All 47 samples of homemade flour/ingredients were contaminated with AFs (0.3–723 ng/g). The overall prevalence of individuals with detectable urinary AFM1 was reduced by 81%, from 15/36 (42%) at baseline to 3/36 (8%) at follow‐up (p = 0.003). Provision of low‐aflatoxin porridge flour was acceptable to caregivers and their infants and successfully reduced the prevalence of detectable urinary AFM1 in infants, thus, confirming its potential to be tested in future large‐scale health outcomes trial.Aflatoxins are toxic secondary metabolites of fungi that colonize staple food crops, such as maize and groundnut, frequently used in complementary feeding. In preparation for a large trial, this pilot study examined if provision of a low‐aflatoxin infant porridge flour made from local maize and groundnuts reduced the prevalence of a urinary aflatoxin biomarker in infants. Thirty‐six infants aged 6–18 months were included from four villages in Kongwa District, Tanzania. The study was conducted over 12 days with a three‐day baseline period and a 10 days where low‐AF porridge flour was provided. Porridge intake of infants was assessed using quantitative 24‐h recalls by mothers. Household food ingredients used in infant porridge preparation and urine samples were collected on Days 1–3 (baseline) and 10–12 (follow‐up). Aflatoxins were measured in household foods, and AFM1 was measured in urine. At baseline and follow‐up, 78% and 97%, respectively, of the infants consumed porridge in the previous 24 h, with a median volume of 220 mL  and 460 mL , respectively ( Tanzanian infants are commonly exposed to AF through porridge consumption in the complementary feeding period. Provision of locally processed low‐AF maize: groundnut complementary porridge flour increased porridge consumption and significantly reduced the prevalence of detectable AFM1 by 81%. Tanzanian infants are commonly exposed to AF through porridge consumption in the complementary feeding period.Interventions to test if consumption of low‐AF local foods can reduce prevalence of AF biomarkers are limited.Provision of locally processed low‐AF maize: groundnut complementary porridge flour increased porridge consumption and significantly reduced the prevalence of detectable AFM1 by 81%.The AFM1 biomarker appeared to be responsive to the intervention. AFs occur in four main forms: aflatoxins B1 (AFB1), aflatoxins B2 (AFB2), aflatoxins G1 (AFG1), and aflatoxins G2 (AFG2). AFB1 is the most prevalent and poisonous, and is classified as a Group I human carcinogen by the International Agency for Research on Cancer , the secondary metabolites of fungi er IARC, , 2012. Ier IARC, , stunteder IARC,  and micrer IARC, . Acute her IARC, .Consumption of AF‐contaminated foods, especially those emanating from susceptible cereals such as maize, oily seeds, and nuts such as groundnuts, is a global public health concern. The global magnitude of exposure to these toxins can vary due to climate, market characteristics, dietary availability and preferences, and food safety control systems. Countries with diversified diets combined with firm regulations that monitor AF levels in susceptible foods are better equipped to protect humans and livestock from ingestion of significant amount of these toxins. However, in countries with limited dietary diversity, widespread production and consumption of susceptible crops, combined with weak enforcement of regulations, ingestion of AF is frequent  study  of Kongwa district of Dodoma region in Tanzania between April and May 2018. Kongwa district was selected because there is high production and reliance on maize and groundnuts Mtaki,  for housCommunity health agents collaboratively created a list of all village infants between 6 and 18 months of age. Research staff randomly select eight households per village by pulling names from a hat. The mother/baby dyads were included if the baby had previous consumption of groundnuts with no reported allergy and no illness at recruitment.2.2The legal limit for processed cereals set by East Africa Community and adopted by Tanzania Bureau of Standards TBS, , 2014b iGrains were milled to obtain porridge flour containing maize and groundnuts at a ratio of 4:1 and sole groundnut flour. Both porridge (1 kg) and groundnut flour (0.5 kg) were supplied to caregivers. Quantities were estimated to last for a week, benchmarked from the quantities reported in the baseline dietary interviews. The proportions were based on the common practice in the study area  methodology  of the grain or flour was available, a sample of 100–250 g was collected. To enhance representative sampling, stocks of 20 kg or less were thoroughly mixed by inverting the storage bag about five times before sampling. Five subsamples of about 100 g each were randomly drawn and mixed to an aggregate sample. In a few households with larger grain stocks (>20 to 100 kg), a sampling spear was used to draw 10 random subsamples of about 100 g each from different parts of the storage bag to obtain an aggregate sample. In both cases, the aggregate sample was mixed thoroughly, and then a subsample of 500 g or less, depending on the stock quantity, was drawn. The samples were tightly packed in dry sealable Ziplock bags and kept at −20°C for AF analysis.On Day 3 of the baseline study, low‐AF flours were provided. Guidance was provided to the mothers to: (a) use the provided preblended porridge flour when making porridge for the index infant; (b) make a thick porridge, with a maximum ratio of four parts water to one part preblended flour; (c) use porridge made from the provided flour when feeding the index infant, but continue to give nutritious sauces and relishes as normal; (d) use the groundnut flour provided instead of any other groundnut flour when cooking food (even family food) that the index infant would eat; (e) place cooked porridge in the vacuum flask provided to keep it safe and hygienic; and (f) continue breastfeeding as normal.After the final baseline visit, mothers practiced feeding porridge and other foods made from the provided flours for 1 week. One visit occurred midway through the week to attempt to resolve any challenges cooking and/or feeding the flours. Additional low AF flours were provided at this visit if needed. Daily at baseline and follow‐up visits, infant urine samples were collected using paediatric urine bags and then transferred to sterile urine containers. These were stored frozen (−20°C) for AFM1 analysis.2.4g using Eppendorf AG 5810. While low matrix filtrates were ready for the assay, high matrix filtrate required an additional step of dilution (10‐fold) with reconstituted wash buffer solution supplied with the ELISA kit. The diluted high‐ and undiluted low‐matrix filtrates were analysed for total aflatoxins using Helica AF ELISA kit lots AF 120117 and ALM 113017 (Helica), respectively, according to the manufacturer's specification. Enyme‐linked immunosorbent assay (ELISA) plates were read on a Microplate Reader (BIOTEK EL808) at 450 nm. Results of unknown samples were compared with the standard curve to obtain AF concentrations in ng/g. We ensured coefficients of variation of not greater than 5% for duplicates. Samples with CV above 5% were reanalysed. Limit of detection for both kits was 0.05 μg/kg.Upon collection, samples of grain and flour were stored at −4°C in the transitory sample receipt laboratory in the Kongwa District Hospital, then transported to Nelson Mandela African Institution of Science and Technology (NM‐AIST) laboratory and stored at −20°C before AF analysis. For grains up to 250 g were homogenized in a heavy‐duty RIRIHONG 500A Multifunctional Grinder to a fine flour. Five grams of the flour was mixed with 25 mL of 70% analytical grade methanol (Loba Chemie) for maize or 80% methanol for high matrix effect samples of groundnut, sorghum, rice, millet, finger millet and composite flour. The mixture was extracted on a Stuart Orbital Incubator SI600C (Bibby Scientific Ltd) mechanical shaker at 200 rpm for 20 min. The mixture was allowed to settle and then centrifuged for 5 min at 24652.5g for 1 min to pellet solids. The supernatant was diluted with distilled water at a 1:10 dilution, and AFM1 was measured in duplicate according to the manufacturer's instructions. ELISA plates were read on a Microplate Reader (BIOTEK EL808) at 450 nm. Samples measuring above the standard curve were repeated following additional dilutions. An internal sample was measured across each plate to assess inter‐plate variation. Samples were analysed in duplicate and those with a coefficient of variation (CV) of greater than 7% were re‐measured. Urine samples from each individual child/day were analysed separately.Urine samples were stored frozen at −20°C in aliquots at the laboratory located in the Kongwa District Hospital, then transported on liquid nitrogen to NM‐AIST laboratory. AFM1 was analysed using the Helica Urinary AFM1 ELISA kits lot AFLM 112917 (Helica) according to the manufacturer's specification (limit of detection of 80 pg/mL) and with modifications described by Smith et al. . Urine s2.6T‐test. Wilcoxon signed‐rank test was used to explain differences between frequency of consumption at baseline and follow‐up visits, and compare prevalence of AFM1 biomarkers before and after the pilot intervention. Spearman's correlation and regression models were used to draw association between age, frequency and volume of porridge consumption.R version 4.12 was used for all analyses. Specifically, amounts of porridge consumed before and after the pilot intervention are reported in median with interquartile ranges, and compared using paired sample 2.7Ethical approval was obtained from Cornell Institutional Review Board (IRB)—USA, and the National Institute for Medical Research (NIMR)‐Tanzania. Administrative permission to visit villages and households was sought from the National through Regional, District and village authorities. Mothers provided informed consent, using a signed written consent form in Swahili to participate in the study.3n = 5) and travel (n = 1) , 10–12 (n = 14) and 13–16 (n = 10) months, and 52.8% were boys . The increase in the volume of consumption appeared to be mostly driven by an increase in the number of daily feedings rather than an increase in any given meal  at baseline to 1 (3%) at follow‐up, the shift in porridge consumption frequency between once and four times a day also increased significantly, This increase in porridge consumption was also witnessed by mothers during the reinforcement visit, where all mothers stated that they were happy with the provided preblended porridge and groundnut flour and that the infant seemed to like the foods made with these ingredients. Seventy‐eight percent of mothers (28/36) said their infant consumed more porridge when made with the provided flour than when made as usual in the household.In addition to thin or thick porridge, babies also consumed family foods. At baseline, stiff porridge had the highest frequency: 22.4% (37/165), mango juice (3%) and others (< 3%). In the follow‐up visit, the percentage of stiff porridge consumption was reduced to 1 out of 165 observations (0.6%), with an increased consumption of fresh roasted maize (4.5%), watermelon (3.9%), orange (3.9%) and pumpkin (3.9%).3.1n = 33), and for groundnut, 5.0–723 μg/kg (n = 8). At baseline groundnuts had a median concentration of 19 times greater than maize  samples were collected, consisting of homemade composite flour or ingredients of porridge flour consumed by infants in the previous 24 h (Table 3.2p = 0.003) at follow‐up. Overall, the intervention reduced the prevalence of detectable urinary AFM1 in infants by 81%. In addition, baseline samples that were detectable (AFM1 range 80–707 pg/mL) tended to be higher than those following the intervention (range 83–143 pg/mL), and notably 6/36 at baseline exceeding 150 pg/mL of AFM1 versus none in the follow‐up  of infants had detectable urinary AFM1 in at least one sample at baseline, which was significantly reduced to 8% (3/36) , and of moderate risk Egypt , respectively, had urinary AFM1 detected in samples with 32/50 detected (range 8–801 pg/mL), 5/42 detected (32–505 pg/mL) and 4/50 (range 5–6 pg/mL), respectively  aged 6–16 months from three Tanzanian villages  relationship between urinary AFM1 and the aflatoxin–albumin biomarker; the latter has been used in studies to assess growth faltering  are clearly of some concern. For example, urinary AFM1 measured in regions regarded as high risk, for example, Guinea (24–48‐month‐old) and Nigeria (3–18‐month‐old, 5This study population had high frequency and quantity of consumption of porridge made with homemade AF‐contaminated ingredients. Our pilot study found high acceptability and low barriers to recommended feeding practices using the provided low‐AF flours. Building from these results, the MMT trial intervention distributed 4:1 maize to groundnut porridge flour and separate groundnut flour, using AFM1 in a cohort of infants to measure AF exposure levels and adherence to intervention.Neema Kassim designed and implemented the study, performed data analysis and interpretation, developed first draft and revised all versions of the manuscript. Francis Ngure designed the study, developed AF‐low food and performed AF analysis of food. Laura Smith designed the study, guided and conducted AFM1 analysis, interpreted data and critically reviewed the final manuscript. Paul C. Turner guided AFM1 analysis, interpreted data and critically reviewed all versions of the manuscripts. Rebecca Stoltzfus  designed the study, interpreted data and critically reviewed all versions of the manuscripts. Edna Makule  implemented the study. Nyabasi Makori implemented the study. Erica Phillips designed and implemented the study, interpreted data and critically reviewed all versions of the manuscripts. All authors have read and approved the final manuscript.The authors declare no conflict of interest.


The accurate segmentation of nuclei is crucial for cancer diagnosis and further clinical treatments. To successfully train a nuclei segmentation network in a fully-supervised manner for a particular type of organ or cancer, we need the dataset with ground-truth annotations. However, such well-annotated nuclei segmentation datasets are highly rare, and manually labeling an unannotated dataset is an expensive, time-consuming, and tedious process. Consequently, we require to discover a way for training the nuclei segmentation network with unlabeled dataset. In this paper, we propose a model named NuSegUDA for nuclei segmentation on the unlabeled dataset (target domain). It is achieved by applying Unsupervised Domain Adaptation (UDA) technique with the help of another labeled dataset (source domain) that may come from different type of organ, cancer, or source. We apply UDA technique at both of feature space and output space. We additionally utilize a reconstruction network and incorporate adversarial learning into it so that the source-domain images can be accurately translated to the target-domain for further training of the segmentation network. We validate our proposed NuSegUDA on two public nuclei segmentation datasets, and obtain significant improvement as compared with the baseline methods. Extensive experiments also verify the contribution of newly proposed image reconstruction adversarial loss, and target-translated source supervised loss to the performance boost of NuSegUDA. Finally, considering the scenario when we have a small number of annotations available from the target domain, we extend our work and propose NuSegSSDA, a Semi-Supervised Domain Adaptation (SSDA) based approach. Nuclei are the fundamental organizational unit of life  unfortunately leads to poor performance due to the domain shift problem  model for nuclei segmentation to close the gap between the annotated source domain and unlabeled target domain. Unsupervised Domain Adaptation methods are capable to minimize the labeling cost by utilizing cross-domain data and aligning the distribution shift between labeled source domain data and unlabeled target domain data. We empirically and carefully observed that, images from different nuclei datasets, even if collected from different organ or cancer types, exhibit dissimilarity although their corresponding segmentation ground-truth labels are quite similar see . In summConducting extensive experiments on two nuclei segmentation datasets we conclude that, our proposed UDA method, NuSegUDA, outperforms fully-supervised model trained on source domain and evaluated on target domain, and baseline generic and biomedical UDA segmentation models. Experimental result (see Section 4) also shows the impacts of training NuSegUDA with proposed image reconstruction adversarial loss, target-translated source images, and feature-level clustering loss. Furthermore, the accuracy of our SSDA model, NuSegSSDA, is highly competitive to the upper bound of fully-supervised model trained in the target domain.Therefore, the main contributions of this paper are: (1) We propose an adversarial learning based Unsupervised Domain Adaptation (UDA) approach, which is applied at both of feature space and output space to solve nuclei segmentation problem for unannotated datasets. (2) Additionally, we incorporate adversarial learning into a reconstruction network to translate source domain images to the target domain, and train proposed model with these target-translated source images. (3) Compared to many of the baselines, our proposed method is simple as it does not depend on any data synthesization or data augmentation. (4) Our proposed UDA framework can be easily extended to Semi-Supervised Domain Adaptation (SSDA) in the scenario where a small portion of the target domain is labeled. (5) Extensive and comprehensive experiments on two datasets have demonstrated the superiority of the proposed methods.In literature, several domain adaptation models have been proposed for generic image segmentation. Isola et al.  applied Domain adaptation has also been employed in different biomedical image segmentation tasks. A multi-connected domain discriminator based UDA model for brain lesion segmentation was proposed by Kamnitsas et al. . Dong etSeveral previous approaches  and Semi-Supervised Domain Adaptation (SSDA) framework. Finally, we discuss the implementations of the proposed models.X of size H×W×3. The input X comes from either the source domain or the target domain. Depending on the problem  and domain , we may also have the corresponding pixel-wise ground-truth label Y of size H×W×1 which is basically a binary mask. Then, using the segmentation network, we want to predict the segmentation output Ŷ of size H×W×1.In our nuclei segmentation problem, we have nuclei histopathology image patches as input Ns annotated images {}, and the target domain has Nt unannotated images {(Xt)}. In the case of Semi-Supervised Domain Adaptation (SSDA) problem, the source domain is the same as it is in UDA problem, and we assume that the target domain has Formally, in Unsupervised Domain Adaptation (UDA) problem, the source domain consists of S), Reconstruction network (R), Prediction Discriminator (DP), and Image Discriminator (DI).We refer our nuclei segmentation Unsupervised Domain Adaptation (UDA) model as NuSegUDA, and the framework is shown in S takes image X as the input and produces the segmentation prediction Ŷ of the same size as the input. Here, X can be either the source domain image Xs, or the target domain image Xt. Hence, the source domain prediction S can be thought as the generator module.The segmentation network S to generate the source domain segmentation predictions Ys. Since in Unsupervised Domain Adaptation (UDA) the ground-truth labels are not available for target images, we can not compute any supervised pixel-level loss for target predictions. In practice, we found that combining dice-coefficient loss and entropy minimization loss is more effective than simply using binary cross-entropy loss for nuclei segmentation tasks. Therefore, we define segmentation loss Lseg as:We train Ys and where S in NuSegUDA although we have two different domains. Since we are particularly looking for nuclei from both domain images, it is very unusual to use multiple segmentation networks. Additionally, using two segmentation networks would increase the number of learnable parameters which would slow down the training process in turn. Therefore, single segmentation network helps to prevent the memory issues and training latency in NuSegUDA.Here, question may arise that why we are using single segmentation network S with only the annotated source data teaches S to make accurate predictions for source images. However, this segmentation network may generate incorrect outputs for target images as there are visual discrepancies between source images and target images , and Hp and Wp are height and width of the prediction discriminator output DP(Ŷt). The details of the Prediction Discriminator DP is discussed in Section 3.2.3.where ŶS to fool the prediction discriminator so that it considers S to generate target domain predictions Ŷt which look similar to source domain ground-truths.The prediction adversarial loss in Equation (4) helps S produces domain-invariant predictions for both domains. In other words, we want to generate the target domain predictions in a way so that they become similar to the source domain predictions. However, it is highly probable that the target predictions are not well-correlated with corresponding target input images. In this scenario, the ability of reconstructing the images from the predictions with similar visual appearance as input images will ensure that there is a correlation between the input image and segmentation output.As we mentioned earlier, the segmentation network R is used in NuSegUDA. In a similar way to Xia and Kulis  is the output of reconstruction network for Ŷt, and C is the number of channels of input image Xt.where, Lrecons (in Equation 5) can not capture the overall pixel distribution of target domain images. To solve this issue, in addition to Lrecons, we also utilize an Image Discriminator DI to distinguish the original images and the reconstructed images. To train R and S to generate original-alike reconstructed images, we define image reconstruction adversarial loss as:Although we use above reconstruction loss to reconstruct the target domain images from its predictions, the reconstructed images may have very different textures and styles (for both of nuclei and background) than the original images see . The reaHi and Wi are height and width of the image discriminator output LadvI trains R and S to reconstruct target domain images of similar distributions  to the original images from target domain.where LadvP helps the segmentation network S to generate target predictions LadvI, reconstruction network R learns to reconstruct target images  to a common prediction subspace R reconstructs the images in target domain. Therefore, using S and R we can translate source domain images Xs to the target domain. Thus, target translated source domain images Xs→t = R(S(Xs)). LadvI on Xs→t. Finally, we train the segmentation network S with {} using following Ltrans loss which is a combination of dice-coefficient loss and entropy minimization loss:In NuSegUDA, Ys and where DP) and Image Discriminator (DI). Prediction Discriminator distinguishes between source domain outputs and target domain outputs, whereas Image Discriminator distinguishes between original images and reconstructed images. We discuss the details of both discriminators in the following.We utilize two discriminators in NuSegUDA: Prediction Discriminator  comes from the source domain or the target domain. To train DP, we use following cross-entropy loss:zp=0 when DP takes target domain prediction as its input, and zp=1 when the input comes from source domain prediction.where Image discriminator We use image discriminator DI in NuSegUDA so that the reconstructed image distribution becomes similar to original image distribution. The input of DI is either the original target image or the reconstructed target image. Then, DI distinguishes whether the input is original or the reconstructed one. Similar to DP, we use following cross-entropy loss to train DI:zi=0 when DI takes reconstructed target image zi=1 when the input comes from original target images Xt.where S is composed of an encoder SE and a decoder SD . Here, the encoder SE works as a feature extractor. Due to the discrepancy of input statistics across domains, there is also a shift of feature distribution in the feature space spanned by SE. Similar to Toldo et al.  and Ft = SE(Xt). Then, the clustering loss is computed as:Given source image fi is the feature vector corresponding to a spatial location of Fs or Ft, ŷi is the corresponding predicted class, and C is the set of semantic classes which is {0, 1} for our nuclei segmentation problem. To compute ŷi, the segmentation prediction Ŷ is downsampled to match the spatial dimension of F. We set the function d(.) to L1 norm. In Equation (12), cj denotes the centroid of semantic class j, which is computed using following formula:where j,ŷi is equal to 1 if ŷi = j, and to 0 otherwise.where δIn Equation (12), the clustering loss is composed of two terms: the first term measures how close the features are from their respective centroids, and the second term measures how far the semantic class centroids are from each other. Therefore, according to the first term, the feature vectors of the same class from same or different domain are tightened around the class feature centroids. And, because of the second term, features from different classes gets a repulsive force applied to feature centroids which moves them apart.S and reconstruction network R:Thus, we minimize the following total loss when training our segmentation network advP, λrecons, λadvI, λtrans, and λcl are the weights to balance corresponding losses.where, λYt when training our segmentation network S. In such scenarios, we extend proposed NuSegUDA framework to NuSegSSDA, a nuclei segmentation SSDA model.In Semi-Supervised Domain Adaptation (SSDA) problem, we aims to ensure the best usages of available target domain annotations In NuSegSSDA, for unannotated target images In our experiments, we use two H&E stained histopathology datasets with ground-truth annotations for nuclei segmentation. Both of the datasets that we used are public. We present the brief of the datasets in the following.Dataset-1 (KIRC) This dataset is taken from Irshad et al.  Naylor et al. , whereas TNBC images were acquired at Curie Institute using Philips Ultra Fast Scanner 1.6RA. Organ difference, cancer type difference, institutional difference, and using different imaging tools and protocols cause the visual difference among the images from these two datasets. See advP, λrecons, λadvI, λtrans, and λcl, respectively. We implement NuSegUDA and NuSegSSDA using PyTorch  In our first experiment, we choose KIRC as source domain and TNBC as target domain, denoted by KIRC → TNBC. In our experiment, we choose U-Net  We conduct another experiment in the similar way to experiment-1 by selecting TNBC as source and KIRC as target domain. This experiment also reflects the excellence of NuSegUDA compared to other approaches in terms of segmentation accuracies (see last three columns of Experiment-1 (KIRC → TNBC) In experiment-1, we assess our Semi-Supervised Domain Adaptation (SSDA) method NuSegSSDA for KIRC → TNBC. Experiment-2 (TNBC → KIRC) In our second experiment, we select TNBC as source and KIRC as target domain. The second experiment also demonstrates the excellence of NuSegSSDA compared to U-Net  decreases when trained with these Xs→t images.etc. see . As a reLadvI gives much worse performance than NuSegUDA which happens because of training NuSegUDA with less-realistic target-translated source domain images. This again validates the effectiveness of LadvI on NuSegUDA. Finally, with all the proposed losses enabled, we achieve the best performing model NuSegUDA for both of the experiments which demonstrates the combined impact of newly proposed image adversarial loss, target-translated source supervised loss, and clustering loss on NuSegUDA. LadvI, NuSegUDA w/o Ltrans, NuSegUDA w/o Lcl, and NuSegUDA.Similarly, NuSegUDA w/o Impacts of different segmentation networks In NuSegUDA, we use U-Net  framework named NuSegUDA for segmenting nuclei in unannotated datasets by utilizing adversarial learning. In NuSegUDA, we apply domain adaptation at both of feature space and output space. We also incorporate image adversarial loss and target-translated source supervised loss into NuSegUDA, and train the model with target-translated source domain images. Extensive and prominent experimental results validate the effectiveness of each of the newly proposed modules and losses, and the superiority of NuSegUDA over baseline models. Finally, assuming we have a few annotations available, we extend our work to Semi-Supervised Domain Adaptation (SSDA). We expect our proposed UDA and SSDA approaches to be very useful in other biomedical image segmentation tasks.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.MH, HM, and JH contributed to the methodology and design of the study. MH implemented the proposed method, organized the experimental section, and wrote the first draft of the manuscript. HM and JH corrected the draft. All authors contributed to the article and approved the submitted version.


These deformations affect the final properties of 3D-printed parts, and proper compensating technology has to be developed in order to minimize these deformations. (2) Methods: Parts were printed with powder composed of nylon plastic infused with glass beads (PA12GB). The HP MJF technology was used during investigations. All parts (specimens) were measured at different points over an extended period to follow the deformations at each point. Different finite element simulations were performed to compare them with real results and assess the viability of using simulations to save time. Various modules of the Digimat software, such as additive manufacturing (AM), material focused (MF), finite element (FE), and computer-aided engineering (CAE), were used to run the simulations. (3) Results: It was found that the printing position of the part in the printer had an impact on deformations. When the part was simulated in a tilted position but alone (deformation: 7.19 mm), the value of the deformation was 1.49 mm greater than when the other parts (two comparable parts) were simulated at the same time (deformation: 5.7 mm). The difference between the simulation with the three parts together (deformation: 5.7 mm) and reality (deformation: 3.44 mm) was 2.26 mm. Finally, the difference between the simulated single part (deformation: 7.19 mm) and the real part (deformation: 3.44) was 3.75 mm. (4) Conclusions: The results of this study will contribute to a better understanding of deformation mechanisms and will suggest solutions for improving the quality of printed parts. Three-dimensional printing is a rapidly growing technology that offers numerous possibilities across various fields. However, one commonly encountered issue is the deformation of printed parts. Methods for minimizing deformations were studied during the 3D printing process using HP MJF technology. Various factors contributing to deformation were investigated, and different techniques for reducing them were explored. Three-dimensional printing of industrial parts is more and more used to create complex and cheaper products. Additive manufacturing has advantages like design freedom and cost reduction, but there are also disadvantages like the printing time and the appearance of warping deformation in the part due to high temperature ,2. ThereDeformation in 3D-printed parts is a common issue and can occur for various reasons.Understanding this mechanism of deformation is crucial for improving the quality and reliability of 3D-printed components. Common problems associated with deformation in 3D-printed parts are warping (caused mainly by different cooling rates across the part), cracking , shrinkage , and layer misalignments. This research is focused on shrinkage and warping .Additive manufacturing (AM) has revolutionized the way we design, produce, and interact with objects, opening up new horizons in various industries. One of the most promising techniques in the field of AM is the high-performance Multi Jet Fusion (HP MJF) process ,7, whichIn this article, the deformation in 3D parts produced through the HP MJF process see  is deeplThe influence of the production method on part deformation and strength is investigated so that engineers can leverage this knowledge to design parts that are more resilient to deformation while maintaining their intended functionalities. The interplay between part orientation, support structures, and build layout plays a pivotal role in mitigating deformation and achieving successful outcomes.In conclusion, this article aims to shed light on the phenomenon of deformation in 3D parts fabricated using the HP MJF process. By comprehending the underlying mechanisms, designers, engineers, and manufacturers can make informed decisions to minimize deformation and optimize the overall manufacturing process ,12,13. TThe HP Multi Jet Fusion (MJF) process involves powdered materials being selectively fused layer by layer to build complex 3D structures  14]. Th. Th14]. 3/h [The material considered in this research was PA12GB. The thickness of one layer of PA12GB is around 0.08 mm, and this technology results in actual productivity of 4115 cm3/h ,22. The 3/h . This te3/h ,25.The HP MJF technique offers impressive benefits, such as high speed, precision, and the ability to produce complex parts with excellent detail resolution and mechanical properties. It also introduces complexities related to thermal, mechanical, and material properties. These factors can contribute to varying degrees of deformation, impacting the final accuracy, dimensional stability, and overall quality of the printed parts.HP MJF has applications in various industries, including aerospace, automotive, healthcare, and consumer goods . Its verFurthermore, the impact of the material properties on deformation behavior was examined, recognizing that different powder materials react differently under the heat and pressure of the MJF process. Identifying the ideal material and manufacturing settings and considering correct mechanical characteristics is vital for obtaining highly accurate and dimensionally stable parts ,29.To carry out this study, the digital definition of the characteristics of the used material had to be the first step. This approach would enable us to obtain a virtual representation that is as faithful as possible to reality, allowing us to anticipate the behavior expected under real conditions.To begin with, FEM analysis was used to numerically define the material, i.e., PA12GB. This is a composite material made by incorporating glass beads into polyamide 12 (PA12), which is a type of nylon, to reinforce the material. Material properties for the combination of PA12 and glass beads were obtained using Digimat MF see  and the The constitutive law used to model PA12 utilized elastoplastic law for the J2 plasticity model and exponential and linear laws for the isotropic hardening model . An isotPhase one: PA12 phase, which is a matrix phase;Phase two: Glass beads phase, which is an inclusion phase with many spherical inclusions, the total mass fraction of which is 0.4 of phase one.Now that the material properties had been defined, the microstructure properties had to be described. The PA12GB microstructure is divided into two phases:The elastic domain simulation with Hooke’s law is possible according to the following equation:A failure model had to be defined within Digimat in order to generate plastic domain behavior and run the analysis. The 3D transversely isotropic Tsai–Hill model was chosen because it incorporates interactions between stress components, even though it does not take into account the tension–compression asymmetry ,33,34, wWith11: Principal strain in the fiber direction.ϵ22: Principal strain perpendicular to the fiber.ϵ33: Principal strain perpendicular to the fiber.ϵ23: Strain perpendicular to the fiber.ϵA: Failure indicator; failure occurs when indicator is more or equal to 1.fX, Y, S are unitless maximum strain parameters.In our case of rounded glass beads, this equation can be simplified because the fiber is globular and its properties are the same in all three directions. This complex equation is used because of its possible use for other (nonglobular) phases ,37.When everything is defined, Digimat can be used to obtain the stress versus strain curve, but this is only a simulated result. Tensile tests on test specimens with various orientations during printing were performed in order to determine the material’s experimental curves. This made it possible to compare the experimental and simulated results.The highest difference was for orientation 0° and 90° ,39, whilEach result is plotted onto the same graph in The Young’s modulus was higher for the 90° orientation, and the rupture occurred at lower strain (compared to 0°) because loading is affected more by the cohesivity between layers . The linThe orientation of parts during the printing process also affects the accuracy of the print. This geometric accuracy is negligible with respect to the thermal deformation caused by the design and thermal expansion of the material.An examination of the material’s microstructure, represented by the representative volume element . This step results in coupled thermal–structural FEM simulation, which can be used to obtain results of deflection and prediction of defects. Elements of each layer are turned on with respect to the movement of the printing head.A rectangular plate with a thick edge on half the perimeter and a rib running diagonally across the piece was chosen. The part was designed in Ansys and measured 150 mm × 200 mm × 20 mm . It is oTo effectively simulate the deformation of the component, it is imperative to select Digimat AM parameters that correspond with real-world conditions. The printing chamber’s dimensions aligned with those employed in the HP printer (380 × 284 × 380 mm). The simulation encompassed a comprehensive full-build simulation, accounting not only for the part itself but also for the surrounding powder material .As outlined in the Materials and Methods selection, we opted for PA12GB based on its technical characteristics outlined in the previous section, which closely approximate the actual material. We maintained the printing process parameters without alteration with a consistent printing temperature of 330 degrees Celsius.However, one parameter was modified. The thickness of each layer of powder, normally 0.08 mm, was increased to 1 mm for reasons relating to computer processing power and storage. For this reason, at the Digimat meshing stage of the part, the voxel size was set to 2 mm, generating a mesh with 410,688 voxels in full build. The process took 963 min, or 16 h 3 min, on a computer with an Intel(R) Core(TM) i5-4590 CPU @3.30 GHz. Given that the number of voxels increases quadratically with decreasing voxel size, this was the best compromise between time and accuracy, as shown in Meshing was then carried out on Digimat layer by layer. The mesh quality is shown in Once the part had been meshed, three simulations were carried out to determine the differences generated by the orientation of the print. The first one was a simulation of the single part oriented horizontally (flat position) in the printing chamber. The simulation considered the coupled structural–thermal solution with element birth function. Heat transfer coefficients were set up by the solution process with respect to the selected material ,45.The deformations for the flat orientation occurred in a circular pattern originating from the center of the component, as illustrated in The second simulation was of the piece tilted as it would be when printed in real life (approximately 45° orientation in the printing chamber). Here, the corner with the most material was at the lowest point , meaning that it would be printed first. The deformation was caused by one main factor: the fact that the part had already begun to cool on the lower part, thus gradually creating greater deformation at the upper end of the part. The maximum deflection in this simulation was 7.72 mm. The difference between this and the previous result was caused by completely different heat transfer during the printing process.The last simulation was of the part at the same angle as before but this time as in the real world, with the part being printed at the same time as the two others (slightly different parts). Here, the simulation took place with three parts, as shown in The part simulated in the previous section was printed as shown with the red arrow in The results were almost the same as those from the previous simulation; however, the deformations were less significant: 5.7 mm for this one  comparedIt can be concluded that the part deforms differently depending on the position in which it is printed. If minimal deformation is desired, it should be printed flat with other parts around it, even though it may tend to deform at multiple points. On the other hand, if a larger deformation at a single point is negligible, it might be preferable to print it inclined. Printing flat parts horizontally is generally minimally used because it causes problems with surface quality, such as sinks or ‘elephant skin’. This flat orientation is shown just for comparing. It will not be used in reality because it will cause the detailing and fusing agent to remain on the upper surface during printing.The measurement of deformations after the printing process has to be performed in order to validate the simulations from the virtual prediction. The difference between the simulation and reality has to be quantified.In order to check whether the simulated results are correct, it is necessary to observe the deformations that occur in the real part. Measurements were taken at different times after the printing process. A total of 15 measurements (15 specimens) were performed at each time in order to obtain statistically correct results (by the 3D measuring station). The first measurement was just after the printing had finished, then two and five days later, and then every week for three weeks. To obtain long-term information, measurements were also taken six and eight weeks after printing.From The only non-negligible value was that at the end of the rib (δh), which should be zero. This value increased then decreased and seemed to stabilize at around 3.42 mm of deformation . It has By comparing the results of advanced simulations with the data obtained from real experiments, a correlation between the virtual world and the physical world can be established. It is obvious that the simulations and reality appeared to be consistent as the deformation occurred at the same point. However, there were a few differences between the values of the maximum deflection in the corner (δh).When the part was simulated in a tilted position but alone (deformation: 7.19 mm), the value of the deformation was 1.49 mm greater than when the other parts were simulated at the same time (deformation: 5.7 mm). The difference between the simulation with the three parts together (deformation: 5.7 mm) and reality (deformation: 3.44 mm) was 2.26 mm. Finally, the difference between the simulated single part (deformation: 7.19 mm) and the real part (deformation: 3.44) was 3.75 mm. This is not an insignificant difference. Further research and development in this area must be carried out to bring virtual and real printing closer together and thereby design parts with minimal deformation. All results are shown in This study delved into the investigation of deformations that occur during the Hewlett-Packard (HP) Multi Jet Fusion (MJF) 3D printing process. The study focused on minimizing deformations by exploring different printing orientations and highlighting the factors that influence the deformation of printed parts.Simulation results were compared with experimental data, demonstrating good consistency between the two, by considering the printing position, the material, the size, and even the quantity of pieces during printing.The difference between reality (after stabilization of dimension in time) and simulation was 2.26 mm. This is the lowest achieved error with maximal accuracy of simulation for a full printing job. Further approximation of results is possible only by FEM mesh refinement. This is limited by the actual available computing power.The insights gained into the deformation mechanisms and simulation methods provide valuable information for optimizing the design of parts printed using the HP MJF technology. This research contributes to enhancing the quality of printed parts and harnessing the full potential of the HP MJF technology across various industrial applications. In summary, this study underscores the significance of comprehending deformation mechanisms and employing effective strategies in order to minimize deformations during HP MJF 3D printing. Future research will continue with the description of material models of different composite polymers and refining results in order to minimize differences between simulations and the parts produced by the MJF (and other) technology. The main limit of this study is the limited computing power. The actual considered size of one element was 2 mm. This value should be lower than the layer size during the actual printing process (0.08 mm) in order to obtain more precise results. This means that elements that are 25 times smaller should be used for FEM simulations. This size of elements was not possible to simulate with the computational power that was used.


Lentinula edodes is known to show high nutritional and organoleptic properties and can be mixed with different by-products in the production of new foods with important functional characteristics. Cocoa pod husk (CPH) is the main by-product in the cocoa industry and presents important bioactivities. In this research, two mixtures were applied based on Lentinula edodes mushroom flour from fifty different strains (strain 1 to strain 50) and cocoa pod husk flour (CPHF): 60% Lentinula edodes mushroom flour from each strain mixed with 40% CPHF (Mixture 1), and 80% Lentinula edodes mushroom flour from each strain mixed with 20% CPHF (Mixture 2). The parameters evaluated were moisture, proteins, fat, total dietary fiber (TDF), ash, carbohydrates, antioxidant capacity, antimicrobial activity, pH and water activity (aw). The multivariate statistical techniques evaluated those samples that exhibited the highest degree of correlation with the distinct chemical and commercial parameters, showing that sample 13 of both mixture flours (M1 and M2) obtained by the mushrooms of L. edodes (strain 13) with CPH indicated the higher significant of the parameters. In addition, the sensory test of the best flour (sample 13) was applied to 50 panelists, presenting the best sensory characteristics, such as color, aroma, texture and general acceptability. Lentinula edodes (shiitake) is the mushroom with the second-highest production worldwide, and represents about 22% of the total production of edible mushrooms [Lentinula edodes is in logs cut from trees and requires long periods of time, causing negative impacts on the environment due to the forest destruction of the trees. The high consumption rates of this edible fungi have promoted the development of technologies based on the use of agricultural waste with high carbon and nitrogen content as substrates in the cultivation of Lentinula edodes. The nutritional, pharmaceutical and organoleptic properties of Lentinula edodes are related to the nutrient composition of the culture substrate and the type of supplements added [Lentinula edodes mushrooms to enrich the nutritional value of food products because they are appetizing and nutritious. Lentinula edodes protein is composed of all essential amino acids in ideal proportions for human nutrition. Additionally, Lentinula edodes shows a high composition of complex carbohydrates, vitamins and minerals and a high amount of trace elements that improve physical fitness and prevent diseases [Lentinula edodes mushrooms can be mixed with various types of products or by-products such as cocoa waste to obtain complementary foods with high nutritional content.ushrooms . The trats added . Furtherts added . Studiesdiseases . ConsequCocoa production worldwide represents around 4.7 million tons of cocoa beans, and is found mainly in African and American countries. There are four different cocoa varieties: National, Criollo, Forastero and Trinitario. Forastero is the variety with the highest harvest in Africa and has important characteristics such as a greater resistance to different pests and higher productivity in comparison to other varieties . EcuadorMultivariate statistical techniques allow the study, representation and interpretation of data resulting from the observation of more than two statistical variables on a sample of individuals; these variables are homogeneous and correlated . MultivaBiplots allow the study of sets of variables in a population of individuals and the determination of data sets with the same characteristics through the use of mathematical operations derived from geometry, matrix calculus and multivariate distributions . These mLentinula edodes mushroom flour and the cocoa pod husk flour) that present the highest chemical and commercial characteristics, such as moisture, proteins, fats, total dietary fiber (TDF), ashes, carbohydrates, antioxidant capacity, antimicrobial activity, pH and water activity (aw). Additionally, the flour mixtures with the highest chemical and commercial characteristics were subjected to a sensory test that was evaluated using the ANOVA test.The main purpose of this research was to use statistical algorithms, GGE Biplot and PCA biplot, to determine the flour mixtures , banana leaves (30%), millet seed (16%), cotton seed husk (5%) and CaCO3 (5%). The fruiting conditions were as follows: relative humidity between 85 and 90%, temperature of 25 ± 1 °C, air recirculation and a lighting period of 12 h [Lentinula edodes strain were performed in triplicate.The fruiting bodies of the 50 different strains of  of 12 h . The culCPH are among the main by-products of the Ecuadorian food industry, and is generated by the high production of fine chocolate. Therefore, it was collected from the AgroFarm Sánchez-Jaramillo in the Province of Manabí. The CPH were placed in boiling water for 10 min to inactivate the enzyme. Then, the fruit bodies of the fifty mushroom strains and the CPH were exposed to direct sunlight until they were dry.The mushrooms and the CPH were ground to powder in a paddle mill for 30 s and passed through a sieve of 200 µm mesh, obtaining fungi flour and cocoa pod husk flour (CPHF), respectively, and the flours were stored at 4 °C.The mixtures were composed using the following formulations:Lentinula edodes strain mixed with 40% CPHF. Fifty samples (1–50) were obtained for the different strains collected.Mixture 1—60% mushroom flour from each Lentinula edodes strain mixed with 20% CPHF. Fifty samples (1–50) were obtained for each of the different strains collected.Mixture 2—80% mushroom flour from a specific 2SO4 (1.25%) and sodium hydroxide NaOH (1.25%) (enzymatic-gravimetric method), ash from the calcination of the sample at 550 °C and carbohydrates determined by using Equation (1): The chemical parameters of the mixtures were analyzed according the AOAC procedures  with cerEquation (1). Percentage of carbohydrates of mixtures.w).The calculated commercial properties of the flour mixtures were antioxidant capacity, antimicrobial activity and technological properties such as pH and water activity  were mixed in a 96-well plate. The mixture of the extract with ethanol was incubated in the dark for 30 min and later it was measured at 515 nm using a microplate reader. The radical scavenging activity (RSA) of DPPH was determined using Equation (2) [The extracts of the flour mixtures were analyzed to determine the antioxidant capacity, and 30 μL of the extract and 270 μL of DPPH methanolic solution (6 × 10tion (2) .(2)RSA(Equation (2). DPPH radical scavenging activity (RSA).Salmonella typhimurium (ABN 572). The bacteria have been deposited at Research and Development Laboratory of Ecuahidrolizados.The antimicrobial activity was determined using the following Gram-positive bacteria: 6 CFU/mL was used to adjust the bacterial suspensions. The extracts of the flour mixtures were dissolved in 30 mL of ethanol, and the ethanolic extracts were evaporated using a rotary evaporator. The extracts were dissolved in 5% DMSO (1 mg mL−1) and added into a Tryptic Soy Broth (TSB) medium (100 μL) with bacterial inoculum (1.0 × 105 CFU per well) at a final volume of 100 µL.A saline solution with a concentration of 1.0 × 10The microplates were subjected to incubation at 170 rpm for one day at a temperature of 38 °C. Subsequent to this incubation period, a solution of commercial dye, hexamethylpararosaniline chloride, commonly known as crystal violet, was introduced at a concentration of 0.1 mg/mL. A further incubation of thirty minutes followed this addition. Samples that demonstrated growth inhibition exhibited a distinct color change. The determination of the bacterial growth inhibitory concentrations (MIC) was accomplished through the microscopic examination of a subculture of the bacterial extract in microtitre plates, which were subsequently incubated for a period of 24 h. The optical density of each well was measured and compared to both a blank and a positive control, the latter being represented by streptomycin . w) for mixtures was measured with a Labmaster AW  at 25 °C [The pH levels of the mixtures were measured with a pH 400 m , diluting the mixtures in distilled water (1:10), and the water activity  and mixture 2 (80% mushroom flour from the L. edodes strain 13 and with 20% CPH flour). The survey presented 9 points: 1 = disliked extremely, 2 = disliked very much, 3 = disliked moderately, 4 = disliked slightly, 5 = neither liked nor disliked, 6 = liked slightly, 7 = liked moderately, 8 = liked very much, 9 = liked extremely. The sensory parameters evaluated were color, aroma, texture and general acceptability [The sensory evaluation test was carried out by 50 panelists during a period of 1 h. The panelists received educational training for 5 min before participating in the sensory test, and the samples of food were provided in a random order. The 4 samples evaluated were only mushroom flour of tability ,28.w)) were measured in triplicate, and the data were subjected to multivariate statistical techniques such as GGE Biplot and PCA Biplot using R software v.4.1.1.The chemical composition and commercial characteristics , ash, carbohydrates, antioxidant capacity, antimicrobial activity, pH and water activity .In addition, the results obtained from the sensory analysis survey were tabulated and subsequently examined using one-way analysis of variance (ANOVA) at the A GGE biplot graph is used to obtain the visualization of the best behavior of a certain genotype in each environment. This is undertaken through interconnecting all the genotypes that are at the extreme points of the graph and their respective perpendicular lines, forming a polygon. This polygon shows the best genotype for each environment and divides the environments into different groups.In the event that the genotypes adapt to different environmental groups and the variation between groups is greater than within the group, a mega-environment is formed, which indicates the genotypes that have a broad or specific adaptation to certain environments or groups of environments.The GGE Biplot model keeps G and GE together and partitions GGE into two multiplicative terms, and therefore the equation is given by ,30:Yij=μThe PCA biplot allows a standardized data set to be obtained in order to extract the main patterns of the potential groups and explore the correlations between the objective indicators. The PCA biplot decreases the amount of the data and shows the relative position of each indicator in a quadrant of the axes of the plane.The PCA biplot method approximates the matrix pression : X≅Xq≅U∑Lentinula edodes flour and CPHF.The research purpose was to identify the flour mixtures with the highest chemical characteristics and commercial properties of The samples of the flour mixtures were made according to the following distribution:Lentinula edodes strain mixed with 40% CPHF) or flour samples corresponding to Mixture 2 (80% mushroom flour from a specific Lentinula edodes strain mixed with 20% CPHF).1–50: Flour samples corresponding to Mixture 1  with CPH presented the highest values in three of the six variables of the analysis: protein, moisture and fat.Lentinula edodes mushroom, showing the highest values in moisture (90/100 g), total dietary fiber (4.6/100 g) and protein (1.6/100 g) and important levels in amino acids, polysaccharides, vitamins and minerals [In the following study, evaluating the potential of CPH as a food additive, it was found that this by-product is rich in total dietary fiber (35.3/100 g dry weight of the husk), carbohydrates 49.3/100 g) and ash (8.9/100 g) . Other r.3/100 g minerals . Lentinula edodes mushroom flour and the CPHF, that present high protein and fiber values allows new food products with an important nutritional composition to be obtained, promoting the cultivation of the Lentinula edodes mushrooms and also the use of a by-product such as CPH in the food industry.The use of two different ingredients in the mixtures, such as w). w); and Cluster 3, “color green”, formed of 11 samples , which showed highest values of antioxidant activity and pH. On the other hand, w); Cluster 2, “color green”, was formed of 22 samples  and presented the highest values of antimicrobial activity and pH; and Cluster 3, “color blue”, was formed of 12 samples  and presented the highest values of antioxidant activity. Sample 13 of both mixtures’ flours (M1 and M2) obtained using the mushrooms of L. edodes (strain 13) with CPH presented the highest values of antioxidant activity and pH. Sample 13 also showed the highest values of antimicrobial activity in the mixture M2, establishing itself as the strain with the best characteristics. Lentinula edodes is an edible mushroom that contains polyphenols and polysaccharides that indicate a high antioxidant capacity [Lentinula edodes against Gram-positive bacteria. The antimicrobial capacity is related to the total content of phenols and ortho-diphenols that are produced in the different extracts (aqueous or methanol), and also the phenolic compounds depend on the morphological and genetic characteristics of the fungi strain [Lentinula edodes is known for its importance in the pharmaceutical area, especially in the prevention of hypercholesterolemia and diabetes [S. aureus and Gram-negative bacteria such as E. coli, obtaining higher values in soluble methanol in comparison with soluble ethyl acetate [capacity . Antioxicapacity . In addii strain . At presdiabetes . In addidiabetes . Differe acetate . The inh acetate . The pH  acetate .Lentinula edodes strain 13 mixed with CPH showed important values of nutritional composition, antimicrobial and antioxidant activities and pH that are characteristic for formulating new products and also can be used in the pharmaceutical sector.GGE and PCA biplots indicated that flour samples of mixtures 1 and 2 that were obtained from the fruit bodies of Lentinula edodes mushrooms present volatile compounds, alcohols, aldehydes, ketones, acids, esters, sulfur compounds and pyrazines, that contribute to the quality and flavor of new foods [Lentinula edodes mushrooms have a toasted aroma or garlic aroma, so they can be used for the development and research of products in the food industry. Currently, the fruiting bodies of mushrooms are evaluated in the condiment industry for their flavor. The high content of compounds of eight carbons, sulfur and aldehydes that present in the composition of the fruiting bodies of L. edodes come from the sawdust substrate that is used in their cultivation [L. edodes mushrooms in their composition [ew foods . Volatilew foods . Dried Ltivation . Resultsposition . Cocoa pposition . The CPHposition .L. edodes strain 13, in mixture 2 (80% mushroom flour from the L. edodes strain 13 and with 20% CPH flour) and mixture 1 (60% mushroom flour from the L. edodes strain 13 and with 40% CPH flour), presented the highest values of aroma, while mixture 2 and the sample of only mushroom flour of L. edodes strain 13 showed color values and mixture 2 indicated the highest values of texture and acceptability. The panelists indicated that mixture 2 presented the highest sensory attribute values compared to the other samples (The results obtained by the 50 panelists who carried out the sensory evaluation indicated that the sample of only mushrooms flour of  samples .Lentinula edodes strain 13 presented the highest values of nutritional parameters such as protein and fiber, antioxidant capacity and antimicrobial activity against Salmonella typhimurium.The GGE and PCA biplots showed that the flour samples of the mixtures 1 and 2 that were obtained from the mushrooms of the Lentinula strain 13 and 20% CPHF.The sensory test, based on a nine-point survey provided to the panelists, showed a general acceptability for the flour sample of mixture 2, composed of 80% mushroom flour of the Lentinula edodes using banana leaves as part of the substrate, and promote the use of cocoa pod husks to as an ingredient in the development of new food and seasoning products.The results obtained from this research encourage the production of The use of multivariate statistical techniques allows a better visualization of the flour samples with the highest antioxidant capacity and antimicrobial activity for use in the pharmaceutical field.


Nanosized metals usually exhibit ultrahigh strength but suffer from low homogeneous plasticity. The origin of a strength–ductility trade-off has been well studied for pure metals, but not for random solid solution (RSS) alloys. How RSS alloys accommodate plasticity and whether they can achieve synergy between high strength and superplasticity has remained unresolved. Here, we show that face-centered cubic (FCC) RSS AuCu alloy nanowires (NWs) exhibit superplasticity of ~260% and ultrahigh strength of ~6 GPa, overcoming the trade-off between strength and ductility. These excellent properties originate from profuse hexagonal close-packed (HCP) phase generation (2H and 4H phases), recurrence of reversible FCC-HCP phase transition, and zigzag-like nanotwin generation, which has rarely been reported before. Such a mechanism stems from the inherent chemical inhomogeneity, which leads to widely distributed and overlapping energy barriers for the concurrent activation of multiple plasticity mechanisms. This naturally implies a similar deformation behavior for other highly concentrated solid-solution alloys with multiple principal elements, such as high/medium-entropy alloys. Our findings shed light on the effect of chemical inhomogeneity on the plastic deformation mechanism of solid-solution alloys. How the random solid solution (RSS) alloys accommodate plasticity and overcome strength-ductility trade-off remain unresolved. Here, the authors reveal atomic-scale plastic deformation of AuCu alloy, which shed light on the effect of chemical inhomogeneity on plastic deformation of RSS alloys. For practical applications, NWs are usually under stress/strain environments, in which both high strength and high ductility are desired. The strength and ductility are directly related to the atomic-scale structural evolution mechanism under strain/stress conditions6. Thus, achieving both high strength and high ductility, as well as revealing atomic-scale mechanisms, is greatly desired in metal science and technology research, which can provide scientific guidance for designing metals with desired properties. Owing to the importance of these issues, a large number of studies have been carried out on nanosized pure metals14, which have shown that nanosized metals exhibit a strength-ductility trade-off phenomenon, i.e., nanosized pure metals exhibit high strengths but suffer from low ductility at room temperature18. At relatively high temperatures, nanostructured metals can exhibit superelongation, but very low strength21. This strength-ductility trade-off phenomenon is based on studies of pure metals, such as Ni, Au, and Cu NWs24. It has also been unclear how to make alloy NWs achieve both high strength and superplasticity. Based on previous classic experiments and theoretical studies conducted on pure metals, NWs exist in a low dislocation density state, high stress is required for the nucleation of new dislocations, and dislocations can easily escape rather than multiply, leading to metallic NWs exhibiting high strength but low ductility27. Many interesting studies have also shown that the plastic deformation in pure metals is dominated by partial dislocation and twinning when the size of metals is below several tens of nanometers29, and full dislocations are rarely observed32. In a pure metal, it is easier to thicken an existing stacking fault (SF) or twin than to nucleate a new SF in the pristine lattice during plastic deformation. Molecular dynamics (MD) simulations have predicted that as the size of pure metals decreases to several nanometers, a phase transition from the FCC to body-centered cubic (BCC) phase36 occurs during deformation. FCC-hexagonal close-packed (HCP) transformation is rarely observed under conventional straining, where the FCC-HCP transformation requires ultrahigh stress39. However, most proposed deformation models are based on nanosized pure metals. For highly concentrated solid-solution alloy NWs, in which the solid-solution atoms are randomly distributed, the accommodation of plastic deformation remains an unresolved issue. Thus, obtaining in situ atomic-scale deformation evidence can determine whether both high strength and superplasticity can be achieved and whether there are novel deformation mechanisms for nanosized concentrated solid-solution alloy NWs.Nanosized face-centered cubic (FCC) metals such as nanowires (NWs) have attracted extensive interest because of their potential applications in micro/nanoelectromechanical devices and flexible electrodesIn this study, we present in situ atomic-scale evidence that a random solid solution (RSS) structure enables AuCu NWs to exhibit superplasticity with ~260% uniform elongation and ultrahigh strength of ~6 GPa. We discovered that the excellent properties originate from the chemical inhomogeneity , which leads to widely distributed and overlapping energy barriers for multiple plasticity mechanisms and enables the AuCu alloy NWs to exhibit several unconventional plastic deformation modes, such as an ultrahigh density of deformation-induced HCP lamellae (including 2H and 4H phases), zigzag-like nanotwins, and reversible FCC–HCP phase transition. These deformation modes hinder the dislocation motion, which ultimately leads to superplasticity and ultrahigh strength in the AuCu NWs.43. Then, ligaments generated using a Fischione 1040 NanoMill served as AuCu NWs, as shown in Fig. AuCu NWs for in situ transmission electron microscopy (TEM) straining experiments were fabricated from thin films deposited on single-crystal NaCl substrates using magnetron sputtering Fig. . First, ε) was calculated using the formula ε = (Ln − L0)/L0, where L0 is the initial length between the two reference points, and Ln is the length after deformation. As shown in Fig. L0). Upon deformation, the NW first experienced elastic elongation and then was subjected to a homogeneous elongation as large as ~260%, as shown in Fig. 41. We also performed the tensile experiments of AuCu NWs under beam blank (only turning on the electron beam for image capture), while the super-elongation was still observed . The NW then underwent Stage 1, in which the plasticity was governed by leading and trailing partial dislocations that formed extended dislocations or full dislocations on multiple gliding systems  coherent twin boundaries . Au and Cu atoms are randomly distributed in a supercell with dimensions of 9.5 nm × 5.5 nm × 26 nm  and ∆γ3 (97.4 mJ/m2) are overall smaller than that of ∆γ3 (99.4 mJ/m2), and the former two distributions largely overlap. Such widely distributed and overlapping energy barriers enable the concurrent activation of separated partial dislocation, HCP phase transformation, and deformation twin generation. This is because the continuous thickening of an existing nanotwin would require a lower energy barrier for the successive shearing of neighboring {111} planes. The chance of this occurring would be low, considering the significantly varied energy barriers for twin thickening owing to the RSS-induced inherent chemical inhomogeneity. Once a plane with a high energy barrier is encountered, the thickening stops and the strain is carried by an SF  nucleated elsewhere at a new site with low ∆γ1. Thus, a high density of HCP nanolamellae and separated sub-nanoscale deformation twins are formed instead of individual thick twins in the RSS AuCu NW. This explains the experimental observations during our in situ nanoscale deformation and large-scale MD simulation.To unveil the intrinsic mechanism of dislocation nucleation for nanoscale twins in RSS AuCu, we employed the nudged-elastic-band (NEB) method to investigate the generalized SF energy (GSFE) surfaces, as illustrated in Fig. mentclasspt{minima59, always along with relatively low ductility  can serve as a proficient strengthening source for the pronounced strain hardening. Specifically, previous studies have predicted that twin and phase boundaries could exert a repulsive force on gliding dislocation, which can be described by τ ∝ 1/λ, where λ is the thickness of the twin or HCP phase78. With ultrahigh density boundaries, dislocation slip is confined in such a way that the elastic strain energy cannot be easily released by means of dislocation propagation. Therefore, a higher stress is required for the nucleation and slip of dislocations for NWs with subaerial-scale HCP nanolamellae and subaerial-scale twins , zigzag-like nanotwins, and ordinary dislocation activities on multiple slip systems, which is very different from the expected thickening of a single twin across the gauge section in a pure FCC metal. Previous MD simulations and experiments have shown that the elastic strain of metallic NWs can approach as high as ~7%80, extended and full dislocations consist of leading followed by trailing partial dislocations, where the distance r between the leading and trailing partial dislocations can be affected by the shear stress σ according to the formula:γ is the SF energy of the metals, and κ1, κ2 are the constants depending on the elastic moduli of the material. At Stage 1, the size of the NW is relatively large, and the corresponding stress is relatively low . It is reasonable that the plastic deformation was governed by leading followed by trailing partial dislocations forming extended dislocations or full dislocations , which was operated in an FEI Titan environmental-TEM . An AuCu film with a thickness of ~30 nm was deposited on a single-crystal NaCl substrate via magnetron sputtering. The film with the substrate was attached to a bimetallic strip extensor using epoxy resin. The NaCl substrate was then etched away with deionized water, leaving a free-standing AuCu thin film on the bimetallic extensor. To prepare the AuCu NW, the AuCu film was first thinned using a precision ion polishing system, forming many variously sized nanovoids. The ligaments between neighboring nanovoids served as AuCu NWs. The NWs were then processed using a Fischione 1040 NanoMill to further thin them and remove the amorphous and carbon deposits around the NWs. Finally, atomic-scale in situ tensile experiments were conducted on the appropriate AuCu NWs using E-TEM.The atomic-scale energy-dispersive X-ray spectroscopy (EDS) mapping of Cu/Au and the corresponding HAADF image were simultaneously captured at the same position. Therefore, the atomic column coordinates in the atomic-scale EDS mapping of Cu/Au were the same as those in the corresponding HAADF images. For HAADF images, the center position of each atom column was calculated using the open-source software Atomap. Then, the position coordinates of the atom columns were mapped to the EDS mappings, which were well-matched, as shown in Supplementary Fig. For the atomic-scale EDS mappings of Cu and Au, the average intensity of the image pixels was calculated within a small neighborhood of the atomic coordinates. This intensity value represents the relative intensity of the element in the atom column and the relative intensities of Au and Cu were Based on the mentclass2pt{minim60Cu40 alloy using large-scale atomic/molecular massive parallel simulator (LAMMPS) packages92. The shear deformation of the Au60Cu40 alloy was simulated in a 9.5 nm × 5.5 nm × 26 nm orthogonal simulation cell containing 96,000 atoms with the x, y, and z axes along the [112],  direction at a shear velocity of 0.01 Å/ps. Deformation data were collected for the following 4 ns under the NVT ensemble with a time step of 2 fs94.To reveal the underlying deformation mechanism, MD simulations were conducted on the RSS Aub = −14 eV.A total of 250 independent AuCu samples with planar fault areas of 2 nm × 2 nm were used to calculate the GSFE energetics. Starting from the initial FCC lattice, a one-layer ISF, two-layer ESF, and three-layer twins were created by introducing Shockley partials with Supplementary InformationPeer Review File


Herein, inspired by the Claisen-Schmidt condensation reaction, two propenone-linked (C=C–C=O) COFs (named Py-DAB and PyN-DAB) are developed based on the base-catalyzed nucleophilic addition reaction of ketone-activated α-H with aromatic aldehydes. The introduction of propenone structure endows COFs with high crystallinity, excellent physicochemical stability, and intriguing optoelectronic properties. Benefitting from the rational design on the COFs skeleton, Py-DAB and PyN-DAB are applied to the extraction of radionuclide uranium. In particular, PyN-DAB shows excellent removal rates (>98%) in four uranium mine wastewater samples. We highlight that such a general strategy can provide a valuable avenue toward various functional porous crystalline materials.The type of reactions and the availability of monomers for the synthesis of  sp2 carbon-conjugated covalent organic frameworks is limited to a few reactions and monomers due to the poor irreversibility of carbon double bonds. Here the authors synthesize propenone-linked covalent organic frameworks for the removal of radionuclide uranium from mine wastewater samples.The synthesis of  In recent years, COFs based on irreversible C=C bond connection have attracted extensive attention for their excellent stability and π-electronic communication characteristics3, which overcome the disadvantages of borate ester or dynamic imine linked COFs in this respect4. Therefore, they have considerable application potential in energy5, environment and health fields7.Covalent organic frameworks (COFs) represent a class of advanced porous crystalline materials, which specifically integrate various functional units into highly ordered periodic arrays and feature highly customizable structure and functionssp2 carbon-conjugated COFs challenging, limiting viable strategies to only a few reactions, including the Aldol reaction8, the Knoevenagel reaction9, and the Horner-Wadsworth-Emmons polycondensation10. However, the monomers used in these reactions must contain numerous electron-withdrawing groups11, which requires cumbersome process and even unavoidably introduces toxic raw materials12. The design and synthesis of sp2 carbon-conjugated COFs will continue to be at the forefront of COFs research as their unparalleled potential for combination in crystallinity, porosity, stability, modularity, and functionality.However, the relatively poor reversibility of C=C bonds makes the synthesis of 14. Compared to the classical reaction of olefin bond formation described above, the essence of the Claisen-Schmidt reaction is that the α-H activated by the ketone structure, which condensates with the aromatic aldehyde under the catalysis of base to form a stable C=C bond. Thus, the monomers used in Claisen-Schmidt reaction are free of cyano and more environmentally friendly. In addition, such the compound formed by Claisen-Schmidt condensation with a propenone (C=C–C=O) structure contains a large π-bond, which makes such materials exhibit excellent photoelectronic performance. Furthermore, introducing ketone structure can enhance the light absorption15, and the lone pair electron resonance effect of oxygen atom can improve the stability of materials16, which will further improve designability and functionality of the interrelated materials.Claisen-Schmidt condensation has been extensively used in organic chemistry to introduce C=C bondssp2-c conjugate COFs (Py-DAB and PyN-DAB) with high crystallinity were successfully synthesized by combining 1,4-diacetophenone (DAB) with 1,3,6,8-tetrakis(4-formylphenyl)pyrene (TFPPy) and 5,5’,5”,5”‘-tetrapicolinaldehyde (TFPPyN), respectively. Py-DAB and PyN-DAB exhibited excellent physicochemical stability and photocatalytic performance due to the introduction of the propenone structure. In particular, benefitting from the rational design on the COFs skeleton, PyN-DAB exhibited a higher radioactive uranium adsorption capacity under visible light through photocatalytic reduction mechanism, and showed excellent removal rates (>98%) in four uranium mine wastewater samples, which overcome the disadvantages of the complex post-modification process of amidoxime-based COFs and the poor selectivity produced by vanadium. We highlighted that such a strategy will enrich the types of sp2-c COFs and provide a valuable avenue toward various functional porous crystal materials.Herein, we report a strategy for synthesizing unsubstituted propenone-linked COFs via the base-catalyzed Claisen-Schmidt reaction between aryl acetyl and aryl aldehydes. Two Initially, the model compound was successfully prepared by the classical method to verify the feasibility of the reaction  than AA mode , proving the staggered AB stacking structure is much more stable17. This may be attributed to the strong dipole moment generated by the heteroatoms (oxygen atoms) in the skeleton to avoid these charged atoms directly located at the top of each other4. The Pawley refinement for these COFs yielded PXRD patterns showed reasonable agreement with the simulation of AB mode   measurement was examined. As shown in Fig. −1) was greatly reduced21. The vibration peak of ketone structure (ca. 1672 cm−1) had a certain degree of retention, and the vibration peak of trans -C=C- (ca. 970 cm−1) newly appeared  NMR spectroscopy was tested. As shown in Fig. 24, and the peak located at around 190 ppm was ascribed to the carbon atoms in the ketone groups25. Similarly, PyN-DAB also exhibited distinct characteristic peaks  spectroscopy. Clearly, the stretching vibration peak of Ar-CHO  surface areas were calculated to be 633.5 and 456.3 m2 g−1, and the pore size distributions centered at 1.31 and 1.33 nm, respectively , strong basicity (9 M KOH), and intense radiation (200 K Gy) for 24 h. The test results showed that all Py-DAB and PyN-DAB exhibited less remarkable cost of crystallinity under the above conditions by PXRD analysis  spectroscopy indicated that Py-DAB and PyN-DAB exhibited broad boundary absorption in the ultraviolet and visible regions, and their optical band gaps were determined to be 2.15 and 1.76 eV by the Kudelka-Munk transform reflection spectra  represent the lower electron transfer resistance (Ret) and higher charge transfer rate . The results show the bandgap narrowing with the formation of Py-DAB and PyN-DAB  to insoluble uranium (IV) has been proposed as a green and viable solution to the health and environmental problems caused by uranium-containing wastewater38.Uranium is a key element related to the sustainable development of the nuclear industrysp2-c conjugated Py-DAB and PyN-DAB were used to photocatalytic uranium reduction. Firstly, the cyclic voltammetry curves of Py-DAB and PyN-DAB were tested, and the onset oxidation potentials of Py-DAB and PyN-DAB were about −1.15 V and −1.28 V  under the conditions of pH 4.0  was still higher than that of COF-PDAN-AO (105 mg g-1)37. In addition, the test results of the Zeta potentials proved that both Py-DAB and PyN-DAB have more negative potential compared to the amine-linked Py-PD, indicating that the introduction of propenone structure can increase the electronegativity of materials skeleton  (3.45-fold)  after 5 cycles  to 1436.4 mg gld) Fig. , which wtes Fig. , which i22+ concentration is about 28.76 ppm, Supplementary Table 41, and the results showed that all removal rates were >90% under visible light irradiation . The U 4f peaks were more pronounced after uranium adsorption in the light than the dark , and even the O-U peak appeared  and U 4f5/2 (392.8 and 391.4 eV) coexisted under visible light irradiation, and the peaks area of UIV is much larger than that of UVI  silent45, PyN-DAB had strong EPR signal with g = 1.99 under visible light irradiation  were selected to perform quantum-chemical calculations by the DFT method. The surface charge density distribution results showed that carbonyl oxygen and pyrazine nitrogen atoms exhibited higher electron cloud density Fig. . In sharely Fig. , provingsp2 carbon-linked COFs, which can stimulate the research on enriching the reaction types and monomers availability, and further explores the broad application prospects of COFs.In summary, we have successfully demonstrated a feasible approach to construct propenone-linked COFs via Claisen-Schmidt condensation reaction. Due to the introduction of propenone structure, Py-DAB and PyN-DAB revealed high crystallinity, excellent physicochemical stability, and wonderful photocatalytic functions. Furthermore, application of them to the photocatalytic reduction of uranium showed that PyN-DAB exhibited stronger adsorption capacity and faster adsorption kinetics for uranium than Py-DAB due to its abundant binding sites and broader negative charge region. It is worth highlighting that PyN-DAB synthesized by the one-pot method showed excellent removal rates (>98%) in four uranium-containing wastewater samples, avoiding the use of toxic cyano monomers and significantly overcoming the drawbacks of amidoxime-based COFs in complex post-modification processes and relatively poor selectivity. This ingenious protocol expands the synthetic toolbox of 2O, tetrahydrofuran (THF), N,N-Dimethylformamide (DMF) and dimethyl sulfoxide (DMSO), and dried under vacuum at 100 °C to obtain Py-DAB solid of 89% yield.A mixture of TFPPy (18.6 mg), DAB (9.7 mg) and 1,4-dioxane solution (1.5 mL) in a Pyrex tube with aqueous KOH  was degassed by three freeze-pump-thaw cycles. The tube was sealed off and heated at 90 °C for 72 h. The yellow solid was filtrated and then washed with H2O, THF, DMF and DMSO, and dried under vacuum at 100 °C to obtain PyN-DAB solid of 75% yield.A mixture of TFPPyN (18.6 mg), DAB (9.7 mg) and 1,4-dioxane solution (1.5 mL) in a Pyrex tube with aqueous KOH  was degassed by three freeze-pump-thaw cycles. Then the tube was sealed off and heated at 90 °C for 72 h. The yellow solid was filtrated and washed with H2O, THF, DMF and DMSO, and dried under vacuum at 80 °C to obtain Py-PD solid of 93% yield.A mixture of TFPPy (24.7 mg), p-Phenylenediamine (8.7 mg), 1,4-dioxane (0.8 mL), mesitylene (1.334 mL), benzyl alcohol (0.8 mL) and acetic acid solution  was degassed by three freeze-pump-thaw cycles. Then the tube was sealed off and heated at 120 °C for 72 h. The yellow solid was filtrated and washed with HSupplementary InformationPeer Review File