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Alternative splicing (AS) events play a crucial role in the tumorigenesis and progression of cancer. Transcriptome data and Percent Spliced In (PSI) values of OV patients were downloaded from TCGA database and TCGA SpliceSeq. Totally we identified 1,472 AS events that were associated with survival of OV and exon skipping (ES) was the most important type. Univariate and multivariate Cox regression analysis were performed to identify survival-associated AS events and develop the prognostic model based on 11-AS events. The immune cells and different response to cytotoxic T lymphocyte associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1) blockers in low-risk and high-risk group of OV patients were analyzed. Ten kinds of immune cells were found up-regulated in low-risk group. Activated B cell, natural killer T cell, natural killer cell and regulatory T cell were associated with survival of OV. The patients in low-risk group had good response to CTLA-4 and PD-1 blockers treatment. Moreover, a regulatory network was established according to the correlation between AS events and splicing factors (SFs). The present study provided valuable insights into the underlying mechanisms of OV. AS events that were correlated with the immune system might be potential therapeutic targets.

Alternative splicing (AS) events play a crucial role in the tumorigenesis and progression of cancer. Transcriptome data and Percent Spliced In (PSI) values of ovarian cancer patients were downloaded from TCGA database and TCGA SpliceSeq. Totally we identified 1472 AS events that were associated with survival of ovarian serous cystadenocarcinoma (OC) and exon skipping (ES) was the most important type. Univariate and multivariate Cox regression analysis were performed to identify survival-associated AS events and developed the prognostic model based on 11-AS events. The immune cells and different response to cytotoxic T lymphocyte associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1) blockers in low-risk and high-risk group of OC patients were analyzed. Ten kinds of immune cells were found up-regulated in low-risk group. Activated B cell, natural killer T cell, natural killer cell and regulatory T cell were associated with survival of OC. The patients in low-risk group had good response to CTLA-4 and PD-1 blockers treatment. Moreover, a regulatory network was established according to the correlation between AS events and splicing factors (SFs). The present study provided valuable insights into the underlying mechanisms of OC. AS events that were correlated with the immune system might be potential therapeutic targets.

Flexible pressure sensors usually require functional materials with both mechanical compliance and appropriate electrical performance. Most sensors based on materials with limited compressibility can hardly balance between high sensitivity and broad pressure range. Here, we prepare a heterophasic ionogel with shape and stiffness memory for adaptive pressure sensors. By combining the microstructure alignment for stiffness changing and shape memory micro-inclusions for stiffness fixing, the heterophasic ionogels reveal tunable compressibility. This controllable pressure-deformation property of the ionogels results in the pressure sensors’ programmable pressure-resistance behavior with tunable pressure ranges, varied detection limits, and good resolution at high pressure. Broad pressure ranges to 220 and 380 kPa, and tunable detection limit from 120 to 330 and 950 Pa are realized by the stiffness memory ionogel sensors. Adaptive detection is also brought out to monitor tiny pressure changes at low stiffness and distinguish different human motions at high stiffness. Using shape and stiffness memory materials in pressure sensors is a general design to achieve programmable performance for more complex application scenarios.

Permanent magnet torque motor (PMTM) is widely used in aerospace, computer numerical control (CNC) machine tools, and industrial robots with many advantages such as high torque density, strong overload capacity, and low torque ripple. With the upgrading of industrial manufacturing, the requirements for the performance of torque motors have become more stringent. At present, how to achieve high output torque and low torque ripple has become a research hotspot of torque motors. In the optimization process, it is necessary to increase the output torque while the torque ripple can be reduced, and it is difficult to get a good result with the single-objective optimization. In this paper, a multi-objective optimization method based on the combination of design parameter stratification and support vector machine (SVM) is proposed. By analyzing the causes of torque ripple, the output torque, efficiency, cogging torque, and total harmonic distortion (THD) of back electromotive force (EMF) are selected as the optimization objectives. In order to solve the coupling problem between the motor parameters, the calculation formula of Pearson correlation coefficient is used to analyze the relationship between the design parameters and the optimization objectives, and the design parameters are layered ac-cording to the sensitivity. In order to shorten the optimization cycle of the motor, SVM is used as a fitting method of the mathematical model. The performance between initial and optimal motors is compared, and it can be found that the optimized motor has a higher torque and lower torque ripple. The simulation results verify the effectiveness of the proposed optimization method.

Objective: Gout is a common arthritis caused by deposition of monosodium urate crystals. Macrophage is crucial in the process of monosodium urate (MSU) -induced inflammation. Although it has been reported that adrenocorticotropic hormone (ACTH) in nature can be used to cure urarthritis, the mechanism concerning macrophage is still not clear. This study aims to explore how natural ACTH can alleviate urarthritis through functional changes in macrophage. Methods: We analysed the variations in VAS pain scores of five patients, knowing the time of action, and detecting the level of cortisol and ACTH in patients 24 hours after the application of ACTH. The effect of natural ACTH on joint inflammation and the level of cortisol in blood in mouse model was evaluated by studies in vivo. In vitro studies we evaluated the effect of natural ACTH on macrophage and revealed different functions of ACTH and dexamethasone on macrophage in the transcriptional level. Results: In patients with acute gout, natural ACTH can quickly alleviate pain and has no effect on the level of cortisol and ACTH. Natural ACTH is able to ease the swelling and inflammatory cell infiltration caused by arthritis, without changing the level of cortisol. Besides, natural ACTH in vitro can alleviate acute gouty inflammation by regulating phagocytosis and polarization of macrophage, which also exert different effects on the transcription of some related genes.Conclusion: Natural ACTH is able to alleviate acute gouty inflammation by regulating macrophage, and this effect differs from that of dexamethasone in the transcriptional level.

Objective Alternative splicing (AS) events play a crucial role in the tumorigenesis and progression of various cancers. In the present study, we aimed to identify specific AS events, which might be prognostic markers and therapeutic targets for ovarian cancer (OV). Methods Transcriptome data, clinical information, and Percent Spliced In (PSI) values were downloaded from TCGA database and TCGA SpliceSeq to explore the role of AS events in the prognosis of OV patients. Univariate and multivariate Cox regression analyses were performed to identify survival-associated AS events and develop multi-AS-based prognostic models. The K-M curves and ROC curves were conducted based on prognostic AS event models. Moreover, a splicing regulatory network was established according to the correlation between AS events and splicing factors (SFs). Finally, we performed functional enrichment analysis by GO terms and KEGG pathways. Results We identified 1,472 AS events that were associated with the survival of OV patients, and exon skipping (ES) was the most important type. We also found that prognostic models based on AS events were good predictors of OV prognosis, which could discriminate the high-risk group from the low-risk group (P

Aiming at the problem that the noise covariance matrix is difficult to get when using the Kalman filter to observe the force, a force ripple suppression strategy of permanent magnet linear synchronous motor based on fuzzy adaptive Kalman filter is proposed. According to the fuzzy control theory, this method takes the error of the q-axis current and rate of the error as fuzzy controller inputs, and takes the parameter in the covariance matrix of the measurement noise as fuzzy controller output, which reduces the burden of parameter tuning and improve disturbance rejection performance. The experimental results show that compared with the Kalman filter with fixed parameters, the method proposed in this paper has better dynamic performance.

As many studies prefer to focus on the accuracy of prediction models in model-based problems of chiller plants, the practicality and feasibility of the models used for field applications are often compromised for a superior performance. We herein present an online predictive control method based on linear regression model for a typical chiller plant from the perspective of real-time application to optimize its overall performance and energy consumption. A multi-input–single-output (MISO) controller is developed for predicting the global coefficient of performance (COP) of the chiller plant to reflect integral characteristics and interactions of components based on the multivariable linear regression (MLR) model. The input variables are much easier to obtain in a practical system compared to variables such as water flow and part load ratio, thereby rendering an excellent model capable of easy implementation and duplication. The performance of this approach is tested and evaluated in a real chiller plant system by comparing it to that obtained using a local control strategy. The results of this study indicate that the online predictive control strategy can enhance the global COP values by 5.35% on average and reduce electricity consumption by 2.70% daily compared to the local control strategy.

The automatic control of Heating, ventilation and Air Conditioning (HVAC) systems aims to achieve the thermal comfort requirements of occupants with minimum energy consumption. The automatic control strategy of existing HVAC systems determines a set value for creating a thermal environment in accordance with relevant design principles and/or occupants’ preferences. An overly-cooled indoor environment may reduce the occupant's thermal comfort and result in excessive energy consumption. In order to improve thermal comfort and save energy, this study proposes an indoor thermal environment optimal control method based on the online monitoring of thermal sensation. First, a smart wristband collects the human physiological data, including wrist skin temperature and heart rate. This is for predicting human thermal sensation, where a fuzzy comprehensive evaluation method is employed to determine the integrated thermal sensation of multi occupants. Then, a linear adjustment algorithm is developed to optimize the indoor temperature set point. In order to evaluate the performance of the thermal sensation-based control method, a series of experiments were conducted using the thermal sensation-based control and set point-based control. The results show that the thermal sensation-based control can adjust the temperature setting in a timely fashion according to the occupants’ integrated thermal sensations, although they do not necessarily state their subjective perception. It is also revealed that the thermal sensation-based control can achieve a more comfortable thermal environment than the set point-based control. Furthermore, the thermal sensation-based control saves 13.8% in daily energy consumption compared to the set point-based control method.

Objectives Acute gout is an inflammatory response to MSU crystals. In our previous research, Sirt1 was shown to have an effect in preventing acute gouty inflammation. In the current study, we aimed to investigate the underlying mechanism involving Sirt1 in acute gout. Methods The cytological changes and Sirt1 expression in the synovium were observed in patients with acute or intermittent gout. The effect of Sirt1 and its mechanism in gout were studied in macrophages, C57BL/6 mice and Sirt1+/− mice. Results Sirt1 expression was increased in the peripheral blood mononuclear cells (PBMCs) of patients with acute gout but not in the chronic tophus tissue. The arthritis score and numbers of inflammatory cells in injured paw tissue from murine gout models were upregulated in Sirt1+/− mice compared with wild-type mice. A PCR array of the paw tissue from murine gout models indicated that Sirt1 activation might attenuate MSU-induced inflammation by altering the polarization state of macrophages. Furthermore, in patients with acute gout, the phagocytosis of MSU crystals by a macrophage was found in a smear of the joint fluid and large amounts of macrophages were also found in the synovium. The activation of Sirt1 in gouty mice actually decreased the tendency toward M1 polarization. The inhibition of PI3K/Akt partially blocked the anti-inflammatory effect of Sirt1 and the translocation of STAT6, and phosphorylated STAT6 expression was decreased in RAW 264.7 cells treated with MSU crystals. Conclusion Our studies revealed that Sirt1 ameliorates MSU-induced inflammation by altering macrophage polarization via the PI3K/Akt/STAT6 pathway.

Background: Rheumatoid arthritis (RA) is a chronic inflammatory arthropathy characterized by excessive synovial hyperplasia and progressive joint destruction. Pro-inflammatory cytokines pla...

Nanocomposite fibers have attracted intensive attentions owing to their promising applications in various fields. However, the fabrication of nanocomposite fibers with super toughness and strong strength under mild conditions remains a great challenge. Here we present a facile flow-induced assembly strategy for the development of super-tough and strong nanocomposite fibers with highly ordered carbon nanotubes (CNTs), which can be induced by directional and fast flow on a grooved hydrogel surface. The prepared nanocomposite fibers show excellent mechanical properties, with a tensile strength up to 643± 27 MPa and toughness as high as 77.3±3.4 MJ m−3 at ultimate strain of 14.8±1.5%. This versatile and efficient flow-induced alignment strategy represents a promising direction for the development of high-performance nanocomposites for practical applications.

Tight oil sandstones have complex pore structure due to multiple pore types and multiscale pore size ranging from several nanometers to dozens of microns. In this study, petrophyiscal properties of tight oil sandstones were quantified based on microscale X-ray computed tomography (μXCT) imaging techniques. The pore types in gray scale images were identified; porosity, pore size distribution (PSD), and pore connectivity were analyzed from segmented image stacks; and their permeability was calculated by representative elementary volume (REV) scale lattice Boltzmann method (LBM). Three types of pores including residual interparticle pores, grain dissolution pores and micro fractures were identified. A multi-threshold segmentation algorithm was applied to segment the image stacks into three constituents, namely, pore, matrix, and clay. The calculated volumetric fraction of the pore constituent from the segmented image stacks was smaller than experimental porosity, because a certain amount of porosity was also contributed by clay constituent. The PSD of the samples was found to be unimodal in the range of 3–69 μm, with the peak at around 10–15 μm. The clay constituent led to the increase in the overall connectivity of the images stacks, indicating that clays bridged the isolated pores. By assigning appropriate porosity and permeability to the clay constituent, calculated permeability of the samples using REV scale LBM well matched the experimental data. This study provides new insight into characterization of pore structure of tight oil sandstones by μXCT technique.

In variable air volume air(VAV) conditioning systems, conventional methods for static pressure reset depends on accurate mathematics models which are often difficult to be built. On the contrary, controls, which are based on a series of control rules derived from the operating and commissioning experience, have the capacity to achieve better effects. A rule-based fuzzy control method (RBFCM) for static pressure reset is proposed using improved Mamdani model and functioning fuzzy subset inference method, which reflects the physical characteristics of the controlled object and is convenient for the engineers to understand and use. The development and implementation of the proposed RBFCM are necessary to demonstrate in detail. Through comparative experiments , the proposed RBFCM is compared with the conventional fixed-step method and itself with different actual domain in a control experiment platform for the VAV air conditioning system. Experimental results indicate that the proposed RBFCM has better control performances and outstanding energy-saving potential if the actual domain can be selected reasonably. A minimum of 33% energy saving rate of the proposed RBFCM with the optimal actual domain is shown through experimental study.

In as-welded state, each region of 2219 aluminum alloy TIG-welded joint shows different microstructure and microhardness due to the different welding heat cycles and the resulting evolution of second phases. After the post-weld heat treatment, both the amount and the size of the eutectic structure or θ phases decreased. Correspondingly, both the Cu content in α-Al matrix and the microhardness increased to a similar level in each region of the joint, and the tensile strength of the entire joint was greatly improved. Post-weld heat treatment played the role of solid solution strengthening and aging strengthening. After the post-weld heat treatment, the weld performance became similar to other regions, but weld reinforcements lost their reinforcing effect on the weld and their existence was more of an adverse effect. The joint without weld reinforcements after the post-weld heat treatment had the optimal tensile properties, and the specimens randomly crack in the weld zone.

This paper presents a novel methodology to study the characteristic of the sleeve electric two-way regulating valves, applied in the HVAC systems. In this paper, the valve admittance is introduced ...

This essay examines the policy response of the federal and regional governments in federations to the COVID-19 crisis. We theorize that the COVID-19 policy response in federations is an outcome of strategic interaction among the federal and regional incumbents in the shadow of their varying accountability for health and the repercussions from the disruptive consequences of public health measures. Using the data from the COVID-19 Public Health Protective Policy Index Project, we study how the variables suggested by our theory correlate with the overall stringency of public health measures in federations as well as the contribution of the federal government to the making of these policies. Our results suggest that the public health measures taken in federations are at least as stringent as those in non-federations, and there is a cluster of federations on which a bulk of crisis policy making is carried by subnational governments. We find that the contribution of the federal government is, on average, higher in parliamentary systems; it appears to decline with the proximity of the next election in presidential republics, and to increase with the fragmentation of the legislative party system in parliamentary systems. Our analysis also suggests that when the federal government carries a significant share of responsibility for healthcare provision, it also tends to play a higher role in taking non-medical steps in response to the pandemic.

The pancreatic disease taxonomy includes ten types of masses (tumors or cysts) [20], [8]. Previous work focuses on developing segmentation or classification methods only for certain mass types. Differential diagnosis of all mass types is clinically highly desirable [20] but has not been investigated using an automated image understanding approach.We exploit the feasibility to distinguish pancreatic ductal adenocarcinoma (PDAC) from the nine other nonPDAC masses using multi-phase CT imaging. Both image appearance and the 3D organ-mass geometry relationship are critical. We propose a holistic segmentation-mesh-classification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information, which is accomplished by combining the anatomical structure and the semantic detection-by-segmentation network. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask (i.e., mask-to-mesh). A new graph-based residual convolutional network (Graph-ResNet), whose nodes fuse the information of the mesh model and feature vectors extracted from the segmentation network, is developed to produce the patient-level differential classification results. Extensive experiments on 661 patients’ CT scans (five phases per patient) show that SMCN can improve the mass segmentation and detection accuracy compared to the strong baseline method nnUNet (e.g., for nonPDAC, Dice: 0.611 vs. 0.478; detection rate: 89% vs. 70%), achieve similar sensitivity and specificity in differentiating PDAC and nonPDAC as expert radiologists (i.e., 94% and 90%), and obtain results comparable to a multimodality test [20] that combines clinical, imaging, and molecular testing for clinical management of patients.

Energy-dissipation elastomers relying on their viscoelastic behavior of chain segments in the glass transition region can effectively suppress vibrations and noises in various fields, yet the operating frequency of those elastomers is difficult to control precisely and its range is narrow. Here, we report a synergistic strategy for constructing polymer-fluid-gels that provide controllable ultrahigh energy dissipation over a broad frequency range, which is difficult by traditional means. This is realized by precisely tailoring the relaxation of confined polymer fluids in the elastic networks. The symbiosis of this combination involves: elastic networks forming an elastic matrix that displays reversible deformation and polymer fluids reptating back and forth to dissipate mechanical energy. Using prototypical poly (n-butyl acrylate) elastomers, we demonstrate that the polymer-fluid-gels exhibit a controllable ultrahigh energy-dissipation property (loss factor larger than 0.5) with a broad frequency range (10−2 ~ 108 Hz). Energy absorption of the polymer-fluid-gels is over 200 times higher than that of commercial damping materials under the same dynamic stress. Moreover, their modulus is quasi-stable in the operating frequency range., In most cases the frequency range of a damping material is adapted to a specific application. Huang et al. design a gel filled with a polymeric fluid that bypasses this problem and offers an unusually broad window over which vibrational energy is effectively dissipated.

Background Endometrial cancer (EC) is one of the three malignant reproductive tumours that threaten women’s lives and health. Glycerophospholipids (GPLs) are important bioactive lipids involved in various physiological and pathological processes, including cancer. Immune infiltration of the tumour microenvironment (TME) is positively associated with the overall survival in EC. Exploring GPL-related factors associated with the TME in endometrial cancer can aid in the prognosis of patients and provide new therapeutic targets. Methods Differentially expressed GPL-related genes were identified from TCGA-UCEC datasets and the Molecular Signatures Database (MSigDB). Univariate Cox regression analysis was used to select GPL-related genes with prognostic value. The Random forest algorithm, LASSO algorithm and PPI network were used to identify critical genes. ESTIMATEScore was calculated to identify genes associated with the TME. Then, differentiation analysis and survival analysis of LPCAT1 were performed based on TCGA datasets. GSE17025 and immunohistochemistry (IHC) verified the results of the differentiation analysis. An MTT assay was then conducted to determine the proliferation of EC cells. GO and KEGG enrichment analyses were performed to explore the underlying mechanism of LPCAT1. In addition, we used the ssGSEA algorithm to explore the correlation between LPCAT1 and cancer immune infiltrates. Results Twenty-three differentially expressed GPL-related genes were identified, and eleven prognostic genes were selected by univariate Cox regression analysis. Four significant genes were identified by two different algorithms and the PPI network. Only LPCAT1 was significantly correlated with the tumour microenvironment. Then, we found that LPCAT1 was highly expressed in tumour samples compared with that in normal tissues, and lower survival rates were observed in the groups with high LPCAT1 expression. Silencing of LPCAT1 inhibited the proliferation of EC cells. Moreover, the expression of LPCAT1 was positively correlated with the histologic grades and types. The ROC curve indicated that LPCAT1 had good prognostic accuracy. Receptor ligand activity, pattern specification process, regionalization, anterior/posterior pattern specification and salivary secretion pathways were enriched as potential targets of LPCAT1. By using the ssGSEA algorithm, fifteen kinds of tumor-infiltrating cells (TICs) were found to be correlated with LPCAT1 expression. Conclusion These findings suggested that LPCAT1 may act as a valuable prognostic biomarker and be correlated with immune infiltrates in endometrial cancer, which may provide novel therapy options for and improved treatment of EC.

OBJECTIVE To assess the association between insulin resistance and gestational diabetes mellitus (GDM) in early pregnancy and find a simple surrogate index of the homeostasis model assessment of insulin resistance (HOMA-IR). METHODS A total of 700 pregnant women were included in this prospective, double-center, observational cohort study. The glucose and lipid metabolic characterization was performed at 6-12 weeks of pregnancy. All participants underwent a 75-g oral glucose tolerance test at 24-28 weeks of pregnancy. Linear regression analysis was applied to find a novel surrogate index of HOMA-IR. Binary logistic analysis was applied to estimate possible associations of different indices with GDM and insulin resistance. RESULTS GDM was diagnosed in 145 of 700 women with singleton pregnancies (20.7%). HOMA-IR was higher in the GDM group than in the normal glucose tolerance (NGT) group and was an individual risk factor for GDM (adjusted risk ratio RR 1.371, 95% confidence interval [CI] 1.129-1.665, P

Background Multiple sclerosis (MS) is a central nervous system disease with a high disability rate. Modern molecular biology techniques have identified a number of key genes and diagnostic markers to MS, but the etiology and pathogenesis of MS remain unknown. Results In this study, the integration of three peripheral blood mononuclear cell (PBMC) microarray datasets and one peripheral blood T cells microarray dataset allowed comprehensive network and pathway analyses of the biological functions of MS-related genes. Differential expression analysis identified 78 significantly aberrantly expressed genes in MS, and further functional enrichment analysis showed that these genes were associated with innate immune response-activating signal transduction (p = 0.0017), neutrophil mediated immunity (p = 0.002), positive regulation of innate immune response (p = 0.004), IL-17 signaling pathway (p Conclusion This study provides new understanding for the etiology/pathogenesis of MS, facilitating an early identification and prediction of MS.

BackgroundEvaluation of monosodium urate (MSU) crystal deposition and related lesion in joints using ultrasound in gout and hyperuricemia patients. MethodsTotal 202 gout patients and 43 asymptomatic hyperuricemia patients were included, the clinical data and ultrasunic assessment results were collected and statistically analyzed. ResultsDeposition of MSU crystals were found in 25.58% (11/43) of the patients with asymptomatic hyperuricemia and 76.24% (154/202) of the patients with gout. In the all examined 1082 joints from gout patients, 33.09% (358/1082) of them were detected MSU crystals. In MSU crystal positive joints, 77.37% (277/358) of them had history of attacks. Among the joints of gouty arthritis, 56.88% (277/487) of them were found MSU crystals. Double contour sign (DCS), hyperechoic aggregate (HAG) and Tophi were found in 32.65% (159/487), 7.80% (38/487) and 24.64% (120/487) of the joints, respectively. DCS and Tophi, but not HAG, appeared increasingly in gout duration extension. In the patients with more than 15 years of gout history, DCS, Tophi and HAG were found in 48.18%, 40.00%, 6.36% of US assessed joints, respectively. In the gout patients, synovial lesion and bone erosion were found in 17.74% (192/1082) and 7.58% (82/1082) of joints, respectively. Synovial lesion was related with HAG, while bone erosion was related to tophi and DCS. Nephrolithiasis was detected in 20.30% (41/202) of gout patients and 4.65% (2/43) of hyperuricemia patients, indicating nephrolithiasis occurred in more gout patients than in hyperuricemia patients.ConclusionHAG is the early sign of MSU crystal deposition in joints. Early urate lowering therapy (ULT) may reduce HAG and ameliorate synovitis and synovial hypertrophy. DCS and tophi are the risk factors of bone erosion. Early ULT should be considered in the gout patients with DCS or tophi. And nephrolithiasis was remarkably relevant to MSU crystal deposition in joints in gout patients.

Breast cancer is the most common cancer in women and is one of the three most common malignancies worldwide. Serum microRNAs (miRNAs/miRs) are ideal biomarkers for tumor diagnosis and prognosis due to their specific biological characteristics. In several different types of cancer, miRNAs are associated with cell migration and invasion. In the present study, miR-25-3p expression levels were detected in tissue and serum samples derived from patients with breast cancer, and the diagnostic and prognostic value of miR-25-3p in breast cancer was evaluated. Cellular function assays were performed to evaluate the role of miR-25-3p in breast cancer. Moreover, dual-luciferase reporter assays and western blotting were performed to investigate the target of miR-25-3p. miR-25-3p expression was upregulated in breast cancer tissue and serum samples compared with normal breast tissue and serum samples. Patients with breast cancer with high serum miR-25-3p levels were more likely to have lymph node metastasis compared with those with low serum miR-25-3p levels. The area under the curve for miR-25-3p in the diagnosis of breast cancer was 0.748, with 57.1% sensitivity and 95.0% specificity. Moreover, the Kaplan-Meier survival curves demonstrated that patients with breast cancer with a low expression of serum miR-25-3p had a higher overall survival rate compared with patients with a high serum miR-25-3p expression. miR-25-3p knockdown suppressed breast cancer cell proliferation and invasion, and transducer of ERBB2, 1 (TOB1) was identified as a potential target gene regulated by miR-25-3p. Therefore, the present study suggested that miR-25-3p regulated cellular functions via TOB1 in breast cancer; therefore, miR-25-3p may serve as a breast cancer biomarker.

The combination of photothermal therapy (PTT) and gene therapy (GT) shows great potential to achieve synergistic anti-tumor activity. However, the lack of a controlled release of genes from carriers remains a severe hindrance. Herein, peptide lipid (PL) and sucrose laurate (SL) were used to coat single-walled carbon nanotubes (SCNTs) and multi-walled carbon nanotubes (MCNTs) to form bifunctional delivery systems (denoted SCNT-PS and MCNT-PS, respectively) with excellent temperature-sensitivity and photothermal performance. CNT/siRNA suppressed tumor growth by silencing survivin expression while exhibiting photothermal effects under near-infrared (NIR) light. SCNT-PS/siRNA showed very high anti-tumor activity, resulting in the complete inhibition of some tumors. It was highly efficient for systemic delivery to tumor sites and to facilitate siRNA release owing to the phase transition of the temperature-sensitive lipids, due to PL and SL coating. Thus, SCNT-PS/siRNA is a promising anti-tumor nanocarrier for combined PTT and GT.

Background Several systematic reviews have suggested that acupuncture is effective against functional constipation, but it is unknown whether variations in treatment effect across subgroups remain consistent. Our purpose of this study is to explore the heterogeneity of treatment effect of acupuncture on functional constipation across subgroups. Methods We will search eleven English and Chinese electronic databases and three clinical trial registries from inception to December 2021. Randomized controlled trials that evaluate acupuncture compared with sham acupuncture or no treatment for functional constipation will be eligible if they report at least one primary outcome. The primary outcomes will include the change in weekly complete spontaneous bowel movements or spontaneous bowel movements from baseline. Two authors will independently identify the relevant studies, assess the risk of bias using the Cochrane RoB 2 tool and contact the primary researchers of the eligible trials for individual patient data. Individual patient data obtained from the original trial author will be standardized and all trial data will be combined into a single database. Generalized linear mixed effects model will be used to determine possible subgroup effects by adding an interaction term for predefined subgroup and treatment. Systematic review registration International Prospective Register of Systematic Reviews (Number: CRD42020188366).

Additional file 1: Figure S1. (a) ROC (receiver operating characteristic) curve and (b) contribution of the risk score in patients who died and survived.

Detecting the airway anomaly can be an essential part to aid the lung disease diagnosis. Since normal human airways share an anatomical structure, we design a graph prototype whose structure follows the normal airway anatomy. Then, we learn the prototype and a graph neural network from a weakly-supervised airway dataset, i.e., only the holistic label is available, indicating if the airway has anomaly or not, but which bronchus node has the anomaly is unknown. During inference, the graph neural network predicts the anomaly score at both the holistic level and node-level of an airway. We initialize the airway anomaly detection problem by creating a large airway dataset with 2589 samples, and our prototype-based graph neural network shows high sensitivity and specificity on this new benchmark dataset. The code is available at https://github.com/tznbz/Airway-Anomaly-Detection-by-Prototype-based-Graph-Neural-Network.

The fungus Irpex Lacteus is effective in decolourising methylene blue, a common dye in printing and dyeing wastewater, and it is of clear significance to investigate its optimal decolourisation process through liquid fermentation. The medium mixed with methylene blue dye was incubated with Irpex Lacteus in a constant temperature shaking flask liquid fermentation equipment, and single factors such as incubation time, temperature, shaking bed speed, dye concentration, pH, carbon and nitrogen sources were screened, and key factors were targeted orthogonal tests were carried out to optimise the best decolourisation process for Irpex Lacteus against methylene blue. The single-factor test revealed that carbon, nitrogen, pH and inoculum were the main factors influencing the decolourisation of methylene blue by Irpex Lacteus. The results of the orthogonal test showed that the decolourisation effect was best when the carbon source was sucrose, the nitrogen source was peptone, pH=3.5, the inoculum level was 7% and the incubation time was 10 days. Irpex Lacteus showed good decolourisation effect on methylene blue and the optimised process conditions provided a new direction for the treatment of printing and dyeing wastewater.

Among sub-items of energy consumption in public buildings, lighting sockets play an important role in energy-saving analysis. So, the energy consumption data quality of lighting sockets is important. However, limited by the initial cost of energy monitoring platform, it is difficult to install electricity meters covering all branches and to retrofit the incompact classification electricity branches, which results in a mixture of the lighting socket energy consumption and other components. In this study, a separation methodology is proposed. First, the abnormal data in the energy monitoring platform are cleaned and screened using a clustering algorithm. Second, the average outdoor air temperature partitioning model (OATPM) method and the k-nearest neighbor (KNN) clustering algorithm method are proposed for identifying and separating the abnormal data. These two methods have complementary advantages in the best applicable scenarios, including calculation accuracy and other aspects. The verification results for three buildings show that the relative error of this separation methodology is less than 15%. Finally, this paper presents the optimization parameters of the KNN method. Through this methodology, building managers need only historical data in an energy monitoring platform to separate the combined power consumption of the lighting sockets and air-conditioning online, independent of detailed information statistics.

Aiming at the problem of the architecture of the automation control system of traditional buildings, Tsinghua University proposed swarm intelligence building technology. However, most of the electrical equipment in completed buildings is not designed for swarm intelligence technology, and a large number of heterogeneous devices can't communicate with Swarm Intelligence Computing network platform, which leads to the difficulty of the system promotion. This paper develops a multi-purpose gateway for swarm intelligence. On the basis of solving the data interaction between terminal equipment of swarm intelligence control system and computing network, it reduces the requirement of network bandwidth through preprocessing of data, and completes the feasibility test of gateway in lighting unit.

Additional file 2: Figure S2. (a) Contribution of the predictive value of patients in the training set. (b) Contribution of the predictive value of patients in the testing set.

The energy crisis is one of the most serious crises in modern society. In this context, building energy prediction is more and more important, as it can guide energy management and efficiency retrofitting, especially for public buildings that often have more energy-using equipment and larger numbers of users. However, existing physical modeling methods and data-driven methods are difficult to predict suitably and accurately. Therefore, the KNN-Matrix algorithm method is developed. It introduces the concept of fuzzy quantification levels for electricity intensity, and uses historical data (after screening and processing) as the prediction data. The validation results on nine buildings with different functions and climate zones show that the average of hourly prediction error is 13.1%. This algorithm relies only on historical data without simulation software and unpredictable parameters as the initial conditions. Moreover, this algorithm can better address noise data, allowing for better accuracy and less data volume.

Background: Heart failure (HF) is a heterogeneous disease characterized by significant metabolic disturbances; however, the breadth of metabolic dysfunction before the onset of overt disease is not well understood. The purpose of this study was to determine the association of circulating metabolites with incident HF to uncover novel metabolic pathways to disease. Methods: We performed targeted plasma metabolomic profiling in a deeply phenotyped group of Black adults from the JHS (Jackson Heart Study; n=2199). We related metabolites associated with incident HF to established etiological mechanisms, including increased left ventricular mass index and incident coronary heart disease. Furthermore, we evaluated differential associations of metabolites with HF with preserved ejection fraction versus HF with reduced ejection fraction. Results: Metabolites associated with incident HF included products of posttranscriptional modifications of RNA, as well as polyamine and nitric oxide metabolism. A subset of metabolite-HF associations was independent of well-established HF pathways such as increased left ventricular mass index and incident coronary heart disease and included homoarginine (per 1 SD increase in metabolite level, hazard ratio, 0.77; P =1.2×10 −3 ), diacetylspermine (hazard ratio, 1.34; P =3.4×10 −3 ), and uridine (hazard ratio, 0.79; P , 3×10 −4 ). Furthermore, metabolites involved in pyrimidine metabolism (orotic acid) and collagen turnover ( N -methylproline) among others were part of a distinct metabolic signature that differentiated individuals with HF with preserved ejection fraction versus HF with reduced ejection fraction. Conclusions: The integration of clinical phenotyping with plasma metabolomic profiling uncovered novel metabolic processes in nontraditional disease pathways underlying the heterogeneity of HF development in Black adults.

We propose weakly supervised training schemes to train end-to-end cell segmentation networks that only require a single point annotation per cell as the training label and generate a high-quality segmentation mask close to those fully supervised methods using mask annotation on cells. Three training schemes are investigated to train cell segmentation networks, using the point annotation. First, self-training is performed to learn additional information near the annotated points. Next, co-training is applied to learn more cell regions using multiple networks that supervise each other. Finally, a hybrid-training scheme is proposed to leverage the advantages of both self-training and co-training. During the training process, we propose a divergence loss to avoid the overfitting and a consistency loss to enforce the consensus among multiple co-trained networks. Furthermore, we propose weakly supervised learning with human in the loop, aiming at achieving high segmentation accuracy and annotation efficiency simultaneously. Evaluated on two benchmark datasets, our proposal achieves high-quality cell segmentation results comparable to the fully supervised methods, but with much less amount of human annotation effort.

ObjectivesThe underlying mechanism of the inflammatory response against Brucellosis caused by Brucella remains poorly understood. This study aimed to determine the role of long non-coding RNAs (lncRNAs) in regulating of inflammatory and anti-Brucella responses.Materials and methodsMicroarray analysis was performed to detect differentially expressed lncRNAs in THP-1 cells infected with an S2308 Brucella strain. The candidate lncRNAs were screened using bioinformatic analysis and siRNAs; bioinformatic prediction and luciferase reporter assay were also conducted, while inflammatory responses was assessed using RT‐qPCR, western blot, immunofluorescence, ELISA, HE, and immunohistochemistry.ResultsThe lncRNA Gm28309 was identified to be involved in regulating inflammation induced by Brucella. Gm28309, localized in the cytoplasm, was down-expressed in RAW264.7 cells infected with S2308. Overexpression of Gm28309 or inhibition of miR-3068-5p repressed p65 phosphorylation and reduced NLRP3 inflammasome and IL-1β and IL-18 secretion. Mechanistically, Gm28309 acted as a ceRNA of miR-3068-5p to activate NF-κB pathway by targeting κB-Ras2, an inhibitor of NF-κB signaling. Moreover, the number of intracellular Brucella was higher when Gm28309 was overexpressed or when miR-3068-5p or p65 was inhibited. However, these effects were reversed by the miR-3068-5p mimic.ConclusionsOur study demonstrates, for the first time, that LncRNAs are involved in regulating immune responses during Brucella infection, and Gm28309, an lncRNA, plays a crucial role in activating NF-κB/NLRP3 inflammasome signaling pathway.

Introduction: After CMS established bundled payments for outpatient peripheral vascular intervention (PVI) in 2008, a large proportion of PVIs shifted to outpatient settings, including ambulatory surgery centers (ASCs) and office-based laboratories (OBLs). Little is known about these free-standing procedural centers, such as their proximity to tertiary care hospitals and the characteristics of patients being treated. Methods: Medicare fee-for-service beneficiaries ≥66 years undergoing femoropopliteal artery PVI from 4/1/15-12/31/17 in outpatient settings were identified in the CMS Carrier file linked to institutional outpatient files. The exposure was treatment at an ASC/OBL versus an outpatient hospital. An area deprivation index of ≤15th percentile identified those who reside in regions of lower socioeconomic status (SES). The primary business address of each ASC/OBL was identified using the National Provider Identifier. The address of the closest tertiary care hospital was identified in the Medicare Hospital database and distances were calculated using a Google Maps API platform. Results: Of 1,155 providers who performed ≥1 PVI in an ASC/OBL, the average distance to the nearest hospital was 1.5±2.2 miles. There was a weak correlation between the proportion of procedures performed at ASCs/OBLs and the distance to the nearest hospital (Figure). Among 686 providers who performed procedures in both ASCs/OBLs and outpatient hospitals, patients treated in ASCs/OBLs had a higher burden of comorbidities and were more often Black, dually-enrolled in Medicare/Medicaid, and residing in lower SES regions. Conclusions: ASCs/OBLs are geographically close to tertiary care centers, with little correlation between the proportion of procedures performed and the distance to the nearest hospital. The heterogeneity of patient characteristics across outpatient settings underscores the need for better measures of quality and appropriateness of PVI procedures.

Background During the course of gene transfection, the interaction kinetics between liposomes and DNA is speculated to play very important role for blood stability, cellular uptake, DNA release and finally transfection efficiency. Results As cationic peptide liposomes exhibited great gene transfer activities both in vitro and in vivo, two peptide lipids, containing a tri-ornithine head (LOrn3) and a mono-ornithine head (LOrn1), were chosen to further clarify the process of liposome-mediated gene delivery in this study. The results show that the electrostatically-driven binding between DNA and liposomes reached nearly 100% at equilibrium, and high affinity of LOrn3 to DNA led to fast binding rate between them. The binding process between LOrn3 and DNA conformed to the kinetics equation: y = 1.663631 × exp (− 0.003427x) + 6.278163. Compared to liposome LOrn1, the liposome LOrn3/DNA lipoplex exhibited a faster and more uniform uptake in HeLa cells, as LOrn3 with a tri-ornithine peptide headgroup had a stronger interaction with the negatively charged cell membrane than LOrn1. The efficient endosomal escape of DNA from LOrn3 lipoplex was facilitated by the acidity in late endosomes, resulting in broken carbamate bonds, as well as the “proton sponge effect” of the lipid. Conclusions The interaction kinetics is a key factor for DNA transfection efficiency. This work provided insights into peptide lipid-mediated DNA delivery that could guide the development of the next generation of delivery systems for gene therapeutics.

BackgroundMillions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. Common ways against cancer include surgical operation, radiotherapy and chemotherapy. However, they are all very harmful for patients. Recently, the anticancer peptides (ACPs) have been discovered to be a potential way to treat cancer. Since ACPs are natural biologics, they are safer than other methods. However, the experimental technology is an expensive way to find ACPs so we purpose a new machine learning method to identify the ACPs.ResultsFirstly, we extracted the feature of ACPs in two aspects: sequence and chemical characteristics of amino acids. For sequence, average 20 amino acids composition was extracted. For chemical characteristics, we classified amino acids into six groups based on the patterns of hydrophobic and hydrophilic residues. Then, deep belief network has been used to encode the features of ACPs. Finally, we purposed Random Relevance Vector Machines to identify the true ACPs. We call this method ‘DRACP’ and tested the performance of it on two independent datasets. Its AUC and AUPR are higher than 0.9 in both datasets.ConclusionWe developed a novel method named ‘DRACP’ and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs.

Interactions between proteins and small molecule metabolites play vital roles in regulating protein functions and controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters and membrane receptors can all be mediated through protein–metabolite interactions (PMIs). Compared with the rich knowledge of protein–protein interactions, little is known about PMIs. To the best of our knowledge, no existing database has been developed for collecting PMIs. The recent rapid development of large-scale mass spectrometry analysis of biomolecules has led to the discovery of large amounts of PMIs. Therefore, we developed the PMI-DB to provide a comprehensive and accurate resource of PMIs. A total of 49 785 entries were manually collected in the PMI-DB, corresponding to 23 small molecule metabolites, 9631 proteins and 4 species. Unlike other databases that only provide positive samples, the PMI-DB provides non-interaction between proteins and metabolites, which not only reduces the experimental cost for biological experimenters but also facilitates the construction of more accurate algorithms for researchers using machine learning. To show the convenience of the PMI-DB, we developed a deep learning-based method to predict PMIs in the PMI-DB and compared it with several methods. The experimental results show that the area under the curve and area under the precision-recall curve of our method are 0.88 and 0.95, respectively. Overall, the PMI-DB provides a user-friendly interface for browsing the biological functions of metabolites/proteins of interest, and experimental techniques for identifying PMIs in different species, which provides important support for furthering the understanding of cellular processes. The PMI-DB is freely accessible at http://easybioai.com/PMIDB.

Porous polymers are of great interest in potential energy storage and environmental remediation applications. However, traditional fabrication methods are either time-consuming or energy-consuming and deteriorate the mechanical strength of polymer materials. In this study, polymerization-induced phase separation was used to realize the template-free fabrication of superflexible macroporous polymers. Since the solvent is also used as a porogen, this method can be widely used to synthesize several porous polymers by carefully choosing the solvent and monomer. Compared to nonstructured polymers, the prepared macroporous polymers exhibited enhanced mechanical strength, superflexibility, multicompressibility, and bending properties. Along with hydrophobicity/oleophilicity and macroporous structures, the as-prepared porous polymers demonstrated controllable oil absorbability and release; furthermore, after infusing with lubrication liquid, these materials can be used as antiwaxing materials. The elastic porous polymers prepared using this simple and universal method show great potential for various applications, including controlled drug release, antiwaxing, and lubrication.

Background Patients with symptomatic and asymptomatic heart failure undergoing noncardiac surgery may benefit from the haemodynamic profile of etomidate. However, the safety of etomidate in this population is unknown. We examined anaesthesiologist variation in etomidate use and assessed its safety using an instrumental variable approach to account for differences in treatment selection. Methods A retrospective cohort study of 19 714 patients with heart failure undergoing noncardiac surgery at two tertiary care institutions from January 2006 to December 2017 was performed. The proportion of etomidate use among 294 anaesthesiologists was examined and adjusted risk differences (aRD) for in-hospital and 30-day mortality were calculated using physician preference for etomidate as an instrumental variable. Results Etomidate was used in 14.3% (2821/19 714) of patients. Preference for etomidate varied substantially among individual anaesthesiologists with the lowest and highest quartile users using etomidate in 0–4.7% and 20.4–66.7% of their own heart failure patients, respectively. The adjusted instrumental variable analysis showed no significant differences in the risk of in-hospital (aRD –0.2%; 95% confidence interval, –2.4%–1.9%; P=0.83) or 30 day mortality (aRD 0.2%; 95% confidence interval, –2.5%–2.9%; P=0.90). Anaesthesiologists with higher preferences for etomidate were more experienced (greater heart failure and total case volume) than anaesthesiologists with lower preferences for etomidate. Conclusions We found substantial variability in anaesthesiologists' preference for etomidate for use in patients with heart failure undergoing noncardiac surgery. There was no association between etomidate use and in-hospital or 30-day mortality. Etomidate is not inferior to other currently used options for induction of general anaesthesia in patients with heart failure.

Motivation: The functional changes of the genes, RNAs and proteins will eventually be reflected in the metabolic level. Increasing number of researchers have researched mechanism, biomarkers and targeted drugs by metabolites. However, compared with our knowledge about genes, RNAs, and proteins, we still know few about diseases-related metabolites. All the few existed methods for identifying diseases-related metabolites ignore the chemical structure of metabolites, fail to recognize the association pattern between metabolites and diseases, and fail to apply to isolated diseases and metabolites. Results: In this study, we present a graph deep learning based method, named Deep-DRM, for identifying diseases-related metabolites. First, chemical structures of metabolites were used to calculate similarities of metabolites. The similarities of diseases were obtained based on their functional gene network and semantic associations. Therefore, both metabolites and diseases network could be built. Next, Graph Convolutional Network (GCN) was applied to encode the features of metabolites and diseases, respectively. Then, the dimension of these features was reduced by Principal components analysis (PCA) with retainment 99% information. Finally, Deep neural network was built for identifying true metabolite-disease pairs (MDPs) based on these features. The 10-cross validations on three testing setups showed outstanding AUC (0.952) and AUPR (0.939) of Deep-DRM compared with previous methods and similar approaches. Ten of top 15 predicted associations between diseases and metabolites got support by other studies, which suggests that Deep-DRM is an efficient method to identify MDPs. Contact: liangcheng@hrbmu.edu.cn. Availability and implementation: https://github.com/zty2009/GPDNN-for-Identify-ing-Disease-related-Metabolites.