The Existence of a Global Attractor for the S-K-T Competition Model with Self-Diffusion

This paper concerns the uniform bounds of the global existence of solutions in time for the S-K-T competition model with self-diffusion. We prove that the system has a global attractor for n<8

Interventional therapy of diabetes mellitus type 2 complicated with acute cerebral hemorrhage by using dexmedetomidine

AbstractObjectiveTo study the effects of dexmedetomidine on cerebral injury, inflammation, oxidative stress and renal function of patients with diabetes mellitus type 2 complicated with acute cerebral hemorrhage.MethodsA total of 98 cases who had been diagnosed with diabetes mellitus type 2 complicated with acute cerebral hemorrhage and treated with interventional therapy in Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from September 2014 January 2016 were chosen to be our study subjects. Among them, 50 cases given dexmedetomidine treatment in the process of anesthesia were included in the dexmedetomidine group (Group A), while the other 48 cases treated with equal amount of normal saline were considered as the negative control group. The postoperative cerebral injury indexes and the serum biochemical indexes were detected after 24 h.ResultsThe contents of serum S100β [(2.1 ± 0.2) μg/L] and neuron-specific enolase (NSE) [(14.2 ± 1.3) μg/mL] in Group A were all significantly lower than serum S100β [(2.9 ± 0.3) μg/L] and NSE [(16.6 ± 1.7) μg/mL] of patients in negative control group. The contents of cerebrospinal fluid S100β [(0.9 ± 0.1) μg/L] and NSE [(10.7 ± 1.3) μg/mL] in Group A were all significantly lower than cerebrospinal fluid S100β [(1.3 ± 0.2) μg/L] and NSE [(15.3 ± 1.7) μg/mL] of patients in negative control group. The contents of erythrocyte sedimentation rate [(11.7 ± 2.5) mm/h], c-reactive protein [(2.3 ± 0.4) mg/L], urea nitrogen [(10.7 ± 1.2) mmol/L] and serum creatinine [(151.6 ± 14.9)] μmol/L in Group A were all significantly lower than erythrocyte sedimentation rate [(23.6 ± 3.8) mm/h], c-reactive protein [(6.9 ± 1.1) mg/L], urea nitrogen [(16.7 ± 1.7) mmol/L] and serum creatinine [(192.5 ± 18.3)] μmol/L of patients in negative control group.ConclusionsThe application of dexmedetomidine in the interventional therapy of diabetes mellitus type 2 complicated with acute cerebral hemorrhage could protect brain and renal functions and reduce systemic inflammatory responses

4-Bromo-3-methyl­anilinium perchlorate 18-crown-6 clathrate

The reaction of 4-bromo-3-methyl­anilinium perchlorate and 18-crown-6 in methanol solution yielded the title compound, C7H9BrN+·ClO4 −·C12H24O6. The protonated 4-bromo-3-methyl­amine unit contains one –NH3 + substituent, resulting in a 1:1 supra­molecular rotator–stator structure, (C7H9Br—NH3 +)(18-crown-6), through three bifurcated N—H⋯(O,O) hydrogen bonds between the ammonium group of the cation and the O atoms of the crown ether mol­ecule

GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting

Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one hand, they failed to capture global dependencies within time series data. On the other hand, the long input sequence usually leads to large model size and high time complexity. To address these limitations, we present GCformer, which combines a structured global convolutional branch for processing long input sequences with a local Transformer-based branch for capturing short, recent signals. A cohesive framework for a global convolution kernel has been introduced, utilizing three distinct parameterization methods. The selected structured convolutional kernel in the global branch has been specifically crafted with sublinear complexity, thereby allowing for the efficient and effective processing of lengthy and noisy input signals. Empirical studies on six benchmark datasets demonstrate that GCformer outperforms state-of-the-art methods, reducing MSE error in multivariate time series benchmarks by 4.38% and model parameters by 61.92%. In particular, the global convolutional branch can serve as a plug-in block to enhance the performance of other models, with an average improvement of 31.93\%, including various recently published Transformer-based models. Our code is publicly available at https://github.com/zyj-111/GCformer

Knockdown of TIGAR by RNA interference induces apoptosis and autophagy in HepG2 hepatocellular carcinoma cells

AbstractApoptosis and autophagy are crucial mechanisms regulating cell death, and the relationship between apoptosis and autophagy in the liver has yet to be thoroughly explored. TIGAR (TP53-induced glycolysis and apoptosis regulator), which is a p53-inducible gene, functions in the suppression of ROS (reactive oxygen species) and protects U2OS cells from undergoing cell death. In this study, silencing TIGAR by RNAi (RNA interference) in HepG2 cells down-regulated both TIGAR mRNA (∼75%) and protein levels (∼80%) and led to the inhibition of cell growth (P<0.01) by apoptosis (P<0.001) and autophagy. We demonstrated that TIGAR can increase ROS levels in HepG2 cells. The down-regulation of TIGAR led to the induction of LC-3 II (specific autophagic marker), the formation of the autophagosome, and increased Beclin-1 expression. 3-MA (3-Methyladenine), an inhibitor of autophagic sequestration blocker, inhibited TIGAR siRNA-enhanced autophagy, as indicated by the decrease in LC-3 II levels. Consequently, these data provide the first evidence that targeted silencing of TIGAR induces apoptotic and autophagic cell death in HepG2 cells, and our data raise hope for the future successful application of TIGAR siRNA in patients with hepatocellular carcinoma (HCC)