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Optically controllable delivery of microparticles excites interesting research and applications in various fields because of the noninvasive and noncontact features. However, long-distance delivery with a static low-power light source remains challenging. Here, the constant-force photonic projectile (CFPP) is employed to achieve long-distance delivery of microparticles with a low-power laser beam. The CFPP takes advantage of photon absorption to create a constant optical force within a large range, surpassing traditional tweezers. The concept of CFPP has been experimentally corroborated by remote control over micrometer-sized absorptive particles (APs) using a simple tilted focused beam. At the laser focus, strong photon absorption results in a large constant optical force that ejects the APs along the optical axis. Furthermore, the additional thermal convection field, which attracts particles from a distance into the working range of the CFPP, is utilized to collect the unbound APs for reuse. Finally, we demonstrate the concept of drug delivery by transporting a small microparticle onto a host particle at a remote location. The proposed CFPP provides a new perspective for drug delivery and heat-enhanced photodynamic therapy.

This research investigates the suitability of PRISMA and Sentinel-2 satellite imagery for retrieving topsoil properties such as Organic Matter (OM), Nitrogen (N), Phosphorus (P), Potassium (K), and pH in croplands using different Machine Learning (ML) algorithms and signal pre-treatments. Ninety-five soil samples were collected in Quzhou County, Northeast China. Satellite images captured soil reflectance data when bare soil was visible. For PRISMA data, a Linear Mixture Model (LMM) was used to separate soil and Photosynthetic Vegetation (PV) endmembers, excluding Non-Photosynthetic Vegetation (NPV) using Band Depth values at the 2100 nm absorption peak of cellulose. Sentinel-2 bare soil reflectance spectra were obtained using thresholds based on NDVI and NBR2 indices. Results showed PRISMA data provided slightly better accuracy in retrieving topsoil nutrients than Sentinel-2. While no optimal predictive algorithm was best, absorbance data proved more effective than reflectance. PRISMA results demonstrated potential for predicting soil nutrients in real scenarios.

A dielectric microsphere concentrates light into a photonic nanojet (PNJ), and swims towards the near-infrared laser in response to the nanojet-mediated force. In contrast, a Janus particle with an opaque metal layer was thought to be impossible to concentrate light into a stable nanojet. However, the Janus particle may experience optical torque owing to the inhomogeneous composition on both sides even in linearly polarized non-resonant light. Herein, we report on topologically protected PNJ produced by a synthetic Janus particle, and observed the backaction force on the Janus particle. Due to symmetry, the counter-propagating beams can both form PNJ on the respective opposite sides, and pull Janus particles towards respective sources. Furthermore, we unveil that the hysteresis on backaction force with respect to the injection power also exists on synthetic Janus particle compared with their dielectric counterparts. Additionally, the magnitude of the backaction force varies between power increase and decrease stages even with the same laser power. We anticipate that the observation offers great possibilities to pull irregular particles by concentrating light with the particle, and such scheme may be applied for parallel particle manipulation and classification.

Heart failure (HF) is a major global cause of morbidity and mortality. This study aimed to elucidate the role of secreted protein acidic and rich in cysteine-related modular calcium-binding protein 2 (SMOC2) in HF development and its underlying mechanism. Using a rat HF model, SMOC2 expression was examined and then knocked down via transfection to assess its impact on cardiac function and damage. The study also evaluated the effects of SMOC2 knockdown on autophagy-related molecules and the transforming growth factor beta 1 (TGF-β1)/SMAD family member 3 (Smad3) signaling pathway. Intraperitoneal injection of the TGF-β agonist (SRI-011381) into the HF rat model was performed to explore the SMOC2-TGF-β1/Smad3 pathway relationship. SMOC2 expression was elevated in HF rats, while its downregulation improved cardiac function and damage. SMOC2 knockdown reversed alterations in the LC3-II/I ratio, Beclin-1, and p62 levels in HF rats. Through transmission electron microscope, we observed that SMOC2 knockdown restored autophagosome levels. Furthermore, SMOC2 downregulation inhibited the TGF-β1/Smad3 signaling pathway, which was counteracted by SRI-011381. In conclusion, SMOC2 knockdown inhibits HF development by modulating TGF-β1/Smad3 signaling-mediated autophagy, suggesting its potential as a therapeutic target for HF.

The operations of the operators are important for nuclear safety, but conventional operating experience feedback and common data-driven methods make it difficult to explicitly find valuable information hidden in these operational sequences that can help the operator to provide advice at the operational level. During the nuclear power plant (NPP) operation, a large amount of historical operating data is accumulated, which records the operational sequences of the operators and the state parameters of equipment. Therefore, this paper proposes the use of association rule techniques to mine the NPP operating data to discover the operational characteristics of operators and reveal their possible impact on the NPP operation. This work helps to improve the operational performance of operators and prevent human-factor events. To this end, the concept of state switching values for describing the operating states of NPPs is proposed to enable the proposed method to be adapted to different practical application scenarios. A sequence segmentation method is proposed to be able to transform historical NPP operating data into a sequence data set for association rule mining. Furthermore, an ensemble algorithm based on sequence pattern mining and sequence rule mining and its postprocessing method are designed. The empirical study was carried out using 20 batches of historical operating data of the cold start-up. A total of 164 original association rules are generated using the proposed method and were analyzed by experts. The recommendations were made for 4 different cases that would improve the operational performance of the operators.

BACKGROUND The North Henan Plain is located in the middle and lower reaches of the Yellow River, and both high arsenic and fluoride groundwater exist. However, the coexistence mechanism of arsenic and fluoride in this sporadic distribution area is unclear. OBJECTIVES To investigate the spatial distribution characteristics and formation mechanism of arsenic and fluoride in shallow groundwater in the North Henan Plain. METHODS 332 groups of shallow groundwater samples were collected and analyzed in the North Henan Plain. Atomic fluorescence spectrometry was used to determine arsenic content, and ion chromatography and inductively coupled plasma emission spectroscopy were used to determine the content of fluoride and other cation-anions. Based on the spatial distribution of arsenic and fluoride, combined with hydrochemical diagrams and factor analysis, three main factors affecting the evolution of groundwater in this area were extracted, and the formation mechanism of high arsenic and high fluoride groundwater in this area was discussed. RESULTS The concentrations of arsenic and fluoride in groundwater were 0.0001-0.1900mg/L and 0.13-4.94mg/L, respectively. The high-arsenic groundwater was mainly distributed in the vertical depth of 15-80m in the front alluvial-diluvial depression of Taihang Mountain and the Yellow River flood fan. The high-fluoride groundwater was mainly distributed in the vertical depth of 7-100m in the modern channel influence zone of the Yellow River. Evaporation and concentration, mineral dissolution/desorption, and redox environment were the main factors controlling the evolution of groundwater in this area. Fluoride had loads of 0.214 and 0.743 in factor F1 (evaporation and concentration) and F2 (mineral dissolution/desorption), respectively. High concentration of F appeared in groundwater with low concentration of Ca2+, and the concentration of F was positively correlated with ρ(Na+)/[ρ(Na+)+ρ(Ca2+)]. The strong evaporation and concentration in the modern channel influence zone of the Yellow River contributed to the dissolution of fluorine-containing minerals, and the irrigation of the Yellow River water increased the concentration of Na+ in groundwater, which further enhanced the dissolution. Arsenic had a load of 0.728 in factor F3 (redox environment). Arsenic was positively correlated with Fe2+ and NH4+, and negatively correlated with NO3- and SO42-. The lower Eh corresponds to the higher arsenic concentration. The reductive environment in the front alluvial-diluvial depression of Taihang Mountain and the Yellow River flood fan was favorable for the reductive dissolution of arsenic containing iron oxides/hydroxides, resulting in the formation of the high-arsenic groundwater. The desorption of arsenate/arsenite/fluoride in the form of anions on the mineral surface caused by the increase of pH value was favorable for the coexistence of arsenic and fluoride in groundwater. However, the correlation between arsenic and fluoride in groundwater in this area was not significant. The high concentration of calcium ions in the high arsenic region was not conducive to the enrichment of fluoride. In contrast, the weak reducing conditions in the high fluoride region were not conducive to the dissolution of arsenic-containing iron oxides/hydroxides. CONCLUSIONS The results clarify the coexistence mechanism of arsenic and fluoride in the North Henan Plain, and enrich the theoretical system of co-contamination of groundwater with high arsenic and fluoride.

Based on the Deep Q-Network(DQN) algorithm of reinforcement learning, an active fault-tolerance method with incremental action is proposed for the control system with sensor faults of the once-through steam generator(OTSG). In this paper, we first establish the OTSG model as the interaction environment for the agent of reinforcement learning. The reinforcement learning agent chooses an action according to the system state obtained by the pressure sensor, the incremental action can gradually approach the optimal strategy for the current fault, and then the agent updates the network by different rewards obtained in the interaction process. In this way, we can transform the active fault tolerant control process of the OTSG to the reinforcement learning agent's decision-making process. The comparison experiments compared with the traditional reinforcement learning algorithm(RL) with fixed strategies show that the active fault-tolerant controller designed in this paper can accurately and rapidly control under sensor faults so that the pressure of the OTSG can be stabilized near the set-point value, and the OTSG can run normally and stably.

In this paper, two arc modes of gas shielded metal arc welding (GMAW), a new pulsed mode (microMIG) and a non-pulsed mode (microMIG-CC), were used to melt ER5356 aluminum alloy wire for thin-wall fabrication. The influence of both arc modes on the melt–drop transition pattern, thin–wall properties, porosity, microstructure and mechanical properties was investigated at the same heat input. The microMIG mode adds SKS-based current time based on conventional pulsed GMAW for short-circuiting the transition, which leads to a faster spread of melt pool temperature. The rapid cooling of the melt pool contributed to the increase of grain nucleation rate and inhibited grain growth, resulting in smaller grain size in the microMIG mode specimens. In addition, the low porosity of microMIG specimens is attributed to the high voltage of the micro-MIG mode resulting in an unfocused heat source, which leads to a shallow penetration depth of the specimens and a reduced hydrogen escape distance. Compared with the microMIG-CC mode, the microMIG mode specimens have smaller grain size and lower porosity, therefore higher tensile strength is obtained. The isotropic property of thin walls is due to the existence of equiaxed grains and the random distribution of grain orientation. However, the aggregation of dislocations and the increase of precipitates make microMIG-CC have higher hardness. Overall, the mechanical properties of the thin wall specimens of WAAM 5356 aluminum alloy are higher than those of the as-cast specimens.

Reversible control over the microparticle motion using light excites interesting applications in optofluidics, microswimmers, artificial optical matter, and biomedical engineering. The dielectric microspheres swim towards the near infrared pulsed laser in response to the backaction force mediated by photonic nanojet. Hereby, we report that the backaction force exhibits hysteretic behaviour owing to the distinguishable responses of the temperature rise inside the nanojet and the temperature rise of the liquid ensemble. Accordingly, the magnitude of backaction force at the same laser power varies between power increase and decrease stages. In order to develop multidimensional manipulation tool, we studied the possibility of using lasers with different spatiotemporal profiles to mediate the backaction force, and developed the counterpropagating beam scheme for reversible control of the particle motion directions. We further harness the hysteresis to reverse the direction of backaction force on dielectric particles in presence of a constant force from a counter-propagating beam with broadband supercontinuum spectrum. In contrast to the microsphere caught in the single beam gradient trap, the microsphere encounters augmented Brownian motion at higher balanced power level. The microsphere would eventually escape from the common region of the paired beams, enabling high throughput morphology analysis for cancer cell classification, biopsy, and diagnosis.

A heat pipe reactor (HPR) is a kind of modular small reactor with broad application prospects, and its dynamic characteristics and nuclear power control are essential to the safe and stable operation of nuclear power plants. Taking the MegaPower HPR as an example, the dynamic characteristics of the HPR are analyzed, and its power control method is designed in this paper. Based on the lumped parameter idea, the equivalent processing of the structure of the HPR core is carried out, and the main parameters of the heat pipe heat exchanger are designed at first. A lightweight dynamic model of the HPR is established using a thermal resistance network, and the accuracy of the model is verified using the solution of the model under the steady-state full power condition. Then, the dynamic characteristics of the HPR without a controller are analyzed respectively with the disturbance reactivity and mass flow rate, indicating strong self-stability and self-regulation of the HPR. Finally, a reinforcement learning (RL) controller based on the twin delayed deep deterministic policy gradient (TD3) algorithm is designed for the HPR power control, and it is adjusted through appropriately setting states, network structures, reward functions, etc. To verify the performance of the controller, a step response simulation ranging from 100%FP to 90%FP, a compound conditions simulation, and a large load change simulation are carried out, respectively. The results show that the RL controller can find the optimal control strategy through training. Meanwhile, it significantly improves the dynamic and steady-state performance of nuclear power compared with uncontrolled case and PID controller case, and it has the ability of power control under all operating conditions.

BACKGROUND Arsenic content in shallow groundwater of the Yellow River alluvial fan plain exceeds the standard. A comprehensive understanding of the arsenic enrichment mode driven by the nitrogen cycle of shallow groundwater in the northern Henan Plain is essential for the sustainable use of groundwater resources and the health of residents. The main area of the North Henan plain is the Yellow River alluvial fan plain. The sedimentary environment in the northern Henan plain is complex, and is influenced by the Yellow River breach, diversion and oscillation, as well as alluvial diluvial in the mountainous area around the basin. The distribution, migration and release mechanism of As, NH4+ and NO3- are quite different under different sedimentary environment conditions. The joint enrichment mechanism of the three is still unclear, which is worth further study. OBJECTIVES To investigate the effect of nitrogen cycling on arsenic migration and enrichment in groundwater in the Northern Henan Plain. METHODS 513 shallow groundwater samples were collected from the northern Henan Plain. Atomic fluorescence spectroscopy was used to determine the arsenic content, atomic absorption spectroscopy and ion chromatography, and other methods for major and trace element analysis. The correlation between nitrate, ammonia nitrogen and arsenic was investigated, and the influence of nitrogen cycle on the migration and enrichment of arsenic in groundwater was studied. RESULTS The over-standard rate of arsenic concentration in shallow groundwater in the study area was 17.3%. The occurrence and transformation modes of nitrogen under different depositional environmental conditions were important driving factors for arsenic enrichment. A large amount of NO3- was produced by nitrification in the groundwater of the alluvial fan in the piedmont alluvial fan, which had the average concentration of 9.3mg/L and was the highest in the district. At the same time, the concentration of arsenic was the lowest in the district, with the average of 1.3μg/L. The good negative correlation between NO3- and As concentration indicated that nitrification produced a large amount of NO3-, which was not conducive to the dissolution of arsenic-containing iron oxide. The depressions in front of the alluvial fan and the Yellow River crater fan with higher NH4+ content were the gathering places of high-arsenic groundwater. The average concentration of arsenic in groundwater from these two areas was 49.7μg/L and 18.9μg/L, respectively, and the exceeding rate reached 87.5% and 71.4%. The good positive correlation between arsenic content and NH4+ in groundwater indicated that the process of denitrification and dissimilative reduction of nitric acid to ammonium (DNRA) consumed NO3- in groundwater and generated a large amount of NH4+, which promoted the reduction and dissolution of iron oxides that adsorbed arsenic, forming an environment rich in arsenic. CONCLUSIONS The nitrogen cycle plays an important role in the migration and enrichment of arsenic. The migration and enrichment mode of arsenic driven by the nitrogen cycle provides a scientific basis for the treatment and supervision of groundwater with high arsenic content.

Since President Xi Jinping proposed the Belt and Road Initiative, China’s investment in Southeast Asia has grown rapidly, with its role as a leading technology exporter giving any host country a significant boost. As a host country, Malaysia’s experience has yet to be explored. This study investigates the advantages of Chinese enterprises locating in Malaysia, and explores the links between Chinese enterprises and Malaysian firms. A qualitative study involving structured interviews were conducted with 30 participants selected from different departments in three of China’s technological firms. Responses addressed the nature of their business including Malaysian government-related companies, employment of Chinese school graduates, technology transfer and industry linkages. These results show that Chinese enterprises’ localization in Malaysia has advantages despite some costs. Nevertheless, Malaysia needs to strengthen its human capital to benefit from Chinese enterprises’ localization.

BACKGROUND A typical irrigation area of the downstream Yellow River (Henan) is an important agricultural planting area in the northern Henan Plain. The shallow water quality in this area is generally poor, which is often used for crop irrigation or livestock drinking, posing a risk to human health. Therefore, the study on the variation characteristics and mechanism of arsenic and fluorine content in groundwater in this area will help to improve the level of understanding of pollution in this area. OBJECTIVES To investigate the spatial variation characteristics and mechanism of arsenic and fluorine concentration. METHODS Based on 327 groups of shallow groundwater samples collected in 2010 and 2020 in the irrigated area, the distribution of arsenic and fluorine in the groundwater of the irrigated area was analyzed, and compared to the evolution characteristics of arsenic and fluorine in the shallow groundwater of the irrigated area in the last ten years. RESULTS In 2020, the number of samples with high arsenic concentration (>10μg/L) and high fluorine concentration (>1mg/L) in shallow groundwater accounted for 26.1% and 26.06%, respectively. The high-arsenic groundwater was distributed in the sedimentary environments with interbedding structure of sediment, such as the Taihang Mountain depression and the Yellow River alluvial plain, with strong reducibility, poor groundwater runoff and strong cation exchange, resulting in high concentration of Ca2+ in the environment. Arsenic concentration increased in 31.8% of water samples and decreased in 36.7% of water samples in the last ten years. The increase of arsenic content was caused by the dissolution and release of manganese oxide due to the enhanced reducibility of groundwater. The variation of water level caused by agricultural irrigation and water replacement in different areas during the last ten years was a potential factor of arsenic concentration change. The high-fluorine groundwater was mainly distributed along the Yellow River in Xinxiang and Puyang, Henan Province. The concentration of fluorine ion was affected by the dissolution of calcareous minerals such as fluorite in the sediments, which made the high-fluorine groundwater appear in the low-calcium environment. In the last ten years, the study area with decreased fluorine ion concentration accounted for 60.2%, whereas areas with increased fluorine ion concentration accounted for 32.1%. The overall change trend was good, but the fluorine ion concentration in the high fluorine area continued to increase. Changes in fluorine concentration were also affected by changes in Ca2+, with further increases (decreases) as Ca2+ concentration decreased (increased). Elevated fluorine in groundwater was distributed along the Yellow River, affected greatly by the Yellow River water supply. So, the groundwater flow condition was good and the cation exchange was weakened, reducing the Ca2+ content. At this time, the arsenic content in the groundwater was less affected by the environment, so the study area with fluorine increase showed an inverse relationship between arsenic and fluorine in terms of regional distribution and evolution. CONCLUSIONS This study will provide support for rational utilization of groundwater in the study area.

Renal cell carcinoma (RCC) is generally acknowledged as the most resistant primary malignancy unresponsive to conventional radiotherapy and chemotherapy treatments. Norcantharidin (NCTD), a therapeutic compound derived from medicinal plants, has been shown to trigger apoptosis, as well as antimetastatic and antioxidant activities in several tumor cells. However, NCTD’s mechanism of antitumor activity in the RCC cell line remains unclear. In this study, we report that NCTD led to a time- and dose-dependent inhibition of cell proliferation. It had also markedly induced apoptosis and G2/M phase cell cycle arrest in a dose-dependent manner by decreasing the expressions of pro-caspase-3, pro-caspase-9, cyclin B1, and pCDC25C while increasing active caspase-3, cleaved-PARP, P21, and pCDC2 levels. Interestingly, NCTD treatment provoked the phosphorylation of extracellular-regulated protein kinase (ERK) and c-Jun-N-terminal kinase (JNK), but not of p38 MAPK. Moreover, SCH772984 and SP600125, ERK and JNK inhibitors, respectively, could partially abolish NCTD-induced apoptosis and G2/M phase cell cycle arrest. Collectively, these findings suggest that NCTD might activate JNK and ERK signaling pathways, consequently inducing apoptosis and G2/M arrest through the modulation of related proteins. This study provided evidence that NCTD is a promising therapeutic drug for the treatment of RCC.

By analyzing the recorded operation data of a nuclear power plant (NPP), its results can serve the fault detection or operation experience feedback. Data missing exists in the recorded operation data. It may lower the data quality and affect the accuracy of the analysis results. In order to improve the data quality, two parts of researches are carried on. Firstly, to locate the missing data accurately the detecting algorithm for missing data of the NPP operation parameters based on wavelet analysis. Different judging basis is proposed for discrete and continuous missing respectively. Then, the filling method based on the hot deck algorithm are studied. As the dynamic properties of the parameters are closely related to the operating state of NPP, the similarity of the operation parameter vectors are formed to express the similarity of the operating states, so as to fulfill the requirements of the hot deck algorithm. To improve the accuracy of the measuring results, taken the differences between the characteristics of the analog parameters and the switch parameters into consideration, the similarity measurements using Mahalanobis distance for the analog parameter vectors and the matching measure for the switch parameter vectors are studied respectively. Finally, the operation data is taken to build the experiment data set for the algorithm verification. The results shows that the designed algorithm performs much better than the mean interpolation method and LSTM.

The main transformer is critical equipment for economically generating electricity in nuclear power plants (NPPs). Dissolved gas analysis (DGA) is an effective means of monitoring the transformer condition, and its parameters can reflect the transformer operating condition. This study introduces a framework for main transformer predictive-based maintenance management. A condition prediction method based on the online support vector machine (SVM) regression model is proposed, with the input data being preprocessed using the information granulation method, and the parameters of the model are optimized using the particle swarm optimization (PSO) algorithm. Using DGA data from the NPP data acquisition system, two experiments are designed to verify the trend tracing and prediction envelope ability of main transformers installed in NPPs with different operating ages of the proposed model. Finally, how to use this framework to benefit the maintenance plan of the main transformer is summarized.

Because of the characteristics of the small water volume of OTSG, it is hard to control the outlet steam pressure when the load is changed or disturbed. This study is devoted to the control of the once-through steam generator (OTSG). A double-layer controller based on the PPO algorithm is proposed to control the outlet steam pressure of OTSG. The bottom layer is the PID controller; it directly regulates the OTSG feed water valve and then controls the steam pressure. The top layer of the controller is the agent based on the PPO algorithm, which is responsible for optimizing the parameters of the PID in real time to obtain better control performance. The agent chooses PID parameters as actions to the environment, and then, the reward value is obtained through the reward function of the environment which enables online learning of the agent. Compared with the PID controller, the simulation experiment result shows that the method not only has a good control performance but also has a good anti-interference ability.

Background: Circular RNAs (circRNA) play an essential role in the tumorigenesis of non-small cell lung cancer (NSCLC). CircDTL is a novel identified circRNA with little information regarding its biological role. However, the role of circDTL in NSCLC has not been investigated yet.Method: In this study, the levels of circDTL in tissues and cells were measured by RT-PCR. Cell viability was measured by the CCK-8 assay. Cell migration and invasion were evaluated using the wound healing assay and transwell assay, respectively. Cell death was measured by the cell death ELISA kit. The levels of Fe2+, ROS, MDA and GSH were measured using the commercial kits. The interactions between miR-1287-5p and circDTL/3′UTR GPX4 were verified by dual-luciferase activity assay. The effects of circDTL on tumor growth were evaluated in vivo.Results: CircDTL was found to be upregulated and acted as an oncogene in NSCLC cells. Knockdown of circDTL promoted both apoptosis and ferroptosis of NSCLC cells. It was identified that circDTL exerts its oncogenic effects via the circDTL/miR-1287-5p/GPX4 axis and GPX4 inhibits both ferroptosis and apoptosis. Finally, this study showed that silencing of circDTL promoted the sensitivity of NSCLC cells to chemotherapeutic agents and inhibited the growth of tumors in vivo.Conclusion: CircDTL acts as an oncogene and exerts its effects via the miR-1287-5p/GPX4 axis in NSCLC, providing a potential therapeutic target for NSCLC cancer therapy.

Abstract Introduction Patients with type 2 diabetes mellitus (T2DM) who fail to meet glycaemic control are at increased risk of diabetes complications. For patients who cannot maintain glycaemic control with oral medication, one recommended option is to add an injectable glucagon-like peptide-1 receptor agonist (GLP-1 RA) to their treatment regimen. The purpose of this study was to examine time to treatment intensification with GLP-1 RAs, including the duration of time that patients did not maintain glycaemic control with oral medication. Methods This was a medical record review conducted in the UK via a physician survey. Patients eligible to have their records reviewed were required to be ≥ 18 years of age, have a confirmed T2DM diagnosis, and have initiated GLP-1 RA treatment for T2DM in the past 6 months. All glycated haemoglobin (HbA1c) values within 5 years prior to GLP-1 RA initiation were collected. Results A total of 113 physicians contributed data for 1096 patients (mean age at the time of GLP-1 RA initiation was 54.9 years, 55.4% were male, and 71.4% were White). Median time from T2DM diagnosis to GLP-1 RA initiation was 6.1 years. Median consecutive time patients taking oral regimens were not under glycaemic control (HbA1c > 7.0%) prior to GLP-1 RA initiation was 13.5 months. Patients treated by general practitioners (GPs) had a significantly longer duration of time with insufficient glycaemic control prior to GLP-1 RA initiation compared with patients treated by diabetes specialists (median time for specialists was 11.0 months vs. 17.0 months for GPs; p = 0.038). Conclusions Results suggest that treatment intensification is often delayed despite consistently poor glycaemic control for more than 12 months, contrary to treatment guideline recommendations. Findings from this study highlight that some T2DM patients may benefit from more rapid treatment intensification, which could improve glycaemic control and reduce the risk for many short- and long-term health complications.

Umbelliferone exhibits extensive pharmacological activity, including anti-immunomodulatory, anti-inflammatory and antigenotoxicity activities. However, its antitumor properties still remain unclear in human renal cell carcinoma (RCC) cells. Our results have revealed that treatment of human RCC cells (786-O, OS-RC-2, and ACHN) with umbelliferone reduced cell proliferation in a concentration-dependent manner and induced dose-dependent apoptotic events. In addition, cell cycle analysis determined that umbelliferone treatment induced cell cycle arrest in the G1 phase in a dose-dependent manner. Furthermore, western blotting analysis showed a dose-dependent decrease in Ki67, MCM2, Bcl-2, CDK2, CyclinE1, CDK4, and CyclinD1 and a dose-dependent increase in Bax in RCC cells cultured with umbelliferone. Similarly, umbelliferone exhibited a dose-dependent reduction of p110γ when using western blotting analyses. Taken together, these results provide an insight into the pharmacology regarding the potential application of umbelliferone, which contributes to cell death by decreasing p110γ protein expression.

BackgroundContralateral prophylactic mastectomy (CPM) in female breast cancer (FBC) is supported by multiple clinical studies and consensus guidelines, but knowledge of preventive contralateral mastectomy in male breast cancer (MaBC) is very limited and its benefits are still controversial.MethodsA retrospective cohort study was enrolled with 4,405 MaBC patients who underwent unilateral mastectomy (UM) or CPM from the Surveillance, Epidemiology, and End Results (SEER) database from 1998 to 2015. A nomogram was built based on the corresponding parameters by competing risks regression to predict the 3-year, 5-year, and 8-year probabilities of BCSD (breast cancer-specific death). C-index and calibration curves were chosen for validation. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to estimate the nomogram’s clinical utility.ResultsA total of 4,197 patients received UM and 208 patients received CPM, with 63-months median follow-up. In the competing risks regression, six variables (surgery, marital status, T-stage, N-stage, histology, tumor grade) were significantly associated with BCSD. Based on these independent prognosis factors, a nomogram model was constructed. The C-index 0.75 (95%CI: 0.73-0.77) in the training cohort and 0.73 (95%CI: 0.71-0.74) in the internal validation group suggested robustness of the model. In addition, the calibration curves exhibited favorably. The NRI values (training cohort: 0.54 for 3-year, 0.55 for 5-year, and 0.49 for 8-year BCSD prediction; validation cohort: 0.51 for 3-year, 0.45 for 5-year, and 0.33 for 8-year BCSD prediction) and IDI values (training cohort: 0.02 for 3-year, 0.03 for 5-year, and 0.04 for 8-year BCSD prediction; validation cohort: 0.02 for 3-year, 0.04 for 5-year, and 0.04 for 8-year BCSD prediction) indicated that the model performed better than the AJCC criteria-based tumor staging alone.ConclusionsThe administration of CPM was associated with the decrease in risk of BCSD in patients with MaBC. The nomogram could provide a precise and personalized prediction of the cumulative risk in patients with MaBC after CPM.

The nuclear power plant systems are coupled with each other, and their operation conditions are changeable and complex. In the case of an operation fault in these systems, there will be a large number of alarm parameters, which can cause humans to be hurt in the accidents under great pressure. Therefore, it is necessary to predict the values of the key parameters of a device system. The prediction of the key parameters’ values can help operators determine the changing trends of system parameters in advance, which can effectively improve system safety. In this paper, a deep learning long short-term memory (LSTM) neural network model is developed to predict the key parameters of a nuclear power plant. The proposed network is verified by simulations and compared with the traditional grey theory. The simulation and comparison results show that the proposed LSTM neural network is effective and accurate in predicting the key parameters of the nuclear power plant.

Nuclear power plant operating data are characterized by a large variety, strong coupling, and low data value density. When using machine learning techniques for fault diagnosis and other related research, feature selection enables dimensionality reduction while maintaining the physical meaning of the original features, thus improving the computational efficiency and generalization ability of the learning model. In this paper, a correlation-based feature selection algorithm is developed to implement feature selection of nuclear power plant operating data. The proposed algorithm is verified by experiments and compared with traditional correlation-based feature selection algorithms. The experiments and comparison results show that the proposed algorithm is effective in realizing the dimensionality reduction of nuclear power plant operating data.

A fault diagnosis framework based on extreme learning machine (ELM) and AdaBoost.SAMME is proposed in a nuclear power plant (NPP) in this paper. After briefly describing the principles of ELM and AdaBoost.SAMME algorithm, the fault diagnosis framework sets ELM algorithm as the weak classifier and then integrates several weak classifiers into a strong one using the AdaBoost.SAMME algorithm. Furthermore, some experiments are put forward for the setting of two algorithms. The results of simulation experiments on the HPR1000 simulator show that the combined method has higher precision and faster speed by improving the performance of weak classifiers compared to the BP neural network and verify the feasibility and validity of the ensemble learning method for fault diagnosis. Meanwhile, the results also indicate that the proposed method can meet the requirements of a real-time diagnosis of the nuclear power plant.

Our previous reports showed that the cyclic-AMP-response element-binding protein (CREB) served as a proto-oncogene in the process of tumorigenesis and mediated the growth and metastatic activity of renal cancer cells. Our study, therefore, explored the role of CREB in sorafenib- -inhibited cell proliferation, migration and invasion. Renal cancer cells were cultured in medium containing sorafenib for 12, 24, 48 and 72 h. The MTT assay was used to study the cytotoxic effects of sorafenib. Cell invasion and migration were assayed in wound healing and transwell experiments, respectively. Protein expression levels were evaluated by western blotting. The results show that sorafenib treatment decreased cell viability in a dose- and time-dependent manner. Sorafenib inhibited cell migration and invasion and decreased the expression of MMP-2 and MMP-9. Moreover, addition of the recombinant plasmid pCI-neo/ CREB (PN) reversed the sorafenib-induced inhibition of cell proliferation, migration and invasion. These results show that CREB is associated with the sorafenib-induced inhibition of proliferation, migration and invasion.

Abstract Background In clinical studies involving a healthy volunteer human challenge model, a valid and reliable measure to assess the evolution of patient-reported symptom type and severity following viral exposure is necessary. This study examines the use of the InFLUenza Patient-Reported Outcome (FLU-PRO) diary as a standardized measure of symptom severity in a healthy volunteer human challenge model. Methods Healthy adults admitted to the NIH Clinical Center (Day − 1) underwent a 9-day inpatient quarantine after intranasal challenge with a wild-type influenza A/H1N1pdm virus (Day 0). Participants completed the 32-item FLU-PRO diary twice daily for 14 days to assess presence, severity, and duration of symptoms across six body systems. Secondary analyses included descriptive statistics to examine FLU-PRO scores over the course of illness and analysis of variance to compare scores on Day 3 post-challenge by presence of viral shedding, and pre-challenge hemagglutinin and neuraminidase inhibition (HAI and NAI) titers. Results All but one subject (99%), who was lost to follow-up, completed twice daily FLU-PRO diaries on all study assessment days. FLU-PRO demonstrated that 61 of 65 subjects reported symptoms (Days: Median 5, Mean 6 ± 7), of whom 37 (61%) had viral shedding. Pre-challenge, 39 (64%) and 10 (16%) subjects had low (

Bladder cancer (BCa) is one of the most common urinary cancers. The present study aims to investigate whether Paeoniflorin (Pae) can exert inhibitory effects on BCa. The results showed that Pae inhibited proliferation of human BCa cell lines in a concentration- and time-dependent manner. Pae and cisplatin (Cis) synergistically inhibited the growth of tumours in RT4-bearing mice. Pae treatment neutralized the body loss induced by Cis. Moreover, Pae induced apoptosis in RT4 cells and increased the activities of caspase3, caspase8 and caspase9. Western blotting and immunohistochemical analysis revealed that the phosphorylated signal transducer and activator of transcription-3 (p-STAT3) level were decreased in Pae-treated RT4 cells and Pae-treated tumour-bearing mice. Furthermore, STAT3 transcriptional target B-cell lymphoma-2 was decreased in Pae-treated RT4 cells. Interestingly, Pae prevented translocation of STAT3 to the nucleus in RT4 cells. Collectively, Pae inhibits the growth of BCa, at least in part, via a STAT3 pathway.

With the development of the Internet era, urban logistics distribution increases. At the same time, the state vigorously promotes the development of the new energy automobile industry and the continuous strengthening of people’s awareness of environmental protection, new energy vehicles began to be favored by logistics enterprises. From the analysis of the national support policy for new energy vehicles, the development of new energy vehicles in recent years and the TCO calculation of their cost advantages, this paper clarifies that new energy vehicles will occupy the mainstream position in urban logistics distribution.