ABSTRACT Pancreatic adenocarcinoma accounts for 90% of pancreatic cancer cases, the deadliest kind. PC patients' poor immunotherapy, chemotherapy, and other responses lead to a generally failed treatment strategy. Thus, understanding molecular processes is essential for creating novel PC therapies. The natural chemical andrographolide (ADG) from Andrographis paniculata shows anticancer properties against various cancer types. The method by which ADG fights pancreatic cancer is unknown. In PC cell lines, ADG inhibited cell proliferation and migration, caused G0/G1 phase arrest, and caused cell death due to reactive oxygen species, iron accumulation, malondialdehyde production, and glutathione (GSH) exhaustion. Ferrostatin‐1 inhibited ADG‐induced cell death. A molecular docking investigation demonstrated that ADG directly binds to heat shock protein 90 (HSP90). ADG suppresses HSP90 expression, and tanespimycin prevents ADG‐induced cytotoxicity, showing that HSP90 is ADG's main target in activating intracellular activities. Tests using immunoprecipitation, degradation, and in vitro ubiquitination showed that the ADG‐HSP90 pair targeted and broke down glutathione peroxidase 4 (GPX4), allowing it to be tagged for destruction. ADG also reduced cell development, caused apoptosis, increased reactive oxygen species and iron, synthesized malondialdehyde, depleted glutathione, and ubiquitinated and degraded GPX4. In subcutaneous in vivo tumors, ferroptosis caused by ADG inhibits tumor development. HSP90 is a new ADG target. After connecting to and complexing with HSP90, ADG targeted and deleted GPX4, triggering ferroptosis in PC. The findings strongly suggest that ADG may treat PC. ADG's pharmacokinetics and other effects must be studied in patients' clinical trials to make it a pancreatic cancer therapy option.
According to the empirical mode decomposition (EMD) theory, a prediction method of support vector machine (SVM) is proposed based on particle swarm optimization. The ensemble EMD method is used to decompose the signal into some intrinsic mode function components which are taken as the input of the SVM to predict the data. All the predicted values are combined, and the weak signals submerged in chaos background, including the transient signal and periodic signal, are detected from the prediction error. Lorenz attractor and the data from the McMaster IPIX radar sea clutter database are used in the simulation. The results show that the proposed method can effectively detect the weak target from chaotic signal. When the signal-to-noise ratio is 102.8225 dB in the chaotic noise background, by using the new method the root mean square error can be reduced by five orders of magnitude, reaching 0.00000033092, while the conventional SVM can reach only 0.049 under the condition of -54.60 dB and the weak target detected in sea clutter has the harmonic characteristics, which shows the prediction model has a lower threshold and error.
In this paper, the adaptive time delay estimation algorithm based on wavelet transform is presented by combing the multiresolution wavelet analysis and LMS algorithm. Furthermore, the validity of this algorithm is studied to estimate the time delay of the chirp signal which is under the chaotic environment. The experimental result shows that the time delay could be estimated precisely despite the SCR (signal to chaotic signal ratio) below -40dB. When this algorithm is applied to the chaotic background which includes Gaussian white noise (SNR=-5dB and SCR=-12dB), the time delay could also be estimated precisely, but the performance of the algorithm drops.
Additional file 5:Promoter analysis of maize ARFs. Auxin response elements are shown in the list. (XLS 20 KB)
This study proposed a quasi-order-based temporal data structure (QOTDS) which differed from conventional, algebraic data management models. Based on this QOTDS, a temporal data index called the temporal quasi-order index (TQOindex) was established. Firstly, the study proposed the concepts of temporal quasi-order (TQO) and linear order partitioning (LOP) of time period sets and discussed the construction algorithm of LOP and the optimum (minimum) properties. On this basis, a temporal data structure was established based on LOP. This structure realized the set-at-a-time data operation-like relational data structure and improved the inquiry efficiency by using multiple threads. Subsequently, in the structural framework of TQO, we discussed the temporal data index (TQOindex) based on quasi-order extensions. This index was effectively applicable to various conventional database platforms depending on the disk (external memory)-based data management and also to big data dynamic index technology relying on the incremental updating mechanism. Finally, a corresponding experimental simulation and comparative evaluation were designed to verify the feasibility and effectiveness of TQOindex. Research and experiments showed that QOTDS were effective at temporal inquiry and management in cases involving the temporal processing and integration mechanisms in new data, such as semantic data, XML data, and moving object data.
The increasing scale of network access in recent years requires an advanced analysis of future network data traffic(NDT) trends to anticipate various network scenarios. However, current prediction methods are limited in accuracy due to the overly complex components of network traffic on the time scale. This paper proposes a time series network traffic prediction(NTP) method based on the ARIMA-LSTM model. The method uses the ARIMA to extract the linear components of the web traffic data and then uses the LSTM to extract the nonlinear components further. It will achieve effective prediction of complex time series. The article validates the effectiveness of the method experimentally on two publicly available datasets. All experimental results show that the ARIMA-LSTM method has satisfactory prediction performance and outperforms several other state-of-the-art works. In sum, this NTP method points to a new orientation for NDT monitoring and analysis.
Weak signal detection has always been a hot spot in the signal processing field. In this paper, the chaotic and large data size characteristics of sea clutter are analyzed, the advantage of the Long and Short Term Memory network (LSTM) is taken to design a weak signal detection method based on deep learning. The reconstructed phase space signal is used as the input of LSTM network, the length of training data is determined by embedding dimension and delay time, and a chaotic prediction model is established to detect weak signals from the prediction error. In order to improve the detection performance, reduce the missing rate of deep learning method for small feature signal, frequency domain conversion of the prediction error is conducted, the spectrum of the prediction error of different distance gates is compared to locate the coordinates of the weak signal. The experimental results show that the sea clutter detection method based on LSTM prediction error frequency domain conversion has strong applicability and higher accuracy, and the detection performance is improved by 30%.
The current thunderstorm monitoring methods ignore the nonlinear and non-stationary characteristics of atmospheric electric field signals, which has a negative effect on the monitoring results. Based on complementary ensemble empirical mode decomposition with adaptive noise and Savitzky-Golay filtering (CEEMDAN-SG), a point charge localization method for the thunderstorm cloud is proposed. After CEEMDAN is used to decompose the electric field signal into a series of intrinsic mode function (IMF) components, the signal is reconstructed after SG filtering of those noise-dominant components. Then, the reconstructed signal is used for point charge localization correction. By changing the signal samples, the decomposition order and SNR of CEEMDAN-SG, CEEMDAN, etc. are compared, and the performance of the method is analyzed. Experiments show that compared to the SNR before reconstruction, the SNR after reconstruction is improved by about 3%. At the same time, the results can match the radar chart well on the time scale. After using the CNN-LSTM network model, it is found that compared with the original signal, the amplitude of the absolute error of the reconstructed signal is larger, so that the signal characteristics can be more displayed. This once again proves the localization effects.
Additional file 2:Sequence identity matrix of maize ARF proteins. BioEdit program were employed to examine sequence identity of 31 maize ARF proteins.a (XLS 24 KB)
Sea clutter is a kind of signal with low signal-to-noise ratio. Fractional Fourier transform is used to gather energy. Fractional Brownian motion model is introduced to model sea clutter. Based on the measured data of IPIX radar, high-order multi-fractal parameters are calculated. Fitting the multi-fractal parameters, choosing the steepest interval and calculating its slope. At the same time, choosing the fractal parameters with scale -30 under HH and VV polarization. The small targets are 0 and 1, respectively. The slope and high-scale fractal parameters of different sea conditions are normalized, and the normalized data are predicted by logistic regression. The simulation results show that FRFT can aggregate the energy of sea clutter signal. Logistic regression model can predict the fractal parameter data of sea clutter FRFT domain, and the prediction accuracy reaches 83.42%.
Atmospheric electric field signal (AEFS) usually superimposes low-frequency noise, which has a negative effect on thunderstorm monitoring. A thunderstorm prediction method based on Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) is proposed. Firstly, AEFS is divided into useful, baseline and noise components by BEADS. After getting the estimated value of the useful signal, the denoised signal is obtained. Then, the AEFS prediction model is built based on BiLSTM. After inputting the AEFS spatial features extracted by CNN into the model, a CNN-BiLSTM hybrid model for thunderstorm prediction is formed. After analyzing the performance of the method, we carried out the experiment in thunderstorm weather. Results show that the SNR of AEFS processed by BEADS is improved effectively. It's worth noting that the determining coefficients before and after BEADS are all above 94.12%, showing a good effect. Finally, the effectiveness of the method is proved again by the coincidence between the predicted results and the radar chart.
This paper analyzes some problems existing in the experiment teaching of universities,compares every kind of solution scheme,and analyses the open teaching laboratory management strategy.The solution scheme for management mode of open laboratory is also presented.In order to cultivate the practicing ability and innovative thinking ability of students,to improve the personal ability and whole level for teachers,and to strengthen the efficiency of laboratory on external competitive ability,different strategies for students,teachers and laboratories are put forward respectively.
Addressing the limitations of manually extracting features from small maritime target signals, this paper explores Markov transition fields and convolutional neural networks, proposing a detection method for small targets based on an improved Markov transition field. Initially, the raw data undergo a Fourier transform, feature fusion is performed on the series, and a spectrogram is generated using Markov transition fields to extract radar data features from both the time domain and frequency domain, providing a more comprehensive data representation for the detector. Then, the InceptionResnetV2 network is employed as a classifier, setting decision thresholds based on the softmax layer’s output, thus achieving controllable false alarms in the detection of small maritime targets. Additionally, transfer learning is introduced to address the issue of sample imbalance. The IPIX dataset is used for experimental verification. The experimental results show that the proposed detection method can deeply mine the differences between targets and the maritime clutter background, demonstrating superior detection performance. When the observation time is set to 1.024 s, the IMIRV2 detector performs best. Cross-validation with different data preprocessing methods and classification models reveals a significant advantage in the performance of the IMIRV2 detector, especially at low signal-to-noise ratios. Finally, a comparison with the performance of existing detectors indicates that the proposed method offers certain improvements.
