To address the problems of large storage requirements, computational pressure, untimely data supply of off-chip memory, and low computational efficiency during hardware deployment due to the large number of convolutional neural network (CNN) parameters, we developed an innovative hardware-friendly CNN pruning method called KRP, which prunes the convolutional kernel on a row scale. A new retraining method based on LR tracking was used to obtain a CNN model with both a high pruning rate and accuracy. Furthermore, we designed a high-performance convolutional computation module on the FPGA platform to help deploy KRP pruning models. The results of comparative experiments on CNNs such as VGG and ResNet showed that KRP has higher accuracy than most pruning methods. At the same time, the KRP method, together with the GSNQ quantization method developed in our previous study, forms a high-precision hardware-friendly network compression framework that can achieve “lossless” CNN compression with a 27× reduction in network model storage. The results of the comparative experiments on the FPGA showed that the KRP pruning method not only requires much less storage space, but also helps to reduce the on-chip hardware resource consumption by more than half and effectively improves the parallelism of the model in FPGAs with a strong hardware-friendly feature. This study provides more ideas for the application of CNNs in the field of edge computing. In recent years, convolutional neural networks have become the most important intelligent algorithms in the field of computer vision ,2,3,4, dTherefore, improvements in CNN algorithms are essential to reduce the number of parameters and calculations. One approach is to directly design lightweight networks having fewer parameters and calculations, such as MobileNet , which uThe object of non-structured pruning is weights. The number of weights is sufficient to ensure a large amount of pruning without affecting the accuracy, and by randomly pruning the weights , we can To combine the advantages of both methods, pattern pruning was proposed ,25,26. PFurthermore, to recover the accuracy loss from pruning, many researchers have used a classical pruning architecture with training, pruning, and retraining . The conTo address the above issues, in this study, we focused on software and hardware co-optimization, considering a model pruning method with high sparsity, high accuracy, and high computational efficiency for easy deployment based on the different characteristics of the three pruning methods mentioned above. First, we developed a pruning method based on the row scale of convolutional kernels, KRP. The KRP method prunes each convolutional kernel according to the importance of each row, and each convolutional kernel retains only one row weight while all the remaining weights are pruned. The pruning granularity of KRP is between non-structured and structured pruning, and it is easy to obtain higher accuracy than that of structured pruning at the same pruning rate. Meanwhile, the KRP method has strong hardware adaptation capability. It ensures the same number of weights remain in each convolutional kernel, like pattern pruning, to avoid the problem of an unbalanced workload. The weight distribution of the convolutional kernels after row-scale pruning has high regularity, which can directly skip all the zero calculations produced by pruning by selecting the row from which the input feature data are entered during the hardware deployment process. Second, we propose a retraining method based on learning rate tracking to replace the conventional retraining approach in the retraining phase. This method reinitializes the learning rates in the retraining phase to their values in the learning rate schedule of the original CNN training process and corresponds the learning rates of the remaining epochs to the values in the schedule to achieve the effect of LR tracking, which can obtain higher training accuracy than conventional retraining in most cases. The whole process is similar to the retraining method proposed in the lottery hypothesis ,36, wherIn this study, we designed a CNN pruning method based on convolutional kernel row-scale pruning. It is highly practical by combining the high pruning rate and high accuracy features of non-structured pruning, high regularity and hardware- friendly features of structured pruning, and same number of remaining weights in each convolutional kernel of pattern pruning.In this study, we developed a retraining method based on LR tracking, which sets the retraining learning rate according to the variation in the original training learning rate, which can more quickly achieve higher training accuracy than conventional retraining methods.In this study, we performed pruning experiments on the CIFAR10 classification dataset  on four In this study, we combined the KRP pruning method with our previously developed GSNQ quantization algorithm to propose a hardware-friendly high-precision CNN compression framework, which can match the original network performance while compressing the network by 27×. We designed a highly pipelined convolutional computation module on an FPGA platform based on this compression framework, which can skip all the zero calculations without excessive indexing and significantly reduce hardware resource consumption.In summary, this study provides a reference for the lightweight deployment of convolutional neural networks in edge applications from both theoretical and experimental aspects. The main work in this study was as follows:The remainder of this paper is organized as follows: Nonstructured pruning and pattern pruning cannot efficiently skip all the zero calculations during hardware deployment, and the random distribution of weights in the convolution kernel caused by unstructured pruning can lead to poor use of the hardware resources. In contrast, structured pruning with high regularity cannot achieve large-scale pruning and match the performance of the original network. To balance the advantages of these methods and obtain a hardware-friendly pruning CNN model with high sparsity, high accuracy, and high regularity, we developed a regular pruning method, KRP, based on the row scale of convolutional kernels, where only one row of weight is kept in each convolutional kernel and all other rows are pruned. The KRP method is shown in The KRP method ensures that each convolutional kernel is transformed into a highly regular sparse matrix with a regular arrangement of zero weights after pruning. In terms of hardware deployment, all the zero calculations produced by pruning are skipped by directly determining which row the input feature map data start to enter according to the weight distribution, which can be efficiently processed by the hardware architecture without too much additional data storage or data indexing. This reduces the amount of storage while reducing the amount of computation and improving the computational efficiency of the deployed CNN models with hardware-friendly features. In terms of software level, the pruning granularity of the KRP pruning method is between that of nonstructured pruning and structured pruning and can obtain higher pruning accuracy more easily than structured pruning.A common method for determining the pruning criteria, i.e., to choose which row in the convolutional kernel should be pruned, is to judge the absolute value of the weights. In many previous studies, researchers have usually assumed that the larger the absolute value of the weight, the more substantial its effect on CNN models; and the smaller the absolute value of the weight, the less its effect on CNN models ,42,43. BJ convolution kernels in a CNN model, for the jth convolution kernel H × H, we first cluster the weights in the convolution kernel by rows to obtain the H group of weight sets, and each group contains H weights. Then, we separately calculate the sum of the absolute values of all weights in each group of the weight set:hth group of weight sets in the kernel, and W(i) denotes the weights in the hth group of weight sets.Assuming that there is a total of After calculating the absolute value of all the weight sets, we sort all the For CNN models that contain both convolutional and fully connected layers, the convolutional layers contain a large number of calculations, while the fully connected layer contains a large number of parameters. Taking the VGG-16 for the ImageNet dataset  as an exMany advanced CNN pruning methods follow a standard pruning framework: training the original CNN model; pruning the CNN model according to different pruning criteria; fine-tuning the pruning model by additional multiple retrainings with a low learning rate (LR) . BecauseIn the study of the lottery hypothesis , a high-We trained an original CNN model for T epochs to obtain a pre-trained model, recorded the learning rate schedule of this training process, and then pruned this model by the KRP pruning method proposed in The horizontal axis indicates the number of training epochs, the vertical axis indicates the corresponding learning rate, r is the initial learning rate of the original network training, and the black line indicates the preset learning rate variation pattern. T indicates the original network training epochs, a in the jth kernel, and jth kernel. This formula is used to construct a structure identical to the original network by setting up a matrix containing only 0 s and 1 s, with 0 s denoting pruned weights, and 1 s denoting unpruned weights. When the network is retrained and the weights are updated, we use the stochastic gradient descent algorithm (SGD) and restrict the update of weights using the constructed mask matrix structure. The specific update method is as follows:E denotes the loss function, a in the jth kernel, a in Equation (2). When A weight update phase occurs in the retraining process, and we need to restrict the pruned weights to keep the value of 0 in this process. Therefore, in this study, we set a mask matrix Algorithm 1 One-time KRP pruning process.Input 1: The pre-trained CNN model: Input 2: Learning rate schedule of the original CNN training process: for do1:  2:   Determine the L1 norm of all rows by Equation (1)3:   Keep the row with the largest L1 norm, prune the other weights, and update end for4:  if there are FC layers5:  6:   Set the pruning rate of FC layers for unstructured pruning and update 7:  Set the LR tracking retraining epoch and retraining learning rate to correspond to 8:  Update the weights with Equation (3)Output: Row-scale regular pruning CNN modelFor CNNs where all convolutional kernels are 3 × 3, such as VGG-16, ResNet-18, ResNet-56, ResNet-110, etc., the pruning rate of the KRP method is relatively low and proposed method easily compensates for accuracy loss. Therefore, for these CNN models, we used a one-time pruning method in this study to complete the pruning of the whole CNN at once, and then retrained based on LR tracking to recover the accuracy, which can significantly reduce the time cost of training. The whole KRP algorithm is shown in Algorithm 1.However, one-time pruning may not be suitable for CNNs containing a large number of 5 × 5, 7 × 7, or 11 × 11 convolutional kernels  was generally low. In contrast, the pruning network obtained by our KRP pruning method had high regularity, similar to that of structured pruning, while being more accurate than that of the structured pruning SOTA model ResRep. The accuracy loss of the final pruning model of KRP was less than 0.8%, essentially achieving one-time lossless pruning with a 70% pruning rate. We also verified in ResNet-56 that the KRP pruning method could exactly obtain a performance matching that of the baseline model at a 63.8% pruning rate, which is much higher than the 50% pruning rate of the structured pruning SOTA model .In conclusion, the proposed pruning algorithm successfully solves the contradiction between the hardware and software performance of currently structured pruning and nonstructured pruning. On the premise of hardware-friendly, our algorithm has higher pruning accuracy at a high pruning rate, which demonstrates its strong application value.We combined the KRP pruning method with the GSNQ quantization method proposed in our previous study  to develWe found that after being compressed by this compression framework, the accuracy of the VGG-16 model exceeded that of the baseline model, while ResNet-56 and ResNet-110 also basically experienced no accuracy loss. Therefore, the proposed pruning and quantization-based CNN compression framework can help to obtain 4-bit fixed-point lossless compression CNN models with a pruning ratio of 70%, which substantially reduces the bandwidth, storage, and computational pressure in FPGAs without losing accuracy. This is important for the practical application of convolutional neural networks.On the FPGA platform, we compared the convolution calculation module based on KRP pruning and GSNQ quantization with other FPGA deployment modules. The input used in the experiment was the 32 × 32 pixels feature maps in the CIFAR10 dataset. The whole FPGA deployment experiment was written in Verilog hardware description language based on a ZYNQ XC7Z035FFG676-2I chip, the on-chip hardware resources of which included 171,900 LUTs, 343,800 FFs, and 900 DSPs. The development environment was the official Xilinx compilation environment: Vivado Design Suite-HLx Editions 2019.2.First, we compared the two designed convolutional computation modules  with twoWe found that our designed module 3 achieved zero on-chip DSP occupation compared with module 1, while the consumption of other resources remained basically the same. Compared with module 2, the on-chip LUT and FF consumption in module 3 was significantly reduced. These were described in detail in our previous study .The proposed pruning and quantization compression framework performed better in terms of on-chip resource consumption. From the resource consumption of module 4, we can see that the KRP pruning method not only reduced the CNN parameters by a large amount, but also helped to reduce on-chip hardware resource consumption while maintaining efficient hardware operation. Compared with the convolution calculation module 3, which only quantized and did not prune, the hardware resource consumption of module 4 was reduced by more than 50%. Compared with modules 1 and 2, module 4 even significantly reduced hardware resource consumption overall, which means that the KRP pruning method can theoretically help to achieve more than twice the parallelism of the original CNN model and has additional advantages in multichannel parallel CNN deployment computation.In most cases, the number of input and output channels of convolutional layers in CNNs was between 32 and 512. Therefore, in the FPGA deployment work, regardless of the method followed to accelerate the CNN computation, a convolutional computation accelerator architecture must be built with at least 32 convolutional computation modules in parallel ,53,54,55We found that, as in the comparison of one convolutional computation module, the GSNQ quantization method well overcame the limitation of on-chip DSP resources on the deployment performance, and module 3 performed better than modules 1 and 2. The KRP pruning-based module 4 further significantly reduced the on-chip hardware resource consumption of FPGA deployment on this basis, occupying the fewest resources. Without the on-chip DSPs limitation, we could continue to stack 32-channel parallelism up to 64-channel or even 128-channel parallelism. The proposed KRP pruning method and designed convolutional computation module can considerably save the on-chip hardware resources of FPGAs and achieve increased parallelism to enable higher computational efficiency, which strongly demonstrates the hardware-friendliness and engineering application value of the proposed KRP pruning method. Although the two cases of quantizing parameters into 4-bit and 3-bit have less impact on the on-chip hardware resource consumption in FPGAs, considering the slightly larger accuracy loss of 3-bit quantization, the 4-bit quantization of the KRP pruning model using the GSNQ method in the proposed CNN compression framework is a comprehensive choice with outstanding performance.In this study, we designed an innovative CNN regular pruning method, KRP, based on the row scale of convolutional kernels and a hardware-friendly and high-accuracy CNN compression framework. The KRP pruning method uses the rows in the convolutional kernel as the basic pruning unit, keeping only one row for each convolutional kernel and pruning all other rows. The pruning granularity is between that of nonstructured pruning and structured pruning, and it can easily obtain higher pruning accuracy than structured pruning methods with high regularity, and all the zero calculations caused by pruning can be skipped during FPGA deployment without much indexing. During model retraining, we proposed a special retraining method based on LR tracking, which can achieve far better results than traditional retraining methods by strictly tracking the learning rate schedule of the original CNN training. The results of several sets of experiments demonstrated that the KRP pruning method can produce lossless network pruning models at a pruning rate of over 60%. The proposed CNN compression framework based on the KRP pruning method and GSNQ quantization method can help to construct a lightweight CNN model that exactly matches the performance of the original model. For the compression models, we designed a high-performance convolutional computation module in FPGA. The results of multiple sets of experiments demonstrated that the KRP pruning method can help to significantly reduce the storage and hardware resource consumption during deployment in FPGA, and more effectively improve the computational parallelism and memory access efficiency with good hardware adaptation capability. The methods described in this paper provide a complete idea for the application of CNNs in the edge computing field, which can help with constructing high-accuracy lightweight CNN models and implementing a high-performance hardware accelerator.


These values are a physical sign of the elastic–plastic state of EPS. It has been shown that the final destruction of cells takes place in EPS when the active strain reaches 50%. Empirical equations are proposed to estimate the residual strain of EPS based on density with determination coefficients varying from 0.744 to 0.986 at a confidence level of 90%. Moreover, graphical interpretations with regression equations for residual strain dependence on density and compressive strength, as well as density and active strain, were proposed with determination coefficients equal to 0.779 and 0.717, respectively.In this paper, we undertake a detailed analysis of the active and passive deformation of expanded polystyrene (EPS), which is used as a thermal insulating layer in building partitions, under short-term compressive loading. The values of residual strain in 10–40 kg/m Expanded polystyrene (EPS) slabs are widely used in the building industry as thermal insulating layers ,2. TheirFurther on the mechanical response, Vilau and Dudescu  presenteEPS under simple compression has elastic properties, and residual deformation only occurs at a certain stage of loading . An overAn important feature of the deformation beyond the elastic limit is the nature of the discharge, such as the process in which the external forces change and in which all areas of the material that experience deformation also experience decreased stress. Unfortunately, only few studies ,27 have However, deformation properties under transient compressive loads  of non-modified EPS have not yet been sufficiently investigated . The res3 EPS slabs was conducted, and respective regression models to determine residual deformation were suggested. Additionally, scanning electron microscopy (SEM) images of the deformed samples were presented for graphical visualization of EPS under compressive loading at different deformations.The aim of the present investigation is to study the deformation process of EPS under short-term compression. In order to achieve the aim, the determination of compressive stress under 15–65% strain of 10–40 kg/mAn experimental study of the deformation of EPS under short-term compression was performed on slabs made by foaming a raw material in the form of beads—rigid granules 0.8–2.5 mm in diameter, which are produced by the leading European companies Styrochem and BASF. All the EPS slabs were produced according to EN 13163  requiremTests were carried out on cubic specimens with an edge length of 50 ± 1 mm, which were cut from slabs with a nominal thickness of 50 mm that are the most typical thermal insulation products for building purposes. The dimensions of the specimens were measured according to EN 12085 , and theShort-term compression of the specimens was performed on a computerized testing machine H10KS ; the compression conducted was unconfined. The experimental samples see  were defo EN 826 . Until tShort-term compression and residual deformation tests were carried out at ambient temperature (23 ± 2) °C and (50 ± 5)% relative humidity. The test specimens of EPS were conditioned at ambient temperature (23 ± 2) °C and (50 ± 5)% relative humidity throughout the duration of the experiment.The density of EPS is the main parameter determining its properties under static loading; thus, the results of the investigation of plastic deformation are presented according to this basic parameter at fixed active deformations For the processing of the experimental data received, the mathematical-statistical methods along with the program package (Statistica) were used .3; and The studied values of residual (plastic) strains are scattered randomly around a mean value nloading . They wes method .The degree of relationship between two variables in the regression scheme with a nonlinear dependence is characterized by the correlation ratio io ηy⋅x2 . BecauseIn addition to the predicted resultant as a single numerical value δ=tα;om f=n−m .The unilateral maximum and minimum limits of the confidence interval for the predicted assessment of resultant indication are 3 to 40 kg/m3, based on their EPS type.Commercially available EPS slabs are characterized by a broadly varying density, such as from 12 kg/m3 and 22–40 kg/m3. Two density ranges were selected in order to evaluate their impact on residual deformations. The structure of the EPS specimen is presented in Therefore, the whole range was selected and all specimens were divided into two groups: 12–22 kg/mUnder compression, the stress continuously grows and exceeds the elastic limit ,38, and The structure of the EPS, depending on the density of the material, is characterized by closed pores of a certain size and the arrangement of the pores in the product. The EPS slabs are made up of a hard polymeric matrix and a gaseous phase. The gaseous phase of EPS materials can take up to 98% of their volume. Whereas cellular materials are classified according to their cellular structure and cell connectivity, unlike polyurethane foam , EPS is The EPS pores are deformed at the places where they connect with each other e,f. For x-axis in The presence of an irreversible deformation is a physical symptom of an elastic–plastic state. The experimental values of the residual (plastic) deformation The results of the statistical processing of the experimental data of the deformation n values . Thus, tTherefore, the value of the coefficient of determination after the active deformation to  factors  , active strain Xi et al.  proposedAccording to the coefficient of determination, the suggested regression is the most acceptable for the model in 3) and compressive stress, The dependence of the deformation equation b:(7)εpl). The curve (8) passes through the point The process of active deformation was analyzed, and the results were presented to demonstrate that the elastic limit exceeds the current values of stress  and εcr ,37). The in Ref. . This see et al.  showed t3. The obtained results also show that the residual deformation at the specified density range increases from 2% to 40% when the active deformation increases from 15% to 60%. Switching parameters in multicriteria regression analysis show that the apparent density, active deformation, and compressive stress are the most important factors in the proper prediction of residual deformation. Compared to previous modeling attempts, the proposed regression models allow for the accurate prediction of the residual deformation at the apparent density range of 12–40 kg/m3, active deformation 15–65%, and compressive stress 80–230 kPa. The implementation of the proposed models makes it possible to accurately determine the appropriate characteristics of EPS during compression, which makes it possible to increase the accuracy and effectiveness of the simulation. The research carried out and the results obtained allow for a better selection of the EPS type and its properties for the corresponding application.It is determined that EPS passes three stages during compression and the final destruction of cells occurs at 50% of active deformation. Moreover, it is shown that the residual deformation is dependent on the apparent density. The relation is described by the regression equation, which allows the prediction of residual deformation at a density range from 12 to 40 kg/m


C-terminal laminin G-like (LG) domains of ProS1 were swapped with those of Gas6, as well as a set of ProS1 mutants with novel added glycosylations within LG1. Alongside wildtype ProS1, only the chimera containing ProS1 LG1 domain stimulated Tyro3 and Erk phosphorylation in human cancer cells, as determined by Western blot. In contrast, Gas6 and chimeras containing minimally the Gas6 LG1 domain stimulated Axl and Akt phosphorylation. We performed in silico homology modelling and molecular docking analysis to construct and evaluate structural models of both ProS1-Tyro3 and Gas6-Axl ligand-receptor interactions. These analyses revealed a contact between the ProS1 LG1 domain and the first immunoglobulin domain of Tyro3, which was similar to the Gas6-Axl interaction, and involved long-range electrostatic interactions that were further stabilised by hydrophobic and polar contacts. The mutant ProS1 proteins, which had added glycosylations within LG1 but which were all outside of the modelled contact region, all activated Tyro3 in cells with no hindrance. In conclusion, we show that the LG1 domain of ProS1 is necessary for activation of the Tyro3 receptor, involving protein-protein interaction interfaces that are homologous to those of the Gas6-Axl interaction.The homologous proteins Gas6 and protein S (ProS1) are both natural ligands for the TAM  receptor tyrosine kinases. ProS1 selectively activates Tyro3; however, the precise molecular interface of the ProS1-Tyro3 contact has not been characterised. We used a set of chimeric proteins in which each of the  •The first LG domain (LG1) of protein S is essential for activation of Tyro3 and downstream signalling in human cancer cells.•The LG1 domain is necessary but not sufficient for TAM activation, as the presence of a tandem LG2 domain is also required.•There is a similarity between the Gas6-Axl and ProS1-Tyro3 contact sites. While Gactivity . The Gasactivity , and abeactivity . Gas6 acactivity , whereasactivity . The affactivity . The Glaactivity ,17.SHBG gene, as well as two repeats that have sequence similarities with the globular domains of the α chain of laminin and laminin-related proteins, such as merosin and Drosophila crumbs [The SHBG-like regions of both ProS1 and Gas6 contain a short segment encoded by exon IX of the a crumbs ,19. The a crumbs . Approxia crumbs . These ca crumbs . For Gasa crumbs ,22. The a crumbs , whilst a crumbs ,24.N-terminal Ig domains of the receptor, specifically a conserved surface patch on the Ig2 domain close to the inter-domain interface [The Axl receptor contains two distinct Gas6-binding epitopes: a high-affinity site on its first Ig-like domain (Ig1) and a low-affinity site on the second Ig domain (Ig2). This may contribute to greater ligand affinity/specificity and explain why Gas6 alone can cause Axl homo-dimerisation in 2:2 stoichiometry and perhaps hetero-dimerisation amongst TAMs . A crystnterface . This isHowever, in contrast to the Gas6-Axl ligand-receptor pairing, relatively little is known about the structure-function relationships behind the role of ProS1 as a TAM ligand. One study using human-bovine ProS1 chimeras as an approach suggested several residues in the LG1 domain to be involved in the activation of Tyro3 , while oA structure comparison of Axl and Tyro3 reveals noticeable differences in their Ig1 domains, whereas their Ig2 domains are quite similar . The Ig In this study, we have investigated the structure-function relationships surrounding the ProS1-Tyro3 ligand-receptor interaction. We have used chimeric and glycosylation mutant constructs to determine the specific TAM ligand properties inherent in those proteins versus the natural ligands ,29. We a22.12, as previously described [The SCC-25 human head and neck cancer cell line which expresses both Axl and Tyro3, and the MGH-U3 bladder cancer cell line, expressing only Tyro3 as TAM receptor , were boescribed .2.2C-terminal globular SHBG region, with each construct possessing a domain that was swapped with the corresponding domain from Gas6 as follows: entire Gas6 SHBG region , Gas6 LG1 domain only  or Gas6 LG2 domain only  , L379T (V3), R404T (V5), G418 N (V6) and Q427 N/K429T (V7). All ProS1 variants were expressed to result in fully γ-carboxylated Gla domains as previously described [N-linked glycosylation for variants V1, V3, V6 and V7. However, no shift was visible for V5, indicating that this variant does not bear a glycosylation but instead a single amino acid switch [Cells were first serum-starved for 24 h, then treated for various time periods with TAM ligand proteins, which were recombinant human Gas6, developed in-house , and Promera II) a. We alsd switch . In ligad switch .2.3ImageJ was used for densitometric quantification of Western blot band intensities [For cell lysis, ice-cold RIPA buffer  was used, supplemented with a cocktail of protease and phosphatase inhibitors. As previously described, SDS-PAGE and western blotting were performed on extracts using specific antibodies to detect activated, phosphorylated forms of Tyro3, Axl, Erk and Akt . The priensities .2.4Protein structures that had been experimentally determined through crystallisation were downloaded from the Protein Data Bank (PDB) . Sequenc2.5N-glycosylation sites were modelled in 3D using the GLYCAM-Web GAG Builder service and the high mannose oligosaccharide library [N-terminal Ig domains of Axl (PDB file: 2c5d) [N-glycosylated sites are present in the LG2 domain of ProS1 ; these were grafted using the GLYCAM-Web GAG Builder service. Further, as two short loops were missing in the X-ray structure of Gas6, they were built using the Swiss-Model server. Gas6 N420 is glycosylated and two sugar rings have been defined in the experimental structure. The X-ray structure of Tyro3 containing two Ig domains (Ig-like C2-type 1 and Ig-like C2-type 2) (PDB file: 1rhf) was used for the docking computations. For Tyro3, putative N-glycosylation sites were modelled in 3D using the GLYCAM-Web GAG Builder service. Similarly, putative N-glycosylation sites were modelled on both Axl Ig domains using the X-ray structure of Axl extracted from the PDB file 2c5d.PDB files were manipulated with the Python scripts available in PDB-Tools  or the M library . Electro library  and the  library . The twoe: 2c5d) . The seq2.6post-hoc Tukey test for multiple comparisons with one control group. Statistical analysis and preparations of graphs were performed using Prism software . The degree of statistical significance is indicated by symbols in the figures and accompanying legends, and p < 0.05 was considered as statistically significant.All experimental data from cells is expressed as mean ± SEM, obtained from a minimum of 3 independent experiments, with each treatment constituting multiple replicate wells per condition. Quantitative data were subjected to analysis by analysis of variance (ANOVA) and 33.1To begin with, SCC-25 cells were used as they were responsive to both ProS1 and Gas6 ligands via Tyro3 and Axl receptors respectively, and furthermore do not express ProS1, as we have previously shown . Out of In contrast, the chimeric proteins that at a minimum contained the Gas6 LG1 domain were able to stimulate Axl phosphorylation to the same extent as wildtype Gas6 c. These Therefore, for both ProS1 and Gas6, the LG1 domain is necessary as well as minimally sufficient to enable the protein to activate its respective TAM receptor and associated downstream signalling pathways.3.2We have previously shown that in MGH-U3 cells, which express Tyro3 as sole TAM receptor, Tyro3 has a broader receptivity and is also activated by Gas6 in addition to ProS1 . However3.3Following the experimental evidence that the LG1 domain of TAM ligands is necessary for TAM functional activation, we set out to propose a model of the Tyro3-ProS1 complex. We first investigated the experimental structure of Gas6 co-crystallised with Axl. The crystal structure of Gas6 in complex with Axl has been reported by Sasaki et al. . In thisIn Gas6, a large electropositive groove is present on what we refer to “Face 1” of the LG1 domain, whilst a clear electronegative region is present on the Ig1 domain of Axl, exactly within the region of the major contact site . Thus, iUsing two online services, pyDockWeb and HawkDock, we then docked Axl domains Ig1 and Ig2 onto the crystal structure of Gas6 (to which we added two missing loops that are far away from the protein-protein interface using the Swiss-Model web server) . Sugar m3.4Modelling ProS1 using the experimental structure of Gas6 was facilitated by the high sequence identity between this region of the two proteins. The new ProS1 model displays some differences in the orientation of the two LG domains and for some loop regions as compared to a previous model that we developed some years ago using the individual LG domains of laminin and plasma SHBG aligned onto the structure of a laminin structure that contained two LG domains . The RMSIn the new ProS1 model, the three known glycosylation sites are solvent exposed, with N458 and N468 located on Face 2 of the LG2 domain and N489 presents on Face 1 of the LG2 domain . The calWe then docked the experimental Tyro3 Ig1-Ig2 domain structure  onto the3.5N-linked glycosylation sites were inserted within the LG1 domain through amino acid substitutions -mediated proteolytic cleavage of coagulantion factors Va and factor VIIIa . In a coIn keeping with the domain dependence of the ProS1-Tyro3 interaction, our results also showed that the chimeric proteins containing at a minimum the Gas6 LG1 domain were able to stimulate Axl, and downstream Akt, phosphorylation with equal strength to wildtype Gas6. These findings are in accordance with the known experimental structural model of the Gas6-Axl interaction, which occurs through at minimum a major contact between Gas6 LG1 and the Ig1 domain of Axl .Our model of the ProS1-Tyro3 interaction was based on the structure of the Gas6-Axl interaction interface and compared with the experimentally determined crystal structure . Gas6 isN-glycosylation sites, one at N23 in the first Ig domain and one at N151 in the second Ig domain. It is also interesting to note that, despite some structural differences and the fact that the Tyro3 Ig structure is slightly bent as compared to Axl, the best ranked position for Tyro3 was highly similar to the position of the Axl Ig1 main contact site with Gas6. In fact, the bend in between the two domains of Tyro3 did not interfere with the docking computations as the main contact areas were located on the Tyro3 Ig1 domain and on the LG1 domain of ProS1. In this orientation, the putative Tyro3 glycosylation sites at N23 and N151 do not impede the interaction with ProS1. N23 is close to the interface, somewhat like Gas6 N420, but the complex could be formed in silico without problems as it is not directly at the interface. In addition, we tested five recombinant mutant variants of ProS1 protein, each of which harboured novel putative glycosylation sites in the LG1 domain . All of these ProS1 variants stimulated Tyro3 and Erk phosphorylation in cells to at least the same extent as wildtype ProS1, hence none of them perturbed the ProS1-Tyro3 interaction. Some of the extra glycosylation sites are close to the interface, but none of them were within the main contact site predicted by the docking. Therefore, these mutants exclude the regions in which they appear as being direct interfaces for the ProS1-Tyro3 interaction, hence further supporting the modelled contact region.Our new model of ProS1 enabled display of some important solvent exposed regions, such as possible binding sites for a complement protein, location of the calcium binding site, and of the glycosylated regions. Moreover, as the sequence identity between ProS1 and plasma SHBG or laminin is much lower than for Gas6, the new model should therefore be more accurate for performing molecular docking experiments. In addition, despite some structural differences, the Ig domains of Axl and Tyro3 are similarly formed by beta-strands connected by loops, and the RMSD between their Ig1 domains is around 2.5 Å. In Tyro3, there are two putative We therefore hypothesise that, as in the case of the main contact site of Gas6-Axl, the ProS1 LG1-Tyro3 Ig1 interaction is driven by long range electrostatic interactions, and as the two proteins get close in space, can be further stabilised by hydrophobic and polar contacts, which then become buried in this predicted orientation. As such, the suggested overall similarity in terms of binding between Gas6/Axl and Gas6/Tyro3 (not shown)  in the m5In summary, the results of this study have elucidated for the first time the location and physical nature of the interacting regions within ProS1 as ligand and Tyro3 as receptor. Furthermore, given the similarity between the Gas6-Axl and ProS1-Tyro3 contact sites, it is therefore likely that a similar nature of contact exists for Mer when interacting with both ligands, although this remains to be studied. In addition, this study further expands the knowledge on the diverse repertoire of protein-protein interactions involving ProS1, reflecting its diverse roles in regulation of blood coagulation, complement activation and the various cellular functions mediated by TAM RTKs.Conceptualisation, SH; Methodology, NK, JA, AT, LC, NS, BV; Data analysis and validation, SH, NK, JA, AT, LC, NS, BV; Resources, SH, JA, BV; Data Curation, NK, BV; Writing—original draft preparation, NK, SH, BV; Writing—review & editing, SH, NK, JA, AT, LC, NS, BV; Funding Acquisition, SH. All authors read and approved the final manuscript.This research received no external funding.The authors declare no conflict of interest.


In contrast, a related increase in properties utilizing point-plane relation prompts a dramatic cyclic degradation. Observed dielectric and piezoelectric properties are rationalized using transmission electron microscopy and time- and cycle-dependent nuclear magnetic resonance paired with X-ray diffraction. Succinct mechanistic understanding is provided by phase-field simulations and driving force calculations of the described dislocation–domain wall interactions. Our 1D-2D defect approach offers a fertile ground for tailoring functionality in a wide range of functional material systems.Dislocations are usually expected to degrade electrical, thermal and optical functionality and to tune mechanical properties of materials. Here, we demonstrate a general framework for the control of dislocation–domain wall interactions in ferroics, employing an imprinted dislocation network. Anisotropic dielectric and electromechanical properties are engineered in barium titanate crystals via well-controlled line-plane relationships, culminating in extraordinary and stable large-signal dielectric permittivity (≈23100) and piezoelectric coefficient (≈2470 pm V 3 single crystal.Dislocations are often perceived as a culprit for degradation in functionality. Here, the authors introduce a general framework for engineering dislocations and domain walls and demonstrate its full potential on a ferroelectric BaTiO Among them, topological one-dimensional (1D) dislocations and their associated elastic strain fields are of fundamental interest in materials science, with the control of dislocations emerging as a powerful approach for radically tailoring the electronic, thermal, and optical properties, such as superconductivity4, electrical/thermal conductivity6, and optical bandgap7. In particular during the last five years, extensive efforts have been made to significantly advance the knowledge about the interaction between dislocations and elementary particles7, such as phonons and electrons. Thanks to the planar strain field surrounded by dislocations, these are strongly scattered only when they propagate perpendicular to the dislocations8. This scenario offers guidance for altering intrinsically anisotropic functionalities, for example, dislocation-induced thermal transport anisotropy9 and enhanced superconductivity4.Defects are ubiquitous in materials10, the dislocations themselves act both as the sites for domain nucleation and as pinning centers for the motion of domain walls. For instance, dislocations can be used as a template for enabling nontrivial domain pinning/depinning phenomena12, and controlling the polarization instability in nanoscale ferroelectrics14. Recently, we addressed a strategy for imprinting dislocation networks with {101}<101> slip systems, using high-temperature creep compression along the [001] direction on BaTiO3 single crystals. The local dislocation–domain wall (DDW) interaction and macroscopic restoring force were highlighted to play essential roles in generating a giant enhancement in large-signal dielectric permittivity and piezoelectric coefficient15. The concept of mechanical dislocation imprinting describes a new paradigm for developing functional materials beyond chemical means . As the material is doped with its own defects without relying on additional elements, this strategy was also coined as self-doping or sustainable doping19. However, dislocation-based anisotropy utilizing 1D-2D defect interactions is still missing, due to the grand challenges of introducing highly oriented dislocations into brittle ceramic oxides20 and accurately quantifying their electrical properties, both parallel to dislocations and perpendicular to dislocations.Interestingly, when dislocations are interacting with defects of higher dimensionality such as domain walls known as topological two-dimensional (2D) defects21), but this behavior stems mainly from the intrinsic crystallographic structure  and not from anisotropic defect interactions. However, a clear picture of the above-predicted domain-wall pinning anisotropy based on geometric line-plane relationships has been lacking. This prevents deploying defect design strategies to engineer multi-dimensional defects of materials.1D-2D defect interactions generally can be classified into point-plane (0-dim/2-dim) interaction  and line-plane (1-dim/2-dim) interaction , as schematically highlighted in Supplementary Fig. 3. Well-chosen single crystal orientations then reveal anisotropic dielectric and electromechanical properties. To introduce directed mesoscopic dislocation structures, BaTiO3 crystals were plastically deformed at 1150 °C by loading along the [110] direction, see Methods and Supplementary Fig. 3. As compared to the reference sample, the domain patterns were altered by the mechanical imprint, see Supplementary Fig. b = [010]  in (110), (100) and (010) cuts, but stabilized the a1–a2 variants in the (001)-cut sample  , but more so converse piezoelectric coefficient (d33*), reveal an almost constant response at a small alternating current (AC) drive but a dramatic increase beyond a pinning field, see Methods and Supplementary Fig. –1 at 50 V mm–1) than the (001)-cut deformed sample (–1). Sub-coercive-signal properties of our previous report15 in the deformed sample with {101} < 101 >  slip systems (ε33 ≈ 5800 and d33*≈ 1890 pm V–1) are comparable to the (001)-cut deformed sample, but smaller than that observed in the (110)-cut deformed sample O3-PbTiO3 with d33* ≈ 2000 pm V–1). We note that both permittivity and d33* of the (110)-cut deformed sample, had a counter-clockwise cycle during loading and unloading hysteresis, while for the (001)-cut deformed sample, the hysteresis went clockwise during increasing/decreasing AC field  Fig. . Both, peld Fig. . As featε33 and d33* decreased significantly at the beginning, then stabilized when measuring along dislocations -cut deformed sample when applying the field perpendicular to dislocations -cut deformed sample increased, leading to an enhanced permittivity at low fields due to the anisotropic dielectric tensor -cut deformed sample were stable.Counter-clockwise and clockwise sub-coercive hystereses are suggested to indicate a difference in stability during AC field cycling. To this end, the prominent role of DDW interactions and their effects on dielectric and piezoelectric properties were obtained by extending AC field cyclic loading (see Methods). For the (001)-cut deformed sample, both ons Fig. . Howeverfor the 1-cut defo for the 0-cut defa1–a2 domain variants of unpoled (001)-cut deformed sample and two a–c variants (a1–c and dominant a2–c) of unpoled (110)-cut deformed sample -cut deformed sample and a1–a2 variants in poled (110)-cut deformed sample -cut cycled sample in Fig. a/c domain ratio of the (001)-cut deformed sample, however, increased from ~47/53 to ~92/8 -cut sample. By contrast, dislocation lines cut across a–c 90° domain walls with individual intersections, and generated the 0-dim/2-dim interaction in the (001)-cut sample  to harvest versatile functionality. Third, wider perspectives of the 1D-2D defect engineering encompass concepts, such as anisotropic domain-wall engineering28 and anisotropy control in dislocation arrangement of ferroics29.On the basis of above-mentioned results, we were able to reconstruct a full picture of 1D-2D defect interactions in BaTiOple Fig. . The domple Fig. . The aniTo mechanistically rationalize and quantify the role of anisotropic DDW interactions in dielectric permittivity and piezoelectric response, we calculated the local pinning phenomenon of both 1-dim/2-dim and 0-dim/2-dim interactions based on the phase-field simulations and the generalized theory of configurational forces (see Methods). The mechanical far-field loading activated the {100}<100> slip systems with two perpendicular slip directions, as confirmed by TEM Fig. . We incla2–c 90° domain wall and the dislocation lines at initial time step. The intersection provides a pinning force of 0.22 nN (corresponding stress is 0.029 MPa) on the domain wall towards the c-domains and increases the amount of a2-domains . The combined reversible and irreversible movement of the domain wall leads to field-dependent piezoelectric and dielectric properties of the material30. The variation of the driving force , deformation mechanisms and nanomechanics.Dislocation networks were mechanically imprinted into BaTiO9 that depends on anisotropy of electric or thermal transport. The established constitutive law in this work is not strictly limited to 1D-2D defect systems and may open a door for engineering multidimensional defects with broader perspectives. For example, the 0-dim/2-dim interaction has individual intersection points reminiscent of point defect–domain wall interactions that are capable of tailoring local domain-wall conductivity in ferroelectrics31. Dislocation-based domain-wall engineering offers a promising route to the systematic development of ferroelectric materials beyond widely used chemical means including chemical doping-associated polarization rotation  and local structural heterogeneity16. In addition, other electronic properties could be anisotropically influenced by dislocations as well. For example, enhanced superconductivity4, and ferromagnetic dislocations30 have been discovered in antiferromagnetic NiO.Our 1D-2D defect approach is fundamentally different from emerging dislocation engineering34 have demonstrated the effects of [110]-type dislocations on the equilibrium position and shape of both ferroelastic 90° and ferroelectric 180° domain walls in ferroelectric single crystals. The pinning force computed for [110]-type dislocations interacting with 180° domain walls is larger than that for dislocations interacting with 90° domain walls. In this work, we reveal that the strength of the pinning force for [100]-type dislocations interacting with both 90° and 180° domain walls strongly depends on the Burgers vector and DDW configuration (see summary in Supplementary Table 0.2Ti0.8O3 film with misfit dislocations (Burgers vector [100])12, while the strong pinning of the 90° domain wall by a pair of misfit dislocations -cut deformed samples cut from a single deformation and obtained from different deformations with the same loading parameters is ~16 V mm–1  with a geometry of 4 × 4 × 8 mm³  were deformed at 1150 °C in uniaxial compression to activate the {100} <100> high temperature slip system with a Schmid factor of 0.541. In this case, the maximized Schmid factor as quantification for the propensity of dislocation slip on the (100) planes leads to generation of dislocations along [001]  using a load-frame  equipped with a linear variable differential transformer (LVDT) for precise displacement measurement. After reaching 2% deformation, the sample was unloaded with a loading rate of 0.5 N s–1 (0.031 MPa s–1) to avoid barreling of the sample. Afterwards, the sample was cooled down to room temperature with a ramp of 1 °C min–1 under a uniaxial compressive stress of 1.25 MPa. Details of the deformation experiments can be found in Supplementary Fig. 3 crystal  [110]-oriented high-quality BaTiOsee Fig. . During entclass1pt{minima–1) prior to electrical characterization. DC-poling of reference and deformed samples was performed under 1 kV mm–1 for 10 min at room temperature.To assess anisotropic behavior, deformed crystals were sliced into smaller pieces Fig.  using a –1, to completely release the strain from polishing procedure40. The annealed TEM slices were mounted on supporting molybdenum grids of 100 mesh  and thinned by Ar ions using a dual ion milling system  into electron transparency.Bright-field TEM and scanning transmission electron microscopy (STEM) images were taken with a JEM-2100F TEM . The deformed crystal was cut parallel to (110), (100), (010), as well as (001) planes into small pieces with a 300-μm thickness and polished using a MultiPrep polishing system  down to 20 μm. To remove any influence from polishing on the dislocations imprinted by high-temperature deformation, the as-polished thin slices were annealed at 200 °C for 30 min with a slow heating/cooling rate of 1 °C min–1. Polarization hysteresis (P–E) loops at room temperature were recorded at 1 Hz using a TF 2000E ferroelectric workstation . Sub-coercive AC field dependence of permittivity was obtained using a lock-in amplifier  in combination with a high voltage amplifier  generated peak AC voltage and current of ±700 V and ±200 mA, respectively. To simultaneously quantify the converse piezoelectric coefficient, d33*, we combined the driving-voltage setup with a laser vibrometer . AC voltage-induced strain and displacement signals through the converse piezoelectric effect were determined by the Doppler effect with a high precision of ±1 pm42. However, the real resolution of the laser vibrometer is limited by background noise (10–20 pm). The cycling experiment in Fig. –1 up to 18 cycles (about 6 h for the whole measurement). The developed displacement as a function of time under different AC voltages was recorded using a self-developed Macro code in the VibSoft-VDD software.Small-signal dielectric permittivity as a function of temperature was quantified with an applied AC field of 1 V (root-mean-square) using a HP 4192 A impedance analyzer  equipped with a furnace  with a heating ramp of 1 °C min3 single crystals were taken using a LEXT laser scanning microscope . Both differential interference contrast (DIC) mode and polarized light mode were used to image domain patterns. For in situ domain observation, the top surface of the sample was additionally polished to optical grade. A transparent gold electrode was sputtered onto the top surface. Domain structures in Supplementary Fig. Optical images  patterns from the adjacent area. Evaluation of the diffraction patterns revealed that the trace of dislocation lines is mainly parallel to the [001] direction. Two beam conditions were adopted to determine the direction of the Burgers vector of dislocations, demonstrating that 137Ba NMR spectra on single crystals  were collected with a Bruker Avance III HD spectrometer  equipped with a wide bore 14.1 T Oxford magnet. A single-axis goniometer NMR probe  with nominal resolution of 0.1° was tuned to 66.71 MHz. An angle of 0° represents the normal vector of the sample holder as parallel to the magnetic field B0. An angle of 20° was set to measure samples exposing a (001) face or a (110) face, respectively. A Hahn-echo sequence with τ = 30 µs and a recycle delay time of 1 s for an acquisition time of 0.05 s was applied. The duration of the 90° pulses was set to 3.5 μs. The number of scans was set to 10240, for a used sample volume of about 4 × 4 × 1 mm3. The pre-scan delay was set to 10 μs. The chemical shift scale was referenced with respect to a 1 M solution of BaCl2 (0 ppm).α1,2 radiation. We used (001)-cut deformed samples (about 4 × 4 × 1 mm3) for XRD experiments on two large (001) faces. XRD patterns as a function of time after poling were recorded to determine the domain ratio  based on the intensity around (200) and (002) peaks. When the sample was poled along [001] and [110], the poling induced a–c and a1–a2 90° domain wall variants  experiments were carried out using a Bruker D8 diffractometer  in Bragg-Brentano geometry using Cu-K43 combining a ferroelectric phase-field model, non-singular solution of dislocations and the extended configurational force theory, we investigate the anisotropic DDW interactions here. For the ferroelectric phase-field model, the free energy of the BaTiO3 single crystal is given in the followingαij, αijkl, αijklmn and αijklmnpq are the phenomenological Landau-Devonshire coefficients. Only αij are linearly dependent on temperature, T is the temperature, TC is the Curie temperature. Pi is the polarization. cijkl is the elastic stiffness tensor, ui,j. Qijkl are the electrostrictive coefficients. bi is the Burgers vector, nj is the normal vector of the slip plane, and 15.15. ω0 is the dielectric permittivity of vacuum and κ is the relative dielectric permittivity of the bulk. The electric field is defined as φ is the electric potential. gijkl are the gradient energy coefficients. The material parameters can be found in refs. Based on our recently established simulation frameworkfi is the body force, q is the volume charge density, and M is the mobility parameter. The stress (σij) and electric displacement (Di) are defined through the constitutive relationsThe evolution of domain structures is governed by the following equations of motion:Fk is calculated based on the generalized theory of configurational forces34V is the volume of the sample, Σkj is the Eshelby stress tensor, δkj is the Kronecker delta, and 34. In the finite element simulation, the nodal configurational force on the I-th node is computed in the postprocessing nel is the number of elements around a node, Ve is the volume of an element, and Gi around the defects. The configurational force theory mentioned above is used to calculate the dislocation induced driving force on the domain wall. Considering a vertical domain wall to move in the x1 direction, we assume the external electric field induced driving force is superposed on the dislocation-induced driving force for simplicityx2 direction and l is the height of the sample, respectively. The corresponding potential energy of the driving force is calculated asL is the distance between the domain wall and dislocation and the domain wall is placed perpendicular to the x1 axis. For a mixed dislocation, the screw component has Burgers vector b and sense vector x3 direction. According to the Peach-Koehler force 45, the pinning force on the domain wall due to the screw dislocation is F = 0. This theoretical estimation means that the interaction between the screw dislocation and the domain wall should be weak and is thus not considered in our simulations.The driving force on the domain wall 3. The periodic boundary condition was applied for the top and bottom surfaces, as well as for the front and back surfaces. The left and right surfaces are traction-free and the applied surface charge density is consistent with the horizontal component of the spontaneous polarization. Considering the boundary conditions, we used the following coordinate systems: (110)-cut deformed sample with x1:x2: [001]; x3:[110], and (001)-cut deformed sample with x1:x2: [100]; x3:[011]. The AC field with amplitude of Epin was applied for (110)-cut deformed sample and (001)-cut deformed sample along the 46. Numerical simulations were carried out on the High Performance Lichtenberg Cluster at Technical University of Darmstadt.The simulation used material parameters at room temperature (25 °C). The sample size for three-dimensional simulations in Fig. ystems: 1-cut defoFurther information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Lasing Reporting Summary


TIMP1. Weighted gene co-expression networkanalysis (WGCNA) yielded multiple transcriptional modules that were sharedbetween BPSD domains or unique to each domain, and NetDecoder was used toanalyze context-dependent information flow through the biological network. Forthe agitation domain, we found that all DEGs and a highly correlatedtranscriptional module were functionally enriched for ECM-related genesincluding TIMP1, TAGLN, and FLNA. Anotherunique transcriptional module also associated with the agitation domain wasenriched with genes involved in post-synaptic signaling, including DRD1,PDE1B, CAMK4, and GABRA4. By comparingcontext-dependent changes in DEGs between cases and control networks,ESR1 and PARK2 were implicated as two highimpact genes associated with agitation that mediated significant informationflow through the biological network. Overall, our work establishes uniquetargets for future study of the biological mechanisms of BPSD and resultant drugdevelopment.Despite the significant burden, cost, and worse prognosis ofAlzheimer’s disease (AD) with behavioral and psychological symptoms ofdementia (BPSD), little is known about the molecular causes of these symptoms.Using antemortem assessments of BPSD in AD, we demonstrate that individual BPSDcan be grouped into 4 domain factors in our sample: affective, apathy,agitation, and psychosis. Then, we performed a transcriptome-wide analysis foreach domain utilizing bulk RNA-seq of post-mortem anterior cingulate cortex(ACC) tissue. Though all 4 domains are associated with a predominantlydownregulated pattern of hundreds of differentially expressed genes (DEGs), mostDEGs are unique to each domain, with only 22 DEGs being common to all BPSDdomains, including  Although dementia is defined largely by memory and cognitive decline whichresults in the loss of the ability to function independently, behavioral andpsychological symptoms in dementia (BPSD) play a large role in a patient’soverall functional level . SignifiBPSD are a heterogeneous group of symptoms, and while each symptom has itsown frequency of presentation, studies have suggested that certain symptoms co-occurat greater rates than others, 24. In Despite the ubiquity and burden of BPSD clinically, very few molecularstudies exist to elucidate the molecular mechanisms underlying BPSD-27, withCommunity-dwelling older adults who later developed dementia and theirinformants were recruited through the Rush Alzheimer’s Disease Center(RADC) memory clinic. As a part of their research visits, trained researchassistants conducted standardized clinical interviews via telephone withinformants, including assessment of the frequency and severity of numerous BPSD.Questions pertaining to BPSD were developed by two neuropsychologists fromclinical descriptions in the literature, observations of patient behaviors, andcaregiver interviews, as described previously-34.This study was approved by an Institutional Review Board of RushUniversity Medical Center (RUMC). For all subjects, consent for brain autopsywas obtained after death from next of kin and a witness by RUMC staff. Inclusioncriteria for subjects in the present study were based on the NINCDS-ADRDAclinical criteria for “probable AD” with diagnosticians blinded topost-mortem findings. Namely,To determine the grouping of BPSD into domains, we performed a principalcomponent analysis with oblimin rotation on responses to BPSD questions from 192th percentile), controls (≤30thpercentile), or neither (31st-69th percentile), and usedthese groupings for subsequent transcriptome analyses. This design permitted anindividual subject to be considered as a case for one domain but as a controlfor another, maximizing the sample sizes of cases and controls for each domain.In choosing a subset of subjects for our final sample, we made sure that eachdomain contained similar proportions of cases to controls and of males tofemales so as to minimize bias due to overrepresentation of any particulargroup. We ultimately chose 60 subjects for biochemical analyses . The quality of RNA was measuredusing an Agilent Bioanalyzer, which produces an RNA Integrity Number (RIN)between 1 and 10, with 10 being the highest quality samples showing the leastdegradation. The RINs of the 60 samples ranged between 5.3-10.0 (88%>7.0), and 1ég of high-quality RNA per sample was used for thetotal RNA-Seq library preparation. RNA-Seq was conducted at the NorthwesternUniversity NUSeq Core Facility. Briefly,_the Illumina TruSeq Stranded Total RNALibrary Preparation Kit was used to prepare sequencing libraries. The Kitprocedure was performed without modifications. This procedure includes rRNAdepletion, remaining RNA purification and fragmentation, cDNA synthesis,3' end adenylation, Illumina adapter ligation, and library PCRamplification and validation. Illumina HiSeq 4000 Sequencer was used to sequencethe libraries with the production of single-end 50 bp reads.Raw data was pre-processed with TrimGalore, including an initial qualitycontrol (QC). Read depth ranged from 50 – 90M. Pseudo-alignment wasperformed with Kallisto with k-mcontrol = 23, ncase = 22; Apathy domain,ncontrol = 23, ncase = 25; Agitation domain,ncontrol = 28, ncase = 23; Psychosis domain,ncontrol = 24, ncase =21. Because overlapping designallowed for individual samples to be analyzed as a case or control depending ontheir individual domain score, the sum of all comparisons should not add up tothe total 59 included samples. We were underpowered to perform differentialexpression analysis by sex, and therefore performed 4 independent analyses, onefor each domain, using the cases and controls identified by our pre-mortemscoring system. DESeq2[RIN, sex, and RNA-isolation batch were identified as contributingsignificantly to variation and were included in the final model, while othervariables including post-mortem interval, Braak, CERAD, NIA-Reagen scores, andage at death did not. This was also analyzed with Eigen-R2, which lm. DESeq2 was usedFunctional Enrichment Analysis was performed using gProfiler2, and is Prior to cellular decomposition, WGCNA, and NetDecoder analyses, countswere converted to transcripts per million (TPM) and underwent covariatecorrection and variance stabilizing transformation via limma. BRETIGEWeighted gene co-expression network analysis (WGCNA) was performed toidentify modules of gene co-expression, 47. We IMPDH2, LAMTOR1, MTFR1L,SMIM7, TMEM50B) were selected based on lowcovariance between cases and controls in our RNA-seq experiment. Eight negativecontrols and six positive controls (NanoString Technologies) were measured as acomponent of QC.RNA fluorescent barcoding was used to perform multiplex measurement of37 agitation domain genes of interest (GoIs), the selection of which wasinformed by WGCNA and differential expression analysis. A customCodeSet/ProbeSet  was designed to measureGoI transcript counts from the RNA samples that remained available following usefor RNAseq (N = 47). In addition to GoIs, five reference genes. Briefly, 50ng of total RNA at a concentration of10ng/μL was incubated with a Reporter CodeSet-hybridization buffer master mix and Capture ProbeSet in athermocycler at 65°C untilsample processing on the following day. Hybridized samples were brought to avolume of 30μL with RNAse-free water and loaded into an nCounter SPRINTcartridge , which was run on annCounter SPRINT profiler. Transcript counts detected by barcode visualization inthe nCounter SPRINT profiler were analyzed using nSolver Analysis Software(v4.0). Differences between group means were evaluated using Welch-Satterthwaitet-tests and a threshold of p < .05 was implemented fordetermination of statistical significance. All 47 measured samples were includedfor analysis, as binding density QC indicated sufficient RNA abundance withoutlane oversaturation, no fields of view were lost during imaging, and assessmentof positive control linearity yielded r2 = 1.0 for each sample.Incubation temperature was then reduced to 4°NetDecoder was performed to compare context-dependent changes ininformation-flow through case and control networks, as previouslydescribed. A fulleThough BPSD are heterogeneous, previous reports have indicated thatcommon symptoms often co-occur at high rates and can be grouped intodomains, 24. HowAfter demonstrating BPSD could be split into 4 domains, we created ascoring system to estimate BPSD burden using a composite of individual symptomseverity and frequency within each domain and grouped individual as cases orcontrols for each domain. Though BPSD are likely to result from dysfunction ofmultiple brain regions, the ACC has repeatedly been implicated as being involvedin all four BPSD domains-51. TherUsing a liberal cut-off for significance of nominal p < 0.05 andfold change > 0.20, we identified hundreds of DEGs for each BPSD domainwith a pattern towards downregulated BPSD for all four domains. Specifically, wefound 207 upregulated and 396 downregulated  DEGs for theaffective domain; 135 upregulated and 788 downregulated (86%) DEGs for theapathy domain; 619 upregulated and 931 downregulated (60%) DEGs for theagitation domain; and 227 upregulated and 519 (70%) downregulated for thepsychosis domain .Importantly, despite an experimental design using the same subjects ascases and controls on a domain-by-domain basis, most of the DEGs weretranscriptionally unique for each domain : 65.8% uTranscriptional signatures can be used to estimate relative cellabundance of the origin tissue. Therefore, we used BRETIGEA to estimate the cellabundance of 6 major cell types – astrocytes, endothelial cells,microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells.Across all four domains, only a decrease in microglia was detected for casescompared to controls in the apathy domain, while all other cell typecompositions were comparable (p < 0.05). The results were similar when adifferent deconvolution algorithm, BisqueMarker, was used (data not shown).TIMP1 being a notableDEG .Interestingly, there were only 22 DEGs that were shared by all four BPSDdomains  and all were downregulated.Functional enrichment analysis yielded pathways related to response tocytokines, integrin-mediated signaling, and collagen trimers , with TIFunctional enrichment analysis of the DEGs were performed for each BPSDdomain. Here, we focus on the results of the agitation domain, as this domain isoften most challenging for families and clinicians due to high risk of safetyissues including aggression, increased risk-taking and impulsivity, etc. see. TranscrTIMP1, TAGLN, and FLNA and FLNA . TIMP1 was also in this domain buthad module membership value slightly below the cut-off for potential hub genes.The synaptic signaling and monoamine module (darkgrey), a unique module withincreased transcription for aggression domain cases, had 39 hubs genes includingCAMK4 ,PDE1B ,DRD1 , andGABRA4 .Hub genes in WGCNA that are connected with a given domain are defined bythe high connectivity within their module as well as significant influence ontranscription with the BPSD domain . The ECMBPSD likely arise from complex changes in multiple biological networks,and the dynamic interactions of genes and proteins within a network may be keyto understanding the difference between the BPSD cases and controls in AD. Whileenrichment analyses suggest important pathways based on overrepresentation ofgenes compared to chance, they cannot inform how biological information maychange in a context-dependent manner – such as a disease vs non-diseasestate. Therefore, we used NetDecoder to compare the‘information-flow’ through each BPSD domain’s cases andcontrols’ networks, which utilizes a process-guided flow algorithm toidentify the weights of information flow from source genes, DEGs, to targetgenes, transcriptional regulators.For conESR1, was identified as the top key target and highimpact gene mediating positive information flow , there is already some evidence suggesting that BPSDmechanisms diverge molecularly. There have been a handful of genetic studiessuggesting overlapping polygenic risk for PPD and neurodegenerative disease, 62,andTreatment of BPSD has also yielded surprising differences from PPD. Forinstance, the HTA-SADD trial found no benefit of two common serotonergicantidepressants for depression in AD, anda CRigorous characterizations of BPSD antemortem with complementary post-mortemtissues for molecular analyses are remarkably scarce, so we sought to optimize ouranalytical power for the samples we were able to obtain. In addition, given the highprevalence of BPSD in AD ( > 95%), finding enough controls without any BPSDwould be very challenging. This required us to adopt an experimental design whereeach individual’s sample could be considered to be a case (25% highest scoreof the domain) or control (25% lowest score of the domain) for a specific BPSDdomain, meaning that some transcriptome could be potentially analyzed multiple timesdepending on the comparison. Therefore, it was encouraging to see such a largenumber of DEGs that are unique to each domain despite the overlapping design, whichmay suggest that each BPSD domain has distinct biological etiologies despite thecommon neuropathological drivers – in this case, AD. These findings may becomparable to other investigations showing distinct pathways associated withcognitive decline in AD at the transcriptomic, proteomic, and methylation levels inbrain tissue without overlapping dependence on being correlated with AD pathology-79, highTIMP1was among the 22 shared DEGs, all of which were downregulated. A major inhibitor toa number of metalloproteases such as MMP3 and MMP9, TIMP1 has beenimplicated in multiple forms of neurodegeneration and neuroinflammation[TIMP1 maintainsbalance in neurodegenerative states while late downregulation may suggest inabilityto achieve homeostasis[TIMP1 leads toloss of homeostasis, which could lead to heightened stochasticity in downstreamprocesses and divergent BPSD. While it is also interesting thatTIMP1 has been suggested as a biomarker in biofluids in bothParkinson’s disease and AD[TIMP1 could be especially fruitful in understanding the earlydrivers of BPSD.Given this overlapping design, it was surprising that so few genes wereshared amongst all the BPSD domains. It is notable that response to cytokines wasimplicated as a functionally enriched pathway, as neuroinflammation is oftenconsidered an integral driver of neurodegeneration and subsequent synapticdysfunction. TIMP1wlammation, 82,witmeostasis. It is tse and AD-86, furtTIMP1, was associated with inhibition of aggressionand irritability in one study[Though our analyses discovered multiple interesting targets that were uniqueto each BPSD domain , serotonin(antipsychotics and antidepressants), and GABA-A receptors (benzodiazepines). As monoDRD1SNPs are associated with greater impulsivity and aggression[Additionally, the finding that DRD1, a GPCR coupled to Gs/α, anddownstream effectors PDE1B and CAMK4 are associated with this module and theagitation domain is in line with some genetic reports that ggression, 90, incggression, 92 and ggression. The rolggression, but howggression, 96 and ggression. It was ESR1 and PARK2. For neurons inthe ventromedial hypothalamus[ESR1+ expression differentiates these cells as onesthat regulate aggression from the behavioral function ofESR1− cells. Despite the importance ofESR1 as a marker of aggression-regulating neurons, the actualfunction of ESR1 in the cell and resultant aggressive behavior isless clear, though knockout of ESR1 in mice leads to reducedaggression[ESR1polymorphisms have been linked with aggression in humans[ESR1’s role in the ACC interms of agitation, aggression, and impulsivity may be a particularly fruitfulavenue for mechanistic understanding and future drug development.Transcriptome datasets are inherently noisy, and predicting how biologicalnetworks regulating transcription, translation, and protein-protein interactionsmight change based on differential gene expression can uncover hidden drivers ofdisease. Using an analytical framework to analyze transcription in thiscontext-dependent way, we uncovered two high interest gene targets related to theagitation domain, othalamus, posteriothalamus, and bedothalamus,ESR1+ eggression and ESR1in humans and songin humans. UnderstThe main novelty of our study is that it is the first, to our knowledge, tostudy the affective, apathy, and agitation domains on the transcriptome-wide level,which is comparable to the many studies of cognition in AD, 75, 77,Supplementary Table S1Supplementary Table S1 Questions and evaluations for behavior andpsychological symptoms in AD patients with dementiaSupplementary Table S2Supplementary Table S2 Notable Affective DEGEnrichments. Using gProfiler2, functional enrichment analysis wasperformed, and the most notable pathways are listed in this table. Sourceindicates the databases queried and include Gene ontology: Molecularfunction (GO:MF), GO: Biological process (GO:BP), GO: Cellular compartment(GO:CC), Kyoto encyclopedia of gene and genomes (KEGG), and Reactome (REAC).Adjusted p-values (P Adj) > 0.05 were considered significant.Supplementary Table S3Supplementary Table S3 Notable Apathy DEG Enrichments.Using gProfiler2, functional enrichment analysis was performed, and the mostnotable pathways are listed in this table. Source indicates the databasesqueried and include Gene ontology: Molecular function (GO:MF), GO:Biological process (GO:BP), GO: Cellular compartment (GO:CC), Kyotoencyclopedia of gene and genomes (KEGG), Reactome (REAC), WikiPathways (WP),CORUM, and Human phenotype ontology (HP). Adjusted p-values (P Adj) >0.05 were considered significant.Supplementary Table S4Supplementary Table S4 Notable Psychosis DEGEnrichments. Using gProfiler2, functional enrichment analysis wasperformed, and the most notable pathways are listed in this table. Sourceindicates the databases queried and include Gene ontology: Molecularfunction (GO:MF), GO: Biological process (GO:BP), GO: Cellular compartment(GO:CC), Kyoto encyclopedia of gene and genomes (KEGG), and Reactome (REAC).Adjusted p-values (P Adj) > 0.05 were considered significant.supplementary materials6Supplementary Fig.S1 BPSD can be split into four domains basedon clustering of symptoms. A) Principal component analysis wasperformed on the symptom questions from the structured interview for eachparticipant, and a Scree plot was constructed to evaluate how many factorsoptimally grouped BPSD. The curve began to asymptote between 3-5 factors.B) Horn’s parallel analysis suggested a 4-factorsolution, where eigenvalues > 1. C) Each BPSD questionwas loaded onto a predominant factor, and these loadings were used to sorteach question into BPSD domains. D,E) Variance explained byeach factor and correlations between factors.Supplementary Fig. S2 Estimated cell abundance for affective,apathy, and psychosis domains. A) Relative cell abundanceestimated with BRETIGIA for affective, B) apathy,C) and psychosis domains. After correcting for multiplecomparisons, there was a significant decrease in microglia detected inApathy domain cases (p < 0.05) but not for any other comparison.Supplementary Fig. S3 Agitation domain case subnetwork.Main differences in case subnetwork compared to control subnetwork. Sourcegenes are indicated as diamonds, intermediate genes as circles, and targetgenes as squares. The color of the node depicts flow difference, with redbeing high flow in case but low in control and blue being high flow incontrol but low in case. Edge colors correspond to directionality ofcorrelation, with red being positive and blue being negative. To aidvisualization, nodes and edges with highest differences in information floware shown.Supplementary Fig. S4 Divergent information flow betweentranscriptional networks in the affective domain. A) Total edgeflow profiles in control versus case subnetworks for the affective domain.Overall, edges display decreased flow in cases versus controls.B) Jaccard index evaluating similarity between case andcontrol subnetworks. C) Venn diagrams depicting overlap (blue)in genes, edges, and paths between cases (yellow) and controls (green).D) Heatmaps showing top 20 network routers and key targetswith the most increased (red) and most decreased (blue) flow difference.Network routers are intermediary genes with the highest difference in flowwhen comparing the case and control subnetworks. Key targets are sinks,transcriptional regulators in this analysis, that have the highestdifference in flow to them when comparing the case and control subnetwork.E) Top 20 impact genes with the most increased (red) andmost decreased (blue) flow differences between case and control subnetwork.Impact scores to determine top impact genes are based on total flowdifference at each node between cases and controls, proportion of newlyestablished interaction in the case subnetwork, and number of edges wherethe expression correlation changes directionality from control to case.Arrows point to notable genes depicted in rest of figure (below) and maintext. F) Differences in information flow between control andcase subnetworks at TGFBR1, G) TGFBRAP1,and H)GSK3B. Node is colored according to the node flowdifferences across case and control subnetworks. Edge thickness, detailed inhistogram on x-axis, represents total amount of edge flow from 0 to 1. Thedirection of flow is determined by overall structure of information flowfrom source to sinks. A red edge depicts a positive gene expressioncorrelation between a pair of protein-protein interactions, while blue edgesrepresent a negative correlation. Histogram depicts number of edges binnedby total flow. Genes with flow amounts to the right of the dashed, red lineare depicted in the corresponding graphic to the right of eachhistogram.Supplementary Fig. S5 Affective domain case subnetwork.Main differences in case subnetwork compared to control subnetwork. Sourcegenes are indicated as diamonds, intermediate genes as circles, and targetgenes as squares. The color of the node depicts flow difference, with redbeing high flow in case but low in control and blue being high flow incontrol but low in case. Edge colors correspond to directionality ofcorrelation, with red being positive and blue being negative. To aidvisualization, nodes and edges with highest differences in information floware shown.Supplementary Fig. S6 Divergent information flow betweentranscriptional networks in the apathy domain. A) Total edge flowprofiles in control versus case subnetworks for the apathy domain. Overall,edges display decreased flow in cases versus controls. B)Jaccard index evaluating similarity between case and control subnetworks.C) Venn diagrams depicting overlap (blue) in genes, edges,and paths between cases (yellow) and controls (green). D)Heatmaps showing top 20 network routers and key targets with the mostincreased (red) and most decreased (blue) flow difference. Network routersare intermediary genes with the highest difference in flow when comparingthe case and control subnetworks. Key targets are sinks, transcriptionalregulators in this analysis, that have the highest difference in flow tothem when comparing the case and control subnetwork. E) Top 20Impact Genes with the most increased (red) and most decreased (blue) flowdifferences between case and control subnetwork. Impact scores to determinetop impact genes are based on total flow difference at each node betweencases and controls, proportion of newly established interaction in the casesubnetwork, and number of edges where the expression correlation changesdirectionality from control to case. Arrows point to notable genes depictedin rest of figure (below) and main text. F) Differences ininformation flow between control and case subnetworks atTRAF6, G)CUL1, and H)CREBBP. Node is colored according to the node flowdifferences across case and control subnetworks. Edge thickness, detailed inhistogram on x-axis, represents total amount of edge flow from 0 to 1. Thedirection of flow is determined by overall structure of information flowfrom source to sinks. A red edge depicts a positive gene expressioncorrelation between a pair of protein-protein interactions, while blue edgesrepresent a negative correlation. Histogram depicts number of edges binnedby total flow. Genes with flow amounts to the right of the dashed, red lineare depicted in the corresponding graphic to the right of eachhistogram.Supplementary Fig. S7 Apathy domain case subnetwork.Main differences in case subnetwork compared to control subnetwork. Sourcegenes are indicated as diamonds, intermediate genes as circles, and targetgenes as squares. The color of the node depicts flow difference, with redbeing high flow in case but low in control and blue being high flow incontrol but low in case. Edge colors correspond to directionality ofcorrelation, with red being positive and blue being negative. To aidvisualization, nodes and edges with highest differences in information floware shown.Supplementary Fig. S8 Divergent information flow betweentranscriptional networks in the psychosis domain. A) Total edgeflow profiles in control versus case subnetworks for the Psychosis Domain.Overall, edges display decreased flow in cases versus controls.B) Jaccard index evaluating similarity between case andcontrol subnetworks. C) Venn diagrams depicting overlap (blue)in genes, edges, and paths between cases (yellow) and controls (green).D) Heatmaps showing top 20 network routers and key targetswith the most increased (red) and most decreased (blue) flow difference.Network routers are intermediary genes with the highest difference in flowwhen comparing the case and control subnetworks. Key targets are sinks,transcriptional regulators in this analysis, that have the highestdifference in flow to them when comparing the case and control subnetwork.E) Top 20 Impact Genes with the most increased (red) andmost decreased (blue) flow differences between case and control subnetwork.Impact scores to determine top impact genes are based on total flowdifference at each node between cases and controls, proportion of newlyestablished interaction in the case subnetwork, and number of edges wherethe expression correlation changes directionality from control to case.Arrows point to notable genes depicted in rest of figure (below) and maintext. F) Differences in information flow between control andcase subnetworks at HSP90AA1, G)ARRB1, and H)YWHAZ. Node is colored according to the node flowdifferences across case and control subnetworks. Edge thickness, detailed inhistogram on x-axis, represents total amount of edge flow from 0 to 1. Thedirection of flow is determined by overall structure of information flowfrom source to sinks. A red edge depicts a positive gene expressioncorrelation between a pair of protein-protein interactions, while blue edgesrepresent a negative correlation. Histogram depicts number of edges binnedby total flow. Genes with flow amounts to the right of the dashed, red lineare depicted in the corresponding graphic to the right of eachhistogram.Supplementary Fig. S9 Notable key target and network routerfor psychosis domain. Despite not affecting enough informationflow to be considered high impact genes, a notable key target inHTT (A) and a notable network router inSCNA (B). HTT encodes huntingtin, which is theprotein implicated in the neurodegenerative condition Huntington’sDisease. There is a notable shift of information flow away from thepost-synaptic protein DLG4 (PSD95) in the control conditiontowards proteins involved in the unfolded protein response.SCNA encodes α-synuclein, which is the proteinimplicated in the neurodegenerative disease Parkinson’s disease.Supplementary Fig. S10 Psychosis domain casesubnetwork. Main differences in case subnetwork compared tocontrol subnetwork. Source genes are indicated as diamonds, intermediategenes as circles, and target genes as squares. The color of the node depictsflow difference, with red being high flow in case but low in control andblue being high flow in control but low in case. Edge colors correspond todirectionality of correlation, with red being positive and blue beingnegative. To aid visualization, nodes and edges with highest differences ininformation flow are shown.Supplementary Fig. S11 WGCNA optimization and moduledendrogram. A) Using the Abbassi-Daloii WGCNA optimizationmethod, 420 Enrichment factors (EF) were computed based on correspondingdifferences in WGCNA parameters. These EFs were plotted against the numberof modules with a weak linear correlation. The module circled in red depictsthe EF of the parameters ultimately chosen for WGCNA, as this was both thehighest EF and resulted in a moderate number of modules. B)Network properties for different soft thresholds visualized by graphingpower by scale free topology fit. Notably, network is signed. Meanconnectivity at different powers is also shown. C) Moduledendrogram depicting clustering of genes into modules based on highco-expression.


The teams interacted significantly less with a “withdrawn” than with an “agitated” relative . The teams confronted with an “agitated” relative showed more unsafe defibrillations, higher ventilation rates, and a delay in starting CPR . The presence of a relative increased frustration, effort, and perceived temporal demands ; in addition, an “agitated” relative increased mental demands and total task load (both p < 0.05 compared to “withdrawn” and control group). The type of leadership condition did not show any effects. Conclusions: Interaction with a relative accounted for up to 25% of resuscitation time. Whereas the presence of a relative per se increased the task load in different domains, only the presence of an “agitated” relative had a marginal detrimental effect on CPR quality (GERMAN study registers number DRKS00024761).Background: Guidelines recommend that relatives be present during cardiopulmonary resuscitation (CPR). This randomised trial investigated the effects of two different behaviour patterns of relatives on rescuers’ perceived stress and quality of CPR. Material and methods: Teams of three to four physicians were randomised to perform CPR in the presence of no relatives (control group), a withdrawn relative, or an agitated relative, played by actors according to a scripted role, and to three different models of leadership (randomly determined by the team or tutor or left open). The scenarios were video-recorded. Hands-on time was primary, and the secondary outcomes comprised compliance to CPR algorithms, perceived workload, and the influence of leadership. Results: 1229 physicians randomised to 366 teams took part. The presence of a relative did not affect hands-on time (91% [87–93] vs. 92% [88–94] for “withdrawn” and 92 [88–93] for “agitated” relatives;  Discussing the presence of a relative during cardiopulmonary resuscitation (CPR) dates back to the 1980s and has been a controversial issue since ,3,4. CurResuscitation per se is a stressful task . StudiesAnalysing the effect of a family member’s presence on the quality of resuscitation in a randomised controlled trial and under controlled conditions is quite challenging in reality, as the causes of cardiac arrest may vary from case to case. Simulations allow team performance to be studied in a realistic and standardised way , both ovhttp://www.aim-arnsberg.de, assessed on 18 April 2022), arranges postgraduate educational, medical courses for physicians from Germany and German-speaking countries, mainly residents in the 2nd to 3rd year in internal medicine, Anaesthesiology, or surgery, who work in emergency and intensive care medicine. Attendees were offered participation in optional simulator-based training and were notified that the simulations would be video-recorded for scientific purposes. Physicians not willing to participate were offered identical workshops, but these were not filmed. The study was conducted according to the rules of the Declaration of Helsinki and authorised by the Ethics Committee of the Medical Association of Westphalia-Lippe , which waived the requirement to provide informed consent. The trial was registered with the German Clinical Trials Registry  and is reported here according to the extensions of the CONSORT statements of the Reporting Guidelines for Health Care Simulation Research  unless otherwise reported. Statistical analysis was conducted using SPSS (version 25). Non-parametric ANOVA  was used for analysing the numerical data, subsequently followed by Mann–Whitney-U tests for independent samples where necessary. To obtain the estimates for differences between the medians and their approximate confidence intervals, the Hodges–Lehmann estimation was applied. The effect of leadership and the interactive term leadership × relative on outcomes was assessed using SPSS’s general linear model procedure with leadership assignment and relative assignment  as fixed factors. The chi-square test was used for testing categorical data. A p = 0.16) between the study groups. Verbal interactions with the “withdrawn” relative took place during a total of 11 [7–16]% of the study time and with the “agitated” relative in 23 [15–30]% . Leadership had no influence on the allocation of interaction time (p = 0.54).After allocation and follow-up, the data of 335 teams  were analysed  had no effect on hands-on time, and there was no significant relationship between hands-on and absolute (p = 0.64) or percentage of time (p = 0.54) spent verbally interacting with the relative.Hands-on time was 91% [87–93] in the control group, 92% [88–94] in the “withdrawn” relative group, and 92% [88–93] in the “agitated” relative group (The secondary outcomes are presented in The NASA-TLX findings are summarised in The present study demonstrates that regardless of their behaviour, the presence of family members during CPR increased rescuers’ perceived task load in several domains. While withdrawn relatives had no impact on the quality of CPR, the presence of an agitated relative was associated with minor effects on the start of CPR and defibrillation patterns.Although family member presence during CPR is still a discussed and controversial issue ,2,3,4, tBy reemphasising the importance of high-quality chest compressions in the current guidelines, we selected hands-on time as the primary outcome variable . We wereHonarmand and Crowley recently described a set of ACLS guideline deviations and their impact on outcomes from in-hospital cardiac arrest ,31. SeleEmergency department personnel were reporting aggravated stress due to the attendance of relatives throughout resuscitation, and six out of twenty interviewees claimed to be hindered in their work ,4. In a In contrast to previous studies , our parThe identical conditions for all teams at all times in a large sample, together with the detailed evaluation of a large number of CPR tasks and subtasks right from the beginning of the scenario, are strengths of our simulation study. In contrast, there are often mentioned limitations in the context of a simulation study, such as the lack of real patients and—in the present study—also real family members. Due to the study centre and location, teams in the trial presented here comprised only physicians, so the results cannot necessarily be transferred to other team compositions.Regardless of their behaviour, relatives present during CPR of a next of kin increase the perceived task load of rescuers involved. The presence of withdrawn relatives has no effect on CPR quality, while the presence of agitated relatives results in minor deviations only. Thus, the CPR quality is not an argument against family presence per se. In the case of massive disruption by agitated family members, teams might consider removing them from the setting to increase patient safety.


JCI, Taguchi, Elias, et al. explore the relationship between cyclin G1 (CG1), an atypical cyclin that induces G2/M proximal tubule cell cycle arrest, and epithelial dedifferentiation during fibrogenesis. While CG1-knockout mice were protected from fibrosis and had reduced G2/M arrest, protection was unexpectedly independent of induction of G2/M arrest. Rather, CG1 drove fibrosis by regulating maladaptive dedifferentiation in a CDK5-dependent mechanism. These findings highlight the importance of maladaptive epithelial dedifferentiation in kidney fibrogenesis and identify CG1/CDK5 signaling as a therapeutic target in CKD progression.Understanding the cellular mechanisms underlying chronic kidney disease (CKD) progression is required to develop effective therapeutic approaches. In this issue of the  Chronic kidney disease (CKD) affects approximately 800 million people worldwide and treatment options are limited . CKD is 2/M phase of the cell cycle, leading to adoption of a senescence-associated secretory phenotype (SASP)  and the acquisition of a transient mesenchymal phenotype. This process is followed by cellular proliferation to replace neighboring epithelia lost through cell death, followed by redifferentiation and restoration of tubular function. Many studies have implicated aberrant proximal tubule injury responses as a central driver of the AKI-to-CKD transition , 3. Somee (SASP) , 5. Thise (SASP) –9. Impores exist , 7.JCI, Taguchi, Elias, et al. , an atypical cyclin induced by p53 and known to regulate G2/M arrest in other contexts, as a key player in the AKI-to-CKD transition  through phosphorylation of tyrosine 15, and phosphorylated CDK5 was detectable in maladaptive proximal tubules of wild-type but not CG1-knockout kidneys. In vitro studies showed that CDK5 expression was sufficient to drive proximal tubule dedifferentiation, suggesting that CDK5 activation mediates CG1-dependent dedifferentiation. Since CG1-knockout mice were protected from the AKI-to-CKD transition, Taguchi, Elias, and colleagues next asked whether CDK5 is also a therapeutic target. They showed convincingly that indeed either pharmacologic inhibition of CDK5 with two different drugs or tubule-specific knockout of ansition  10)..Cdk5 pro2/M arrest as a central, required cell state for the development of fibrosis and CKD, as had been concluded previously. Deletion of CG1 inhibited both G2/M arrest and fibrosis but subsequent induction of G2/M arrest did not reverse this protective phenotype, providing strong evidence that, in this context at least, proximal tubule G2/M arrest was not sufficient to drive fibrogenesis (2/M arrest still plays roles in CKD, perhaps acting in addition to other profibrotic pathways rather than as a necessary and sufficient state.There are several implications of this work. Perhaps most importantly, these studies shift focus away from Gogenesis . Instead2/M. These questions also await further experimental investigation.Another intriguing implication of Taguchi, Elias, et al.  that cleThat profibrotic cellular dedifferentiation in CKD can be targeted therapeutically by inhibition of CDK5 not only validates the kinase as a therapeutic target in fibrosis, but also suggests that other downstream pathways could represent additional antifibrotic targets as well. The signaling pathways either upstream of CG1 or downstream of CDK5 in proximal tubules remain undefined. Mitochondrial dynamics and dysfunction are increasingly recognized to play critical roles in driving both recovery from AKI as well as the progression of CKD . Mitocho2/M arrest from dedifferentiation and progression of fibrosis. They implicate a CG1/CDK5 signaling axis in regulating proximal tubule dedifferentiation and fibrosis and validate these proteins as therapeutic targets in CKD (In summary, Taguchi, Elias, and colleagues uncouple Gs in CKD .