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Constructing energy-efficient database systems to reduce economic costs and environmental impact has been studied for 10 years. With the emergence of the big data age, along with the data-centric and data-intensive computing trend, the great amount of energy consumed by database systems has become a major concern in a society that pursues Green IT. However, to the best of our knowledge, despite the importance of this matter in Green IT, there have been few comprehensive or systematic studies conducted in this field. Therefore, the objective of this article is to present a literature survey with breadth and depth on existing energy management techniques for database systems. The existing literature is organized hierarchically with two major branches focusing separately on energy consumption models and energy-saving techniques. Under each branch, we first introduce some basic knowledge, then we classify, discuss, and compare existing research according to their core ideas, basic approaches, and main characteristics. Finally, based on these observations through our study, we identify multiple open issues and challenges, and provide insights for future research. It is our hope that our outcome of this work will help researchers develop more energy-efficient database systems.

In this paper, we propose a unified framework for an abstractive summarization method which uses the prompt language model and a pointer mechanism. The abstractive summarization problem usually includes a text encoder and a text decoder. Current methods usually employ an encoder-decoder architecture to condense and paraphrase a document. To better paraphrase a document, we propose a unified framework for an abstractive summarization model that only uses a topic-sensitive decoder. Our model has a prompt input module, a text decoder and a pointer mechanism. We apply our model to Xsum, Gigaword, and CNN/DailyMail summarization datasets, and experimental results demonstrate that our model has achieved state-of-the-art results on the Xsum dataset and comparable results on the other two datasets.

Background. Cancers of digestive system have high case-fatality rate. It is important to find more appropriate methods in diagnosing and predicting gastrointestinal malignances. And thrombospondin-2 (TSP-2) was reported to have the functions, although results were not identical. So we performed this meta-analysis to clarify the significance of TSP-2 in this area. Methods. PubMed, Embase, Web of Science, Cochrane Library, and Clinicaltrial.gov were searched for relevant studies. Data were extracted from these involved records. For the meta-analysis of diagnostic test, bivariate mixed effect model was used to estimate diagnostic accuracy. For prognosis part, HRs and their 95% CIs were pooled to compare the overall survival (OS) and disease-free survival (DFS) between patients with high TSP-2 and low TSP-2. Results. Nine records were eligible for the analysis of diagnostic test. Pooled results were as follows: sensitivity 0.60 (0.52, 0.68), specificity 0.96 (0.91, 0.98), positive likelihood ratio (PLR) 15.4 (7.3, 32.2), negative likelihood ratio (NLR) 0.42 (0.34, 0.50), and diagnostic odds ratio (DOR) 37 (18, 76). While in prognosis part, 10 articles were included. Patients with increased TSP-2 had shorter OS ( HR = 1.64 , 95% CI = 1.21 -2.22); however, no difference was found in DFS between TSP-2 high and low groups ( HR = 1.44 , 95% CI = 0.28 -7.33). Conclusions. TSP-2, as a diagnostic marker, has a high specificity but a moderate sensitivity. Meanwhile, it plays a role in predicting OS. Therefore, making TSP-2 a routine assay could be beneficial to high-risk individuals and patients with digestive malignances.

Graph convolutional networks (GCNs) show great potential in recommendation applications, as they have excellent performance in propagation node information propagation and capturing high-order connectivity in user-item interaction graphs. However, in the current recommendation model based on GCN, incoming information from neighbors is aggregated during information propagation, and some of this information may be noisy due to negative information. Additionally, the over-smoothing problem occurs when the model layers are stacked too high. During the embedding learning of users in the graph convolution operation, an important factor is that high-order neighbor users with no interest are involved, leading to similar embedding for users with no interest. These issues can degrade the recommendation performance. To address these problems, this paper proposes a method called IMPLayerGCN. In this method, high-order graph convolution is performed within subgraphs, which are composed of users with similar interests and their interaction items. The higher-order graph convolution is carried out in the subgraph and the layer representation in the process of GCN re-information propagation and node update is refined. The convolution process uses a symmetrical matrix. This approach avoids the spread of negative information from higher-order neighbors to embedded learning.

Background and Aims. At present, a targeted drug has not been found for patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study assessed the effects of different therapeutic schedules on patients with COVID-19. Methods. The clinical characteristics, the prognosis of patients with western medicine (WM) treatment, and the combination of traditional Chinese medicine and western medicine (TCM-WM) treatment were retrospectively explored from January 20, 2020 to February 20, 2021. Results. 19 patients (15.20%) and 7 patients (5.60%) in the WM treatment group developed into severe type and critically ill, which were higher than 5 patients (9.43%) and 0 patients (0.00%) in the TCM-WM treatment group ( p = 0.306 and p = 0.08 ). The time from admission to severe in the WM treatment group was significantly shorter than that of the TCM-WM treatment group (7.5 vs. 11.2, p < 0.001 ). Compared with patients in the TCM-WM treatment group, the average stay time and the negative nucleic acid time of patients in the WM treatment group were both significantly longer (both p < 0.05 ). Besides, there existed no statistical difference for the safety of the two treatment options and nucleic acid test positive 14 days after discharge between the two groups. In line with the performance for severe patients, the average stay, the nucleic acid negative time, and the days of hormone therapy in the WM treatment group were all significantly longer than that of the TCM-WM treatment group (32.5 vs. 18.8, p < 0.001 ; 24.5 vs. 14.5, p < 0.001 ; 6.5 vs. 3.0, p < 0.001 ). Conclusions. TCM-WM has better performance in both the disease progression and treatment of severe patients. We recommended that timely TCM-WM should be treated with patients with COVID-19.

Seed cells are an important part of bone tissue engineering. In this study, we investigated methods for the isolation, culture and identification of rat bone marrow mesenchymal stem cells (BMSCs) in vitro. BMSCs from SD rats were isolated and cultured using the whole bone marrow adherent method. Morphological observation and flow cytometry were used for the detection of cell surface markers. The osteogenic and adipogenic differentiation of cultured cells was induced and evaluated. BMSCs could be effectively isolated and purified using the whole bone marrow adherent method. The primary BMSCs cultured in vitro formed spindle-shaped, spiral and vigorous cell colonies. The flow cytometry results showed that CD90 was highly expressed on the cell surface, but CD34 and CD45 were not expressed. After culture in osteogenic and adipogenic induction medium, positive Alizarin Red and Oil Red O staining was observed, indicating the potential for multidirectional differentiation. The whole bone marrow adherent method is simple and practical, which makes the isolation, culture and amplification of BMSCs in vitro easy, and the method has little effect on cell activity and is an effective method for obtaining high-purity BMSCs.

Hepatitis B surface antigen (HBsAg) persists after liver transplantation in almost all patients receiving HBsAg-positive grafts. Chronic hepatitis B virus (HBV) infection is one of the main causes of hepatocellular carcinoma (HCC). We aimed to investigate possible interactions between HBsAg-positive donors, HCC, HBV-related transplant indication, and long-term outcomes. This retrospective study enrolled 1176 patients from two centers between January 2015 and May 2019, of which 135 (11.5%) were HBsAg-positive and 1041 (88.5%) were HBsAg-negative donors. Cox regression models were fitted to study the association between variables and patient and graft survival. In univariate and multivariate analyses, the donor HBsAg status was not significantly associated with patient and graft survival in the entire cohort, but there was a significant interaction between HBsAg-positive donors and HCC, independent of HBV-related transplant indication. The cumulative incidence of patient and graft survival was significantly lower in the subgroup of HCC recipients receiving HBsAg-positive grafts, but no significant difference was found in recipients with benign liver disease. In a subgroup analysis of HCC recipients, HBsAg-positive donors were significantly associated with an increased risk of HCC recurrence (hazard ratio: 1.73; 95% confidence interval: 1.20-2.48; p = 0.003) and similar results were obtained after propensity score matching analysis. We showed excellent outcomes of using HBsAg-positive grafts in patients with benign liver disease, regardless of HBV-related transplant indications. However, positive grafts should be used with caution in recipients with HCC, which are associated with an increased risk of HCC recurrence.

To address the problem of inadequate feature extraction and binary code discrete optimization faced by deep hashing methods using a relaxation-quantization strategy, a novel deep attention-guided hashing method with pairwise labels (DAHP) is proposed to enhance global feature fusion, better learn the contextual information of image features to effectively enhance the feature representation, and solve the problem of losing feature information in discrete optimization by optimizing the loss function. First, we introduce a new concept called the anchor hash code generation(AHCG) algorithm, we train the ResNet with the position attention and channel attention mechanisms with the anchor points in Hamming space as supervised information, we fit the binary code representing the picture to the vicinity of each anchor point, and finally, we use the optimized loss function to calculate the pairwise loss and the anchor loss, allowing the hash function to generate hash code with strong discriminative power. The experiments were conducted on four benchmark datasets, and the retrieval accuracy of the proposed method outperformed the retrieval accuracies of the state-of-the-art methods.

The sixth-generation network (6G) is expected to achieve a fully connected world, which makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging distributed computing paradigm. In Vehicular Edge Computing (VEC), FL is used to protect consumer data privacy. However, using FL in VEC will lead to expensive communication overheads, thereby occupying regular communication resources. In the traditional FL, the massive communication rounds before convergence lead to enormous communication costs. Furthermore, in each communication round, many clients upload large quantity model parameters to the parameter server in the uplink communication phase, which increases communication overheads. Moreover, a few straggler links and clients may prolong training time in each round, which will decrease the efficiency of FL and potentially increase the communication costs. In this work, we propose an efficient-communication approach, which consists of three parts, including ``Customized'', ``Partial'', and ``Flexible'', known as FedCPF. FedCPF provides a customized local training strategy for vehicular clients to achieve convergence quickly through a constraint item within fewer communication rounds. Moreover, considering the uplink congestion, we introduce a partial client participation rule to avoid numerous vehicles uploading their updates simultaneously. Besides, regarding the diverse finishing time points of federated training, we present a flexible aggregation policy for valid updates by constraining the upload time. Experimental results show that FedCPF outperforms the traditional FedAVG algorithm in terms of testing accuracy and communication optimization in various FL settings. Compared with the baseline, FedCPF achieves efficient communication with faster convergence speed and improves test accuracy by 6.31% on average. In addition, the average communication optimization rate is improved by 2.15 times.

Camera surveillance is widely used in residential areas, highways, schools and other public places. The monitoring and scanning of sudden abnormal events depend on humans. Human anomaly monitoring not only consumes a lot of manpower and time but also has a large error in anomaly detection. Video anomaly detection based on AE (Auto-Encoder) is currently the dominant research approach. The model has a highly symmetrical network structure in the encoding and decoding stages. The model is trained by learning standard video sequences, and the anomalous events are later determined in terms of reconstruction error and prediction error. However, in the case of limited computing power, the complex model will greatly reduce the detection efficiency, and unnecessary background information will seriously affect the detection accuracy of the model. This paper uses the AE loaded with dynamic prototype units as the basic model. We introduce an attention mechanism to improve the feature representation ability of the model. Deep separable convolution operation can effectively reduce the number of model parameters and complexity. Finally, we conducted experiments on three publicly available datasets of real scenarios (UCSD Ped1, UCSD Ped2 and CUHK Avenue). The experimental results show that compared with the baseline model, the accuracy of our model improved by 1.9%, 1.4% and 6.6%, respectively, across the three datasets. Compared with many popular models, the validity of our model in anomaly detection is verified.

Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As a result of these two properties, we show that outliers are susceptible to a mechanism called fluctuation. This article proposes a method called fluctuation-based outlier detection (FBOD) that achieves a low linear time complexity and detects outliers purely based on the concept of fluctuation without employing any distance, density or isolation measure. Fundamentally different from all existing methods. FBOD first converts the Euclidean structure datasets into graphs by using random links, then propagates the feature value according to the connection of the graph. Finally, by comparing the difference between the fluctuation of an object and its neighbors, FBOD determines the object with a larger difference as an outlier. The results of experiments comparing FBOD with eight state-of-the-art algorithms on eight real-worlds tabular datasets and three video datasets show that FBOD outperforms its competitors in the majority of cases and that FBOD has only 5% of the execution time of the fastest algorithm. The experiment codes are available at: https://github.com/FluctuationOD/Fluctuation-based-Outlier-Detection.

Background We explored whether stem cell therapy was effective for animal models and patients with Crohn’s disease (CD). Methods We searched five online databases. The relative outcomes were analyzed with the aid of GetData Graph Digitizer 2.26 and Stata 16.0 software. The SYRCLE risk of bias tool and the MINORS tool were used to assess study quality. Results We evaluated 46 studies including 28 animal works (n = 567) and 18 human trials (n = 360). In the animal studies, the disease activity index dramatically decreased in the mesenchymal stem cell (MSC) treatment groups compared to the control group. Rats and mice receiving MSCs exhibited longer colons [mice: standardized mean difference (SMD) 2.84, P = 0.000; rats: SMD 1.44, P = 0.029], lower histopathological scores (mice: SMD − 4.58, p = 0.000; rats: SMD − 1.41, P = 0.000) and lower myeloperoxidase levels (SMD − 6.22, P = 0.000). In clinical trials, stem cell transplantation reduced the CD activity index (SMD − 2.10, P = 0.000), the CD endoscopic index of severity (SMD − 3.40, P = 0.000) and simplified endoscopy score for CD (SMD − 1.71, P = 0.000) and improved the inflammatory bowel disease questionnaire score (SMD 1.33, P = 0.305) compared to control values. CD patients maintained high remission rates for 3–24 months after transplantation. Conclusions Stem cell transplantation is a valuable supplementary therapy for CD.

Most existing recommendation methods focus on the improvement of recommender accuracy while ignoring the influence of recommended explanation. Recommender explainability is an efficient way to help consumers make much more suitable decisions and enhance their acceptance and trustfulness in recommender systems (RSs). Incorporating a knowledge graph (KG) into RS is a promising way to improve recommender results while enhancing the strength of explanation. In this paper, an interactive rules-guided recommender (IR-Rec) framework based on KG is proposed. First, an existing KG is enriched by introducing it to facts about e-commerce. Then, a number of paths are extracted from the enhanced KG for user-item interactions that are able to ascertain the underlying reason for recommendations according to the semantic strength of the KG. These paths are summarized into some common behavior rules that have the ability to explain the underlying motivations of users. According to the characteristics of users, items, and rules, different neural networks are designed, such as a graph convolutional network , to learn more accurate embeddings. Furthermore, recommendations that meet users’ personalized interests are made by combining the public behavior rules and their individual features. Extensive experiments are carried out on four Amazon datasets for top- K recommendation. All the results show that the proposed method performs better than other respective baselines and demonstrate the effectiveness of rule extraction for making recommendations.

Peng L, Huang YT, Chen J, et al. Cancer Manag Res. 2018;10:5471–5477. The Editor and Publisher of Cancer Management and Research wish to retract the published article. Concerns were raised regarding the alleged duplication of flow cytometry images in Figure 3. Specifically, The image for Figure 3A, Ctrl, appears to have regions of unexpected duplication with the image for Figure 3A, 2 Gy. The image for Figure 3A, 5 Gy, appears to have regions of unexpected duplication with the image for Figure 3A, Osthole. The authors agreed with the concerns that had been raised and provided data related to the study, however it was unable to verify the validity of the published work. Therefore, the decision was made to retract the article and the authors were notified of this. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

Hepatitis B surface antigen (HBsAg) loss or seroconversion is an ideal treatment endpoint for patients with chronic hepatitis B but is rarely achievable in hepatitis B e-antigen (HBeAg)-positive patients using existing treatment strategies. In this study, the effect of pegylated interferon (peg-IFN) alfa-2b plus tenofovir disoproxil fumarate (TDF), granulocyte-macrophage colony-stimulating factor (GM-CSF), and hepatitis B vaccine was evaluated. This randomized controlled trial was conducted at nine liver centers in Chinese university hospitals from May 2018 to July 2020. Patients (n = 303) enrolled were randomly administered peg-IFN-α-2b combined with TDF, GM-CSF, and hepatitis B vaccine (experimental group); peg-IFN-α-2b plus TDF (control group 2); or interferon-α-2b alone (control group 1). The primary efficacy endpoint was HBsAg seroconversion at 48 weeks and the secondary endpoint included safety. No differences in baseline HBsAg levels were observed among the groups. The primary endpoint was achieved in three (3.0%), one (1.03%), and one (1.19%) patient in the experimental group, control group 2, and control group 1, respectively. The incidence of HBsAg seroconversion at week 48 was not significantly different among the three groups (p = 0.629). However, the decrease in serum levels of HBsAg at week 48 was significantly higher in the experimental and control group 2 compared with that in control group 1 (p = 0.008 and 0.006, respectively). No significant difference between the experimental and control group 2 was observed (p = 0.619). Adverse events were not significantly different among the groups except for the lower incidence of neutropenia in the experimental group. Peg-IFN-α-2b combined with TDF, GM-CSF, and hepatitis B vaccine is not superior to peg-IFN-α-2b combined with TDF in HBeAg-positive naïve patients. Clinical Trials Registration: ChiCTR1800016173.

Channel attention mechanisms have attracted more and more researchers because of their generality and effectiveness in deep convolutional neural networks(DCNNs). However, the signal encoding methods of the current popular channel attention mechanisms are limited. For example, SENet uses the full-connection method to encode channel relevance, which is parameters-costly; ECANet uses 1D-Convolution to encode channel relevance, which is parameter fewer but can only encode per k adjacent channels in a fixed scale. This paper proposes a novel dilated efficient channel attention module(DECA), which consists of a novel multi-scale channel encoding method and a novel channel relevance feature fusion method. We empirically show that different scale channel relevance also contributes to performance, and fusing various scale channel relevance features can obtain more powerful channel feature representation. Besides, we widely use the weight-sharing method in the DECA module to make it more efficient. Specifically, we have applied our module to the real-life fire image detection task to evaluate its effectiveness. Extensive experiments on different backbone depths, detectors, and fire datasets have shown that the average performance boost of DECA module is more than 4.5% compare to the baselines. Meanwhile, DECA outperforms other state-of-art attention modules while keeping lower or comparable parameters in the experiments. The experimental results on different datasets also shown that the DECA module holds great generalization ability.

Background and Aims Timely and effective assessment scoring systems for predicting the mortality of patients with hepatitis E virus-related acute liver failure (HEV-ALF) are urgently needed. The present study aimed to establish an effective nomogram for predicting the mortality of HEV-ALF patients. Methods The nomogram was based on a cross-sectional set of 404 HEV-ALF patients who were identified and enrolled from a cohort of 650 patients with liver failure. To compare the performance with that of the model for end-stage liver disease (MELD) scoring and CLIF-Consortium-acute-on-chronic liver failure score (CLIF-C-ACLFs) models, we assessed the predictive accuracy of the nomogram using the concordance index (C-index), and its discriminative ability using time-dependent receiver operating characteristics (td-ROC) analysis, respectively. Results Multivariate logistic regression analysis of the development set carried out to predict mortality revealed that γ-glutamyl transpeptidase, albumin, total bilirubin, urea nitrogen, creatinine, international normalized ratio, and neutrophil-to-lymphocyte ratio were independent factors, all of which were incorporated into the new nomogram to predict the mortality of HEV-ALF patients. The area under the curve of this nomogram for mortality prediction was 0.671 (95% confidence interval: 0.602–0.740), which was higher than that of the MELD and CLIF-C-ACLFs models. Moreover, the td-ROC and decision curves analysis showed that both discriminative ability and threshold probabilities of the nomogram were superior to those of the MELD and CLIF-C-ACLFs models. A similar trend was observed in the validation set. Conclusions The novel nomogram is an accurate and efficient mortality prediction method for HEV-ALF patients.

BACKGROUND As human placenta-derived mesenchymal stem cells (hP-MSCs) exist in a physiologically hypoxic microenvironment, various studies have focused on the influence of hypoxia. However, the underlying mechanisms remain to be further explored. AIM The aim was to reveal the possible mechanisms by which hypoxia enhances the proliferation of hP-MSCs. METHODS A hypoxic cell incubator (2.5% O2) was used to mimic a hypoxic microenvironment. Cell counting kit-8 and 5-ethynyl-20-deoxyuridine incorporation assays were used to assay the proliferation of hP-MSCs. The cell cycle was profiled by flow cytometry. Transcriptome profiling of hP-MSCs under hypoxia was performed by RNA sequencing. CD99 mRNA expression was assayed by reverse transcription-polymerase chain reaction. Small interfering RNA-mediated hypoxia-inducible factor 1α (HIF-1α) or CD99 knockdown of hP-MSCs, luciferase reporter assays, and the ERK1/2 signaling inhibitor PD98059 were used in the mechanistic analysis. Protein expression was assayed by western blotting; immunofluorescence assays were conducted to evaluate changes in expression levels. RESULTS Hypoxia enhanced hP-MSC proliferation, increased the expression of cyclin E1, cyclin-dependent kinase 2, and cyclin A2, and decreased the expression of p21. Under hypoxia, CD99 expression was increased by HIF-1α. CD99-specific small interfering RNA or the ERK1/2 signaling inhibitor PD98059 abrogated the hypoxia-induced increase in cell proliferation. CONCLUSION Hypoxia promoted hP-MSCs proliferation in a manner dependent on CD99 regulation of the MAPK/ERK signaling pathway in vitro.

The convolution module in Conformer is capable of providing translationally invariant convolution in time and space. This is often used in Mandarin recognition tasks to address the diversity of speech signals by treating the time-frequency maps of speech signals as images. However, convolutional networks are more effective in local feature modeling, while dialect recognition tasks require the extraction of a long sequence of contextual information features; therefore, the SE-Conformer-TCN is proposed in this paper. By embedding the squeeze-excitation block into the Conformer, the interdependence between the features of channels can be explicitly modeled to enhance the model’s ability to select interrelated channels, thus increasing the weight of effective speech spectrogram features and decreasing the weight of ineffective or less effective feature maps. The multi-head self-attention and temporal convolutional network is built in parallel, in which the dilated causal convolutions module can cover the input time series by increasing the expansion factor and convolutional kernel to capture the location information implied between the sequences and enhance the model’s access to location information. Experiments on four public datasets demonstrate that the proposed model has a higher performance for the recognition of Mandarin with an accent, and the sentence error rate is reduced by 2.1% compared to the Conformer, with only 4.9% character error rate.

Neural headline generation models have recently shown great results since neural network methods have been applied to text summarization. In this paper, we focus on news headline generation. We propose a news headline generation model based on a generative pre-training model. In our model, we propose a rich features input module. The headline generation model we propose only contains a decoder incorporating the pointer mechanism and the n-gram language features, while other generation models use the encoder-decoder architecture. Experiments on news datasets show that our model achieves comparable results in the field of news headline generation.

As one of the most popular high-performance data processing technology, existing task and resource scheduling strategies for stream computing platforms are suffering from the problem of triggering hysteresis, which seriously affects the cluster performance. To address this problem, the idea of performance prediction based on timeline series data is proposed. Firstly, the performance variation rule of the stream processing platform is analyzed, which provides a basis for proposing the performance prediction model. Secondly, the basic topology paradigm, periodic load prediction model, and real-time throughput prediction model are proposed as the theoretical foundation for performance prediction. Thirdly, the performance prediction algorithm is proposed to predict the variation trend of the processing load and throughput of the cluster. The processing load is predicted periodically while the throughput of the cluster is predicted in a real-time manner. Finally, the performance evaluation algorithm is proposed to evaluate the cluster performance based on the prediction results and trigger the corresponding scheduling strategies in advance. The experimental results showed that the prediction accuracy of the proposed algorithm meets the requirements in practical applications. Meanwhile, the proposed method improves the performance of stream computing performance by triggering the scheduling strategies in advance.

Rationale: Acute lung injury (ALI)-recruited mononuclear phagocytes play a pivotal role in lung injury and repair. This study investigated the types of recruited mononuclear phagocytes and the immunotherapeutic effects of allograft mesenchymal stem cells (MSCs) in a mouse model of lipopolysaccharide (LPS)-induced ALI. Methods: C57BL/6 mice were orotracheally instilled with LPS (20 mg/kg). Compact bone-derived MSCs were administered orotracheally 4 h after LPS inhalation. Mononuclear phagocytes recruited in the lung tissues were characterized at different timepoints by high-dimensional analysis including flow cytometry, mass cytometry, and single-cell RNA sequencing. Results: Eight mononuclear phagocyte subsets recruited to LPS-challenged lungs were precisely identified. On day 3 after LPS administration, both Ly6ChiCD38+ and Ly6ClowCD38+ monocytes were recruited into acutely injured lungs, which was associated with increased secretion of neutrophil chemokines. Ly6ChiCD38+ monocytes differentiated into M1 macrophages on day 3, and subsequently differentiated into CD38+ monocyte-derived dendritic cells (mo-DCs) on day 7, while Ly6ClowCD38+ monocytes differentiated into CD11b+CD38+ DCs on day 7. When ALI mice were treated with MSCs, the mortality significantly reduced. Notably, MSCs reduced the amount of M1 macrophages and reduced the secretion of neutrophil chemokines on day 3. Furthermore, MSCs reduced the number of CD38+ mo-DCs and CD11b+CD38+ DCs on day 7, suppressing the antigen presentation process. Recruited mononuclear phagocyte subsets with a high level of CD38 exhibited an activated phenotype and could secrete higher levels of cytokines and chemokines. Conclusions: This study characterized the dynamic functions and phenotypes of recruited mononuclear phagocytes in ALI mice and MSC-treated ALI mice.

Background Lymphatic metastasis is one of the main factors affecting prognosis in esophageal squamous cell carcinoma (ESCC). Vascular endothelial growth factor-C (VEGF-C) is an important factor that promotes lymphangiogenesis. Survivin also plays a significant role in lymphatic invasion. However, the role and mechanism of their co-expression are still unclear in ESCC. The purpose of this study was to investigate whether the co-expression of VEGF-C and survivin could be a potential marker to predict patient prognosis and survival in ESCC. Methods The levels of VEGF-C, vascular endothelial growth factor receptor 3 (VEGFR-3), survivin, and Ki-67 were determined by immunohistochemistry (IHC) in 97 ESCC patient tumors. The correlations of co-expression of VEGF-C and survivin with pathological features and survival results were also assessed. Results High VEGF-C expression was observed in 64.9% of the patients and significantly correlated with T stage (P=0.024), node status (P=0.038), and lymph node metastasis (P=0.015). High survivin expression was significantly associated with T stage (P=0.013), N stage (P=0.016), lymph node metastasis (P=0.005), and differentiation (P=0.044) in 67.0% of the patients. Co-expression of VEGF-C and survivin (V+S+) was significantly associated with T stage (P

In the era of big data, as the amount of streaming data continues to increase, stream processing tasks (SPTs) face serious challenges in real-time processing scenarios with low latency and high throughput. However, much of the current literature on the performance of SPTs pays attention to the reactive approach, which cannot well avoid the problem of system crashes due to the inherent performance volatility. In this paper, a novel throughput prediction method based on ExtraTree for SPTs is presented to address these challenges. A volatility detection algorithm was proposed to obtain the reasonable metric values after the performance volatility of SPTs was studied. Moreover, a selection algorithm of regression function was proposed to output the performance values of SPTs under a relative stead state. Furthermore, a ExtraTree-based algorithm was proposed to predict the throughput of SPTs. The experimental results from two open-source benchmarks running on Apache Flink, a popular stream processing system (SPS), indicated that the average of the accuracy and efficiency of the proposed method could achieve 90.535% and 0.835 s/10,000 samples, which proved the effectiveness of the proposed method on the task of predicting the throughput of SPTs.

Bis (2-ethylhexyl)-2,3,4,5-tetrabromophthalate (TBPH) is an extensively used novel brominated flame retardant that is present ubiquitously in the environment and in biota. However, there is inadequate data on its potential hepatotoxicity to humans. In this study, high-coverage quantitative metabolomics based on

The effect of hypoxia on mesenchymal stem cells (MSCs) is an emerging topic in MSC biology. Although long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) are reported to play a critical role in regulating the biological characteristics of MSCs, their specific expression and co-expression profiles in human placenta-derived MSCs (hP-MSCs) under hypoxia and the underlying mech anisms of lncRNAs in hP-MSC biology are unknown.To reveal the specific expression profiles of lncRNAs in hP-MSCs under hypoxia and initially explored the possible mechanism of lncRNAs on hP-MSC biology.Here, we used a multigas incubator (92.5% NWe identified 289 differentially expressed lncRNAs and 240 differentially expressed mRNAs between the hypoxia and normoxia groups. Among them, the lncRNAOur results revealed the specific expression characteristics of lncRNAs and mRNAs in hypoxia-cultured hP-MSCs and that lncRNA

Outlier detection is an important research direction in the field of data mining. Aiming at the problem of unstable detection results and low efficiency caused by randomly dividing features of the data set in the Isolation Forest algorithm in outlier detection, an algorithm CIIF (Cluster-based Improved Isolation Forest) that combines clustering and Isolation Forest is proposed. CIIF first uses the k-means method to cluster the data set, selects a specific cluster to construct a selection matrix based on the results of the clustering, and implements the selection mechanism of the algorithm through the selection matrix; then builds multiple isolation trees. Finally, the outliers are calculated according to the average search length of each sample in different isolation trees, and the Top-n objects with the highest outlier scores are regarded as outliers. Through comparative experiments with six algorithms in eleven real data sets, the results show that the CIIF algorithm has better performance. Compared to the Isolation Forest algorithm, the average AUC (Area under the Curve of ROC) value of our proposed CIIF algorithm is improved by 7%.

The execution time prediction for query tasks in graph database has become difficult and challenging due to the complexity of query plan and system. It is difficult for Database Administrators (DBA) or Database Management System (DBMS) to catch the accurate execution time during and before the execution of a query task. Before executing a query task, predicting its execution time can help the DBA or DBMS to efficiently management in the fields of load management, task scheduling, permission control, progress monitoring, system scale customization, etc. Therefore, accurately and efficiently predicting the execution time for query tasks is a key technology in these fields. In this paper, motivated by the combination of artificial intelligence technologies and graph database theories, we first propose a novel deep learning method to predict the execution time for query tasks in graph database. First, each query plan tree of tasks is encoded into an operation sequence. Second, top-20 features are selected from 68 candidate system features using random forest (RF), and the selected top-20 features are reduced to five principal components using principal component analysis (PCA). Finally, an accurate and efficient model based on the long short-term memory (LSTM) is designed and implemented to predict the execution time. The model can predict the execution time in advance before executing a query task in graph database. The experimental results from six kinds of benchmarks with the public data set Yelp show that the average accuracy of the proposed model can reach 81.34% with a high prediction efficiency rate, which proves the feasibility of the deep learning method. In particular, the proposed model can achieve the state-of-the-art prediction performance for query task execution time.

How to quickly identify high-risk populations is critical to epidemic control. We developed and validated a risk prediction model for screening SARS-CoV-2 infection in suspected cases with an epidemiological history. A total of 1019 patients, ≥13 years of age, who had an epidemiological history were enrolled from fever clinics between January 2020 and February 2020. Among 103 (10.11%) cases of COVID-19 were confirmed. Multivariable analysis summarized four features associated with increased risk of SARS-CoV-2 infection, summarized in the mnemonic COVID-19-REAL: radiological evidence of pneumonia (1 point), eosinophils < 0.005 × 109/L (1 point), age ≥ 32 years (2 points), and leukocytes < 6.05 × 109 /L (1 point). The area under the ROC curve for the training group was 0.863 (95% CI, 0.813 - 0.912). A cut-off value of less than 3 points for COVID-19-REAL was assigned to define the low-risk population. Only 10 (2.70%) of 371 patients were proved to be SARS-CoV-2 positive, with a negative predictive value of 0.973. External validation was similar. This study provides a simple, practical, and robust screening model, COVID-19-REAL, able to identify populations at high risk for SARS-CoV-2 infection.

Hypoxia-inducible factors (HIFs) regulate oxygen sensing and expression of genes involved in angiogenesis and erythropoiesis. Polycythemia has been observed in patients with hepatocellular carcinoma (HCC), but the underlying molecular basis remains unknown. Liver tissues from 302 HCC patients, including 104 with polycythemia, were sequenced for HIF2A mutations. A germline HIF2A mutation was detected in one HCC patient with concurrent polycythemia. Three additional family members carried this mutation, but none exhibited polycythemia or were diagnosed with HCC. The gain-of-function mutation resulted in a HIF-2α protein that was transcribed normally but resistant to degradation. HIF-2α target genes EDN1, EPO, GNA14, and VEGF were significantly upregulated in the tumor bed but not in the surrounding liver tissue. Polycythemia resolved upon total resection of the tumor tissue. This newly described HIF2A mutation may promote HCC oncogenesis.

The placenta and umbilical cord are pre-eminent candidate sources of mesenchymal stem cells (MSCs). However, placenta-derived MSCs (P-MSCs) showed greater proliferation capacity than umbilical cord-derived MSCs (UC-MSCs) in our study. We investigated the drivers of this proliferation difference and elucidated the mechanisms of proliferation regulation. Proteomic profiling and Gene Ontology (GO) functional enrichment were conducted to identify candidate proteins that may influence proliferation. Using lentiviral or small interfering RNA infection, we established overexpression and knockdown models and observed changes in cell proliferation to examine whether a relationship exists between the candidate proteins and proliferation capacity. Real-time quantitative polymerase chain reaction, western blot analysis, and immunofluorescence assays were conducted to elucidate the mechanisms underlying proliferation. Six candidate proteins were selected based on the results of proteomic profiling and GO functional enrichment. Through further validation, yes-associated protein 1 (YAP1) and β-catenin were confirmed to affect MSCs proliferation rates. YAP1 and β-catenin showed increased nuclear colocalization during cell expansion. YAP1 overexpression significantly enhanced proliferation capacity and upregulated the expression of both β-catenin and the transcriptional targets of Wnt signaling, CCND1, and c-MYC, whereas silencing β-catenin attenuated this influence. We found that YAP1 directly interacts with β-catenin in the nucleus to form a transcriptional YAP/β-catenin/TCF4 complex. Our study revealed that YAP1 and β-catenin caused the different proliferation capacities of P-MSCs and UC-MSCs. Mechanism analysis showed that YAP1 stabilized the nuclear β-catenin protein, and also triggered the Wnt/β-catenin pathway, promoting proliferation.

To solve the problems of a poor manual garbage sorting environment, including heavy tasks and low sorting efficiency, we propose the Lightweight Feature Fusion Single Shot Multibox Detector (L-SSD) algorithm to realize intelligent trash classification and recognition. Since waste has a small volume and the image resolution of garbage is always low, the algorithm that we propose is an enhanced single shot multibox detector (SSD) with a lightweight and novel feature fusion module. This SSD can significantly improve the performance of rubbish detection. In this feature fusion module, features from different layers with different scales are connected in series. A new feature pyramid was generated by using downsampling blocks, which will be fed to appointed multibox detectors to predict the final detection results. Due to the extremely unbalanced ratio of positive samples to negative samples, which leads to a low accuracy of SSD, Focal Loss using balanced cross-entropy is employed, which is provided by easy examples that corresponds to difficult samples with a decline in the loss weight. Thus, the training is biased towards meaningful samples. We have replaced the backbone network of VGG16 with ResNet-101 to achieve more accurate detection. We analyzed the performance of a nonmaximum suppression (NMS) algorithm and discovered that Soft-NMS was more suitable for learning better image representations. The strategy of Soft-NMS is to suppress the undesirable detection box rather than remove it completely. The experimental results show that the L-SSD exceeds a large number of state-of-the-art object detection algorithms in both accuracy and speed.

The volume and type of streaming data is increasing rapidly, thus, real-time processing scenarios for streaming data have continued to increase. The inherent volatility of streaming data causes large changes in the throughput volatility of stream processing platforms, making evaluations of the maximum sustainable throughput (MST) for such platforms a challenge. To address these problems and improve the low efficiency of manual evaluations, a naïve method is typically used to evaluate the MST of platforms by periodically measuring throughput. The throughput volatility is detected using a skip sliding window that is defined as a bounded sequence in which a limited number of throughput values stored to measure volatility in a skipping fashion in each data growth cycle, which is a fixed time interval with respect to the continuous data rate increases. A skipping sliding window that is used to describe the procedure with which the MST is calculated in an approximate way is based on a sliding window, which is used usually in the context of stateful streaming operators. Then, according to the throughput volatility, the method judges whether the MST has been reached using a latency guarantee. However, because this method has low efficiency and a large error rate, we propose an adaptive MST evaluation method that adds a data-growth factor function to the naïve method cycle that dynamically and adaptively tunes the data rate for each data growth cycle. The experimental results from four open-source benchmarks running on three mainstream stream processing platforms show that the adaptive MST evaluation method has a lower error rate and executes faster compared with the naïve evaluation method. Moreover, the proposed method is insensitive to parameter variations and is suitable for mainstream stream processing platforms.

Mesenchymal stem cells (MSCs) have attracted interest for their potential to alleviate liver injury. Here, the protective effect of MSCs on carbon tetrachloride (CCl4)-induced acute liver injury (ALI) was investigated. In this study, we illustrated a novel mechanism that ferroptosis, a newly recognized form of regulated cell death, contributed to CCl4-induced ALI. Subsequently, based on the in vitro and in vivo evidence that MSCs and MSC-derived exosomes (MSC-Exo) treatment achieved pathological remission and inhibited the production of lipid peroxidation, we proposed an MSC-based therapy for CCl4-induced ALI. More intriguingly, treatment with MSCs and MSC-Exo downregulated the mRNA level of prostaglandin-endoperoxide synthase 2 (Ptgs2) and lipoxygenases (LOXs) while it restored the protein level of SLC7A11 in primary hepatocytes and mouse liver, indicating that the inhibition of ferroptosis partly accounted for the protective effect of MSCs and MSC-Exo on ALI. We further revealed that MSC-Exo-induced expression of SLC7A11 protein was accompanied by increasing of CD44 and OTUB1. The aberrant expression of ubiquitinated SLC7A11 triggered by CCl4 could be rescued with OTUB1-mediated deubiquitination, thus strengthening SLC7A11 stability and thereby leading to the activation of system XC− to prevent CCl4-induced hepatocyte ferroptosis. In conclusion, we showed that MSC-Exo had a protective role against ferroptosis by maintaining SLC7A11 function, thus proposing a novel therapeutic strategy for ferroptosis-induced ALI.

In most of the existing multi-task learning (MTL) models, multiple tasks’ public information is learned by sharing parameters across hidden layers, such as hard sharing, soft sharing, and hierarchical sharing. One promising approach is to introduce model pruning into information learning, such as sparse sharing, which is regarded as being outstanding in knowledge transferring. However, the above method performs inefficiently in conflict tasks, with inadequate learning of tasks’ private information, or through suffering from negative transferring. In this paper, we propose a multi-task learning model (Pruning-Based Feature Sharing, PBFS) that merges a soft parameter sharing structure with model pruning and adds a prunable shared network among different task-specific subnets. In this way, each task can select parameters in a shared subnet, according to its requirements. Experiments are conducted on three benchmark public datasets and one synthetic dataset; the impact of the different subnets’ sparsity and tasks’ correlations to the model performance is analyzed. Results show that the proposed model’s information sharing strategy is helpful to transfer learning and superior to the several comparison models.

Background The radial arteriovenous fistula (AVF) is a rare complication occurring after transradial cardiac catheterization. Patients with AVF typically present with signs of venous dilation, such as swelling or palpable thrills. However, neurological complications secondary to radial AVFs are rare. This paper reported a rare case of ischemic steal syndrome that occurred 11 months after the transradial cardiac catheterization, most likely as a consequence of radial arteriovenous fistula. Case presentation This paper described a case of a 73-year-old female, who complained of right forearm swelling and radial 1–3 fingers numbness for several months after the catheterized stent surgery through radial approach. Upon Clinical examination, this patient presented with a slight bump and palpable thrill at the distal third of right forearm, and the sensory of radial 1–3 fingers and pinch force was compromised. The Ultrasonography and computed tomography angiography (CTA) of the upper extremity revealed AVF between the right radial artery and the adjacent vein. Microsurgery was performed successfully to ligate the fistula and reconstruct the radial artery. The numbness has gradually improved about 1 week after the surgery, with no recurred swelling. The two-point pinch force and digital sensitivity recovered at the 20-month follow-up. What’s more, due to the scarcity of cases, the optimal therapy for iatrogenic radial AVF is controversial. Accordingly, we provided a literature review of previous reports of catheter-related radial AVFs and proposed an algorithm to manage them. Conclusions We believe that once an AVF is diagnosed, early treatment options such as compression or surgery are necessary to relieve symptoms and prevent further complications. Otherwise, serious complications can occur, including the ischemic steal syndrome.

Dynamic changes in metabolites may affect liver disease progression, and provide new methods for predicting liver damage. We used ultra-performance liquid chromatography-mass spectroscopy to assess serum metabolites in healthy controls (HC), and patients with acute hepatitis E (AHE) or hepatitis E virus acute liver failure (HEV-ALF). The principal component analysis, partial least squares discriminant analysis, and discriminant analysis of orthogonal projections to latent structures models illustrated significant differences in the metabolite components between AHE patients and HCs, or between HEV-ALF and AHE patients. In pathway enrichment analysis, we further identified two altered pathways, including linoleic acid metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis, when comparing AHE patients with HCs. Linoleic acid metabolism and porphyrin and chlorophyll metabolism pathways were significantly different in HEV-ALF when compared with AHE patients. The discriminative performances of differential metabolites showed that taurocholic acid, glycocholic acid, glycochenodeoxycholate-3-sulfate, and docosahexaenoic acid could be used to distinguish HEV-ALF from AHE patients. The serum levels of glycocholic acid, taurocholic acid, deoxycholic acid glycine conjugate, and docosahexaenoic acid were associated with the prognosis of HEV-ALF patients. Dynamic changes in serum metabolites were associated with AHE infection and severity. The identified metabolites can be used to diagnose and predict the prognosis of HEV-ALF.

Outlier detection aims to identify rare, minority objects in a dataset that are significantly different from the majority. When a minority group (defined by sensitive attributes, such as gender, race, age, etc.) does not represent the target group for outlier detection, outlier detection methods are likely to propagate statistical biases in the data and generate unfair results. Our work focuses on studying the fairness of outlier detection. We characterize the properties of fair outlier detection and propose an appropriate outlier detection method that combines adversarial representation learning and the LOF algorithm (AFLOF). Unlike the FairLOF method that adds fairness constraints to the LOF algorithm, AFLOF uses adversarial networks to learn the optimal representation of the original data while hiding the sensitive attribute in the data. We introduce a dynamic weighting module that assigns lower weight values to data objects with higher local outlier factors to eliminate the influence of outliers on representation learning. Lastly, we conduct comparative experiments on six publicly available datasets. The results demonstrate that compared to the density-based LOF method and the recently proposed FairLOF method, our proposed AFLOF method has a significant advantage in both the outlier detection performance and fairness.

Objectives. There are no detailed reports on the long-term outcome of patients treated with tenofovir disoproxil fumarate (TDF) compared with entecavir (ENT) following liver transplantation. We aimed to assess the association between TDF use and long-term outcome compared to recipients using ENT. Methods. This retrospective observational study included patients who underwent liver transplantation between January 2015 and May 2019 at the First Affiliated Hospital of Zhejiang University School of Medicine and Hangzhou Shulan Hospital. Cox regression, propensity score matching (PSM), and inverse probability of treatment weighting (IPTW) were performed to assess HBV recurrence, renal dysfunction, and patient survival in liver transplant patients treated with TDF compared with ENT. Results. A total of 907 patients met the inclusion criteria and were included in the final analysis, among which, there were 109 (12.0%) patients treated with TDF and 798 (88.0%) patients treated with ENT. During the follow-up period, 166 patients died, 15 (13.8%) in the TDF group, and 151 (18.9%) in the ENT group. No significant association was found between TDF or ENT use and patient survival (HR, 0.72, 95% CI 0.41-1.23; P = 0.226 ) by PSM analysis. Similarly, differences in the antiviral agents were not significantly associated with hepatitis B recurrence (HR, 1.19, 95% CI 0.62-2.28; P = 0.603 ), but TDF use was significantly related to renal dysfunction after liver transplantation (HR 1.70, 95% CI, 1.21-2.37; P = 0.002 ). Similar results were obtained in subsequent sensitivity analyses. Conclusions. In this study, the results showed that the use of TDF after liver transplantation is as safe and effective as the use of ENT in preventing hepatitis B recurrence. However, renal function in recipients treated with TDF requires careful monitoring.

With database management systems becoming complex, predicting the execution time of graph queries before they are executed is one of the challenges for query scheduling, workload management, resource allocation, and progress monitoring. Through the comparison of query performance prediction methods, existing research works have solved such problems in traditional SQL queries, but they cannot be directly applied in Cypher queries on the Neo4j database. Additionally, most query performance prediction methods focus on measuring the relationship between correlation coefficients and retrieval performance. Inspired by machine-learning methods and graph query optimization technologies, we used the RBF neural network as a prediction model to train and predict the execution time of Cypher queries. Meanwhile, the corresponding query pattern features, graph data features, and query plan features were fused together and then used to train our prediction models. Furthermore, we also deployed a monitor node and designed a Cypher query benchmark for the database clusters to obtain the query plan information and native data store. The experimental results of four benchmarks showed that the average mean relative error of the RBF model reached 16.5% in the Northwind dataset, 12% in the FIFA2021 dataset, and 16.25% in the CORD-19 dataset. This experiment proves the effectiveness of our proposed approach on three real-world datasets.

Background: Although increasing preclinical studies have emphasized the benefits of exosome-related therapies, the efficacy of mesenchymal stromal cell (MSC)-derived extracellular vesicles (EV) for liver injury is unclear. In this work, a pooled analysis was conducted to explore the overall effect of MSC-EV in animal models. Methods: A systematic search of the PubMed, EMBASE, Web of Science, and Cochrane Library databases was performed, from initiation to February 2022, for preclinical studies with liver disease models. The treatment outcomes were evaluated based on liver function, histological analysis, and inflammatory cytokines. Results: After screening, 39 studies were included. Pooled analyses demonstrated that MSC-EV therapy significantly improved liver functions (ALB, ALT, AST, ALP, and γ-GT), promoted the repair of injured liver tissue (damaged area, Ishak’s score), reduced inflammatory factors (TNF-α, IL-1β, IL-6, and IFN-γ), and increased an anti-inflammatory cytokine (IL-10) compared to the placebo control group. Subgroup analyses indicated that MSC-EV had therapeutic effects on liver fibrosis (n = 16), acute liver injury (n = 11), non-alcoholic fatty liver disease (n = 3), autoimmune hepatitis (n = 4), and hepatic ischemia-reperfusion injury (n = 6). Additionally, the therapeutic effect of EV was comparable to that of MSCs. Conclusion: MSC-EV have therapeutic potential for acute and chronic liver diseases.