Lithium-Excess Research of Cathode Material Li2MnTiO4 for Lithium-Ion Batteries

Lithium-excess and nano-sized Li2+xMn1−x/2TiO4 (x = 0, 0.2, 0.4) cathode materials were synthesized via a sol-gel method. The X-ray diffraction (XRD) experiments indicate that the obtained main phases of Li2.0MnTiO4 and the lithium-excess materials are monoclinic and cubic, respectively. The scanning electron microscope (SEM) images show that the as-prepared particles are well distributed and the primary particles have an average size of about 20–30 nm. The further electrochemical tests reveal that the charge-discharge performance of the material improves remarkably with the lithium content increasing. Particularly, the first discharging capacity at the current of 30 mA g−1 increases from 112.2 mAh g−1 of Li2.0MnTiO4 to 187.5 mAh g−1 of Li2.4Mn0.8TiO4. In addition, the ex situ XRD experiments indicate that the monoclinic Li2MnTiO4 tends to transform to an amorphous state with the extraction of lithium ions, while the cubic Li2MnTiO4 phase shows better structural reversibility and stability

Clinical Efficacy of Temozolomide and Its Predictors in Aggressive Pituitary Tumors and Pituitary Carcinomas: A Systematic Review and Meta-Analysis

Background: A growing number of evidences suggest that TMZ applications can generate impressive benefits for APT and PC patients. However, the definite role of TMZ for individuals remains unclarified due to the variation between studies. And the predictive factors to alter its efficacy remain debatable.Objective: To evaluate the long-term effectiveness and safety profile of TMZ in the treatment of pituitary malignancies, and delineate the predictors during its clinical employment.Results: A literature retrieval was conducted from online databases for studies published up to December 31, 2020. Twenty one studies involving 429 patients were identified. TMZ exhibited 41% radiological overall response rate (rORR). The biochemical response rate was determinate in 53% of the functioning subset. Two-year and 4-year survival rate were 79 and 61%, respectively. TMZ prolonged the median PFS and OS as 20.18 and 40.24 months. TMZ-related adverse events occurred in 19% of patients. Regarding predictors of TMZ response, rORR was dramatically improved in patients with low/intermediate MGMT expression than those with high-MGMT (>50%) (p < 0.001). The benefit of TMZ varied according to functioning subtype of patients, with greater antitumor activities in functioning subgroups and fewer activities in non-functioning sets (p < 0.001). Notably, the concomitant therapy of radiotherapy and TMZ significantly increased the rORR (p = 0.007).Conclusion: TMZ elicits clinical benefits with moderate adverse events in APT and PC patients. MGMT expression and clinical subtype of secreting function might be vital predictors of TMZ efficacy. In the future, the combination of radiotherapy with TMZ may further improve the clinical outcomes than TMZ monotherapy

Transcriptome profiling reveals the role of ZBTB38 knock-down in human neuroblastoma

ZBTB38 belongs to the zinc finger protein family and contains the typical BTB domains. As a transcription factor, ZBTB38 is involved in cell regulation, proliferation and apoptosis, whereas, functional deficiency of ZBTB38 induces the human neuroblastoma (NB) cell death potentially. To have some insight into the role of ZBTB38 in NB development, high throughput RNA sequencing was performed using the human NB cell line SH-SY5Y with the deletion of ZBTB38. In the present study, 2,438 differentially expressed genes (DEGs) in ZBTB38−/− SH-SY5Y cells were obtained, 83.5% of which was down-regulated. Functional annotation of the DEGs in the Kyoto Encyclopedia of Genes and Genomes database revealed that most of the identified genes were enriched in the neurotrophin TRK receptor signaling pathway, including PI3K/Akt and MAPK signaling pathway. we also observed that ZBTB38 affects expression of CDK4/6, Cyclin E, MDM2, ATM, ATR, PTEN, Gadd45, and PIGs in the p53 signaling pathway. In addition, ZBTB38 knockdown significantly suppresses the expression of autophagy-related key genes including PIK3C2A and RB1CC1. The present meeting provides evidence to molecular mechanism of ZBTB38 modulating NB development and targeted anti-tumor therapies

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Vibration Power Flow of an Infinite Cylindrical Shell Submerged in Viscous Fluids

In the previous investigations of the vibroacoustic characteristics of a submerged cylindrical shell in a flow field, the fluid viscosity was usually ignored. In this paper, the effect of fluid viscosity on the characteristics of vibration power flow in an infinite circular cylindrical shell immersed in a viscous acoustic medium is studied. Flügge’s thin shell theory for an isotropic, elastic, and thin cylindrical shell is employed to obtain the motion equations of the structure under circumferential-distributed line force. Together with the wave equations for the viscous flow field as well as continuity conditions at the interface, the vibroacoustic equation of motion in the coupled system is derived. Numerical analysis based on the additional-damping numerical integral method and ten-point Gaussian integral method is conducted to solve the vibroacoustic coupling equation with varying levels of viscosity. Then, the variation of the input power flow against the nondimensional axial wave number in the coupled system with different circumferential mode numbers is discussed in detail. It is found that the influence of fluid viscosity on the vibroacoustic coupled system is mainly concentrated in the low-frequency band, which is shown as the increase of the crest number and amplitude of the input power flow curves

Research on preload relaxation for composite pre-tightened tooth connections

Preload is the primary reason why pre-tightened tooth connections (PTTC) can transfer relatively large loads. However, creep of the composite would cause the preload relaxation, resulting in reducing bearing capacity of the connection. To study the preload relaxation of PTTC caused by the creep of composites, a prediction formula is deduced by converting the viscoelastic problem to an elastic problem using Laplace transform. Meanwhile, long-term experimental research on the preload relaxation of composite pre-tightened tooth connection with different initial preloads and different geometry sizes was made. The theoretical results are compared with experimental data obtained by long-term experiment, and the results indicate that the calculation formula can predict the preload relaxation well in linear viscoelastic state. The preload relaxation mainly occurs at the beginning of loading and it tends to be steady in the middle and later periods

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

Safety is an essential topic to the architecture, engineering and construction (AEC) industry. However, traditional methods for structural health monitoring (SHM) and jobsite safety management (JSM) are not only inefficient, but also costly. In the past decade, scholars have developed a wide range of deep learning (DL) applications to address automated structure inspection and on-site safety monitoring, such as the identification of structural defects, deterioration patterns, unsafe workforce behaviors and latent risk factors. Although numerous studies have examined the effectiveness of the DL methodology, there has not been one comprehensive, systematic, evidence-based review of all individual articles that investigate the effectiveness of using DL in the SHM and JSM industry to date, nor has there been an examination of this body of evidence in regard to these methodological problems. Therefore, the objective of this paper is to disclose the state of the art of current research progress and determine the relevant gaps, challenges and future work. Methodically, CiteSpace was employed to summarize the research trends, advancements and frontiers of DL applications from 2010 to 2020. Next, an application-focused literature review was conducted, which led to a summary of research gaps, recommendations and future research directions. Overall, this review gains insight into SHM and JSM and aims to help researchers formulate more types of effective DL applications which have not been addressed sufficiently for the time being

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

Safety is an essential topic to the architecture, engineering and construction (AEC) industry. However, traditional methods for structural health monitoring (SHM) and jobsite safety management (JSM) are not only inefficient, but also costly. In the past decade, scholars have developed a wide range of deep learning (DL) applications to address automated structure inspection and on-site safety monitoring, such as the identification of structural defects, deterioration patterns, unsafe workforce behaviors and latent risk factors. Although numerous studies have examined the effectiveness of the DL methodology, there has not been one comprehensive, systematic, evidence-based review of all individual articles that investigate the effectiveness of using DL in the SHM and JSM industry to date, nor has there been an examination of this body of evidence in regard to these methodological problems. Therefore, the objective of this paper is to disclose the state of the art of current research progress and determine the relevant gaps, challenges and future work. Methodically, CiteSpace was employed to summarize the research trends, advancements and frontiers of DL applications from 2010 to 2020. Next, an application-focused literature review was conducted, which led to a summary of research gaps, recommendations and future research directions. Overall, this review gains insight into SHM and JSM and aims to help researchers formulate more types of effective DL applications which have not been addressed sufficiently for the time being