Genotype-phenotype analysis of three Chinese families with Jervell and Lange-Nielsen syndrome

Long QT syndrome (LQTS) is characterized by QT prolongation, syncope and sudden death. This study aims to explore the causes, clinical manifestations and therapeutic outcomes of Jervell and Lange-Nielsen syndrome (JLNS), a rare form of LQTS with congenital sensorineural deafness, in Chinese individuals.Three JLNS kindreds from the Chinese National LQTS Registry were investigated. Mutational screening of KCNQ1 and KCNE1 genes was performed by polymerase chain reaction and direct DNA sequence analysis. LQTS phenotype and therapeutic outcomes were evaluated for all probands and family members.We identified 7 KCNQ1 mutations. c.1032_1117dup (p.Ser373TrpfsX10) and c.1319delT (p.Val440AlafsX26) were novel, causing JLNS in a 16-year-old boy with a QTc (QT interval corrected for heart rate) of 620 ms and recurrent syncope. c.605-2A>G and c.815G>A (p.Gly272Asp) caused JLNS in a 12-year-old girl and her 5-year-old brother, showing QTc of 590 to 600 ms and recurrent syncope. The fourth JLNS case, a 46-year-old man carrying c.1032G>A (p.Ala344Alasp) and c.569G>A (p.Arg190Gln) and with QTc of 460 ms, has been syncope-free since age 30. His 16-year-old daughter carries novel missense mutation c.574C>T (p.Arg192Cys) and c.1032G>A(p.Ala344Alasp) and displayed a severe phenotype of Romano-Ward syndrome (RWS) characterized by a QTc of 530 ms and recurrent syncope with normal hearing. Both the father and daughter also carried c.253G>A (p.Asp85Asn; rs1805128), a rare single nucleotide polymorphism (SNP) on KCNE1. Bizarre T waves were seen in 3/4 JLNS patients. Symptoms were improved and T wave abnormalities became less abnormal after appropriate treatment.This study broadens the mutation and phenotype spectrums of JLNS. Compound heterozygous KCNQ1 mutations can result in both JLNS and severe forms of RWS in Chinese individuals.SCI(E)CPCI-S(ISTP)PubMed0MEETING ABSTRACT267-75

Rapid Determination of Nitrate Nitrogen Isotope in Water Using Fourier Transform Infrared Attenuated Total Reflectance Spectroscopy (FTIR-ATR) Coupled with Deconvolution Algorithm

Nitrate is a prominent pollutant in water bodies around the world. The isotopes in nitrate provide an effective approach to trace the sources and transformations of nitrate in water bodies. However, determination of isotopic composition by conventional analytical techniques is time-consuming, laborious, and expensive, and alternative methods are urgently needed. In this study, the rapid determination of 15NO3− in water bodies using Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) coupled with a deconvolution algorithm and a partial least squares regression (PLSR) model was explored. The results indicated that the characteristic peaks of 14NO3−/15NO3− mixtures with varied 14N/15N ratios were observed, and the proportion of 15NO3− was negatively correlated with the wavenumber of absorption peaks. The PLSR models for nitrate prediction of 14NO3−/15NO3− mixtures with different proportions were established based on deconvoluted spectra, which exhibited good performance with the ratio of prediction to deviation (RPD) values of more than 2.0 and the correlation coefficients (R2) of more than 0.84. Overall, the spectra pretreatment by the deconvolution algorithm dramatically improved the prediction models. Therefore, FTIR-ATR combined with deconvolution and PLSR provided a rapid, simple, and affordable method for determination of 15NO3− content in water bodies, which would facilitate and enhance the study of nitrate sources and water environment quality management

Big data-enabled multiscale serviceability analysis for aging bridges☆

This work is dedicated to constructing a multi-scale structural health monitoring system to monitor and evaluate the serviceability of bridges based on the Hadoop Ecosystem (MS-SHM-Hadoop). By taking the advantages of the fault-tolerant distributed file system called the Hadoop Distributed File System (HDFS) and high-performance parallel data processing engine called MapReduce programming paradigm, MS-SHM-Hadoop features include high scalability and robustness in data ingestion, fusion, processing, retrieval, and analytics. MS-SHM-Hadoop is a multi-scale reliability analysis framework, which ranges from nationwide bridge-surveys, global structural integrity analysis, and structural component reliability analysis. This Nationwide bridge survey uses deep-learning techniques to evaluate the bridge serviceability according to real-time sensory data or archived bridge-related data such as traffic status, weather conditions and bridge structural configuration. The global structural integrity analysis of a targeted bridge is made by processing and analyzing the measured vibration signals incurred by external loads such as wind and traffic flow. Component-wise reliability analysis is also enabled by the deep learning technique, where the input data is derived from the measured structural load effects, hyper-spectral images, and moisture measurement of the structural components. As one of its major contributions, this work employs a Bayesian network to formulate the integral serviceability of a bridge according to its components serviceability and inter-component correlations. Here the inter-component correlations are jointly specified using a statistics-oriented machine learning method (e.g., association rule learning) or structural mechanics modeling and simulation

Generating Multi-group Homogenized Cross-sections Using Continuous-energy Monte Carlo Method for Fast Reactor Analysis

The deterministic two-step method for the fast reactor neutronics calculation, composed of cross-section homogenization and diffusion or transport core calculations, was widely applied in the fast reactor engineering design and analysis field. The homogenized cross-section calculation method based on Monte Carlo with continuous-energy and fine geometry can provide high-precision cross-sections for advanced fast reactors. The current development status and trends of coupling Monte Carlo-generated homogenized cross-sections with diffusion and transport core calculations were briefly reviewed in this paper. The methods discussed include the Monte Carlo flux-volume homogenization method, the superhomogenization equivalence technique (SPH), and the Monte Carlo flux-moment homogenization method (MHT). The MET-1000 metal fuel fast reactor is used as a benchmark. The SPH equivalent techniques are widely used to preserve the reaction rates of a reference heterogeneous model and a homogenous model. In this paper, the SPH was applied to the control rods' cross-section address to improve the diffusion core calculations. This equivalence technique reduces the overestimation of the control rod worth using the diffusion core solver from 13.5% to 0.35% and improves power distribution prediction accuracy. With SPH correction, the MC/diffusion in this work exhibits about <±4% error as the insertion of control rods in power distribution. For the transport core calculations, the reasons for core reactivity overestimation were quantitatively analyzed, and the MHT method was developed. The basic principle of the MHT homogenization method is to incorporate the anisotropy of the total cross-section concerning the incident angle into the scattering matrix. This allows for the consideration of cross-section anisotropy while maintaining the generality of the generated total cross-section within the core transport solver. The MHT reduces the error of the transport core solving of MET-1000 by 698 pcm. The factors that cause the residual bias were discussed, but there is only about 130 pcm unsolved bias. The flux-volume homogenization method exhibits uneven error distribution, tending to underestimate the power at the inner core top and overestimate the power at the outer core bottom, with errors ranging from -3.63% to +4.02%. The MHT homogenization method reduces power prediction errors, with errors ranging between -2.39% and +2.76%, and achieves a more uniform error distribution. This work presented Monte Carlo homogenized cross-section generation methods applicable to diffusion and transport core calculations for fast reactor neutronics analysis. The MHT homogenization method provides a novel approach for generating cross-sections suitable for core transport calculations in Monte Carlo simulations. However, further validation is needed with different core solvers and fast reactors such as small fast reactors and more heterogenous fast reactors. The Monte Carlo homogenization method still requires extensive research in various aspects, including the generation of discontinuous factors, the BN leakage model, and methods for handling historical effects

Network-based drug repurposing for potential stroke therapy

Stroke is the leading cause of death and disability worldwide, with a growing number of incidences in developing countries. However, there are currently few medical therapies for this disease. Emerged as an effective drug discovery strategy, drug repurposing which owns lower cost and shorter time, is able to identify new indications from existing drugs. In this study, we aimed at identifying potential drug candidates for stroke via computationally repurposing approved drugs from Drugbank database. We first developed a drug-target network of approved drugs, employed network-based approach to repurpose these drugs, and altogether identified 185 drug candidates for stroke. To validate the prediction accuracy of our network-based approach, we next systematically searched for previous literature, and found 68 out of 185 drug candidates (36.8 %) exerted therapeutic effects on stroke. We further selected several potential drug candidates with confirmed neuroprotective effects for testing their anti-stroke activity. Six drugs, including cinnarizine, orphenadrine, phenelzine, ketotifen, diclofenac and omeprazole, have exhibited good activity on oxygen-glucose deprivation/reoxygenation (OGD/R) induced BV2 cells. Finally, we showcased the anti-stroke mechanism of actions of cinnarizine and phenelzine via western blot and Olink inflammation panel. Experimental results revealed that they both played anti-stroke effects in the OGD/R induced BV2 cells via inhibiting the expressions of IL-6 and COX-2. In summary, this study provides efficient network-based methodologies for in silico identification of drug candidates toward stroke

Preparation of hydrogels based on poplar cellulose and their removal efficiency of Cd(II) from aqueous solutions

Industrial heavy metal-contaminated wastewater is one of the main water pollution problems. Adsorbents are a promising method for the removal of heavy metal contaminants. Herein, polyaspartic acid/carboxymethyl poplar sawdust hydrogels (PASP/CMPP) and ascorbic acid/carboxymethyl poplar sawdust hydrogels (VC/CMPP) were prepared by aqueous polymerization using alkalized poplar sawdust (CMPP) as the substrate and PASP and vitamin C (VC) as modifiers. The effective results, provided by the characterization analysis of SEM and BET, indicate that the surface of the PASP/CMPP hydrogel has a larger number of loose pores and a larger pore volume than the VC/CMPP hydrogel. The treatment effects of the two hydrogels on simulated wastewater containing Cd(II) were investigated by a batch of experiments. The results showed that PASP/CMPP had a better adsorption effect than VC/CMPP under the same adsorption conditions. Interestingly, the solid concentration effect was found in the process of sorption kinetics and sorption isotherms. The sorption kinetic curves of Cd(II) on PASP/CMPP were well-fitted by the quasi-second-order kinetics under different adsorbent concentrations. The adsorption conforms to Langmuir and Freundlich adsorption isotherm models. More importantly, PASP/CMPP composites are expected to be used as a new kind of environmental adsorbent for wastewater treatment. HIGHLIGHTS Hydrogels of PASP/CMPP were prepared.; The hydrogels of PASP/CMPP exhibit enhanced sorption capacities for Cd(II).; The hydrogels of PASP/CMPP are potential sorbents for wastewater treatment.; The solid concentration effect was found in the process of sorption kinetics and sorption isotherms.; The synthesis of PASP/CMPP hydrogels provides a win-win strategy.

Development of a neuroevolution machine learning potential of Pd-Cu-Ni-P alloys

Pd-Cu-Ni-P alloy is an ideal model system of metallic glass known for its exceptional glass-forming ability. However, few correlation of structures with properties was systematically investigated owing to a lack of interatomic potential. In this work, a neuroevolution machine learning potential (NEP) with efficiency close to embedded atom method (EAM) potentials is developed. Its accuracy has been compared to density functional theory (DFT) calculations. For energy, force and virial, the training errors are 6.0 meV/atom, 111.1 meV/Å and 21.5 meV/atom, respectively. By means of this NEP, several thermodynamic parameters such as glass transition temperatures and pair distribution functions of Pd40Cu30Ni10P20 and Pd40Ni40P20 liquid and glassy alloys as well as their short-range orders, tensile and compression strengths, transport properties etc. have been evaluated by a series of molecular dynamics simulations. A good agreement with DFT calculations and previous experiments indicates this NEP provides an accurate and efficient scheme in the analysis and exploration of microstructures, thermodynamic and kinetic properties of Pd-Cu-Ni-P alloys

Systems Pharmacology-Based Strategy to Investigate the Mechanism of Ruangan Lidan Decoction for Treatment of Hepatocellular Carcinoma

epatocellular carcinoma (HCC) is one of the leading contributors to cancer mortality worldwide. Currently, the prevention and treatment of HCC remains a major challenge. As a traditional Chinese medicine (TCM) formula, Ruangan Lidan decoction (RGLD) has been proved to own the effect of relieving HCC symptoms. However, due to its biological effects and complex compositions, its underlying mechanism of actions (MOAs) have not been fully clarified yet. In this study, we proposed a pharmacological framework to systematically explore the MOAs of RGLD against HCC. We firstly integrated the active ingredients and potential targets of RGLD. We next highlighted 25 key targets that played vital roles in both RGLD and HCC disease via a protein-protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Furthermore, an ingredient-target network of RGLD consisting of 216 ingredients with 306 targets was constructed, and multilevel systems pharmacology analyses indicated that RGLD could act on multiple biological processes related to the pathogenesis of HCC, such as cellular response to hypoxia and cell proliferation. Additionally, integrated pathway analysis of RGLD uncovered that RGLD might treat HCC through regulating various pathways, including MAPK signaling pathway, PI3K/Akt signaling pathway, TNF signaling pathway, and ERBB signaling pathway. Survival analysis results showed that HCC patients with low expression of VEGFA, HIF1A, CASP8, and TOP2A were related with a higher survival rate than those with high expression, indicating the potential clinical significance for HCC. Finally, molecular docking results of core ingredients and targets further proved the feasibility of RGLD in the treatment of HCC. Overall, this study indicates that RGLD may treat HCC through multiple mechanisms, which also provides a potential paradigm to investigate the MOAs of TCM prescription

Near room temperature LaFe₁₁.₆Si₁.₄/PrₓCo₇ magnetocaloric composites with excellent mechanical and thermal properties

LaFe11.6Si1.4/10wt%PrxCo7 (x = 1, 2, 3, 5) magnetocaloric composites were prepared by spark plasma sintering (SPS) and diffusion annealing. The phase composition, microstructure, magnetic, mechanical and thermal properties were studied. The addition of PrxCo7 binder can be used to tune the phase fraction and magnetic properties. Pr2Co7 binder promoted the formation of 1:13 phase. LaFe11.6Si1.4/10wt%Pr2Co7 composites possess the highest 1:13 content (~ 89 wt%). The Curie temperature (TC) can be tuned in the technologically useful range of 280–347 K. Relatively large maximum magnetic entropy change (− ∆SM)max (2 T) of 1.45−3.16 J/kg K and refrigeration capacity (RC) (2 T) of 117–137 J/kg were obtained. These composites exhibit superior compressive strength of 1005–1250 MPa and excellent thermal conductivity of 17.68–27.77 W/m K. Thus, LaFe11.6Si1.4/10wt%PrxCo7 magnetocaloric composites possess excellent mechanical and thermal properties, with adjustable magnetic properties and have the potential application for near room temperature magnetic refrigeration.This work was supported by the Guangzhou Municipal Science and Technology Project (Grant No. 201904010030); the Natural Science Foundation of Guangdong Province (Grant No. 2022A1515012578, 2019A1515010970); the National Natural Science Foundation of China (Grant Nos. 51874143, 52066001); the Foundation of State Key Laboratory of Baiyunobo Rare Earth Resource Researches and Comprehensive Utilization (Grant No. 2020Z2218)

Study of Gong tone matching to five colors based on event-related potential

Objective: To investigate the neural mechanism of Gong tone matching to the five colors. Methods: The experiment was designed using event-related potential technique, following the S1→S2 paradigm, and dividing into two arms according to the Gong tone being played in harmonics or open strings. For each arm, S1 was the playing of the Gong tone, followed by S2, where subjects, based on their first reaction, were required to make a yes/no judgement to whether the tone matches to a randomly-picked color out of the five colors. During this process, the event-related potentials were recorded. Results: Gong tone played in harmonics, most participants matched it with black and cyan; while in the open strings arm, most matched it with black. The average amplitudes of P300 at PZ, FZ, and CZ points were compared. For the Gong tone played in harmonics, there was a significant difference between those who matched it with black and those who did otherwise (P < .01). There was also a significant difference between those who matched it with cyan and those who did otherwise (P < .01). For the open strings arm, there was a significant difference between those who matched it with black and those who did otherwise (P < .01). Conclusion: Our comparison of the average amplitudes of P300 suggests a commonality in the neural mechanism underlying categorical thinking that is not fundamentally altered by playing style. However, behavioral theory suggests that playing style does have an influence on categorization. The Gong tone can be matched with any of the five colors, which accords with the thought of central Earth in the five-element theory. This provides evidence of the similarity within five elements correspondences existing alongside the indirect matching between five tones and five colors. Keywords: Central earth, Categorical thinking, Five-element theory, Neural mechanis