Impacts of Millipedes on Acari and Collembola Communities-A Microcosm Experiment.

Ecosystem engineers influence the structure and function of soil food webs through non-trophic interactions. The activity of large soil animals, such as earthworms, has a significant impact on the soil microarthropod community. However, the influence of millipedes on soil microarthropod communities remains largely unknown. In this microcosm experiment, we examined the effects of adding, removing, and restricting millipede activity on Acari and Collembola communities in litter and soil by conducting two destructive sampling sessions on days 10 and 30, respectively. At the time of the first sampling event (10 d), Acari and Collembola abundance was shown to increase and the alpha diversity went higher in the treatments with millipedes. At the time of the second sampling event (30 d), millipedes significantly reduced the Collembola abundance and alpha diversity. The results were even more pronounced as the millipedes moved through the soil, which caused the collembolans to be more inclined to inhabit the litter, which in turn resulted in the increase in the abundance and diversity of Acari in the soil. The rapid growth of Collembola in the absence of millipedes significantly inhibited the abundance of Acari. The presence of millipedes altered the community structure of Acari and Collembola, leading to a stronger correlation between the two communities. Changes in these communities were driven by the dominant taxa of Acari and Collembola. These findings suggest that millipedes, as key ecosystem engineers, have varying impacts on different soil microarthropods. This study enhances our understanding of biological interactions and offers a theoretical foundation for soil biodiversity conservation.

Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.

Identifying the side effects related to drugs is beneficial for reducing the risk of drug development failure and saving the drug development cost. We proposed a graph reasoning method, RKDSP, to fuse the semantics of multiple connection relationships, the local knowledge within each meta-path, the global knowledge among multiple meta-paths, and the attributes of the drug and side effect node pairs. We constructed drug-side effect heterogeneous graphs consisting of the drugs, side effects, and their similarity and association connections. Multiple relational transformers were established to learn node features from diverse meta-path semantic perspectives. A knowledge distillation module was constructed to learn local and global knowledge of multiple meta-paths. Finally, an adaptive convolutional neural network-based strategy was presented to adaptively encode the attributes of each drug-side effect node pair. The experimental results demonstrated that RKDSP outperforms the compared state-of-the-art prediction approaches.

Hypothermia protects the integrity of corticospinal tracts and alleviates mitochondria injury after intracerebral hemorrhage in mice.

Disruption of corticospinal tracts (CST) is a leading factor for motor impairments following intracerebral hemorrhage (ICH) in the striatum. Previous studies have shown that therapeutic hypothermia (HT) improves outcomes of ICH patients. However, whether HT has a direct protection effect on the CST integrity and the underlying mechanisms remain largely unknown. In this study, we employed a chemogenetics approach to selectively activate bilateral warm-sensitive neurons in the preoptic areas to induce a hypothermia-like state. We then assessed effects of HT treatment on the integrity of CST and motor functional recovery after ICH. Our results showed that HT treatment significantly alleviated axonal degeneration around the hematoma and the CST axons at remote midbrain region, ultimately promoted skilled motor function recovery. Anterograde and retrograde tracing revealed that HT treatment protected the integrity of the CST over an extended period. Mechanistically, HT treatment prevented mitochondrial swelling in degenerated axons around the hematoma, alleviated mitochondrial impairment by reducing mitochondrial ROS accumulation and improving mitochondrial membrane potential in primarily cultured cortical neurons with oxyhemoglobin treatment. Serving as a proof of principle, our study provided novel insights into the application of HT to improve functional recovery after ICH.

A nomogram for predicting the necessity of tracheostomy after severe acute brain injury in patients within the neurosurgery intensive care unit: A retrospective cohort study.

Objective: This retrospective study was aimed to develop a predictive model for assessing the necessity of tracheostomy (TT) in patients admitted to the neurosurgery intensive care unit (NSICU). Method: We analyzed data from 1626 NSICU patients with severe acute brain injury (SABI) who were admitted to the Department of NSICU at the Affiliated People's Hospital of Jiangsu University between January 2021 and December 2022. Data of the patients were retrospectively obtained from the clinical research data platform. The patients were randomly divided into training (70%) and testing (30%) cohorts. The least absolute shrinkage and selection operator (LASSO) regression identified the optimal predictive features. A multivariate logistic regression model was then constructed and represented by a nomogram. The efficacy of the model was evaluated based on discrimination, calibration, and clinical utility. Results: The model highlighted six predictive variables, including the duration of NSICU stay, neurosurgery, orotracheal intubation time, Glasgow Coma Scale (GCS) score, systolic pressure, and respiration rate. Receiver operating characteristic (ROC) analysis of the nomogram yielded area under the curve (AUC) values of 0.854 (95% confidence interval [CI]: 0.822-0.886) for the training cohort and 0.865 (95% CI: 0.817-0.913) for the testing cohort, suggesting commendable differential performance. The predictions closely aligned with actual observations in both cohorts. Decision curve analysis demonstrated that the numerical model offered a favorable net clinical benefit. Conclusion: We developed a novel predictive model to identify risk factors for TT in SABI patients within the NSICU. This model holds the potential to assist clinicians in making timely surgical decisions concerning TT.

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.

As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.

Multi-Functional Regulation on Buried Interface for Achieving Efficient Triple-Cation Perovskite Solar Cells.

Mixed-cation perovskite solar cells (PSCs) have attracted much attention because of the advantages of suitable bandgap and stability. It is still a challenge to rationally design and modify the perovskite/tin oxide (SnO2) heterogeneous interface for achieving highly efficient and stable PSCs. Herein, a strategy of one-stone-for-three-birds is proposed to achieve multi-functional interface regulation via introducing N-Chlorosuccinimide (NCS) into the solution of SnO2: i) C═O functional group in NCS can induces strong binding affinity to uncoordinated defects (oxygen vacancies, free lead ions, etc) at the buried interface and passivate them; ii) incomplete in situ hydrolysis reactions can occur spontaneously and adjust the pH value of the SnO2 solution to achieve a more matchable energy level; iii) effectively releasing the residual stress of the underlying perovskite. As a result, a champion power conversion efficiency (PCE) of 24.74% is achieved with a device structure of ITO/SnO2/Perovskite/Spiro-OMeTAD/Ag, which is one of the highest values for cesium-formamidinium-methylammonium (CsFAMA) triple cation PSCs. Furthermore, the device without encapsulation can sustain 94.6% of its initial PCE after the storage at room temperature and relative humidity (RH) of 20% for 40 days. The research provides a versatile way to manipulate buried interface for achieving efficient and stable PSCs.

Earthworms mediate the influence of polyethylene (PE) and polylactic acid (PLA) microplastics on soil bacterial communities.

There is a growing body of evidence that suggests that both biodegradable and conventional (non-degradable) microplastics (MP) are hazardous to soil health by affecting the delivery of key ecological functions such as litter decomposition, nutrient cycling and water retention. Specifically, soil fauna may be harmed by the presence of MPs while also being involved in their disintegration, degradation, migration and transfer in soil. Therefore, a comprehensive understanding of the interactions between MPs and soil fauna is essential. Here, we conducted a 120-day soil microcosm experiment applying polyethylene (PE) and polylactic acid (PLA), in the absence/presence of the earthworm Eisenia nordenskioldi to estimate the relative singular and combined impact of MPs and earthworms on the soil bacterial community. Our findings revealed contrasting effects of PE and PLA on the composition and diversity of soil bacteria. All treatments affected the community and network structure of the soil bacterial community. Compared to the control (no MPs or earthworms), PE decreased bacterial alpha diversity, while PLA increased it. Patescibacteria were found to be significantly abundant in the PE group whereas Actinobacteria and Gemmatimonadetes were more abundant in PE, and PLA and earthworms groups. The presence of earthworms appeared to mediate the impact of PE/PLA on soil bacteria, potentially through bacterial consumption or by altering soil properties (e.g., pH, aeration, C availability). Earthworm presence also appeared to promote the chemical aging of PLA. Collectively, our results provide novel insights into the soil-fauna-driven impact of degradable/nondegradable MPs exposure on the long-term environmental risks associated with soil microorganisms.

Dual-modal radiomics for predicting cervical lymph node metastasis in papillary thyroid carcinoma.

BACKGROUND: Preoperative prediction of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) is significant for surgical decision-making. OBJECTIVE: This study aims to develop a dual-modal radiomics (DMR) model based on grayscale ultrasound (GSUS) and dual-energy computed tomography (DECT) for non-invasive CLNM in PTC. METHODS: In this study, 348 patients with pathologically confirmed PTC at Jiangsu University Affiliated People's Hospital who completed preoperative ultrasound (US) and DECT examinations were enrolled and randomly assigned to training (n = 261) and test (n = 87) cohorts. The enrolled patients were divided into two groups based on pathology findings namely, CLNM (n = 179) and CLNM-Free (n = 169). Radiomics features were extracted from GSUS images (464 features) and DECT images (960 features), respectively. Pearson correlation coefficient (PCC) and the least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation were then used to select CLNM-related features. Based on the selected features, GSUS, DECT, and GSUS combined DECT radiomics models were constructed by using a Support Vector Machine (SVM) classifier. RESULTS: Three predictive models based on GSUS, DECT, and a combination of GSUS and DECT, yielded performance of areas under the curve (AUC) = 0.700 [95% confidence interval (CI), 0.662-0.706], 0.721 [95% CI, 0.683-0.727], and 0.760 [95% CI, 0.728-0.762] in the training dataset, and AUC = 0.643 [95% CI, 0.582-0.734], 0.680 [95% CI, 0.623-0.772], and 0.744 [95% CI, 0.686-0.784] in the test dataset, respectively. It shows that the predictive model combined GSUS and DECT outperforms both models using GSUS and DECT only. CONCLUSIONS: The newly developed combined radiomics model could more accurately predict CLNM in PTC patients and aid in better surgical planning.

Interactions between phenanthrene and polystyrene micro/nano plastics: Implications for rice (Oryza sativa L.) toxicity.

Micro/nano plastics (MPs/NPs) are widely distributed and are one of the global pollutants of current concern. Micro/nano plastics can adsorb a variety of persistent organic pollutants, and different particle sizes and surface charges affect the biological effects of MPs/NPs. Therefore, how the compound pollution of MPs/NPs with different particle sizes and organic pollutants produces toxic effects on plants needs to be further studied. We investigated the toxic effects of phenanthrene (Phe) and amino-modified PS (PS-NH2) with two particle sizes (50 nm, 5 µm) on rice. The stress mechanism of PS-NH2 was different between the two particle sizes. Moreover, 50 nm PS-NH2 inhibited stomatal conductance and transpiration rate, reduced photosynthetic rate, significantly enriched GO functions such as "DNA repair" and "DNA double-strand break," and caused severe DNA damage in rice. Notably, 5 µm PS-NH2 affected the gene expression of "photosynthetic lighting" and "photosynthetic antenna protein" in rice, decreased chlorophyll content, and inhibited rice growth. The toxicity of 50 nm PS-NH2 was stronger. In addition, we found that Phe reduced the toxicity of PS-NH2 with different particle sizes, and the relief effect of 50 nm PS-NH2+Phe was more evident. Further, 50 nm PS-NH2+Phe alleviated the toxicity by stimulating the activities of antioxidant enzymes, reducing oxidative damage to chloroplasts, and inhibiting photosynthesis. However, 5 µm PS-NH2+Phe can reduce the stress by reducing the degree of membrane lipid peroxidation, activating metabolic pathways related to the cell wall and cell membrane formation, and plant antitoxin biosynthesis. The results contribute to the understanding of the mechanism of toxicity of MPs/NPs and polycyclic aromatic hydrocarbons (PAHs) to crops.

Marine algal polysaccharides as future potential constituents against non-alcoholic steatohepatitis.

Non-alcoholic steatohepatitis (NASH) is one of the most chronic and incurable liver diseases triggered mainly by an inappropriate diet and hereditary factors which burden liver metabolic stress, and may result in liver fibrosis or even cancer. While the available drugs show adverse side effects. The non-toxic bioactive molecules derived from natural resources, particularly marine algal polysaccharides (MAPs), present significant potential for treating NASH. In this review, we summarized the protective effects of MAPs on NASH from multiple perspectives, including reducing oxidative stress, regulating lipid metabolism, enhancing immune function, preventing fibrosis, and providing cell protection. Furthermore, the mechanisms of MAPs in treating NASH were comprehensively described. Additionally, we highlight the influences of the special structures of MAPs on their bioactive differences. Through this comprehensive review, we aim to further elucidate the molecular mechanisms of MAPs in NASH and inspire insights for deeper research on the functional food and clinical applications of MAPs.

Bioactivity of polysaccharides derived from bivalves.

Bivalves have high diversity, widely distributed in various aquatic environments, including saltwater, brackish water and freshwater. Bivalves are known to rich in polysaccharides and have wide applications in functional foods, pharmaceuticals, and industrial research. Despite many relevant reports are available, the information is poorly organized. Therefore, in this study, we conducted a comprehensive scientific review on the potential bioactivity of polysaccharides derived from bivalves. In general, the polysaccharides derived from bivalves possess various bioactive properties, including anticancer, antioxidant, anticoagulant and immunomodulatory activities. The bioactivity of these biomolecules highly depends on the bivalve species, extraction methods, purification methods, dosages, etc. The information in this study can provide an overview of the bioactivities of bivalve polysaccharides. This is very useful to be used as a guide for identifying the health benefits of polysaccharides derived from different bivalve species.

Radiomics features from whole thyroid gland tissue for prediction of cervical lymph node metastasis in the patients with papillary thyroid carcinoma.

OBJECTIVE: We aimed to develop a clinical-radiomics nomogram that could predict the cervical lymph node metastasis (CLNM) of patients with papillary thyroid carcinoma (PTC) using clinical characteristics as well as radiomics features of dual energy computed tomography (DECT). METHOD: Patients from our hospital with suspected PTC who underwent DECT for preoperative assessment between January 2021 and February 2022 were retrospectively recruited. Clinical characteristics were obtained from the medical record system. Clinical characteristics and rad-scores were examined by univariate and multivariate logistic regression. All features were incorporated into the LASSO regression model, with penalty parameter tuning performed using tenfold cross-validation, to screen risk factors for CLNM. An easily accessible radiomics nomogram was constructed. Receiver Operating Characteristic (ROC) curve together with Area Under the Curve (AUC) analysis was conducted to evaluate the discrimination performance of the model. Calibration curves were employed to assess the calibration performance of the clinical-radiomics nomogram, followed by goodness-of-fit testing. Decision curve analysis (DCA) was performed to determine the clinical utility of the established models by estimating net benefits at varying threshold probabilities for training and testing groups. RESULTS: A total of 461 patients were retrospectively recruited. The rates of CLNM were 49.3% (70 /142) in the training cohort and 53.3% (32/60) in the testing cohort. Out of the 960 extracted radiomics features, 192 were significantly different in positive and negative groups (p < 0.05). On the basis of the training cohort, 12 stable features with nonzero coefficients were selected using LASSO regression. LASSO regression identified 7 risk factors for CLNM, including male gender, maximum tumor size > 10 mm, multifocality, CT-reported central CLN status, US-reported central CLN status, rad-score, and TGAb. A nomogram was developed using these factors to predict the risk of CLNM. The AUC values in each cohort were 0.850 and 0.797, respectively. The calibration curve together with the Hosmer-Lemeshow test for the nomogram indicated good agreement between predicted and pathological CLN statuses in the training and testing cohorts. Results of DCA proved that the nomogram offers a superior net benefit for predicting CLNM compared to the "treat all or none" strategy across the majority of risk thresholds. CONCLUSION: A nomogram comprising the clinical characteristics as well as radiomics features of DECT and US was constructed for the prediction of CLNM for patients with PTC, which in determining whether lateral compartment neck dissection is warranted.

Diagnosis of cerebral microbleed via VGG and extreme learning machine trained by Gaussian map bat algorithm.

Cerebral microbleed (CMB) is a serious public health concern. It is associated with dementia, which can be detected with brain magnetic resonance image (MRI). CMBs often appear as tiny round dots on MRIs, and they can be spotted anywhere over brain. Therefore, manual inspection is tedious and lengthy, and the results are often short in reproducible. In this paper, a novel automatic CMB diagnosis method was proposed based on deep learning and optimization algorithms, which used the brain MRI as the input and output the diagnosis results as CMB and non-CMB. Firstly, sliding window processing was employed to generate the dataset from brain MRIs. Then, a pre-trained VGG was employed to obtain the image features from the dataset. Finally, an ELM was trained by Gaussian-map bat algorithm (GBA) for identification. Results showed that the proposed method VGG-ELM-GBA provided better generalization performance than several state-of-the-art approaches.

Development of a Coal Dust Concentration Sensor Based on the Electrostatic Induction Method.

In order to solve the problems of easy blockage and difficult maintenance of the current coal dust concentration sensor, a coal dust concentration sensor based on the electrostatic induction method was designed. Based on the analysis of the principle of electrostatic induction dust concentration detection, an electrostatic induction dust concentration sensor composed of a electrostatic detection electrode, a dust extraction fan, an induction signal processing circuit, an insulator, a shield, and other parts was designed. The influence of the length and width of the electrostatic detection electrode and the particle flow rate on the standard deviation of the induction signal was analyzed through experiments to optimize them. The induction signals on the electrostatic detection electrode at different dust concentrations were determined in tests, and the mathematical relationship between the standard deviation of the induction signal and the dust concentration was obtained. According to segment multiple-curve height fitting, the maximum deviation between the detected value and actual dust concentration does not exceed 10%.

A new approach: Evaluation of necroptosis and immune status enables prediction of the tumor microenvironment and treatment targets in pancreatic cancer.

Growing evidence indicates a potential correlation between necroptosis and pancreatic cancer, and the relationship between necroptosis, immune infiltration and the microenvironment in pancreatic cancer has drawn increasing attention. However, two-dimensional phenotype and prognostic assessment systems based on a combination of necroptosis and immunity have not been explored. In our present study, we explored the pancancer genomics signature of necroptosis-related molecules, identifying necroptosis-related molecule mutation profiles, expression profiles, and correlations between expression levels and methylation/CNV levels. We identified distinct necroptotic as well as immune statuses in pancreatic cancer, and a high necroptosis phenotype and high immunity phenotype both indicated better prognosis than a low necroptosis phenotype and low immunity phenotype. The two-dimensional phenotype we constructed has ideal discriminative effects on pancreatic cancer prognosis, inflammation, and the immune microenvironment. The "high-necroptosis and high-immunity (HNHI)" group exhibited the best prognosis and the highest proportion of infiltrating immune cells. The NI score can be used to predict patient prognosis and is correlated with the immune microenvironment score, chemotherapeutic drug IC50, and tumor mutational burden. In addition, it may be useful for predicting the effect of individualized chemotherapy and immunotherapy. Our study also revealed that SLC2A1 is associated with both necroptosis and immunity and acts as a potential oncogene in pancreatic cancer. In conclusion, the two-dimensional phenotype and NI score we developed are promising tools for clinical multiomics applications and prediction of chemotherapy and immunotherapy response and present benefits in terms of precision medicine and individualized treatment decision-making for pancreatic cancer patients.

Molecular mechanisms of toxicity and detoxification in rice (Oryza sativa L.) exposed to polystyrene nanoplastics.

Nanoplastics (NPs) are an emerging threat to higher plants in terrestrial ecosystems. However, the molecular of NP-related phytotoxicity remains unclear. In the present study, rice seedlings were exposed to polystyrene (PS, 50 nm) NPs at 0, 50, 100, and 200 mg/L under hydroponic conditions to investigate the induced physiological indices and transcriptional mechanisms. We found that 50, 100, and 200 mg/L PS significantly reduced root (53.05%, 49.61%, and 57.58%, respectively) and shoot (54.63%, 61.56%, and 62.64%, respectively) biomass as compared with the control seedlings. The activities of antioxidant enzymes, including catalase (CAT), peroxidase (POD), superoxide dismutase (SOD), and ascorbate peroxidase (APX), were significantly activated in all PS treatment groups, indicating that PS inhibited plant growth and induced oxidative stress. Transcriptome analyses showed that PS modulated the expression of the genes involved in cell detoxification, active oxygen metabolism, mitogen-activated protein kinase (MAPK), and plant hormone transduction pathways. Our study provides new insights into phytotoxicity by demonstrating the potential underlying toxicity of PS NPs in higher plants.

BCH1 expression pattern contributes to the fruit carotenoid diversity between peach and apricot.

Peach (Prunus persica L. Batsch) and apricot (Prunus armeniaca L.) are two species of economic importance for fruit production in the genus Prunus. Peach and apricot fruits exhibit significant differences in carotenoid levels and profiles. HPLC-PAD analysis showed that a greater content of ß-carotene in mature apricot fruits is primarily responsible for orange color, while peach fruits showed a prominent accumulation of xanthophylls (violaxanthin and cryptoxanthin) with yellow color. There are two ß-carotene hydroxylase genes in both peach and apricot genomes. Transcriptional analysis revealed that BCH1 expresses highly in peach but lowly in apricot fruit, showing a correlation with peach and apricot fruit carotenoid profiles. By using a carotenoid engineered bacterial system, it was demonstrated that there was no difference in the BCH1 enzymatic activity between peach and apricot. Comparative analysis about the putative cis-acting regulatory elements between peach and apricot BCH1 promoters provided important information for our understanding of the differences in promoter activity of the BCH1 genes in peach and apricot. Therefore, we investigated the promoter activity of BCH1 gene through a GUS detection system, and confirmed that the difference in the transcription level of the BCH1 gene resulted from the difference of the promoter function. This study provides important perspective to understanding the diversity of carotenoid accumulation in Prunus fruits such as peach and apricot. In particular, BCH1 gene is proposed as a main predictor for ß-carotene content in peach and apricot fruits during the ripening process.

Efficient Layer-Wise N:M Sparse CNN Accelerator with Flexible SPEC: Sparse Processing Element Clusters.

Recently, the layer-wise N:M fine-grained sparse neural network algorithm (i.e., every M-weights contains N non-zero values) has attracted tremendous attention, as it can effectively reduce the computational complexity with negligible accuracy loss. However, the speed-up potential of this algorithm will not be fully exploited if the right hardware support is lacking. In this work, we design an efficient accelerator for the N:M sparse convolutional neural networks (CNNs) with layer-wise sparse patterns. First, we analyze the performances of different processing element (PE) structures and extensions to construct the flexible PE architecture. Second, the variable sparse convolutional dimensions and sparse ratios are involved in the hardware design. With a sparse PE cluster (SPEC) design, the hardware can efficiently accelerate CNNs with the layer-wise N:M pattern. Finally, we employ the proposed SPEC into the CNN accelerator with flexible network-on-chip and specially designed dataflow. We implement hardware accelerators on Xilinx ZCU102 FPGA and Xilinx VCU118 FPGA and evaluate them with classical CNNs such as Alexnet, VGG-16, and ResNet-50. Compared with existing accelerators designed for structured and unstructured pruned networks, our design achieves the best performance in terms of power efficiency.

Total synthesis of bi-magnolignan.

Bi-magnolignan, isolated from the leaves of Magnolia officinalis, has shown excellent physiological activity against tumor cells. An efficient strategy for the first total synthesis of bi-magnolignan is reported. The bi-dibenzofuran skeleton was constructed via functional group interconversions of commercially available materials 1,2,4-trimethoxybenzene and 4-allylanisole. Then, the dibenzofuran skeleton was afforded by subsequent Suzuki coupling and intramolecular dehydration. The total synthesis of natural product was accomplished through FeCl3 catalyzed oxidative coupling.

Manifestation of the malignant progression of glioma following initial intracerebral hemorrhage: A case report.

BACKGROUND: Intracranial hemorrhage is extremely rare during the initial stages of glioma. Here, we report a case of glioma with unclassified pathology and intracranial bleeding. CASE SUMMARY: After the second surgery for intracerebral hemorrhage, the patient experienced weakness in the left arm and leg, but could walk unassisted. One month after discharge, the weakness in the left limbs had exacerbated and the patient also suffered from headaches and dizziness. A third surgery was ineffective against the rapidly growing tumor. Intracerebral hemorrhage may be the initial symptom of glioma in some rare cases, and atypical perihematomal edema can be used for diagnosis during an emergency. Certain histological and molecular features seen in our case were similar to that of glioblastoma with a primitive neuronal component, which is termed diffuse glioneuronal tumor with features similar to oligodendroglioma and nuclear clusters (DGONC). The patient underwent three surgeries to remove the tumor. The first tumor resection had been performed when the patient was 14-years-old. Resection of the hemorrhage and bone disc decompression were performed when the patient was 39-years-old. One month after the last discharge, the patient underwent neuronavigation-assisted resection of the right frontotemporal parietal lesion plus extended flap decompression. On the 50th d after the third operation, computed tomography imaging showed rapid tumor growth accompanied by brain hernia. The patient was discharged and died 3 d later. CONCLUSION: Glioma can present as bleeding in the initial stage and should be considered in such a setting. We have reported a case of DGONC, which is a rare molecular subtype of glioma with a unique methylation profile.