Evodiamine induces extrinsic and intrinsic apoptosis of ovarian cancer cells via the mitogen-activated protein kinase/phosphatidylinositol-3-kinase/protein kinase B signaling pathways

AbstractObjectiveTo explore the effects of evodiamine on ovarian cancer cells and the mechanisms underlying such effects.MethodsHuman ovarian cancer cells HO-8910PM were treated with evodiamine at 0, 1.25, 2.5, and 5 μM for 1-4 d. 3-(4,5-Dimethiylthiazol2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was used to detect the growth inhibition rate of evodiamine-treated HO-8910PM cells. The cell cycle was observed via propidium iodide (PI) staining. Apoptosis induction was assessed via Annexin V-fluorescein isothiocyanate/propidium iodide (Annexin V-FITC/PI) double staining assay. To verify the mechanism of apoptosis, caspase-dependent apoptotic pathway-related protein was detected by Western blot analysis. The expression levels of mitogen-activated protein kinase (MAPK) and/or phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt) pathway-related proteins were also investigated.ResultsEvodiamine significantly inhibited the proliferation of HO-8910PM cells in a dose- and time-dependent manner. Evodiamine induced G2/M arrest with an increase of cyclin B1 level, and promoted cell apoptosis with a decrease of B cell lymphoma/lewkmia-2 (Bcl-2) and an increase of Bcl-2-associated X protein (Bax) level. In addition, evodiamine treatment led to the activation of caspase-8, caspase-9, and caspase-3 and the cleavage of poly (ADP-ribose)-polymerase (PARP). Evodiamine targeted the MAPK and/or PI3K/Akt pathways by reducing the expression and activity of PI3K, Akt, and extracellular signal-regulated kinase mitogen-activated protein kinase (ERK1/2 MAPK) and the activity of p38 MAPK.ConclusionEvodiamine can inhibit the growth of ovarian cancer cells by G2/M arrest and intrinsic and extrinsic apoptosis. In addition, evodiamine-induced PI3K/Akt, ERK1/2 MAPK, and p38 MAPK signaling may be involved in cell death

Analysis of 126 hospitalized elder maxillofacial trauma victims in central China

Background: The aim of this study was to analyzed the characteristics and treatment of maxillofacial injuries in the elder patients with maxillofacial injuries in central China. Material and Methods: We retrospectively analyzed the characteristics and treatment of maxillofacial injuries in the patients over the age of 60 to analyze the trends and clinical characteristics of maxillofacial trauma in elder patients from the First Affiliated Hospital of Zhengzhou University (from 2010 to 2013) in central China and to present recommendations on prevention and management. Results: Of the 932 patients with maxillofacial injuries, 126 aged over 60 years old accounting for 13.52% of all the patients (male:female, 1.74:1; mean age, 67.08 years old). Approximately 52% of the patients were injured by falls. The most frequently observed type of injuries was soft tissue injuries (100%), followed by facial fractures (83.05%). Of the patients with soft tissue injuries, the abrasions accounted the most, followed by lacerations. The numbers of patients of midface fracture (60 patients) were almost similar to the number of lower face fractures (66 patients). Eighty two patients (65.08%%) demonstrated associated injuries, of which craniocerebral injuries were the most prevalent. One hundred and four patients (82.54%) had other systemic medical conditions, with cardiovascular diseases the most and followed by metabolic diseases and musculoskeletal conditions. Furthermore, the study indicated a relationship between maxillofacial fractures and musculoskeletal conditions. Only 13 patients (10.32%) sustained local infections, of whom had other medical conditions. Most of the facial injuries (85.71%) in older people were operated including debridement, fixing loose teeth, reduction, intermaxillary fixation and open reduction and internal fixation (ORIF). Conclusions: Our analysis of the characteristics of maxillofacial injuries in the elder patents may help to promote clinical research to develop more effective treatment and possibly prevent such injuries

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.Comment: Accepted by AAAI 2023, appendix included. 9 pages + 5 pages appendix, 12 figures, in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23), Feb 202

Response characteristics of root to moisture change at seedling stage of Kengyilia hirsuta

Kengyilia hirsuta is an important pioneer plant distributed on the desertified grassland of the Qinghai-Tibet Plateau. It has strong adaptability to alpine desert habitats, so it can be used as a sand-fixing plant on sandy alpine land. To study the response mechanisms of root morphological and physiological characteristics of K. hirsuta to sandy soil moisture, 10%, 25% and 40% moisture levels were set up through potted weighing water control method. The biomass, root-shoot ratio, root architecture parameters, and biochemical parameters malondialdehyde, free proline, soluble protein, indole-3-acetic acid, abscisic acid, cytokinin, gibberellin, relative conductivity and antioxidant enzyme activities were measured in the trefoil stage, and the response mechanisms of roots at different moisture levels were analyzed. The results showed that with the increase of soil moisture, root morphological indexes such as root biomass, total root length, total root volume and total root surface increased, while the root topological index decreased continuously. The malondialdehyde content, relative conductivity, superoxide dismutase activity, peroxidase activity, catalase activity, free proline content, soluble protein content, abscisic acid content and cytokinin content at the 25% and 40% moisture levels were significantly decreased compared with the 10% level (P< 0.05). Thus, the root growth of K. hirsuta was restricted by the 10% moisture level, but supported by the 25% and 40% moisture levels. An artificial neural network revealed that total root length, total root surface area, root link average length, relative conductivity, soluble protein, free proline and moisture level were the key factors affecting root development. These research results could contribute to future agricultural sustainability

RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through use of both synthetic data and real-world data. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently. We open-source the code, data, and floor plan at https://github.com/DataScienceLab18/IndoorToolKit

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

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve