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Abstract MicroRNAs (miRNAs) are a key class of endogenous non-coding RNAs that play a pivotal role in regulating diseases. Accurately predicting the intricate relationships between miRNAs and diseases carries profound implications for disease diagnosis, treatment, and prevention. However, these prediction tasks are highly challenging due to the complexity of the underlying relationships. While numerous effective prediction models exist for validating these associations, they often encounter information distortion due to limitations in efficiently retaining information during the encoding-decoding process. Inspired by Multi-layer Heterogeneous Graph Transformer and Machine Learning XGboost classifier algorithm, this study introduces a novel computational approach based on multi-layer heterogeneous encoder—machine learning decoder structure for miRNA-disease association prediction (MHXGMDA). First, we employ the multi-view similarity matrices as the input coding for MHXGMDA. Subsequently, we utilize the multi-layer heterogeneous encoder to capture the embeddings of miRNAs and diseases, aiming to capture the maximum amount of relevant features. Finally, the information from all layers is concatenated to serve as input to the machine learning classifier, ensuring maximal preservation of encoding details. We conducted a comprehensive comparison of seven different classifier models and ultimately selected the XGBoost algorithm as the decoder. This algorithm leverages miRNA embedding features and disease embedding features to decode and predict the association scores between miRNAs and diseases. We applied MHXGMDA to predict human miRNA-disease associations on two benchmark datasets. Experimental findings demonstrate that our approach surpasses several leading methods in terms of both the area under the receiver operating characteristic curve and the area under the precision-recall curve.

Abstract Background Circulating atherogenic index of plasma (AIP) levels has been proposed as a novel biomarker for dyslipidemia and as a predictor of insulin resistance (IR) risk. However, the association between AIP and the incidence of new-onset stroke, particularly in individuals with varying glucose metabolism status, remains ambiguous. Methods A total of 8727 participants aged 45 years or older without a history of stroke from the China Health and Retirement Longitudinal Study (CHARLS) were included in this study. The AIP was calculated using the formula log [Triglyceride (mg/dL) / High-density lipoprotein cholesterol (mg/dL)]. Participants were divided into four groups based on their baseline AIP levels: Q1 (AIP ≤ 0.122), Q2 (0.122 0.562). The primary endpoint was the occurrence of new-onset stroke events. The Kaplan–Meier curves, multivariate Cox proportional hazard models, and Restricted cubic spline analysis were applied to explore the association between baseline AIP levels and the risk of developing a stroke among individuals with varying glycemic metabolic states. Results During an average follow-up of 8.72 years, 734 participants (8.4%) had a first stroke event. The risk for stroke increased with each increasing quartile of baseline AIP levels. Kaplan–Meier curve analysis revealed a significant difference in stroke occurrence among the AIP groups in all participants, as well as in those with prediabetes mellitus (Pre-DM) and diabetes mellitus (DM) (all P values 0.05). Restricted cubic spline analysis also demonstrated that higher baseline AIP levels were associated with higher hazard ratios for stroke in all participants and those with glucose metabolism disorders. Conclusions An increase in baseline AIP levels was significantly associated with the risk of stroke in middle-aged and elderly individuals, and exhibited distinct characteristics depending on the individual’s glucose metabolism status.

The melt pools, the most basic units of the components fabricated by the selective laser melting (SLM) technology, play an important role in the mechanical properties of the structures. A self-developed in-situ tensile observation platform was used to carry out the in-situ tensile test of SLMed AlSi10Mg alloy specimens under the observation of optical microscope. With a series of obtained experimental data on mechanical properties and metallurgical images, combined with the digital image correlation(DIC) technology, the melt pool of the specimen and the strain of defects were analyzed, and the deformation and fracture mechanism of the SLMed AlSi10Mg alloy specimens was obtained. The results show that the proposed method successfully obtains the deformation field evolution data of the melt pool and defects, which provides experimental and theoretical support for the further study of crack extension characteristics and fatigue life prediction of SLMed metallic material components.

ObjectivesWilson disease (WD) is a rare autosomal recessive disorder caused by a mutation in the ATP7B gene. Neurological symptoms are one of the most common symptoms of WD. This study aims to construct a model that can predict the occurrence of neurological symptoms by combining clinical multidimensional indicators with machine learning methods.MethodsThe study population consisted of WD patients who received treatment at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 2021 to September 2023 and had a Leipzig score ≥ 4 points. Indicators such as general clinical information, imaging, blood and urine tests, and clinical scale measurements were collected from patients, and machine learning methods were employed to construct a prediction model for neurological symptoms. Additionally, the SHAP method was utilized to analyze clinical information to determine which indicators are associated with neurological symptoms.ResultsIn this study, 185 patients with WD (of whom 163 had neurological symptoms) were analyzed. It was found that using the eXtreme Gradient Boosting (XGB) to predict achieved good performance, with an MCC value of 0.556, ACC value of 0.929, AUROC value of 0.835, and AUPRC value of 0.975. Brainstem damage, blood creatinine (Cr), age, indirect bilirubin (IBIL), and ceruloplasmin (CP) were the top five important predictors. Meanwhile, the presence of brainstem damage and the higher the values of Cr, Age, and IBIL, the more likely neurological symptoms were to occur, while the lower the CP value, the more likely neurological symptoms were to occur.ConclusionsTo sum up, the prediction model constructed using machine learning methods to predict WD cirrhosis has high accuracy. The most important indicators in the prediction model were brainstem damage, Cr, age, IBIL, and CP. It provides assistance for clinical decision-making.

In this study, gas tungsten arc welding (GTAW) based dual wire additive manufacturing (D-WAAM) is adopted to fabricate the functionally graded material (FGM) from stainless steel (SS) 304 to Fe–40Al, using SS 304 and aluminum alloy (AA) 1070 wires. The Al/Fe atomic ratio varies from 0 to 40 % by varying the ratio of the wire feed speeds of the SS and AA wires. The chemical composition, phases, microstructure, and mechanical properties of the FGM are investigated. The results showed that with the increase of Al content, microstructures evolve as follows: γ-Fe → γ-Fe + α-Fe → α-Fe → Fe3Al → FeAl. Cracks occur in FeAl region, caused by the high stress and the low plasticity. Dislocation walls prevent the large grains from slipping, leading to the loss of plasticity. The microhardness of SS 304 to Fe–40Al FGM ranges from 188HV0.5 to 558HV0.5. As Al content increases, the ultimate compressive strength decreases from 1820 MPa to 1061 MPa. The results on microstructure and mechanical properties provide guidance for the preparation of steel - aluminum alloy FGMs.

Abstract Adolescent cannabis use increases the risk for cognitive impairments and psychiatric disorders. Cannabinoid receptor type 1 (Cnr1) is expressed not only in neurons and astrocytes, but also in microglia, which shape synaptic connections during adolescence. However, the role of microglia in mediating the adverse cognitive effects of delta-9-tetrahydrocannabinol (THC), the principal psychoactive constituent of cannabis, is not fully understood. Here, we report that in mice, adolescent THC exposure produces microglial apoptosis in the medial prefrontal cortex (mPFC), which was exacerbated in a model of 16p11.2 duplication, a representative copy number variation (CNV) risk factor for psychiatric disorders. These effects are mediated by microglial Cnr1, leading to reduction in the excitability of mPFC pyramidal-tract neurons and deficits in social memory in adulthood. Our findings suggest the microglial Cnr1 may contribute to adverse effect of cannabis exposure in genetically vulnerable individuals.

ObjectiveTo examine the implementation of diabetes health management project and its influence on knowledge awareness, health behavior, medication, and blood glucose control among the diabetes patients with the standardized management for the improvement of the project. MethodsUsing multistage random sampling, we recruited 3 995 diabetes patients aged 35 years and over and being followed-up in the Survey for Assessment on Chronic Disease Patients Management in the National Primary Public Health Services in 8 provincial level administrative divisions. A questionnaire survey and peripheral blood glucose test were conducted among the participants during August – October 2018. Binary logistic regression model was used to analyze the impact of standardized management on diabetes patients′ knowledge awareness, health behavior, medication and blood glucose control. ResultsOf the 3 938 participants with complete information, 71.28%, 88.69%, 51.98% reported having standardized management (receiving 4 times and more health care follow-ups per year), medication for diabetes, and effective blood glucose control (fasting and postprandial blood glucose < 7.0 and < 10.0 mmol/L), respectively. Compared with those without standardized management, the participants with the management reported significantly higher ratios of relevant knowledge awareness (56.11% vs. 41.54%), better dietary diversity score (48.79% vs.43.26%), and effective blood glucose control (53.16% vs. 47.96%) (all P < 0.05) but significantly lower ratios of current smoking (10.17% vs. 12.49%) and alcohol consumption during past one year (13.34% vs. 18.78%) (both P < 0.05 ). Binary logistic regression analysis showed that the standardized management on diabetes patients was significantly correlated positively with the patients′ better knowledge awareness and better dietary diversity, and negatively with the patients′ current smoking and alcohol drinking (all P < 0.05). ConclusionThe standardized management on diabetes patients was positively related to the patients′ better knowledge awareness and dietary diversity but negatively related to the patients′ current smoking and alcohol drinking, suggesting that the management achieved good primary outcomes in China.

The reclamation disposal of oily sludge, which is a hazardous waste from the extraction, transportation, storage, and refining of crude oil, is a paramount challenge for environmental protection and resource recycle. Herein, a catalytic pyrolysis approach with the participation of CaO was adopted for oil resource recovery. The results show that the optimal pyrolysis temperature for recovering oil was 500 °C, in which the pyrolysis oil yield was 44.37%. CaO could act as a catalyst during the pyrolysis process, thus promoting the formation of light components in the pyrolysis oil. The light components in pyrolysis oil increased from 5.08% to 16.67% with the participation of CaO. Meanwhile, the addition of CaO immobilized As, Cr, Pb and Zn into the pyrolysis slag, thus decreasing their migration into pyrolysis oil and gas. The migration of Ni displayed a different trend, and part of Ni entered into the pyrolysis oil and gas. The BCR continuous extraction experiments display that the highly biological-activity heavy metals (i.e., F1, F2 and F3 form) was transformed to a more stable state (i.e., F4 form). These results demonstrate that the catalytic pyrolysis approach with the participation of CaO not only improve the yield and quality of pyrolysis oil, but also reduce the emission and mobility of heavy metals.

Profiling gene expression while preserving cell locations aids in the comprehensive understanding of cell fates in multicellular organisms. However, simple and flexible isolation of microregions of interest (mROIs) for spatial transcriptomics is still challenging. We present a laser-induced forward transfer (LIFT)-based method combined with a full-length mRNA-sequencing protocol (LIFT-seq) for profiling region-specific tissues. LIFT-seq demonstrated that mROIs from two adjacent sections could reliably and sensitively detect and display gene expression. In addition, LIFT-seq can identify region-specific mROIs in the mouse cortex and hippocampus. Finally, LIFT-seq identified marker genes in different layers of the cortex with very similar expression patterns. These genes were then validated using in situ hybridization (ISH) results. Therefore, LIFT-seq will be a valuable and efficient technique for profiling the spatial transcriptome in various tissues.

DNA 5-methylcytosine (5mC) is widely present in multicellular eukaryotes, which plays important roles in various developmental and physiological processes and a wide range of human diseases. Thus, it is essential to accurately detect the 5mC sites. Although current sequencing technologies can map genome-wide 5mC sites, these experimental methods are both costly and time-consuming. To achieve a fast and accurate prediction of 5mC sites, we propose a new computational approach, BERT-5mC. First, we pre-trained a domain-specific BERT (bidirectional encoder representations from transformers) model by using human promoter sequences as language corpus. BERT is a deep two-way language representation model based on Transformer. Second, we fine-tuned the domain-specific BERT model based on the 5mC training dataset to build the model. The cross-validation results show that our model achieves an AUROC of 0.966 which is higher than other state-of-the-art methods such as iPromoter-5mC, 5mC_Pred, and BiLSTM-5mC. Furthermore, our model was evaluated on the independent test set, which shows that our model achieves an AUROC of 0.966 that is also higher than other state-of-the-art methods. Moreover, we analyzed the attention weights generated by BERT to identify a number of nucleotide distributions that are closely associated with 5mC modifications. To facilitate the use of our model, we built a webserver which can be freely accessed at: http://5mc-pred.zhulab.org.cn.

The development of wire arc additive manufacturing (WAAM) provides a new solution for manufacturing aluminum alloy (AA) components used in lightweight mobile heat storage equipment. AA5356 component is deposited at a wire feed speed of 7.3 m/min and a welding speed of 1000 mm/min with the cold metal transfer-advanced mode. Adjusting the deposition angle to 90° between layers can enhance the microstructure and mechanical properties of the WAAM AA5356 component. The quasi in situ idea is proposed to investigate the microstructures, defects evolution, and mechanical property differences during the successive deposited. The results showed that the average grain size increases with increasing deposition height. The microstructures are uniform, except for the interlayer with refined equiaxed grains. The porosity increases gradually from 1.23% to 1.75% as the deposition height increases, and the average diameter of the micropores increases from 16.54 μm to 21.91 μm. In contrast, the microhardness of WAAM AA5356 component gradually decreases, and the microhardness of the interlayer area is higher than that of the innerlayer. The ultimate tensile strength, yield strength, and elongation are 286 MPa, 142 MPa, and 23%, respectively. Compared with casting AA5356, the ultimate tensile and yield strengths are 32.2% and 40.5% greater, respectively, while the elongation is almost equal. As the grain sizes increase with the heat accumulation effect, the microhardness decreases and the elongation increases while the tensile strength decreases, mainly due to the high proportion of large micropores in the top area.

To solve the chattering problem caused by discontinuous switching function in traditional sliding mode observer, a piecewise square root switching function with continuously varying characteristics is designed, and its stability is analyzed by using Lyapunov stability criterion. Secondly, according to the relationship among bus current, switching state and phase current, a single bus resistance sampling current reconstruction scheme without current sensors is adopted, which effectively reduces the cost of motor system. Finally, the feasibility and effectiveness of the proposed scheme are verified by simulation.

Abstract Background Lung cancers arising in never smokers have been suggested to be substantially different from lung cancers in smokers at an epidemiological, genetic and molecular level. Focusing on non-small cell lung cancer (NSCLC), we characterized lung cancer patients in China looking for demographic and clinical differences between the smoking and never-smoking subgroups. Methods In total, 891 patients with NSCLC, including 841 with adenocarcinoma and 50 with squamous cell carcinoma, were recruited in this study. Association of smoking status with demographic and clinical features of NSCLC was determined, and risk factors for lymph node metastasis and TNM stage were evaluated using Multivariate logistic regression analysis. Results In patients with adenocarcinoma, never smokers showed a younger age at diagnosis (54.2 ± 12.7vs. 59.3 ± 9.4, p adjusted

Synaptic dysfunction plays a crucial role in the pathogenesis of Alzheimer's disease (AD). α/β-hydrolase domain-containing 6 (ABHD6) contributes to synaptic dysfunctions, and ABHD6 inhibition has shown potential therapeutic value in neurological disorders. However, the role of ABHD6 in AD has not been fully defined. In this study, we demonstrated that adeno-associated virus (AAV) mediated shRNA targeting ABHD6 in hippocampal neurons attenuated synaptic dysfunction and memory impairment of APPswe/PS1dE9 (APP/PS1) mice, while it didn't affect the amyloid-beta (Aβ) levels and neuroinflammation in the brains. In addition, intraperitoneal injection of wwl70, a specific inhibitor of ABHD6, improved synaptic plasticity and memory function in APP/PS1 mice, which might attribute to the activation of endogenous cannabinoid signaling. Furthermore, wwl70 significantly decreased the Aβ levels and neuroinflammation in the hippocampus of AD mice, and enhanced Aβ phagocytized by microglia. In conclusion, for the first time our data have shown that ABHD6 inhibition might be a promising strategy for AD treatment, and wwl70 is a potential candidate for AD drug development pipeline.

Synaptic plasticity impairment plays a critical role in the pathogenesis of Alzheimer’s disease (AD), and emerging evidence has shown that microRNAs (miRs) are alternative biomarkers and therapeutic targets for synaptic dysfunctions in AD. In this study, we found that the level of miR-431 was downregulated in the plasma of patients with amnestic mild cognitive impairment and AD. In addition, it was decreased in the hippocampus and plasma of APPswe/PS1dE9 (APP/PS1) mice. Lentivirus-mediated miR-431 overexpression in the hippocampus CA1 ameliorated synaptic plasticity and memory deficits of APP/PS1 mice, while it did not affect amyloid-β levels. Smad4 was identified as a target of miR-431, and Smad4 knockdown modulated the expression of synaptic proteins, including SAP102, and protected against synaptic plasticity and memory dysfunctions in APP/PS1 mice. Furthermore, Smad4 overexpression reversed the protective effects of miR-431, indicating that miR-431 attenuated synaptic impairment at least partially by Smad4 inhibition. Thus, these results indicated that miR-431/Smad4 might be a potential therapeutic target for AD treatment.

Abstract Percutaneous laser discectomy is one common and effective treatment for cervical radicular pain. Currently, the surgery is performed with blind cannulation technique, mainly relies on the experience of surgeon. However, it still remains unsafe and difficult to reach the target. As an alternative, ultrasound-guided cannulation provides visualization of important structures, thus increasing the precision and safety. The primary goal of this study is to report the detail of the ultrasound-guided technique in the percutaneous laser cervical discectomy. The secondary purpose is to evaluate the feasibility of the novel therapy. This is a single center, feasibility study conducted in one teaching hospital. Thirteen intervertebral discs in 9 patients presented with cervical radicular pain. Accuracy of the cannulation with ultrasonic guidance was confirmed by the anterior–posterior and lateral view of fluoroscopy. We compared the pain severity pre- and post-treatment with Visual Analogue Score (VAS), and functional improvement was assessed with the modified Macnab Criteria and Neck Disability Index (NDI) respectively. Ultrasonic short-axis was used to scan the cervical nerve root, and its transition was used to identify the distinct intervertebral space. Following the recognition of targeted cervical level, the ultrasound probe was moved medially for the visualization of the surface of the cervical vertebrae. In plane cannulation was then applied to avoid the injury of the vessels. The location of cannula was confirmed by the fluoroscopic imaging. Low-power laser was set for the cervical disc ablation in this cohort. The majority of the surgical sites maintained in the C5/6 level (38%), and 31% for the C6/7 level respectively. Despite the distinct cervical level, the tip of needle was properly placed near by the targeted intervertebral disc in all participants, which was confirmed by the imaging of fluoroscopy. We did not observe any obvious complications during the procedure. The mean VAS decreased from 7.6 ± 1.1 to 2.3 ± 2.7 one month after discharge, and 2.1 ± 2.6 at the last follow-up (median duration of nine months). All patients reported significant improvement of NDI up to last follow-up (p = 0.011). Meanwhile, the good to excellent rate was reported in 8 of 9 patients (89%) according to the modified Macnab Criteria. The finding of this feasibility assessment indicates the ultrasound-based cannulation technique is capable of guiding the cannulation for the percutaneous laser discectomy. It may facilitate identifying the corresponding site of cervical intervertebral disc and prevent the damage of vessel.

Abstract Background Rapid progress over the last decade has added numerous agents targeting specific cellular signaling pathways to the treatment armamentarium for advanced cancer. However, many of these agents can cause hypertension resulting in major adverse cardiovascular event. Methods and results A systematic literature search was performed on the databases PubMed and Google Scholar for papers published in English until December 2020. This review summarizes the risk, mechanism, diagnosis, and management of hypertension in cancer patients undergoing targeted therapy. The risk and pathogenesis of hypertension vary widely with different classes of targeted agents. Currently there is a paucity of data investigating optimal management of hypertension with targeted therapy. A practical approach is discussed with a focus on the goal of blood pressure control as well as drug selection based on the mechanism of hypertension in the context of advanced cancer, treatment toxicity, comorbidity, and drug-drug interactions. This review also discusses many studies that have explored hypertension as a biomarker for cancer treatment efficacy and as a pharmacodynamic biomarker to titrate drug dose. Conclusions The diversity of targeted agents has provided important insights into the pathogenesis of hypertension in cancer patients. The underlying mechanism may provide a guidance to the management of hypertension. Further studies are needed to investigate optimal treatment and hypertension as a biomarker for cancer treatment.

Abstract Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver metabolic syndrome which affects millions of people worldwide. Recently, improving mitochondrial function and autophagic ability have been proposed as a means to prevent NAFLD. It has been previously described that high-temperature requirement protein A2 (HtrA2/Omi) favors mitochondrial homeostasis and autophagy in hepatocytes. Thus, we explored the effects of HtrA2/Omi on regulating mitochondrial function and autophagy during NAFLD development. High-fat diet (HFD)-induced NAFLD in mice and free fatty acids (FFAs)-induced hepatocytes steatosis in vitro were established. Adeno-associated viruses (AAV) in vivo and plasmid in vitro were used to restore HtrA2/Omi expression. In this study, we reported that HtrA2/Omi expression considerably decreased in liver tissues from the HFD-induced NAFLD model and in L02 cells with FFA-treated. However, restoring HtrA2/Omi ameliorated hepatic steatosis, confirming by improved serum lipid profiles, glucose homeostasis, insulin resistance, histopathological lipid accumulation, and the gene expression related to lipid metabolism. Moreover, HtrA2/Omi also attenuated HFD-mediated mitochondrial dysfunction and autophagic blockage. TEM analysis revealed that liver mitochondrial structure and autophagosome formation were improved in hepatic HtrA2/Omi administration mice compared to HFD mice. And hepatic HtrA2/Omi overexpression enhanced mitochondrial fatty acid β-oxidation gene expression, elevated LC3II protein levels, induced LC3 puncta, and decreased SQSTM1/p62 protein levels. Furthermore, hepatic HtrA2/Omi increased respiratory exchange ratio and heat production in mice. Finally, HtrA2/Omi overexpression by plasmid significantly diminished lipid accumulation, mitochondrial dysfunction, and autophagic inhibition in FFA-treated L02 hepatocytes. Taken together, we demonstrated that HtrA2/Omi was a potential candidate for the treatment of NAFLD via improving mitochondrial functions, as well as restoring autophagic flux.

Punding is a rare condition triggered by dopaminergic therapy in Parkinson’s disease (PD), characterized by a complex, excessive, repetitive, and purposeless abnormal movement, and its pathogenesis remains unclear. We aimed to assess the brain structure alterations related to punding by using multipametric magnetic resonance imaging (MRI). Thirty-eight PD patients (19 with punding and 19 without punding) from the Parkinson’s Progression Marker Initiative (PPMI) were included in this study. Cortical thickness was assessed with FreeSurfer, and the integrity of white matter fiber tracts and network topologies were analyzed by using FMRIB Software Library (FSL) and Pipeline for Analyzing braiN Diffusion imAges (PANDA). PD patients with punding showed a higher apathy score and more severe cortical atrophy in the left superior parietal, right inferior parietal, and right superior frontal gyrus, and worse integrity of the right cingulum cingulate tract compared to those without punding. On the other hand, no significant difference in structural network topologies was detected between the two groups. These data suggest that the specific area of destruction may be an MRI biomarker of punding risk, and these findings may have important implications for understanding the neural mechanisms of punding in PD.

The progression of Alzheimer’s disease (AD) correlates with the propagation of hyperphosphorylated tau (pTau) from the entorhinal cortex to the hippocampus and neocortex. Neutral sphingomyelinase2 (nSMase2) is critical in the biosynthesis of extracellular vesicles (EVs), which play a role in pTau propagation. We recently conjugated DPTIP, a potent nSMase2 inhibitor, to hydroxyl-PAMAM-dendrimer nanoparticles that can improve brain delivery. We showed that dendrimer-conjugated DPTIP (D–DPTIP) robustly inhibited the spread of pTau in an AAV-pTau propagation model. To further evaluate its efficacy, we tested D-DPTIP in the PS19 transgenic mouse model. Unexpectantly, D-DPTIP showed no beneficial effect. To understand this discrepancy, we assessed D-DPTIP’s brain localization. Using immunofluorescence and fluorescence-activated cell-sorting, D-DPTIP was found to be primarily internalized by microglia, where it selectively inhibited microglial nSMase2 activity with no effect on other cell types. Furthermore, D-DPTIP inhibited microglia-derived EV release into plasma without affecting other brain-derived EVs. We hypothesize that microglial targeting allowed D-DPTIP to inhibit tau propagation in the AAV-hTau model, where microglial EVs play a central role in propagation. However, in PS19 mice, where tau propagation is independent of microglial EVs, it had a limited effect. Our findings confirm microglial targeting with hydroxyl-PAMAM dendrimers and highlight the importance of understanding cell-specific mechanisms when designing targeted AD therapies.

Allostery is fundamental to many biological processes. Due to the distant regulation nature, how allosteric mutations, modifications, and effector binding impact protein function is difficult to forecast. In protein engineering, remote mutations cannot be rationally designed without large-scale experimental screening. Allosteric drugs have raised much attention due to their high specificity and possibility of overcoming existing drug-resistant mutations. However, optimization of allosteric compounds remains challenging. Here, we developed a novel computational method KeyAlloSite to predict allosteric site and to identify key allosteric residues (allo-residues) based on the evolutionary coupling model. We found that protein allosteric sites are strongly coupled to orthosteric site compared to non-functional sites. We further inferred key allo-residues by pairwise comparing the difference of evolutionary coupling scores of each residue in the allosteric pocket with the functional site. Our predicted key allo-residues are in accordance with previous experimental studies for typical allosteric proteins like BCR-ABL1, Tar, and PDZ3, as well as key cancer mutations. We also showed that KeyAlloSite can be used to predict key allosteric residues distant from the catalytic site that are important for enzyme catalysis. Our study demonstrates that weak coevolutionary couplings contain important information of protein allosteric regulation function. KeyAlloSite can be applied in studying the evolution of protein allosteric regulation, designing and optimizing allosteric drugs, and performing functional protein design and enzyme engineering.

Measuring the characteristics of seawater constituent is in great demand for studies of marine ecosystems and biogeochemistry. However, existing techniques based on remote sensing or in situ samplings present various tradeoffs with regard to the diversity, synchronism, temporal-spatial resolution, and depth-resolved capacity of their data products. Here, we demonstrate a novel oceanic triple-field-of-view (FOV) high-spectral-resolution lidar (HSRL) with an iterative retrieval approach. This technique provides, for the first time, comprehensive, continuous, and vertical measurements of seawater absorption coefficient, scattering coefficient, and slope of particle size distribution, which are validated by simulations and field experiments. Furthermore, it depicts valuable application potentials in the accuracy improvement of seawater classification and the continuous estimation of depth-resolved particulate organic carbon export. The triple-FOV HSRL with high performance could greatly increase the knowledge of seawater constituents and promote the understanding of marine ecosystems and biogeochemistry.

The effects of cooling rate, Ti content, and casting temperature on titanium compounds for high titanium steel were investigated. In-situ observation of high titanium steel during remelting and solidification was carried out by using a High Temperature Confocal Scanning Laser Microscope (HTCSLM), and the observed results were in good agreement with the thermodynamic and kinetic calculations. The observation and calculation results both show that the inclusions in high titanium steel first precipitate in the form of TiN, followed by TiC precipitates as temperature decreases, eventually forming TiCxN1-x type inclusions at room temperature. The initial precipitation temperature of the inclusions increases with the increase of Ti content in molten steel, whereas casting temperature has little effect on the initial precipitation temperature of inclusions. In addition, the size of TiN inclusions increases with the increase of Ti content in steel but decreases with the increase in cooling rate.

Chronic stress induces peripheral and intracerebral immune changes and inflammation, contributing to neuropathology and behavioral abnormalities relevant to psychiatric disorders such as depression. Although the pathological implication of many peripheral factors such as pro‐inflammatory cytokines, hormones, and macrophages has been demonstrated, the roles of circulating extracellular vesicles (EVs) for chronic stress mechanisms remain poorly investigated. Here, we report that chronic social defeat stress (CSDS)‐induced social avoidance phenotype, assessed by a previously untested three‐chamber social approach test, can be distinguished by multiple pro‐inflammatory cytokines and EV‐associated molecular signatures in the blood. We found that the expression patterns of miRNAs distinguished the CSDS‐susceptible mice from the CSDS‐resilient mice. Social avoidance behavior scores were also estimated with good accuracy by the expression patterns of multiple EV‐associated miRNAs. We also demonstrated that EVs enriched from the CSDS‐susceptible mouse sera upregulated the production of pro‐inflammatory cytokines in the LPS‐stimulated microglia‐like cell lines. Our results indicate the role of circulating EVs and associated miRNAs in CSDS susceptibility, which may be related to pro‐inflammatory mechanisms underlying stress‐induced neurobehavioral outcomes.

Abstract Background Early postnatal overfeeding could result in metabolic imprinting that decreases energy expenditure following white adipose tissue (WAT) gain throughout life. This research investigated whether curcumin (CUR) supplementation could promote WAT browning and activate thermogenesis in postnatal overfed rats. Methods and results This study adjusted the size of litters to three (small litters, SL) or ten (normal litters, NL) to mimic early postnatal overfeeding or normal feeding from postnatal day 3. From postnatal week 3 (weaning period), SL rats were fed a standard diet (SL) or a diet supplemented with 1% (SL1% CUR) or 2% (SL2% CUR) CUR for ten weeks. At postnatal week 13, SL rats with 1% or 2% CUR supplementation had lower body weight and less WAT gain and had an increased lean mass ratio, and their glucose tolerance and blood lipid levels had recovered to normal when compared to SL rats that did not receive the supplement. Moreover, the increased heat generation were consistent with the expression levels of uncoupling protein 1 (UCP1) and other browning-related genes in the subcutaneous adipose tissue (SAT) of the SL2% CUR rats but not in the SL1% CUR rats. In addition, 2% CUR dietary supplementation enhanced the serum norepinephrine levels in SL rats, with upregulated mRNA levels of β3-adrenergic receptor (β3-AR) in SAT. Conclusion Dietary CUR supplementation attenuates body fat gain and metabolic disorders in SL, which might be induced by promoting browning of SAT and energy expenditure. Moreover, the benefits were more obvious in SL with 2% CUR supplementation.

A novel ratiometric fluorescent probe was constructed for sensitive assay of hydrogen peroxide (H2O2) and glucose, which utilized the synergistically enhanced effects of Ce3+ and Fe2+ on copper nanoclusters (CuNCs) and coumarin. In the CuNCs-Ce3+/Fe2+-coumarin system, Ce3+ triggered the aggregation-induced emission phenomenon of CuNCs, and Fe2+ catalyzed the Fenton reaction to efficiently yield hydroxyl radical (•OH). In the presence of H2O2, the 625-nm red fluorescence of CuNCs was sharply quenched owing to the oxidation of CuNCs to Cu(II) by •OH, but the 460-nm blue fluorescence of 7-hydroxycoumarin from the oxidation of coumarin by •OH dramatically increased. Based on the reversible changes in two fluorescence signals, a satisfactorily ratiometric probe was constructed for H2O2 assay with a detection limit (LOD) of 0.6 μM accompanied by a visual color variation from red to blue. For glucose assay, this ratiometric probe gave a linear range of 3.2–160 μM and LOD of 0.96 μM owing to the oxidization of glucose to yield H2O2 in the presence of glucose oxidase and O2. Overall, the newly developed ratiometric probe shows a great prospect in real applications for visual assay of H2O2 and glucose by our naked eyes.

N6-methyladenosine (m6A) is one of the most important RNA modifications, which is involved in many biological activities. Computational methods have been developed to detect m6A sites due to their high efficiency and low costs. As one of the most widely utilized model organisms, many methods have been developed for predicting m6A sites of Saccharomyces cerevisiae. However, the generalization of these methods was hampered by the limited size of the benchmark datasets. On the other hand, over 60,000 low resolution m6A sites and more than 10,000 base resolution m6A sites of Saccharomyces cerevisiae are recorded in RMBase and m6A-Atlas, respectively. The base resolution m6A sites are often obtained from low resolution results by post calibration. In view of these, we proposed a two-stage deep learning method, named MTDeepM6A-2S, to predict RNA m6A sites of Saccharomyces cerevisiae based on RNA sequence information. In the first stage, a multi-task model with convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) deep framework was built to not only detect the low resolution m6A sites but also assign a reasonable probability for the predicted site. In the second stage, a transfer-learning strategy was used to build the model to predict the base resolution m6A sites from those low resolution m6A sites. The effectiveness of our model was validated on both training and independent test sets. The results show that our model outperforms other state-of-the-art models on the independent test set, which indicates that our model holds high potential to become a useful tool for epitranscriptomics analysis.

Objectives: Twenty-four-hour ambulatory blood pressure monitoring (ABPM) is recommended in patients with white coat hypertension (HTN) by national guideline, but was poorly utilized, and is available only in very limited subspecialty clinics. We aim to examine the feasibility and utility of ABPM in a primary care setting in the diagnosis and management of white coat HTN including the implementation and modification of antihypertensive therapy. Methods: Patients who have elevated blood pressure in clinic office but normal blood pressure readings at home are eligible for 24-h ambulatory BP monitoring. We analyzed data from patients who were suspected to have white coat HTN in the last 2 years underwent ABPM in our practice. Results: Among 68 patients, 54 patients met the selection criteria. ABPM showed that 13 patients had normal BP (24%), while 41 patients (76%) had persistent HTN. Among these patients with persistent HTN, 28 patients had intervention including 24 patients prescribed with new anti-hypertensive medication or medication increase, 3 patients prescribed with additional lifestyle modification and one started CPAP.13 patients with slightly elevated BP (133/77 for average daytime BP) didn’t have medication adjustment. Conclusion: ABPM has substantial utility in the diagnosis and management of white coat HTN in a primary care setting.

Combined antiretroviral therapy ushered an era of survivable HIV infection in which people living with HIV (PLH) conduct normal life activities and enjoy measurably extended lifespans. However, despite viral control, PLH often experience a variety of cognitive, emotional, and physical phenotypes that diminish their quality of life, including cognitive impairment, depression, and sleep disruption. Recently, accumulating evidence has linked persistent CNS immune activation to the overproduction of glutamate and upregulation of glutaminase (GLS) activity, particularly in microglial cells, driving glutamatergic imbalance with neurological consequences. Our lab has developed a brain-penetrant prodrug of the glutamine antagonist 6-diazo-5-oxo-L-norleucine (DON), JHU083, that potently inhibits brain GLS activity in mice following oral administration. To assess the therapeutic potential of JHU083, we infected mice with EcoHIV and characterized their neurobehavioral phenotypes. EcoHIV-infected mice exhibited decreased social interaction, suppressed sucrose preference, disrupted sleep during the early rest period, and increased sleep fragmentation, similar to what has been reported in PLH but not yet observed in murine models. At doses shown to inhibit microglial GLS, JHU083 treatment ameliorated all of the abnormal neurobehavioral phenotypes. To explore potential mechanisms underlying this effect, hippocampal microglia were isolated for RNA sequencing. The dysregulated genes and pathways in EcoHIV-infected hippocampal microglia pointed to disruptions in immune functions of these cells, which were partially restored by JHU083 treatment. These findings suggest that upregulation of microglial GLS may affect immune functions of these cells. Thus, brain-penetrable GLS inhibitors like JHU083 could act as a potential therapeutic modality for both glutamate excitotoxicity and aberrant immune activation in microglia in chronic HIV infection.

People living with HIV (PLH) have significantly higher rates of cognitive impairment (CI) and major depressive disorder (MDD) versus the general population. The enzyme neutral sphingomyelinase 2 (nSMase2) is involved in the biogenesis of ceramide and extracellular vesicles (EVs), both of which are dysregulated in PLH, CI, and MDD. Here we evaluated EcoHIV-infected mice for behavioral abnormalities relevant to depression and cognition deficits, and assessed the behavioral and biochemical effects of nSMase2 inhibition. Mice were infected with EcoHIV and daily treatment with either vehicle or the nSMase2 inhibitor (R)-(1-(3-(3,4-dimethoxyphenyl)-2,6-dimethylimidazo[1,2-b]pyridazin-8-yl)pyrrolidin-3-yl)-carbamate (PDDC) began 3 weeks post-infection. After 2 weeks of treatment, mice were subjected to behavior tests. EcoHIV-infected mice exhibited behavioral abnormalities relevant to MDD and CI that were reversed by PDDC treatment. EcoHIV infection significantly increased cortical brain nSMase2 activity, resulting in trend changes in sphingomyelin and ceramide levels that were normalized by PDDC treatment. EcoHIV-infected mice also exhibited increased levels of brain-derived EVs and altered microRNA cargo, including miR-183-5p, miR-200c-3p, miR-200b-3p, and miR-429-3p, known to be associated with MDD and CI; all were normalized by PDDC. In conclusion, inhibition of nSMase2 represents a possible new therapeutic strategy for the treatment of HIV-associated CI and MDD.

As an active remote sensing technology, airborne LIDAR can work at all times while emitting specific wavelengths of laser light that can penetrate seawater. Airborne LIDAR bathymetry (ALB) records an object’s full return waveform, including the water surface, water column, seafloor, and the objects on it. Due to the seawater’s absorption and scattering and the seafloor’s reflectivity effect, the seafloor’s amplitude of seafloor echoes varies greatly. Seafloor echoes with low signal-to-noise ratios are not easily detected using waveform processing methods, which can lead to insufficient seafloor topography depth and incomplete seafloor topography coverage. To extract faint seafloor echoes, we proposed a depth extraction method based on the PointConv deep learning model, called FWConv. The method assumed that spatially adjacent echoes were correlated. We converted all the spatially adjacent multi-frame waveforms into a point cloud. Each point represented a bin value in the waveform, and the points’ properties contained spatial coordinates and the amplitude in the waveform. In the semantic segmentation of these point clouds using deep learning models, we considered not only each centroid’s amplitude, but also its neighboring points’ distance and amplitude. This enriched the centroids’ features and allowed the model to better discriminate between background noise and seafloor echoes. The results showed that FWConv could extract faint seafloor echoes in the experimental area and was not easily affected by noise, and that the correctness reached 99.82%. The number of point clouds increased by 158%, and the seafloor elevation accuracy reached 0.20 m concerning the multibeam echo sounder data.

As one of the most important post-transcriptional modifications of RNA, 5-cytosine-methylation (m5C) is reported to closely relate to many chemical reactions and biological functions in cells. Recently, several computational methods have been proposed for identifying m5C sites. However, the accuracy and efficiency are still not satisfactory. In this study, we proposed a new method, m5Cpred-XS, for predicting m5C sites of H. sapiens, M. musculus, and A. thaliana. First, the powerful SHAP method was used to select the optimal feature subset from seven different kinds of sequence-based features. Second, different machine learning algorithms were used to train the models. The results of five-fold cross-validation indicate that the model based on XGBoost achieved the highest prediction accuracy. Finally, our model was compared with other state-of-the-art models, which indicates that m5Cpred-XS is superior to other methods. Moreover, we deployed the model on a web server that can be accessed through http://m5cpred-xs.zhulab.org.cn/, and m5Cpred-XS is expected to be a useful tool for studying m5C sites.

Abstract Background As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functions such as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA, researchers can better understand the exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost. However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement. Results In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVM offered substantially higher prediction accuracy than previously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites. Conclusion In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species. The result shows that our model outperformed the existing state-of-art models. Our model is available for users through a web server at https://zhulab.ahu.edu.cn/m5CPred-SVM .

The accumulation of insoluble amyloid β (Aβ) peptides is one of the pathological changes in Alzheimer's disease (AD), which induced synaptic plasticity impairment and excitatory amino acid toxicity associated with decreased memory function. Xingnaojing (XNJ), a well-known prescription in traditional Chinese medicine, has been used for the treatment of stroke for many years in China. In this study, we aim to investigate the therapeutic effects of XNJ in a hippocampus of Aβ1–42 induced mouse model of AD which showed significant memory loss and impaired synaptic morphology and function. Treatment of XNJ could attenuate spatial and working memory dysfunction, increase dendritic spine density and improve long-term potential (LTP) induction. In addition, XNJ treatment significantly increased the level of N-methyl-d-aspartate receptors (NMDARs) and inhibit the NMDA/α-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) ratio in AD mice. XNJ treatment also activated the AKT/mechanistic target of rapamycin (mTOR) pathway, while inhibition of the mTOR pathway by rapamycin could reverse the protective effects of XNJ treatment. In conclusion, XNJ protected against synaptic plasticity and memory impairment in AD mice via the activation of AKT/mTOR signaling pathway, suggesting XNJ as an alternative treatment for AD.

Abstract Background The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions. Results In this study, we built a new computational model, iPNHOT, to effectively predict hot spot residues on protein-nucleic acids interfaces. One training data set and an independent test set were collected from dbAMEPNI and some recent literature, respectively. To build our model, we generated 97 different sequential and structural features and used a two-step strategy to select the relevant features. The final model was built based only on 7 features using a support vector machine (SVM). The features include two unique features such as ∆SASsa1/2 and esp3, which are newly proposed in this study. Based on the cross validation results, our model gave F1 score and AUROC as 0.725 and 0.807 on the subset collected from ProNIT, respectively, compared to 0.407 and 0.670 of mCSM-NA, a state-of-the art model to predict the thermodynamic effects of protein-nucleic acid interaction. The iPNHOT model was further tested on the independent test set, which showed that our model outperformed other methods. Conclusion In this study, by collecting data from a recently published database dbAMEPNI, we proposed a new model, iPNHOT, to predict hotspots on both protein-DNA and protein-RNA interfaces. The results show that our model outperforms the existing state-of-art models. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPNHOT/ .

A compact Super Wide band (SWB) antenna fed by coplanar waveguide (CPW) is proposed in this paper. The size of the antenna is 140 × 100mm2 (0.16λ × 0.12λ). The antenna originates from 3D volcano smoke antenna, and consists of a prolonged radiation patch and a symmetrical ground. Both the patch and ground were designed as smoothing structure, which can improve the impedance matching through a wide band. The simulated and measured bandwidth show that the antenna has a well-matched impedance bandwidth from 400MHz to higher than 20GHz under VSWR = 2 criterion, and the bandwidth ratio is greater than 50:1. The gain, efficiency and radiation pattern were all measured, and the maximum gain and efficiency are 6.3dBi and 96%, respectively. The antenna was also used to measure the ambient radiation frequency (RF) density in an urban area of Nanjing in China, which on the other hand proves the efficiency of this design.

As one of the most important posttranslational modifications (PTMs), protein lysine glycation changes the characteristics of the proteins and leads to the dysfunction of the proteins, which may cause diseases. Accurately detecting the glycation sites is of great benefit for understanding the biological function and potential mechanism of glycation in the treatment of diseases. However, experimental methods are expensive and time-consuming for lysine glycation site identification. Instead, computational methods, with their higher efficiency and lower cost, could be an important supplement to the experimental methods. In this study, we proposed a novel predictor, BERT-Kgly, for protein lysine glycation site prediction, which was developed by extracting embedding features of protein segments from pretrained Bidirectional Encoder Representations from Transformers (BERT) models. Three pretrained BERT models were explored to get the embeddings with optimal representability, and three downstream deep networks were employed to build our models. Our results showed that the model based on embeddings extracted from the BERT model pretrained on 556,603 protein sequences of UniProt outperforms other models. In addition, an independent test set was used to evaluate and compare our model with other existing methods, which indicated that our model was superior to other existing models.

Alzheimer's disease (AD), a heterogeneous neurodegenerative disorder, is the most common cause of dementia accounting for an estimated 60–80% of cases. The pathogenesis of AD remains unclear, and no curative treatment is available so far. Increasing evidence has revealed a vital role of non-coding RNAs (ncRNAs), especially long non-coding RNAs (lncRNAs), in AD. LncRNAs contribute to the pathogenesis of AD via modulating amyloid production, Tau hyperphosphorylation, mitochondrial dysfunction, oxidative stress, synaptic impairment and neuroinflammation. This review describes the biological functions and mechanisms of lncRNAs in AD, indicating that lncRNAs may provide potential therapeutic targets for the diagnosis and treatment of AD.

Abstract It is important to investigate the mechanical performances of (Gd0.9Yb0.1)2Zr2O7 (GYbZ) materials deposited on irregular substrates for improving new thermal barrier coatings. Three-point bending fracture characteristics of freestanding GYbZ coating prepared by supersonic plasma sprayed (SPS) technique were investigated with the help of digital image correlation technique. The cracking time, crack propagation path, and mechanical properties of GYbZ coating were obtained. Meanwhile, the X-ray computed tomography technique was introduced to scan the microstructure of freestanding GYbZ coatings, which are used to establish three-dimensional (3D) finite element model by using the Avizo software. The brittle cracking criterion was applied to describe the bending fracture process of GYbZ coatings. The critical cracking strain was estimated as 0.36%±0.03% by repeatedly comparing the difference between the experimental and simulated curves. The results would be extended to predict the dangerous region and failure mechanisms of GYbZ coatings deposited on irregular substrate during finite element simulations.

5-methylcytosine (m5C) is one of the most common and abundant post-transcriptional modifications (PTCMs) in RNA. Recent studies showed that m5C plays important roles in many biological functions such as RNA metabolism and cell fate decision. Because most experimental methods that determine m5C sites across the transcriptome are time-consuming and expensive, it is urgent to develop accurate computational methods to identify m5C sites effectively. A benchmark dataset is important for developing and evaluating computational methods. In this work, we constructed four different datasets according to the data redundancy and imbalance. Based on these datasets, we generated three different kinds of features, i.e., KNFs (K-nucleotide frequencies), KSNPFs (K-spaced nucleotide pair frequencies), and pseDNC (pseudo-dinucleotide composition), and then used a support vector machine (SVM) to build our models. Based on the imbalanced and nonredundant dataset, Met935, we extensively studied the three kinds of features and determined an optimal combination of the features. Based on the feature combination, we built models on the three different datasets and compared them with state-of-the-art models. According to the predictive results of the stringent jackknife test, the models based on the three features, 4NF, 1SNPF, and pseDNC, are superior or comparable to other methods. To determine the best model between the models based on the imbalanced dataset Met935 and the balanced dataset Met240, we further evaluated the two models on an independent test set Test1157. Our results demonstrate that the model based on the balanced dataset Met240 achieved the highest recall (68.79%) and the highest Matthews correlation coefficient (MCC) (0.154). In addition, the model is also superior to other state-of-the-art methods according to the integrated parameter MCC on the independent test set. Thus, we selected the model based on Met240 as our final model, which was named RNAm5CPred. In addition, a web server for RNAm5CPred (http://zhulab.ahu.edu.cn/RNAm5CPred/) has been provided to facilitate experimental research. Keywords: 5-methylcytosine site, post-transcriptional modification, support vector machine, nucleotide composition, prediction

Air interlayers are often used as a part of thermal insulation structure and form a system with solid materials to reduce heat transfer. However, the heat transfer process in the composite structure is affected by many factors at high temperatures. In this paper, a series of heat preservation systems with air gap and solid structure were proposed. Based on the research background of heat preservation in the riser of ingot casting, the heat transfer process in the composite structure was systematically studied by means of numerical simulation and experiment. The heat transfer laws under different geometric structures, thermophysical properties of materials, and surface emissivity were obtained, which could provide theoretical basis for the design of heat preservation structures at high temperatures.

Excited-state molecular dynamics may be too complex to be resolved by femtosecond spectroscopic studies. Here the authors resolve the competing pathways in the excited state dynamics of methyl bromide by attosecond transient absorption spectroscopy, from excitation to fragmentation.

Abstract Background Hypertension is one of the major side effects associated with abiraterone in the treatment of advanced prostate cancer. The specific contribution of abiraterone to hypertension has not been defined. We performed a systematic review and meta-analysis of randomized clinical trials to determine its overall risk. Methods Databases including Pubmed (up to July 2018) and Google scholar (up to July 2018) were searched to identify relevant studies. Eligible studies were prospective randomized clinical trials with prostate cancer treated with abiraterone and prednisone. The incidence and relative risk (RR) of hypertension was calculated using random-effects or fixed-effects model depending on the heterogeneity of included studies. Results A total of five studies including 5445 patients were selected for analysis. Among patients receiving abiraterone, the overall incidences of all grade and high grade (grade 3 and 4) were 21.9% (95% CI: 13.6–33.2%) and 10.2% % (95% CI: 6.9–11.6%). Abiraterone was associated with a significantly increased risk of hypertension of all grade with a relative risk of 1.80 (95% CI: 1.47–2.19%, p

Internet application providers now have more incentive than ever to collect user data, which greatly increases the risk of user privacy violations due to the emerging of deep neural networks. In this paper, we propose TensorClog-a poisoning attack technique that is designed for privacy protection against deep neural networks. TensorClog has three properties with each of them serving a privacy protection purpose: 1) training on TensorClog poisoned data results in lower inference accuracy, reducing the incentive of abusive data collection; 2) training on TensorClog poisoned data converges to a larger loss, which prevents the neural network from learning the privacy; and 3) TensorClog regularizes the perturbation to remain a high structure similarity, so that the poisoning does not affect the actual content in the data. Applying our TensorClog poisoning technique to CIFAR-10 dataset results in an increase in both converged training loss and test error by 300% and 272%, respectively. It manages to maintain data's human perception with a high SSIM index of 0.9905. More experiments including different limited information attack scenarios and a real-world application transferred from pre-trained ImageNet models are presented to further evaluate TensorClog's effectiveness in more complex situations.

NiTi alloy’s shape memory effect provides additional restoring force under temperature loads, making it an ideal material for gaskets. However, its yield stress is too large to form the initial seal. In this paper, by combining the advantages of corrugated structure and NiTi alloy’s shape memory effect, a NiTi alloy corrugated gasket is proposed. Its mechanical properties were studied using experiments and the finite element method. The influences of geometric parameters on gasket performance were discussed. The results show that the shape memory effect can greatly improve the contact stress of gaskets. The corrugation can effectively reduce the pre-tightening force. The contact stress of NiTi alloy corrugated gasket is significantly affected by plate thickness, gasket height, and corrugation pitch and shows a high nonlinear characteristic. The proposed finite element method (FEM) and the gasket contact stress prediction model are accurate and engineering available.

Bolted flange connections are commonly used in process industries. Their sealing performance is greatly affected by the gasket. In this paper, a NiTi alloy corrugated gasket was simulated to reveal its sealing performance, considering the gasket surface roughness, shape memory effect and superelastic effect. A fluid–structure coupling analysis method that takes the real surface morphology of the gasket contact zone was proposed, and a leakage rate prediction model was established. The results showed that NiTi shape memory effect could enhance the sealing reliability in service and lower the leakage rate. The leakage rate of the NiTi alloy corrugated gasket is positively correlated with the internal pressure of the medium and the roughness of the sealing surface. The prediction model of the NiTi alloy corrugated gasket leakage rate has good reliability with an average error of about 16.81% compared with the simulation.

Mechanisms underlying cognitive impairment (CI) in hypertensive patients remain relatively unclear. The present study aimed to explore the relationship among serum exosomal microRNAs (miRNAs), cerebrovascular reactivity (CVR), and cognitive function in hypertensive patients. Seventy-three hypertensive patients with CI (HT-CI), 67 hypertensive patients with normal cognition (HT-NC), and 37 healthy controls underwent identification of exosomal miRNA, multimodal magnetic resonance imaging (MRI) scans, and neuropsychological tests. CVR mapping was investigated based on resting-state functional MRI data. Compared with healthy subjects and HT-NC subjects, HT-CI subjects displayed decreased serum exosomal miRNA-330-3p. The group difference of CVR was mainly found in the left frontal lobe and demonstrated that HT-CI group had a lower CVR than both HT-NC group and control group. Furthermore, both the CVR in the left medial superior frontal gyrus and the miRNA-330-3p level were significantly correlated with executive function (r = −0.275, P = 0.021, and r = −0.246, P = 0.04, respectively) in HT-CI subjects, and the CVR was significantly correlated with the miRNA-330-3p level (r = 0.246, P = 0.040). Notably, path analysis showed that the CVR mediated the association between miRNA-330-3p and executive function. In conclusion, decreased miRNA-330-3p might contribute to CI in hypertensive patients by decreasing frontal CVR and could be a biomarker of early diagnosis.