Search

This paper presents a novel approach for structure extraction of the cluster sparse system identification. Different from adopting $$\ell _1$$ -norm constraint to regularize the sparsity in the improved proportionate normalized least mean square (IPNLMS) algorithm, we directly work with the block sparse structure via $$\ell _{1,0}$$ -norm constraint. In particular, we develop a cluster sparse IPNLMS by the block $$\ell _0$$ norm regularization, named IPNLMS-BL0 method. The cluster sparse constraint is regarded as an extended version for the sparse constraint term. On the other hand, the iterations of IPNLMS-BL0 are derived by the steepest descent strategy. Then, we provide the analysis of block size choices of the cluster sparse constraint, computational complexity, and steady-state error of the proposed method. Various simulations are designed to test the performance of the IPNLMS-BL0 algorithm and its counterparts to identify and track the unknown sparse systems. The results are provided and analyzed to confirm the effectiveness and superiority of the proposed IPNLMS-BL0 algorithm.

Taizhou is a typical coastal area in East China where drinking water is mainly supplied by reservoir water. The water quality of the reservoirs may be affected to some extent by human activities an...

Recently, high levels of Cr6+ in groundwater have been found and are threatening public health in the Guanzhong Basin of China. For this reason, this study aims to specify the occurrence and spatial distribution of groundwater Cr6+ and to analyze the favorable hydrogeochemical environment elevating its concentration in the Guanzhong Basin. The impacts of Cr6+ on human health were also estimated based on the health risk assessment model recommended by the USEPA. Results show that 45.40% and 37.36% of the groundwater samples contain Cr6+ concentration lower than 10 μg/L and ranging within 11–50 μg/L, respectively. And they are predominantly of HCO3-Ca and HCO3-Ca(Mg) water type. About 17.24% of the total water samples present Cr6+ concentrations exceeding the acceptable limit for drinking purpose of 50 µg/L, and are mainly classified as HCO3-Na water type. Low Cr6+ groundwater is mainly observed in the alluvial aquifer. Cr6+ concentration in the samples from the loess aquifer is higher due to low groundwater velocity caused by the low permeability of loess, as verified by the relationship between Cr6+ and major ions. The relationship between Cr6+ and pH and molar ratio of Na+/(Na+ + Ca2+) suggest that alkaline environment and cation exchange are beneficial to high concentration of Cr6+ in groundwater. Industrial activities are also responsible for the elevation of Cr6+ to some extent. Health risk assessment results show that the adults and children in the study area face higher carcinogenic risks than non-carcinogenic risk induced by Cr6+.

In our previous study, we have shown that AVE 0991, a nonpeptide analogue of Ang-(1–7), ameliorates cognitive decline and inhibits NLRP3 inflammasome of astrocytes in Alzheimer's disease model mice. Additionally, several studies have suggested that activation of autophagy appears to effectively inhibit the progression of neuroinflammation. However, it is unclear whether AVE 0991 can modulate astrocyte autophagy to suppress neuroinflammation in Alzheimer's disease. In our study, we showed that 30 days of intraperitoneal administration of AVE 0991 improved cognitive deficits and neuronal death in APP/PS1 Alzheimer's disease model mice. Moreover, AVE 0991 treatment greatly suppressed astrocyte-mediated inflammation and up-regulated the expression of autophagy. Furthermore, the inhibitory effect of AVE 0991 on the expression of inflammatory factors was reversed by 3-MA, an autophagy inhibitor. These findings suggest that regulation of autophagy is critical for inhibiting astrocyte neuroinflammatory responses and demonstrate a potential neuroprotective mechanism by which AVE 0991 could suppress neuroinflammatory responses by enhancing autophagy.

Triggering receptor expressed on myeloid cells-like 2 (TREML2) is a newly identified susceptibility gene for Alzheimer's disease (AD). It encodes a microglial inflammation-associated receptor. To date, the potential role of microglial TREML2 in neuroinflammation in the context of AD remains unclear. In this study, APP/PS1 mice were used to investigate the dynamic changes of TREML2 levels in brain during AD progression. In addition, lipopolysaccharide (LPS) stimulation of primary microglia as well as a lentivirus-mediated TREML2 overexpression and knockdown were employed to explore the role of TREML2 in neuroinflammation in the context of AD. Our results show that TREML2 levels gradually increased in the brains of APP/PS1 mice during disease progression. LPS stimulation of primary microglia led to the release of inflammatory cytokines including interleukin-1β, interleukin-6, and tumor necrosis factor-α in the culture medium. The LPS-induced microglial release of inflammatory cytokines was enhanced by TREML2 overexpression and was attenuated by TREML2 knockdown. LPS increased the levels of microglial M1-type polarization marker inducible nitric oxide synthase. This effect was enhanced by TREML2 overexpression and ameliorated by TREML2 knockdown. Furthermore, the levels of microglial M2-type polarization markers CD206 and ARG1 in the primary microglia were reduced by TREML2 overexpression and elevated by TREML2 knockdown. LPS stimulation increased the levels of NLRP3 in primary microglia. The LPS-induced increase in NLRP3 was further elevated by TREML2 overexpression and alleviated by TREML2 knockdown. In summary, this study provides the first evidence that TREML2 modulates inflammation by regulating microglial polarization and NLRP3 inflammasome activation. These findings reveal the mechanisms by which TREML2 regulates microglial inflammation and suggest that TREML2 inhibition may represent a novel therapeutic strategy for AD.

Long non‐coding RNA (lncRNA) lnc‐ISG20 has been found aberrantly up‐regulated in the glomerular in the patients with diabetic nephropathy (DN). We aimed to elucidate the function and regulatory mechanism of lncRNA lnc‐ISG20 on DN‐induced renal fibrosis. Expression patterns of lnc‐ISG20 in kidney tissues of DN patients were determined by RT‐qPCR. Mouse models of DN were constructed, while MCs were cultured under normal glucose (NG)/high glucose (HG) conditions. The expression patterns of fibrosis marker proteins collagen IV, fibronectin and TGF‐β1 were measured with Western blot assay. In addition, the relationship among lnc‐ISG20, miR‐486‐5p, NFAT5 and AKT were analysed using dual‐luciferase reporter assay and RNA immunoprecipitation. The effect of lnc‐ISG20 and miR‐486/NFAT5/p‐AKT axis on DN‐associated renal fibrosis was also verified by means of rescue experiments. The expression levels of lnc‐ISG20 were increased in DN patients, DN mouse kidney tissues and HG‐treated MCs. Lnc‐ISG20 silencing alleviated HG‐induced fibrosis in MCs and delayed renal fibrosis in DN mice. Mechanistically, miR‐486‐5p was found to be a downstream miRNA of lnc‐ISG20, while miR‐486‐5p inhibited the expression of NFAT5 by binding to its 3'UTR. NFAT5 overexpression aggravated HG‐induced fibrosis by stimulating AKT phosphorylation. However, NFAT5 silencing reversed the promotion of in vitro and in vivo fibrosis caused by lnc‐ISG20 overexpression. Our collective findings indicate that lnc‐ISG20 promotes the renal fibrosis process in DN by activating AKT through the miR‐486‐5p/NFAT5 axis. High‐expression levels of lnc‐ISG20 may be a useful indicator for DN.

Increasing power cycle efficiency is an important way to reduce the cost of the solar thermal power generation. The power generation system using a supercritical carbon dioxide (s-CO2) Brayton cycle has the advantages of high cycle efficiency, small equipment size and low corrosion. Then, low temperature waste heat generated by the s-CO2 Brayton cycle can be used as the low temperature heat source for multi-effect desalination. This paper discusses a s-CO2 Brayton cycle integrated with a multi-effect seawater desalination system. The heat input of the s-CO2 Brayton cycle is from a central-tower solar receiver. The residual heat of the s-CO2 Brayton cycle is the heat input of the multi-effect distillation system. The results show that the integration of multi-effect desalination system does not affect the s-CO2 Brayton cycle efficiency. With the increase of split ratio of the compressor, Brayton cycle efficiency increases and then decreases, and the water production decreases and then increases. With the increase of turbine inlet temperature, Brayton cycle efficiency increases while freshwater production varies insignificantly. After optimizing the design of the cogeneration system, solar thermal power efficiency is 24.04%, the cost of electricity generation can be reduced, the LCOE is 0.081$/kWh. by using the waste heat generated from the power cycle. realized electricity-water cogeneration, the fresh water production from 5-effect MED is 459m3/day, and the unit cost of freshwater UPC of 0.81$/m3.

Chondroitin sulfate (CS) is a kind of linear polysaccharide that is covalently linked to proteins to form proteoglycans. Chondroitin sulfate proteoglycans (CSPGs) consist of a core protein, with one or more CS chains covalently attached. CSPGs are precisely regulated and they exert a variety of physiological functions by binding to adhesion molecules and growth factors. Widely distributed in the nervous system in human body, CSPGs contribute to the major component of extracellular matrix (ECM), where they play an important role in the development and maturation of the nervous system, as well as in the pathophysiological response to damage to the central nervous system (CNS). While there are more than 30 types of CSPGs, this review covers the roles of the most important ones, including versican, aggrecan, neurocan and NG2 in the pathogenesis of neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and multiple sclerosis. The updated reports of the treatment of neurodegenerative diseases are involving CSPGs.

A Ga hybridization strategy is proposed for simultaneously enhancing the near-infrared activity and extending the bandwidth of Bi-activated photonic glass. Systematic studies on the near-infrared optical responses of Ga/Bi and Al/Bi co-doped silica glasses are performed. It is interesting to note that Ga/Bi co-doped glasses have a similar near-infrared emission center to Al/Bi co-doped glass, while the former is more effective in improving near-infrared activity. The different luminescence mechanisms of Ga/Bi and Al/Bi co-doped silica glasses are elucidated, and the corresponding microstructure–optical response relationship is discussed. In addition, the Ga/Bi co-doped silica optical fiber is successfully prepared, and the principal fiber amplifier device is fabricated. Furthermore, amplified spontaneous emission and broadband on-off gain are realized. The results suggest that Ga-hybridized Bi-activated photonic glass is a promising gain material for broadband fiber amplifiers.

C19H16Br4CuN2O2: monoclinic, P21/c (no. 14), a = 10.2649(3) Å, b = 10.1903(3) Å, c = 21.2494(6) Å, β = 100.522(1)°, V = 2185.36(11) Å3, Z = 4, wR 2 = 0.0730, T = 169.0 K.

C21H23Br4CoN6O3, monoclinic, P21/c (no. 14), a = 16.2300(4) Å, b = 20.9661(6) Å, c = 7.8269(2) Å, β = 97.2250(11)°, V = 2642.19(12) Å3, Z = 4, R gt (F) = 0.0336, wR ref (F 2) = 0.0981, T = 150.0 K.

C21H19Cl4FeN4O2S, monoclinic, P21/c (no. 14), a = 16.304(2) Å, b = 11.1675(15) Å, c = 14.1041(16) Å, β = 109.322(4)°, V = 2423.3(5) Å3, Z = 4, R gt (F) = 0.0802, wR ref(F 2) = 0.1769, T = 150.0 K.

Summary In multicentre research, individual-level data are often protected against sharing across sites. To overcome the barrier of data sharing, many distributed algorithms, which only require sharing aggregated information, have been developed. The existing distributed algorithms usually assume the data are homogeneously distributed across sites. This assumption ignores the important fact that the data collected at different sites may come from various subpopulations and environments, which can lead to heterogeneity in the distribution of the data. Ignoring the heterogeneity may lead to erroneous statistical inference. We propose distributed algorithms which account for the heterogeneous distributions by allowing site-specific nuisance parameters. The proposed methods extend the surrogate likelihood approach (Wang et al. 2017; Jordan et al. 2018) to the heterogeneous setting by applying a novel density ratio tilting method to the efficient score function. The proposed algorithms maintain the same communication cost as existing communication-efficient algorithms. We establish a nonasymptotic risk bound for the proposed distributed estimator and its limiting distribution in the two-index asymptotic setting, which allows both sample size per site and the number of sites to go to infinity. In addition, we show that the asymptotic variance of the estimator attains the Cramér–Rao lower bound when the number of sites is smaller in rate than the sample size at each site. Finally, we use simulation studies and a real data application to demonstrate the validity and feasibility of the proposed methods.

Acousticvector sensors have been widely used for direction-of-arrival (DOA) estimation in hydroacoustics and aeroacoustics. The orientation of the acoustic vector sensor must be measured, e.g., by an attitude sensor, before its application to DOA estimation. However, the measured orientation generally deviates slightly from its actual orientation because of imperfect attitude measurements. This letter investigates how random deviations in the orientation degrade the performance of DOA estimation. We derive a closed-form approximation of the hybrid Cramer-Rao bound for DOA estimation by modeling the deviations as zero-mean Gaussian variables with small variances. A relationship based on a determinant-differential formula is introduced to avoid a complicated brute-force solution, yielding a sufficiently simple expression with quantitative observations. Numerical results illustrate the high accuracy and effectiveness of the closed-form approximation.

Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients’ data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases., Thus far, pleiotropy analysis using individual-level Electronic Health Records data has been limited to data from one site. Here, the authors introduce Sum-Share, a method designed to efficiently and losslessly integrate EHR and genetic data from multiple sites to perform pleiotropy analysis.

This article presents formulas to calculate the source depth based on the arrival angle and time delay between the direct and surface-refracted arrivals measured in deep water from a broadband signal. Using the formulas, the data autocorrelation is transformed to a correlation sequence as a function of assumed source depth, which can be simply averaged over a long time to estimate the source depth in a noisy environment. Both the simulated and real data verify the effectiveness and efficiency of the proposed method.

There is considerable epidemiological evidence indicating that air pollution has adverse effects on human health and is closely related to respiratory diseases, including chronic obstructive pulmonary disease (COPD). These effects, which can be divided into short- and long-term effects, can manifest as an exacerbation of existing symptoms, impaired lung function, and increased hospitalization and mortality rates. Long-term exposure to air with a high concentration of pollutants may also increase the incidence of COPD. The combined effects of different pollutants may become more complex in the future; hence, there is a need for more intensive research on specific at-risk populations, and formulating corresponding protective strategies is crucial. We aimed to review the epidemiological evidence on the effect of air pollution on COPD, the possible pathophysiological mechanisms underlying this effect, as well as protective measures against the effects of air pollutants in patients with COPD.

Hepatocellular carcinoma (HCC) is one of the dominating tumors causing death due to lack of timely discovery and valid treatment. Abnormal increase of Rac GTPase activating protein 1 (RACGAP1) has been verified to be an oncogene in plenty tumors. The profound mechanism of RACGAP1 was rarely reported in HCC. In this study, we explored the function and mechanism of RACGAP1 in HCC through multiple analysis and experiments. RACGAP1 expression was up-regulated in HCC samples and the high expression of RACGAP1 was an independent prognostic risk factor for HCC patients. Meanwhile, RACGAP1 promoted developments of HCC both in vitro and in vivo. We verified that RACGAP1 promoted proliferation of HCC via PI3K/AKT/CDK2 and PI3K/AKT/GSK3β/Cyclin D1 signaling pathway. RACGAP1 accelerated the invasion and metastasis of HCC via phosphorylation of GSK3β and nuclear translocation of β-catenin. Furthermore, by luciferase reporter assay and Chromatin immunoprecipitation (ChIP) assay, we confirmed Recombinant GA Binding Protein Transcription Factor Alpha (GABPA) regulated the transcription of RACGAP1. All these findings revealed that RACGAP1 promotes the progression of HCC through a novel mechanism, which might be a new therapeutic target for HCC patients.

In research synthesis, publication bias (PB) refers to the phenomenon that the publication of a study is associated with the direction and statistical significance of its results. Consequently, it may lead to biased (commonly optimistic) estimates of treatment effects. Visualization tools such as funnel plots have been widely used to investigate PB in univariate meta-analyses. The trim and fill procedure is a nonparametric method to identify and adjust for PB. It is popular among applied scientists due to its simplicity. However, most visualization tools and PB correction methods focus on univariate outcomes. For a meta-analysis with multiple outcomes, the conventional univariate trim and fill method can only account for different outcomes separately and thus may lead to inconsistent conclusions. In this article, we propose a bivariate trim and fill procedure to simultaneously account for PB in the presence of two outcomes that are possibly associated. Based on a recently developed galaxy plot for bivariate meta-analysis, the proposed procedure uses a data-driven imputation algorithm to detect and adjust PB. The method relies on the symmetry of the galaxy plot and assumes that some studies are suppressed based on a linear combination of outcomes. The method projects bivariate outcomes along a particular direction, uses the univariate trim and fill method to estimate the number of trimmed and filled studies, and yields consistent conclusions about PB. The proposed approach is validated using simulated data and is applied to a meta-analysis of the efficacy and safety of antidepressant drugs.

ObjectivesWe propose a communication-efficient transfer learning approach (COMMUTE) that efficiently and effectively incorporates multi-site healthcare data for training risk prediction models in a target population of interest, accounting for challenges including population heterogeneity and data sharing constraints across sites.MethodsWe first train population-specific source models locally within each institution. Using data from a given target population, COMMUTE learns a calibration term for each source model, which adjusts for potential data heterogeneity through flexible distance-based regularizations. In a centralized setting where multi-site data can be directly pooled, all data are combined to train the target model after calibration. When individual-level data are not shareable in some sites, COMMUTE requests only the locally trained models from these sites, with which, COMMUTE generates heterogeneity-adjusted synthetic data for training the target model. We evaluate COMMUTE via extensive simulation studies and an application to multi-site data from the electronic Medical Records and Genomics (eMERGE) Network to predict extreme obesity.ResultsSimulation studies show that COMMUTE outperforms methods without adjusting for population heterogeneity and methods trained in a single population over a broad spectrum of settings. Using eMERGE data, COMMUTE achieves an area under the receiver operating characteristic curve (AUC) around 0.80, which outperforms other benchmark methods with AUC ranging from 0.51 to 0.70.ConclusionCOMMUTE improves the risk prediction in the target population and safeguards against negative transfer when some source populations are highly different from the target. In a federated setting, it is highly communication efficient as it only requires each site to share model parameter estimates once, and no iterative communication or higher-order terms are needed.

Large collaborative research networks provide opportunities to jointly analyze multicenter electronic health record (EHR) data, which can improve the sample size, diversity of the study population, and generalizability of the results. However, there are challenges to analyzing multicenter EHR data including privacy protection, large-scale computation, heterogeneity across sites, and correlated observations. In this paper, we propose a federated algorithm for generalized linear mixed models (Fed-GLMM), which can flexibly model multicenter longitudinal or correlated data while accounting for site-level heterogeneity. Fed-GLMM can be applied to both federated and centralized research networks to enable privacy-preserving data integration and improve computational efficiency. By communicating only a limited amount of summary statistics, Fed-GLMM can achieve nearly identical results as the gold-standard method where the GLMM is directly fitted on the pooled dataset. We demonstrate the performance of Fed-GLMM in both numerical experiments and an application to longitudinal EHR data from multiple healthcare facilities.

For deep-water faulted sandstone reservoirs, the general practice is to design long horizontal wells improving well productivity. During the project implementation stage, well tests are performed on all drilled wells to evaluate well productivity accurately. Furthermore, multisize chokes are often utilized in a shorten test time for loosen formation, high test cost, and high well productivity. Nevertheless, the conventional productivity evaluation approach cannot accurately evaluate the well test productivity and has difficulty in determining the underneath pattern. As a result, the objective of this paper is to determine a productivity evaluation method for multisize chokes long horizontal well test in deep-water faulted sandstone reservoir. This approach introduces a productivity model for long horizontal wells in faulted sandstone reservoir. It also includes the determination of steady-state test time and the productivity evaluation method for multisize chokes. In this paper, the EGINA Oilfield, a deep-water faulted sandstone reservoir, located in West Africa was chosen as the research target. Based on Renard and Dupuy’s steady-state equation, the relationship between the productivity index per meter and the length of horizontal section was derived. Consequently, this relationship is used to determine the productivity pattern for long horizontal wells with the same geological features, which can provide more accurate productivity evaluations for tested wells and forecast the well productivity for untested wells. After implementing this approach on the EGINA Oilfield, the determined relationship is capable to accurately evaluate the test productivity for long horizontal wells in reservoirs with similar characteristics and assist in examination and treatment for horizontal wells with abnormal productivity.

Photothermal control has been widely used in the tuning of microelectronic devices due to its laser-assisted noncontact manipulation and cost-effective advantages. The Nd3+ heavily doped NaGdF4 nanocrystals (NCs) were proposed as a heat source at nanoscale, and its light-to-heat conversion efficiency was evaluated to be above 80%. Here, we obtained a whispering gallery mode lasing emission from the prepared polymer-coated silica microcavity under a 532 nm pulsed laser. The nanoheaters are combined with the microcavity to trigger the photothermal effect at the irradiation of a second 793 nm continuous wave (CW) laser, thereby realizing all-optical tuning of the resonance wavelength by changing the effective refractive index of the microcavity. By measuring the shift of lasing modes on exposure to CW laser with different power intensities, the maximum tuning range and sensitivity of 2 nm and 1.96 nm/(W·mm−2) were achieved as the power intensity increased from 0 to 1.02 W/mm2. Our work provides a method for developing a new generation of all-optical tunable lasers based on nanoscale photothermal effect.

Diabetic nephropathy (DN) is a complication of diabetes that is increasing in prevalence in China. Extracellular vesicles (EVs) carrying microRNAs (miRs) may represent a useful tool in the development of therapies for DN. Here, we report that EVs released by adipose-derived mesenchymal stem cells (ADSCs) during DN contain a microRNA, miR-26a-5p, that suppresses DN. Using bioinformatic analyses, we identified differentially expressed miRs in EVs from ADSCs and in DN and predicted downstream regulatory target genes. We isolated mesenchymal stem cells (MSCs) from adipose tissues and collected EVs from the ADSCs. We exposed mouse glomerular podocytes and MP5 cells to high glucose (HG), ADSC-derived EVs, miR-26a-5p inhibitor/antagomir, Toll-like receptor 4 (TLR4) plasmids, or the NF-κB pathway activator (phorbol-12-myristate-13-acetate, or PMA). We used the cell counting kit-8 (CCK-8) assay and flow cytometry to investigate the impact of miR-26a-5p on cell viability and apoptosis and validated the results of these assays with in vivo experiments in nude mice. We found that in DN, miR-26a-5p is expressed at very low levels, whereas TLR4 is highly expressed. Of note, EVs from ADSCs ameliorated the pathological symptoms of DN in diabetic mice and transferred miR-26a-5p to HG-induced MP5 cells, improving viability while suppressing the apoptosis of MP5 cells. We also found that miR-26a-5p protects HG-induced MP5 cells from injury by targeting TLR4, inactivating the NF-κB pathway, and downregulating vascular endothelial growth factor A (VEGFA). Moreover, ADSC-derived EVs transferred miR-26a-5p to mouse glomerular podocytes, which ameliorated DN pathology. These findings suggest that miR-26a-5p from ADSC-derived EVs protects against DN.

Excessive lipid deposition is a hallmark of NAFLD. Although much has been learned about the enzymes and metabolites involved in NAFLD, few studies have focused on the role of long noncoding RNAs (lncRNAs) in hepatic lipid accumulation. Here, using in vitro and in vivo models of NAFLD, we found that the lncRNA Gm15622 is highly expressed in the liver of obese mice fed a HFD and in murine liver (AML-12) cells treated with free fatty acids. Investigating the molecular mechanism in the liver-enriched expression of Gm15622 and its effects on lipid accumulation in hepatocytes and on NAFLD pathogenesis, we found that Gm15622 acts as a sponge for the microRNA miR-742-3p. This sponging activity increased the expression of the transcriptional regulator SREBP-1c and promoted lipid accumulation in the liver of the HFD mice and AML-12 cells. Moreover, further results indicated that metformin suppresses Gm15622 and alleviates NAFLD-associated lipid deposition in mice. In conclusion, we have identified an lncRNA Gm15622/miR-742-3p/SREBP-1c regulatory circuit associated with NAFLD in mice, a finding that significantly advances our insight into how lipid metabolism and accumulation are altered in this metabolic disorder. Our results also suggest that Gm15622 may be a potential therapeutic target for managing NAFLD.

A chemical-looping hydrogen generation technology (CLH) has considerable potential in renewable energy exploration because it can produce hydrogen and capture carbon dioxide at low energy. This study designs a novel system combining solar energy harvesting and chemical-looping hydrogen technology for hydrogen production, carbon dioxide capture, and power generation. The system is analyzed using commercial process simulation software. The effects of major factors including solar heating rate are investigated. When solar power absorbed by Fe3O4 solid particle flow is less than 132.75 kJ/mol, the yield of hydrogen and the mass fraction of the captured CO2 in the system increase with increasing solar heating rate. When solar power absorbed by Fe3O4 solid particle flow is larger than 132.75 kJ/mol, the yield of hydrogen and the mass fraction of the captured CO2 decrease with increasing solar heating rate. A set of particle energy storage equipment is added to the system to make sure stable operation given solar irradiance fluctuates. Finally, the yield coefficient X of hydrogen, H2(molar)/CH4(molar) in the system is stabilized at 3.8, the System energy efficiency η is stabilized at 79% and the mass fraction of captured carbon dioxide is stabilized above 93%.

TA29 titanium alloy forging was heated by duplex annealing. The fatigue crack growth behaviour of the alloy at room temperature, 400 °C, 500 °C and 600 °C was studied. The relationship between the fatigue crack growth rate (Da/dN) and the stress intensity factor amplitude (△K) of TA29 alloy at different temperatures was revealed by Paris formula. The fatigue fracture morphology of fatigue crack growth specimens was analysed by scanning electron microscopy (SEM). The results showed that the TA29 titanium exhibited good crack growth resistance at room temperature. With the increase of test temperature, the C value of the Paris formula increased, the m value decreased, and the fatigue crack growth rate increased. From the fatigue fracture morphology of the specimens, it was found that the fractures of fatigue crack growth specimens at different temperatures exhibited typical pre-splitting zone, steady-state expansion zone and rapid expansion zone. As the temperature increased, the range of the pre-cracking zone was larger, the range of the steady-state extension zone and the rapid expansion zone were smaller. At room temperature, there was no obvious fatigue strips in the steady-state expansion zone, and the transient fracture zone exhibited the characteristics of cleavage fracture, while the steady-state expansion zone at high temperature showed obvious fatigue strips and secondary cracks, and the transient fracture zone was a typical ductile fracture mechanism.

Background A long noncoding RNA (lncRNA), ZNFX1 antisense RNA 1 (ZFAS1), was increased in multiple cancers, including hepatocellular carcinoma (HCC), resulting in malignancy development and progression. However, the mechanisms involving the interaction between ZFAS1 and microRNA-624 (miRNA-624) remain largely unknown. Therefore, the goal of this study was to probe the functional role of ZFAS1 in the development of HCC and its underlying mechanism. Methods Firstly, differentially expressed lncRNAs in HCC tissues were screened out by microarray. Subsequently, the prognostic effect of ZFAS1 patients with HCC was analyzed by the Kaplan-Meier analysis and The Cancer Genome Atlas database. ZFAS1 regulation on miRNA-624 was determined after si-ZFAS1 and/or miRNA-624 inhibitor were transfected into HepG2 and SMMC7721 cell lines. Finally, the effects of ZFAS1 on the growth and metastasis of HCC were observed by in vivo tumorigenesis and metastasis tests. Results ZFAS1 was overexpressed in HCC tissues and cells and indicated worse prognosis and shorter survival in patients with HCC. Silencing of ZFAS1 inhibited the malignancy of HCC cells, but miR-624 inhibitor could partially reverse the repressive role of si-ZFAS1. Moreover, ZFAS1 induced the extracellular-regulated protein kinases/c-Jun N-terminal kinase (ERK/JNK)/P38 pathway by binding to midkine (MDK) through miR-624, thus promoting the occurrence of HCC. Conclusion Collectively, ZFAS1 depletion inhibited the occurrence of HCC by downregulating the MDK/ERK/JNK/P38 pathway through restoring miR-624 expression. Inhibition of ZFAS1 may act as an innovative target to suppress occurrence in HCC.

BACKGROUND: Apatinib is a small-molecule multitargeted tyrosine kinase inhibitor. Apatinib has demonstrated encouraging antitumor activities. This study aimed to observe the efficacy and safety of apatinib for the treatment of multiple brain micrometastases. CASE SUMMARY: We report two patients with multiple brain micrometastases after failure of second-line treatment. Both patients had extracerebral metastases. When the patients took 250 mg/d apatinib orally, the intracerebral lesions disappeared. The extracerebral lesions were partially alleviated. Both patients had a progression-free survival of more than 12 mo and were still stable. The safety was good. The main adverse events (AEs) were mild hypertension and proteinuria, which could be controlled. CONCLUSION: Apatinib has clear efficacy and good tolerance in patients with multiple brain micrometastases after failure of second-line treatment.

Electronic Health Records (EHR) contain extensive patient data on various health outcomes and risk predictors, providing an efficient and wide-reaching source for health research. Integrated EHR data can provide a larger sample size of the population to improve estimation and prediction accuracy. To overcome the obstacle of sharing patient-level data, distributed algorithms were developed to conduct statistical analyses across multiple clinical sites through sharing only aggregated information. However, the heterogeneity of data across sites is often ignored by existing distributed algorithms, which leads to substantial bias when studying the association between the outcomes and exposures. In this study, we propose a privacy-preserving and communication-efficient distributed algorithm which accounts for the heterogeneity caused by a small number of the clinical sites. We evaluated our algorithm through a systematic simulation study motivated by real-world scenarios and applied our algorithm to multiple claims datasets from the Observational Health Data Sciences and Informatics (OHDSI) network. The results showed that the proposed method performed better than the existing distributed algorithm ODAL and a meta-analysis method.

Graphical abstract, Synthetic lethality is thought to play an important role in anticancer therapies. Herein, to understand the potential distributions and relationships between synthetic lethal interactions between genes, especially for pairs deriving from different sources, we performed an integrative analysis of genes at multiple molecular levels. Based on inter-species phylogenetic conservation of synthetic lethal interactions, gene pairs from yeast and humans were analyzed; a total of 37,588 candidate gene pairs containing 7,816 genes were collected. Of these, 49.74% of genes had 2–10 interactions, 22.93% were involved in hallmarks of cancer, and 21.61% were identified as core essential genes. Many genes were shown to have important biological roles via functional enrichment analysis, and 65 were identified as potentially crucial in the pathophysiology of cancer. Gene pairs with dysregulated expression patterns had higher prognostic values. Further screening based on mutation and expression levels showed that remaining gene pairs were mainly derived from human predicted or validated pairs, while most predicted pairs from yeast were filtered from analysis. Genes with synthetic lethality were further analyzed with their interactive microRNAs (miRNAs) at the isomiR level which have been widely studied as negatively regulatory molecules. The miRNA–mRNA interaction network revealed that many synthetic lethal genes contributed to the cell cycle (seven of 12 genes), cancer pathways (five of 12 genes), oocyte meiosis, the p53 signaling pathway, and hallmarks of cancer. Our study contributes to the understanding of synthetic lethal interactions and promotes the application of genetic interactions in further cancer precision medicine.