esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Teaching Path Planning with DRL
The output is the next knowledge point to be learned. The procedure of the PPO algorithm is shown in Algorithm 1:  
```
1:Initial policy parameters \(\theta_{0}\) and value function parameters \(\varphi_{0}\)
2:for\(k=1\) to \(N\)do
3:for\(i=1\) to \(M\)do
4: Run policy \(\pi_{k}=\pi(\theta_{k})\) in environment for T time steps
5: Compute advantage estimates \(\hat{A}_{i}\) based on the current value function \(V_{\varphi_{k}}\)
6:endfor
7: Collect set of trajectories \(D_{k}=\tau_{i}\)
8: Update the policy by maximizing the objective: \[\theta_{k+1}=arg\underset{\varphi}{max}\frac{1}{|D_{k}|T}\sum_{\tau\in D_{k} }\sum_{t=0}^{T}min\] \[(\frac{\pi_{\theta}(a_{t}|s_{t})}{\pi_{\theta_{k}}(a_{t}|s_{t})}A^ {\pi_{\theta_{k}}}(S_{t},a_{t}),g(\varepsilon,A^{\pi_{\theta_{k}}}(S_{t},a_{t })))\] via stochastic gradient ascent with Adam
9: Fit value function by regression on mean-squared error: \[\varphi_{k+1}=arg\underset{\varphi}{min}\frac{1}{|D_{k}|T}\sum_{ \tau\in D_{k}}\sum_{t=0}^{T}\] \[grade=\sum_{i=0}^{K}P_{i}\]0 via stochastic gradient ascent with Adam
10:endfor
```  
**Algorithm 1** PPO-Clip  
* First, we initialize various parameters and then train through K iterations. In a training iteration, collecting samples is the primary activity, which uses an actor neural network to generate a strategy to capture a period of an environmental change trajectory. In our algorithm, the actor neural network used to generate the strategy and the critic neural network used to calculate the value are both LSTM. The input of the actor is the sequence of the observation space of the whole class, the knowledge points of the previous teaching, and the time interval from the previous teaching. The output is the currently selected teaching knowledge point sequence. After receiving the knowledge that it is about to be taught, the environment model returns the teaching observation and reward. At the time of sample collection, the specific data of the sample are the current observation, the knowledge point for teaching, the observation after teaching, and the reward obtained. * According to the collected data, the advantage value is calculated through a critic neural network. The input of the critic is the same as the actor, and the output value is used to calculate the advantage. Then, we train the actor neural network based on Line 8. * Then we use Line 9 to train the critic neural network. * Looping the above steps \(k\) times is a complete training process. It is worth mentioning that we used the same random projection method as in [20] to reduce the input dimension so that a larger number of knowledge points can be processed.
## 3 Results
### Effect of Salt Stress on the Transcriptome of A.__mongholicus__Leaves__
#### 3.3.2 GO and KEGG Enrichment of DEGs  
In the GO functional analysis of all comparison groups (screening conditions: FDR \(<\) 0.05 and 1 log\({}_{2}\)(Fold Change) \(\mid\)\(>\) 1), the differential gene enrichment was as follows: in the DT-Treat_vs_DT-CK group, 6600 differential genes were identified, and 42 GO terms were significantly enriched, including 22, 2, and 18 terms enriched in the BP, CC, and MF categories, respectively. Biological processes were primarily associated with cellular  
Figure 3: Venn diagram showing differentially expressed genes.  
Figure 2: Physiological and biochemical changes in ‘LQ’ and ‘DT’ _A.__mongholicus_ under salt stress. Note: (**A**) SOD, (**B**) POD, (**C**) CAT, (**D**) MDA, (**E**) H\({}_{2}\)O\({}_{2}\), (**F**) OFR, (**G**) Soluble protein, (**H**) Soluble sugar, (**I**) Proline, (**J**) Plant height, (**K**) Root length, (**L**) Root thickness, and (**M**) Fresh weight. SOD: superoxide dismutase; POD: peroxidase; CAT: catalase; MDA: malondialdehydedehyde; H\({}_{2}\)O\({}_{2}\): hydrogen peroxide; OFR: oxidized free radicals; different lower-case letters in the graphs indicate significant differences (\(p<\) 0.05).  
processes, metabolic pathways, and stress responses. Protein complexes and cellular anatomical structures were significantly enriched within cellular components, whereas molecular functions were primarily related to binding and catalytic activities (Figure 4A).  
In the LQ-Treat_vs_LQ-CK group, 9255 differential genes were identified, and 44 GO terms were significantly enriched, including 22, 2, and 20 terms enriched in the BP, CC, and MF categories, respectively. Biological processes were significantly enriched in cellular processes, metabolic pathways, and stress responses. Protein complexes and cellular anatomical structures were the primary enriched cellular components, whereas molecular functions were primarily related to binding and catalyzing activities (Figure 4B).  
In the KEGG pathway annotation, the DT-Treat_vs_DT-CK group showed 5891 out of 6600 differential genes annotated to 143 pathways, with significant enrichment in pathways such as metabolic pathways (ko01100, 1134 genes), biosynthesis of secondary metabolites (ko01110, 688 genes), and starch and sucrose metabolism (ko00500, 123 genes), along with phytohormone signaling (ko04075, 139 genes) and motor protein pathways (ko04814, 87 genes) (Figure 5A).  
Figure 4: GO enrichment histogram. Note: (**A**) DT-Treat_vs_DT-CK and (**B**) LQ-Treat_vs_LQ-CK.  
In the LQ-Treat_vs_LQ-CK group, 7887 out of 9255 differential genes were annotated to 141 pathways, with significant enrichment in pathways such as metabolic pathways (ko01100, 1541 genes), biosynthesis of secondary metabolites (ko01110, 907 genes), and starch and sucrose metabolism (ko00500, 148 genes), along with plant hormone signaling (ko04075, 207 genes) and the MAPK signaling pathway (ko04016, 130 genes), among others (Figure 5B).
## 1 Introduction  
Semiconducting lead halide perovskites (LHPs) with an ABX\({}_{3}\) structure are potential semiconductor materials for use in high-performance electronic, photonic, and biosensing devices because of their outstanding physic characteristics, ease of processing, and low cost. Additionally, by adjusting the proportion of all elements of the A-site, B-site, and X-site in perovskite materials, their emission peaks can be tuned. They can exist in the form of perovskite or as perovskite-like crystals. For example, the emission wavelength range of MA (methylammonium)-based lead halide perovskite light-emitting diodes is 410 to 780 nm, achieved by changing the ratio of halide (Cl, Br, and I) in perovskite's active layer [1; 2; 3; 4; 5; 6; 7; 8; 9]. Therefore, the \"perovskite red wall\" issue was addressed in 2017 [7; 8; 9]. Similarly, the \"perovskite ultraviolet wall\" problem also exists.  
To solve the \"perovskite red wall\" issue, the mixed-cation compound FAPbl\({}_{3}\)-CsPbI\({}_{3}\) (FA\({}^{+}\) being too large and Cs\({}^{+}\) being too small) material system emerged. Initially, the two-dimensional (2D), phenylethylamine (PEA)-based perovskite received more attention due to its better material stability in ambient atmosphere environments than 3D perovskite materials, owing to van der Waals forces in the 2D Ruddlesden-Popper (RP)-phase perovskite of the A-site phenylethylamine cation (PEA\({}^{+}\)) [11; 12; 13]. In the \"perovskite ultraviolet wall\" case, the bandgap widened when the PEA-based halide was introduced into the perovskite material due to the quantum size effect and formed the quasi-2D perovskite material. Therefore, in this study, we employed (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl perovskite to grow on c-face sapphire (0001) substrates with a 2 \(\upmu\)m thick AlN epilayer prepared via MOCVD. A peak shift in emission wavelength from 400 nm to 372 nm was observed due to the strain force caused by the lattice mismatch [14]. Additionally, AlN and (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl perovskite are wide bandgap materials. On the other hand, AlN can be applied to UV devices for disinfection and high-power components for automobiles. During component operation, the remaining power is passed to the surrounding environment and semiconductor material itself in the form of heat. This causes the temperature of the component to increase locally, which worsens electronic characteristics such as mobility, saturation velocity, and conductivity. Because the thermal conductivity of AlN is very good, it is very suitable as a substrate material [15]. Thus, the relationships between contact behaviors and device applications are important. By utilizing the tunable bandgap of perovskite materials, those with emissions in the near-UV region and AlN exhibit similar optical and electronic characteristics. Therefore, we also investigated the contact mechanism between Ag and (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl on an AlN/sapphire substrate heterostructure.
## 3 Results
### Phases Observed in U-Mo Prior to Irradiation
Specimens prepared from the grain boundary also revealed a non-uniform decoration by thin platelet-shaped U-C (carbides) phases, as shown in Figure 5a with an iso-concentration map of carbon (C). A 1D profile was taken across a grain boundary containing a carbide precipitate (Figure 5b), which shows clear enrichment of carbon, suggesting a U-C phase. Grain boundaries without U-C precipitates (shown in Figure 5c) show a slight depletion of Mo and a corresponding increase in U. Measurement of enrichment was carried out across the grain boundary to ensure that the presence of carbide phases does not affect enrichment in the fuel plate. The 1D profile indicates a uniform enrichment of 235U across the grain boundary with and without carbide precipitates. This specimen prepared from the grain boundary also showed the presence of \(\alpha\)-U phases adjacent to Mo-enriched \(\gamma\)-U-Mo phase, reiterating previous findings. Figure 4: 1D profile taken across different features observed in a single tip from corresponding cylinder. (**a**) Mo-enriched U-Mo and \(\alpha\)-U (**b**) and \(\alpha\)-U sandwiched between Mo-enriched \(\gamma\)-U-Mo. (**c**) \(\gamma\)’-U\({}_{2}\)Mo, \(\alpha\)-U, and Mo-enriched \(\gamma\)-U-Mo, and (**d**) across the grain boundary, Mo-enriched \(\gamma\)-U-Mo phase. (grey: U, red: Mo, green: Si).
## 2 Materials and Methods
### Statistical Evaluation of Satellite Precipitation Products  
Different methods were used to compare the 3B42 V7 and IMERG V05 products with the gauge precipitation data from the Taferiat station, depending on temporal evolution by considering (sub-hourly, daily, monthly, and yearly) time steps. However, both studied satellite products have different spatial and temporal resolutions of (0.25\({}^{\circ}\)/3 h 3B42 V7 and (0.1\({}^{\circ}\)/30 min for IMERG V05), respectively, while the rain gauge station is located at the basin outlet and provides 10-min precipitation measurements of precipitation and runoff. For a reliable comparison, the used method is to plot the point precipitation data from the gauges to the same grid scale as the SPPs, either by spatial interpolation or simply by calculating the average. In addition, ref. [39] pointed out that the interpolation can lead to uncertainties due to systematic errors of the gauge density. Therefore, in our case, a direct comparison was used, extracting the numerical data from the SPPs and measuring the precipitation, adjusting them simultaneously to compare them to each other.  
Furthermore, to evaluate the ability of the SPPs to estimate extreme rainfall events, it was decided to use them as input data in the HEC-HMS hydrological model. Indeed, the rainfall measurement station is not precise and poorly distributed spatially, especially in the mountainous regions of the High Atlas, which is a real issue for research work on hydrological modeling and flood forecasting. Consequently, it is important to evaluate the rainfall estimated by the satellites to demonstrate their ability to provide significant information and to approve their use as an alternative source of rainfall measurement data, especially during extreme precipitation events.
## 4 Parametric Study FEM Buried Pipe under Haul Road
### Analysis of Results
#### 4.3.2 Trench Width and Pipe Bedding Height  
On the other hand, it is important to point out that there are other parameters which can increase the stresses on the pipe: the width of the trench and the height of the pipe bedding.  
Diagrams of the circumferential displacements on a 36'' pipe are shown in Figure 24 with an amplification factor of 5. It is observed that, when the trench width parameter is modified, the deflection increases as its width increases and, therefore, the stresses also increase, becoming closer to the allowable stress on the pipe. It is thus important to consider trenches with the minimal possible width, which also benefits the costs. However, it should be considered that it is necessary to compact the layers of the lateral backfill of the pipe with a compaction equipment, so that a suggested minimal adequate width, from the lateral wall of the pipe to the lateral wall of the trench, could be 300 mm.  
On the other hand, bedding heights of 1/12 OD and 1/6 OD has been considered in the model. The results show that increasing the bedding height reduces the deflection pipe. Figure 25 shows the effect of the bedding height. This effect is not very relevant, so one of the conclusions of this study is that is not necessary to increase more than 1/12 OD the bedding height.  
It is important to note that the bedding height of the pipe is relevant during pipe installation only if the pipe is installed with internal wooden supports (struts) that prevent the pipe from deflecting due to its own weight. This fact is more significant for pipes with a very high diameter/thickness ratio.
## 2 Mathematical Model
### Constraints--Disposal Facility  
The number of constraints related to the disposal facility is \(N+4\) such that \(N+1\) of them are nonlinear, and three of the constraints are box constraints.  
\[\sum_{i\in\mathcal{Z}}p_{i,j}x_{i,j}-p_{max}y_{j}\leq 0,\quad j\in \mathcal{N} \tag{26}\] \[d_{CA}-g(p_{max},d_{DT})=0\] (27) \[p_{max}\in\left[p_{max}^{low},p_{max}^{up}\right]\] (28) \[d_{CA}\in\left[d_{CA}^{low},d_{CA}^{up}\right]\] (29) \[d_{DT}\in\left[d_{DT}^{low},d_{DT}^{up}\right] \tag{30}\]  
The constraints (26) and (27) are the nonlinear constraints of this model. The constraint (26) ensures that the heat power of the canisters disposed is allowable while the constraint (27) defines the dependence between the variables \(d_{CA}\), \(p_{max}\), and \(d_{DT}\). In our case, this nonlinear function \(g:\mathbb{R}^{2}\rightarrow\mathbb{R}\) is approximated with a piece-wise linear function (see Appendix A). Finally, the box constraints (28)-(30) give lower and upper bounds for variables \(p_{max}\), \(d_{CA}\), and \(d_{DT}\), respectively.  
Finally, we give some boundaries for the variables:  
\[x_{i,j}\geq 0,\quad z_{i,j}\geq 0\quad\text{for all}\quad i\in \mathcal{Z},\,j\in\mathcal{N},\] \[y_{j}\geq 0\quad\text{for all}\quad j\in\mathcal{N},\] \[e_{ON}^{j}\in\{0,1\},\quad e_{OFF}^{j}\in\{0,1\},\quad e_{j}\in \{0,1\}\quad\text{for all}\quad j\in\mathcal{N}\] \[s_{i,j}\in\{0,1\},\quad r_{i,j}\in\{0,1\}\quad\text{for all}\quad i \in\mathcal{Z},\,j\in\mathcal{N}.\]  
To conclude, the model has nine objectives such that 6 are nonlinear and 3 are linear. The rest of the dimensions of the model are depending on two parameters: the number of periods \(N\) and the number of the removals from the reactor \(Z\). Number of constraints is \(5(N(Z+1)+1)+2Z\), where are \(Z(5N+2)+4N+1\) linear, \(N+1\) nonlinear and 3 box constraints. The total number of variables is \(4N(Z+1)+3\) and \(N(2Z+1)+3\) of them are non-negative continuous variables and \(N(2Z+3)\) are binary variables. Evidently, with any realistic values of \(N\) and \(Z\), for example \(N=19\) and \(Z=11\) when one period is five years, the size of the problem will come quite large.
## 2 System
### Offset Adjustment
\begin{array}{l}\sum\limits_{n=0}^{N}\left(Ax_{n}^{4}+By_{n}^{2}x_{n}^{2}+ Cz_{n}^{2}x_{n}^{2}+Dx_{n}^{3}y_{n}+Ey_{n}z_{n}x_{n}^{2}+Fz_{n}x_{n}^{3}+Gx_{n}^{3}+ Hy_{n}x_{n}^{2}+Iz_{n}x_{n}^{2}\right)=\sum\limits_{n=0}^{N}x_{n}^{2}\\ \sum\limits_{n=0}^{N}\left(Ax_{n}^{2}y_{n}^{2}+By_{n}^{4}+Cz_{n}^{2}y_{n}^{2}+ Dx_{n}y_{n}^{3}+Ey_{n}^{3}z_{n}+Fz_{n}x_{n}y_{n}^{2}+Gx_{n}y_{n}^{2}+Hy_{n}^{3}+ Iz_{n}y_{n}^{2}\right)=\sum\limits_{n=0}^{N}y_{n}^{2}\\ \sum\limits_{n=0}^{N}\left(Ax_{n}^{2}z_{n}^{2}+By_{n}^{2}z_{n}^{2}+Cz_{n}^{4}+ Dx_{n}y_{n}z_{n}^{2}+Ey_{n}z_{n}^{3}+Fz_{n}^{3}x_{n}+Gx_{n}z_{n}^{2}+Hy_{n}z_{n}^{ 2}+Iz_{n}^{3}x_{n}\right)=\sum\limits_{n=0}^{N}z_{n}^{2}\\ \sum\limits_{n=0}^{N}\left(Ax_{n}^{2}y_{n}+By_{n}^{3}x_{n}+Cz_{n}^{2}x_{n}y_{n}+ Dx_{n}^{2}y_{n}^{2}+Ey_{n}^{2}z_{n}^{3}x_{n}+Fz_{n}x_{n}^{2}y_{n}+Gx_{n}y_{n}^{2}x_{n}+ Iz_{n}x_{n}y_{n}\right)=\sum\limits_{n=0}^{N}x_{n}y_{n}\\ \sum\limits_{n=0}^{N}\left(Ax_{n}^{2}y_{n}z_{n}+By_{n}^{3}z_{n}+Cz_{n}^{3}y_{n}+ Dx_{n}y_{n}^{2}z_{n}+Ey_{n}^{2}z_{n}^{2}+Fz_{n}^{2}x_{n}y_{n}+Gx_{n}y_{n}z_{n}+ Hy_{n}^{2}z_{n}+Iz_{n}x_{n}y_{n}\right)=\sum\limits_{n=0}^{N}y_{n}z_{n}\\ \sum\limits_{n=0}^{N}\left(Ax_{n}^{2}z_{n}+By_{n}^{2}z_{n}^{2}x_{n}+Cz_{n}^{3}x_ {n}y_{n}+Dx_{n}y_{n}^{2}z_{n}+Ey_{n}^{2}z_{n}^{2}+Fz_{n}^{2}x_{n}y_{n}+Gx_{n}y_{ n}z_{n}+Hy_{n}^{2}z_{n}+Iz_{n}^{2}x_{n}y_{n}\right)=\sum\limits_{n=0}^{N}y_{n}z_{n} \\ \sum\limits_{n=0}^{N}\left(Ax_{n}^{2}z_{n}^{2}+By_{n}^{2}z_{n}^{2}x_{n}+Cz_{n}^ {3}x_{n}+Dx_{n}^{2}y_{n}z_{n}+Ey_{n}z_{n}^{2}x_{n}+Fz_{n}^{2}x_{n}^{2}+Gx_{n}^{ 2}x_{n}+Hy_{n}z_{n}x_{n}+Iz_{n}x_{n}\right)=\sum\limits_{n=0}^{N}z_{n}x_{n}\\ \sum\limits_{n=0}^{N}\left(Ax_{n}^{2}y_{n}+By_{n}^{3}+Cz_{n}^{2}y_{n}+Dx_{n}y_{ n}^{2}+Ey_{n}^{2}z_{n}^{2}+Fz_{n}x_{n}y_{n}+Gx_{n}y_{n}+Hy_{n}^{2}+Iz_{n}y_{n} \right)=\sum\limits_{n=0}^{N}y_{n}\\ \sum\limits_{n=0}^{N}\left(Ax_{n}^{2}z_{n}^{2}+By_{n}^{2}z_{n}+Cz_{n}^{3}+Dx_{n} y_{n}z_{n}+Ey_{n}^{2}z_{n}^{2}+Fz_{n}^{2}x_{n}+Gx_{n}z_{n}+Hy_{n}z_{n}+Iz_{n}^{2}x_{n} \right)=\sum\limits_{n=0}^{N}z_{n}\\ \sum\limits_{n=0}^{N}\left(Ax_{n}^{2}z_{n}^{2}+By_{n}^{2}z_{n}^{2}+Cz_{n}^{3}+ Dx_{n}y_{n}z_{n}+Ey_{n}z_{n}^{2}+Fz_{n}^{2}x_{n}+Gx_{n}z_{n}+Hy_{n}z_{n}+Iz_{n}^{2}x_{n} \right)=\sum\limits_{n=0}^{N}z_{n}\\ \end{array} \right. \tag{14}\]  
In Equation (14), by expanding the left-hand side and factoring out the nine parameters \(A\) to \(I\) from the summation, it can be expressed as the following matrix using \(A\) to \(I\). Note that the summation \(\sum_{n=0}^{N}\) applies to all elements in the matrix shown below; however, it is omitted here for simplicity. \[\left[\begin{array}{cccccccc}x_{n}^{4}&y_{n}^{2}x_{n}^{2}&z_{n}^{2}x_{n}^{3} y_{n}&y_{n}x_{n}^{2}x_{n}^{2}&z_{n}x_{n}^{3}&x_{n}^{3}&y_{n}x_{n}^{2}&z_{n}x_{n}^{2}\\ x_{n}^{2}y_{n}^{2}&y_{n}^{4}&z_{n}^{2}y_{n}^{4}&z_{n}y_{n}^{2}&y_{n}^{2}z_{n
# Pathophysiologyiology and Treatment of Soriasis: From Clinical Practice to Basic Research
## 3 Pathogenesis of Psoriasis
### Cell Signaling Pathways Related to Soriasis  
The pathogenesis of psoriasis is intricately linked to T cell immunity. The disease process begins with the activation of antigen-presenting cells (APCs) within the skin, such as dermal dendritic cells and epidermal Langerhans cells, in response to external or internal antigens, including skin trauma or infection. Upon activation, these APCs migrate from the skin to nearby lymph nodes, where they present antigens to naive CD45 RA+ T cells, initiating T cell activation [40]. Then, T cells migrate into the affected skin and release cytokines that drive keratinocyte hyperproliferation, disrupt normal cellular differentiation, and perpetuate inflammatory responses through positive feedback loops in cell signaling. This cascade results in the sustained formation of psoriatic skin lesions. Therefore, cell signaling pathways are central to the pathogenesis of psoriasis. Understanding these pathways is crucial for developing effective therapeutic approaches, as they represent key intervention points in managing the disease. Figure 1 is a depiction of the current psoriasis cell signaling pathway.
# Research on Gangue Detection Algorithm Based on Cross-Scale Feature Fusion and Dynamic Pruning
## 5 Conclusions  
This paper proposes an improved algorithm, YOLOv5s, for coal gangue detection on a conveyor belt. The aim is to address challenges such as low contrast in coal gangue, low recognition accuracy, susceptibility to false negatives, and difficulties in hardware deployment due to the large weight of existing models.  
In the YOLOv5s backbone network, ConvNext is utilized to ensure more comprehensive feature extraction for small targets. Optimizing the feature pyramid network, the improved BiFPN structure is tailored to better align with the YOLOv5s algorithm, thereby enhancing the multiscale feature fusion in the network. During anchor box detection, the ESIOU loss function is employed to expedite the convergence speed of the model. Finally, dynamic channel pruning is applied to eliminate redundant channels, reducing both the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Weight/Mb** & **mAP/\%** & **Inference Time/ms** \\ \hline SSD & 94.8 & 65.1 & 34.9 \\ Faster-RCNN & 206.7 & 81.8 & 67.4 \\ YOLOv3 & 123.5 & 84.8 & 40.8 \\ YOLOv5s & 13.7 & 83.7 & 15.3 \\ Textual algorithm & 8.8 & 85.5 & 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of results of different algorithms.  
Figure 9: Visual comparison of model detected results. (**a**) Detection results of yolov5s. (**b**) Improved detection results of yolov5s.  
model's weight and computational load, thereby achieving network lightweighting. The experimental results indicate a notable improvement in the proposed coal and gangue detection algorithm compared to the original one. The model weight is 8.8 Mb, reduced by 38.8%, achieving a detection accuracy of 93.5%. The detection time for a single image is 9.8 ms, with a 56.2% increase in inference speed. The algorithm meets the recognition and detection requirements for coal and gangue on industrial conveyor belts, facilitating the subsequent deployment for the application of robotic arms to grab and sort.  
Conceptualization, H.W. and P.F.; methodology, H.W. and P.F.; software, H.W.; validation, H.W.; formal analysis, H.W.; investigation, H.W.; resources, P.F.; data curation, H.W.; writing--original draft preparation, H.W.; writing--review and editing, H.W.; visualization, H.W.; supervision, X.M.; project administration, X.M.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanghai Nature Foundation, grant number 21 ZR1425800, and the National Nature Foundation, grant number 5217237.  
Data available on request from the authors.  
The authors declare no conflicts of interest.