The effect of the incorporation of various types of residual chia flour  on the technological quality of bread was investigated. The various types of chia flour were used to substitute 5 and 10% wt/wt of wheat flour in the bread formulations. The water absorption, dough development time and stability of blends with the presence of mucilage and the incorporation of 10% wt/wt of chia flour demonstrated the highest values in comparison with the other ones. The specific volume of the flour variants with 5% wt/wt of chia flour with mucilage were similar to the control bread; while those formulated with chia flour without mucilage exhibited a lesser volume. The incorporation of 10% wt/wt of chia flour in the formulations caused a decrease in the technological quality of the bread as expected. The bread crust and crumb colour parameters were mainly influenced by the level of chia flour substitution, which resulted in a decrease in lightness and h values. The substitution of wheat flour with 5% wt/wt of chia flour counterparts with mucilage improved the technological quality of the breads. The different oil content of the chia flours did not show any significant influence on overall quality or texture.  Nowadays, consumers demand food products that have a high nutritional value and provide additional health benefits through the incorporation of new ingredients and natural products whose composition gives protective effects against certain diseases . In this context, the incorporation of by-products derived from oilseeds, pseudocereals and other non-traditional crops could be of great interest for the development of new functional products .Bread is a staple food, and in view of the high frequency of its consumption it can be considered a good delivery system for combining natural ingredients with nutritional and functional properties . In receSalvia hispanica L.) is an ancient seed that has been revalued in recent years because of its nutritional value [In this context, chia  as functional ingredients, and to evaluate the technological quality of the final bakery products.Salvia hispanica L.) seeds were supplied by Nutracéutica Sturla S.R.L., Argentina. The seeds were cleaned manually and foreign matter, such as stones, dirt and broken seeds, was removed. Then they were packed in hermetic plastic vessels and stored at 5 °C until use. Before starting a test, the seeds were taken out of the refrigerator and allowed to temper at room temperature.Chia  was used as a starter for the breadmaking process. All the chemicals and solvents used were of analytical grade.Commercial Spanish refined wheat flour was purchased from the local market. The flour alveograph parameters were tenacity, P: 60.3; extensibility, L: 64.0 mm; and deformation work, W: 151.7 × 10Whole chia flour (WCh) was obtained from black chia seeds ground in a laboratory grinder .Semi-defatted chia flour with mucilage (SDCh) was obtained by cold pressing of chia seeds and further milling of the press-cake by the supplier .Defatted chia flour with mucilage (DCh) was obtained from SDCh after oil extraction with n-hexane in a Soxhlet apparatus  with thermal cycles at 80 °C, 8 h, following the IUPAC Standard Method .The semi-defatted or defatted chia flours without mucilage  were obtained from chia seeds after extraction of mucilage according to Capitani et al.  with modA farinograph  with a 300 g kneader was used to evaluate the impact of chia ingredients of the flour on the mixing behaviour, according to an Official Standard Method with minor modifications . The theThe breads were prepared according to the formulation described by Iglesias-Puig and Haros  with somThe ash and moisture of the flours were determined using AOCS procedure methods Ba 5a-49 and Ba 2a-38, respectively . Protein3) were measured 1 h after removal from the oven. method [3/g), and the buns shape height/width ratio (or bun vaulting) of the central slice (cm/cm) was analysed. The images were acquired using a high Performance Full-HD Digital Microscope  with an image resolution of 16 megapixels, an identical settings of illumination was used for all images; then, they were analysed using the ImageJ/Fiji software (http://rsb.info.nih.gov/ij/) and the cells/cm2 were recorded [Loaf weight (g) and volume (cm. method . Specifirecorded . L* (lightness), C* (chroma) and abh (hue angle) of the baked loaves (crumb and crust) using a digital colorimeter , previously calibrated with the white plate supplied by the manufacturer. The instrument settings were as follows: illuminant D65, visual angle of 10° and calibration with Specular Component Included (SCI). From the parameters L* (lightness), a* (redness to greenness) and b* (yellowness to blueness) of the baked loaves (crumb and crust) the total colour difference ΔE was calculated by the equation:Crust and crumb colours were evaluated by determining the tristimulus parameters Each determination was made in the centre of the bread by the extraction of five central slices from each sample; the evaluation was carried out in triplicate.A Texture Profile Analysis (TPA) was performed, using a TA-XT2i texturometer . Two 10-mm thick slices of each of three buns were compressed in the center of the Texture Analyzer platform using a cylindrical probe of 25 mm in diameter under the following conditions: speed of 1.7 mm/s for the test; speed of 10 mm/s for the post-test; 40% compression and 5 g trigger force. Firmness, Springiness, Cohesiveness, Resilience, Chewiness and Gumminess were recorded. The breads were then packed into polyethylene plastic bags and stored at room temperature (24 ± 2 °C) for the shelf life evaluation. The texture profile analysis was repeated after 1, 3 and 5 days of storage. Crumb slices with a thickness of 2 cm were compressed 50%. Four replicates from two different batches were analysed and averaged. The parameters recorded were hardness, chewiness, cohesiveness, springiness and resilience.To), peak temperature (Tp), conclusion temperature (Tc) and enthalpy of gelatinization (ΔGH). To analyse amylopectin retrogradation, heated-cooled pans were stored at 20 °C for 15 days, and heated again in the calorimeter from 30 to 130 °C at 10 °C/min.The flour’s thermal properties during baking of the fermented dough (gelatinization) and changes induced during storage of the bread (amylopectin retrogradation) were measured on a differential scanning calorimeter . Dough samples that were fermented (30–40 mg) were weighed directly in DSC stainless steel pans  and hermetically sealed . An empty pan was used as the reference. Briefly, the samples were kept at 30 °C for 1 min, heated from 30 to 110 °C at 11.7 °C/min, kept at this temperature for 5 min, and cooled to 30 °C at 50 °C/min, to simulate the temperature profile in the centre of the bread crumb during baking. A data analysis system (Mettler Toledo Star System) was used to determine the following parameters: Onset temperature (p < 0.05). Data were processed using the Statgraphics Plus statistical package .Statistical analysis was performed by ANOVA at 5% significance level, followed by Tukey’s multiple comparison tests (Proximate composition of the chia and wheat flours is shown in p < 0.05).The protein content of the semi-defatted and fully defatted chia flours (with or without mucilage) was higher than that of the whole chia flour. The SDCh and DCh samples had total dietary fibre contents of 24.6 and 32.5%, respectively. These results were slightly higher than those of SDCh-OMu (22.6%) and DCh-OMu (30.3%), indicating the efficiency of the mucilage extraction. The defatted flours, DCh and DCh-OMu, had the highest dietary fibre and ash contents, with a significant decrease in lipid content , C* (Chroma), and abh (hue) values. The extraction of oil and/or mucilage from chia flour increased L* and abh, while the C* values decreased, and therefore DCh-OMu showed higher values for L* and abh, and a lower value for C* . This effect could be related to the dilution of gluten and the difficulty of mixing fibre and wheat flour homogeneously. The DDT of the breads with SDCh or DCh was significantly prolonged than the breads with SDCh-OMu or DCh-OMu. This parameter was lowest in the case of bread with 5% wt/wt DCh-OMu (3.8 min) and highest in bread with 10% wt/wt SDCh (7.25 min). These results suggest that the modification of DDT was probably due mainly to the effect of the presence of chia mucilage in these systems. The stability of the dough containing chia flour without mucilage was comparable to that of the control dough, while the presence of mucilage in the dough produced an elevated stability, which was similar for both levels of substitution of chia flour. The control dough showed a remarkable higher mixing tolerance index (MTI) than those observed for the doughs with chia flour (p < 0.05). The different levels of chia flour substitution did not produce significant differences in MTI. However, significant differences between the various types of chia flour were recorded: WCh, SDCh and DCh had the lowest MTI values (p < 0.05).The inclusion of chia flour in the bread formulations had a considerable effect on the dough development time (DDT); when it was compared with the control system a significant increase was observed . On the other hand, the substitution with chia ingredients without mucilage showed an increment in FQN but in a smaller proportion than the previous ones (p < 0.05). A high FQN means that a wheat flour is strong, it weakens late and slowly, whereas weak flour weakens early shown a low FQN [The FQN for the control dough was modified by chia flours addition . This pa low FQN . The preGH) (amount of energy required for this process) of 1.43 J/g of dough. The addition of 5% of WCh did not significantly alter the thermal properties, except to formulation with 5% of DCh which presented a significant increase on the temperatures and ΔGH (p < 0.05). The obtained results were according to those reported by Iglesias-Puig and Haros [To, Tp) than the previous ones, except in those obtained with the addition of chia flours without mucilage  (p < 0.05).The thermal properties of the samples are listed in nd Haros  who founp < 0.05). The contents of these components increased as the substitution levels of the various chia flours augment in the bread samples. The protein contents of the bread samples formulated with 5 or 10% wt/wt of semi-defatted or fully defatted chia flour had average values of 13.60 and 15.24%, respectively. Bread samples fortified with 10% DCh had the highest (7.62%) dietary fibre level.The chemical compositions of the bread samples containing different levels and types of chia flour are presented in When the breadmaking process ended, the shape, specific volume and pore size of the breads were evaluated and the results are presented in The area of cells as a percentage of total slice area was used to describe crumb properties quantitatively. High values indicate a more open texture . The micThe incorporation of 5% wt/wt of semi-defatted or fully defatted chia flour with or without mucilage  resulted in a large average pore size b–f. HoweL* and abh values (p < 0.05). These parameters showed a similar behaviour both in the crumb and in the crust of the bread samples. High L* values indicate high light reflectance, which suggests a light-coloured bread. The reduction of L* related to the incorporation of 10% wt/wt of chia flour could be due to the colour of this raw material. The chroma values (C*) of the crust decreased when the proportion of chia flour increased from 5 to 10%, while in crumb the C* values were not significantly affected by the incorporation of chia flour, irrespective of the amount and type incorporated. There were also significant differences in crust and crumb colour in comparison with the control, with values greater than 5 and therefore perceptible to the consumer, mainly with the incorporation of 10% of chia flour.The texture parameters of the breads with different levels of substitution and types of chia flour are shown in p < 0.05). The hardness of the loaves formulated with SDCh and DCh was significantly different, at both levels of substitution , from that of the other samples, indicating a positive effect on the crumb matrix .p < 0.05).There was a significant decrease in cohesiveness when the level of chia flour increased, especially in the cases of SDCh and DCh, which could suggest that chia ingredients affect the strength of the internal bonds making up the crumb  , showed a similar behaviour to that of cohesiveness in the bread crumb. The inclusion of a higher amount of chia flour supported in chewiness and gumminess, and the presence of mucilage in the chia flour had a similar effect .GH) (amount of energy required for this process) of 1.43 J/g of dough. The addition of 5% of WCh did not significantly alter the thermal properties, except to formulation with 5% of DCh which presented a significant increase on the temperatures and ΔGH (p < 0.05). The obtained results were according to those reported by Iglesias-Puig and Haros [To, Tp) than the previous ones, except in those obtained with the addition of chia flours without mucilage  (p < 0.05).The thermal properties of the samples are listed in nd Haros  who founTo values of the samples with different chia flours (data not shown). These results suggest that the addition of chia flours would not affect the bread staling. An opposite behaviour was observed by Iglesias-Puig and Haros [The evolution of amylopectin retrogradation is one of the main mechanisms involved in bread staling. After 15 days of storage at 20 °C, the retrogradation peak of the control system presented important changes respect to the initial time (t = 0), beginning at 40.4 °C and ending at 67.9 °C, with an enthalpy of 5.66 J/g of dough. A similar trend to the control bread was recorded after storage for the transition enthalpy and nd Haros , who fouThis study shows that it is possible to make bread products with the characteristics of functional foods by adding chia seed by-products. The small incorporation of various types of chia flour in the formulations significantly increased the levels of proteins, dietary fibre and ash in the final products, compared with the wheat control. The presence of mucilage in the chia flours, at both levels studied, had a remarkable effect on the farinograph parameters evaluated, indicating higher water absorption, prolongation of dough development time and stability than in the case of the dough variants without mucilage.The formulations containing 5% wt/wt of chia flour with mucilage  presented the higher technological potential. However, the higher substitution (10% wt/wt) of wheat flour with the various types of chia flour affected the quality parameters, such as a decrease in the specific volume, an increase in the crumb hardness, and changes in the crumb and crust colour.After the extraction of the oil and/or the mucilage of chia, the residual flours are produced, could have a nutritional and technologic potential application to be utilised not only in the cereal industry, but in the food industry in general.


To tackle real-world challenges, deep and complex neural networks are generally used with a massive number of parameters, which require large memory size, extensive computational operations, and high energy consumption in neuromorphic hardware systems. In this work, we propose an unsupervised online adaptive weight pruning method that dynamically removes non-critical weights from a spiking neural network (SNN) to reduce network complexity and improve energy efficiency. The adaptive pruning method explores neural dynamics and firing activity of SNNs and adapts the pruning threshold over time and neurons during training. The proposed adaptation scheme allows the network to effectively identify critical weights associated with each neuron by changing the pruning threshold dynamically over time and neurons. It balances the connection strength of neurons with the previous layer with adaptive thresholds and prevents weak neurons from failure after pruning. We also evaluated improvement in the energy efficiency of SNNs with our method by computing synaptic operations (SOPs). Simulation results and detailed analyses have revealed that applying adaptation in the pruning threshold can significantly improve network performance and reduce the number of SOPs. The pruned SNN with 800 excitatory neurons can achieve a 30% reduction in SOPs during training and a 55% reduction during inference, with only 0.44% accuracy loss on MNIST dataset. Compared with a previously reported online soft pruning method, the proposed adaptive pruning method shows 3.33% higher classification accuracy and 67% more reduction in SOPs. The effectiveness of our method was confirmed on different datasets and for different network sizes. Our evaluation showed that the implementation overhead of the adaptive method regarding speed, area, and energy is negligible in the network. Therefore, this work offers a promising solution for effective network compression and building highly energy-efficient neuromorphic systems in real-time applications. In recent years, as the prediction of Moore’s law slows down prominently, neuromorphic computing has been widely regarded as a promising approach for large-scale computing. Neuromorphic systems are constructed following biological principles existing in our central nervous systems, which features in massive parallelism, collocated memory, and processors, and asynchronous event-driven computation .Generally considered as the third generation of neural network models, spiking neural networks (SNNs) have started a paradigm shift in the brain-inspired research exploration. Different from artificial neural networks (ANNs), SNNs are well-known for its capability of accurately capturing neural dynamics and biological behaviors of the central nervous system and processing spatio-temporal information. With energy-efficient computation and parallel information processing features, SNNs are widely adopted for building neuromorphic hardware systems . In suchDuring early brain development, creations of synaptic connections between neurons exponentially increase with the numerous stimuli coming from environments every day . The rapWhile weight pruning has been widely applied in different ANNs, the benefits that weight pruning could provide for SNNs have yet to be explored. Limited works have reported applying weight pruning in SNNs so far . Rathi eThe main contributions of this work are summarized as follows.•A simple online adaptation scheme for the pruning threshold is presented, which can change the threshold dynamically over time during training. It also considers the spatial difference of the connection strength of neurons in one layer with the previous layer and adapts the threshold over the neurons based on their firing activity.•The proposed method is demonstrated to be more effective in retaining classification accuracy after pruning than the constant threshold weight pruning and neuron pruning methods. It resulted in a 67% reduction in synaptic operations (SOPs) while outperforming the previously reported soft weight pruning method by 3.33%. The advantage of the proposed method was confirmed in the SNN on different datasets with different network sizes.•In terms of training, the proposed online adaptive pruning method outperformed post-training pruning methods by providing more than 30% reduction in training SOPs when the pruning percentage is larger than 90% while providing more than 3% higher classification accuracy, which shows significant potential for developing high-performance and energy-efficient online neuromorphic learning system.•The overhead of implementing the proposed method in a neuromorphic system is demonstrated to be insignificant in terms of processing speed, area, and energy.This paper is organized as follows: section “Methods and Results” introduces different neural models used in this work and the SNN architecture. It then presents an overview of our methods, algorithmic implementation details, and pruning results for each method. In section “Comparisons and Discussions,” different pruning methods are discussed and compared. Section “Conclusion” concludes this work.To model spiking neurons, the leaky integrated-and-fire (LIF) model was used in this work because of its computational efficiency and capability of capturing the essential features of information processing in the nervous system . The modm is the time constant, vr is the resting membrane potential, ge is the excitatory conductance associated with an excitatory channel, Eexc is the reverse potential of the channel, gi is the conductance associated with an inhibitory channel, Einh is the reverse potential of the channel. The model resets the membrane potential to vr and generates a spike if the membrane potential reaches a defined threshold vth. Synaptic conductance follows a time-varying dynamics governed by where τg is the conductance, τg is the time constant, wij is the synaptic weight from the presynaptic neuron j to the postsynaptic neuron i, and j.where Spike-timing-dependent plasticity relates the synaptic plasticity to the relative timing difference between a presynaptic spike and a postsynaptic spike. A triplet-based STDP model was used in this work because of its biological plausibility and easy implementation . It overxj is the trace variable associated with the firing event of the presynaptic neuron j, i, respectively, and τs is the corresponding time constant. When the presynaptic neuron (or postsynaptic) fires, the related trace xj (or yi) is reset to 1. The weight updates are carried out as below.where pre and μpost are the corresponding weight updating rates.where μIn this work, a two-layer SNN architecture was adopted, as shown in Pruning is a natural process existing in human brains to maintain their efficient function. It is widely adopted in neural networks to reduce network complexity and improve energy efficiency. Various works have demonstrated the practical effectiveness of weight pruning in reducing the number of parameters and computational operations without losing accuracy . For exawth) is adapted and remains different for all the excitatory neurons according to their firing activity. Fewer weights are removed for the neurons with lower thresholds. Moreover, over time, the threshold for each neuron is increased so that more weights are pruned at later pruning stages.However, in the previously reported online pruning methods, pruning was conducted with a constant threshold for all the synaptic weights during training. It is not an effective approach to select the non-critical weights since weights change over time during training. A large threshold can mistakenly remove many important weights at the beginning of training and severely affect the functions of the neurons with weak synaptic connections, leading to substantial performance degradation of the network, while a small threshold is not able to remove some non-critical weights at the end. In this work, we will present different pruning while training methods by adapting the pruning threshold over time and neurons. The overview of the proposed pruning process is depicted in To perform pruning while training effectively, we need to determine when to start the pruning process. If the pruning process starts too early, important weights that have a profound impact on the output could be mistakenly pruned away, which will deteriorate network performance. On the other hand, if it starts too late, the network might not have enough training cycles to compensate for the accuracy loss and reduced improvement in training energy efficiency. To find this critical point, we have observed how the network dynamics evolve with time by monitoring firing activities and weight updates of excitatory neurons. tm and an initial pruning threshold wth(t) = f(t), where wth(t) is the purning threshold at time t, and f(t) is the adaptation function. To select a suitable adaptation function, we propose two different exponential functions (f1 and f2) and a linear function (f3), described below.During training, synaptic weights are randomly initialized and updated according to the input features over time. Some weights approach high value and contribute largely to network performance, whereas some are reduced to zero and less critical. The first adaptation scheme is to adapt the pruning threshold over time during training. The pruning process is illustrated in andtm, wmax is the maximum value of weights, a,b, and c are the corresponding adaptation factors in the functions. These three functions are all confined in the range wmax to avoid pruning the entire network. f1 function increases the threshold slowly at the beginning and rapidly at the end, while the f2 function has the opposite effect. The linear function (f3) keeps the same updating rate.where f1, f2, and f3, as described above. For each adaptation function, the results of network connectivity vs. initial threshold (wth(t0)) and accuracy vs. connectivity are presented, where the connectivity is defined as the percentage of non-zero weights in the total weights. The impact of different values of the corresponding adaptation factors  is also studied. In a = 1, b = 1, and c = 0, they all are equivalent to the constant pruning case. By increasing the corresponding adaptation factors, a smaller initial threshold is needed to reach certain connectivity. In b > 1, the connectivity becomes very small (<15%) even when a very low threshold is used. This is because the adaptation function f2 increases the threshold value very rapidly at the early phase of pruning process and hence results in a high threshold most of the time, as shown in f1, a = 1.3 is slightly better than a = 1.5 and hence selected as the optimized value for a. In f2, all the three cases (>1) have similar overall performance, but b = 1.1 is able to reach higher accuracy with larger connectivity and thus selected as the optimized value for b. In f3, c = 0.01 shows the best overall performance and thus is selected.We simulated SNNs with the proposed online pruning method. Online pruning with a constant threshold was also included as a reference to demonstrate the effectiveness of our proposed pruning methods and will be referred to as online constant pruning hereinafter. The excitatory neurons in the network play different roles in contributing to network performance. The connection strength of these neurons to the input layer is different. A stronger connection makes the neuron more resilient to pruning, whereas a weaker connection makes the neuron more susceptible. Applying a constant threshold for all the neurons can not effectively take the difference into consideration, and a large threshold can significantly deteriorate the function of the neurons with a weak connection. Thus, we propose an adaptation scheme over neurons by adapting the pruning threshold over all the excitatory neurons. The aim is to ensure that a smaller threshold is applied for weaker neurons and a larger threshold is for stronger neurons. So, we can balance the connection strength of all the neurons after pruning to achieve large network sparsity and maintain high classification accuracy.n-th group (Gn). The grouping scheme is explained as follows. Firstly, a spike count interval is defined as SI. Starting from the neuron with the minimum spike count, we group all the neurons with spike counts within , where S is the minimum spike count of the ungrouped neurons. In this way, the neurons in the same group have a spike count difference not larger than SI, so we can fairly sort the neurons with similar connection strength into one group. Across all the groups, the pruning threshold f(n), where n is the group index. In The adaptation scheme is illustrated in f1, f2, and f3. Different values of the adaptation factor  associated with each function were used in the simulation. The results are presented in a = 1.15, b = 1.05, and c = 0.03, respectively.We simulated SNNs with the proposed online pruning method that uses the three different adaptation functions, namely fN(n), and assigned to the following groups. After the adaptation process over neurons is completed, the global threshold is reset and updated with the time adaptation function fT(k), where k is the pruning-step index. This combined method takes into consideration both the time evolution of synaptic weights and the spatial difference of firing activity of neurons during training.In the adaptation scheme over time, the threshold changes with time, but all the neurons share the same threshold. In contrast, the adaptation scheme over neurons considers the spatial difference of firing activities of all the neurons and adapts the threshold over them, but the thresholds for neuron groups are constant over time. A full adaptation scheme combines these two schemes by adapting the pruning threshold for each neuron over time during training. The algorithmic implementation is described in f1 gives the best performance improvement over the constant pruning method, i.e., the highest accuracy when connectivity is smaller than 15% and similar accuracy to other functions otherwise. This can be attributed to the fact that f1 allows the pruning threshold to grow slowly at the early phase of the pruning process and hence more weights to be trained. It increases the threshold rapidly at the end, which guarantees largely reduced network connectivity. f1 gives the best performance. Moreover, after selecting the adaptation function as f1, we studied the effect of the spike count interval on the performance. The results are shown in Firstly, we will compare the three different adaptation functions and select the best spike count interval. f1 was used. The time adaptation factor, the neurons adaptation factor, and the spike count interval were optimized and selected as 1.2, 1.15, and 30, respectively. The comparison among the proposed pruning methods is shown in To apply adaptation over both time and neurons, we combined the proposed adaptive pruning methods with the selected adaptation functions and adaptation factors. In this approach, the pruning threshold is increased over time and adapted across all the excitatory neurons. The comparisons among the proposed adaptive pruning methods for MNIST dataset and Fashion-MNIST dataset are shown in For the proposed online pruning method, it is crucial to find the right starting point for pruning during training. If pruning starts too early, weights are not learned enough, and hence some critical weights can be mistakenly removed. While the weights are learned for enough time after 30,000 training images, the remaining training process will further fine-tune the unpruned critical weights as they get more chance for STDP updates when more weights are pruned. This is due to the dynamics of the STDP learning rule that the weights with more contribution to the neural firing are strengthened more often. So if pruning starts during the last stage of training, the unpruned weights will not have enough chance to be fine-tuned to preserve good network performance. The adaptation will not be carried out effectively, and there will be less improvement in accuracy and training energy efficiency. In section “Methods and results,” we decided to start pruning after training over 30,000 images based on the change of network dynamics. To further investigate the impact of the number of pre-pruning training images, we have obtained pruning results for the various number of pre-pruning training images which are presented in Additionally, the selection of an adaptation function and the corresponding adaptation factors can be further optimized with more choices of functions and a finer grid of factor values. However, this is not in the scope of this work that aims to demonstrate the effectiveness of the proposed adaptive pruning method.In general, for neuromorphic hardware systems, like TrueNorth, SpiNNaker, and Loihi, the fundamental operation is the synaptic event that occurs when a spike is transmitted from a source neuron to a target neuron. So the computational energy of an SNN is proportional to the synaptic activity . PruningThe defined FOM is used on a per-network basis to help identify the best network connectivity for that specific network, as demonstrated in Neuron pruning is one of the structured weight pruning strategies, which eliminates all the weights associated with the pruned neurons and reduces the network complexity proportionally. However, directly removing neurons from the network could cause severe deterioration of network performance. We compared the proposed online weight pruning methods with an online adaptive neuron pruning method presented in our previous work . The comAn online soft weight pruning method for unsupervised SNNs was reported in Comprehensive comparisons among different pruning methods in terms of accuracy loss and SOPs are provided in The proposed adaptive pruning algorithm can be implemented in hardware systems without adding significant overhead. To investigate the overhead, we chose three metrics: processing speed, area, and energy.For energy overhead, the number of basic operations, such as addition, comparison, and memory access, was estimated in As for area overhead, the number of essential digital gates and memories required to implement the pruning algorithm and equivalent NAND gates was estimated in The proposed adaptive method would be effective in improving the compression rate and preserving good network performance in other neural networks, as different threshold adaptation techniques have also been applied to improve the pruning performance in other neural networks.The iterative pruning method has been the most successful and popular pruning technique in ANNs, which relies on numerous cycles of training and pruning in order to induce sparsity in weight matrices and preserve network performance . This meIn order to eliminate the need for iterative threshold tuning, many works have explored to adapt threshold values for neurons in different layers by training the thresholds together with weights . These mIn this work, we proposed an online adaptive weight pruning method that adapts the pruning threshold over time and neurons during training in an unsupervised SNN. The effects of the threshold adaptation over time and neurons were studied individually. Different functions used to adapt the threshold were applied and compared. It is demonstrated that both adaptation over time and neurons can improve the network performance against an online constant weight pruning method. The adaptation enables the network to reserve critical weights when the network is not trained enough at the early phase of training and balance the connection strength of excitatory neurons in the network to avoid largely deteriorating the performance of weak neurons. So, combining the two adaptation schemes can further improve network performance. The online adaptive pruning method provides better performance than the post-training pruning method, suggesting that it can not only improve training energy efficiency but also achieve higher accuracy. Regarding the computational cost, the number of SOPs was analyzed, which shows that the proposed online adaptive pruning method can significantly reduce the SOPs/image during both training and inference. Furthermore, comparisons with the previous works reveal that our method can lead to better accuracy and a more significant reduction in SOPs. The implementation overhead of the proposed method was evaluated in terms of processing speed, area, and energy, which is proven to be negligible in the network. Therefore, the proposed online adaptive pruning method provides a promising approach for reducing network complexity and improving energy efficiency with good performance in SNNs for real-time applications.The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.WG, MF, HY, AE, and KS: conceptualization. WG, MF, HY, AE, and KS: methodology. WG: software, algorithms and writing – original draft preparation. WG, MF, and HY: investigation and validation. MF, HY, AE, and KS: writing – review and editing. AE and KS: supervision. KS: project administration. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The regulation of multiple bacterial phenotypes was found to depend on different cyclic dinucleotides (CDNs) that constitute intracellular signaling second messenger systems. Most notably, c-di-GMP, along with proteins related to its synthesis, sensing, and degradation, was identified as playing a central role in the switching from biofilm to planktonic modes of growth. Recently, this research topic has been under expansion, with the discoveries of new CDNs, novel classes of CDN receptors, and the numerous functions regulated by these molecules. In this review, we comprehensively describe the three main bacterial enzymes involved in the synthesis of c-di-GMP, c-di-AMP, and cGAMP focusing on description of their three-dimensional structures and their structural similarities with other protein families, as well as the essential residues for catalysis. The diversity of CDN receptors is described in detail along with the residues important for the interaction with the ligand. Interestingly, genomic data strongly suggest that there is a tendency for bacterial cells to use both c-di-AMP and c-di-GMP signaling networks simultaneously, raising the question of whether there is crosstalk between different signaling systems. In summary, the large amount of sequence and structural data available allows a broad view of the complexity and the importance of these CDNs in the regulation of different bacterial behaviors. Nevertheless, how cells coordinate the different CDN signaling networks to ensure adaptation to changing environmental conditions is still open for much further exploration. Komagataeibacter (Gluconacetobacter) xylinus ; anProteins containing GGDEF domains synthesize mainly 3’-5’ c-di-GMP (c-di-GMP) molecules, while proteins containing SMODS domain synthesize preferentially 3’-5’ cGAMP (cGAMP) molecules and proteins containing DAC domain synthesize mainly 3’-5’ c-di-AMP (c-di-AMP) molecules. Even though CDN molecules are mainly synthesized by prokaryotic cells, eukaryotic cells also synthesize CDNs such as 2’-3’ cGAMP by cGAS enzymes. These three classes of CDN synthetases do not share structural similarities, have different residues involved in substrate binding, and possess different catalytic mechanisms. Therefore, they are not homologs and probably evolved independently to catalyze analogous chemical reactions. Members of families within the CD-NTases superfamily, such as SMODS and cGAS, often do not share detectable primary sequence similarity but adopt a Pol-β-like nucleotidyl transferase fold, suggesting a common origin followed by divergent evolution ,6,64,65.Proteins containing GGDEF domains require an accessory domain that sense different signals to regulate the GGDEF homodimerization and consequently its enzymatic activity . Each GGGGDEF structure and structural similarities with other protein domains. The GGDEF domain has an overall structure composed of a central five-stranded β sheet surrounded by five α helices  and one The catalytic domains of class III adenylyl cyclase (AC) and guanylyl cyclase (GC) are involved in the conversion of adenosine triphosphate (ATP) to 3′-5′ cyclic AMP (cAMP) and in the conversion of guanosine triphosphate (GTP) to 3′-5′ cyclic GMP (cGMP), respectively C 73,74],7473,74]H)-pyrimidinone 5′-phosphate (FAPy)  and [D/E]h[D/E]h motifs  fold, a highly diverse superfamily of proteins C,E 95]..95]. NTah motifs A.i) proteins containing DZF domain  such as nuclear factor NF90 and NF45; (ii) the catalytic domain of Poly(A) polymerase, PAP,  , ID 2FB5) ; and DacID 6HVN) . As desc, ID 2FB5Listeria monocytogenes (PDBID 4RV7), the ATP ligand is located in a well-defined cavity made up by the N-terminus of α4, loop β5-β6, loop β4-α4, and loop α3-β3  .Streptomyces cell development BldD, REC domain of the transcriptional regulator VspT protein, N-terminal domain of the ATPase of the Type II secretion system MshE protein (T2SSE_N domain), MerR domain of BrlR protein, AAA+ ATPase domain  of the transcriptional regulator FleQ protein, and CA domain  of the cell cycle kinase CckA protein  or Lactococcus lactis pyruvate carboxylase (LIPC), TrkA_C domain of potassium transporter A (KtrA), CBS domain of OpuC carnitine transporter, and RNA structures such as ydaO-yuaA riboswitches , pyruvate carboxylase domain  of switches . For cGAThe function of each CDN receptor, as well as the residues involved in ligand binding, are described in more detail in STING proteins are localized on the endoplasmic reticulum membrane of eukaryotic cells and are CDN sensors that, when bound, regulate the induction of type I interferons (IFN-α and IFN-β), thus eliciting the intracellular signals of the invasion by bacteria and/or viruses, and activating the innate immune response to attack the pathogen. STING proteins can directly sense the pathogen invasion by interaction with bacterial CDNs  or indirectly by binding to eukaryotic 2’-3’ cGAMP through its C-terminal domain . It is controversial whether STING binds 2′-5′ cGAMP preferentially in relation to other CDNs, or binds all of them with the same affinity . Rhodobacter sphaeroides, or chemotaxis by MapZ protein and alginate secretion by Alg44 to promote biofilm formation in Pseudomonas aeruginosa (Other important c-di-GMP receptors are proteins containing PilZ domains PF07238). PilZ domains regulate twitching and swarming motility via the flagellar regulator YcgR protein [238. PilZruginosa . PilZ doPseudomonas aeruginosa that regulates the synthesis of the Pel exopolysaccharide , c-di-GMP II riboswitch (RF01786), and c-di-AMP riboswitch . C-di-GMP I riboswitch and c-di-GMP II riboswitch bind c-di-GMP molecules while c-di-AMP riboswitch binds c-di-AMP molecules ,129,130.Mycobacterium smegmatis was the first c-di-AMP receptor discovered [Escherichia coli, there are three systems responsible for potassium uptake, namely, Trk, Kdp, and Kup. In the case of Trk system, four genes are constitutively expressed and TrkA is the predominant potassium transporter at neutral pH. The Kdp-ATPase system is induced at low potassium concentrations and under conditions of osmotic stress. The Kup, formerly TrkD, is activated when TrkA and Kdp activities are not sufficient [Bacillus subtilis, a novel high-affinity transporter KimA (formerly YdaO) has recently been characterized and the expression of KimA and KtrAB is negatively regulated by c-di-AMP riboswitches [Staphylococcus aureus, where c-di-AMP binds to the KtrA protein and to the universal stress protein (USP) domain of the KdpD sensor kinase inhibiting the expression of Kdp potassium transporter components. In this manner, c-di-AMP appears to be a negative regulator of potassium uptake in different Gram-positive bacteria [The TetR-like transcriptional factor, DarR, from scovered , where cscovered . Anotherfficient ,132,133.switches . When thswitches . A similbacteria ,134.One of the most well understood receptors for c-di-AMP is KtrA, which binds c-di-AMP through its C-terminal domain (RCK_C or TrkA_C) to cause inactivation of the KtrA function . c-di-AMLactococcus lactis by a pyruvate carboxylase, LlPC protein, and Listeria monocytogens pyruvate carboxylase, LmPC protein, respectively. In both cases, c-di-AMP binds to the pyruvate carboxylase domain (HMGL-like domain in the Pfam)  . LIPC foThe huge repertoire of CDN receptors demonstrates the complexity of CDN signaling networks in bacteria. Additionally, CDNs may regulate different bacterial behaviors at different speeds through regulation of gene transcription by transcriptional factors, protein translation by riboswitches, and directly by regulating the function of different classes of protein. The cyclisation between two nucleotides of the most common CDNs involves the formation of a phosphodiester bond that links the C3’ of one pentose ring with the C5’ of another, resulting in a 3’-5’ cyclic dinucleotide. This kind of cyclisation creates a two-fold symmetry between two pentose rings of dinucleotides. Only cGAMP has been reported to present not only a 3’-5’ linkage, but also being found with a 2’-3’ one that contains two distinct phosphodiester linkages, one between C3′ of AMP and C5′-phosphate of GMP, and the other between C5′-phosphate of AMP and C2’ of GMP A.syn or anti conformation in relation to the pentose by the N-glycosidic bond, and only one of the structures, the FimX EAL domain from Xanthomonas citri (PDBID: 4FOK) [syn conformation, which is the less stable state of the molecule. The conformation C3’-endo/C3’-endo is the more representative for the c-di-GMP and c-di-AMP molecules, while cGAMP is preferentially in C3’-endo/C2’-endo conformation   has one ormation A.closed conformation (shaped as a horseshoe) is when the two base rings are face-to-face; 2—open conformation is when the two base rings are far from each other in an elongated conformation and; 3—intermediate conformation (shaped as a boat) is when the bases are not in the closed conformation or in the open conformation  to an extended form (open conformation) allowing significant binding flexibility. The c-di-GMP bases may interact with the protein binding site by stacking with arginine or phenylalanine/tyrosine residues through the hydrophobic surface of the base. The c-di-GMP bases may also interact with acidic residues (aspartate or glutamate) through Watson–Crick–edge interaction or with arginine residue through Hoogsteen–edge interaction [The overall conformation of the ligand can be classified in three conformations with respect to the base proximity: 1—ormation A. The coectively . The comectively  and by Sectively . In botheraction . Kd of about 1 mM under physiological salt conditions [The c-di-GMP molecule in solution is found in a fast equilibrium between a monomeric state and as a dimer with intercalated bases with a nditions . Neverthnditions . Lookingnditions  and the nditions , respectBacillus, Clostridium, Streptomyces, Listeria, and Mycobacterium, produce both signaling molecules and possess a wide array of GGDEF genes, following the general trend of having close to as many genomes with both DAC and GGDEF as possible  proteins containing GGDEF domain that synthesizes mainly c-di-GMP; (ii) CD-NTases enzymes that have the catalytic domain known as SMODS and synthesizes mainly cGAMP; and (iii) DAC proteins that have a catalytic domain called DAC domain  that synthesizes mainly c-di-AMP. These CDN synthetases do not share structural similarities, use different residues for substrate binding, and probably possess different catalytic mechanisms suggesting that they probably evolved independently to catalyze similar chemical reactions.As evidence of the importance and ubiquity of bacterial CDNs, it is interesting to note that mammalian cells evolved to sense bacteria by detecting these molecules to stimulate the immune system to counterattack infections. Thus, the use of CDNs as adjuvants in vaccines has been considered, as they can be used as stimulators of the innate immune system ,195.The huge repertoire of CDN receptors and the complexity of CDN signaling networks in bacteria are shown in this review. Surprisingly, different CDNs share conformational similarities even in the pocket of different classes of receptors. CDNs can regulate bacterial behaviors at different speed levels, directly regulating protein function for a faster response or, more slowly, by affecting gene transcription or protein translation. Remarkably, different CDN second messenger systems may coexist in many organisms, which would imply that both the control of their synthesis and turnover and the specificity of their sensors are carefully tuned.Therefore, the new discoveries reviewed in this paper open up questions about how bacteria coordinate the three mains bacterial CDNs: are they interconnected to regulate the same bacterial phenotype, or do they act independently? Do bacteria use the three CDNs as second messengers or is one chosen? Are the CDN signaling pathways conserved in different bacteria? Will other CDNs be discovered to also be second messengers?


The interrelationship between the strain gradient, electric polarity, and the electromechanical property is scrutinized by detecting of the lattice distortion using atomic scale strain analysis, and also by detecting the depolarized electric field using differential phase contrast technique. We further demonstrate that the domain walls can be manipulated and aligned in specific directions deterministically using a scanning tip, which produces a surficial strain gradient. Our findings provide the comprehensive observation of a flexopiezoelectric phenomenon that is artificially controlled by externally induced strain gradients.The emergence of a domain wall property that is forbidden by symmetry in bulk can offer unforeseen opportunities for nanoscale low-dimensional functionalities in ferroic materials. Here, we report that the piezoelectric response is greatly enhanced in the ferroelastic domain walls of centrosymmetric tungsten trioxide thin films due to a large strain gradient of 10 Observation of a strain-gradient-induced piezoresponse at domain walls remains a challenge. Here, the authors find the piezoelectric response to be enhanced in the ferroelastic domain walls of centrosymmetric tungsten trioxide thin films due to a large strain gradient over a wide width of the wall. The manifestations of ferroelasticity include superelasticity, shape memory, and diffusionless martensitic transformation2. Since the resulting strain field is not spatially uniform, the strain gradient—the rate at which the strain changes through a position—gives rise to flexoelectricity4. On a macroscopic scale the polarization induced by flexoelectricity is typically negligible due to the low strain gradients, of ~1 m−1. However, nanoscale materials and devices can tolerate strain gradients as large as 106 m−1, and this can lead to a substantial amount of flexoelectric polarization which may be comparable to or even larger than conventional ferroelectric polarizations7.Ferroelasticity is a mechanoelastic phenomenon in which a crystalline lattice spontaneously deforms9 can undergo a large amount of lattice deformation and/or structural reconstruction. Because of this feature, ferroelastic domain walls in centrosymmetric, nonpolar materials have attracted much attention not only for their influence on conductivity10 and superconductivity11 but also for interfacial polarity phenomena, such as the nonlinear optical effect14 and field-induced gating effect on electronic conduction16. Recent theoretical studies have also shown the importance of antiferrodistortive octahedral rotations and tilts and polar displacements at ferroelastic domain walls19. The interplay of complex ferroelastic twin patterns and flexoelectrical polarity has been theoretically studied in terms of kink and junction generation reminiscent of polar vortices20.Under these conditions, the walls or boundaries between two crystallographically different domains21 because most ferroelastic domain walls are too narrow in width23 or undergo atomic reconstruction, generating unique interfacial properties that are totally different from the bulk24. Therefore, although a piezoresponse is expected to be present in the ferroelastic domain walls upon symmetry breaking, the direct detection of a strain gradient-induced piezoresponse at the domain walls still remains a tantalizing challenge.One of the most promising ways of visualizing the polar property at the domain walls is to utilize the piezoresponse based on converse piezoelectricity. However, this observation has rarely been made3)26, an extensively studied ferroelastic material that shows periodically ordered domain walls in a herringbone structure, where the intervals between the domain walls can be scaled simply by film thickness25. This binary oxide looks like an A-site vacant perovskite structure, and thus, WO3 is elastically flexible, as supported by experimental and theoretical results. These include a small Young’s modulus and shear modulus28, polaronic transport29, and various temperature-dependent structural phase transitions caused by phonon softening31. Accordingly, the domain walls in WO3 provide an ideal platform to explore the possibility of flexopiezoelectricity.To visualize the piezoresponse at the ferroelastic domain walls with large strain gradients, the so-called flexopiezoelectricity, we choose epitaxially grown tungsten trioxide  at intervals of a few 10 nm. These fine stripes, actually fine-domain walls, are attributed to the surface slope changes at the twin boundaries between monoclinic A1 and A2 ferroelastic domains or between B1 and B2 ones. Mosaic rotations occur alternatingly in a clockwise or counterclockwise manner, as schematically shown in Fig. The typical WOage Fig. , consistYAO and YAO  image taken along the zone axes of 001]YAO Fig. . TEM imaYAO Fig. YAO zone axis, selected area electron diffraction (SAED) patterns were obtained for the areas of the A and B domains . A simulation of the electron diffraction patterns depending on the zone axis clearly agreed with the experimental results . It is worth mentioning that the hierarchical twin structure appeared as a metastable (sustainable itself) structure compatible with the substrate, not an elastically deformed state forced by strong substrate clamping. We have often observed that the domain structure remains in TEM specimens in areas where the substrate has been removed.From the entclass1pt{minimax, y, z) whose basis axes are parallel to the axes of the pseudocubic WO3 unit cell, and  where the y′-axis (x′-axis) is parallel (perpendicular) to the domain wall along [110]. It is worth noting that the shear strain gradient x′, y′, z′) coordinate  over two crystallographically distinguished domains indicated by ‘A’ and ‘B’ and their boundaries. We expect that the two neighboring domains are coherently connected to each other, thereby leading to a large strain gradient at the interface Fig. . We alsoentclass1pt{minima3 thin film. The piezoresponse signals along lines 1 and 2 show a tip-orientation dependence , where the surface piezoelectricity was reduced at the domain walls along with an increase in conductivity. In contrast, no enhanced electronic conduction was detected at any macro-domain wall in our study. This discrepancy is presumed to be due to the direction of electric polarization and the relevant surface screening charge. For example, if the flexoelectric polarization was oriented along the out-of-plane direction in our monoclinic WO3, electronic carriers must have been induced on the surface suppressing the piezoresponses.Meanwhile, our domain walls deduced from the monoclinic WOPi) of the free energy density of the undeformed parent material,χip represent the elastic compliance tensor, vacuum permittivity, and electric susceptibility tensor, respectively. According to the previous PFM result, The piezoelectric response can be loosely understood to occur when a local lattice deformation with a strain gradient breaks the inversion symmetry, thereby making the local structure piezoelectrically active. In a stricter sense, the piezoelectric response at the domain wall is necessarily described by a phenomenological trilinear term related to the order parameters of the strain a and b. The WO3 has spontaneously nonzero lateral longitudinal strains (εxx and εyy) which are defined as relative deviations from the reference cubic cell. The A and B domains can be transformed into each other through azimuthal rotation around the c-axis by ±90°  measurements Fig. . In the 3 thin films had a wide width of ~20 nm or larger, without significant dislocations , decreased (or increased) by ~1.5% along the line profile perpendicular to the domain wall within the analysis range  by the elastic compliance tensor , strain gradient (~106 m−1), vacuum permittivity (~8.85 × 10−12 C V−1 m−1), and electric susceptibility   coefficient . In this way, the induced flexoelectric polarization was roughly estimated to be ~13 µC cm−2 .Although the flexoelectric polarization is a well-known and universal property in dielectric materials, our observation of the piezoresponse at a domain wall bearing a strain gradient is a hidden phenomenon. It is worth noting that the relative ratio for the measured flexopiezoelectric response to the flexoelectric polarization normalized by the elastic compliance is ~10, which is of the same order of magnitude as the ratio of the intrinsic flexopiezoelectric constant (30 V) to the intrinsic flexoelectric constant  tiplls Fig. .Fig. 5MeTo understand the shear strain gradients induced by a frictional force, we performed finite-element modeling (FEM) (details in the ‘Methods’ section). The shear strains were maximized right next to the contact area (dashed circle) and had opposite signs on the left and right sides of the tip trail. The maximum value (~0.025) of the induced shear strain was larger than that of the static twin domains (~0.013), which means that the induced shear strains are enough to switch the domains and consequently the domain walls.40). Reversible switching needs to be emphasized because it implies the lack of linear or planar defects, which leads to wall pinning during the alignment of the domain walls.Based on this effect, we executed three consecutive scans with a normal force of ~100 nN Fig. . The kinc-axis is aligned normal to the substrate by mosaic rotation. The facing bc surfaces of two neighboring fine-domains (either A1 and A2 or B1 and B2) show good compatibility with each other. So, the sharp interface of two crystals can be energetically stable. On the other hand, the macro-domain wall is at the boundary of the two crystallographically different A- and B-domains, where the in-plane crystal axes are transformed into each other by an azimuthal rotation of +−90°. The difference between the a- and b-axis lattice parameters induces a local deformation of unit cells at an {110}WO-type interface even if the compatibility relation for a symmetric W wall is satisfied. In this situation, a gradual wide lattice deformation occurs in the soft material. If a material is sufficiently rigid elastically, the elastic energy loss is too high to take the deformation. Instead, it prefers to produce other surface reconstructions alongside defect formation, within a relatively narrow interfacial region. This raises a basic question—which physical parameters quantitatively determine the width of the domain wall?We also questioned why the macro-domain wall has a wide width while the fine-domain wall appears as a sharp interface. The fine-domains form in such a way that the monocline 20.Domain wall widths are determined by competition between an on-site energy and an inter-site energy . In this ferroelastic system, elastic energy such as the Hook’s law with a quadratic dependence on strain plays the role of on-site energy, while the quadratic dependence of strain gradient acts as an inter-site interaction. The smaller the elastic energy compared to the gradient energy, the wider the domain wall width. Our phase field simulation also supports the importance of the Ginzburg gradient energy in controlling the domain wall width (Supplementary Note wm), which increases with film thickness (t) according to the scaling of a quasi-Kittel’s law (wm ∝ t0.6) (ref. 25). We performed additional PFM characterization of several samples with different film thicknesses  tensor, so the effect can be nonzero regardless of the presence or absence of inversion symmetry. These findings will have a broad impact on nanoscale materials and device research. For example, corrugated 2D materials have significant transverse strain gradients and, as a result, the piezoelectric response should appear, according to this knowledge. We expect the piezoelectric response can be generally induced by wrinkling or bending any flexible material.3 is a representative example where the flexopiezoelectricity is significantly manifested, because it has a large dielectric constant  and it is also elastically soft28. (A large flexopiezoelectric effect at the same intrinsic coefficient can emerge as a result). More interestingly, many unusual phenomena can originate from A-site vacant perovskites, such as fast ionic penetration/migration41, polaronic conduction29, superconductivity11, and many polymorphic phases as well as soft modes31, can be further coupled to the flexopiezoelectricity. From a microscopic point of view, it is also interesting to clarify that the A-site vacant space also acts as a buffer space, capable of enduring a large elastic deformation in the future.WO3 is a helpful example for understanding the high-order effects of piezoelectricity or flexoelectricity. It offers a phenomenological base for answering quantitatively and symmetry-wise how piezoelectric coefficients can be modified by strain gradients, or how flexoelectric coefficients can be changed by strain. Just as the value of the flexovoltage coefficient is universally valid for many perovskite oxides35, so is the estimate of the intrinsic flexopiezoelectric coefficient .Consequently, WO−1 through the direct observation of the lateral piezoresponse (~6 pm V−1) and strain gradient (~106 m−1) at the ferroelastic domain walls in epitaxial WO3 films. Based on the lateral flexopiezoelectricity at the twin walls and mechanical switching, and considering the strain-gradients, we suggest that the shear strain-gradients at the twin walls of a soft material induce flexoelectric polarizations and sufficiently detectable piezoresponses at the twin walls.We have revealed the existence of flexopiezoelectricity at ferroelastic domain walls using an integrative approach, employing high-resolution angle-resolved lateral PFM in conjunction with STEM. We not only measured the details of the atomic arrangement and strain gradient by STEM but also visualized a flexoelectric field by detecting depolarized electric fields using the DPC-STEM technique. We determined the intrinsic flexopiezoelectric coefficient to be ~30 J C3 thin films were grown on orthorhombic YAlO3 substrates with an (110)YAO surface by pulsed laser deposition. WO3 layers were grown at 600–800 °C with an oxygen pressure of 70–100 mTorr. After deposition, they were cooled down to room temperature with an oxygen pressure of 500 mTorr. Our films stabilized the monoclinic structural phase γ-WO3 . A detailed x-ray diffraction analysis was reported in a previous paper25.Epitaxial WOYAO, YAO, and YAO. The reason for this is that the lateral displacement is detected as a tip torsional mode, which is normal to the cantilever axis as a projected component. The signals appear or fade away depending on the tip orientations, so that the piezoresponse vector is oriented parallel to the domain walls. The lateral piezoresponse vector at each spatial position can be quantitatively determined by integrating all the scan data and performing trigonometric curve fitting for the measured signals as a function of the tip-orientation angle. By trigonal fitting the in-plane real signals as a function of the orientation angle, position-sensitive angle-resolved PFM images were gained, as shown in Fig. 33 . For the simulation, an isotropic and elastic 2D layer of WO3 was introduced with an elastic constant of ~1011 Pa and Poisson’s ratio of ~0.25 (refs. 28). Constraints on the four fixed boundaries were exerted, and a frictional force of 40 nN on the tip area of the WO3 surface was presumed. The size of the 2D layer was 120 nm × 120 nm with 18 × 18 elements, and the diameter of the tip contact area was assumed to be ~15 nm.The shear strain on the WOSupplementary InformationPeer Review File


The large family of deubiquitinating enzymes (DUBs) are involved in the regulation of a plethora of processes carried out inside the cell by protein ubiquitination. Ubiquitination is a basic pathway responsible for the correct protein homeostasis in the cell, which could regulate the fate of proteins through the ubiquitin–proteasome system (UPS). In this review we will focus on recent advances on the molecular mechanisms and specificities found for some types of DUBs enzymes, highlighting illustrative examples in which the regulatory mechanism for DUBs has been understood in depth at the molecular level by structural biology. DUB proteases are responsible for cleavage and regulation of the multiple types of ubiquitin linkages that can be synthesized inside the cell, known as the ubiquitin-code, which are tightly connected to specific substrate functions. We will display some strategies carried out by members of different DUB families to provide specificity on the cleavage of particular ubiquitin linkages. Finally, we will also discuss recent progress made for the development of drug compounds targeting DUB proteases, which are usually correlated to the progress of many pathologies such as cancer and neurodegenerative diseases. The ubiquitin–proteasome System (UPS) is the major pathway for the degradation of more than 80% of intracellular proteins. Ubiquitination is the major signal to label proteins for degradation, it controls their fates and contributes to the correct cellular homeostasis during all cell cycle phases ,2. AfterDeubiquitination refers to the process by which ubiquitin molecules are removed from ubiquitin-labeled proteins under the proteolytic action of deubiquitinating enzymes, which cleaves off the isopeptide bond between the C-terminal tail of ubiquitin and the target lysine, thus changing the fate of ubiquitinated proteins and in some instances by preventing them from degradation ,6. DeubiUbiquitin can form different types of chain linkages, depending on the lysine residue used in ubiquitin as a target to build poly-ubiquitin chains. Additionally, four different types of protein ubiquitination in substrates enrich the diversity in ubiquitination, namely monoubiquitination, multi-monoubiquitination, homotypic polyubiquitination, and heterotypic polyubiquitination ,9. In hoAccording to sequence and structure similarity, deubiquitylating enzymes can be divided into seven families, each presenting a unique structural fold: Ubiquitin-specific proteases (USPs), Ubiquitin C-terminal hydrolases (UCHs), Ovarian tumor proteases (OTUs), Machado–Joseph Disease protease family (MJDs), MINDY protease family (MIU-containing DUB family), JAMM family , and the newly discovered ZUFSP/Mug105 family (zinc finger with UFM1-specific peptidase domain protein/C6orf113/ZUP1) ,13 Tabl. Amid thUBD (ubiquitin-binding domain) is a type of modular domain that can non-covalently bind to ubiquitin or ubiquitinated substrates, which is widely present in deubiquitinating enzymes. Almost 20 different UBDs families have been discovered so far. According to the structure of UBDs, they are divided into 5 categories: α helix, Zinc finger, PH domain, Ubc-like, and others. The most common in DUBs are UBA (ubiquitin-associated domain), UBL (ubiquitin-like modifiers), UIM (ubiquitin-interacting motif), ZnF-UBP (zinc finger ubiquitin-specific protease domain), and ZnF-A20s (A20-type zinc fingers) ,15,16. RThe mechanism by which deubiquitinating enzymes exert their function in the cell can be complex, in some cases it is not only deubiquitination, but it can also promote ubiquitination by recycling ubiquitin molecules, proofreading of the ubiquitination process, and decomposing ubiquitination inhibitors . In thisThe large number of deubiquitinating enzymes are not only playing a role in the normal physiological activities of cells, but can also be relevant for the occurrence and development of tumorigenesis and other pathologies ,22,23. TSeveral DUBs are involved in the regulation of the Wnt/β-catenin signaling. For example, overexpression of USP5 will cause an increase in the amount of transcription factor FoxM1, which will increase the content of β-catenin, resulting in faster cell proliferation and carcinogenesis in many tumors . UCH37 cMost DUBs reduce the degradation of protein targets by protecting them from the proteasome, thereby a high concentration of TGF-β, promoted by specific DUBs, might lead to tumor occurrence and metastasis. Deubiquitinating enzyme USP10 promotes the metastasis of hepatocellular carcinoma metastasis by deubiquitinating and stabilizing Smad4 . But theDUBs also play an important role in regulating the NF-κB pathway. OTULIN/CYLD can interact with LUBAC (linear ubiquitin chain assembly complex) through the PUB (PNGase/UBA or UBX) domain of HOIP , thereby inhibiting the NF-κB pathway and exerting anti-tumor effects . StudiesThe Akt pathway promotes the survival and proliferation of cells in the organism, and its expression is increased in cancer cells . The incDrosophila melanogaster by bioinformatics methods ..Ins-2 ofIn other JAMM members, the Ins-1 loop has a permanently active β-hairpin structure, such as in AMSH or AMSH-LP, but the Ins-1 loop of Rpn11 is closed and inactive. According to the crystal structure of the Rpn11-Rpn8 heterodimer bound to ubiquitin, the Ins-1 loop of Rpn11 changes its conformation with different states of the proteasome, before and after ubiquitin-binding, from the closed state to the β-hairpin active state . The InsRpn11 is an important potential target for drug therapy . RPN11 mProstate cancer (PC) is a common male malignant tumor . AndrogeUAF1 interacts with the distal end of the USP12 Finger domain, causing a series of structural changes in USP12, which are then transmitted to the essential ubiquitin contact loop adjacent to the catalytic cleft, thereby enhancing the activity of USP12 B,C 174,,174,177.WDR20 (WD repeat-containing protein 20) is a single domain protein containing WD40-repeat motifs . USP12 cInterestingly, it has been shown that USP12-Ub and UAF1 have two possible binding sites: The Finger domain and the back of the ubiquitin-binding cleft . The secAlso, UAF1 can interact with USP1 and USP46 in addition to USP12 . USP1 isIn addition to PC, USP12 is involved in other diseases. USP12 is a potential target for the treatment of Hepatocellular Carcinoma (HCC), which can prevent G2/M and trigger apoptosis . Also, UIn this review, we have introduced and described the mechanisms for the catalytic regulation in different types of DUBs. Most of the discovered DUBs have a similar basic mechanism of deubiquitinating activity for cleavage of the isopeptide bond, but with the presence of regulatory domains adjacent to the catalytic domain or by the formation of tetrameric complex assemblies, DUBs have developed specific strategies to regulate the interaction and cleavage of the different types of linkages of the ubiquitin-code, which will probably reveal layers of complexity that are still not completely understood.Deubiquitinating enzymes play an essential role in the process of life, and the occurrence and development of many diseases are directly related to them ,22,23. TAs one of the most well-studied DUBs, USP7 has many small molecule inhibitors. USP7 is currently an important targeted protein for tumor therapy. Studies have found that USP7 inhibitors P5091 and Parthenolide inhibits Wnt signaling and colorectal tumor growth; specific inhibitor HBX 19818 can selectively reduce the activity of USP7; USP7 small-molecule inhibitors P5091 and P22077 can induce tumor cell apoptosis by enhancing intracellular oxidative stress response and endoplasmic reticulum stress response ,190,191.It is worth noting that there are still many unsolved problems in the research of deubiquitinating enzymes, and it is likely that novel regulatory mechanisms, similar to the examples depicted in this review, will be uncovered for other DUBs in the future. The development of inhibitory DUB compounds, to treat tumor growth and other pathologies, are based on a better understanding of the structure and regulatory mechanism in the context of each particular DUB. Research on DUB inhibitor drugs has made some progress, but more efforts will be needed to overcome all steps required for clinical application and drug development. As the research goes further deeply into DUBs molecular mechanisms, these questions will gradually be answered, and they will provide new ideas and new methods for the treatment of pathologies such as neurodegenerative and cancer diseases.


Background: In Mexico, the fortification of corn and wheat flours with iron, zinc, and folic acid and the restoration of B-vitamins is a mandatory program. However, the monitoring and evaluation (M&E) of this fortification process is not well understood. Thus, the purpose of the study was to understand the M&E of the food fortification program in Mexico, with an emphasis on technology research and development. Methods: Open-ended exploratory interviews were conducted with food technology representatives (n = 9), food science academic faculty (n = 1), president of a private tortilla-making federation (n = 1), and representatives of the federal monitoring agency (n = 2). Interviews were transcribed and themes were identified using the content analysis methodology. Inter-rater reliability was assessed by calculating an intraclass correlation coefficient (ICC) between the raters (n = 3). Results: A total of 49 codes were identified that resulted in three overarching themes, manufacturing/processing, monitoring logistics, and nutrition. Overall, there is a need for more robust internal and external M&E with Mexico’s fortification program to improve the manufacturing/processing of fortifying the tortillas, the monitoring of this fortification program, and the impact the fortified tortillas have on the nutritional status of the Mexican population. The overall ICC was 0.87. Conclusions: The present study can be used to gain insight into Mexico’s fortification program and to inform food fortification policymakers of best practices. Micronutrient malnutrition (hidden hunger) afflicts more than 2 billion people worldwide, especially women and children. Predominately those affected reside in low-resource regions and food insecure, low-income households ,2. The mp < 0.05)  has never touched us”.Through the interviews, participants discussed that the lowered monitoring capacity  has affected external monitoring checkup frequency. These participants discussed that the external monitoring can result in product recalls or manufacturing suspension. The participants indicated that the size of the food production will dictate the frequency of external monitoring visits, such that smaller production facilities will have fewer external monitoring checkups compared to larger companies. For example, a corn flour producer stated, “As the participants mentioned, government auditors review several aspects of the fortification process during their visits such as the sanitation conditions and the overall upkeep of these facilities, but these reviews are infrequent and inconsistent. They also mentioned that the fortification is monitored such as inputs and outputs of flour and premix, but the record keeping may not be accurate as alluded to by a government representative:During the visits we review the addition of micronutrients. We have a NOM 251 that speaks of all Good Manufacturing Practices. We monitor everything that is the facilities, the process water, the drainage, the toilets, the ventilation… We also check the warehouse with special attention to the storage of flour that has been added and the premix, and so all this is documented. Then in the warehouse they must have a record of inputs and outputs”.“Customers are currently occupying the qualitative techniques [to analyze micronutrient content] for faster determinations. Because [of cost] they cannot send all the batches to analyze”.As indicated by the participants, internal monitoring for small scale flour producers is accomplished using qualitative methods for iron determination in a yes/no binary system on a more frequent basis than the external validated analysis in authorized laboratories. As they mentioned, the qualitative method is utilized more often than the validated analysis due to its rapid and low-cost nature, but it is not always precise. When the researchers asked the participants if an alternative method was available to detect micronutrients as opposed to the qualitative method, the small-scale flour producers agreed an alternative technology would be better if it were cost-effective, faster, and more precise. As a vitamin and mineral premix producer stated, “In Mexico City, the tortillas are made of 70% nixtamal and 30% flour. [Thus] the fortification is diluted”. Whereas a Tortilla-maker in Queretaro stated, “We are using 95% pure corn [non-fortified] and 5% flour [fortified] [to make tortillas]”. Which was reinstated by a corn flour producer, “The problem we have… is that mixing nixtamal masa with nixtamalized corn flour makes the product [the tortilla] not 100% [flour]. So, although we fortify the flour correctly, the final consumer, is no longer acquiring [the micronutrients] in the tortilla”.Participants described how nixtamalized corn flour is processed into tortillas, a main staple commodity for the Mexican diet. They indicated that the tortilla recipes vary from tortillería to tortillería. The recipes utilized by tortilleros (the people who make tortillas) can be made from fresh nixtamal masa (non-fortified), nixtamalized corn flour (fortified), or any ratio of both together. These differences are often based on local consumer preferences. Participants indicated that as a result of these variations, added micronutrient content in tortillas is likely to fluctuate. A research professor described how, “Participants mentioned several reasons that tortilla producers use a blend of masa nixtamal and fortified nixtamalized corn flour. These reasons may include cost, texture, shelf-life, and flavor. As the president of a tortilla-making federation explained:It [the flour] is added with softener, bleach, and some substance that serves to give strength or that does not break the tortilla when you make a taco. But, we are removing [decreasing the use of] those flours, to give a little texture, or softness to the tortilla. Why have we reduced the consumption of flour? First, we find it is more expensive to produce tortillas with flour than we do our own nixtamal… Without using a lot of corn [fortified] flour, we already have the softness, we have a very good texture, we have very good resistance of the tortilla to make a taco”.“[People eat the tortillas] for the taste. The fortified flours have a characteristic flavor. Here, we recognize the taste if you give someone a fortified flour tortilla. If you go to the north of the country, they are used to eating fortified flour. If you go to the center of the country and to the south of the country, they are not so used to eating fortified flours”.This was further verified by a research professor, “Since the implementation of the fortification program, it was noted by participants that the Mexican government has yet to conduct an efficacy trial to assess the program’s impact on the prevalence of iron deficiency or IDA amongst the population. Assessing efficacy is an important part of a successful program’s M&E process. Because of this limitation, it was noted that it is difficult to assess the impact the process of combining fortified nixtamalized corn flour and non-fortified masa nixtamal, in different quantities, has on health outcomes. Without a population-based approach to evaluate the fortification program’s efforts in reducing micronutrient deficiencies, it was noted that participants such as the government or the industry cannot be sure that the program is a cost-effective approach to improving public health as mentioned by a government representative:We have not measured the contribution [the fortification program] on the health of the population. However, we believe that it is contributing [to their health] since the entire population practically consumes this, be it bread or tortilla”.“Participants stated that the fortification program is currently perceived to be reaching limited segments of the Mexican population, such as those with a higher economic power, those situated in urban regions, and those in the north of the country. This is problematic for the program’s intended impact, which is to improve the micronutrient status on all populations throughout the country. Previous literature has documented higher iron deficiency prevalence in rural areas and the central and south of the country compared to other areas within the country ,32, indiThe fortification programs are for the population that has significant nutritional deficiencies. Our product is fortified because it is mandatory. But our product does not normally go to the population [that needs it], but to a higher purchasing sector”.“As the diets of the Mexican population evolve, the demand for tortillas is diminishing . Thus, pWe, as Mexicans, have changed our diet, we have stopped eating tortillas. We should see the consequences that this change in diet has had on these problems [micronutrient deficiencies] that are occurring”.“The purpose of the present work was to understand the context of the fortification program in Central Mexico and to identify strengths and weaknesses related to monitoring and evaluation (M&E) and to recommend strategies for improving its M&E. Three main themes were derived from the interviews through content analysis: 1. Monitoring logistics; 2. Manufacturing and processing; and 3. Nutrition. The main findings highlight the inconsistencies with program design and practice, such as the monitoring strategies, evaluation of the program impact, and actual consumption of nixtamalized tortillas made of flour. Stakeholder insights offered potential strategies for improving the program, such as increased internal monitoring technical capacity, increased fortification education for tortilleros, and raising awareness of the nutritional differences between nixtamalized tortillas made of fortified flour and tortillas made of masa nixtamal.The interviews revealed that in recent years, the access and use of authorized laboratories in Mexico for analyzing the micronutrient content of flour samples has declined. Furthermore, a lack of authorized laboratories ultimately increases the cost for nixtamalized fortified corn flour producers to monitor their products, as sending samples across state lines will accrue more taxes and fees for the analysis. For these reasons, the M&E component of the NOM-247 has decreased in volume and frequency of monitoring checks. The frequency of checks is correlated to the size of the company, as smaller companies will be audited less frequently. A parallel case can be seen in fortification programs in Africa and Asia, where similar budget restraints affect M&E rigor and frequency .Literature shows that having a rigorous M&E component, in any mass mandatory fortification policy, is critical for understanding whether the program is reaching its nutritional goals within the context success . AdditioThe iron spot test is often recommended as a qualitative methodology for internal monitoring . In thisFurthermore, through the results of this study, the actions in case of non-compliance are not well understood. In the Compliance section (Section 12) of the NOM-247-SSA1-2008, it only states:12. Compliance with the standard“12.1. The monitoring of the application of this rule corresponds to the Ministry of Health in the terms and applicable legal provisions”.The stakeholders mentioned product recalls or manufacturing suspensions as possible actions to take in the case of non-compliance, but there is no publicly documented protocol that supports a standardized procedure.Fortified nixtamalized corn flour producers sell household-size packages of fortified nixtamalized corn flour that is sold in supermarkets or convenience stores. Additionally, it is known that these producers sell large, bulk quantities of fortified nixtamalized corn flour to tortillerías (neighborhood tortilla producing facilities). However, it is up to the tortilla producer’s discretion to produce their tortillas using all fortified nixtamalized corn flour, or whether they mix fortified nixtamalized corn flour into the traditionally used masa nixtamal. Prior literature indicates that in Mexico, tortillas are often made from a mix of fortified nixtamalized corn flours (dried) with traditional masa nixtamal (wet) ,36. The These variances in tortilla-making are dependent on regional consumer preferences. For instance, in Mexico City, the final blend may contain around 70% masa nixtamal and 30% fortified nixtamalized corn flour. However, in the state of Querétaro, an owner of several tortillerías uses only 5% fortified nixtamalized corn flour. This difference between flour ratios across regions highlights the uncertainty in the amount of fortified nixtamalized corn flour that is consumed by people who purchase their tortillas from tortillerías and not mainstream supermarkets. This variance undermines the potential for nutritional impact and dilutes the micronutrients reaching the consumers, as indicated by the stakeholders.IDA prevalence in Mexican children under 5 years of age decreased since the implementation of the mandatory flour fortification program to 23.3% from the years 1999 to 2006, but increased in 2016 to 26.9% see  9,10]. . 10]. ThThe assumption that Mexico’s corn flour fortification program is responsible for the reduction in countrywide anemia prevalence is contrary to other studies that demonstrate a lack of efficacy using corn flour fortified with electrolytic iron, an iron fortificant recommended by Mexico’s fortification policy, but with limited bioavailability . A randoBioavailability of iron sources are heavily influenced by factors such as disease state and dietary patterns. Malaria, commonly present in developing countries, inhibits iron absorption due to increased inflammation. Electrolytic iron is highly susceptible to binding to phytates in cereals such as corn . Thus, fAdditional limitations to identify the impact the fortification program has on nutritional outcomes of the population are related to socioeconomic status and geographic variation. It was identified, in the interviews, that the principal beneficiaries of this program would be people who live in the north of the country (where consuming 100% flour tortillas is more common), people in urban areas (who purchase 100% flour tortillas from convenience markets), or the upper class . In the 2012 ENSANUT, 30.5% and 31.9% of women between the ages of 20–49 years were iron deficient in the central and south region of the country, respectively, while 22.8% of women of similar age in the north of the country were iron-deficient . SimilarComprehensive evaluation of fortification programs requires adequate and constant M&E . Based oThe quality of fortified products can also be enhanced through educating tortilleros on the importance of using fortified flours in tortillas for nutritional deficiencies. One way of doing this is to conduct continuous trainings to improve processes in the form of in-person seminars. Park et al. found that one in-person 30-min training and a 2-week follow-up training amongst low-technical skilled food handlers, increased their knowledge of food safety practices by 30% . ConsumeOne limitation of the present study is that an interview with the largest corn flour producer (70% of the market) was not able to be conducted for privacy and access reasons. Furthermore, interviews were geographically limited within the states of Estado de Mexico and Querétaro and do not represent the perspectives of stakeholders from every part of the country. The results presented within this research are specific to the context of Mexico’s program and should not be extrapolated to other contexts without conducting similar interviews with other stakeholders. Finally, the interviews were limited to those people available and willing to participate, but did not include NGOs, other government offices beyond the regulatory agency, or beneficiaries (consumers).In conclusion, the present study was a qualitative evaluation of Mexico’s flour fortification program using interviews with stakeholders from various perspectives: academic, public sector, and private sector. It was found that there are a reduced number of laboratories capable of providing accurate and reliable data to support M&E activities of the program. Nixtamalized corn flour producers are interested in inexpensive, fast, and precise alternatives to support their quality control processes. The fortification efforts in central Mexico may be diluted by mixing masa nixtamal with fortified nixtamalized corn flour for motives of texture, shelf-life, cost, and flavor. Finally, the principal beneficiaries of the program may be those found in urban areas, in parts of the country where consuming tortillas made from 100% flour is more common, and/or with a higher socioeconomic power. Recommendations to improve the program include the implementation of simpler technologies for frequent internal monitoring of the fortification process and to have an external entity evaluate the impact of the program on the anemia prevalence in the country. Future studies should include a more expansive qualitative assessment of the program, including interviews with consumers, as well as the larger production companies.