RT-SRTS: Angle-agnostic real-time simultaneous 3D reconstruction and tumor segmentation from single X-ray projection

Radiotherapy is one of the primary treatment methods for tumors, but the organ movement caused by respiration limits its accuracy. Recently, 3D imaging from a single X-ray projection has received extensive attention as a promising approach to address this issue. However, current methods can only reconstruct 3D images without directly locating the tumor and are only validated for fixed-angle imaging, which fails to fully meet the requirements of motion control in radiotherapy. In this study, a novel imaging method RT-SRTS is proposed which integrates 3D imaging and tumor segmentation into one network based on multi-task learning (MTL) and achieves real-time simultaneous 3D reconstruction and tumor segmentation from a single X-ray projection at any angle. Furthermore, the attention enhanced calibrator (AEC) and uncertain-region elaboration (URE) modules have been proposed to aid feature extraction and improve segmentation accuracy. The proposed method was evaluated on fifteen patient cases and compared with three state-of-the-art methods. It not only delivers superior 3D reconstruction but also demonstrates commendable tumor segmentation results. Simultaneous reconstruction and segmentation can be completed in approximately 70 ms, significantly faster than the required time threshold for real-time tumor tracking. The efficacies of both AEC and URE have also been validated in ablation studies. The code of work is available at https://github.com/ZywooSimple/RT-SRTS.

Small extracellular vesicles facilitate epithelial-mesenchymal transition in chronic rhinosinusitis with nasal polyps via the miR-375-3p/QKI axis

BACKGROUND

Epithelial-mesenchymal transition (EMT) plays a crucial role in the pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP). However, the involvement of small extracellular vesicles (sEVs) in EMT and their contributions to CRSwNP has not been extensively investigated.

METHODS

SEVs were isolated from nasal mucosa through ultracentrifugation. MicroRNA sequencing and reverse-transcription quantitative polymerase chain reaction were employed to analyze the differential expression of microRNAs carried by sEVs. Human nasal epithelial cells (hNECs) were used to assess the EMT-inducing effect of sEVs/microRNAs. EMT-associated markers were detected by western blotting and immunofluorescence. Dual-luciferase reporter assay was performed to determine the target gene of miR-375-3p. MicroRNA mimic, lentiviral, and plasmid transduction were used for functional experiments.

RESULTS

In line with the greater EMT status in eosinophilic CRSwNP (ENP), sEVs derived from ENP (ENP-sEVs) could induce EMT in hNECs. MiR-375-3p was elevated in ENP-sEVs compared to that in control and nonENP. MiR-375- 3p carried by ENP-sEVs facilitated EMT by directly targeting KH domain containing RNA binding (QKI) at seed sequences of 913-919, 1025-1033, and 2438-2444 in 3'-untranslated region. Inhibition of QKI by miR-375-3p overexpression promoted EMT, which could be reversed by restoration of QKI. Furthermore, the abundance of miR-375-3p in sEVs was closely correlated with the clinical symptom score and disease severity.

CONCLUSIONS

MiR-375-3p-enriched sEVs facilitated EMT by suppressing QKI in hNECs. The association of miR-375-3p with disease severity underscores its potential as both a diagnostic marker and a therapeutic target for the innovative management of CRSwNP.

LoGo-GR: A Local to Global Graphical Reasoning Framework for Extracting Structured Information From Biomedical Literature

In the biomedical literature, entities are often distributed within multiple sentences and exhibit complex interactions. As the volume of literature has increased dramatically, it has become impractical to manually extract and maintain biomedical knowledge, which would entail enormous costs. Fortunately, document-level relation extraction can capture associations between entities from complex text, helping researchers efficiently mine structured knowledge from the vast medical literature. However, how to effectively synthesize rich global information from context and accurately capture local dependencies between entities is still a great challenge. In this paper, we propose a Local to Global Graphical Reasoning framework (LoGo-GR) based on a novel Biased Graph Attention mechanism (B-GAT). It learns global context feature and information of local relation path dependencies from mention-level interaction graph and entity-level path graph respectively, and collaborates with global and local reasoning to capture complex interactions between entities from document-level text. In particular, B-GAT integrates structural dependencies into the standard graph attention mechanism (GAT) as attention biases to adaptively guide information aggregation in graphical reasoning. We evaluate our method on three publicly biomedical document-level datasets: Drug-Mutation Interaction (DV), Chemical-induced Disease (CDR), and Gene-Disease Association (GDA). LoGo-GR has advanced and stable performance compared to other state-of-the-art methods (it achieves state-of-the-art performance with 96.14%-97.39% F1 on DV dataset, advanced performance with 68.89% F1 and 84.22% F1 on CDR and GDA datasets, respectively). In addition, LoGo-GR also shows advanced performance on general-domain document-level relation extraction dataset, DocRED, which proves that it is an effective and robust document-level relation extraction framework.

Spatiotemporally continuous PM2.5 dataset in the Mekong River Basin from 2015 to 2022 using a stacking model

With the potential to cause millions of deaths, PM2.5 pollution has become a global concern. In Southeast Asia, the Mekong River Basin (MRB) is experiencing heavy PM2.5 pollution and the existing PM2.5 studies in the MRB are limited in terms of accuracy and spatiotemporal coverage. To achieve high-accuracy and long-term PM2.5 monitoring of the MRB, fused aerosol optical depth (AOD) data and multi-source auxiliary data are fed into a stacking model to estimate PM2.5 concentrations. The proposed stacking model takes advantage of convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM) models and can well represent the spatiotemporal heterogeneity of the PM2.5-AOD relationship. In the cross-validation (CV), comparison with CNN and LightGBM models shows that the stacking model can better suppress overfitting, with a higher coefficient of determination (R2) of 0.92, a lower root mean square error (RMSE) of 5.58 μg/m3, and a lower mean absolute error (MAE) of 3.44 μg/m3. For the first time, the high-accuracy PM2.5 dataset reveals spatially and temporally continuous PM2.5 pollution and variations in the MRB from 2015 to 2022. Moreover, the spatiotemporal variations of annual and monthly PM2.5 pollution are also investigated at the regional and national scales. The dataset will contribute to the analysis of the causes of PM2.5 pollution and the development of mitigation policies in the MRB.

Mechanistic study of Eu single atoms occupying four vacancy centers as potential electrocatalysts for the oxygen reduction reaction

The oxygen reduction reaction (ORR) on the oxygen electrode plays a critical role in rechargeable metal-air batteries, and the development of electrochemical energy storage and conversion technologies for the ORR is of great significance. In this study, the catalytic performance of rare earth-doped graphene (EuNxC6-x-Gra) as an electrocatalyst for the ORR was investigated. The results showed that a majority of the catalysts exhibited good ORR catalytic activity under acidic conditions, with some approaching or even surpassing commercial Pt-based catalysts (ηORR = 0.45 V). Particularly, EuN2C4-2-Gra demonstrated an ηORR of 0.38 V. It has been observed that the f-band center of Eu atoms increases with an increasing number of N atoms, and the charge distribution exhibits a "U" shape. There is a decreasing trend from N0 to N3 and an increasing trend from N4 to N6. By incorporating the proportional relationship of the adsorption free energies of reaction intermediates (ΔG*ads), a volcano diagram was constructed to rapidly assess catalytic activity. Finally, an intrinsic characteristic descriptor φ was formulated to quantitatively describe the relationship between φ and ηORR, providing a new tool for predicting and designing catalysts. This will provide guidance for the development and design of high-performance rare earth single atom catalysts.

A novel network security situation assessment model based on multiple strategies whale optimization algorithm and bidirectional GRU

The rapid development of the internet has brought about a comprehensive transformation in human life. However, the challenges of cybersecurity are becoming increasingly severe, necessitating the implementation of effective security mechanisms. Cybersecurity situational awareness can effectively assess the network status, facilitating the formulation of better cybersecurity defense strategies. However, due to the low accuracy of existing situational assessment methods, situational assessment remains a challenge. In this study, a new situational assessment method, MSWOA-BiGRU, combining optimization algorithms and temporal neural networks, was proposed. Firstly, a scientific indicator system proposed in this research is used to calculate the values of each indicator. Then, the Analytic Hierarchy Process is used to derive the actual situation values, which serve as labels. Taking into account the temporal nature of network traffic, the BiGRU model is utilized for cybersecurity situational assessment. After integrating time-related features and network traffic characteristics, the situational assessment value is obtained. During the evaluation process, a whale optimization algorithm (MSWOA) improved with a mix of strategies proposed in this study was employed to optimize the model. The performance of the proposed MSWOA-BiGRU model was evaluated on publicly available real network security datasets. Experimental results indicate that compared to traditional optimization algorithms, the optimization performance of MSWOA has seen significant enhancement. Furthermore, MSWOA-BiGRU demonstrates superior performance in cybersecurity situational assessment compared to existing evaluation methods.

Identification of membrane protein types via deep residual hypergraph neural network

A membrane protein's functions are significantly associated with its type, so it is crucial to identify the types of membrane proteins. Conventional computational methods for identifying the species of membrane proteins tend to ignore two issues: High-order correlation among membrane proteins and the scenarios of multi-modal representations of membrane proteins, which leads to information loss. To tackle those two issues, we proposed a deep residual hypergraph neural network (DRHGNN), which enhances the hypergraph neural network (HGNN) with initial residual and identity mapping in this paper. We carried out extensive experiments on four benchmark datasets of membrane proteins. In the meantime, we compared the DRHGNN with recently developed advanced methods. Experimental results showed the better performance of DRHGNN on the membrane protein classification task on four datasets. Experiments also showed that DRHGNN can handle the over-smoothing issue with the increase of the number of model layers compared with HGNN. The code is available at https://github.com/yunfighting/Identification-of-Membrane-Protein-Types-via-deep-residual-hypergraph-neural-network.

[Risk factors analysis of protein energy wasting in children with chronic kidney disease]

Objective: To analyze the clinical characteristics and risk factors of protein energy wasting (PEW) in children with chronic kidney disease (CKD). Methods: Clinical data of 231 children with chronic kidney disease hospitalized in Beijing Children's Hospital affiliated to Capital Medical University from January 2018 to January 2023 were retrospectively analyzed to explore the incidence of PEW. According to the diagnostic criteria of CKDPEW, they were divided into a CKDPEW group and a non PEW group. The comparison between the groups was performed by independent-sample t test and Chi-squared test, and the risk factors were analyzed by multivariate Logistic regression. Results: Among the 231 children, there were 138 males and 93 females, with a visiting age of 9.9 (7.9, 16.0) years; 6 cases were in stage 1, 14 cases in stage 2, 51 cases in stage 3, 36 cases in stage 4, and 124 cases in stage 5. A total of 30 children (13.0%) with CKD PEW were diagnosed at the age of 7. 1 (3.8, 13.2) years, including 1 case in stage 1, 1 case in stage 2, 5 cases in stage 3, 5 cases in stage 4, and 18 cases in stage 5. There were a total of 201 cases (87.0%) in the non PEW group, diagnosed at the age of 11.8 (8.5, 12.2) years, including 5 cases in stage 1, 13 cases in stage 2, 46 cases in stage 3, 31 cases in stage 4, and 106 cases in stage 5. The Chi-squared test and t test showed that the systolic blood pressure, diastolic blood pressure, birth weight and carbon dioxide binding capacity of the CKD PEW group were lower than those of the non PEW group ((109±22) vs. (120±20) mmHg (1 mmHg=0.133 kPa), (72±19) vs. (79±16) mmHg, (2.9±0.5) vs. (3.2±0.6) kg, (17±4) vs. (19±4) mmol/L,t=2.85, 2.14, 0.67, 2.63, all P<0.05). Multivariate logistic regression analysis showed that carbon dioxide binding capacity and birth weight were independent protective factors of CKDPEW in children (OR=0.81 and 0.36, 95%CI=0.73-0.90 and 0.17-0.77, respectively; both P<0.01); the risk of PEW in CKD children decreased by 0.187 times for every 1 mmol/L increment in carbon dioxide binding capacity, and 0.638 times for every 1 kg increment in birth weight. Conclusions: The incidence of protein energy expenditure in children with chronic kidney disease is lower than that in the previous researches. PEW can appear in CKD 1-2 stage, and attention should be paid to it in the early stage of CKD in clinical practice. Low birth weight CKD children are susceptible to PEW, and actively correcting metabolic acidosis can reduce the risk of CKDPEW.

DNA protein binding recognition based on lifelong learning

In recent years, research in the field of bioinformatics has focused on predicting the raw sequences of proteins, and some scholars consider DNA-binding protein prediction as a classification task. Many statistical and machine learning-based methods have been widely used in DNA-binding proteins research. The aforementioned methods are indeed more efficient than those based on manual classification, but there is still room for improvement in terms of prediction accuracy and speed. In this study, researchers used Average Blocks, Discrete Cosine Transform, Discrete Wavelet Transform, Global encoding, Normalized Moreau-Broto Autocorrelation and Pseudo position-specific scoring matrix to extract evolutionary features. A dynamic deep network based on lifelong learning architecture was then proposed in order to fuse six features and thus allow for more efficient classification of DNA-binding proteins. The multi-feature fusion allows for a more accurate description of the desired protein information than single features. This model offers a fresh perspective on the dichotomous classification problem in bioinformatics and broadens the application field of lifelong learning. The researchers ran trials on three datasets and contrasted them with other classification techniques to show the model's effectiveness in this study. The findings demonstrated that the model used in this research was superior to other approaches in terms of single-sample specificity (81.0%, 83.0%) and single-sample sensitivity (82.4%, 90.7%), and achieves high accuracy on the benchmark dataset (88.4%, 80.0%, and 76.6%).

TrGPCR:GPCR-ligand Binding Affinity Predicting based on Dynamic Deep Transfer Learning

Predicting G protein-coupled receptor (GPCR)-ligand binding affinity plays a crucial role in drug development. However, determining GPCR-ligand binding affinities is time-consuming and resource-intensive. Although many studies used data-driven methods to predict binding affinity, most of these methods required protein 3D structure, which was often unknown. Moreover, part of these studies only considered the sequence characteristics of the protein, ignoring the secondary structure of the protein. The number of known GPCR for affinity prediction is only a few thousand, which is insufficient for deep learning training. Therefore, this study aimed to propose a deep transfer learning method called TrGPCR, which used dynamic transfer learning to solve the problem of insufficient GPCR data. We used the Binding Database(BindingDB) as the source domain and the GLASS(GPCR-Ligand Association) database as the target domain. We also introduced protein secondary structures, called pockets, as features to predict binding affinities. Compared with DeepDTA, our model improved by 5.2% on RMSE(root mean square error) and 4.5% on MAE(mean squared error).

Extracting biomedical relation from cross-sentence text using syntactic dependency graph attention network

In biomedical literature, cross-sentence texts can usually express rich knowledge, and extracting the interaction relation between entities from cross-sentence texts is of great significance to biomedical research. However, compared with single sentence, cross-sentence text has a longer sequence length, so the research on cross-sentence text information extraction should focus more on learning the context dependency structural information. Nowadays, it is still a challenge to handle global dependencies and structural information of long sequences effectively, and graph-oriented modeling methods have received more and more attention recently. In this paper, we propose a new graph attention network guided by syntactic dependency relationship (SR-GAT) for extracting biomedical relation from the cross-sentence text. It allows each node to pay attention to other nodes in its neighborhood, regardless of the sequence length. The attention weight between nodes is given by a syntactic relation graph probability network (SR-GPR), which encodes the syntactic dependency between nodes and guides the graph attention mechanism to learn information about the dependency structure. The learned feature representation retains information about the node-to-node syntactic dependency, and can further discover global dependencies effectively. The experimental results demonstrate on a publicly available biomedical dataset that, our method achieves state-of-the-art performance while requiring significantly less computational resources. Specifically, in the "drug-mutation" relation extraction task, our method achieves an advanced accuracy of 93.78% for binary classification and 92.14% for multi-classification. In the "drug-gene-mutation" relation extraction task, our method achieves an advanced accuracy of 93.22% for binary classification and 92.28% for multi-classification. Across all relation extraction tasks, our method improves accuracy by an average of 0.49% compared to the existing best model. Furthermore, our method achieved an accuracy of 69.5% in text classification, surpassing most existing models, demonstrating its robustness in generalization across different domains without additional fine-tuning.

[A comparative study of the curative effects between butterfly-shaped flap and propeller flap based on the dorsal branch of digital artery in repairing the wound in volar aspect of finger]

Objective: To compare the curative effects of butterfly-shaped flap based on the dorsal branch of digital artery (hereinafter referred to as butterfly-shaped flap) and propeller flap based on the dorsal branch of digital artery (hereinafter referred to as propeller flap) in repairing the wound in volar aspect of finger. Methods: A retrospective cohort study was conducted. From August 2018 to April 2022, 16 patients with finger palmar wounds admitted to Ruijin Hospital of Shanghai Jiao Tong University School of Medicine and 7 patients with finger palmar wounds admitted to General Hospital of PLA Central Theater Command met the inclusion criteria, including 14 males and 9 females, aged 25 to 64 years. After debridement or resection of skin benign tumor, the wounds ranged from 0.5 cm×0.5 cm to 1.5 cm×1.5 cm. According to the different rotation axes of flap pedicle during wound repair, the patients were divided into butterfly-shaped flap group (8 cases) and propeller flap group (15 cases), and their wounds were repaired by butterfly-shaped flap (with area of 0.5 cm×0.5 cm-1.5 cm×1.3 cm) or propeller flap (with area of 0.7 cm×0.5 cm-1.5 cm×1.5 cm) , respectively. In propeller flap group, wounds in the donor sites were repaired by full-thickness skin grafts taken from the palms of wrists or the groin. The surgical time, postoperative complications, flap survival, and wound healing time of patients in the two groups were recorded. Data were statistically analyzed with independent sample t test, Mann Whitney U test, or Fisher's exact probability test. Results: The surgical time and postoperative wound healing time of patients in butterfly-shaped flap group ((43±9) min and (13.1±0.8) d, respectively) were both significantly shorter than those in propeller flap group ((87±16) min and (16.7±4.6) d, respectively, with t values of -7.03 and -2.86, respectively, P<0.05). The postoperative flap survival and complications of patients between the two groups were both similar (P>0.05). Conclusions: For repairing the wound in volar aspect of finger, the butterfly-shaped flap has more advantages in comparison with the traditional propeller flap. The butterfly-shaped flap has a short surgical time and fast postoperative recovery, which is worthy of clinical promotion.

Anomaly detection method for building energy consumption in multivariate time series based on graph attention mechanism

A critical issue in intelligent building control is detecting energy consumption anomalies based on intelligent device status data. The building field is plagued by energy consumption anomalies caused by a number of factors, many of which are associated with one another in apparent temporal relationships. For the detection of abnormalities, most traditional detection methods rely solely on a single variable of energy consumption data and its time series changes. Therefore, they are unable to examine the correlation between the multiple characteristic factors that affect energy consumption anomalies and their relationship in time. The outcomes of anomaly detection are one-sided. To address the above problems, this paper proposes an anomaly detection method based on multivariate time series. Firstly, in order to extract the correlation between different feature variables affecting energy consumption, this paper introduces a graph convolutional network to build an anomaly detection framework. Secondly, as different feature variables have different influences on each other, the framework is enhanced by a graph attention mechanism so that time series features with higher influence on energy consumption are given more attention weights, resulting in better anomaly detection of building energy consumption. Finally, the effectiveness of this paper's method and existing methods for detecting energy consumption anomalies in smart buildings are compared using standard data sets. The experimental results show that the model has better detection accuracy.

Sublethal effects of chlorantraniliprole on growth, biochemical and molecular parameters in two chironomids, Chironomus kiiensis and Chironomus javanus

Pesticide residues have serious environmental impacts on rice-based ecosystems. In rice fields, Chironomus kiiensis and Chironomus javanus provide alternative food sources to predatory natural enemies of rice insect pests, especially when pests are low. Chlorantraniliprole is a substitute for older classes of insecticides and has been used extensively to control rice pests. To determine the ecological risks of chlorantraniliprole in rice fields, we evaluated its toxic effects on certain growth, biochemical and molecular parameters in these two chironomids. The toxicity tests were performed by exposing third-instar larvae to a range of concentrations of chlorantraniliprole. LC₅₀ values at 24 h, 48 h, and 10 days showed that chlorantraniliprole was more toxic to C. javanus than to C. kiiensis. Chlorantraniliprole significantly prolonged the larval growth duration, inhibited pupation and emergence, and decreased egg numbers of C. kiiensis and C. javanus at sublethal dosages (LC₁₀ = 1.50 mg/L and LC₂₅ = 3.00 mg/L for C. kiiensis; LC₁₀ = 0.25 mg/L and LC₂₅ = 0.50 mg/L for C. javanus). Sublethal exposure to chlorantraniliprole significantly decreased the activity of the detoxification enzymes carboxylesterase (CarE) and glutathione S-transferases (GSTs) in both C. kiiensis and C. javanus. Sublethal exposure to chlorantraniliprole also markedly inhibited the activity of the antioxidant enzyme peroxidase (POD) in C. kiiensis and POD and catalase (CAT) in C. javanus. Expression levels of 12 genes revealed that detoxification and antioxidant abilities were affected by sublethal exposures to chlorantraniliprole. There were significant changes in the expression levels of seven genes (CarE6, CYP9AU1, CYP6FV2, GSTo1, GSTs1, GSTd2, and POD) in C. kiiensis and ten genes (CarE6, CYP9AU1, CYP6FV2, GSTo1, GSTs1, GSTd2, GSTu1, GSTu2, CAT, and POD) in C. javanus. These results provide a comprehensive overview of the differences in chlorantraniliprole toxicity to chironomids, indicating that C. javanus is more susceptible and suitable as an indicator for ecological risk assessment in rice ecosystems.

A Sub-flow Adaptive Multipath Routing Algorithm for Data Centre Network

In response to the fast and intensive traffic changes and the presence of redundant links in the network topology in data centre networks, multipath routing has become the dominant approach. Existing multipath routing algorithms are still inadequate in terms of adapting to dynamic changes in the network state quickly and the cooperation of path selection and sub-flow assignment. Therefore, this paper proposes a sub-flow adaptive multipath routing algorithm for data centre networks (SAMP). The deep deterministic policy gradient (DDPG) algorithm is introduced. DDPG combines deep learning (DL) and reinforcement learning (RL) to implement different network state changes quickly, especially in the process of topology, to achieve dynamic migration of the optimised decisions that have been learned. An adaptive multipath routing model for subflows is established to accomplish collaborative scheduling of path selection and subflow assignment based on the real-time state of the network. The experimental results show that the algorithm can be well adapted to the data centre network, and when compared with several traditional methods, it reduces the latency by an average of 31.3%, improves the task transmission success rate by an average of 14.9%, and increases the throughput by 37.1%.

[Endoscopic resection of type D trigeminal schwannoma through nasal sinus approach]

Objective: To examine the feasibility and surgical approach of removing type D trigeminal schwannoma through nasal cavity and nasal sinus under endoscope. Methods: Eleven patients with trigeminal schwannoma who were treated in the Department of Otorhinolaryngology, Qilu Hospital of Shandong University from December 2014 to August 2021 were analyzed retrospectively in this study. There were 7 males and 4 females, aged (47.5±13.5) years (range: 12 to 64 years). The neoplasm involved the pterygopalatine fossa, infratemporal fossa, ethmoidal sinus, sphenoid sinus, cavernous sinus, and middle cranial fossa. The size of tumors were between 1.6 cm×2.0 cm×2.0 cm and 5.7 cm×6.0 cm×6.0 cm. Under general anesthesia, the tumors were resected through the transpterygoid approach in 4 cases, through the prelacrimal recess approach in 4 cases, through the extended prelacrimal recess approach in 2 cases, and through the endoscopic medial maxillectomy approach in 1 case. The nasal endoscopy and imaging examination were conducted to detect whether neoplasm recurred or not, and the main clinical symptoms during follow-up. Results: All the surgical procedures were performed under endonasal endoscope, including Gross total resection in 10 patients. The tumor of a 12-year-old patient was not resected completely due to huge tumor size and limited operation space. One patient was accompanied by two other schwannomas located in the occipital region and the ipsilateral parotid gland region originating from the zygomatic branch of the facial nerve, both of which were removed concurrently. After tumor resection, the dura mater of middle cranial fossa was directly exposed in the nasal sinus in 2 cases, including 1 case accompanied by cerebrospinal fluid leakage which was reconstructed by a free mucosal flap obtained from the middle turbinate, the other case was packed by the autologous fat to protect the dura mater. The operation time was (M(IQR)) 180 (160) minutes (range: 120 to 485 minutes). No complications and deaths were observed. No recurrence was observed in the 10 patients with total tumor resection during a 58 (68) months' (range: 10 to 90 months) follow-up. No obvious change was observed in the facial appearance of all patients during the follow-up. Conclusion: Type D trigeminal schwannoma involving pterygopalatine fossa and infratemporal fossa can be removed safely through purely endoscopic endonasal approach by selecting the appropriate approach according to the size and involvement of the tumor.

Identification of Membrane Protein Types based using Hypergraph Neural Network

Introduction:

Membrane proteins play an important role in living organisms as one of the main components of biological membranes. The problem in membrane protein classification and prediction is an important topic of membrane proteomics research because the function of proteins can be quickly determined if membrane protein types can be discriminated.

Methods:

Most current methods to classify membrane proteins are labor-intensive and require a lot of resources. In this study, five methods, Average Block (AvBlock), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Histogram of Orientation Gradient (HOG), and Pseudo-PSSM (PsePSSM), were used to extract features in order to predict membrane proteins on a large scale. Then, we combined the five obtained feature matrices and constructed the corresponding hypergraph association matrix. Finally, the feature matrices and hypergraph association matrices were integrated to identify the types of membrane proteins using a hypergraph neural network model (HGNN).

Results:

The proposed method was tested on four membrane protein benchmark datasets to evaluate its performance. The results showed 92.8%, 88.6%, 88.2%, and 99.0% accuracy on each of the four datasets.

method:

Average block (AvBlock), discrete cosine transform (DCT), discrete wavelet transform (DWT), histogram of orientation gradients (HOG) and pseudo-PSSM (PsePSSM) are used to extract evolutionary features, next, we propose a hypergraph neural network model (HGNN) for integrating five features to identify membrane protein types

Conclusion:

Compared to traditional machine learning classifier methods, such as Random Forest (RF), Support Vector Machine (SVM), etc., HGNN prediction performance was found to be better.

result:

For performance evaluation, the proposed method was tested on four membrane protein benchmark Datasets. On the four Datasets, the method of this paper obtained 92.8%, 88.6%, 88.2%, and 99.0

G Protein-Coupled Receptor Interaction Prediction Based on Deep Transfer Learning

G protein-coupled receptors (GPCRs) account for about 40% to 50% of drug targets. Many human diseases are related to G protein coupled receptors. Accurate prediction of GPCR interaction is not only essential to understand its structural role, but also helps design more effective drugs. At present, the prediction of GPCR interaction mainly uses machine learning methods. Machine learning methods generally require a large number of independent and identically distributed samples to achieve good results. However, the number of available GPCR samples that have been marked is scarce. Transfer learning has a strong advantage in dealing with such small sample problems. Therefore, this paper proposes a transfer learning method based on sample similarity, using XGBoost as a weak classifier and using the TrAdaBoost algorithm based on JS divergence for data weight initialization to transfer samples to construct a data set. After that, the deep neural network based on the attention mechanism is used for model training. The existing GPCR is used for prediction. In short-distance contact prediction, the accuracy of our method is 0.26 higher than similar methods.

POS0320 EPIDEMIOLOGY AND HEALTHCARE RESOURCE UTILIZATION OF PATIENTS WITH EGPA IN THE UNITED KINGDOM

Background

Eosinophilic granulomatosis with polyangiitis (EGPA) is characterized by eosinophilic inflammation of small with or without medium arteries. EGPA is a rare disease with varying prevalence and incidence rates globally. To date, limited information is available on the prevalence, incidence and burden of disease in the United Kingdom (UK).

Objectives

The objectives were to estimate the prevalence and incidence of EGPA, and to describe the healthcare resource utilization (HCRU) among patients with EGPA in the UK.

Methods

This retrospective database study used the UK-based Clinical Practice Research Datalink (CPRD)-AURUM database linked to the Hospital Episode Statistics (HES). Prevalence was estimated from 2005 to 2019, and incidence was estimated from 2006 to 2019. HCRU was assessed in the 12-months following the first recorded diagnosis of EGPA (index date), and included hospitalizations, emergency room visits, procedures, outpatient specialist visits, primary care visits, and oral corticosteroid use.

Results

764 people were identified with EGPA in the UK. The prevalence of EGPA, reported in the database, increased from 22.7 to 45.6 per 1,000,000 persons from 2005 to 2019 (Figure 1), whereas the incidence of EGPA from 2006 to 2019 ranged from 2.28 to 4.00 per 1,000,000 person-years. 377 patients with EGPA were successfully linked to the CPRD-HES database. Patient characteristics were as follows: mean age (SD) was 57 years (14.2); 49% were male; 81% had asthma; and 11% had peripheral neuropathy prior to the index date. For patients with EGPA, 19% had an EGPA-related hospitalization and 50% had any-cause hospitalization within 1 year of the index date (Table 1). The mean length of stay was, 18 days and 16 days for EGPA-related and any-cause hospitalizations, respectively. 52% of patients with EGPA had undergone a medical procedure, 89% of patients with EGPA had an outpatient visit to a specialist. Almost all patients with EGPA visited a general practitioner within 1 year of their EGPA diagnosis (97%) and averaged 16.0 visits in 1 year. A significant proportion of the EGPA population were prescribed OCS; most EGPA patients had a prescription in the 0–3 months after the index date (64%), and patients on average had a prescription for OCS for 6 out of the 12 months after the index date.

Table 1.

HCRU among patients with EGPA

HCRUNumber of patients N (%) [total days]Number of events per patient, Mean (SD)Total EGPA cohort (N)377 EGPA-specific hospitalizations72 (19.10)1.2 (1) EGPA-specific hospitalizations  length of stay[1283]17.8 (23.3) Any-cause hospitalizations188 (49.87)1.7 (1) Any-cause hospitalizations length of stay[2992]15.9 (23.7) Any-cause A & E events19 (5.04)1.8 (2) Any-cause outpatient visits334 (88.59)9.8 (7) Any procedures undertaken196 (51.99)6.8 (6) General Practitioner visits366 (97.08)16.0 (11)

A&E, Accident and Emergency; EGPA, eosinophilic granulomatosis with polyangiitis; HCRU, healthcare resource utilization.

Figure 1.

Prevalence of EGPA in the UK from 2005 to 2019

Prevalence is expressed as cases per 1,000,000 persons. EGPA, eosinophilic granulomatosis with polyangiitis; UK, United Kingdom.

Conclusion

The prevalence of EGPA increased over the study period in the UK, and the data show significant HCRU within 1 year of the first recorded diagnosis of EGPA. Almost all of the patients with EGPA were found to frequently visit the primary care physician and seek specialist care, and almost half required hospitalization. Funding: GSK [207888]

Acknowledgements

Funding: GSK [207888]

Disclosure of Interests

Jeremiah Hwee Shareholder of: GSK, Employee of: GSK, Qinggong Fu Shareholder of: GSK, Employee of: GSK, Lorraine Harper Speakers bureau: Viopharm (2021), Roche (2017), Consultant of: GSK (2021), Viopharm (2021), Grant/research support from: Viopharm (researcher initiated project), MSD (researcher initiated project), Krishnarajah Nirantharakumar Consultant of: Boehringer Ingelheim (Consultancy on real world evidence), Grant/research support from: AstraZeneca, Vifor and Boehringer Ingelheim (Investigator led grants), Ruchika Goel: None declared, Rupert Jakes Shareholder of: GSK, Employee of: GSK

Metabolism-based ventilation monitoring and control method for COVID-19 risk mitigation in gymnasiums and alike places

Gymnasiums, fitness rooms and alike places offer exercise services to citizens, which play positive roles in promoting health and enhancing human immunity. Due to the high metabolic rates during exercises, supplying sufficient ventilation in these places is essential and extremely important especially given the risk of infectious respiratory diseases like COVID-19. Traditional ventilation control methods rely on a single CO₂ sensor (often placed at return air duct), which is often difficult to reflect the human metabolic rates accurately, and thus can hardly control the infection risk instantly. Thus, to ensure a safe and healthy environment in places with high metabolism, a real-time metabolism-based ventilation control method is proposed. A computer vision algorithm is developed to monitor human activities (regarding human motion amplitude and speed) and an artificial neural network is established for metabolic prediction. Case studies show that the proposed metabolism-based ventilation control method can reduce the infection probability down to 4.3-6.3% while saving 13% of energy in comparison with the strategy of fixed-fresh-air ventilation. In the development of healthy and sustainable society, gymnasiums and alike exercise places are essential and the proposed ventilation control method is a promising solution to decrease the risk of COVID-19 while preserving features of energy saving and carbon emission reduction.

[Comparison of clinical features between sporadic pancreatic neuroendocrine tumors and those associated with multiple endocrine neoplasia type 1]

Objective: To compare the clinical features of multiple endocrine adenoma type 1 (MEN-1) associated pancreatic neuroendocrine neoplasms (pNENs) as well as sporadic pNENs. Methods: The clinical data of 28 sporadic pNENs patients and 10 MEN-1-related pNENs patients admitted to the First Affiliated Hospital of Nanjing Medical University from January 2010 to June 2021 were collected. Meanwhile, by searching PubMed database and reviewing the clinical data of 20 foreign patients with MEN-1-related pNENs which were reported at the same time.Compare and analyze the similarities and differences between MEN1-associated pNENs and sporadic pNENs in clinical features, such as family history, blood tests, pathological diagnostic indicators, tumor grade, stage and metastasis, treatment and prognosis and so on. Results: A total of 58 pNENs patients were included, and there were 30 MEN1-related pNENs patients and 28 sporadic pNENs patients. Eighteen patients (60%) had a family history of MEN1-related pNENs, and the mean age of onset was (35.3±13.0)years. There were no patients (0) with family history of sporadic pNENs, and the mean age of onset was(55.3±13.4)years. In contrast, the differences in family history, age of onset and NSE were statistically significant(all P<0.05).Among the pathological diagnostic indicators, there were 19 patients (63.3%) with Grade G2 of MEN1-related pNENs, and 25 patients (83.3%) with somatostatin receptor 2(SSTR2) negative. In sporadic pNENs, there were 16 patients (57.1%) with Grade G2 and 9 patients (32.1%) with SSTR2 negative. The differences in pathological grade, immunohistochemistry (Chromogranin A, CD56, and somatostatin receptor 2, SSTR2) between the two groups were statistically significant(all P<0.05). In terms of tumor staging and metastasis, 21 patients with MEN-1-related pNENs had metastasis (70%) and 20 patients with stage Ⅰ and Ⅱ AJCC (71%) in all. Eight patients with sporadic pNENs had metastasis (26.7%) and 8 patients were with stage Ⅰ and Ⅱ AJCC (28.6%). By contrast, the differences in total metastasis rate, AJCC stage and distant metastasis between the two groups were statistically significant(all P<0.05). In terms of treatment and prognosis, there was no statistical significance in the differences between surgical treatment and prognosis (P>0.05), and the difference was also not statistically significant in survival rate between them (P>0.05). Conclusions: There are no significant differences between MEN1-related pNENs and sporadic pNENs in terms of treatment, prognosis, and survival rate, but there are significant differences in clinical features, pathological features and the staging and grading of tumors. The rate of tumor grade, stage and metastasis of sporadic pNENs is higher.

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks

Membrane proteins are an essential part of the body's ability to maintain normal life activities. Further research into membrane proteins, which are present in all aspects of life science research, will help to advance the development of cells and drugs. The current methods for predicting proteins are usually based on machine learning, but further improvements in prediction effectiveness and accuracy are needed. In this paper, we propose a dynamic deep network architecture based on lifelong learning in order to use computers to classify membrane proteins more effectively. The model extends the application area of lifelong learning and provides new ideas for multiple classification problems in bioinformatics. To demonstrate the performance of our model, we conducted experiments on top of two datasets and compared them with other classification methods. The results show that our model achieves high accuracy (95.3 and 93.5%) on benchmark datasets and is more effective compared to other methods.

A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction

Calculating and predicting drug-target interactions (DTIs) is a crucial step in the field of novel drug discovery. Nowadays, many models have improved the prediction performance of DTIs by fusing heterogeneous information, such as drug chemical structure and target protein sequence and so on. However, in the process of fusion, how to allocate the weight of heterogeneous information reasonably is a huge challenge. In this paper, we propose a model based on Q-learning algorithm and Neighborhood Regularized Logistic Matrix Factorization (QLNRLMF) to predict DTIs. First, we obtain three different drug-drug similarity matrices and three different target-target similarity matrices by using different similarity calculation methods based on heterogeneous data, including drug chemical structure, target protein sequence and drug-target interactions. Then, we initialize a set of weights for the drug-drug similarity matrices and target-target similarity matrices respectively, and optimize them through Q-learning algorithm. When the optimal weights are obtained, a new drug-drug similarity matrix and a new drug-drug similarity matrix are obtained by linear combination. Finally, the drug target interaction matrix, the new drug-drug similarity matrices and the target-target similarity matrices are used as inputs to the Neighborhood Regularized Logistic Matrix Factorization (NRLMF) model for DTIs. Compared with the existing six methods of NetLapRLS, BLM-NII, WNN-GIP, KBMF2K, CMF, and NRLMF, our proposed method has achieved better effect in the four benchmark datasets, including enzymes(E), nuclear receptors (NR), ion channels (IC) and G protein coupled receptors (GPCR).

Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map

DNA contains the genetic information for the synthesis of proteins and RNA, and it is an indispensable substance in living organisms. DNA-binding proteins are an enzyme, which can bind with DNA to produce complex proteins, and play an important role in the functions of a variety of biological molecules. With the continuous development of deep learning, the introduction of deep learning into DNA-binding proteins for prediction is conducive to improving the speed and accuracy of DNA-binding protein recognition. In this study, the features and structures of proteins were used to obtain their representations through graph convolutional networks. A protein prediction model based on graph convolutional network and contact map was proposed. The method had some advantages by testing various indexes of PDB14189 and PDB2272 on the benchmark dataset.

[Prognostic significance of DEK-NUP214 fusion gene in patients with acute myeloid leukemia after allogeneic hematopoietic stem cell transplantation]

Objective: To investigate the dynamic change and clinical impact of DEK-NUP214 fusion gene in patients with acute myeloid leukemia (AML) receiving allogeneic hematopoietic stem cell transplantation (allo-HSCT). Methods: Real-time quantitative polymerase chain reaction (RQ-PCR) and multicolor flow cytometry (FCM) were used to detect DEK-NUP214 gene expression and leukemia-associated immunophenotype (LAIP) in 15 newly diagnosed patients with positive DEK-NUP214 and receiving allo-HSCT from September 2012 to September 2017 at Peking University People's Hospital. The clinical outcome was analyzed using Kaplan-Meier survival curves. The impact of DEK-NUP214 expression was analyzed by log-rank test. Results: The subjects were followed-up with a median period of 657 (62-2 212) days. The median DEK-NUP214 expression level at diagnosis was 488% (274%-1 692%). Thirteen patients achieved complete remission before allo-HSCT. Thirteen patients had a residual DEK-NUP214 expression of 0.38% (0.029%-738.9%) before allo-HSCT. After allo-HSCT, DEK-NUP214 expression in 9/13 patients remained positive, which dropped by around 500 folds (5.7-5 663.0 folds) within a month post-transplant. Five patients died and 2 patients relapsed. The 3-year cumulative incidence of relapse in patients with positive DEK-NUP214 before transplant was 17.5%±11.3% and the 3-year overall survival was 60.5%±13.8%. After allo-HSCT, DEK-NUP214-negative patients had a better outcome. Conclusion: Quantitative monitor of DEK-NUP214 fusion gene could be a sensitive indicator of MRD status after allo-HSCT.

Research on RNA secondary structure predicting via bidirectional recurrent neural network

BACKGROUND

RNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary structure with pseudoknots has been proved to be an NP-hard problem. Traditional machine learning methods can not effectively apply protein sequence information with different sequence lengths to the prediction process due to the constraint of the self model when predicting the RNA secondary structure. In addition, there is a large difference between the number of paired bases and the number of unpaired bases in the RNA sequences, which means the problem of positive and negative sample imbalance is easy to make the model fall into a local optimum. To solve the above problems, this paper proposes a variable-length dynamic bidirectional Gated Recurrent Unit(VLDB GRU) model. The model can accept sequences with different lengths through the introduction of flag vector. The model can also make full use of the base information before and after the predicted base and can avoid losing part of the information due to truncation. Introducing a weight vector to predict the RNA training set by dynamically adjusting each base loss function solves the problem of balanced sample imbalance.

RESULTS

The algorithm proposed in this paper is compared with the existing algorithms on five representative subsets of the data set RNA STRAND. The experimental results show that the accuracy and Matthews correlation coefficient of the method are improved by 4.7% and 11.4%, respectively.

CONCLUSIONS

The flag vector introduced allows the model to effectively use the information before and after the protein sequence; the introduced weight vector solves the problem of unbalanced sample balance. Compared with other algorithms, the LVDB GRU algorithm proposed in this paper has the best detection results.

Empirical Potential Energy Function Toward ab Initio Folding G Protein-Coupled Receptors

Approximately 40-50 percent of all drugs targets are G protein-coupled receptors (GPCRs). Three-dimensional structure of GPCRs is important to probe their biophysical and biochemical functions and their pharmaceutical applications. Lacking reliable and high quality free function is one of the ugent problems of computational predicting the three-dimensional structure in this community. We proposed a GPCR-specified energy function composed of four novel empirical potential energy terms: a two-dimensional contact energy force field, knowledge-based helix pair connection distance energy term, knowledge-based helix pair angle restraint energy term and a disulfide bond energy term. To validate the energy function, we employed an ab initio GPCR three-dimensional structure predictor to test if the energy function improved the accuracy of prediction. We evaluated 28 solved GPCRs and found that 21(75 percent) targets were correctly folded (TM-score>0.5). Also, the average TM-score using the energy function was 0.54, which was improved 134 percent than the TM-score 0.23 for MODELLER energy function and 170 percent than the TM-score 0.20 for Rosetta membrane energy function. The results confirmed that our empirical potential energy function toward ab initio folding is competitive to state-of-the-art solutions for structural prediction of GPCRs.

POS0885 HIGH INCIDENCE AND MORTALITY OF PNEUMOCYSTIS JIROVECI INFECTION IN ANTI-MDA5-ANTIBODY POSITIVE DERMATOMYOSITIS: EXPERIENCE FROM A SINGLE CENTER

Background:

Idiopathic inflammatory myopathies (IIM) was associated with a significantly higher risk of opportunistic infections that including Pneumocystis jiroveci pneumonia(PJP) which is potentially fatal opportunistic infection. However, no prior studies have evaluated the PJP infection in subtypes of IIM.

Objectives:

To investigate the incidence rate and mortality rate of PJP infection in subgroups of IIM patients according to myopathy specific antibodies.

Methods:

In the first part, we reviewed 463 consecutive patients with IIM retrospectively to analyze incidence of PJP infection. In the next part, we enrolled 30 consecutive PJP infection patients with any rheumatic disease was to identify the mortality rate and risk factors. Kaplan-Meier curve with log rank test was used to access differences in survival. Univariate and multivariate analyses were performed to identify prognostic factors using Cox regression.

Results:

We found that 12(7.5%) PJP cases occurred in 160 anti-MDA5-ab-positive DM patients, while only two (0.7%) PJP cases were found in 303 anti-MDA5-ab-negtive DM/PM patients(P < 0.05). PJP infection typically happened in the first two months of the treatment for anti-MDA5-ab-positive DM patients who have a significant decrease in the CD4+ T cell counts and Lymphocyte counts (P < 0.05). Only two (16.7%) anti-MDA5-ab-positive DM patients recover from PJP, with lethally higher mortality than those PJP infection with other rheumatic diseases (83.3% vs. 38.9%, P < 0.05). We found no association between the time to anti-PJP treatment and treatment outcomes in anti-MDA5-ab-positive DM; yet we confirmed in PJP infection with other rheumatic diseases that anti-PJP treatment within 6 days crucially increased the survival (P < 0.05).

Conclusion:

PJP infection has alarming high incidence and mortality in anti-MDA5-ab-positive DM patients. Unlike PJP infection with other rheumatic diseases, timely treatment for PJP doesn’t improve the prognosis of this particular subtype. Therefore, the necessity of further study of PJP prophylaxis treatment in anti-MDA5-ab-positive DM patients is verified.

References:

[1]Hsu CY, et al. Comparing the burdens of opportunistic infections among patients with systemic rheumatic diseases: a nationally representative cohort study. ARTHRITIS RES THER 2019, 21(1):211.

Acknowledgements:

The authors thank Dr. An Sun,

Disclosure of Interests:

None declared

Effects of lipids and surfactants on the fermentation production of echinocandin B by Aspergillus nidulans

AIMS

Echinocandin B (ECB) is a kind of lipopeptide antifungal antibiotic, as well as the key precursor of antifungal drug Anidulafungin. Its efficient bioproduction plays an important role in promoting the industrial production of Anidulafungin.

METHODS AND RESULTS

In this study, methyl oleate and Tween 80 were firstly used to enhance the ECB fermentation by Aspergillus nidulans, the results showed that the ECB titre was significantly enhanced with the addition of methyl oleate and Tween 80. Among the lipids, methyl oleate was found to play a pivotal role in increasing the ECB titre to 2123 mg l^-1 , which was more than five times higher than that of the control. The addition of Tween 80 in the medium resulted in ECB titre increased to 2584 mg l^-1 . The scanning electron microscope (SEM) and N-phenyl-1-naphthylamine (NPN) assay indicated that Tween 80 could influence the cell membrane permeability of A. nidulans, and enhance the intracellular and extracellular substance exchange, therefore lead to the increasing of ECB titre.

CONCLUSIONS

Methyl oleate and Tween 80 are optimal carbon sources and surfactants for efficient ECB biosynthesis respectively.

SIGNIFICANCE AND IMPACT OF THE STUDY

Surfactant was used in ECB fermentation for the first time, which provided feasible ideas for optimizing the fermentation process of other fungi.

[Correlation of cerebrospinal fluid amyloid β-protein 42 and neurofilament light protein levels with postoperative neurocognitive dysfunction in elderly patients]

OBJECTIVE

To detect cerebrospinal fluid levels of amyloid beta- protein 42 (Aβ42) and neurofilament light protein (NFL) and explore their correlation with postoperative neurocognitive dysfunction (PNCD) in elderly patients.

OBJECTIVE

A total of 90 elderly patients undergoing hip or knee replacement with joint epidural anesthesia in our Hospital between January, 2017 and December, 2018 were recruited in this study. The levels of Aβ42 and NFL in the cerebrospinal fluid were detected using ELISA. Simple cognitive status assessment scale (MMSE) was used to evaluate the cognitive status of the patients 1 day before and 7 days after the surgery. All the patients underwent neurocognitive function tests, and the z-score method was used to determine the occurrence of PNCD. Spearman rank correlation analysis was used to analyze the correlation of Aβ42 and NFL levels in the cerebrospinal fluid with MMSE scores. Receiver operating characteristic curve (ROC) was used to analyze the predictive value of cerebrospinal fluid Aβ42 and NFL levels for PNCD.

OBJECTIVE

PNCD occurred in 38 of the 90 elderly patients, with an incidence of 42.2%. The level of Aβ42 in the cerebrospinal fluid was significantly lower in PNCD group than in the nonPNCD group (1.96 vs 2.54 ng/mL; t=3.29, P < 0.05); the concentration of NFL in the cerebrospinal fluid was significantly higher in PNCD group than in non- PNCD group (4.59 vs 3.16 ng/mL; t=3.72, P < 0.05). Aβ42 level in the cerebrospinal fluid was positively correlated while NFL was negatively correlated with the MMSE score of the patients (r=-0.659, P < 0.05; r=-0.626, P < 0.05). ROC curve analysis showed that the area under the curve (AUC) of cerebrospinal fluid Aβ42 and NFL levels were 0.744 and 0.768, respectively; the AUC of their combination was 0.847 for prediction of PNCD.

OBJECTIVE

Elderly patients with PNCD have significantly higher levels of Aβ42 and NFL in the cerebrospinal fluid than those without PNCD. Both Aβ42 and NFL levels in the cerebrospinal fluid can help to predict the occurrence of POCD in elderly patients, and their combination has a higher diagnostic value.

[The effect of donor cytomegalovirus serological status on the outcome of allogeneic stem cell transplantation]

Objective: Donor cytomegalovirus (CMV) serological negative status may have an adverse effect on the outcome of allogeneic hematopoietic stem cell transplantation (allo-HSCT), while there is inadequate data for Chinese people. This study is to explore the impact of donor CMV serological status on the outcome of CMV seropositive patients receiving allo-HSCT. Methods: Our study retrospectively analyzed 16 CMV seropositive patients with hematological malignancies receiving allogeneic grafts from CMV seronegative donors (antibody IgG negative) at Peking University People's Hospital from March 2013 to March 2020, which was defined as D^-/R⁺ group. The other 64 CMV seropositive patients receiving grafts from CMV seropositive donors at the same period of time were selected as matched controls through a propensity score with 1∶4 depending on age, disease state and donor-recipient relationship (D⁺/R⁺ group). Results: Patients in D^-/R⁺ group developed CMV DNAemia later than patients in the D⁺/R⁺ group (+37 days vs. +31 days after allo-HSCT, P=0.011), but the duration of CMV DNAemia in D^-/R⁺ group was longer than that of D⁺/R⁺ group (99 days vs. 34 days, P=0.012). The rate of CMV reactivation 4 times or more in D^-/R⁺ group was 4/16, significantly higher than that of D⁺/R⁺ group (4.7%, 3/64, P=0.01). The incidences of refractory CMV DNAemia (14/16 vs. 56.3%, P=0.021) and CMV disease (4/16 vs. 4.7%, P=0.01) in D^-/R⁺ group were both higher than those in D⁺/R⁺ group. In addition, the application of CMV-CTL as the second-line antiviral treatment in D^-/R⁺ group was more than that in D⁺/R⁺ group. Univariate analysis and multivariate analysis suggested that CMV serological negativity is an independent risk factor for refractory CMV DNAemia and the duration of CMV infection. The cumulative incidence of aGVHDⅡ-Ⅳ, cGVHD, 3-year probability of NRM, overall survival, and the cumulative incidence of relapse were all comparable in two groups. Conclusions: Although there is no significant effect on OS and NRM, the incidence of refractory CMV DNAemia, the frequency of virus reactivation, and the development of CMV disease in D^-/R⁺ group are higher than those in controls. Therefore, CMV seropositive donors are preferred for CMV seropositive patients.

Cloud data acquisition and processing model based on blockchain

Data outsourcing has gradually become a mainstream solution, but once data is outsourced, data owners will without the control of the data hardware, there is a possibility that the integrity of the data will be destroyed objectively. Many current studies have achieved low network overhead cloud data set verification by designing algorithmic structures (e.g., hashing, Merkel verification trees); however, cloud service providers may not recognize the incompleteness of cloud data to avoid liability or business factors fact. There is a need to build a secure, reliable, non-tamperable, and non-forgeable verification system for accountability. Blockchain is a chain-like data structure constructed by using data signatures, timestamps, hash functions, and proof-of-work mechanisms. Using blockchain technology to build an integrity verification system can achieve fault accountability. Blockchain is a chain-like data structure constructed by using data signatures, timestamps, hash functions, and proof-of-work mechanisms. Using blockchain technology to build an integrity verification system can achieve fault accountability. This paper uses the Hadoop framework to implement data collection and storage of the HBase system based on big data architecture. In summary, based on the research of blockchain cloud data collection and storage technology, based on the existing big data storage middleware, a large flow, high concurrency and high availability data collection and processing system has been realized.

A Building Energy Consumption Prediction Method Based on Integration of a Deep Neural Network and Transfer Reinforcement Learning

With respect to the problem of the low accuracy of traditional building energy prediction methods, this paper proposes a novel prediction method for building energy consumption, which is based on the seamless integration of the deep neural network and transfer reinforcement learning (DNN-TRL). The method introduces a stack denoising autoencoder to extract the deep features of the building energy consumption, and shares the hidden layer structure to transfer the common information between different building energy consumption problems. The output of the DNN model is used as the input of the Sarsa algorithm to improve the prediction performance of the target building energy consumption. To verify the performance of the DNN-TRL algorithm, based on the data recorded by American Power Balti Gas and Electric Power Company, and compared with Sarsa, ADE-BPNN, and BP-Adaboost algorithms, the experimental results show that the DNN-TRL algorithm can effectively improve the prediction accuracy of the building energy consumption.

[Therapeutic effect of tonsillectomy on IgA nephropathy after kidney transplantation]

Objective: To observe the clinical effect of tonsillectomy on IgA nephropathy (IgAN) after renal transplantation. Methods: From March 2011 to July 2018, 201 kidney transplantation recipients who were diagnosed of IgAN by transplant renal biopsy in the Department of Organ Transplantation of the First Affiliated Hospital of Sun Yat-sen University were retrospectively reviewed, of which 18 patients underwent tonsillectomy after renal biopsy. The clinical data of the 18 patients were collected, patient and kidney survival time and function of the transplanted kidney were analyzed. Results: Of the 18 recipients, 13 were male and 5 were female, with an average age of (36.0±10.9) years. All 18 patients survived during follow-up. Two patients returned to dialysis treatment 10 months and 14 months after tonsillectomy, respectively. The creatinine was 94 (78, 133) μmol/L, 95 (74, 139) μmol/L, 106 (87, 158) μmol/L and 95(81, 147) μmol/L before tonsillectomy, 3 months, 1 year and 2 years after tonsillectomy, respectively (P=0.206). Urinary protein quantification was 0.31 (0.16, 1.38) g/24 h, 0.34 (0.10, 1.42) g/24 h, 0.33 (0.11, 0.56) g/24 h and 0.25 (0.10, 0.50) g/24 h at the same time points, respectively (P=0.104). The two patients who returned to dialysis were diagnosed of IgAN by transplant renal biopsy because of elevated creatinine, proteinuria and hematuria, 9 years and 4 years after kidney transplant respectively. Renal biopsy suggested that glomerular and segmental sclerosis were 7/24, 5/24 and 1/6, 2/6, respectively. Additionally, interstitial fibrosis and tubular atrophy (IF/TA) were both occupied 30% in the biopsies, and tonsillectomy was performed 461 days and 1 077 days after diagnosis of IgAN, respectively. Conclusions: Tonsillectomy can maintain the stability of renal function and prevent the aggravation of proteinuria in IgAN patients after renal transplantation. However, if pathology suggests obvious glomerulosclerosis or IF/TA, tonsillectomy may not be effective.

[Effects of FoxO6 on proliferation and invasion of colorectal cancer cells]

Objective: To investigate the effects and the mechanism of FoxO6 on the proliferation and invasion of colorectal cancer cells. Methods: FoxO6 siRNA was transfected into colorectal cancer cell HCT116 and SW480. The overexpression vector pcDNA.3.1-c-Myc was constructed and co-transfected into HCT116 and SW480 cells with FoxO6 siRNA. Real-time fluorescent quantitative PCR (RT-qPCR) and western blot were used to detect the mRNA and protein expressions of FoxO6, c-Myc, and p21 in HCT116 and SW480 cells. Bromodeoxyuridine (BrdU) was used to detect cell proliferation and Transwell assay was performed to detect the invasion ability of these cells. SW480 cells transfected with FoxO6 shRNA lentivirus (LV-FoxO6) and were injected into the right armpit of BAL b/c nude mice to construct a tumor-bearing mode and the tumor volumes were measured on the days of 10, 13, 16, 19, 22, and 25 after injection. Results: The FoxO6 mRNA were 0.91±0.04, 1.72±0.07, and 2.03±0.06, and protein expression were 0.70±0.04, 1.35±0.08, and 1.56±0.07 in normal colon cell FHC, colorectal cancer cells HT116 and SW480, respectively. The protein and mRNA levels of FoxO6 in HCT116 and SW480 were significantly higher than those in FHC (both P<0.05). Knockdown of FoxO6 in HCT116 and SW480 cells decreased the mRNA and protein expressions of FoxO6 (both P<0.05), the cell proliferation ability (absorbances were 0.26±0.07 and 0.27±0.06, both P<0.05), cell invasion ability (the invaded cell numbers were 42.3±3.3 and 45.7±4.1, both P<0.05), and the mRNA and protein expressions of c-Myc, while increased the mRNA and protein expressions of p21 (both P<0.01). Overexpression of Myc in FoxO6 silenced HCT116 and SW480 cells decreased the expression of p21, while increased the cell proliferation ability (absorbances were 0.54±0.09 and 0.58±0.07, both P<0.01) and invasion ability (the invaded cell numbers were 79.2±5.9 and 80.5±6.4, both P<0.01). On the 25th day after cell inoculation in nude mice, the tumor volume of LV-FoxO6 group was (190.6±36.2) mm(3), significantly lower than (437.8.6±69.2) mm(3) of LV-NC group (P<0.05). Conclusion: FoxO6 promotes the proliferation and invasion of colorectal cancer cells through facilitating c-Myc mediated p21 expression inhibition.

Influence of built environment on quality of life changes in African-American patients with non-metastatic breast cancer

Research links the built environment to health outcomes, but little is known about how this affects quality of life (QOL) of African American breast cancer patients, especially those residing in disadvantaged neighborhoods. Using latent trajectory models, we examined whether the built environment using Google Street View was associated with changes in QOL over a 2-year follow-up in 228 newly diagnosed African American breast cancer patients. We measured QOL using the RAND 36-Item Health Survey subscales. After adjusting for covariates, improvement in emotional well-being and pain over time was greater for women living on streets with low-quality (vs. high-quality) sidewalks.

Ranking near-native candidate protein structures via random forest classification

Background

In ab initio protein-structure predictions, a large set of structural decoys are often generated, with the requirement to select best five or three candidates from the decoys. The clustered central structures with the most number of neighbors are frequently regarded as the near-native protein structures with the lowest free energy; however, limitations in clustering methods and three-dimensional structural-distance assessments make identifying exact order of the best five or three near-native candidate structures difficult.

Results

To address this issue, we propose a method that re-ranks the candidate structures via random forest classification using intra- and inter-cluster features from the results of the clustering. Comparative analysis indicated that our method was better able to identify the order of the candidate structures as comparing with current methods SPICKR, Calibur, and Durandal. The results confirmed that the identification of the first model were closer to the native structure in 12 of 43 cases versus four for SPICKER, and the same as the native structure in up to 27 of 43 cases versus 14 for Calibur and up to eight of 43 cases versus two for Durandal.

Conclusions

In this study, we presented an improved method based on random forest classification to transform the problem of re-ranking the candidate structures by an binary classification. Our results indicate that this method is a powerful method for the problem and the effect of this method is better than other methods.