The marked overexpression of cyclin‐dependent kinase 5 (CDK5) or Notch1 receptor, which plays critical roles in pancreatic ductal adenocarcinoma (PDAC) development, has been detected in numerous PDAC cell lines and tissues. Although, a previous study has demonstrated that CDK5 inhibition disrupts Notch1 functions in human umbilical vein endothelial cells, the mechanism underlying Notch1 activation regulated by CDK5 remains unclear. Herein, we identified a physical interaction between CDK5 and Notch1 in PDAC cells, with the Notch1 peptide phosphorylated by CDK5/p25 kinase. CDK5 blockade resulted in the profound inhibition of Notch signaling. Accordingly, CDK5 inhibition sensitized PDAC cell proliferation and migration following Notch inhibition. In conclusion, CDK5 positively regulates Notch1 function via phosphorylation, which in turn promotes cell proliferation and migration. The combinational inhibition of CDK5 and Notch signaling may be an effective strategy in the treatment of PDAC. CDK5 and Notch1 interact physically in PDAC cells. CDK5 blockade resulted in the profound inhibition of Notch signaling. CDK5 inhibition sensitized cells proliferation and migration following Notch inhibition. Roscovitine  was used to inhibit CDK5 kinase activity. The γ‐secretase inhibitor DAPT  was used to prevent Notch cleavage and nuclear entry. The inhibitors were diluted in dimethyl sulfoxide (DMSO), which was also used as a control.Human BxPC‐3 and HPAC PDAC cells were obtained from National Infrastructure of Cell Line Resource  and were grown in DMEM (Dulbecco's Modified Eagle's medium) supplemented with 10% fetal bovine serum , 100 units/mL penicillin, and 100 mg/mL streptomycin  at 37°C in humidified air with 5% CO2.2Cells were washed with cold PBS, collected by scraping, and lysed in lysis buffer  containing protease inhibitor cocktail . Cells were lysed on ice for 40 min, followed by centrifugation at 12000 × g for 40 min at 4°C, and the supernatant was removed. For immunoprecipitaion, 1 mg of protein was incubated with 2 μg of specific antibodies at 4°C overnight. Subsequently, 30 μL protein A/G agarose  was added, and samples were incubated at 4°C for 2 h. Beads were then washed five times using the lysis buffer. Between washes, the beads were collected by centrifugation at 1000 × g for 3 min at 4°C. The precipitated proteins were eluted from the beads by resuspending the beads in 2 × protein loading buffer and boiling for 10 min. Immunoblotting was then performed. For routine immunoblotting, cells were washed twice with cold PBS, then lysed in RIPA buffer. Equal amounts of protein were loaded onto SDS–PAGE and transferred onto polyvinydene fluoride (PVDF) blotting membranes. The membranes were blocked with 5% fat‐free milk then probed with indicated antibodies at 4°C. The primary antibodies were as follows: anti‐Notch1 , anti‐CDK5 , anti‐p35 , anti‐HA , and anti‐β‐actin . Members were then washed and incubated with anti‐rabbit or anti‐mouse horseradish peroxidase‐conjugated antibodies  for 1 h at room temperature. Signals were detected using a chemiluminescence Western Blot scanner . Protein levels were normalized to β‐actin levels and quantified using ImageJ software. Densitometry analyses of the blots for Figure 2.3® Green Realtime PCR Master Mix  and an Mx3000P instrument (Stratagene Laboratories). The primer sequences for qPCR are listed in Table Total RNA from HPAC and BxPC‐3 cells that had undergone indicated treatment was isolated using Trizol . For reverse transcription and polymerase chain reaction (RT‐PCR), first‐strand cDNA was synthesized form 1 μg of total RNA using SuperScript Kit  according to the supplier's manual. Quantitative RT‐PCR was performed using a SYBR2.4Wild‐type CDK5 was cloned into the pCDNA3.1 vector. Point mutations were established using the Q5 Site‐Directed Mutagenesis Kit  with primer pairs matching target regions. The following primers were used for mutagenesis: D144 N forward primer, 5’‐GAAATTGGCTAATTTTGGCCTGG‐3’ and reverse primer, 5’‐AGCTCCCCATTCCTGTTTATTAG‐3’; and K33 T forward primer, 5’‐GTGGCTCTGACACGGGTGAGG‐3’ and reverse primer, 5’‐GATCTCATGAGTCTCCCGG‐3’.siRNA specific for CDK5 was synthesized by Shanghai GenePharma Co., Ltd. . The CDK5 siRNA sequence was 5’‐GGGAGAUCUGCCUACUCAATT‐3’. The negative control siRNA (NcRNA) sequence was 5’‐UUCUCCGAACGUGUCACGUTT‐3’.2.5For cells immunostaining, cells were rinsed twice in PBS and fixed for 15 min in 4% paraformaldehyde/PBS at room temperature. After rinsing, permeabilization was achieved in 0.4% Triton X‐100/PBS for 20 min. Indicated antibodies incubation were performed in PBS overnight at 4°C, following blocking in 10% serum/PBS for 10 min. After being rinsed in PBS, cells were incubated with Alexa Fluor 488‐conjugated anti‐mouse IgG  and Alexa Fluor 594‐conjugated anti‐rabbit IgG  for 1 h at room temperature. Cells were DAPI‐treated to stain nuclei and mounted with anti‐fade medium. Images were captured using a confocal laser‐scanning microscope .Human pancreatic cancer tissue microarrays were purchased from Xi'an Alena Biotechnology Co., Ltd. of China. Following dewaxing, rehydration and antigen retrieval, the samples were stained with Notch1 and CDK5 antibodies at 4°C overnight. The samples were then incubated with Alexa Fluor 488‐conjugated anti‐rabbit IgG  and Alexa Fluor 594‐conjugated anti‐mouse IgG  for 1 h at room temperature. Nuclei were conterstained with DAPI. The stained tissues were visualized using a confocal laser‐scanning microscope .2.62, 1 mM EGTA, 0.4 mM EDTA, 0.25 mM DTT, 50 ng/μL BAS, and 100 µM ATP. The reactions were performed for 30 min at 32.6°C. Following the kinase assays, the Notch1 peptide was subjected to TiO2 affinity chromatography according to the manufacturer's manual . Liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) was performed using a Q Exactive mass spectrometer . The peptide‐based enzyme activity assay is a highly sensitive and convenient method.Assays were performed using 100 µM Notch1 peptide  and 7.8 nM CDK5/p25  in 5 mM MOPS (pH 7.2), 5 mM MgCl2.7http://gepia.cancer‐pku.cn/) was used to analyze Notch1, CDK5, p35, and p39 RNA sequencing expression data based on The Cancer Genome Altas (TCGA) and the Genotype‐Tissue Expression (GTEx) projects.The online database Gene Expression Profiling Interactive Analysis ‐2, 5‐diphenyltetrazolium bromide] (0.5 mg/ml) by viable cells to form purple formazan product after 2 h incubation. Experiments were performed in triplicate on indicated conditions, and repeated at least three times.2 incubator.For a colony assay, 1000 cells were plated in six‐well plates to assess the effect of CDK5 and/or Notch inhibitors on colony formation after 10 days of incubation at 37°C in 5% CO5 cells/well) and incubated for 24 h. A scratch was made using a 1‐mL pipette tip, followed by treatment of cells with indicated inhibitors. Images were captured at 0 and 48 h using an inverted microscope. ImageJ software was used to calculate the area of the scratch. Then, the percentage of wound closure was calculated and compared with that of the control.For a migration assay, HPAC cells were seeded in a six‐well plate , which predicts protein phosphorylation sites and potential kinase activity. This analysis revealed the presence of 29 CDK5 phosphorylation sites in human Notch1 with a score greater than 0.5. To determine whether CDK5 affects the phosphorylation of Notch1, we determined whether CDK5 could phosphorylate a Notch1 peptide  containing three potential CDK5 phosphorylation sites , respectively. The blockade of CDK5 by roscovitine markedly inhibited the viability of both BxPC‐3 and HPAC cells, as assessed by the MTT assay Figure . After pTo identify whether the regulation of Notch signaling was related to the kinase activity of CDK5, we treated HPAC cells with the enforced expression of two dominant‐negative CDK5 (dnCDK5) proteins with an HA tag. The dnCDK5 constructs were D144 N and K33 T kinase‐dead mutants that abolish the activation of endogenous CDK5 by p35 when overexpressed.3.4To determine the combined effect of CDK5 and Notch1 signaling on pancreatic cancer cells, we measured the effect of blocking CDK5 and/or Notch signaling on cell proliferation and migration. DAPT is a γ‐secretase inhibitor that inhibits the Notch signaling pathway. As several references have demonstrated that pharmacologic inhibition of CDK5 or Notch signaling had the same effect as knockdown of CDK53.5To verify the relevance of CDK5 signaling and Notch signaling in PDAC pathogenesis, we examined the expression and interaction between CDK5 and Notch1 in clinical PDAC specimens.Notch1, CDK5, p35, and p39 were overexpressed in pancreatic adenocarcinoma (PAAD) samples (Figure Based on the GEPIA database, we observed that s Figure . The higs Figure . Consists Figure . To inves Figure , suggest4In the present study, we unraveled the physical association between CDK5 and Notch1 and revealed their colocalization in the cytoplasm. During intracellular processing, full‐length precursor Notch1 is transported to the trans‐Golgi apparatus and then cleaved into two linked peptides.The inhibition of CDK5 by roscovitine significantly reduced the 120 kD cleavage product of Notch1 in both BxPC‐3 and HPAC cells, which resulted in the significant accumulation of full‐length Notch1 in HPAC cells Figure . The difThe inhibition of CDK5 decreased Notch1 cleavage but not Notch1 expression, further downregulating Notch1 signaling Figure . This inHigh expressions of CDK5, p35, or p39 and hyper‐activation of CDK5 signaling have been reported in several cancers, including breast, ovarian, and colorectal cancer.The authors have no conflict of interest to report.Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.


The obtained results show that the rheological stress of the alloy decreases with an increase in deformation temperature and increases with an increase in the strain rate. The alloy is completely dynamically recrystallized at 653 K, and the entire structure is formed of homogeneous crystals/grains, with small secondary phase particles distributed at the crystal boundary. The mean apparent activation energy of hot compression deformation is 153.5 kJ/mol. The Mg–8 wt.% Sn–1.5 wt.% Al alloy exhibits excellent plastic deformation properties, an expansive thermal processing interval, and a narrow instability zone under the test temperature and deformation rate. The optimal process parameters of the alloy comprise deformation temperatures between 603 and 633 K and strain rates of 0.03 to 0.005 s−1.Mg–Sn–Al alloy is a new type of heat-resistant magnesium alloy with great potential and the hot deformation process of this alloy is of great significance for its application. The microstructure, hot deformation behavior, textural evolution, and processing map of a Mg–8 wt.% Sn–1.5 wt.% Al alloy were studied. A Gleeble 1500 D thermo-mechanical simulator was used. The temperature of deformation was 653 to 773 K, the strain rate was 0.001–1 s They haeratures .2Sn phase [5RE [17Al12 phase, and due to the presence of Mg2Sn phase at the grain boundaries, the thermal properties and creep properties of the alloy are obviously improved; Kang D H, et al. [Tin is a commonly used metal in industry, and it has a relatively low price . Tin hasSn phase . It has ase [5RE . Owing tase [5RE ,15. Mg–Sase [5RE , so alumase [5RE . Elsayedase [5RE  investig, et al.  studied , et al.  studied , et al. . Therefo, et al. . To obta2 was used as the protective gas, as well as the tetrafluoroethane used as the cooling gas, and the ratio of them was 10:1. The crucibles were first preheated to 300 °C before the metals were added. Then, the ingots  were added in turn and heated at 750 °C, insulated for 30 min and stirred until the metals had completely melted. Finally, the melt was cooled in water.A Mg–8 wt.% Sn–1.5 wt.% Al ingot was cast by using magnesium (99.95 wt.%), tin (99.98 wt.%), and aluminium (99.95 wt.%). Ar−1. Sandpapers  were used to prepare the metallographic samples; after that, the samples were polished on the metallographic polishing machine (YMP-2)  for 35 s, with diamond polishing paste (1.0) coated on a polishing cloth. Electron backscattered diffraction (EBSD) samples were electrolytically polished  on the basis of mechanical polishing, and nitric acid (20%) alcohol solution was used for that. The metallographic microstructures were obtained by optical microscopy (Carl Zeiss 2000 MAT) , and the grain boundaries and textures were observed by scanning electron microscopy   instrument equipped with an EBSD system.Homogenization heat treatment  was performed in a chamber furnace (SX–G30103). For the compression test, to achieve height reduction of up to 60%, samples with 10 mm diameter and 15 mm height were placed on the gleeble–1500 D thermal analog testing machine ; the heating rate was 10 K/s, and the insulation time was 60 s. The experimental temperatures were 653, 693, 733, and 773 K, and the strain rates were 0.001, 0.01, 0.1, and 1 sThe stress–strain curves of the samples are shown in It can be found that the peak stress varies with the temperature and the deformation strain rate . The plaQ), temperature (T), and Zener–Hollomo parameters:Q is the deformation activation energy, R is the gas constant (8.314 J/K), and T is the absolute temperature (in K).Zener and Sellars ,24 use tThe abovementioned relationship can be expressed by the exponential, power exponential, and hyperbolic sinusoidal function models . For theAt the high-stress level:A1, A2, n1, α, and β are the material constants, which can be represented by the following relationship:α and n1 can be obtained from Equations (2) and (3). Taking the natural logarithm of Equations (2) and (3), respectively:n1 and β values can be obtained from Correct once again:Taking the natural logarithm of Equation (4):Then, taking the temperature as the independent variable to obtain partial differential:The two partial differentials in Equation (9) can be obtained according to N is 3.88, A is 4.32 × 109.Substitute α into Equation (8), and draw Constitutive equation of the alloy is:Q = 153.5 kJ/mol) and strain index (n = 3.88) of the alloy are obtained, which can verify and predict the machining properties of the alloy.Calculation using the constitutive equation shows that the deformation activation energy (P) absorbed per unit volume of the alloy during hot deformation is composed of two parts; one is heat dissipation (G), and the other is energy dissipation (J) related to the microstructure change during deformation. The relationship between them, the flow stress, and the strain rate can be expressed as follows:Based on the dynamic material model (DMM), the technique of metal processing mapping has been previously applied to the study of the hot deformation mechanism of magnesium alloys ,27,28. AJ to the linear dissipation is the power dissipation factor, expressed by η, as shown in Equation (12). In general, the greater the power dissipation factor η, the better the processing property of the material.m is the strain rate-sensitive coefficient of flow stress, and it can be obtained from The ratio of Prasad et al.  have useThe relationship between (m) can be expressed as:a, b, c, and d are all temperature-dependent constants.The strain rate-sensitive exponent  of 0.43 appeared at 693 K, with the strain rate of 0.002 s−1, and the second is 0.37, which appeared at 733 K, with the strain rate of 0.01 s−1, which are all the suitable parameters for hot deformation. The minimum η is 0.24, which appears near the instability zone, and the second minimum η is 0.25, which appears at 773 K, 1 s−1; neither 653 K nor 773 K is the appropriate temperature for hot deformation.After calculating Temperature has a great effect on the hot deformation of the alloy. When the temperature is lower, dynamic recrystallization cannot fully complete; thus, refined crystalline strengthening of the alloy is difficult to achieve a. When tWhen the strain rate is constant (0.001), the grain size of the alloy sharply increases with an increase in temperature . It is s−1 at 733 K. The recrystallized grain size grows perpendicular to the direction of compression and along the tensile grain boundaries; the secondary phase shows a striped structure (1–b3). When the strain rate is 1, 0.1, 0.01, and 0.001 s−1, the grain size of the alloy is 19, 30, 62, and 76 µm, respectively (1–c3).The recrystallized grain size of the Mg–8 wt.% Sn–1.5 wt.% Al alloy decreases with an increase in the strain rate when the strain rate is 0.001, 0.01, 0.1, and 1 structure . The rectructure b1–b3. Whectively c1–c3.−1 deformation, the texture is not strictly along the basic (0001) direction; it is beneficial to reduce anisotropy by reducing the strain rate.The c-axis of deformed grains was perpendicular to the compression direction at different temperatures. The textural strength decreased  from 4. increaseThe hot deformation behavior of the Mg–8 wt.% Sn–1.5 wt.% Al alloy is studied by the hot compression test. Compared with other magnesium alloys, the processing map of the Mg–8 wt.% Sn–1.5 wt.% Al alloys also show certain instability and suitable zones for processing. However, the instability zones on the processing map of the alloy are small and the machinable zones are large. The deformation of the alloy also requires a certain deformation activation energy, which is 153.5 kJ/mol. The effects of the strain rate and deformation temperature on the flow stress of the alloy are discussed. The deformation activation energy, strain index, and hardening rate curve of the tested alloy are calculated; finally, the flow stress equation of plastic deformation is established. It is determined that the alloy has a wide hot working interval and can complete the thermoplastic deformation well. So, the alloy is easily deformed, which is important for its development and application.The main conclusions are as follows:(1) Steady-state rheological characteristics of the Mg–8 wt.% Sn–1.5 wt.% Al alloy exist in the process. The flow stress increases with an increase in the deformation degree and gradually decreases to a certain steady-state value after reaching a certain peak, which has clear dynamic recrystallization behavior characteristics. With a decrease in the deformation rate and an increase in the deformation temperature, the flow stress decreases. By comparing the influence of strain rate and deformation temperature on the flow stress curve, it is determined that the change in deformation temperature is more sensitive to the influence of the curve.Q) is 153.5 KJ/mol; the stress index (n) is 3.88; the average stress factor (α) is 0.034. The constitutive relationship (Equation (10)) of the alloy during hot compression deformation is calculated, which can verify and predict the machining properties of the alloy.(2) During hot deformation, the stress, strain rate, and deformation temperature rheological relationship is expressed by the hyperbolic sine function, i.e., −1 to 19 µm at 1 s−1) with an increase in the strain rate, and the strength of the texture in the alloy decreases (from 4.536 to 2.742). Therefore, the increase of the deformation temperature will cause the growth of the grain size, as well as weaken the texture; on the contrary, the increase of deformation strain rate will reduce the grain size, as well as strengthen the texture.(3) By analyzing the microstructure of the Mg–8 wt.% Sn–1.5 wt.% Al alloy during deformation at different temperatures and strain rates, it is determined that the grain size of the alloy sharply increases (from 38 µm at 653 K to 134 µm at 733 K) with an increase in temperature, and the strength of the texture in the alloy decreases (from 4.410 to 4.189). At the same temperature, the grain size of the alloy decreases (from 76 µm at 0.001 s


Many current convolutional neural networks are hard to meet the practical application requirement because of the enormous network parameters. For accelerating the inference speed of networks, more and more attention has been paid to network compression. Network pruning is one of the most efficient and simplest ways to compress and speed up the networks. In this paper, a pruning algorithm for the lightweight task is proposed, and a pruning strategy based on feature representation is investigated. Different from other pruning approaches, the proposed strategy is guided by the practical task and eliminates the irrelevant filters in the network. After pruning, the network is compacted to a smaller size and is easy to recover accuracy with fine-tuning. The performance of the proposed pruning algorithm is validated on the acknowledged image datasets, and the experimental results prove that the proposed algorithm is more suitable to prune the irrelevant filters for the fine-tuning dataset. Samples and filters are linked in the proposed algorithm through feature representation, applied to find the irrelevant filters in the pretrained network.Different from other pruning algorithms, the proposed algorithm aims to find the suitable network structure for practical tasks, and the pruned filters are redundant for the task on fine-tuning dataset.The proposed algorithm is verified on multiple datasets and pretrained networks. The acknowledged datasets are applied to prove the effectiveness of the proposed idea, and the experimental results are encouraging and interesting.In the last decades, the rapid development of deep learning is promoted, and various novel neural networks are emerging endlessly, especially the convolutional neural networks (CNNs). In image classification, the convolutional layer was playing a more and more important role. However, with the improvement of the CNNs performance, the number of network parameters increased as well, which makes the CNNs difficult to apply in practical application. Moreover, there is a large number of redundant parameters in the CNNs structure , which iThe rest of this paper is structured as follows: Network pruning algorithm has been widespread concerned, which is one of the most immediate and effective ways to compact and accelerate the CNNs. The most notable one must be the “Deep Compression” proposed by Han et al.  in 2016 Most of the pruning methods measured the weight of convolutions first, then pruned the lightweight filters, and fine-tuned the remaining network. Li et al.  comparedIn most practical applications, the networks need only do a single job which is much easier than the object classification on ImageNet. Using the pretrained network may waste many computation resources. Therefore, the purpose of the pruning approach in this paper is to find an appropriate network for simple tasks. Considering the correlation between the source image and convolution feature maps, the CNNs pruning algorithm based on the feature representations is proposed, which bridges the relationships between the sample and convolutions, and guides the network to choose the desired filters.  Simple and useful: in the network propagation process, the convolutional layers are the most time-consuming part. Therefore, pruning the filters in convolutional layers is the most efficient way to accelerate the network. Irrelevant convolution will increase the operating burden of the system, and an effective pruning algorithm can make the network work more efficiently in practical applications. Comparing with other compression methods, network pruning is much simpler and easier to use.  Easier fine-tuning: training from scratch in the same network structure may achieve the same performance , but the  More interpretable: the artificial neural network is a great technique, which is a black box and hard to be interpreted. Up to now, the complete explanation of the CNNs has not been given by any researcher. The proposed pruning method focuses on practical tasks and is guided by the correlation between the sample and feature representation. Compared with other pruning algorithms, the proposed method has better explanatory and more convincing.The major advantages of the proposed method are listed as follows:In the forward propagation of simple CNNs, image data are fed into the network and processed layer by layer. Except for the final layer, each layer accepts the output of the former layer and outputs the intermediate results as the latter layer's input. As the output of convolutional layers, the feature representation implies the response of convolution filters to the input data. Hence, the feature representation links the filters with the input data, and the correlation between filters and samples can be revealed based on the feature representation. High correlation filters play an important in sample feature extraction, which can clearly distinguish objects from the background. Conversely, pruning the low correlation filters will not affect the network performance in practical application.Therefore, how to use the feature representation to guide the filter selection is the major problem in this paper. Each convolution filter generates a feature map, and the feature map presents the response of the filter to the image. In general, a feature map is a gray image, and the brighter the feature map is, the stronger the response of the feature is. Therefore, the importance of the filter can be sorted simply by the brightness of the feature map. However, some feature maps with lower brightness might be important as well because the weight of the filter is smaller. So the feature maps need to be normalized, and the foreground and background are applied to evaluate the intensity of response in convolution filters.P, which includes n convolutional layers. Also, suppose the pruning sample be S = , label = , where  is the sample image with the label,  is the center point of object location, and w and h mean the width and height of object bounding box. After forward propagation, feature maps fm in each convolutional layer are got, and irrelevant filters can be found based on feature representation. Before pruning the filters, the feature response of foreground within the bounding box rt and the feature response of background rb should be calculated at first according to equations  represents the left-top point of bounding box in feature map fm; w′ and h' mean the width and height of bounding box in fm, respectively; W′ and H′ are the width and height of fm; W and H denote the width and height of input image, respectively; λl is diffusion coefficients in l-th convolutional layer, which is used to evaluate the expansion of receptive field in feature map, λ0 = 0. Considering the effect of the edge features, the target area in the feature map is slightly larger than the original labeled position. Because of the expansion of the receptive field after each convolutional layer, the range of the foreground feature should be enlarged with the deepening of the network. However, considering the depth of the current network, the foreground feature range will exceed the scope of the feature map after several enlargements. Therefore, should the enlargement of the receptive field be considered? If yes, does it have any impacts on network pruning? Those questions will be discussed in the experimental part.Training classification network only needs the class label while training detection network not only needs the labels of categories but also needs the locations of objects. In training, the detector will select the background area as the negative samples, and there is no need to prepare negative samples deliberately. Similarly, the proposed pruning algorithm needs the object location as well, and negative samples in training are also selected in the background. If the response intensity in the foreground region is stronger than that in the background region, the filter should be pruned. If the response intensity in the foreground region is stronger than that in the background region, the filter can be considered to extract the effective feature of the object; conversely, the redundant filter is hard to classify the object with background and should be eliminated by the pruning method. Suppose the pretrained network be quations –4), and, andP, wrt and rb will lead to wrong pruning. Hence, after calculating rt and rb, it is necessary to compute the correlation R between the feature map fm and the object in the input images by the following equation:Since some objects in the background have similar features to the foreground objects, the feature response of the background and foreground will be similar. When the foreground region is small, only comparing with R > 0, the feature map is relevant to the input image; conversely, the filter should be subtracted from the network. The same as other pruning methods, after pruning the former convolutional layer, the channel number and the corresponding weights in the latter layers need to be rectified.If After the pruning, the network needs to be fine-tuned for recovering the best performance. Each step is illustrated in If the above pruning method is used for network pruning iteratively, the more different samples are inputted into the pruning structure, the more filters are pruned. However, pruning based on different samples iteratively may affect the network feature extraction, which is misled by low relevance samples. To apply the pruning method in multiple samples, the pruning cartogram is advised in this section.D = { |k = 1, 2, 3, ...,nd}, where  is the k-th image with the label in the dataset and nd is the number of samples in the dataset. Initialize the pruning cartogram Co = {|i = 1, 2, 3, ..., n, j = 1, 2, 3, ...,mi}, where  means that the j-th filter fij in i-th layer li has been pruned cij times and cij = 0; mi denotes the filter number in i-th layer.The same object in different images with identical viewing angle has similar representation features. For example, in the classification of people and vehicles, there are some differences between people individuals, but the essential people characteristics distinguish people from vehicles. Since people have legs and arms while vehicles have not, legs and arms are the essential features of people, directly classifying people and vehicles. Transforming to the pruning mechanism, although there are some differences between individuals, all the objects in the same category have identical features that are the essential features of this category. On the contrary, among different kinds of objects, different features are the main points to distinguish different objects, which need to be preserved. Thus, in multiple sample pruning tasks, the pruning cartogram is provided to count the pruning times of each filter. Similar to the pruning method based on filter weight, the pruning strategy in this part also depends on the pruning priority obtained by the pruning cartogram. Pruning times determine the correlation between the feature map and the object in the input images, and irrelevant filters are more frequently pruned. Suppose the pruning dataset be rt within the bounding box and the background response rb out of bounding box at first and then get the correlation value R of the filter fij based on equation , i = 1, 2, 3, ..., n, j = 1, 2, 3, ...,mi} is obtained, where oij denotes the ranking result based on cij. Finally, prune the filters which are satisfying equation .The results are shown in Focusing on the redundant parameters and hard training problems of neural networks, a convolutional neural network pruning algorithm based on feature representations is proposed. The feature maps of the convolutions in each layer are calculated through the network iteration. The response intensity of the foreground and background features is obtained according to the feature map with the bounding box label. Then the correlation between the filters and the object is bridged owing to the feature representations, which is the basis of the pruning algorithm. Further, the pruning strategy is extended from the single pruning sample to multiple pruning images. By extracting the essential features of the object, the pruning cartogram is advised to guide the pruning direction. The pruning operation is carried out based on the final pruning cartogram, which speeds up the network operation and is easy to fine-tune.The proposed pruning algorithm is also verified on well-known datasets by some interesting experiments. The experimental results show that the proposed algorithm is better than the filter weight-based pruning method and training from scratch if the computation resources or training data are limited. For imitating the practical situation, the proposed method was also compared with another famous pruning algorithm in the experiment. The result presented that our pruning method is more suitable for pruning the redundant filters when the fine-tuning dataset is different from the pretraining dataset. And what is the optimal network structure? How to find that? How to train the network with little samples? Those are part of our ongoing work.


Wheat flour was partially replaced by debittered acorn flour (DAF) with 0%, 10%, 15%, 20% as well as 25%. Rheological properties of wheat/acorn dough and quality and in vitro digestibility of its noodles were determined. Results showed that DAF addition significantly improved pasting viscosity and dough stability time while excessive addition weakened the protein network and decreased maximum fermentation height. Furthermore, noodles with substitutions exhibited promising technological properties as a food ingredient for noodle making  but poor extensibility, smaller lightness values, and a slight deterioration in cooking quality. Furthermore, PCA and correlation analysis demonstrated a significant relationship between textural and cooking properties and pasting and mixing parameters. Moreover, SEM images of acorn noodles presented coarser surfaces but a tighter cross-section structure. Finally, in vitro digestibility results indicated that DAF addition significantly reduced the susceptibility of the starches to enzyme hydrolysis, while the addition of acorn flour slightly decreased the overall acceptability. Thus, the partial substitution of wheat flour with acorn flour can favorably be used in noodles formulation. Quercus spp.) is one of the most species-rich tree groups that comprise more than 500 species plus several hybrids distributed throughout temperate and tropical regions [Oak , the global food system must make reasonable improvements to adapt to the concept of sustainable development . ExploriThe addition of functional flour obtained from acorns in bread, cake, and biscuit have been recently evoked ,10. HoweAlthough acorn flour in making gluten-free foods has been extensively studied, there are no studies conducted over combining acorn flour with wheat flour to produce Chinese noodles. In this context, the work aimed to assess acorn flour’s making performance, in vitro digestibility, and sensory properties as a partial substitution for wheat flour in noodle making and explore the relationship between dough characteristics and noodle properties. Hopefully, the results would provide insights for developing functional acorn staple food industry with high profitability and thus promote global sustainability.Commercial wheat flour (WF) was obtained from Shaanxi Bull Flour Co., Ltd., BaoJi, China. Acorn fruits were randomly collected from acorn planting areas located in Luonan County, Shaanxi province. Other chemicals and reagents used in the current study were of analytical grade and purchased from Sanli Chemical Reagent Co, Yangling, China.The premixed WF and debittered acorn flour (DAF) after removing tannins with substituting 10%, 15%, 20%, and 25%, the premixed WF and debittered acorn flour (DAF) were thoroughly homogenized by rotating and shaking in a self-sealing bag and named DAF10, DAF15, DAF20, DAF25, respectively.The removal of tannin was performed as described by Zhang et al.  with sliProximate compositions of moisture, protein, fat, and ash of samples were analyzed using AACC methods (AACC 2000). A starch assay kit determined the total starch contents . The dough was washed and gluten retained was collected and weighed to determinate the content wet gluten. Apparent amylose content was determined according to the method of Chrastil .Pasting properties of WF, DAF, and mixed flour were studied with a Rapid Visco Analyzer  using the method by Bhattacharya, et al. . Flour  using the “Chopin +” protocol described by Moreira et al. . SamplesDough fermentation properties were determined with a Rheofermentometer F4  following the method of Huang et al. . ReconciAfter pre-experiment, it was determined that the final replacement ratio of acorn powder is 0%, 10%, 15%, 20%, and 25%. Chinese noodle fabrication with different proportions of acorns was as follows: 200 g mixed flour , 40% distilled water, and 0.75% salt were put into a small dough mixer , and mixed for 10 min. After incubated for 40 min at room temperature, the dough crumbles were passed through a small noodle machine 6 times. The final size of the noodles was 1 mm thick, 3.0 mm wide, and 220 mm long.Optimum cooking timeThe optimum cooking time was determined according to the method of Guo et al. . GeneralWater absorption and cooking loss1 is the weight of fresh acorn noodles, W2 is the weight of cooked acorn noodles, and W3 is the weight of remaining solid matter.Water absorption and cooking loss were analyzed as described by Raungrusmee et al.  with somColor attributes for samples were measured using a CR-310 Chromameter  and expressed as L* (lightness), a* (+redness/−greenness) and b* (+yellowness/−blueness) values.TPA, shear, and tensile tests of fresh and cooked acorn noodles were performed using a TA-XT Plus texture analyzer  and refer to the method of Liu et al. . The meaFresh and cooked noodles were dried at −80 °C overnight and lyophilized the next day, then the samples were fixed on the sample stage with conductive double-sided tape and sputtered with a thin layer of gold. The surface- and cross-sectional images were observed 500× at an accelerating voltage of 5 kV by SEM .∞ corresponds to the equilibrium percentage of starch hydrolyzed after 180 min; k is the first-order rate coefficient.The multi-enzymatic method was carried out to analyze noodle digestibility as Bustos et al.  describe20 food professionals performed sensory evaluation following 9 point hedonic scale for sensory attributes viz., appearance, color, flavor, softness, thickness, elasticity, overall acceptability.p ≤ 0.05) using Minitab Statistical Software . Correlation analysis and PCA  were conducted using origin 2021.All assays were performed thrice, and the values are expressed as mean ± standard deviation (SD). Data were subjected to analysis of variance (ANOVA) followed by Turkey’s test  is the time for the maximum consistency of the dough to remain constant during mixing. Stab. increased with increasing substitution level, suggesting that acorn flour improved the strength of the dough and the tolerance for mixing, which might be attributed to the covalent and non-covalent interactions between proteins and polyphenols derived from acorn flour. The results align with previous reports where the substitution of wheat flour by acorn flour could increase stab. ,30. DuriC5 and the difference between C5 and C4 values have been closely associated with the retrogradation tendency of the flour. However, no statistically significant difference was observed between the different proportions of substitutions. Meanwhile, there was no significant difference between different substitution levels  in mechanical weakening. α is used to measure the rate of weakening of the protein network, while β represents the gelatinization speed. The increasing replacement rate of acorn flour resulted in a rise in α value and showed a significant difference when the addition ratio raised to 20%. Samples with adding substitutions presented significantly higher β values than wheat flour, but there is no significant difference between different replacement ratios. These results showed that partial replacement of wheat flour with acorn flour could speed up the denaturation speed of the protein network and the gelatinization speed of starch.2 gas cells and destroyed dough network, which is consistent with the results of the mixing assay. VT is the total volume of gas released during fermentation. It was represented that acorn flour had no significant effect on CO2 production.The effect of adding acorn flour on the fermentation properties of dough is shown in As shown in Correlation analysis was performed among rheological properties of dough, and noodle quality showed in Three color parameters, L*, a*, and b* of different substitutions levels, were evaluated and summarized in Textural parameters could often be taken as an essential indicator that affects the taste and noodle acceptance. The effect of acorn flour substitution  on textural properties, including TPA, shear, and stretch parameters of fresh and cooked noodles, are listed in Furthermore, the adhesiveness of fresh noodles didn’t show substantial differences until the acorn concentration reached 25%. Fresh noodles’ chewiness and springiness were first reduced and subsequently increased, and the DAF10 showed minimal springiness and chewiness. Moreover, acorn flour significantly affected cooked noodles’ hardness, cohesiveness, gumminess, and chewiness. Correlation analysis showed that the hardness of fresh noodles was highly positively correlated with the most pasting properties, W.A., Stab., T.W, α, and β , and resistance to extension (RE) but reduced the extensibility of fresh noodles . FurtherWhen comparing the cross-section images A3–E3, it∞ (1 − e − kt), and k (kinetic constant) and C∞  were obtained in ∞ of the control was significantly higher than acorn-enriched noodles, while no significance between different substitution levels. Moreover, no significance was detectable in the k value among the whole samples.All the experimental values were substituted into the equation C = C∞, HI, and GI all reached the minimum value when the substitution ratio was 15% and then slightly increased. It should be recognized that the enzymatic susceptibility of starch depends not only on the noodles components but also on the structural state of the substrate. Excessive substitution destroyed the protein starch matrix, making the starch more susceptible to degradation by enzymes, and this result coincides with other findings [Hydrolysis Index (HI) and Predicted Glycemic Index (PGI) were calculated as previously described  and showfindings ,39.p < 0.05) similar to the control. Nevertheless, all noodles prepared with acorn flour were liked moderately and liked slightly by panelists. Sensory ratings of cooked noodles from appearance, color, flavor, softness, stickiness, elasticity, and overall liking were evaluated by a trained panel and tabulated in Principal component analysis (PCA) was carried out to visualize the similarities and differences between mixed flours and the interrelationship between the properties of flours, doughs, and noodles, and the results are shown in TF, WS, firmness, gumminess, adhesiveness, and hardness are close to DAF20 and DAF25, indicating that they are highly correlated. Furthermore, the pasting properties, mixing properties, and textural properties are close in the plot, suggesting that they are correlated, consistent with the correlation analysis. Furthermore, DAF10 and DAF15 are not far away from each other, indicating a similarity in their nature. In addition, the L*, HI, and PGI were located close to WF while opposite to a*, b*, and cooking loss, which confirmed a positive effect of acorn flour on glycemia.Partial replacement of wheat flour by debittered acorn flour significantly affected the rheological properties of dough and the quality of noodles. The pasting, mixing, textural properties, and in vitro digestibility were improved by adding acorn flour. In addition, the dough added acorn powder showed longer stabilization time, better gelatinization stability, and a more ordered cross-sectional structure. However, high levels of wheat flour replacement caused a deterioration in cooking quality and an unattractive appearance. Furthermore, the Correlation and PCA analysis indicated that dough’s rheological properties determine the nature of the noodles to a certain extent. Given the above results, this experiment recommends 10% to 15% acorn flour replacing wheat flour to produce acorn noodles from the nutrition value quality perspective. However, further study is needed to investigate the applicability of acorn flour in other food and the mechanism by which the acorn flour influences the characteristics of pasta.


Deep learning grew in importance in recent years due to its versatility and excellent performance on supervised classification tasks. A core assumption for such supervised approaches is that the training and testing data are drawn from the same underlying data distribution. This may not always be the case, and in such cases, the performance of the model is degraded. Domain adaptation aims to overcome the domain shift between the source domain used for training and the target domain data used for testing. Unsupervised domain adaptation deals with situations where the network is trained on labeled data from the source domain and unlabeled data from the target domain with the goal of performing well on the target domain data at the time of deployment. In this study, we overview seven state-of-the-art unsupervised domain adaptation models based on deep learning and benchmark their performance on three new domain adaptation datasets created from publicly available aerial datasets. We believe this is the first study on benchmarking domain adaptation methods for aerial data. In addition to reporting classification performance for the different domain adaptation models, we present t-SNE visualizations that illustrate the benefits of the adaptation process. Deep neural networks were used for a variety of tasks such as image classification, segmentation, image generation, and speech recognition ,2. These domains , and thu domains ,6. DA ca domains .This benchmarking study focuses on unsupervised DA methods for classification without access to any labeled target domain data. Several surveys were conducted on the topic of domain adaptation ,12,13,14We present the first benchmarking study of domain adaptation on aerial datasets.We construct six different aerial datasets for domain adaptation by carefully selecting the common classes and balancing the datasets.We consider seven models for unsupervised domain adaptation and report their performance on the aerial datasets.The main contributions of this paper are as follows:The remainder of this work is organized as follows. K in both source and target domains, and In unsupervised domain adaptation, the source domain where training takes place is described as modality  or diffemodality ,29. TL rMost DA methods can be categorized into divergence based and adversarial-based methods . The div domains ,38,39,40 domains ,33,41. M domains ,43,44, d domains , etc., aIn the adversarial-based DA, a domain discriminator is used to encourage domain confusion by using an adversarial objective. This objective minimizes the distance between the empirical source and target mapping distributions. This is further categorized based on the use of generative versus nongenerative models. The generative model makes use of generative adversarial networks (GANs) to create synthetic target data, which is in turn used to train the target model ,47,48. IIn most cases of TL and DA, the source and target domains have the same classes, and this is called closed set DA. When the classes in the source and target are not identical, the problem is called open set DA  and is mAll the DA algorithms that we have used for this benchmarking study were the top performing models with source code publicly available from each of the DA categories.This section offers a description of each DA method used in our study. We begin with Domain Symmetric Networks (SymNets) , Robust G, and the fully connected C. The domain discriminator D, which is symmetric to C is added on top of G to distinguish between the features of the samples from the two domains.The Domain Symmetric Network (SymNets)  utilizesThe architecture of SymNets is described in K number of classes, The SymNets design consists of two parallel task classifiers for x as belonging to the source and target domains respectively.To make G is learned by the following objective:The category-level confusion loss makes use of the labeled source samples and the feature extractor G is learned by the following objective,For the domain-level confusion loss, the unlabeled target samples are used as the individual class label is not required for domain-level confusion. For the target sample, the feature extractor The entropy minimization principle  is used The overall training objective is obtained by combining Equations (1)–(6) as,Robust Spherical Domain Adaptation (RSDA)  proposesaptation ,62,63,64F goes through adversarial training with a domain discriminator D such that F is able to distinguish between the source and target domains. A CNN such as ResNet [F which is mapped onto a sphere. The spherical feature space also has a classifier (C) and discriminator (D). The spherical neural network consists of spherical perceptron and spherical logistic regression layers. The robust pseudo-label loss defined in spherical space makes use of the pseudo-labels of the target domain and the Gaussian mixture model.The source domain has a labeled dataset, s ResNet  is used H is the entropy of the distribution. To define the form of the robust pseudo-label loss, kth element. A random variable The spherical adversarial training loss is defined as,her DANN  or MSTN her DANN  as the bor (MAE) . wϕ(xjt)The probability The spherical neural network (SNN) is an extension of MLP from Euclidean to spherical space. The features on the spherical space are obtained by normalizing the feature vectors, The spherical linear transform The SP layer is then,The SLR layer is described similar to the Euclidean logistic regression as,Gradually Vanishing Bridge for Adversarial Domain Adaptation  proposesThe gradually vanishing bridge framework is an end-to-end network. The bridge layer on the generator outputs the domain specific properties, called The bridge layer associated with the discriminator represents the distance between the current discriminator function and the ideal decision boundary to be achieved. The discriminator also receives additional discriminative power from the bridge layer. The overall objective of the network is given by the equation below which shows that the network is trained by minimizing classification loss, adversarial transfer loss, and the reconstruction loss for the generator and maximizing the adversarial transfer loss for the discriminator.Gradually Vanishing Bridge  can be af, the classifier prediction g is able to capture multi-modal structures in the adversarial domain adaptation. Domain variations in both feature representation f and classifier prediction g can be represented at the same time via conditioning. Thus, CDAN is presented as a minimax optimization problem consisting of two error terms: G, which needs to be minimized for reduced source risk, and G and the domain discriminator D across both domains. Here, This method improves adversarial domain adaptation methods by addressing two shortcomings. First, when the joint distributions of features and classes across both domains are not identical, adapting only the feature representation will not be enough as deep representations usually transition from general to specific as training progresses in deep networks. The second shortcoming occurs when the feature distribution is multi-modal, which often occurs in real-life scenarios. In this case, the adversarial network performs poorly. These challenges are overcome by training the generator and discriminator on information related to the domains such as its respective labels or similar modality objects. By adapting the feature representation Most domain adaptation techniques require a definite class-wise relationship between the source and target domain classes. Universal Adaptation Network (UAN)  is an inp and a target domain q are given. The common label set shared by source and target domains is denoted by C and C, respectively. The target data are totally nonannotated, and the target label set is defined only for the purpose of explanation of the setting. Commonness between the source and target domains is defined as the Jaccard distance of the label sets In UAN, during training, a source domain F, classifier G, adversarial domain discriminator D and a nonadversarial domain discriminator x from both domains are processed through the feature extractor F to get features G to get the probability D. Source similarity weights The Universal Adaptation Network consists of a feature extractor L is the cross-entropy loss. Therefore, the objective functions are the following.Here, Source Hypothesis Transfer (SHOT) proposes a discrepancy based domain adaptation technique using hypothesis transfer from source domain d dimension feature, i.e., k dimensional logits, i.e., k. During the domain adaptation procedure on the target domain During training the network on the source domain K-dimensions and The loss function for the source training is categorical cross-entropy loss with label smoothing. The label smoothing component helps to achieve evenly separated clustering in the embedding space. The overall source training loss can be written as follows.e et al.  proposede et al.  backboneStructurally Regularized Deep Clustering (SRDC)  is a disThe joint network method consists of an embedding function and a classifier function. The assumptions for the method are that the classes used are discriminative within a domain but the same classes in different domains are geometrically close. After getting the softmax predictions of the target data, the network minimizes the The above objective is optimized by alternating between the auxiliary distribution step and the network update step. Since both the domains share the same label space, joint training pushes instances from the same classes into the same regions, thus achieving feature alignment between the two domains. The clusters are also enhanced by selecting soft source samples, and this is done by reassigning the weights to the source after every epoch, based on their similarity to the target samples.This section describes the aerial datasets used in this benchmarking study. Since there are no aerial datasets dedicated to domain adaptation, we considered publicly available aerial datasets for classification and utilized their shared classes for unsupervised domain adaptation.The Aerial Image Dataset (AID)  is a datThe UC Merced Land Use Dataset (UCM)  is a pubThe NWPU-RESISC45  dataset The Continual Learning Benchmark for Remote Sensing (CLRS)  dataset This dataset was created as part of the xView  2018 DetThe Dataset for Object deTection in Aerial images (DOTA)  dataset To the best of our knowledge, there are no existing datasets designed for aerial domain adaptation. For closed-set DA, the classes between the source and target domain datasets are the same. Using the datasets described in this section, we create three aerial DA datasets for our benchmarking study. Each of these datasets are made by taking the common classes between two of the aerial datasets. There are differences in the image characteristics between the source and target domain based on their GSDs and the sensors that were used to collect the data. The first DA dataset is between AID and UCM, with 9 classes in common between them. The shared classes and number of samples for the AID-UCM DA dataset is in The next dataset is created by considering the common classes between NWPU and CLRS. The 6 shared classes and samples in each class for the NWPU-CLRS DA dataset is in The third dataset is created by taking the common classes between DOTA and xView. The 5 shared classes and samples in each class for the DOTA-xView DA dataset is in The code for the DA methods in our study was obtained from the URLs in SymNets: the backbone and implementation of SymNets follows the original described in [G is a pretrained ResNet-50 [G is fine-tuned depending on the dataset and the classifier G. The optimization used is SGD with a momentum of 0.9 and batch size of 128. The strategy described in [p is the progress of training epochs linearly changing from 0 to 1, ribed in . The feaesNet-50  excludinribed in  to updatRSDA: the backbone network architecture was kept the same as the original RSDA implementation in [F, C, D and the parameters F, C, and D. The following procedures are then run alternately:F, C, and D and estimate F, C, and D and the distance of a sample from the spherical class center for a class Fix lgorithm .F, C, and D and fixing F, C, and D are trained as a standard domain adaptation training by the progressive adversarial training strategy described in [Optimizing ribed in  with theation in . The trarom DANN  Lbas to F, is set to a ResNet-50 [F, C, and D is done by SGD with a momentum of 0.9 and learning rates of C and D and 10 times that of F. Following the method in [p is the optimizing progress that linearly changes from 0 to 1. The alternating iteration is performed 10 times and each step runs SGD for 5000 steps.The feature extractor, esNet-50  pretrainethod in , the leaCDAN-GD: for these experiments, the backbone network is a ResNet-50 [esNet-50  which isGVB-GD: in these experiments, the backbone network is a ResNet-50 [esNet-50  which isUAN: the backbone or feature extractor is an ImageNet pretrained ResNet-50 [esNet-50  followinSHOT: in these experiments, the network architecture is the same as described in their Github implementation. The backbone is a pretrained ResNet-50 [p is the training process from 0 to 1. We have also set the hyperparameter esNet-50  where thSRDC: The backbone network is a ResNet-50 [esNet-50  which isIn this section, we compare the performance using the overall accuracy and F1 scores of the different DA models on the aerial datasets. We also report the confusion matrix and t-SNE plots for the xView to DOTA domain adaptation scenario.In this section, we will discuss the results of the xView to DOTA adaptation in more detail with the help of confusion matrices  and t-SNWhile there is an improvement in the performance for all classes across all DA methods, the most confusion appears to be between small vehicle and large vehicle or between ship and storage tank. This could be due to the fact that at small resolutions, small and large vehicles appear similar as do ships and storage tanks.To visualize the features, we make use of t-Distributed Stochastic Neighbor Embedding (t-SNE) , which iTo visualize features in this benchmarking study, we show t-SNE plots with the source and target domain features of the network before and after adaptation. Prior to the adaptation process, the t-SNE plot is obtained when the model is trained only on the source domain. The t-SNE plot after adaptation is used to visualize the improvement in the alignment of source and target domain features after the model is adapted to the target domain. In this section, we show the t-SNE plots for all the DA methods in When the domain adaptation process is successful, the source and target domains have near perfect alignment. From the t-SNE plots of the DA methods, SymNets, RSDA and SHOT seem to have the best source and target domain alignment after adaptation, as seen in We presented a benchmarking study of seven unsupervised domain adaptation methods  on three custom aerial DA datasets. These datasets were created by taking the common classes between AID-UCM, DOTA-xView, and NWPU-CLRS. We reported the accuracy for each of the methods considered on the aerial datasets to determine their efficacy. The confusion matrices and t-SNE plots of the methods on the xView-DOTA dataset were also reported.For the AID to UCM adaptation, SymNets does the best, while SHOT reports the most improvement for the UCM to AID adaptation task. For CLRS to NWPU and NWPU to CLRS adaptation, SHOT shows the best performance. Finally, for the DOTA to xView dataset, SHOT has highest accuracy, while SymNets does best for the xView to DOTA adaptation. Overall, SHOT and SymNets are the best performing models for the task of aerial domain adaptation. This is further evidenced by observing the confusion matrix of SymNets for the xView to DOTA adaptation, where after adaptation, the diagonal gets stronger. The t-SNE plots of SymNets and SHOT both show good alignment of the source and target domains after adaptation, which is further evidence of the efficacy of these methods.


Signal transduction, the ability of cells to perceive information from the surroundings and alter behavior in response, is an essential property of life. Studies on tyrosine kinase action fundamentally changed our concept of cellular regulation. The induced assembly of subcellular hubs via the recognition of local protein or lipid modifications by modular protein interactions is now a central paradigm in signaling. Such molecular interactions are mediated by specific protein interaction domains. The first such domain identified was the SH2 domain, which was postulated to be a reader capable of finding and binding protein partners displaying phosphorylated tyrosine side chains. The SH3 domain was found to be involved in the formation of stable protein sub-complexes by constitutively attaching to proline-rich surfaces on its binding partners. The SH2 and SH3 domains have thus served as the prototypes for a diverse collection of interaction domains that recognize not only proteins but also lipids, nucleic acids, and small molecules. It has also been found that particular SH2 and SH3 domains themselves might also bind to and rely on lipids to modulate complex assembly. Some lipid-binding properties of SH2 and SH3 domains are reviewed here. Every cell must process information from its environment and appropriately respond to any changes. An understanding the mechanisms that make this possible has long been a goal of biological research. In the three decades since the first reports that modular protein-binding domains interact with plasma membrane receptors and their downstream effectors, it has been established that such domains are involved in almost every corner of signaling in eukaryotes . They alSadowski et al. first described a conserved, noncatalytic domain in cytoplasmic protein tyrosine kinases (PTKs), which comprises approximately 100 amino acid residues . AnalysiThe cloning of the viral Crk oncogene, which encodes a small nonenzyme protein containing an SH2 domain, revealed a second noncatalytic element, which is also found in Src and Abl kinases . The SrcThe assembly of signaling complexes in different pathways usually occurs at the plasma membrane. Cell membranes are composed of a large number of different lipid molecules with variations in their head group and acyl chain structures. Alterations in local lipid composition also represent information that can be deciphered by lipid-binding domains . Lipid-bThe distinct protein-binding domains contain consensus amino acid sequences and, in many cases, specific secondary and occasionally tertiary structures. The individual domains within a span of amino acids in specific proteins are thus postulated on the basis of their homologies to consensus amino acid sequences and their folds. On the other hand, there are always disparities in the sequences that might allow further specificity. Moreover, these differences may result in the gain of a new binding ability towards additional ligands or the exchange of the originally defined binding target for others. Nevertheless, there is still a paucity of predictive data for protein-binding domains, meaning that they must be studied in vitro and in cells on an individual basis to characterize their functions. Interestingly, many SH2 domains have been shown to bind lipids in addition to phosphorylated tyrosine motifs. Conversely, some characterized SH3 domains do not recognize the canonical PXXP sequence, but instead bind to other protein motifs or ultimately stick to lipids.  The lipiSince its discovery, the approximately 100 amino acid-long SH2 domain has been identified in a wide variety of proteins in different eukaryotes, but primarily in metazoans. The human genome encodes 111 different SH2 proteins, including kinases, phosphatases, adaptors, and other signaling molecules with 121 SH2 domains altogether . StructuThe Abl tyrosine kinase SH2 domain was one of the first scrutinized in terms of lipid binding . Abl is d values in the micromolar range, and only eight SH2 domains displayed no detectable binding. Eighteen of the SH2 domains with high affinity for plasma membrane-mimetic vesicles were further examined to investigate the possible selectiveness towards different phosphoinositide species [Recently, Park at al. published a study in which they thoroughly and systematically investigated the potential roles of lipids in regulating SH2 domain-mediated protein–protein interactions and cellular signaling activities . Out of  species . Among t species . On the  species . In the above referred work, Park at al. also exemplified the physiological importance of the lipid-binding activities of SH2 domains with PTK6 and ZAP70 . The rolZAP70 is a Syk family tyrosine kinase with a central role in T cell antigen receptor signaling . ZAP70 hThe SH2 domain of Lck, another key player in TCR signaling, has been dissected by Sheng et al. . Lck is Based on the results of their previous systematic study on SH2 domain lipid binding, Kim et al. have investigated C1-Ten/Tensin2 . This prOne of the most recent studies on SH2 domain–lipid interactions focused on Vav2 , a ubiqu10 helix folded together into a β-sandwich of two antiparallel β-sheets [Among the domains identified so far, the most abundant is the family of SH3 domains, which comprises more than 300 members nested in over 200 proteins in humans ,66. The β-sheets . SH3 domβ-sheets ,68. The β-sheets ,70. For β-sheets . Class Iβ-sheets .Teyra et al. have comprehensively surveyed the specificity landscape of human SH3 domains in an unbiased manner via peptide-phage display and deep sequencing . Based oAlthough the SH3 domain was originally accepted to mediate inter- and intramolecular protein–protein interactions in a constitutive manner, an accumulating body of evidence suggests more nuanced functions. The phosphorylation state of the tyrosine in the atypical binding motif RKXXY has been shown to regulate their binding interactions, suggesting that peptide recognition by the SH3 domain might be coupled to phosphotyrosine signaling . On the Interestingly, c-Src, the protein after which the SH domains were named, has been shown to directly bind acidic phospholipids on several sites of the protein ,77. WhilAnalyses of some special SH3 domains have revealed that variants of the domain may mediate interactions with lipids rather than with protein sequences.  AdhesionPRAM-1  is an ADAP homologue that also contains a fragment with the hallmarks of an hSH3 domain . The proThe calcium/calmodulin-dependent serine protein kinase (CASK)-interacting protein 1, or Caskin1, is enriched in neural synapses in mammals and regulates cortical actin filaments . Based oThe membrane-born lipid mediator lysophosphatidic acid elicits many physiologic responses by acting on cognate G protein-coupled receptors as well as on intracellular target proteins . LysophoThe SH2 domain was originally defined as a reader module in signal transduction specialized for the recognition of newly formed phosphotyrosine sites; however, many different pathways employ this matching pair of moieties. Accumulating evidence suggests that additional lipid-binding abilities that vary in distinct SH2 domains nested in individual proteins contribute to specificity in the precise deciphering and translation of cellular information held in altered membrane lipid composition. In this manner, the composition and assembly–disassembly dynamics of a signaling hub formed around a freshly arisen phosphotyrosine are also influenced by lipid second messengers via the SH2 domain. The SH3 domain is widely accepted as a protein domain that provides stable interactions for the formation of constitutive signaling sub-complexes. However, a handful of SH3 domains have been reported to specialize in lipid recognition instead of protein binding. With little experimental data, it remains to be elucidated whether the lipid-binding ability of SH3 domains is a recent innovation in domain evolution that recycles a structure for extremely specialized functions in niche roles or if it is an as yet overlooked general potential of the SH3 domain.Additional specific biological responses might be controlled by the lipid-binding properties of some SH2 and SH3 domains, which could support spatiotemporal selection of the appropriate responding signaling protein from the repertoire of possible proteins for a given trigger. Experimental data are needed to address the questions stemming from the observations reviewed here.


Relative deprivation is associated with collective and individual variables in psychology. However, so far, there are few studies on measuring the relative deprivation of university students. Therefore, this study designs the University Students’ Relative Deprivation Questionnaire (USRDQ), verifies its validity and reliability, and then uses it to measure the mental health of students.After reviewing the relevant studies and conducting a theoretical analysis and an open questionnaire survey, this article determined the structural dimension of USRDQ. A total of 103 university students were selected to take the open questionnaire survey, 200 were selected to engage in the item analysis, exploratory factor analysis, and internal consistency reliability test, 257 were selected to engage in the confirmatory factor analysis, and 287 were selected to take the retest reliability.2/df = 2.64, NFI = 0.89, CFI = 0.93, GFI = 0.91, RMSEA = 0.08). The Cronbach’s α coefficient of the questionnaire is 0.916, and the coefficients of the three factors range from 0.805 to 0.934. The results of the survey show that the relative deprivation of students is quite high with a mean of 76.78 and a standard deviation of 16.96.The USRDQ includes 19 items under the three dimensions, namely, social comparison, cognitive evaluation, and emotional experience. Factor loads range from 0.49 to 0.87, which accounted for 63.39% of the total variation. The questionnaire has good fitting indicators (χ Relative deprivation refers to a cognitive evaluation of an individual on his or her disadvantageous situation and the resultant feelings of anger and resentment after making a social comparison with others . This coRelative deprivation can be divided into relative deprivation of an individual  and relative deprivation of a group (referred to as groups). Relative deprivation has major impacts on many consequence factors, such as group behavior, individual achievement and deviant behavior, intergroup attitudes, intergroup discrimination, aggressive behavior, prosocial tendencies , nationaRelative deprivation has three dimensions, namely, social comparison, cognitive evaluation, and emotional experience . Social Researchers have paid much attention to relative deprivations influencing factors and consequence variables and have achieved productive results. However, studies on measuring tools of relative deprivation are not adequate. At present, there are mainly three ways to measure relative deprivation. The first way uses statistical indicators in the socioeconomic field to measure relative deprivation, such as the Gini coefficient  and the The second way uses social comparison results to reflect the level of relative deprivation. For example, in the study of The third way develops a relative deprivation scale. Using the Individual Relative Deprivation Scale (IRDS), Although there were numerous indicators to measure relative deprivation, few studies probed into how the measurement reflected the essence of mind for relative deprivation, and empirical data to verify the study were lacking .Relative deprivation is associated with poor mental health. One study proved that perceived relative deprivation in childhood was likely to link with sub-syndrome psychotic symptoms . ResearcThe analysis on relative deprivation measurement showed that previous scales were less reliable and valid, and failed to meet the criteria of psychometrics according to the “Standards for educational and psychological testing” . Given tBased on the abovementioned conceptual analysis, this article agrees that relative deprivation should include three dimensions. The first dimension is social comparison, in which an individual compares his/her current situation with that in the past, that of others in his/her group, and that of others in other groups. The second dimension is cognitive evaluation, in which an individual believes that his/her disadvantageous situation is unfair after making a social comparison. The third dimension is emotional experience, in which after an individual finds out that he/she is in a disadvantageous situation, he/she will feel anger, resentful, and unbalanced.Sample 1: for an open questionnaire survey, the survey was carried out in universities in Wuhan. University students and postgraduates were selected on a class basis. A total of 118 questionnaires were sent out, and 103 valid questionnaires were collected, including 46 from male students and 57 from female students; 47 were from juniors, 39 from seniors, and 17 from postgraduates; and these students aged from 21 to 28 years, with an average age of (22 ± 3) years.Sample 2: for the analysis of questionnaire items, reliability analysis, and exploratory factor analysis, the survey was carried out in universities in Wuhan, Tianjin, and Chongqing. University students and postgraduates were selected on a class basis. A total of 230 questionnaires were sent out, and 200 valid questionnaires were collected, including 78 from male students, 111 from female students, and 9 from without identified gender; 2 were from freshmen, 70 from sophomores, 69 from juniors, 26 from seniors, 24 from postgraduates, and 9 from without identified grade; and these students aged from 20 to 26 years, with an average age of (22 ± 2) years.Sample 3: for CFA and analysis of the questionnaire score, the survey was carried out in universities in Wuhan, Chongqing, and Zhengzhou. University students and postgraduates were selected on a class basis. A total of 300 questionnaires were sent out, and 257 valid questionnaires were collected, including 96 from male students, 144 from female students, and 17 from without identified gender; 27 were from freshmen, 81 from sophomores, 74 from juniors, 28 from seniors, 30 from postgraduates, and 17 from without identified grades; and these students aged from 19 to 26 years, with an average age of (22 ± 3) years.Sample 4: for the retest of questionnaire reliability, the survey was carried out in universities in Zhengzhou, Wuhan, and Chongqing. Graduate students and postgraduates were selected on a class basis. A total of 300 questionnaires were sent out, and 287 questionnaires were collected, including 124 from male students and 163 from female students; 32 were from freshmen, 90 from sophomores, 88 from juniors, 30 from seniors, and 47 from postgraduates; these students aged from 19 to 27 years, with an average age of (23 ± 3) years. The respondents of the survey are shown in An open questionnaire survey was conducted among the abovementioned students. The survey focused on the perception of an individual on relative deprivation, including how to compare oneself with others, the perceived situation after comparison, the feelings after comparison, how to treat the comparison results, and the fairness of situation of an individual. A total of 118 questionnaires were sent out, of which 103 valid questionnaires were collected. The remaining 15 copies were invalid. The research conducted a statistical analysis to the item frequency and classified those with high frequencies. The frequency was between 11 and 103. Items that fell into the category of social comparison were as follows: after being compared with different reference objects, an individual found himself/herself in a disadvantageous situation (frequency 103) and an individual made less achievements compared with those having similar abilities (frequency 103). Items that fell into the category of cognitive evaluation were as follows: an individual thought it was unfair for himself/herself to be inferior to others (frequency 51), and under the same conditions, an individual should be treated the same as others (frequency 11). Items that fell into the category of emotional experience were as follows: an individual felt angry (frequency 34), resentful, and frustrated after finding himself/herself inferior to others (frequency 31).First, by referring to previous questionnaires , the resSPSS 21.0 was used for the item analysis, exploratory factor analysis, and reliability analysis, and AMOS 20.0 was used for the CFA. This study chose items that could differentiate low- and high-score groups using item analysis, and extract principal dimensions of USRDQ using the exploratory factor analysis. AMOS 20.0 was used to test, verify, and compare the structural dimensions of USRDQ through the exploratory factor analysis.t-test finds that each item of the questionnaire is highly different from one another, the decision value is greater than 3, and the significance is p < 0.001. Second, an item-score correlation test was carried out. Results show that the correlation between each item and the total score of the questionnaire is relatively high, the correlation coefficients are from 0.42 to 0.76, and the significance is p < 0.01.First, the item analysis was conducted to the pretest questionnaire. Scores of each respondent were added, and items were arranged from that with the highest score to the one with the lowest score. The first 27% of the items were considered in the high-score group, and the latter 27% were considered in the low-score group. A verification test of these items was conducted. The The average score of the relative deprivation questionnaire is (96.31 ± 21.42). The average score for social comparison is (34.23 ± 8.31), that for cognitive evaluation is (30.71 ± 7.34), and that for emotional experience is (31.53 ± 7.31).p < 0.001.The exploratory factor analysis was used to make descriptive statistics of the survey. The analysis results are shown in 2 = 2,256.90 . KMO value is 0.89. These results indicate that the overall correlation matrix has common factors and is suitable for the factor analysis.First, the Bartlett’s sphere test and KMO test were conducted. In the Bartlett’s sphere test, χThe principal component analysis method was used to extract factors. The factors with eigenvalues greater than 1 were selected. The orthogonal maximum variance method for rotation factor was set, and the factor load criteria greater than 0.4 were used in the exploratory factor analysis. As a result, the common degree value of each item ranges from 0.36 to 0.89. The gravel chart shows that, starting from the fourth factor, the curve gradually becomes flat, indicating that it is appropriate to extract the three factors.According to the suggestions by r = 0.49 and 0.55, p < 0.001), the score of cognitive evaluation is positively correlated with those of emotional experience , and the score of all these three dimensions is positively correlated with the total score of the questionnaire .The three factors are shown in This study used AMOS 20.0, a maximum likelihood method to conduct the CFA on the questionnaire. Missing data were filled using the series mean method. Results show a good fitting of each index in Then, the study compared the compete models using the CFA. The compete model consists of a single factor model and a two-factor model. AMOS 20.0 was used for model comparison, and the fitness index is summarized in According to the contrast model, this study believed that the three-factor model was the best one for theory hypothesis and data analysis.Content validity refers to the extent to which the items on a test are fairly representative of the entire domain that the test seeks to ensure. Items in this questionnaire were identified based on related theories and the open questionnaire survey and were finally determined after discussions with psychologists and PhDs in social psychology. This study intended to ensure that USRDQ interpreted relative deprivation correctly and addressed the features of university students. When designing the questionnaire, the description, details, semantics, and other aspects of the items were evaluated repeatedly in order to ensure that the questionnaire had high content validity. As relative deprivation is an important social variable, it can be used to predict lots of significant consequence variables, such as collective action, individual achievement, individual deviance, intergroup attitudes, and mental health .Internal consistency reliability: the internal reliability for the entire USRDQ is 0.916, and the internal consistency reliabilities of social comparison, cognitive evaluation, and emotional experience are 0.934, 0.869, and 0.805, respectively.Retest reliability: the retest reliability for the entire USRDQ is 0.919, and the retest reliabilities of social comparison, cognitive evaluation, and emotional experience are 0.932, 0.853, and 0815, respectively. The reliability of the relative deprivation and its dimension is shown in The reliability analysis and validity confirm analysis prove that USRDQ has satisfying reliability and validity. According to the suggestions by From the analysis, it can be found that university students have a high level of relative deprivation, with an average score of 76.78 and a standard deviation of 19.69. The average score of social comparison is 27.82, and the standard deviation is 8.65. The average score of cognitive evaluation is 25.21, and the standard deviation is 6.18. The average score of emotional experience is 24.06, and the standard deviation is 5.82. The general results of the survey are shown in The gender difference test was applied to the total score of the questionnaire and the score of each dimension, and results show that there is no significant difference between men and women in the overall relative deprivation and each dimension.t = 0.248 (p = 0.805); for social comparison, t = 0.64 (p = 0.523); for cognitive evaluation, t = 0.806 (p = 0.421); for emotional experience, t = 0.661 (p = 0.510). Gender differences are shown in For the overall relative deprivation, F  = 54.79, p < 0.01. The analysis of ANOVA reveals that relative deprivation level increases with the grade. The ANOVA is summarized in The ANOVA analysis of grade has found that grade has a significant difference in relative deprivation, post-hoc test using the LSD methods, and the multiple comparisons were used to identify the difference in grades. The relative deprivation level of sophomores and freshmen exists a significant difference . The relative deprivation level of juniors and sophomores exists a significant difference . The relative deprivation level of seniors and juniors exists a significant difference . Finally, the relative deprivation level of postgraduates and seniors exists no significant difference .Then, the research continued the In this study, after reviewing the literature and conducting an open questionnaire survey, reliability analysis, and validity analysis, the research designed the USRDQ, which included three dimensions, namely, social comparison, cognitive evaluation, and emotional experience. Respondents were required to make social comparison with different reference objects, evaluate the results after comparisons, and find out how they feel after evaluations. The USRDQ addressed the six questions proposed by The three dimensions of relative deprivation are closely related to each other, and they are complementary and mutually reinforcing. Social comparison is a necessary condition for relative deprivation. In social comparison, an individual compares his/her own situation with various reference objects. After making a social comparison, an individual will inevitably evaluate the comparison results, that is, what kind of status and situation he/she is compared with reference objects. Cognitive evaluation results in emotional experience. If an individual finds himself/herself inferior to others in certain aspects and believes that the disadvantageous situations are unfair and due to reasons other than himself/herself, he/she will produce anger and resentment. The individual will feel resentful about the social system and believes that it results in his/her disadvantageous situation. Resentment gives rise to the judgment of people about fairness. It is a temporary moral emotion with a clear focus, that is, the unfair institutional mechanism . Anger iThis questionnaire designed not only meets the requirements of psychometrics but also fits the basic concept of relative deprivation. Previous studies emphasized that relative deprivation included cognitive component and emotional component. This study found that among the three dimensions of relative deprivation, social comparison, and cognitive evaluation belonged to the cognitive component, and emotional experience belonged to the emotional component. This study is consistent with the theory of relative deprivation. Social comparison is a necessary component of relative deprivation. After making a social comparison, an individual will ask about his/her own situation. This is the cognitive component of relative deprivation. Please note that the cognitive component is not always true, as it is likely to be influenced by prejudice. When an individual finds that he/she is inferior to others in a certain aspect and believes that such disadvantageous situation is unfair, anger and resentment will occur.Relative deprivation is divided into relative deprivation of an individual and relative deprivation of a group. Compared to previous relative deprivation questionnaires, the one designed in this study is more in line with the nature of relative deprivation and meets the criteria of psychometrics. The previous method using socioeconomic statistical indicators to reflect the relative deprivation lacked reliability and validity, as it ignored internal psychological feelings. However, USRDQ considers the nature of relative deprivation as a psychological feeling and it is more appropriate to measure cognitive evaluation and emotional experience produced by social comparisons.The previous method focused on the characteristics of social comparison but paid little attention to emotional experience. The research thought it was incomplete that neglect the feeling aspect of relative deprivation, USRDQ measures social comparison, cognitive evaluation, and emotional experience of relative deprivation. The measurement is multifaceted. Compared with the relative deprivation questionnaire in previous studies, USRDQ includes the aspects of different social comparisons and has a wide range of applications. In terms of context and structural dimensions, USRDQ is superior to the PRDS . In termThe factor dimension obtained through the exploratory factor analysis of this study is basically the same as the theoretically constructed structure, which is a three-dimensional structure that includes social comparison, cognitive evaluation, and emotional experience. Items of this questionnaire are based on relevant scales and theories . The corAt present, many people feel that they are in a state of being deprived, and they have not achieved what they deserve. The relative deprivation level is quite high. Groups with low social status and income present a high level of relative deprivation. Even government officials, white-collar workers, and intellectuals are trapped in the same situation. Relative deprivation reflects how people react to social changes in their circumstances. This phenomenon may be related to the selection of comparison objects and the way to make social comparison. In a stable social order and under stable social norms, people usually choose to compare with those who are similar to them in ability or in certain respects. However, the society is changing thoroughly. People may not necessarily compare with those who have the homogeneity, but with those who are different from them, such as celebrities, high-ranking government officials, and wealthy businessmen, finding only that they are in a disadvantageous situation. This is because people tend to compare with those who are superior to them. Therefore, this study believes that, to reduce relative deprivation, people should start from selecting appropriate comparison objects. The public should be guided to choose suitable comparison objects and make a reasonable comparison.The survey shows that the current university students have a high level of relative deprivation. There are similar findings for other groups. In the demography factor difference analysis, the research found that there is no significant difference in relative deprivation in gender, but with the grade, there exists a significant difference. And the relative derivation has increased along with the grade raise. The research suppose that the situation of relative deprivation of university students may be the same of campus life, such as the schoolwork, friendship, date and love, examination, strive to get certification, further study, job hunting, and daily file. Whether female or male students, they may need to resolve the similar questions at all. Therefore, the research found a non-significant difference in relative deprivation. Regarding grade factors, the students may confront difficult tasks and problems when they get to grow. When they get a higher grade year by year may face the following problems: tougher academic tasks, the more competition to get progress, the tension in daily life, the difficulty to make choice, and the with those predicament and challenge exacerbate, the students had confront increased stress, but the coping capacity was have not promoted synchronized, the students may turn into differentiation, varied to be well-adapted or maladjustment. The complicated variation of students has caused the social comparison, cognitive evaluation, and emotional feeling to be changed, so the relative deprivation was increased with the grade.After the open questionnaire survey was conducted and the opinions of psychologists and doctors in social psychology were solicited, the items for the questionnaire were finally determined. When designing the questionnaire, the description, details, semantics, and other aspects of the items were evaluated repeatedly in order to ensure that the questionnaire had high content validity. The reliability test indicates that the relative deprivation questionnaire and every item has high internal consistency reliability and retest reliability, all of which meet the criteria of psychometrics. In summary, all indicators, such as reliability and validity, of the USRDQ meet the requirements of psychometrics and can be used to measure the relative deprivation of university students. Only when the relative deprivation is measured effectively, it can interpret the comparison of people upward.The USRDQ designed in this study measured relative deprivation resulted from social comparisons with reference objects. As respondents in this study are all university students, the validity of the questionnaire has not been verified for other groups, which may limit the application of the questionnaire. However, the questionnaire contains the main contents of social comparison of relative deprivation, so it can also be applied to other groups. Based on this questionnaire, future studies can focus on designing a new relative deprivation questionnaire for other groups  or social members to see whether structural dimensions of relative deprivation should be adjusted, and test the reliability and validity of the questionnaire.The USRDQ designed in this study has high validity and reliability. USRDQ targets the university students and is more accurate in measuring relative deprivation than other scales. The research proved the relative deprivation theory of The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.LJ contributed to data analyses, writing of the manuscript, critical review of the manuscript, and formal analysis.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.