esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Phases
#### 4.2.2 Nitrides  
As described in Section 3.4, the GH weld showed the lowest content of nitrides and no nitrides in some cases, despite having longer times at the critical temperature ranges; the GR weld showed the highest amount of nitrides and the FR weld was in between. These differences observed in terms of nitride content in the welds can be explained by the difference in cooling rates (Figure 7), influencing the nitrogen diffusion from ferrite to austenite. In addition, the level of micro-inclusions might play a role acting as nucleation sites for austenite and pinning the ferrite grain boundaries preventing grain growth, thus shortening the distance for nitrogen and other elements to diffuse.  
To illustrate the importance of the latter phenomenon, in the GR weld, it was observed that the root pass (GTAW) contained significant amounts of nitrides after only 6 s in the critical temperature range, whilst the second weld pass (GMAW) did not present nitrides. The cooling rate (based on the cooling time t\({}_{12/8}\)) of both passes was the same (38 \({}^{\circ}\)C/s). In this case, when cooling rates and therefore the time for nitrogen diffusion is the same, it is expected that larger ferrite grain sizes implies a longer distance for nitrogen to diffuse until reaching the austenite. Thus, precipitation of nitrides was found in the GTAW weld with a larger grain size. There was an absence of nitrides in the as-deposited GH welds, and there were very few nitrides in the reheated weld passes, despite having longer times inthe critical range compared with the other welds. This is explained by a low cooling rate (Figure 7), which allows more nitrogen diffusion to take place.
## 2 Forward Equations
### Coupled Equations  
It is observed that the Green's function matrix \(\mathbf{G}\) can be eliminated from the reflection- and transmission-based representations by subtracting Equation (13) from Equation (10). This leads to  
\[\underbrace{\begin{pmatrix}\mathbf{O}\\ \mathbf{O}\\ \mathbf{O}\\ \mathbf{O}\\ \mathbf{O}\\ \end{pmatrix}}_{\mathbf{O}}=\frac{1}{2}\begin{pmatrix}\mathbf{I}&-\mathcal{R }_{ll}\mathcal{Z}&-\mathcal{T}_{ll}\mathcal{Z}&\mathbf{O}\\ -\mathcal{R}_{ll}\mathcal{Z}&\mathbf{I}&\mathbf{O}&-\mathcal{T}_{ll}\mathcal{Z }\\ -\mathcal{T}_{ll}\mathcal{Z}&\mathbf{O}&\mathbf{I}&-\mathcal{R}_{ll}\mathcal{Z }\\ \mathbf{O}&-\mathcal{T}_{ll}\mathcal{Z}&-\mathcal{R}_{ll}\mathcal{Z}&\mathbf{I} \\ \end{pmatrix}}_{\mathbf{G}^{(5)}}\underbrace{\begin{pmatrix}\mathbf{F}_{ll}^{-} \\ \mathcal{Z}\mathbf{F}_{ll}^{+}\\ \mathcal{Z}\mathbf{F}_{l}^{-}\\ \mathcal{Z}\mathbf{F}_{l}^{-}\\ \end{pmatrix}}_{\mathbf{F}}. \tag{16}\]  
Here, we divided by a factor 2 to achieve a better amplitude balance with the matrices that were derived in the previous sections. After the substitution of Equation (9), we obtain  
\[\underbrace{\frac{1}{2}\begin{pmatrix}\mathcal{R}_{ll}\mathbf{F}_{ll}^{+}\\ -\mathcal{Z}\mathbf{F}_{ll}^{+}+\mathcal{T}_{ll}\mathbf{F}_{ll}^{-}\\ \mathcal{R}_{l}\mathbf{F}_{ll}\mathbf{F}_{ll}\\ -\mathcal{Z}\mathbf{F}_{ll}^{-}+\mathcal{T}_{ll}\mathbf{F}_{ll}^{+}\\ \end{pmatrix}}_{\mathbf{G}^{(5)}}=\underbrace{\frac{1}{2}\begin{pmatrix} \mathbf{I}&-\mathcal{R}_{ll}\mathcal{Z}&-\mathcal{T}_{ll}\mathcal{Z}&\mathbf{ O}\\ -\mathcal{R}_{ll}\mathcal{Z}&\mathbf{I}&\mathbf{O}&-\mathcal{T}_{ll}\mathcal{Z}\\ -\mathcal{T}_{ll}\mathcal{Z}&\mathbf{O}&\mathbf{I}&-\mathcal{R}_{ll}\mathcal{Z }\\ \mathbf{O}&-\mathcal{T}_{ll}\mathcal{Z}&-\mathcal{R}_{ll}\mathcal{Z}&\mathbf{I} \\ \end{pmatrix}}_{\mathbf{A}_{Cou}}\mathbf{G}^{(5)}\underbrace{\begin{pmatrix} \mathbf{F}_{ll}^{+}\\ \mathcal{Z}\mathbf{F}_{ll}^{+}\\ \mathcal{Z}\mathbf{F}_{ll}^{-}\\ \end{pmatrix}}_{\mathbf{F}_{m}}. \tag{17}\]  
We refer to 17 as a system of coupled equations, which can be interpreted as another inverse problem for \(\mathbf{F}_{m}\). Although we have increased the number of rows signficantly (up to 16,384 in our running example) by not applying the window operator \(\Theta^{[r]}\), the matrix \(\mathbf{A}_{Cou}\) is still rank-deficient, as shown in Figure 2(a) (blue curve). However, adding the rows of either \(\mathbf{A}_{Mar}\) or \(\mathbf{A}_{Aux}\) to the rows of \(\mathbf{A}_{Cou}\) results in a full-rank matrix, as illustrated by the magenta and cyan curves in Figure 2(b). An intuitive understanding of this observation is that the subtraction of Equation (13) from Equation (10) (which was required for the construction of \(\mathbf{A}_{Cou}\)) has reduced the row space of our system matrix, which can be compensated for by adding complementary rows from either the Marchenko or auxiliary system.
## Nomenclature  
\begin{tabular}{l l l l} \(V_{an},V_{bn},V_{ca}\) & Line to neutral voltages & \(I^{\prime}{}_{r}\) & Motor rotor current \\ \(Ia,Ib,Ic\) & Three phase current & \(I_{m}\) & Motor excitation current \\ \(Ia_{0},Ia_{1},Ia_{2}\) & Sequence currents (zero, positive, negative, respectively) & \(\eta_{syn}\) & Synchronous motor speed (rpm) \\ \(V_{a0},V_{a1},V_{a2}\) & Sequence voltages (zero, positive, negative, respectively) & \(J_{th,peak}\) & Peak threshold value \\ \(R_{1}\) & Motor stator resistance & \(\eta_{m}\) & Motor speed (rpm) \\ \(R_{2}\)\({}^{\prime}\) & Motor-reflected rotor resistance & \(V_{\phi}\) & Motor Phase Voltage \\ \(X_{1}\) & Motor stator reactance & \(fe\) & Electrical frequency (50 Hz) \\ \(X_{2}\)\({}^{\prime}\) & Motor-reflected rotor reactance & \(P\) & Motor number of poles \\ \(X_{m}\) & Motor excitation reactance & \(H\) & Healthy condition \\ \(I_{s}\) & Motor stator current & \(F\) & Faulty condition (single phasing) \\ \(v(t)\) & Vibration measured signal & & \\ \end{tabular}
## 3 Results
### Volatile Compounds of the Parmigiano Reggiano Cheese (SPME-GC-MS Results)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compound** & **Retention Time (min)** & **Relative Abundance** \\ \hline
**Esters** & & \\ \hline Pentanoic acid, 4-methyl-, ethyl ester & 13.940 & 6297 \\ Heptanoic acid, ethyl ester & 18.147 & 30,246 \\ Octanoic acid, ethyl ester & 20.505 & 2,238,440 \\ Methyl 5-acetyl-2-methoxybenzoate & 25.046 & 88,972 \\ Ethanol, 2-nitro-, propionate (ester) & 26.477 & 90,749 \\ Pentanoic acid, heptyl ester & 27.884 & 77,044 \\ Decanoic acid, ethyl ester & 28.673 & 5,507,764 \\ Propanoic acid, 2-methyl-, ethyl ester & 30.985 & 52,758 \\ Propanoic acid, 2-methyl-, methyl ester & 31.080 & 18,892 \\ p-Chlorophenyl benzylcarbamate & 31.435 & 20,258 \\ Propanoic acid, 2-methyl-, propyl ester & 36.069 & 7322 \\
1,2-Benzenedicarboxylic acid, bis(2-methylpropyl) ester & 53.361 & 819,636 \\ \hline
**Acids** & & \\ \hline Propanedio acid, dihydroxy- & 3.020 & 238,653 \\ Acetic acid & 23.015 & 7,740,104 \\ Propanoic acid & 26.330 & 180,981 \\ Butanoic acid & 28.938 & 43,581,336 \\ Pentanoic acid, 3-methyl- & 30.092 & 287,245 \\ Butanoic acid, 3-methyl- & 30.429 & 453,424 \\ Pentanoic acid & 31.843 & 11,805,742 \\ Propanedio acid, propyl- & 32.294 & 6,824,393 \\
8-Chlorocapric acid & 32.527 & 17,089 \\ Hexanoic acid & 35.248 & 53,899,542 \\ Heptanoic acid & 38.295 & 1,046,044 \\ Octanoic acid & 40.970 & 11,434,240 \\ Nonanoic acid & 43.615 & 226,077 \\ n-Decanoic acid & 46.123 & 1,912,506 \\
9-Decenoic acid & 47.788 & 65,217 \\ Dodecanoic acid & 52.601 & 51,535 \\
2-(Heptyloxycarbonyl)benzoic acid & 53.390 & 6489 \\ Benzoic acid & 53.717 & 49,625 \\ \hline
**Hydrocarbons** & & \\ \hline
1-Heptene, 5-methyl- & 2.390 & 153,314 \\ Decane, 2,2-dimethyl- & 2.432 & 14,203,627 \\ Propane, 2-(ethanyloxy)- & 3.586 & 1,158,015 \\
2,2,4,4-Tetramethyloctane & 4.330 & 463,598 \\ Ether, 2-ethylhexyl tert-butyl & 5.783 & 101,048 \\ D-Limonene & 11.659 & 32,651 \\ Hexadecane & 27.475 & 10,458 \\ Heptadecane, 2,6,10,15-tetramethyl- & 30.522 & 32,718 \\ Tridecane, 1-iodo- & 30.526 & 36,630 \\ Eicosane & 30.538 & 80,505 \\
1H-Indene, 1-methylene- & 31.415 & 2155 \\ \hline
**Maillard products** & & \\ \hline Pyrazine, 2,6-dimethyl- & 17.539 & 50,361 \\
2,3,5-Trimethyl-6-ethylpyrazine & 24.335 & 32,674 \\ \hline
**Miscellaneous** & & \\ \hline N-Hydroymethyl-2-phenylacetamide & 36.638 & 14,590 \\
1-Gala-1-ido-octose & 38.358 & 1145 \\
3,4-Anhydro-d-galactosan & 52.615 & 2442 \\
2-Propanol, 1-chloro-, phosphate (3:1) & 58.172 & 192,862 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The histogram depicted the major compounds from the Parmigiano Reggiano cheese as described above. There was a clear distinction in the flavor profiles between the undegraded and the degraded cheese samples.
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 4 Experimental Results
### MFCC vs. Log Filter Bank Energy  
We compared the performance of two commonly-used features in speech recognition. The first one is mel-frequency cepstral coefficients (MFCCs) and the other one is log filterbank energies. Figure 8 shows the performance differences of four phoneme groups according to the change of window sizes. Blue lines are MFCC results and orange lines are the results of log filter bank energy features [37,38,52]. In most cases, blue lines are below orange lines, showing less phoneme recognition errors. Based on these results, we chose MFCC in the phoneme group models and the overall model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**File** & **Begin** & **End** & **Text/Phoneme** \\ \hline \hline \multirow{3}{*}{sa.txt} & \multirow{3}{*}{0} & \multirow{3}{*}{46797} & She had your dark \\  & & & suit in greasy \\  & & & wash water all \\  & & & year. \\ \hline \hline \multirow{3}{*}{sa1.phn} & 0 & 3050 & h\# \\  & 3050 & 4559 & sh \\  & 4559 & 5723 & ix \\ \cline{1-1}  & 5723 & 6642 & hv \\ \cline{1-1}  & 6642 & 8772 & eh \\ \cline{1-1}  & 8772 & 9190 & dcl \\ \cline{1-1}  & 9190 & 10,337 & jh \\ \cline{1-1}  &... &... &... \\ \cline{1-1}  & 40,313 & 42,059 & y \\ \cline{1-1}  & 42,059 & 43,479 & in \\ \cline{1-1}  & 43,479 & 44,586 & arr \\ \cline{1-1}  & 44,586 & 46,720 & h\# \\ \hline \hline \end{tabular}
\end{table}
Table 4: An example of the phonetic transcripts in the TIMIT corpus. The audio file is “dr1/rcjf0/sa1.wav” in the provided CD-ROM, and its sentence transcript is “sa1.txt” in the same directory. The phonetic transcript file is “sa1.phn”, and full timings of phonemes are provided as well.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phoneme** & **Fricatives** & **Affricates** & **Stops** & **Nasals** & **Vowels/Mixed** \\ \hline
**Train set** & 21,424 & 2031 & 22,281 & 14,157 & 75,257 \\
**Core test set** & 1114 & 95 & 1114 & 736 & 3955 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Phoneme token counts of the TIMIT train and core test sets, grouped by phoneme classes.
# Multi-Source Satellite Imagery and Machine Learning
## 6 Ablation Analysis
### Individual Parameter Sets and Classifiers  
Geographic parameters (DPGs) provide the best overall performance with 51.53% accuracy and 40.35% MCC achieved with XGBoost.  
The spectrometric parameters (PSOLIs) are also efficient, although less affluent than PG.  
PCA parameters (PPCAs) perform the worst in accuracy and MCC, but they can still improve performance when combined with other sets.  
Figure 12: Histogram showing the results for each type of geological data, based on parameter fusion sets and the classifiers applied.  
By comparing the datasets of geographic parameters (DGPs), OLI spectrometric parameters (DOLIPs), and PCA parameters (DPCAPs), the goal is to identify the data or combinations of data that most influence classification performance. Algorithm performance is evaluated using criteria such as accuracy, MCC (Matthews Correlation Coefficient), and execution time.  
Geographic Settings (DGPs) achieve the best accuracy results, with Gradient Boosting reaching 51.53%, followed by Random Forest (49.14%) and XGBoost (47.78%).  
OLI spectrometric parameters (DOLIPs) achieve the best accuracy with Random Forest (52.59%), outperforming Gradient Boosting and XGBoost in accuracy and MCC.  
PCA parameters (DPCAPs) show lower accuracy in all algorithms, with Random Forest remaining the best performer in this category, with an accuracy of 41.98%.  
Regarding execution time, Random Forest is the fastest on all datasets, followed by XGBoost and Gradient Boosting.  
Table 10 provides valuable insight into the relative impact of different datasets on classification performance. It shows that DGP and DOLIP ensembles are more effective at improving classification results than DPCAP, with Random Forest consistently performing across all datasets.
## 2 Study Site and Available Data
### Available Data
#### 2.2.1 Meteorological Information  
Records from the Refugio Poqueria weather station (PG2, [64]) were used in the study at the plot scale. This weather station is equipped with different sensors that generate 5 min data records pertaining to precipitation, solar radiation, long-wave radiation, wind velocity, temperature, air humidity, and atmospheric pressure. Data aggregated on a daily scale were used in this study.  
At the catchment scale, meteorological daily information from the three nearest weather stations (Figure 1, small blue dots) has been interpolated by spatial algorithms that consider topographic effects and aggregated at the catchment scale. Specific spatial interpolation algorithms for each of the available variables were used [65]. Precipitation-elevation linear gradients have been locally defined for each variable (i.e., precipitation, temperature, and radiation) by using historical datasets and a least square error method. Thereafter, the residuals were calculated by using linear gradients. The square inverse distance weighting (IDW2) was used for interpolation by using the three closest stations and added to the theoretical value calculated using the linear gradient [65; 66; 67]. This data has been used for computing average meteorological information at the catchment scale.
## 3 Results
### Site Water Balance and Efficiency  
Summarising these results to estimate the water balance for each site (Tables 2 and 3) shows accumulated annual rainfall of 4139.2 m\({}^{3}\) ha\({}^{-1}\) at site **a**, and 4199.2 m\({}^{3}\) ha\({}^{-1}\) at site **b** for the 2017/2018 cropping season. The accumulated AET using the CMRSET algorithm was 6686.7 m\({}^{3}\) ha\({}^{-1}\) for site **a**, and 7294.4 m\({}^{3}\) ha\({}^{-1}\) for site **b**. Using the ETLook algorithm, the estimates of accumulated AET were 4051.9 m\({}^{3}\) ha\({}^{-1}\) for site **a**, and 4066.0 m\({}^{3}\) ha\({}^{-1}\) for site **b**. Clearly, the annual estimates for ETLook were, again, lower than for the CMRSET algorithm. As a result, the overall cumulative deficits for the ETLook estimates were quite small, suggesting irrigation might not be needed if rainfall were to be harvested effectively. Using the reported water use by NSW DPIE, the calculated efficiencies were site **a** = 74% (no active on-farm dam), efficiency site **b** = 38.3% (active on-farm dam) (CMRSET). The efficiencies for ETLook were, of course, very small, and due to the negative estimates of the overall deficit not easily calculated.  
Figure 4: Average mean daily values by month for reference ET (Etr, blue bars) and AET for site **a** (orange bars) and site **b** (green bars) using the CMRSET approach (**left**) and ETLook algorithm (**right**) indicating much lower estimates of AET from the ETLook algorithm compared with the CMSRET approach.  
This points to a difficulty of calculating efficiencies based on cumulative rainfall for a month and ignoring any possible runoff losses. Clearly in some of the months with high rainfall totals, almost none of this rainfall was captured for effective evapotranspiration. As a result, it is also difficult to calculate efficiencies by month.  
Figure 5: Monthly sum of AET for study site **a** using the CMRSET approach and ETLook algorithm. Colour scale for predicted AET ranges from 0–150 mm.  
Figure 6: Monthly sum of AET for study site **b** using the CMRSET approach and ETLook algorithm. Colour scale for predicted AET ranges from 0–150 mm.
## 3 Numerical Simulations of Gas Explosion along Pipelines with Abrupt Cross-Section Area Changes
### Change Laws of Explosion Temperature in the Pipe  
Figure 8 shows the change in peak temperature with respect to the propagation distance from the ignition source for different diameter pipes with fuel volumes of 0.18 m\({}^{3}\), 0.23 m\({}^{3}\), and 0.28 m\({}^{3}\). Taking the simulation results for a 0.18 m\({}^{3}\) fuel volume and 0.36 m diameter pipe, before the abrupt cross-section change, the peak temperature at measuring point 1 is 2426 K. As the propagation distance increases, the peak temperature gradually in  
Figure 7: Comparison of numerical simulations and experimental results.  
creases to 2499 K. After an abrupt increase in cross-section area (measuring points 3 and 4), the peak temperature decreases sharply. Then, after an abrupt decrease in cross-section area, the peak temperature changes from 1872 K (measuring point 6) to 1591 K (measuring point 7). From measuring points 7-10, the peak temperature decreases from 1591 K to 1363 K. The peak temperature clearly decreases at the two abrupt changes in the cross-section area. This trend demonstrates that the counter-current effect formed by the interaction between pressure wave and reflection wave at these two places restrains the propagation of the flame. This indicates that an abrupt change in cross-section pipe area has a certain influence on flame propagation characteristics [35].  
Considering Figure 8a,c, for a constant pipe diameter of 0.36 m, a larger fuel volume results in a greater decrease in peak temperature. For a constant fuel volume of 0.18 m\({}^{3}\), a larger ratio of abrupt cross-section area change induces a more obvious decline in peak temperature.
# CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR
Moving forward, the BiLSTM network extracts temporal features, allowing the model to understand dependencies and prioritize different parts of the sequence effectively through a time attention mechanism. Finally, a fully connected layer is used to identify the modulation mode. To evaluate the way in which this was performed in the CNN-BiLSTM-DNN framework under challenging conditions with low signal-to-noise ratios, extensive testing was conducted on the RML2016.10b and RML2016.10a public datasets. The main contributions of the CNN-BiLSTM-DNN framework proposed in this paper are as follows:  
* We combine a CNN, BiLSTM, a DNN, and an attention mechanism in a hybrid neural network architecture to leverage their complementarity and synergy for extracting and classifying spatiotemporal features. The CNN is used to learn the spatial features of I/Q signals. The BiLSTM network can extract bidirectional time series features in the time dimension and effectively avoid the problems of gradient explosion and gradient vanishing, and fully connected (FC) deep neural networks achieve effective feature classification. * The signal preprocessing (SP) module is used to process the original I/Q signal, which effectively filters out additive white Gaussian noise and lays a solid foundation for subsequent feature extraction. By including the attention mechanism module in the model, it is possible to elevate the model's representation capabilities, minimize the interference caused by invalid targets, enhance the target of interest's recognition effect, and ultimately elevate the model's overall performance. The structure of this study is as follows: the signal model and the signal preprocessing methods used in this work are introduced in the second part. The structure of the convolutional bidirectional long short-term memory deep neural networks suggested in this paper is shown in the Section 3, along with a detailed explanation of each module's makeup and purposes. Section 4 describes and analyzes the experimental setup and results. The Section 5 concludes with a summary of the study and looks at the advantages and disadvantages of the recommended approach.
# A Semantic Segmentation Method with Emphasis on the Edges for Automatic Vessel Wall Analysis
## 1 Introduction
Disentangling the edge information from the fused features is essential for improving the performance of the CNN architecture. In this work, we proposed a two-stream deep network architecture entitled EVSegNet, which emphasizes the edges for vessel segmentation. One stream incorporates the Dense Upsampling Convolution (DUC) and Hybrid Dilated Convolution (HDC) for texture feature extraction, which can process multiscale objects. Meanwhile, another stream focuses on the feature extraction of edge information. The outputs of the two streams will be ultimately fused. The proposed EVSegNet was evaluated and compared with the standard U-Net and modified U-Net to validate the effectiveness of vessel wall segmentation based on MR vessel wall images. In summary, the major contribution of this work is that we propose a fully automated vessel wall segmentation used for MR vessel wall images that emphasizes edge information. The paper is organized as follows. The materials and methods are introduced in Section 2. Sections 2.1-2.5 describe how to segment the vessel wall. Section 2.6 is implementation. Next, results are given in Section 3. We then discuss the results in Section 4. Finally, the conclusion is given in Section 5.
# Inhibition of _Tp53_ Mutant Oral Cancer by Reactivating p53
## 1 Introduction  
Many types of cancers exhibit p53 mutations [1]. Since p53 is important for cellular apoptosis, cancer cells with p53 mutations may be resistant to chemotherapy [2]. In particular, oral carcinomas have a much higher incidence of p53 mutations [3, 4]. Thus, a treatment strategy focused on the functional recovery of mutant p53 could be a promising approach [5]. Chemical chaperones can bind to proteins and induce conformational changes [6]. Mutant p53 may hide the amino acids which are important for its phosphorylation and acetylation [7]. Chemical chaperone-induced conformational changes may expose these amino acids which have not been exposed in the mutant state.  
p53 is a transcription factor involved in the regulation of apoptosis-associated gene expression [1]. These genes are involved in p53-dependent apoptosis [1]. To induce the expression of apoptosis-associated genes, p53 needs to be phosphorylated and acetylated in the cytoplasm and translocated into the nucleus [7, 8]. Many class I histone deacetylase (HDAC) inhibitors have shown anticancer effects by increasing the acetylation of important proteins, including p53 [9].  
The chemical chaperone, 4-hexylInscrinol (4 HR), can induce dormancy in microorganisms against environmental stress [10]. Chronic administration of 4 HR per os can preventspontaneous tumour development in experimental animals [11]. 4 HR induces cellular apoptosis in cancer cells [12]. 4 HR has been recognised as a class I HDAC inhibitor [13]. In addition, 4 HR can increase endoplasmic reticulum (ER) stress which may be due to the induction of protein conformational changes [14]. Increased ER stress following 4 HR administration may induce p53 independent apoptosis [15]. Therefore, 4 HR treatment can induce cancer cell apoptosis in a p53-dependent as well as independent manner. Phosphorylation of p53 is closely associated with p53 function [16]. Acetylation of p53 increases its DNA-binding affinity and activates target gene expression [17]. Cancer cells having mutations in the p53 DNA binding domain may show difficulty in activation of target gene expression. Therefore, increased phosphorylation and acetylation will be important markers for evaluating the functional recovery of p53 in _TP53_ mutant cancer. However, phosphorylation and acetylation levels of p53 after 4 HR treatment in _TP53_ mutant cancer has not been studied.  
The objectives of this study were to (1) evaluate phosphorylation and acetylation levels of p53 after 4 HR treatment in p53 mutant oral cancer cells, (2) evaluate HDAC activity after 4 HR treatment in p53 mutant oral cancer cells, and (3) evaluate the expression levels of apoptosis-associated proteins after 4 HR treatment in p53-silenced oral cancer cells.