Rule-Guided Compositional Representation Learning on Knowledge Graphs

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Besides, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.Comment: The full version of a paper accepted to AAAI 202

Mortality and Pre-Hospitalization use of Renin-Angiotensin System Inhibitors in Hypertensive COVID-19 Patients.

Background There has been significant controversy regarding the effects of pre-hospitalization use of renin-angiotensin system (RAS) inhibitors on the prognosis of hypertensive COVID-19 patients. Methods and Results We retrospectively assessed 2,297 hospitalized COVID-19 patients at Tongji Hospital in Wuhan, China, from January 10th to March 30th, 2020; and identified 1,182 patients with known hypertension on pre-hospitalization therapy. We compared the baseline characteristics and in-hospital mortality between hypertensive patients taking RAS inhibitors (N=355) versus non-RAS inhibitors (N=827). Of the 1,182 hypertensive patients (median age 68 years, 49.1% male), 12/355 (3.4%) patients died in the RAS inhibitors group vs. 95/827 (11.5%) patients in the non-RAS inhibitors group (p<0.0001). Adjusted hazard ratio for mortality was 0.28 (95% CI 0.15-0.52, p<0.0001) at 45 days in the RAS inhibitors group compared with non-RAS inhibitors group. Similar findings were observed when patients taking angiotensin receptor blockers (N=289) or angiotensin converting enzyme inhibitors (N=66) were separately compared with non-RAS inhibitors group. The RAS inhibitors group compared with non-RAS inhibitors group had lower levels of C-reactive protein (median 13.5 vs. 24.4 pg/mL; p=0.007) and interleukin-6 (median 6.0 vs. 8.5 pg/mL; p=0.026) on admission. The protective effect of RAS inhibitors on mortality was confirmed in a meta-analysis of published data when our data were added to previous studies (odd ratio 0.44, 95% CI 0.29-0.65, p<0.0001). Conclusions In a large single center retrospective analysis we observed a protective effect of pre-hospitalization use of RAS inhibitors on mortality in hypertensive COVID-19 patients; which might be associated with reduced inflammatory response

Using microneedle array electrodes for non-invasive electrophysiological signal acquisition and sensory feedback evoking

Introduction: Bidirectional transmission of information is needed to realize a closed-loop human-machine interaction (HMI), where electrophysiological signals are recorded for man-machine control and electrical stimulations are used for machine-man feedback. As a neural interface (NI) connecting man and machine, electrodes play an important role in HMI and their characteristics are critical for information transmission.Methods: In this work, we fabricated a kind of microneedle array electrodes (MAEs) by using a magnetization-induced self-assembly method, where microneedles with a length of 500–600 μm and a tip diameter of ∼20 μm were constructed on flexible substrates. Part of the needle length could penetrate through the subjects’ stratum corneum and reach the epidermis, but not touch the dermis, establishing a safe and direct communication pathway between external electrical circuit and internal peripheral nervous system.Results: The MAEs showed significantly lower and more stable electrode-skin interface impedance than the metal-based flat array electrodes (FAEs) in various testing scenarios, demonstrating their promising impedance characteristics. With the stable microneedle structure, MAEs exhibited an average SNR of EMG that is more than 30% higher than FAEs, and a motion-intention classification accuracy that is 10% higher than FAEs. The successful sensation evoking demonstrated the feasibility of the MAE-based electrical stimulation for sensory feedback, where a variety of natural and intuitive feelings were generated in the subjects and thereafter objectively verified through EEG analysis.Discussion: This work confirms the application potential of MAEs working as an effective NI, in both electrophysiological recording and electrical stimulation, which may provide a technique support for the development of HMI

Improving the Robustness of Electromyogram-Pattern Recognition for Prosthetic Control by a Postprocessing Strategy

Electromyogram (EMG) contains rich information for motion decoding. As one of its major applications, EMG-pattern recognition (PR)-based control of prostheses has been proposed and investigated in the field of rehabilitation robotics for decades. These prostheses can offer a higher level of dexterity compared to the commercially available ones. However, limited progress has been made toward clinical application of EMG-PR-based prostheses, due to their unsatisfactory robustness against various interferences during daily use. These interferences may lead to misclassifications of motion intentions, which damage the control performance of EMG-PR-based prostheses. A number of studies have applied methods that undergo a postprocessing stage to determine the current motion outputs, based on previous outputs or other information, which have proved effective in reducing erroneous outputs. In this study, we proposed a postprocessing strategy that locks the outputs during the constant contraction to block out occasional misclassifications, upon detecting the motion onset using a threshold. The strategy was investigated using three different motion onset detectors, namely mean absolute value, Teager–Kaiser energy operator, or mechanomyogram (MMG). Our results indicate that the proposed strategy could suppress erroneous outputs, during rest and constant contractions in particular. In addition, with MMG as the motion onset detector, the strategy was found to produce the most significant improvement in the performance, reducing the total errors up to around 50% (from 22.9 to 11.5%) in comparison to the original classification output in the online test, and it is the most robust against threshold value changes. We speculate that motion onset detectors that are both smooth and responsive would further enhance the efficacy of the proposed postprocessing strategy, which would facilitate the clinical application of EMG-PR-based prosthetic control

Thyroid hormone T4 mitigates traumatic brain injury in mice by dynamically remodeling cell type specific genes, pathways, and networks in hippocampus and frontal cortex

The complex pathology of mild traumatic brain injury (mTBI) is a main contributor to the difficulties in achieving a successful therapeutic regimen. Thyroxine (T4) administration has been shown to prevent the cognitive impairments induced by mTBI in mice but the mechanism is poorly understood. To understand the underlying mechanism, we carried out a single cell transcriptomic study to investigate the spatiotemporal effects of T4 on individual cell types in the hippocampus and frontal cortex at three post-injury stages in a mouse model of mTBI. We found that T4 treatment altered the proportions and transcriptomes of numerous cell types across tissues and timepoints, particularly oligodendrocytes, astrocytes, and microglia, which are crucial for injury repair. T4 also reversed the expression of mTBI-affected genes such as Ttr, mt-Rnr2, Ggn12, Malat1, Gnaq, and Myo3a, as well as numerous pathways such as cell/energy/iron metabolism, immune response, nervous system, and cytoskeleton-related pathways. Cell-type specific network modeling revealed that T4 mitigated select mTBI-perturbed dynamic shifts in subnetworks related to cell cycle, stress response, and RNA processing in oligodendrocytes. Cross cell-type ligand-receptor networks revealed the roles of App, Hmgb1, Fn1, and Tnf in mTBI, with the latter two ligands having been previously identified as TBI network hubs. mTBI and/or T4 signature genes were enriched for human genome-wide association study (GWAS) candidate genes for cognitive, psychiatric and neurodegenerative disorders related to mTBI. Our systems-level single cell analysis elucidated the temporal and spatial dynamic reprogramming of cell-type specific genes, pathways, and networks, as well as cell-cell communications as the mechanisms through which T4 mitigates cognitive dysfunction induced by mTBI

5-Fluorouracil targets thymidylate synthase in the selective suppression of TH17 cell differentiation

While it is well established that treatment of cancer patients with 5-Fluorouracil (5-FU) can result in immune suppression, the exact function of 5-FU in the modulation of immune cells has not been fully established. We found that low dose 5-FU selectively suppresses TH17 and TH1 cell differentiation without apparent effect on Treg, TH2, and significantly suppresses thymidylate synthase (TS) expression in TH17 and TH1 cells but has a lesser effect in tumor cells and macrophages. Interestingly, the basal expression of TS varies significantly between T helper phenotypes and knockdown of TS significantly impairs TH17 and TH1 cell differentiation without affecting the differentiation of either Treg or TH2 cells. Finally, low dose 5-FU is effective in ameliorating colitis development by suppressing TH17 and TH1 cell development in a T cell transfer colitis model. Taken together, the results highlight the importance of the anti-inflammatory functions of low dose 5-FU by selectively suppressing TH17 and TH1 immune responses

Identification of miRs-143 and -145 that Is Associated with Bone Metastasis of Prostate Cancer and Involved in the Regulation of EMT

The principal problem arising from prostate cancer (PCa) is its propensity to metastasize to bone. MicroRNAs (miRNAs) play a crucial role in many tumor metastases. The importance of miRNAs in bone metastasis of PCa has not been elucidated to date. We investigated whether the expression of certain miRNAs was associated with bone metastasis of PCa. We examined the miRNA expression profiles of 6 primary and 7 bone metastatic PCa samples by miRNA microarray analysis. The expression of 5 miRNAs significantly decreased in bone metastasis compared with primary PCa, including miRs-508-5p, -145, -143, -33a and -100. We further examined other samples of 16 primary PCa and 13 bone metastases using real-time PCR analysis. The expressions of miRs-143 and -145 were verified to down-regulate significantly in metastasis samples. By investigating relationship of the levels of miRs-143 and -145 with clinicopathological features of PCa patients, we found down-regulations of miRs-143 and -145 were negatively correlated to bone metastasis, the Gleason score and level of free PSA in primary PCa. Over-expression miR-143 and -145 by retrovirus transfection reduced the ability of migration and invasion in vitro, and tumor development and bone invasion in vivo of PC-3 cells, a human PCa cell line originated from a bone metastatic PCa specimen. Their upregulation also increased E-cadherin expression and reduced fibronectin expression of PC-3 cells which revealed a less invasive morphologic phenotype. These findings indicate that miRs-143 and -145 are associated with bone metastasis of PCa and suggest that they may play important roles in the bone metastasis and be involved in the regulation of EMT Both of them may also be clinically used as novel biomarkers in discriminating different stages of human PCa and predicting bone metastasis

Design and Test of a Jet Remote Control Spraying Machine for Orchards

Aimed at issues associated with the poor air supply and poor automatic targeting accuracy of existing orchard sprayers, this paper designs a jet-type orchard remote control sprayer with automatic targeting which is suitable for standardized orchards in hilly and mountainous areas. By optimizing the structure of the diversion box, the uniformity of deposition and penetration ability of the pesticide droplets to the fruit tree canopy are improved, and a uniform wild field distribution is realized simultaneously. An accurate positioning of the fruit tree canopy space orientation is achieved through automatic targeting and azimuthal adjustment systems. When the target is detected, the solenoid valve is controlled to open, and vice versa, and the distance from the nozzle to the fruit tree canopy is adjusted in real time to improve the utilization rate of pesticides. The test results show that the effective range of the jet-type orchard remote control sprayer is no more than 3.5 m, and the maximum flow rate range is 6~6.5 L/min. Within the effective spraying range, the farther the distance is, the higher the automatic targeting accuracy. The pesticide droplets sprayed by the spraying machine have a certain penetration ability, and the uniformity of the droplets is good, which solves solidification problems caused by the penetration of pesticide into the soil. This research provides a reference for jet spraying operation and automatic targeting spraying structure design

Forecasting Network Interface Flow Using a Broad Learning System Based on the Sparrow Search Algorithm

In this paper, we propose a broad learning system based on the sparrow search algorithm. Firstly, in order to avoid the complicated manual parameter tuning process and obtain the best combination of hyperparameters, the sparrow search algorithm is used to optimize the shrinkage coefficient (r) and regularization coefficient (λ) in the broad learning system to improve the prediction accuracy of the model. Second, using the broad learning system to build a network interface flow forecasting model. The flow values in the time period [T−11,T] are used as the characteristic values of the traffic at the moment T+1. The hyperparameters outputted in the previous step are fed into the network to train the broad learning system network traffic prediction model. Finally, to verify the model performance, this paper trains the prediction model on two public network flow datasets and real traffic data of an enterprise cloud platform switch interface and compares the proposed model with the broad learning system, long short-term memory, and other methods. The experiments show that the prediction accuracy of this method is higher than other methods, and the moving average reaches 97%, 98%, and 99% on each dataset, respectively

Forecasting Network Interface Flow Using a Broad Learning System Based on the Sparrow Search Algorithm

In this paper, we propose a broad learning system based on the sparrow search algorithm. Firstly, in order to avoid the complicated manual parameter tuning process and obtain the best combination of hyperparameters, the sparrow search algorithm is used to optimize the shrinkage coefficient (r) and regularization coefficient (λ) in the broad learning system to improve the prediction accuracy of the model. Second, using the broad learning system to build a network interface flow forecasting model. The flow values in the time period [T−11,T] are used as the characteristic values of the traffic at the moment T+1. The hyperparameters outputted in the previous step are fed into the network to train the broad learning system network traffic prediction model. Finally, to verify the model performance, this paper trains the prediction model on two public network flow datasets and real traffic data of an enterprise cloud platform switch interface and compares the proposed model with the broad learning system, long short-term memory, and other methods. The experiments show that the prediction accuracy of this method is higher than other methods, and the moving average reaches 97%, 98%, and 99% on each dataset, respectively