MicroRNA-140-5p inhibits cellular proliferation, migration and invasion by downregulating AKT/STAT3/NF-κB pathway in breast carcinoma cells

MicroRNA-140-5p (miR-140-5p) plays a pivotal role in human cancers. However, its role and molecular mechanisms in breast carcinoma are not fully explored. Using miR-140-5p transfected breast cancer cell line MDA-MB-231, several in vitro experiments were performed and described in this paper. They consist of the cell proliferation assay, wound healing assay, transwell assay, colony formation assays and qRTPCR. Expression levels of target proteins were determined using western blotting. In addition, experiments on animal models were performed to study the possible role of miR-140-5p in tumorigenesis of breast carcinoma cells. The induction of experimental breast tumor in mice model was achieved through the incorporation of MDA-MB-231 tumor cells subcutaneously into the middle left side of the mice. The results showed that miR-140-5p up-regulation significantly suppresses proliferation, cellular invasion and migration of breast carcinoma cells. Furthermore, miR-140-5p up-regulation stops breast cancer cells at G0/G1 phase. The results of the animal model indicated that up-regulation of miR-140-5p suppresses its tumorigenic ability. Moreover, we also found that miR-140-5p up-regulation reduces the phosphorylation level of STAT3, p65, and AKT. In addition, miR-140-5p overexpression significantly decreases CDK2 expression while increasing E-cadherin expression level. These data revealed that miR-140-5p suppressed tumor progression of breast carcinoma cells through inhibition of the AKT/STAT3/NF-κB pathway. Taken the present study results together, we can conclude that miR-140-5p may act as a novel target in microRNA-targeting anticancer strategy for the treatment of breast cancer

Physical and virtual water transfers for regional water stress alleviation in China

Water can be redistributed through, in physical terms, water transfer projects and virtually, embodied water for the production of traded products. Here, we explore whether such water redistributions can help mitigate water stress in China. This study, for the first time to our knowledge, both compiles a full inventory for physical water transfers at a provincial level and maps virtual water flows between Chinese provinces in 2007 and 2030. Our results show that, at the national level, physical water flows because of the major water transfer projects amounted to 4.5% of national water supply, whereas virtual water flows accounted for 35% (varies between 11% and 65%at the provincial level) in 2007. Furthermore, our analysis shows that both physical and virtualwater flows do not play a major role in mitigating water stress in the water-receiving regions but exacerbate water stress for the water-exporting regions of China. Future water stress in the main water-exporting provinces is likely to increase further based on our analysis of the historical trajectory of the major governing socioeconomic and technical factors and the full implementation of policy initiatives relating to water use and economic development. Improving water use efficiency is key to mitigating water stress, but the efficiency gains will be largely offset by the water demand increase caused by continued economic development. We conclude that much greater attention needs to be paid to water demand management rather than the current focus on supply-oriented management

Stock Market Prediction via Deep Learning Techniques: A Survey

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction

A multifunctional tripodal fluorescent probe for the recognition of Cr3+, Al3+, Zn2+ and F− with controllable ESIPT processes

Three 4-(benzo[d]thiazol-2-yl)-2,5-dihydroxybenzaldehyde fluorophores were introduced to construct a tripodal multifunctional ESPIT fluorescence probe L. The fluorescent analysis revealed that probe L exhibited excellent recognition capabilities towards Cr3+, Al3+, Zn2+ and F− ions with large Stokes shifts. Furthermore, under optimal conditions, the detection limit of probe L towards Cr3+, Al3+, Zn2+ and F− were low, of the order of 10−8 M, which indicated that probe L was sensitive to these four ions. Interestingly, the fluorescent and 1H NMR titration experiments revealed that the recognition mechanism of probe L towards the ions Cr3+, Al3+, Zn2+ and F− were different. The presence of Cr3+ and Al3+ recovered the ESIPT, but the presence of Zn2+ trigger a moderate deprotonation of the phenolic OH and induced an ESIPT red-shifted (60 nm) emission wavelength. Finally, the presence of F− completely deprotonated the free phenolic OH and a remarkable red-shifted (130 nm) ESIPT emission was observed. In other words, the ESIPT process of probe L is controllable. Furthermore, the utility of probe L as a biosensor in living cells (PC3 cells) towards Cr3+, Al3+ and Zn2+ ions has been demonstrated

The NLRP3 inflammasome is involved in resident intruder paradigm-induced aggressive behaviors in mice

Background: Aggressive behaviors are one of the most important negative behaviors that seriously endangers human health. Also, the central para-inflammation of microglia triggered by stress can affect neurological function, plasticity, and behavior. NLRP3 integrates stress-related signals and is a key driver of this neural para-inflammation. However, it is unclear whether the NLRP3 inflammasome is implicated in the development of aggressive behaviors.Methods: First, aggressive behavior model mice were established using the resident intruder paradigm. Then, aggressive behaviors were determined with open-field tests (OFT), elevated plus-maze (EPM), and aggressive behavior tests (AT). Moreover, the expression of P2X7R and NLRP3 inflammasome complexes were assessed by immunofluorescence and Western blot. The levels of NLRP3 and inflammatory cytokines were evaluated using enzyme-linked immunosorbent assay (ELISA) kits. Finally, nerve plasticity damage was observed by immunofluorescence, transmission electron microscope, and BrdU staining.Results: Overall, the resident intruder paradigm induced aggressive behaviors, activated the hippocampal P2X7R and NLRP3 inflammasome, and promoted the release of proinflammatory cytokines IL-1β in mice. Moreover, NLRP3 knockdown, administration of P2X7R antagonist (A804598), and IL-1β blocker (IL-1Ra) prevented NLRP3 inflammasome-driven inflammatory responses and ameliorated resident intruder paradigm-induced aggressive behaviors. Also, the resident intruder paradigm promoted the activation of mouse microglia, damaging synapses in the hippocampus, and suppressing hippocampal regeneration in mice. Besides, NLRP3 knockdown, administration of A804598, and IL-1Ra inhibited the activation of microglia, improved synaptic damage, and restored hippocampal regeneration.Conclusion: The NLRP3 inflammasome-driven inflammatory response contributed to resident intruder paradigm-induced aggressive behavior, which might be related to neuroplasticity. Therefore, the NLRP3 inflammasome can be a potential target to treat aggressive behavior-related mental illnesses

Three-point Step Size Gradient Method with Relaxed Generalized Armijo Step Size Rule

Based on differences of points and differences of gradients over the most recent three iterations, together with the Taylor's theorem, two forms of the quasi-Newton equations at the recent iteration are constructed. By using the two forms of the quasi-Newton equation and the method of least squares, three-point step size gradient methods for solving unconstrained optimization problem are proposed. It is proved by using the relaxed generalized Armijo step size rule that the new method is of global convergence properties if the gradient function is uniformly continuous. Moreover, it is shown that, when the objective function is pseudo-convex (quasi-convex) function, the new method has strong convergence results. In addition, it is also shown under some suitable assumptions that the new method is of super-linear and linear convergence. Although multi-piont information is used, TBB has the feature of simplicity, low memory requirement and only first order information being used, the new method is very suitable for solving large-scale optimization problems. Numerical experiments are provided and the efficiency, robustness and analysis of TBB are confirmed

A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

One-class support vector machine (OCSVM) is an efficient data-driven mineral prospectivity mapping model. Since the parameters of OCSVM directly affect the performance of the model, it is necessary to optimize the parameters of OCSVM in mineral prospectivity mapping. Trial and error method is usually used to determine the “optimal” parameters of OCSVM. However, it is difficult to find the globally optimal parameters by the trial and error method. By combining OCSVM with the bat algorithm, the intialization parameters of the OCSVM can be automatically optimized. The combined model is called bat-optimized OCSVM. In this model, the area under the curve (AUC) of OCSVM is taken as the fitness value of the objective function optimized by the bat algorithm, the value ranges of the initialization parameters of OCSVM are used to specify the search space of bat population, and the optimal parameters of OCSVM are automatically determined through the iterative search process of the bat algorithm. The bat-optimized OCSVMs were used to map mineral prospectivity of the Helong district, Jilin Province, China, and compared with the OCSVM initialized by the default parameters (i.e., common OCSVM) and the OCSVM optimized by trial and error. The results show that (a) the receiver operating characteristic (ROC) curve of the trial and error-optimized OCSVM is intersected with those of the bat-optimized OCSVMs and (b) the ROC curves of the optimized OCSVMs slightly dominate that of the common OCSVM in the ROC space. The area under the curves (AUCs) of the common and trial and error-optimized OCSVMs (0.8268 and 0.8566) are smaller than those of the bat-optimized ones (0.8649 and 0.8644). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets predicted by the common and trial and error-optimized OCSVMs account for 29.61% and 18.66% of the study area respectively, and contain 93% and 86% of the known mineral deposits. The mineral targets predicted by the bat-optimized OCSVMs account for 19.84% and 14.22% of the study area respectively, and also contain 93% and 86% of the known mineral deposits. Therefore, we have 0.93/0.2961 = 3.1408 < 0.86/0.1866 = 4.6088 < 0.93/0.1984 = 4.6875 < 0.86/0.1422 = 6.0478, indicating that the bat-optimized OCSVMs perform slightly better than the common and trial and error-optimized OCSVMs in mineral prospectivity mapping