening (HTS), allowing for the swift identification of bioactive compounds from previously overlooked scaffolds due to their inherent fluorescence properties.

The fate of sulfonamides in microenvironments of rape and hot pepper rhizosphere soil system.

Sulfonamides (SAs) in agricultural soils can be degraded in rhizosphere, but can also be taken up by vegetables, which thereby poses human health and ecological risks. A glasshouse experiment was conducted using multi-interlayer rhizoboxes to investigate the fate of three SAs in rape and hot pepper rhizosphere soil systems to examine the relationship between the accumulation and their physicochemical processes. SAs mainly entered pepper shoots in which the accumulation ranged from 0.40 to 30.64 mg kg-1, while SAs were found at high levels in rape roots ranged from 3.01 to 16.62 mg kg-1. The BCFpepper shoot exhibited a strong positive linear relationship with log Dow, while such relationship was not observed between other bioconcentration factors (BCFs) and log Dow. Other than lipophilicity, the dissociation of SAs may also influence the uptake and translocation process. Larger TF and positive correlation with log Dow indicate preferential translocation of pepper SAs. There was a significant (p < 0.05) dissipation gradient of SAs observed away from the vegetable roots. In addition, pepper could uptake more SAs under solo exposure, while rape accumulated more SAs under combined exposure. When SAs applied in mixture, competition between SAs might occur to influence the translocation and dissipation patterns of SAs.

The phloem and xylem structure of plants and the neutral and ionic partitioning of sulfonamides (SAs) influence the uptake and translocation of SAs.A significant (p < 0.05) dissipation gradient of SAs was observed away from the vegetable roots.Combined exposure could promote the correlation between log BCF and log D_ow.

Study of Acoustic Emission Signal Noise Attenuation Based on Unsupervised Skip Neural Network.

Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials' structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals.

Semantic Segmentation of Surface Cracks in Urban Comprehensive Pipe Galleries Based on Global Attention.

Cracks inside urban underground comprehensive pipe galleries are small and their characteristics are not obvious. Due to low lighting and large shadow areas, the differentiation between the cracks and background in an image is low. Most current semantic segmentation methods focus on overall segmentation and have a large perceptual range. However, for urban underground comprehensive pipe gallery crack segmentation tasks, it is difficult to pay attention to the detailed features of local edges to obtain accurate segmentation results. A Global Attention Segmentation Network (GA-SegNet) is proposed in this paper. The GA-SegNet is designed to perform semantic segmentation by incorporating global attention mechanisms. In order to perform precise pixel classification in the image, a residual separable convolution attention model is employed in an encoder to extract features at multiple scales. A global attention upsample model (GAM) is utilized in a decoder to enhance the connection between shallow-level features and deep abstract features, which could increase the attention of the network towards small cracks. By employing a balanced loss function, the contribution of crack pixels is increased while reducing the focus on background pixels in the overall loss. This approach aims to improve the segmentation accuracy of cracks. The comparative experimental results with other classic models show that the GA SegNet model proposed in this study has better segmentation performance and multiple evaluation indicators, and has advantages in segmentation accuracy and efficiency.

Implication of lncRNA MSTRG.81401 in Hippocampal Pyroptosis Induced by P2X7 Receptor in Type 2 Diabetic Rats with Neuropathic Pain Combined with Depression.

Major depressive disorder (MDD) is a common complication of diabetes and is often observed alongside diabetic neuropathic pain (DNP) as a comorbidity in diabetic patients. Long non-coding RNA (lncRNA) plays an important role in various pathophysiological processes. The P2X7 receptor is responsible for triggering inflammatory responses, such as pyroptosis, linked to pain and depression. The aim of this study was to investigate the effect of lncRNA MSTRG.81401 on hippocampal pyroptosis induced by the P2X7 receptor in diabetic rats with DNP combined with MDD (DNP + MDD). Our results showed that the expression of lncRNA MSTRG.81401 was significantly elevated in the hippocampus of DNP + MDD rats compared with the control group. Following the administration of shRNA targeting lncRNA MSTRG.81401, a notable elevation in mechanical and thermal pain thresholds was observed in rats with comorbid DNP and MDD. Additionally, significant improvements in depression-like behaviors were evident in the open-field test (OFT), sucrose preference test (SPT), and forced swim test (FST). In the DNP + MDD rats, elevated levels in hippocampal P2X7 receptor mRNA and protein were observed, along with increased co-expression of P2X7 and the astrocytic marker glial fibrillary acidic protein (GFAP). Meanwhile, in DNP + MDD rats, the heightened mRNA expression of NOD-like receptor protein 3 (NLRP3), apoptosis-associated speck-like protein (ASC), pyroptosis-related protein Gasdermin D (GSDMD), caspase-1, IL-1ß, IL-18, and TNF-α was detected, in addition to increased serum levels of IL-1ß, IL-18 and TNF-α. After shRNA treatment with lncRNA MSTRG.81401, the above abnormal changes in indicators for pyroptosis and inflammation were improved. Therefore, our study demonstrates that shRNA of lncRNA MSTRG.81401 can alleviate the pain and depression-like behaviors in diabetic rats associated with the comorbidity of DNP and MDD by inhibiting the hippocampal P2X7 receptor-mediated pyroptosis pathway and pro-inflammatory responses. This suggests that the P2X7R/NLRP3/caspase-1 implicated pyroptosis and inflammatory scenario may serve as a potential target for the management of comorbid DNP and MDD in diabetes.

Quantitative determination of flow rate variations in reservoir Eco-scheduling: A case study of Yangqu dam in the upper yellow river.

Accurate quantification of flow dynamics during reservoir ecological scheduling hinders the maintenance of normal reproductive activities in downstream riverine fish. This study proposed a quantitative method for determining the flow rate changes in reservoir ecological scheduling. The approach utilized the daily flow rate and daily flow-rate increment to characterize the flow process. Adopting the perspective of shifting spawning grounds of adhesive egg-laying fish species in response to flow rate variations, we introduced the Spawning Ground Overlap Rate as an indicator and utilized it to determine flow rate changes. Focusing on the downstream area of the Yangqu Hydropower Station in the upper reaches of the Yellow River, we calculated the distribution of spawning grounds and the Spawning Ground Overlap Rate in the region. We set a threshold for the Spawning Ground Overlap Rate to restrict the flow rate changes. The results indicated that during the fish spawning period, the ecological flow range in the downstream area of the Yangqu Dam was 480-1200 m3/s. It was required to maintain a daily flow rate change of less than 49.45 m3/(s·d) and a maximum seven-day flow difference of less than 227.76 m3/s to maintain the optimal level of spawning ground overlap rate. Additionally, it was necessary to keep the daily flow rate change below 123.83 m3/(s·d) and the maximum seven-day flow difference below 368.84 m3/s to maintain the minimum spawning ground overlap rate. The findings provide foundational data for determining flow dynamics during the ecological scheduling of the spawning period for viscous-spawning fish.

[Cloning and functional verification of farnesyl pyrophosphate synthase gene(AvFPS) from Aconitum vilmorinianum].

Aconitum vilmorinianum is an authentic and superior medicinal herbal in Yunnan, which is rich in yunaconitine and other diterpene alkaloids. Diterpene alkaloids are its main active components. Farnesyl pyrophosphate synthase(FPS) is a key enzyme in the terpene biosynthetic pathway and plays an important role in diterpene alkaloid biosynthesis. Functional studies of FPS help to reveal the molecular mechanism of diterpene alkaloid biosynthesis. In this study, one FPS gene(AvFPS) was selected based on the transcriptome data of A. vilmorinianum. Its full-length sequence was cloned, and bioinformatic analysis, functional verification, and gene expression analysis were performed. The open reading frame(ORF) of AvFPS was 1 056 bp, encoding 351 amino acids. Its molecular weight was 41 kDa. AvFPS had two typical conserved functional domains of isopentenyl transferase, " DDIMD" and " DDYXD". The recombinant protein of AvFPS was expressed in Escherichia coli, and purified recombinant protein was used for in vitro enzymatic reaction. The results revealed that AvFPS was able to catalyze the synthesis of farnesyl pyrophosphate(FPP). The results of qRT-PCR analysis showed that AvFPS was expressed in the roots, stems, leaves, and flowers of A. vilmorinianum, with the highest expression level in the roots. The expression level of AvFPS was significantly up-regulated by MeJA induction. This study clarified the catalytic function of AvFPS, revealed the expression pattern of AvFPS in different tissue, as well as at different time induced by MeJA, and provided a reference for a deeper understanding of the function of FPS in the biosynthesis of diterpenoid components.

PDA-PRGCN: identification of Piwi-interacting RNA-disease associations through subgraph projection and residual scaling-based feature augmentation.

BACKGROUND: Emerging evidences show that Piwi-interacting RNAs (piRNAs) play a pivotal role in numerous complex human diseases. Identifying potential piRNA-disease associations (PDAs) is crucial for understanding disease pathogenesis at molecular level. Compared to the biological wet experiments, the computational methods provide a cost-effective strategy. However, few computational methods have been developed so far. RESULTS: Here, we proposed an end-to-end model, referred to as PDA-PRGCN (PDA prediction using subgraph Projection and Residual scaling-based feature augmentation through Graph Convolutional Network). Specifically, starting with the known piRNA-disease associations represented as a graph, we applied subgraph projection to construct piRNA-piRNA and disease-disease subgraphs for the first time, followed by a residual scaling-based feature augmentation algorithm for node initial representation. Then, we adopted graph convolutional network (GCN) to learn and identify potential PDAs as a link prediction task on the constructed heterogeneous graph. Comprehensive experiments, including the performance comparison of individual components in PDA-PRGCN, indicated the significant improvement of integrating subgraph projection, node feature augmentation and dual-loss mechanism into GCN for PDA prediction. Compared with state-of-the-art approaches, PDA-PRGCN gave more accurate and robust predictions. Finally, the case studies further corroborated that PDA-PRGCN can reliably detect PDAs. CONCLUSION: PDA-PRGCN provides a powerful method for PDA prediction, which can also serve as a screening tool for studies of complex diseases.

Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks.

BACKGROUND: As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters. RESULTS: Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment. CONCLUSIONS: BioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/ .

The Structure of Terbium in the Ferromagnetic State.

Metals typically crystallize in highly symmetric structures due to their nondirectional and nonsaturated metallic bonds. Here, we report that terbium metal in its ferromagnetic state adopts an unusual low-symmetry orthorhombic structure with a Cmcm space group. A similar structure has been previously observed only in a few actinide metals with bonding 5f electrons at ambient pressure, such as uranium, neptunium, and plutonium, but with different nearest coordination numbers and bond-length variations. The Tb atom occupies the 4c site (0, ∼0.1661, 1/4), building up -[Tb-Tb]- layers stacking along the b-axis. Our first-principles many-body calculations of the crystal field splitting in the correlated Tb 4f-shell demonstrate that the Cmcm structure for ferromagnetic terbium is stabilized by magneto-elastic forces due to a secondary order of quadrupolar moments in the ferromagnetic state. These findings are significant for further understanding of the nature of terbium, including its electron structure, energy bands, phonons, and magnetism.