Search

The new product development (PD) project is a complex network of communications involving trust relationships among teams. Trust is prominent, influencing the team positions and organizational performance indirectly. To manage the coordination complexity in PD projects, in this paper, we build a model of mutual trust among teams and further identify core teams to optimize the PD organizational network structure. First, we identified the technical interdependency and emotional closeness that influence the transmission behavior of the tie strength in the PD organizational network. Then, we examined how the presence of a common third party in the organizational network affects the trust transferability between interdependent teams. We modelled the structural similarity based on the trust transferability. To identify the core teams, which typically have high importance as well as diverse knowledge in the organizational network, we improved the LeaderRank centrality with trust transferability related to common recipients/sources to evaluate the importance of teams and present the team attributes (i.e. expertise) diversity. To build the group around core teams, we used the core teams as the input parameter (i.e. the initial clustering seeds) of the K-means clustering algorithms. The clustering results reinforce several managerial practices in an industrial example, including how trust transferability impacts the optimal organizational network structure and how to build an organizational network structure based on core teams.

Fe–N–C electrocatalysts, as a representative of platinum group metal-free (PGM-free) catalysts, exhibit a comparable oxygen reduction reaction (ORR) activity but insufficient stability to that of c...

Element doping strategy has been proven to be an efficient route to both activate the catalytic sites and optimize the electronic structure for transition metal dichalcogenides. However, the specif...

To study the influence of B4C particle size on the microstructure and damping capacities of (B4C+Ti)/Mg composites, in situ reactive infiltration technique was utilized to prepare Mg-matrix composites. The microstructure, produced phases and damping capacities of the composites prepared with different particle size of B4C were characterized and analyzed. The results show that the reaction between B4C and Ti tends to be more complete when finer B4C particle was used to prepare the composites. But the microstructure of the as-prepared composites is more homogenous when B4C and Ti have similar particle size. The strain-dependent damping capacities of (B4C+Ti)/Mg composites improve gradually with the increase of strain amplitude, and composites prepared with coarser B4C particles tend to have higher damping capacities. The temperature-dependent damping capacities improve with increasing the measuring temperatures, and the kind of damping capacities of the composites prepared with 5 B4C are inferior to those of coarser particles. The dominant damping mechanism for the strain-damping capacity is dislocation damping and plastic zone damping, while that for the temperature-damping capacity is interface damping or grain boundary damping.

Multifunctional motion sensors have broad applications in the field of the internet of things (IoT). An octopus-shaped self-powered motion sensor is proposed to monitor the acceleration, motion dir...

Single-atom catalysts often exhibit unexpected catalytic activity for many important chemical and biological reactions with respect to their bulk counterparts, and have been recognized as potential substitutes for natural enzymes. Here we report a biomimetic composite, yolk–shell Pd1@Fe1, that features two compatible single-atom systems with atomically dispersed Fe1 sites in a N-doped carbon shell and Pd1 sites in a yolk derived from a metal–organic framework. Directly utilizing the O2 and H2 sources generated on-site from the electrocatalytic overall water splitting, the as-synthesized yolk–shell Pd1@Fe1 could simultaneously catalyse nitroaromatic hydrogenation and alkene epoxidation reactions and lead to a cascade synthesis of amino alcohols. Our findings provide a versatile strategy to integrate different single metal sites within one system to allow the continuous and easy synthesis of complex compounds for various challenging reactions. Single-atom catalysts can exhibit improved catalytic performance with respect to their bulk counterparts. Now, the authors introduce a yolk@shell catalyst with spatially separated Pd and Fe single sites that simultaneously catalyse nitroaromatic hydrogenation and alkene epoxidation reactions, leading to a cascade synthesis of amino alcohols.

ZrO2 is one of the most stable metal oxides which is applicable to various chemical reactions in harsh environments. However, the photocatalytic performance of ZrO2 is relatively poor due to the negligible use of the solar spectrum caused by the wide bandgap (Eg = 5.3 eV). Here, we report plasmon enhanced Au nanoparticles decorated onto ZrO2 through a facile tannic acid-reduction method. The Au/ZrO2 heterojunctions exhibited efficient and stable photocatalytic activity of reducing CO2 into main CO and CH4, at the rates of 25.6 μmol g-1 h-1 and 5.1 μmol g-1 h-1 at most, respectively, approximately 6-fold enhanced compared to the pristine ZrO2, under simulated solar light. The reduction rates could also be improved over 10-fold under visible light when Au nanoparticles were loaded onto ZrO2. UV-Vis diffuse reflectance spectra confirmed the enhanced visible-light absorption of Au/ZrO2 caused by localized surface plasmon resonance (LSPR). Electrochemical impedance spectra (EIS) and photocurrent tests proved the more efficient charge transport and electron-hole separation of Au/ZrO2 heterojunctions. This study demonstrates an effective strategy of LSPR effects to improve the photocatalytic performances of semiconductors.

This paper presents a new approach to filter signals for discrete-time physical problems with stochastic uncertain in the presence of random data transmission delays, out-of-order packets and correlated noise. To deal with the packet disorder, the system model synthesizing the transmission delays and out-of-order packets from the plant to the filter is established by utilizing signal reconstruction schemes based on the zero-order-holder and logic zero-order-holder. A robust finite horizon Kalman filter is proposed by augmenting the state-space model and minimizing the error covariance. To further improve the filtering performance, a linear estimation-based delay compensation strategy is proposed by employing the reorganized time-stamped measurements. Moreover, for solving the missing measurement problem whilst reducing the computational costs, an artificial delay compensation approach is established using an one-step prediction approach. Simulation results show the effectiveness of the proposed method.

Coronavirus disease 2019 (COVID-19) is a newly emerging infectious disease. Our understanding of the clinical characteristics of liver damage and the relationship with disease severity in COVID-19 is still limited. To investigate the serum hepatic enzyme activities in different phenotypes of COVID-19 patients, evaluate their relationship with the illness severity and analyze the correlation of glycyrrhizin treatment and abnormal liver enzyme activities, one hundred and forty-seven patients with COVID-19 were enrolled in a retrospective study that investigated hepatic dysfunction. Liver alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactic dehydrogenase (LDH), Y-glutamyl transferase (GGT), superoxide dismutase (SOD), and alkaline phosphatase (ALP) were analyzed in these patients. Patients with diammonium glycyrrhizinate (DG) treatment were further investigated. Of the 147 patients, 56 (38.1%) had abnormal ALT activity and 80 (54.4%) had abnormal AST activity. The peak of abnormal hepatic enzyme activities occurred at 3 to 6 days after on admission. Serum AST and LDH levels were elevated, while the SOD level was decreased in severe and critical patients, compared with mild cases. DG treatment may alleviate the abnormal liver enzyme activities in non-critical COVID-19 patients. Abnormal liver functions may be observed in patients with COVID-19, and were associated with SARS-CoV-2-induced acute liver damage. Glycyrrhizin treatment may be an effective therapeutic approach for the outcome of abnormal hepatic enzyme activities in severe COVID-19 cases. Serum hepatic enzyme tests may reflect the illness severity and should be monitored.

The electrocatalytic carbon dioxide reduction reaction (CO2RR) offers an attractive route to fuels and feedstocks from renewable energy. Gold is active for the electrochemical CO2RR to CO, while the competing hydrogen evolution reaction is unavoidable. Here, we report a synergistic strategy, via introducing atomically dispersed Fe to tune the electronic structure of the Au nanoparticle, to improve the CO selectivity. By using operando X-ray absorption and infrared spectroscopies, we reveal the dynamic structural evolution and the adsorption of reactant intermediates at the single-atom Fe1/Au interface. During the reaction, the interaction between Fe and Au atoms becomes stronger, and the Fe1/Au synergies affect the adsorption of reaction intermediates, thus improving the selectivity of CO up to 96.3% with a mass activity of 399 mA mg-1. These results highlight the significant importance of synergistic modulation for advancing the single-atom decorated nanoparticle catalysis.

To solve the copyright protection problem of a colour image, a new blind colour image watermarking method combining a discrete cosine transform (DCT) in the spatial domain is presented in this study. The advantages of the spatial-domain watermarking algorithm and frequency-domain one are made full use in this scheme. Based on the different quantisation steps in red, green, and blue three-layer images, the processes of watermark embedding and blind extraction are completed in the spatial domain without a real DCT domain. The scheme is realised by using the unique features of the direct current (DC) coefficient and the relativity of DC coefficients between adjacent pixel blocks. This scheme can effectively solve the problems of the large-capacity colour image watermarking algorithm, such as long-running time and weak robustness. Comparing with other advanced watermarking algorithms, the presented scheme has better invisibility, stronger robustness, and higher real-time performance.

The large-arc discharge problem occurs in the processing of magnesium alloy by micro-arc oxidation. It will cause local ablation of ceramic coating and even magnesium alloy matrix material. Thus, clarifying the mechanism and influencing factors is a necessary approach to avoid the large-arc discharge problem. In this study, a new “cooling time” parameter based on frequency and duty cycle is proposed to study the large-arc discharge trend and understand the relationship between large-arc discharge and process parameters. Then, the mechanism of the large-arc discharge phenomenon and the action of the cooling time were investigated by the theory of local-arc discharge mechanism and load characteristics of micro-arc oxidation. The results show that the mechanism of large-arc discharge is that the arc discharge is continuous in situ, and the aggregation or merger of arc discharge occurrence. Therefore, the large-arc problem can be resolved effectively through discretizing the arc discharge by adjusting the cooling time and regulating the pulse frequency and duty cycle. The cooling time made the location area of arc discharge and oxides fully cooled, and provided time for the sufficient discharge of the load capacitance, to effectively inhibit the problem of large-arc discharge.

Actinide-based catalysts have been regarded as promising candidates for N

Many eukaryotic transcription factors (TF) form homodimer or heterodimer complexes to regulate gene expression. For example, dimerization properties of the basic leucine zipper (bZIP) family play a critical role in regulating the unique biological functions in all eukaryotes. However, the molecular mechanism underlying the binding sequence and functional specificity of homo- versus heterodimers remains elusive. To fill this gap, we developed a double DNA Affinity Purification sequencing (dDAP-seq) technique that maps heterodimer DNA binding sites in an endogenous genome context. Our genome-wide binding profiles of twenty pairs of C/S1 bZIP heterodimers and S1 homodimers in Arabidopsis revealed that heterodimerization significantly expands the DNA binding preferences of bZIP TFs. Analysis of the heterodimer target genes in stress response and development suggest heterodimerization gives rise to regulatory responses that are distinct from the homodimers. In addition to the classic ACGT elements recognized by plant bZIPs, we found that the C/S1 heterodimers bound to motifs that might share an origin with the GCN4 cis-elements in yeast that diverged from plants more than one billion years ago. Importantly, heterodimer binding specificities can be distinguished by their relative preference for ACGT motifs versus GCN4-related motifs. More broadly, our study demonstrates the potential of dDAP-seq in deciphering the DNA binding specificities of interacting TFs that are key for combinatorial gene regulation.

The incorporation of two different cyanide building blocks of [(Tp

The atomic-level understanding of the dynamic evolution of the surface structure of bimetallic nanoparticles under industrially relevant operando conditions provides a key guide for improving their catalytic performance. Here, we exploit operando X-ray absorption fine structure spectroscopy to determine the dynamic surface reconstruction of Cu/Au bimetallic alloy where single-atom Cu was embedded on the Au nanoparticle, under electrocatalytic conditions. We identify the migration of isolated Cu atoms from the vertex position of the Au nanoparticle to the stable (100) plane of the Au first atom layer, when the reduction potential is applied. Density functional theory calculations reveal that the surface atom migration would significantly modulate the Au electronic structure, thus serving as the real active site for the catalytic performance. These findings demonstrate the real structural change under electrochemical conditions and provide guidance for the rational design of high-activity bimetallic nanocatalysts.

Single atom catalysts (SACs) as the bright star catalysts have attracted great attention, constituting a series of intriguing properties for catalytic reactions. Herein, a simple and ingenious stra...

This article presents a soft crawling robot prototype with a simple architecture inspired by inchworms. The robot functionally integrates the torso (body) and feet in a monolithic curved structure that only needs a single shape memory alloy coil and differential friction to actuate it. A novel foot configuration is proposed, which makes the two feet, with an anti-symmetrical friction layout, can be alternately anchored, to match the contraction–recovery sequence of the body adaptively. Based on the antagonistic configuration between the shape memory alloy actuator and the elastic body, a vertically auxiliary spring was adopted to enhance the interaction mechanism. Force and kinematic analysis was undertaken, focusing on the parametric design of the special foot configuration. A miniature robot prototype was then 3D-printed (54 mm in length and 9.77 g in weight), using tailored thermoplastic polyurethane elastomer as the body material. A series of experimental tests and evaluations were carried out to assess its performance under different conditions. The results demonstrated that under appropriate actuation conditions, the compact robot prototype could accomplish a relative speed of 0.024 BL/s (with a stride length equivalent to 27% of its body length) and bear a load over five times to its own weight.

Given the increasing amounts of data and high feature dimensionalities in forecasting problems, it is challenging to build regression models that are both computationally efficient and highly accurate. Moreover, regression models commonly suffer from low interpretability when using a single kernel function or a composite of multi-kernel functions to address nonlinear fitting problems. In this paper, we propose a bi-sparse optimization-based regression (BSOR) model and corresponding algorithm with reconstructed row and column kernel matrices in the framework of support vector regression (SVR). The BSOR model can predict continuous output values for given input points while using the zero-norm regularization method to achieve sparse instance and feature sets. Experiments were run on 16 datasets to compare BSOR to SVR, linear programming SVR (LPSVR), least squares SVR (LSSVR), multi-kernel learning SVR (MKLSVR), least absolute shrinkage and selection operator regression (LASSOR), and relevance vector regression (RVR). BSOR significantly outperformed the other six regression models in predictive accuracy, identification of the fewest representative instances, selection of the fewest important features, and interpretability of results, apart from its slightly high runtime.

Single-atom catalysts not only maximize metal atom efficiency, they also display properties that are considerably different to their more conventional nanoparticle equivalents, making them a promising family of materials to investigate. Herein we developed a general host–guest strategy to fabricate various metal single-atom catalysts on nitrogen-doped carbon (M1/CN, M = Pt, Ir, Pd, Ru, Mo, Ga, Cu, Ni, Mn). The iridium variant Ir1/CN electrocatalyses the formic acid oxidation reaction with a mass activity of 12.9 $${{{\rm{A}}\,{\rm{mg}}^{-1}_{{\rm{Ir}}}}}$$ whereas an Ir/C nanoparticle catalyst is almost inert (~4.8 × 10−3 $${{{\rm{A}}\,{\rm{mg}}^{-1}_{{\rm{Ir}}}}}$$). The activity of Ir1/CN is also 16 and 19 times greater than those of Pd/C and Pt/C, respectively. Furthermore, Ir1/CN displays high tolerance to CO poisoning. First-principle density functional theory reveals that the properties of Ir1/CN stem from the spatial isolation of iridium sites and from the modified electronic structure of iridium with respect to a conventional nanoparticle catalyst. Single-atom catalysts maximize metal atom efficiency and exhibit properties that can be considerably different to their nanoparticle equivalent. Now a general host–guest strategy to make various single-atom catalysts on nitrogen-doped carbon has been developed; the iridium variant electrocatalyses the formic acid oxidation reaction with high mass activity and displays high tolerance to CO poisoning.

Point absorber wave energy converter is one of the most effective wave energy harness devices. Most of the wave energy converters generate energy by oscillating the floating body. Usually, the power-take-off system is simplified as a linear spring and a linear damper. However, the narrow frequency bandwidth around a particular resonant frequency is not suitable for real vibrations applications. Thus, a nonlinear hardening spring and a linear damper are applied in the power-take-off system. The bandwidth of hardening mechanism is discussed. The dynamic model of wave energy converter is built in regular waves with time domain method. The results show that the nonlinear wave energy converter has higher conversion efficiency than the linear wave energy converter more than the natural frequency state. The conversion efficiency of the nonlinear wave energy converter in the low frequency state is closed to the linear converter. The amplitude of the incident wave, the damping of the nonlinear wave energy converter and the nonlinear parameter [Formula: see text] affect the energy capture performance of the wave energy converter.

Supervised cross-modal hashing has attracted widespread concentrations for large-scale retrieval task due to its promising retrieval performance. However, most existing works suffer from some of following issues. Firstly, most of them only leverage the pair-wise similarity matrix to learn hash codes, which may result in class information loss. Secondly, the pair-wise similarity matrix generally lead to high computing complexity and memory cost. Thirdly, most of them relax the discrete constraints during optimization, which generally results in large cumulative quantization error and consequent inferior hash codes. To address above problems, we present a Fast Discrete Cross-modal Hashing method in this paper, FDCH for short. Specifically, it firstly leverages both class labels and the pair-wise similarity matrix to learn a sharing Hamming space where the semantic consistency can be better preserved. Then we propose an asymmetric hash codes learning model to avoid the challenging issue of symmetric matrix factorization. Finally, an effective and efficient discrete optimal scheme is designed to generate discrete hash codes directly, and the computing complexity and memory cost caused by the pair-wise similarity matrix are reduced from O ( n 2 ) to O ( n ) , where n denotes the size of training set. Extensive experiments conducted on three real world datasets highlight the superiority of FDCH compared with several cross-modal hashing methods and demonstrate its effectiveness and efficiency.

Supervised cross-modal hashing has gained increasing research interest on large-scale retrieval task owning to its satisfactory performance and efficiency. However, there are still some issues to be further addressed: (1) most of them fail to capture the inherent data structure effectively due to the complex correlations among heterogeneous data points; (2) most of them obtain continuous solutions firstly and then quantize the continuous solutions to generate hash codes directly, which causes large quantization error and consequent suboptimal retrieval performance; (3) most of them suffer from relatively high memory cost and computational complexity during training procedure, which makes them unscalable. In this paper, to address above issues, we propose a supervised hashing method for cross-modal retrieval dubbed Efficient Discrete Supervised Hashing (EDSH). Specifically, the sharing space learning with collective matrix factorization and semantic embedding with class labels are seamlessly integrated to learn hash codes. Therefore, the feature based similarities and semantic correlations are both preserved in hash codes, which makes the learned hash codes more discriminative. Then an efficient discrete optimal scheme is designed to handle the scalable issue. Instead of learning hash codes bit-by-bit, hash codes matrix can be obtained directly which is more efficient. Extensive experimental results on three public datasets show that our EDSH produces a superior performance in both accuracy and scalability over several existing cross-modal hashing approaches.

Catalysts for CO oxidation reaction are mainly based on oxide/hydroxide materials with multicomponent active sites. Here, we report a nonoxide/hydroxide material, atomically dispersed dual-metal si...

Developing low-cost, active, and stable electrocatalysts toward acidic oxygen evolution reaction (OER) is highly desirable for proton electrolyte membrane electrolyzers to produce renewable H2 ener...

Three-dimensional tactile sensing in smart devices is witnessing an increasing demand. In this study, a three-dimensional tactile sensor is proposed according to the principle of triboelectricity by using polydimethylsiloxane (PDMS) and polyethylene oxide (PEO) films. Use of PDMS-ZnO composite films increases the output voltage of the electrodes. The sensor is designed with a cross beam-shaped pressure head to enable sensing normal forces in five directions. The working mechanism of the sensor is analyzed using the finite element method. The calculation results indicate a linear relationship between the output voltage and the force. The simulation results also demonstrate that the output voltage is linearly correlated with the applied force in the range of 1–30 N. Test results show that the sensor can sense the magnitude, frequency, and direction of the force as well as measure the duration of the force. The hardness of the object has a significant effect on the output voltage. Finally, the sensor is tested on a manipulator grasping an object to demonstrate its ability to sense the contact and sliding of the object.

Oxide-derived Cu materials are most commonly used as electrocatalysts for the carbon dioxide reduction reaction (CO2RR). Previous studies have proved that Cu+ and residual subsurface oxygen species can enhance the CO2RR activity; however the stable presence of Cu+ remains a subject of debate. Here, we design a strategy of single-atom Sn anchored on Cu2O nanosheets to stabilize the key Cu+ species for electroreduction of CO2. Operando synchrotron X-ray absorption spectroscopy and statistics analysis distinguish the active Cu+ and reduced Cu+ species, and reveal that the constructed Sn–O–Cu sites with charge transfer can significantly enhance the resistance of copper oxides to reduction. Operando infrared spectroscopy suggests that the survival of Cu+ species on the catalyst surface promotes the adsorption of *CO during the CO2RR, leading to the obvious improvement of CO2-to-CO conversion. Our results demonstrate the role of a single-atom-modifier in both stabilizing Cu+ species and enhancing the CO2RR selectivity of oxide-derived Cu catalysts.

The efficient electrochemical hydrogen evolution reaction (HER) plays a key role in accelerating sustainable H2 production from water electrolysis, but its large-scale applications are hindered by the high cost of the state-of-the-art Pt catalyst. In this work, submonolayer Pt was controllably deposited on an intermetallic Pd3Pb nanoplate (AL-Pt/Pd3Pb). The atomic efficiency and electronic structure of the active surface Pt layer were largely optimized, greatly enhancing the acidic HER. AL-Pt/Pd3Pb exhibits an outstanding HER activity with an overpotential of only 13.8 mV at 10 mA/cm2 and a high mass activity of 7834 A/gPd+Pt at -0.05 V, both largely surpassing those of commercial Pt/C (30 mV, 1486 A/gPt). In addition, AL-Pt/Pd3Pb shows excellent stability and robustness. Theoretical calculations show that the improved activity is mainly derived from the charge transfer from Pd3Pb to Pt, resulting in a strong electrostatic interaction that can stabilize the transition state and lower the barrier.

LncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is reported to be highly expressed in myocardial I/R injury and closely related to autophagy. However, the exact biological role of MALAT1 and its underlying mechanism in myocardial I/R injury remain to be elucidated. We established incultured H9C2 cardiomyocytes an oxygen-glucose deprivation and reoxygenation (OGD/R) model for 6 h and then reoxygen-glucose for 4 h. We measured cell damage and autophagy levels after OGD/R by real-time quantitative PCR and Western blot. The relationships between miR-20b and MALAT1, beclin1 were confirmed by luciferase reporter assay. We found that the expression of MALAT1 and beclin1, cell damage levels (lactate dehydrogenase (LDH) release, 222.4 ± 29.4 vs. 577.5 ± 27.4 U/L; creatine kinase MB isoenzyme (CK-MB), 1.0 ± 0.2 vs. 4.3 ± 0.4; cardiac troponin I (cTn-I), 1.0 ± 0.3 vs. 3.0 ± 0.3; p

Intracranial artery calcification (IAC) is an important risk factor for cerebral infarction and a key biomarker for intracranial artery stenosis. Gamma-glutamyl transferase (GGT) has been independently associated with increased cardiovascular events and coronary calcification. Our study assessed whether GGT is an independent factor for IAC in acute ischemic stroke (AIS) patients. This cross-sectional study involved a total of 754 patients with AIS (mean age: 65 ± 13.2 years). All the patients had received brain computed tomography angiography (CTA) examination to evaluate IAC. Further, serum GGT levels and other biochemical parameters were analyzed. The average GGT level in patients who died was also significantly increased (37.0 ± 26.8 vs 29.0 ± 21.5 U/L, p = 0.012). Partial correlation analysis showed that serum GGT levels were associated with NIHSS score at admission after adjustment for age and gender was considered (r = 0.150, p = 0.001). Logistic regression analysis showed that serum GGT levels independently predicted all-cause mortality (OR = 1.036, 95% CI: 1.014–1.060, p = 0.002), NIHSS scores (β = 0.051, 95% CI: 0.020–0.082, p = 0.001) and IAC scores (β = 0.006, 95% CI: 0.003–0.014, p = 0.005) in male patients. Each SD (standard deviation) increase of serum GGT levels was also associated with risk of all-cause mortality (OR = 2.272, 95% CI: 1.364–3.787, P = 0.002). GGT levels in patients with severe IAC were significantly elevated (37.6 ± 33.6 vs 28.6 ± 19.2, p

A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes.We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61).Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs.The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC.The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.

CRISPR/Cas has recently emerged as the most reliable system for genome engineering in various species. However, concerns about risks associated with the CRISPR/Cas technology are increasing on potential unintended DNA changes that might accidentally arise from CRISPR gene editing. Developing a system that can detect and report the presence of active CRISPR/Cas tools in biological systems is therefore very necessary. Here, we developed four real-time detection systems that can spontaneously indicate the presence of active CRISPR-Cas tools for genome editing and gene regulation including CRISPR/Cas9 nuclease, base editing, prime editing, and CRISPRa in plants. Using the fluorescence-based molecular biosensors, we demonstrated that the activities of CRISPR/Cas9 nuclease, base editing, prime editing, and CRISPRa can be effectively detected in transient expression via protoplast transformation and leaf infiltration (in

AIM The purpose of this study was to compare percutaneous endoscopic lumbar discectomy (PELD) and the microscopic tubular technique and evaluate the outcomes of surgery. MATERIAL AND METHODS We collected information through retrospective analysis of patients with far-lateral lumbar disc herniation (FLLDH) from June 2015 to October 2018. Twenty-six patients underwent paraspinal muscle-splitting microscopic-assisted discectomy (MD) and thirty patients underwent PELD surgery by the same surgical team. Data included the duration of the operation, duration of intraoperative radiation exposure, and average duration of hospitalization. Pre- and postoperative pain scores and neurological functions were recorded using the visual analog scale (VAS) score and Oswestry disability index (ODI). RESULTS Fifty-six patients remained in the study over the 12-24 months period. The mean operating time was 65.83 ± 16.64 min in the PELD group, mean duration of radiation exposure was 154.98 ± 64.26 mGy, and average of hospitalization was 3.43 days. The mean operating time was 44.96 ± 16.87 min in the MD group, duration of radiation exposure was 42.12 ± 17.28 mGy, and duration of hospitalization was 4.12 days. There were two patients with postoperative transient dysesthesia and one underwent reoperation seven months after surgery in the PELD group. One patient had postoperative transient dysesthesia in the MD group. Except low back pain at three months (p 0.05), all patients in both groups showed significant improvement in VAS and ODI scores compared with pre-operation and until final follow-up (p 0.05). CONCLUSION Both techniques are minimally invasive, effective, and safe for treating FLLDH in selected patients. Compared with the PELD technique, the MD procedure offers a wider field of vision during operation, shorter operation time, fewer postoperative complications, and shorter learning curve.

For the purpose of studying compact strip production (CSP) funnel-shaped mold and flexible thin-slab rolling (FTSR) funnel-shaped mold, a three-dimensional (3D) multi-field coupling mathematical model was established to describe the electromagnetic braking (EMBr) continuous casting process. To investigate the metallurgical effect of EMBr in the CSP and FTSR funnel-shaped thin-slab molds, a Reynolds-averaged Navier–Stokes (RANS) turbulence model, together with an enthalpy–porosity approach, was established to numerically simulate the effect of ruler EMBr on the behaviors of melt flow, heat transfer, solidification, and inclusion movement in high-speed casting. The simulation results indicate that the application of ruler EMBr in the CSP and FTSR molds shows great potential to improve the surface temperature of molten steel and reduce the penetration depth of downward backflow. This contributes to the melting of the slag rim near the meniscus region and facilitates the floating removal of the inclusions in the molten pool. In addition, in comparison with the case of no EMBr, the parametric study shows that the braking effect of ruler EMBr with an electromagnetic parameter of 0.5 T can enhance the upward backflow in the two high-speed thin-slab molds. The enhanced upward backflow can successfully entrain the inclusions to the top of the mold and improve the activity of surface fluctuations to avoid the formation of the slag rim. For instance, for the ruler EMBr applied to the FTSR mold, the maximum amplitude of surface fluctuation and the floatation removal quantity of inclusions with a diameter of 100 μm are increased by 4.6 percent and 51 percent, respectively.

Improving metal loading and controlling the coordination environment is nontrivial and challenging for single-atom catalysts (SACs), which have the greatest atomic efficiency and largest number of interface sites. In this study, a matching bidentate ligand (MBL) anchoring strategy is designed for the construction of CuN