Negaprion acutidens (sicklefin lemon shark) is distributed in the Indo-Pacific and in close association with coral reefs. Under the protection of the Dongsha Atoll National Park, a small but well-established juvenile population of N. acutidens inhabiting coastal areas of Dongsha Island was recently observed to display site fidelity by using acoustic telemetry. This study was designed to reveal the fine scale genetic structure and relatedness within and among 5 juvenile shark cohorts inhabiting 3 sampling sites at Dongsha Island. A total 188 juveniles were caught and sampled between 2016 and 2017, and genotyped with twelve loci. They were assigned to 5 year cohorts (2013–2017) based on the body length and date they were caught, also assigned to 3 sites based on where they were caught. Among five cohorts, the percentage of unrelated pairs within a cohort is more than 62% in average, suggesting a potential high mortality during their early life stage. The results of Fst and assignment testing showed that there was no significant genetic structure between sites and cohorts indicating that there was no fine scale genetic structure, even though the juveniles possessed strong site fidelity. A small effective population size (Ne) was detected (Ne = 86.7) which indicates the presence of a potentially isolated and vulnerable population at Dongsha. These results provide the genetic diversity as a baseline for future management and conservation of N. acutidens in the South China Sea.  Information on abundance and distribution of most of the shark species is relatively scarce, with deficient data occurring in 45% of the species, according to red list assessments of the International Union for Conservation of Nature (IUCN)3. According to the report of Food and Agriculture Organization of the United Nations (FAO), global landing of chondrichthyans increased since 1950s, reaching a maximum in 2000  before declining. However, global landings data are likely to represent a gross underestimation of actual landings due to illegal fishing. The high value of shark fins has led to an increase in illegal shark finning, resulting in an increase in the number of discarded bodies which often go unreported4. In Southeast Asia, the shark catch records are often inadequate and incomplete5. Lam and Sadovy6 suggested all known shark fisheries in the South China Sea collapsed between the 1970s and the 1990s and of the 109 species historically present in the South China Sea, only 18 species were recorded in their fish market surveys between 2006 and 2008. Additionally, sharks observed in the markets are almost all subadults, which is a sign of overfishing and inappropriate management. Therefore, there is an urgent need to enhance conservation efforts and implement management plans for these rapidly declining species on either a regional or global scale.Sharks are the most endangered group of marine animals due to their vulnerability to fishing pressureNegaprion is comprised of two species, including Negaprion brevirostris (lemon shark) an inhabitant of shallow, inshore waters throughout the western Atlantic also occurring in eastern Pacific and eastern Atlantic and Negaprion acutidens (sicklefin lemon shark), which is found in similar habitats across the Indo-Pacific region. Both species are associated with reefs. The former species has been overexploited7 and the latter species is locally extinct in India and Thailand, endangered in Southeast Asia, and considered vulnerable throughout its range . Both species have shown strong site fidelity9. The lemon shark, N. brevirostris, is one of the most well-studied elasmobranch species in the world. The juvenile phase has been found to utilize shallow coastal environments (lagoons) as nursery habitat. Strong site fidelity has been revealed by genetic and acoustic telemetry in different regions of the western Atlantic11. Additionally, female lemon sharks exhibit natal philopatry to one of these sites 12. This understanding of the connectivity, behavior, and spatial ecology of elasmobranch specie is crucial for the development of effective conservation strategy and management plans.The genus N. acutidens), largely because of its overall scarcity due to recent population decline . This species has been extirpated in India, Thailand, and Bali (Indonesia) due to overexploitation, but it remains relatively abundant in Indian Ocean and coastal areas of Australia13. Schultz et al.14 conducted the first genetic study on sicklefin lemon shark in the region between western Australia to central Pacific showing a minor genetic structure which may be due to the isolation by oceanic distance in between, the juveniles exhibit site fidelity by a narrow home range, similar to the closely related lemon shark16. There are only a few records of the occurrence of sicklefin lemon shark in the South China Sea18. The Dongsha Island (20° 40′ 34.5″N 116° 49′ 31.7″ E) is a remote island, part of Dongsha Atoll which is located at the northern boundary of the South China Sea approximately 450 km from Taiwan. The island is 2.8-km long and 0.865-km wide with an embedded lagoon (64 ha). A recent survey of the fish composition in the embedded lagoon showed that juvenile N. acutidens are among the three most abundant fish taxa found in the lagoon18. Like other coastal sharks, sicklefin lemon sharks, use shallow coastal habitats such as seagrass beds and lagoons as nursery grounds, then gradually expand their territory as they grow older9. In addition, site fidelity has been observed in not only juveniles but also resident adults of N. acutidens19. Based on the results of long-term acoustic telemetry data for studying the movement of juveniles inhabit around Dongsha Island, juveniles inhabit different sites around the island  exhibited strong site fidelity (Chen unpublished data). These behaviors related to fidelity for specific breeding and nursery areas may result in genetic structure despite the high mobility of adults on a relatively large scale (100 s–1000 s km) 21. This aggregation of juvenile sharks indicate Dongsha Island may represent as an essential habitat for sicklefin lemon shark to reproduce or as nursing ground like other sharks23 and have disproportionate management implications for shark conservation24. In the present study, in order to reveal the spatial and temporal genetic structure of juvenile N. acutidens at Dongsha Island, we developed eight novel microsatellite loci in combination with four previously published microsatellite loci to determine the genotype of 188 juveniles collected during 2016 and 2017. This information was used to determine whether the site fidelity affects spatial genetic structure around the Island.In contrast, little is known about the movements, behavior, and reproductive biology of the sicklefin lemon shark  while we observed the caught juveniles with open or recently closed umbilical scar between April and May  from 2013 to 2017 to estimate the average total length of newborn pup  of juveniles Table . Additioup Table . Using tTL = Total length of captured juvenile, TL = Average total length of newborn pup, GR = Average growth rate of juvenile (cm/month).25. In total, eight novel microsatellite loci (Table O), expected (HE) heterozygosities and Fst were estimated using Microsatellite Analyzer (MSA) 26. An exact test on genotype counts was done to test for significant deviations from Hardy–Weinberg equilibrium by GENEPOP. MICRO-CHECKER, version 2.2.3 27 was used to check for the possible occurrence of null alleles and allelic dropout. STRUCTURE, version 2.2 29 was used to infer population structure and assign individuals to clusters based on microsatellite genotype. The software was run using the admixture model that assumes that all individuals are potentially of mixed ancestry, and assigns each individual to a designated population  with a partial probability. Ten independent runs, incorporating a burn-in of 1 000 000 Markov chain-Monte Carlo iterations followed by 1 000 000 replicates of data collection. For data set of location, we set K = 1 to 3 because we assigned 188 juveniles to 3 populations according to where they were caught . For cohort data sets, we set K = 1 to 5 because we assigned 188 juveniles  to 5 cohorts . Structure Harvester 0.6.9430 was used based on the ΔK obtained through each run to determine the most likely number of clusters for each data set. CLUMPP31 was used to summarize the results of replicates under the best K-value. To further understand the clustering patterns, genetic distance-based (Pairwise Nei genetic distance) principal coordinate analysis (PCoA) was carried out using GENALEX 6.532. Analysis of molecular variance (AMOVA) was further performed by separating 188 individuals into two groups based on the PCoA result with ‘poppr’ package33 in R.Genomic DNA was extracted from muscle tissue using commercial DNA extraction kits . Through the raw sequences from a paired-end Illumina Miseq sequencing run, 41,546 contiqs (> 500 bp) were obtained after QC and assembly. A total of 84 sequences with different microsatellite motifs were isolated and primers were designed by Primer 3 (version 0.4.0) including 78 dinucleotide, 2 complex-nucleotide, 2 tetra-nucleotide and 2 hexa-nucleotide repeats. all quality filtered and assembled reads were used for screening repeated motifs following the methods described in 34, computing 5000 iterations for each year cohort. The program calculates the maximum likelihood relationship between individual pairs. It determines which of the following yield the greatest likelihood: parent offspring (PO), full-sibling (FS), half sibling (HS) and unrelated (U) categories. Meanwhile, COLONY version 2.0.6.6 was also used with marker type error rate as 0.001 for each locus, both male and female were polygamy to determine whether the relationship of a pair of juveniles belongs to one of three possible candidate relationships, including full-sibling, half-sibling and non-sibling. In order to quantify the effective population size of the N. acutidens inhabiting the waters of Dongsha, we used NeEstimator v2.135 based on linkage disequilibrium (LD) for two allele frequency thresholds (0.02 and 0.05) and one without any constraints.Maximum likelihood estimates of pairwise relatedness coefficients and genealogical relationships were calculated with the software ML-RELATEAll field work was carried on in accordance with the relevant guidelines and regulations under Marine National Park Headquarters permit No. 1051000471 and No. 106000672. This study was approved by the Institutional Animal Care and Use Committee (IACUC) at National Sun Yat-sen University and conducted the experiments following the IACUC guidelines. All methods are reported in accordance with ARRIVE guidelines.The observed heterozygosity and expected heterozygosity of each locus in each population were given in the Table The average juvenile growth rate was 0.868 cm/month, and the average total length of newborn pup was 67.47 cm. Ten juveniles were assigned to the 2013 cohort, 24 juveniles were assigned to 2014, 85 juveniles were assigned to 2015, 45 juveniles were assigned to 2016, and remaining 24 juveniles were assigned to 2017 Table .P > 0.05).Among them, we developed, characterized and applied 8 novel polymorphic microsatellite loci including seven dinucleotide loci and one tetra-nucleotide loci, with another 4 published loci Table  to genotP < 0.01, 13.4% of variance). The estimated effective population size (Ne) by the linkage disequilibrium method under the allele frequency thresholds of 0.02, 0.05 and no constraint were 86.7 (95% CI = 75.3–100.6), 87.9 (95% CI = 71.7–109.5) and 199.9 (95% CI = 170.2–239.1), respectively.The results of STRUCTURE showed that K = 2 has the highest delta K for both location and year cohort data sets. The bar charts represent the results of the assignment test while K = 2 Fig. . Both baP < 0.05, unpaired t-test). Among these cohorts, an average of 91% of the pairwise relationships were unrelated  relationship in each cohort. The first order genetic relationships  accounted for 2%, 10%, 13%, 11% and 10% of all pairwise relationships in year 2013, 2014, 2015, 2016 and 2017, respectively Fig. . In contN. acutidens has a relatively restricted home range at St. Joseph Atoll in the Indian Ocean9, northern Australia (< 1.9 km2) 36 and even smaller in western Australia16 (0.6 ± 0.04 km2). And fine spatial side fidelity was also detected between three sites  where juveniles were collected in the present study. The results of pairwise Fst test showed no genetic structure among these three sites which suggest that this behavior may not affect the fine scale genetic structure of juvenile N. acutidens inhabit in the water of Dongsha Island. Surprisingly, we observed a clear genetic partition based on the PCoA plot . Collection of tissue samples from adults could allow us to verify our findings by reconstructing the pedigree between adults and juveniles inhabiting the Dongsha. Additionally, further study using popup satellite tagging is needed to better understand adult movement in the Dongsha Atoll.Even previous studies showed that the juvenile N. acutidens is placentally viviparous, producing 1–13 (mean = 9.3) pups per litter39. With an extremely low first order genetic relationship found in each year cohort and high sampling intensity of the capture-recapture experiment (monthly), we suggest that the natural mortality rate of newborn sicklefin lemon sharks may be very high in Dongsha population, resulting in survival of only a few pups per litter to reach the juvenile stage instead of causing by incomplete sampling.Our results of relationship test also showed that an average of 91% and 62% of the pairwise relationships were unrelated by ML-RELATE and COLONY, respectively. This evidence may also suggest that we observed an outcross population in Dongsha Island. Previous study documented in French Polynesia showed two times higher first-order genetic relationship in Moorea than in Dongsha. Although the Moorea study focused on only 40 mature sharks and our study focused on juveniles only , we suggest the genetic network of different geographic populations may vary. Additionally, N. acutidens in the South China Sea with high juvenile abundance in the shallow habitats18, (Chen unpublished data) and it is located far from other potential habitats such as West Luzon coast (500 km), Spratly Islands (1160 km) and Paracel Islands (670 km). Although previous study has showed that N. acutidens could potentially travel up 300 km between Moorea and Bora Bora15. Unlike French Polynesia which contains many islands adjacent to each other to serve as stepping-stones to support their seasonal movement, Dongsha Island is relatively isolated. The migratory path to West Luzon coast and Spratly Islands crosses more than 500 km deep ocean basin without any island in between, therefore, the only possible migratory path may follow the continental shelf toward west to reach Hanan Island or Paracel Islands and the travel distance is more than 650 km. Under the protection of the Dongsha Atoll National Park, the nursery ground at Dongsha Island may have acted as the population source for other local populations and for the whole northern South China Sea area since 2013. It is important to improve understanding of the effective population size (Ne) of this putative source population for purposes of assessing extinction risk and improving conservation management of this threatened species42. Frankham et al.43 proposed threshold requirements of Ne ≥ 100 to avoid inbreeding depression and Ne ≥ 1000 to retained evolutionary potential. Considering these recommendations, the estimated Ne of N. acutidens  is insufficient for short-term and long-term health of this species, perhaps representing an isolated population at Dongsha Island44. However, the Ne estimated by microsatellite may lack accuracy and precision when derived from insufficient numbers of samples and loci45. Dudgeon and Ovenden46 provided guidelines for establishing the minimum numbers of samples (91 samples or 20% of the census population) at least 10 loci in order to obtain adequate precision and finite values for estimating Ne of the zebra shark, Stegostoma fasciatum. In the present study, we used 188 samples with 9 microsatellite loci, which should be enough to obtain a fairly robust Ne estimation under the threshold provided by46. Therefore, the Ne value of the present study suggest that the Dongsha Island population is relatively isolated with potential to suffer inbreeding depression in the future.The Dongsha Island is the only known nursery ground for N. acutidens in Dongsha. In present study, we have provided robust evidence that a well-established population of N. acutidens inhabits Dongsha waters, using the Dongsha Island as nursery ground. We found that the site fidelity observed in juveniles is not affecting the fine scale genetic structure, and no genetic structure was found among cohorts. Surprisingly, the principal components analysis showed that the population in Dongsha might be comprised of individuals derived from two breeding families. Additionally, the small Ne value that we obtained indicates the juvenile N. acutidens found in Dongsha may represent a small, isolated and outcross population. These results provide useful insights for future management and conservation of the sicklefin lemon shark in the South China Sea.In conclusion, the coupling of genetic analyses with long-term demographic experiments is crucial to improving our understanding of the movement patterns of the Supplementary Information.


Marine cyanobacteria are of particular interest for biotechnology applications, thanks to their rich diversity, their robustness to environmental changes, and their metabolic capabilities with potential for therapeutics and chemicals production without requiring freshwater. The additional cyanobacterial properties, such as efficient photosynthesis, are also highly beneficial for biotechnological processes. Due to their capabilities, research efforts have developed several genetic tools for direct metabolic engineering applications. While progress toward a robust genetic toolkit is continuously achieved, further work is still needed to routinely modify these species and unlock their full potential for industrial applications. In contrast to the understudied marine cyanobacteria, genetic engineering and synthetic biology in freshwater cyanobacteria are currently more advanced with a variety of tools already optimized. This mini-review will explore the opportunities provided by marine cyanobacteria for a greener future. A short discussion will cover the advances and challenges regarding genetic engineering and synthetic biology in marine cyanobacteria, followed by a parallel with freshwater cyanobacteria and their current genetic availability to guide the prospect for marine species.The current economic and environmental context requests an accelerating development of sustainable alternatives for the production of various target compounds. Biological processes offer viable solutions and have gained renewed interest in the recent years. For example, photosynthetic chassis organisms are particularly promising for bioprocesses, as they do not require biomass-derived carbon sources and contribute to atmospheric CO Modern society faces various challenges that must be addressed promptly to preserve most living organisms. The current environmental crisis is particularly pressing and threatening and demands that the present industrial and economical behaviors are entirely revised . As such2. These prokaryotes are significant contributors to global carbon fixation in many environments, participating in ecosystem maintenance. The tremendous diversity and strain-specific variation of pigments and photosystems  maxima, another rising filamentous cyanobacterium, is gaining interest for its use as a food supplement as it offers a higher protein content than its freshwater counterpart Arthrospira platensis. Indeed, Arthrospira sp., and primarily A. platensis, have been extensively exploited for food, cosmetics and pharmaceutical applications. Their unique spiral morphology enables to achieve high biomass cultivations, further harvested for downstream processes  is arguably the most genetically amenable marine cyanobacterium -Cas tools have revolutionized genome editing in various organisms, allowing rapid and markerless genomic modifications. This RNA-guided method, reviewed elsewhere , causes PCC 7120 , a tremePCC 7120  but stilS. elongatus PCC 7942 was introduced in Anabaena sp. PCC 7120 genome for direct tool transferability without requiring additional cloning steps (Anabaena, hypothesized to be more suitable for production of complex secondary metabolites. This further illustrates that pathway transferability between unicellular and filamentous species might not be as straight-forward as tool transferability, likely due to metabolic differences. In addition, tools developed specifically for filamentous species, such as Arthrospira platensis, have also been reported. In fact, optimization of the genetic engineering techniques led to the development of a potential oral therapy for Campylobacter infections (Some of the reported tools have been adapted in both unicellular and filamentous species, suggesting that morphology is not impeding tool development. In fact, a previous CRISPR tool showed transferability in phylogenetically distinct unicellular and filamentous species . In anotng steps , allowinfections , harnessSynechocystis sp. PCC 6803 (Finally, an important tool for routine and rapid genetic engineering is shuttle vectors, able to replicate within the cyanobacterial population. This prevents time-consuming genomic integration for genetic element and tool characterization and provides higher expression levels. The broad-host range replicon RSF1010 has been used widely in freshwater cyanobacteria  and addiPCC 6803 . AlthougSynechococcus species have been and are still leading the field. On the other hand, despite the current knowledge and techniques available on genetic systems, some species continue to resist genetic modifications, as exemplified by the studied Prochlorococcus. While some biological challenges must participate in this discrepancy, additional aspects, such as historical attention and devoted resources, further contribute to the uneven genetic accessibility of marine cyanobacteria.The potential of marine cyanobacteria for various biotechnological applications and, more importantly, sustainable compound bioproduction is remarkable. The diversity of these species and their unique metabolic properties, evolved to survive in changing environments, offer tailored possibilities for industrial bioprocesses, such as large-scale photobioreactors for target bioproduction. To fully unlock their potential, marine cyanobacteria must be genetically accessible to further engineer strains for industrial purposes. While some progress has already been made, the current genetic toolkit remains insufficient. In fact, a clear discrepancy persists between species where some Synechocystis sp. PCC 6803 and PCC 7338 showed how close species can be, although protein regulation reflected their respective natural habitat (Prochlorococcus, for which engineering obstacles remain unclear (Moreover, the genetic toolkit for freshwater cyanobacteria is undeniably more extensive, with complex tools developed for several genera. Considering how phylogenetically close some marine and freshwater species are, the transferability of methods is highly probable. In fact, a proteomic analysis of  habitat . Similar habitat , suggest unclear .Escherichia coli, for which, for example, sophisticated CRISPR tools, such as prime-editing (However, it is worth pointing out that, despite the discussed advances in freshwater species, cyanobacteria in general remain behind in terms of complex genetic tools compared to model prokaryotes, such as -editing , have beBB: conceptualization, initial writing, and manuscript revision. KS: conceptualization and manuscript revision. Both authors contributed to the article and approved the submitted version.


With the advancement of medicine, more and more researchers have turned their attention to the study of fetal genetic diseases in recent years. However, it is still a challenge to detect genetic diseases in the fetus, especially in an area lacking access to healthcare. The existing research primarily focuses on using teenagers’ or adults’ face information to screen for genetic diseases, but there are no relevant directions on disease detection using fetal facial information. To fill the vacancy, we designed a two-stage ensemble learning model based on sonography, Fgds-EL, to identify genetic diseases with 932 images. Concretely speaking, we use aggregated information of facial regions to detect anomalies, such as the jaw, frontal bone, and nasal bone areas. Our experiments show that our model yields a sensitivity of 0.92 and a specificity of 0.97 in the test set, on par with the senior sonographer, and outperforming other popular deep learning algorithms. Moreover, our model has the potential to be an effective noninvasive screening tool for the early screening of genetic diseases in the fetus. According to a study conducted in 1988, a disease with a significant genetic component is likely to affect at least 7.9 percent of newborns ,3. GenetWith the continuous advancement of ultrasonic diagnosis techniques and the accumulation of clinical experience, prenatal ultrasound has become widely used in prenatal genetic disease screening owing to the diverse advantages it offers, including real-time acquisition, low costs, no radiation, and noninvasiveness . There aComputer vision has long been concerned with medical image analysis issues . Deep leIn this study, we developed Fgds-EL , a two-stage ensemble learning model that used a convolutional neural network (CNN) and Random Forest (RF) to automatically screen for genetic diseases in fetal ultrasound imaging. The Fgds-EL uses an aggregate of fetal facial regions to improve performance and robustness. Additionally, we analyzed the screening performance of subnetworks from Fgds-EL and show the contribution of image regions to the decision by interpretable techniques . MoreoveThe main contributions of this paper are as follows: (1) the development of a two-stage ensemble learning model based on fetal facial, named Fgds-EL, for robust and accurate screening genetic diseases; (2) evaluating the performance of Fgds-EL in screening for fetal genetic diseases from ultrasound images; (3) the decision-making area of the Fgds-EL is visualized using the heat map to discover the anatomical structures related to genetic diseases; and (4) comparing the performance of the Fgds-EL to that of sonographers.n = 333) from the initial list of 1000 cases, those without genetic results, 3D volume images and complete clinical data. Thus, our dataset contained 556 normal pregnancies and 111 pregnancies with genetic anomalies. All examinations have been performed and diagnosed before the research by one or two ultrasound specialists with more than five years of experience. Overall, our datasets contain 845 normal pregnancy images and 275 genetic disease images from 667 cases, with a gestational age distribution of 14.8 ± 2.6 (range 11–27). Details on the development set and test set are described in In this study, we collected ultrasound fetal profile images from around 1000 pregnant women with an age range of 23–38 years who underwent prenatal diagnosis at the Guangzhou Women and Children’s Medical Center. We excluded . Each participant signed an informed consent form.At first, we converted all the images into grayscale images. Following that, we rescaled the images to 400 × 440 pixels in size and normalized the pixel values to a range of 0 to 1. In addition, we augmented the training data set with distortion, zoom in, tilt, zoom out, and crop. All preprocessing steps make use of open-source Python libraries: OpenCV, scikit-image, and NumPy.Following data preprocessing, a total of 20,000 images, including 10,000 normal pregnancy images and 10,000 genetic disease images, are used to develop Fgds-EL.Both local and global facial features have been considered as significant variations for the detection of genetic diseases. Our deep learning architecture is inspired by an experiment conducted to predict the genetic diseases of the whole face, including the upper face, middle face, and lower face ,28. In oIn the subgraph acquisition part a, the ulIn subgraph A, Network A identified facial differences to estimate the probability of a patient presenting with a genetic disease and the probability of the patient not presenting with a genetic disease from the facial image of size 440 × 400 pixels. Network B embedded three networks  with the architectures described in We use RF as the stacking model of sub-networks in ensemble learning, and RF fuses the disease probability of each network to estimate the comprehensive probability of a patient presenting with a genetic disease and the comprehensive probability of a patient not presenting with a genetic disease. To prevent overfitting, we adjusted the parameters of the training set. The number of parameters of the decision tree is 43, the maximum depth of the decision tree is 3, and the other parameters are the default parameters.−5. During the training process, the callback function is used to keep an eye on the validation set loss function, and the best model is saved.Networks A, B, and RF were trained in two stages. First, Networks A and B were used to predict the disease probability of each network. Following that, RF is used to combine each network’s prediction probability to obtain the overall classification performance. In the CNN model, at least one residual block is added to each network, and batch normalization optimization is carried out in each convolution layer. Learning rate optimization was performed using the Adam optimizer, with an initial learning rate of 1 × 10We implemented and trained Fgds-EL, and all the functionality, experiments, and analysis were implemented using Python  and Google Tensorflow 2.0 (for the implementation of the deep learning architecture).To comprehensively evaluate the excellent performance of our proposed Fgds-EL model, we trained the other models, including classic convolution network models, such as ResNet , VGG 3131, and DIn the statistical analysis, six quantitative indicators are used to evaluate the screening performance, such as accuracy (ACC), sensitivity (SEN), specificity (SPEC), and F1 score. F1 score, also known as balanced F score, is defined as the harmonic average of the precision and recall rates. Additionally, we use the receiver operating characteristic (ROC) curve and the area under the receiver ROC curve (AUC) to assess the screening performance between different models and networks. Additionally, ROC analysis is performed to determine the optimal operating thresholds by using the outputs of the models on the tuning dataset. We also use the distribution of risk scores related to the genetic disease determined by Fgds-EL in test sets.To compare the Fgds-EL’s performance to that of a doctor, we recruited three sonographers with varying levels of clinical experience . Fgds-EL and each sonographer were given 100 images from the test set and were asked to independently determine whether the fetus had certain genetic diseases. Grad-CAM is used to visualize the decision-making ability of our model by adding a visualization layer to the CNN model . This meInstead of retraining the model or making changes to it, Grad-CAM’s approach simply takes a single image into the network to extract the top layer feature maps and uses backpropagation to compute the gradients to generate a category gradient matrix. In this way, we can obtain a feature map associated with the category information, and then overlay it with the original image to obtain a heat map that can explain the model’s decision logic.A total of 932 images  are retrieved for developing the ensemble learning model. Following data preprocessing, a total of 20,000 images, including 10,000 normal pregnancy images and 10,000 genetic disease images, are used to develop Fgds-EL, and 158 images  are used to test our model. In the original dataset, the top three genetic diseases in our datasets are Down syndrome , trisomy 18 , and trisomy 13 . The other genetic diseases  are 30.9% (75/252), including Turner syndrome, 1q21.1 microdeletion, 15q11-q13 duplication syndrome, 15q26.1-q26.3 deletion and 20p13 duplication, Helsmoortel-van der AA syndrome, 4P syndrome, etc. Details on the development set and test set are described in The development and validation processes of the Fgds-EL are shown in For screening genetic diseases, the optimal algorithm Fgds-EL achieves an area under the receiver operating characteristic curve (AUC) of 0.986, a sensitivity of 0.92, and a specificity of 0.97 in the test set. We also calculate the performance of each subnetwork to find out how different parts of the fetal face affect the screening for genetic diseases. Network A achieves the best performance in the subnetwork, including an AUC of 0.857, a sensitivity of 0.76, and a specificity of 0.81 by training subgraph A. Network B.1 achieves an AUC of 0.889, a sensitivity of 0.78, and a specificity of 0.86 by training subgraph B1, and Network B.2 obtains an AUC of 0.66, a sensitivity of 0.70, and a specificity of 0.63 by training subgraph B2. In the bone detection subgraph, Network B.3 attains an AUC of 0.711, a sensitivity of 0.52, and a specificity of 0.93 by training subgraph B3. Network B.1 shows the best performance in subnetworks, which indicates that subgraph B1 contains information that is essential for screening genetic diseases. Further information encompassing the F1 score of Fgds-EL and the subnetwork is also displayed in The distribution of the predicted scores and the confusion matrices related to the categories determined by the Fgds-EL and sub-networks in test sets are shown in The screening results of the Fgds-EL for genetic diseases are shown in Since the proposed model combines several CNN models, we also measure the processing speed of a single sample. The results show that the model loading time of Fgds-EL is 2.638 s, and the model prediction time is 0.942 s, which is slightly higher than ResNet-50 and lower than DenseNet. In the recent deep learning model, Swin Transfomer has the fastest processing speed, while ResNest50 and RepLKNet have the slowest processing speeds. The processing speed of all the models is shown in RF is an ensemble learning classifier based on the decision tree with good model interpretability. Fgds-EL used an ensemble learning model based on a RF, which is stacked by networks A, B.1, B.2, and B.3. The importance score can reflect the contribution of each subgraph to Fgds-EL screening performance and help us understand the logic of the model. The importance scores of network A, network B.1, network B.2, and network B.3 are 0.53, 0.16, 0.03, and 0.32, respectively, as shown in To explore the interpretability of the Fgds-EL in screening genetic diseases, we use the heatmaps created by Grad-CAM algorithms to visualize the regions that contributed most to the model’s decisions. We find that heatmaps highlight the mouth, nose, jaw, and forehead regions. Furthermore, the heatmap from the sub-network highlights some abnormalities of the facial features of genetic diseases in more detail. According to the heatmap, there are three findings shown as follows. (1) Network B3 is able to accurately identify information such as the nasal and jaw bones of the fetus, which are often overlooked in the analysis of the global image. (2) Network B2 focuses on the morphological features of the skull, and the flattened forehead of trisomy 13 is accurately identified by the heat map. (3) The heat map of Network B1 focuses on the richer information of facial details. Examples of images and corresponding heatmaps of genetic disease are shown in As shown in For screening genetic disease from the test set, the junior sonographer achieves an accuracy of 0.63 with a sensitivity of 0.42 and a specificity of 0.73, the attending achieves an accuracy of 0.74 with a sensitivity of 0.79 and a specificity of 0.72, and the senior obtains an accuracy of 0.91 with a sensitivity of 0.88 and a specificity of 0.93, while the Fgds-EL acquires an accuracy of 0.93 with a sensitivity of 0.91 and a specificity of 0.95. The performances of these three sonographers are presented in The goal of this study is to develop a significant ensemble learning algorithm (named Fgds-EL) to screen for genetic diseases in the fetus. The workflow of our deep learning model for screening fetal genetic diseases is shown in Chromosomal diseases are the most common genetic diseases and the leading causes of congenital disability. Abnormal facial manifestations usually accompany patients with chromosomal genetic syndromes ,39,40,41Our current method calculates the risk of genetic diseases  by using ultrasonographic images of the head through a routine structural scan in the first and mid-trimester. This means that original images are readily available and do not increase the additional workload of ultrasound technicians. It has the potential to be widely applicable in remote or underdeveloped areas. We hope that the model will reduce the time it takes to refer patients with suspected genetic diseases and speed up the process of diagnosing them.Deep learning is regarded as a high-risk decision due to the lack of interpretability in diagnosis, and is even referred to as “a black box.” To investigate this issue, we use the RF algorithm for ensemble learning and the importance score to visualize the contribution of subnetworks to the final decision. We also create heatmaps using the Grad-CAM technique  to visuaIn the research domain, potential applications include: (1) studying the facial abnormalities of fetal genetic diseases and analyzing the facial risk areas; (2) discovering new genetic diseases; and (3) performing a correlation analysis between genetic diseases and facial characteristics. In the clinical domain, potential applications in the future could include: (1) It can be packaged into low-cost screening software and deployed to the cloud or server, which would be a convenient and cost-effective approach for promoting the early detection of genetic disease; (2) deployed to hospitals at all levels for graded diagnosis and treatment to reduce medical errors and medical waste; (3) it provides suggestions for diagnoses for doctors to consider as a complementary diagnostic technique; and (4) large-scale, cross-regional genetic disease screening.Although the present study proves the potential of our Fgds-EL in screening genetic diseases, the model has several limitations, which we wish to address in the near future: (1) Our study only used a small number of cases of genetic diseases from one center, which may affect the robustness of the model. In the next study, we will collect data from different races  to improve the robustness of the model. (2) In addition to the cases included in this study, there are many rare genetic diseases with typical facial abnormalities. The next study needs to combine multiple institutions to collect these rare cases. (3) Only one standard section of ultrasonic images is used in this study. In the following study, we will explore the use of other standard sections for genetic disease screening. In addition, while our model appears well suited for a screening purpose , it cannot provide a specific diagnosis based on images. We expect to collect more ultrasonic facial images of genetic diseases in each category and then develop an AI model to realize this function. (4) We did not compare the differences between our model and the integrated test and the NIPT in this experiment, we hope to collect data for comparative experiments in future studies. (5) The model has been tested against three human raters, but the number of human raters involved is relatively small. In the future, we will conduct large-scale comparative research.In conclusion, we created Fgds-EL, a deep learning model based on the ensemble learning framework, to screen for fetal genetic diseases. Fgds-EL could also predict a risk score for trisomy 21, trisomy 18, trisomy 13, and other rare genetic diseases in an automated, accurate, and quantitative manner. As a new technology for clinic grading, this AI model can assist medical institutions in conducting large-scale fetal genetic disease screening while also reducing the use of medical resources.


Bifidobacterium animalis A12 was used for the development of fermented sausage. The growth activity, tolerance, and enzyme activity of B. animalis A12 and its contribution to the texture and flavour of fermented sausages were evaluated. Additionally, the sensory texture, flavour components, and amino acid nutrients during the fermentation process were assessed. B. animalis had high tolerance to NaCl and nitrite, and B. animalis A12 had protease and lipase activities. The pH value of sausage fermented with B. animalis A12 was lower than that of sausage fermented without any fermentation strain. Hexanal, heptanal, decanal, cis-2-decanal, and 4-methoxy-benzaldehyde are the unique aldehydes flavour components of fermented sausages in the A12 group. The highest content of volatile flavour substances and amino acids, and the color and texture characteristics of fermented sausage in the experimental group at 18 h were better than those at other times. These results suggest that B. animalis A12 has the potential to be used as a starter culture for im-proving flavour and texture in fermented sausage. Fermented sausage is a meat product prepared by microbial fermentation and mixed with meat batter and ingredients. It has a long shelf life and a unique flavour. According to moisture content, fermented sausage is mainly divided into fermented dry sausage and fermented semi-dry sausage.Lactobacillus, Lactococcus, Pediococcus, Leuconostoc [Weissella [Staphylococci and Micrococcus, most of which are S. xylose and S. carnivores [Bifidobacteria, which can benefit host health when ingested properly. LAB, one of the most important varieties of fermented meat products, and one of the earliest and most widely used probiotics in food, have been widely used as starters to improve product quality and safety. Studies have demonstrated the feasibility of probiotics in fermented sausages. Gao et al. found that LAB C2 could effectively improve the sensory characteristics, colour, and flavour of fermented sausages [Lactobacillus plantarum and Lactobacillus ferment decreased rapidly during maturation, which improved the safety of meat products. Cheng et al. inoculated L. plantarum and Pediococcus pentosaceus into sausage, and the results showed that the mixed starter had the potential to improve the quality of sausage [L. plantarum to fermented sausages could produce highly-conjugated linoleic acid [Traditional fermented sausage is mainly produced by natural fermentation, but the safety and small scale of the obtained products cannot be guaranteed. Therefore, to improve product quality and safety and further realise large-scale production, most researchers control the fermentation process by inoculating starters. Inoculation of appropriate probiotics in fermented sausages can prolong storage time and improve nutritional value and meat quality. The microorganisms in fermented sausage are mainly divided into lactic acid bacteria, including conostoc , and Weieissella . The maieissella . Other mrnivores . By relernivores , and cansausages . Xi et asausages  found th sausage . Özer aneic acid . Lactobacillus casei addition on the formation of volatile compounds in fermented sausage production and found that esters and alcohols were high [P. pentosaceus, L. brevis, L. bentus, and L. fermentans isolated from harbin air-dried sausages were precursors for the decomposition of sarcoplasmic proteins to form flavour compounds. Although a large number of probiotics have been reported for fermented sausage, the application of Bifidobacterium animalis alone has not been reported. Therefore, the present study expands on current knowledge and highlights new techniques for meat processing.Sidira et al. studied the effect of ere high . Hu et aere high  detectedere high  found thB. animalis A12 can inhibit the production of glucose and hinder the transport of glucose, thereby reducing blood glucose levels and exerting a potential hypoglycaemic effect [B. animalis A12 can effectively inhibit obesity induced by a high-fat diet [B. animalis A12 to fermented sausages as a nutritional functional food.c effect . In addifat diet . TherefoIn this study, the tolerance evaluation and enzyme production capacity of the experimental strains were first carried out, and then applied to fermented sausage to evaluate the sensory, textural, and hydrolytic amino acids and volatile flavour components of fermented sausage. This provides a basis for the development of functional foods using probiotics.P. pentosaceus S2, Staphylococcus vitulinus T1, and B. animalis A12 (CGMCC No.17308) were inoculated into MRS and nutrient liquid medium for amplification and culture to the third generation.The activated strains S2, T1, and A12 were inoculated in sterilised 200 mL MRS and nutrient liquid medium at 1% proportion, respectively, and cultured on a shaking table at 37 °C and 200 rpm. Using sterile medium as the control, the samples were collected at 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, and 24 h of culture, and the OD600 value and pH value of the fermentation broth were determined.Different concentrations of sodium chloride  and nitrite  were added to the liquid MRS and nutrient medium, respectively, for sterilisation. The activated strains S2, T1, and A12 were inoculated in MRS and nutrient liquid medium containing different concentrations of sodium chloride and sodium nitrite and cultured in a constant temperature incubator at 37 °C for 24 h. The OD600 value was determined.The activated strains S2, T1, and A12 were inoculated into the PCA solid medium supplemented with 2% casein at 200 μL according to the Oxford cup method. Sterile water was used as a control, and it was placed in 37 °C constant temperature incubator for 5 days. Plastic droppers were used to put 3–5 drops of 10% trichloroacetic acid in the culture medium around the colony drops and the size of the transparent circle was measured.The activated strains S2, T1, and A12 were inoculated on PCA solid medium supplemented with 12% lard oil and 1 mL 1.6% bromocresol purple solution according to the Oxford cup method. A transparent circle was observed when sterile water was added as a control and the cells were cultured at 37 °C for 5 days.The experimental raw materials (pork meat (85%), back fat (15%)) and excipients , soy protein isolate (2%), iso-Vc-Na (0.05%), sodium tripolyphosphate (0.3%), sodium nitrite (0.003%), salt (1.8%), sugar (0.2%), glucose (1%), ice water (30%), monascus red (0.1%).)—were mixed in proportion, the pre-treated bacterial suspension was inoculated into the A12 and experimental group, and the mixture was evenly mixed, after enema, fermentation, fermented sausage at different times was taken for index detection. The entire experiment was repeated thrice.B. animalis A12 freeze-dried powder (9 × 1011 CFU/kg), fermentation temperature (40 ℃), humidity (95%), time (18 h); experimental group: P. pentosaceus S2, Staphylococcus vitulinus T1, and B. animalis A12 (1:3:1) were inoculated. The inoculation amount was 4 × 107 cfu/g, Sampling at .CK group: no bacterial inoculation and natural fermentation; A12 group: adding Ten students who had studied sensory evaluation were selected as judges to score the appearance (X1), organisation (X2) and flavour (X3) of the fermented sausage . Before According to GB 5009.237-2016, a calibrated pH meter  was inserted into the sausage to determine the pH value.L* (lightness), a* (red) and b* (yellow) value of fermented sausage were measured and recorded after whiteboard correction. There were 3 parallel samples for each sample, 5 points for each parallel sample, a total of 15 measured values, and the average of 15 measured values was used as the analytical standard.The colour was measured with a colorimeter  using an 8 mm port size, illuminant D65, and 10° standard observer. The The texture was measured with a texture analyser , which was repeated three times and the average value was taken. Using the texture profile analysis (TPA) method, the P50 cylindrical probe was used to cut the fermented sausage into samples with a diameter of 12.7 mm and a length of 20.0 mm. The compression rate was 2.0 mm/s before the test, and 1.0 mm/s during the test. After testing, the compression rate was 1.0 mm/s, the compression ratio was 75%, and the pause time between the two compressions was 5.2 s, and the following textural parameters were calculated: hardness (N), chewiness (N* mm), and elasticity (mm).An amount of 10.0 g of fermented sausage was weighed and dissolved in 10 mL of 6 mol/L HCl. Phenol was added and frozen and vacuum filled with nitrogen seal. It was cooled by hydrolysis. The hydrolysate was filtered through medium-speed qualitative filter paper and the filtrate was dried. Finally, pH 2.2 sodium citrate buffer was added to fully dissolve the samples, and after mixing, the samples were filtered through a 0.22 μm filter membrane. Finally, the amino acid analyzer was used for determination and analysis. Instrument conditions: analytical column: 4.6 mm × 60 mm; resin: 2622#; column temperature: 57 °C; reaction column temperature: 135 °C; buffer: citric acid, sodium citrate buffer; chromogenic solution: ninhydrin solution.The volatile flavour components of fermented sausage were determined by GC-MS. Accurately weighed 10.0 g cut sausage samples were added to the sample bottle, 1 μL of 0.816 μg/μL 2-methyl-3-heptanone was added as the internal standard, and the bottle was closed. After the sample bottle was preheated in a 55 °C water bath for 10 min, an SPME needle was inserted into the sample bottle, and the fibre head was placed on top of the sample bottle. After the volatile flavour substances in the sample bottle were adsorbed for 40 min, they were removed and inserted into the GC inlet for desorption for 10 min. GC conditions: TG-Wax MS polar column ; the carrier gas was high purity helium (purity > 99.99%); flow rate: 1.5 mL/min; use no shunt mode, keep 2 min. The temperature program was as follows: inlet temperature 250 °C; column temperature 40 °C; 3 min, 5 °C/min rate to 200 °C; maintained for 1 min; and finally, 8 °C/min rate to 230 °C, and maintained for 10 min. The MS conditions were as follows: transmission line temperature, 230 °C; electron energy, 70 eV; electron ion source temperature, 280 °C; mass scanning range 40–600 u; full scan mode.p < 0.05. GraphPad  was used to analyze the GC-MS data and construct a heatmap.Experimental data were expressed as mean ± standard deviation (SD). All experiments were repeated three times. SPSS 22.0 software  was used to analyze the data by independent sample T test to determine significant differences. Statistical significance was set at The growth curve is an intuitive reflection of the growth vigour of the strain. As shown in The pH value is an important index of raw material carbohydrate decomposition, which can inhibit the growth of pathogenic microorganisms and improve the stability of the product. As shown in p < 0.05). S2 maintained a high OD600 at a NaCl concentration of 7.5–10%, indicating that the strain had high tolerance to NaCl. It has been reported in the literature that 2.0% NaCl is the ideal addition amount of fermented sausage [B. animalis A12 has good growth ability within the concentration range of the commercial starter strains S2 and T1, indicating that B. animalis A12 is suitable for the fermentation of meat products.As shown in  sausage . B. animB. animalis A12 was similar to that of commercial starter culture, and all three strains had high OD600 values, indicating that Bifidobacterium animalis A12 could adapt to the nitrite content in the experiment and maintain good growth ability, which could be used as meat product starter culture.Nitrite is a commonly used food additive in meat products, which has colour development and antibacterial effects and helps to improve meat quality . AccordiThe lipase and protease production results for the three strains are shown in p < 0.05). The sensory quality of sausages fermented by A12 was significantly improved, with a unique flavour. This indicated that B. animalis A12 played an important role in the sensory quality of fermented sausages. After 18 h of fermentation, the pH of A12 group decreased significantly . Previous research results showed that after inoculation with L. plantarum and simulated S. aureus, pH decreased, which was similar to our results [L* value  and a* value  of the A12 group were significantly higher than those of the CK group, which may be because the metabolites of A12 have reducibility and improve the colour of the sausage. Eduardo et al. [B. animalis A12 could effectively improve the colour of sausage from the perspective of comprehensive quality, which might be due to the low pH value of fermented sausage by B. animalis A12, which promotes the decomposition of nitrite to NO and then generates nitroso myoglobin, thereby improving the colour of the product [p < 0.05) and chewiness  of the A12 fermented sausage were significantly lower than those of the CK group (p < 0.05), which was consistent with the experimental results of Kanno et al. [p < 0.05). Essid and Hassouna found that inoculation with Staphylococcus xylosus and L. plantarum could increase the elasticity of fermented sausages [B. animalis A12 can effectively improve the texture of fermented sausage, indicating that it can be used as a sausage starter.As shown in  results . Therefoo et al.  confirme product ,21. The o et al. . This maVolatile flavour compounds in the two fermented sausages are shown in p < 0.05). This indicated that the main flavour components in the sausage were aldehydes, followed by acids and alcohols. Among them, nonanal is the most abundant and important volatile flavour component in A12 fermented sausage, which is described as a lemon and plastic flavour [B. animalis A12 can significantly improve the flavour of fermented sausage.The proportion of aldehydes was the highest in these two groups. The content of aldehydes in the A12 group accounted for approximately 62% of the total volatile components, and its significantly higher than that in the CK group . The results showed that the inoculation starter had good aminopeptidase activity and could significantly improve the amino acid content in fermented sausages. The total amount of essential amino acids was 469.2 mg/kg, accounting for 46.2% of the total amino acid content, while the total amount of non-essential amino acids was 546.44 mg/kg, accounting for 53.8% of the total amino acid content. Asp and Glu are considered delicious amino acids that contribute to the umami taste of food [B. animalis A12 plays a positive role in the amino acid content of fermented sausages, which is most obvious at 18 h.The contents of hydrolysed amino acids in the sausages at different fermentation times are shown in  of food . At 18 hp < 0.05), indicating that the flavour compounds of sausage were directly related to fermentation time. Among the five time points of fermented sausage, aldehydes were the most abundant, with 4, 16, 20, 20, and 20 species detected, with total contents of 95.23, 160.89, 340.30, 416.56, and 284.76 μg/kg, respectively. The proportions were 53.00, 68.92, 66.41, 66.39, and 64.28%, respectively, which indicated that the flavour substances in sausage produced more flavour substances in the fermentation process, indicating that the processing technology of sausage was helpful to promote the formation of flavour substances in sausage. Among them, the contents of n-octanal, nonanal, hexanal, and heptanal, which are the main components of sausage flavour, increase with the extension of fermentation time and reach the highest value at 12–18 h. Aldehydes are generally produced by degradation or oxidation of fatty acids. Owing to their low perception threshold, they become the key substances that constitute the characteristic flavour of meat [ of meat .Alcohols have less significant effects on meat aroma than aldehydes, but they also play a key role in the overall flavour . AlcoholThe total ketone content in sausage first increased and then decreased with time. Ketone compounds are related to citric acid and lactose metabolism and can also be produced through amino acid decomposition . MethylhThe sausage samples also contained small amounts of acids, esters, alkanes, and other compounds. 2-pentylfuran is a heterocyclic compound with the greatest impact on flavour and is mainly derived from fat oxidation.B. animalis A12 on pH, colour, texture, hydrolysis of amino acids, and volatile flavour components of fermented sausages.Current research has shown that the application of probiotics in sausage fermentation is beneficial for improving the quality and nutritional value ,40. TherBifidobacterium, similar to the results reported in the literature [During the fermentation process, some basic indicators change, which determines the quality of the final product. These changes are mainly caused by fermentation acidification, pH reduction, nitrate reduction to nitrite and nitroso myoglobin formation, proteolysis, lipolysis, and oxidation ,42. LAB terature ,53,54,55To grow the strain during fermentation and maturation, it is important to have tolerance to acids, salts, and nitrite . The proB. animalis A12 can promote amino acid production and accelerate protein breakdown in fermented sausages.During the processing and storage of fermented sausages, proteins are gradually degraded into peptides, small peptides, and amino acids. Amino acids are the final product of protein degradation, and their content and composition ratio play an important role in improving the taste of fermented sausages. Gly, Ala, Ser, and Pro are known to be sweet, while Asp and Glu are umami. Studies have shown that with the increase in microbial enzyme activity in the late fermentation of fermented sausage, the content of amino acids in the mature process increased, resulting in the significant release of amino acids ,53, whicThe volatile flavour compounds of the fermented sausages were analysed using GC-MS. Some studies have shown that probiotics have a positive effect on the formation of volatile flavours in sausage ,55. The B. animalis cannot be used alone in fermented sausage, and that it must be combined with other probiotics [B. animalis A12 was used alone, the sensory quality of fermented sausage was improved. In addition, the hydrolysed amino acids and volatile flavour components were enhanced, indicating that there may be differences between strains. Therefore, it is necessary to strengthen this discussion further in the future.Previous studies have reported that obiotics . In thisB. animalis A12 in fermented sausage had a positive effect. First, the tolerance evaluation of B. animalis A12 showed that it exhibited acid resistance, salt resistance, nitrite resistance, and enzyme production. At the same time, the sensory score, colour, and texture were improved when B. animalis A12 was applied to fermented sausage, and the content of hydrolysed amino acids and volatile flavour components was highest in sausage fermented for 18 h. Hexanal, heptanal, decanal, cis-2-decanal, and 4-methoxy-benzaldehyde are the unique aldehydes flavour components of fermented sausages in the A12 group. Overall, the addition of B. animalis A12 improved the quality of fermented sausages; it can be used as a potential probiotic auxiliary culture for the development of functional fermented sausage.Our findings showed that the application of


Rice-fish farming refers to the cultivation of fish in rice fields. China’s Guizhou Province has a long history (over 1000 years) of Rice-fish farming. Carp (golden-backed carp) that have been cultivated for generations in rice fields have evolved to be extremely adaptable to that environment. Unfortunately, farmers and enterprises have found that the feeding ability and growth rate of golden-backed carp are becoming increasingly weaker and slower, respectively. Therefore, the present study carried out to examine the population genetics of golden-backed, and the results indicated that the status of germplasm resources of this species is not very good. The aim of this study was to further elucidate the status of golden-backed carp germplasm resources in this province. In conclusion, this work can provide reliable fundamental data for the future conservation and breeding of golden-backed carp.Cyprinus carpio var. Jinbei) cultured in paddy fields in Guizhou Province, China. Five populations of golden-backed carp in Liping County, Jinping County, Huangping County, Congjiang County and Duyun City in Guizhou Province were subjected to high-throughput sequencing by 2b-RAD technology, and their genetic diversity and genetic differentiation were analysed. Based on sequencing, 44,896 SNP loci were obtained, and all five population genetic diversity indicators showed low diversity. In the NJ tree, the Congjiang and Liping populations were mixed together, and the other three groups formed a cluster. A cross-validation error box plot and pong cluster plot were constructed to show the K value results. When K = 1, the cross-validation error rate was the lowest. Principal component analysis showed that the Duyun population formed a group separate from the group comprising the other four populations. The genetic differentiation index and genetic distances between the Duyun population and the remaining four populations were greater than 0.05, indicating population differentiation. The genetic diversity of the five populations of golden-backed carp in Guizhou Province was low, the genetic differentiation of the Duyun population was the most significant, and the Duyun population was separate from the other four groups.The aim of this study was to assess the current status of the germplasm resources of golden-backed carp ( Rice-fish farming refers to the cultivation of fish in the water bodies of rice fields. Fish swim in and out of the water in paddy fields, loosening the soil for rice and feeding on insects, mollusks, and plankton, which can greatly reduce pesticide input . Fish maCyprinus carpio var. Jinbei) (tentative name) because of the two stable golden stripes on both sides of its dorsal fin and a pattern resembling a golden butterfly on its head  in Zhejiang Province, China, and they found that the genetic diversity of this population in rice fields was lower than that of the wild population. Through landscape genetic analysis, it was found that farmer behaviour is a key factor affecting the genetic diversity of Qingtian field fish [b genes and used PCR amplification and a direct sequencing method to study five geographical populations of rice-farm golden-backed carp in Guizhou Province, and we found that the genetic diversity of this carp in different regions of Guizhou ranged from low to high, and that there were different degrees of genetic differentiation [In recent years, some scholars have successively studied the genetic diversity and genetic differentiation of carp in rice paddy fields. Ren et al. used microsatellite and direct sequencing technologies to explore nuclear loci and mitochondrial D-loop region and eld fish . Gan Baoeld fish . In earlntiation .The above studies were limited to two genes, and the polymorphic information content was found to be insufficient, thus reducing the support of high-throughput genome sequencing. Therefore, in this study, 2b-RAD technology was used to perform high-throughput sequencing of 75 individuals in five groups in Guizhou Province, and the genetic diversity and genetic differentiation of the golden-backed carp groups in Guizhou Province were further explored. An in-depth understanding of the characteristics and current status of germplasm resources for golden-backed carp will also provide basic data for the selection and breeding of this fish species.Through a survey, it was found that there are golden-backed carp in five regions of Guizhou Province, China. From August to October 2020, 75 golden-backed carp samples were collected from Jinping County (JP), Liping County (LP), Congjiang County (CJ), Huangping County (HP) and Duyun City (DY) in Guizhou Province . Fifteen2, and then the golden-backed carp was returned to the rice field for further breeding. The caudal fin sample was stored in anhydrous ethanol at −20 °C, DNA was extracted using an animal genome extraction kit from Qingke Biotechnology Co., Ltd., Beijing, China, and the obtained DNA samples were analysed by a spectrophotometer to detect the OD values of the samples, which was between 1.8 and 2.0. The samples were stored at −20 °C for later use.The tail fin of each live golden-backed carp was cut to 0.5 cmThe 2b-RAD libraries were prepared at Qingdao OE Biotech Co., Ltd. , as described in Wang . For eachttps://www.ncbi.nlm.nih.gov/assembly/GCF_000951615.1/, accessed on 8 December 2021) were extracted based on the enzyme’s recognition site, which served as a reference for SNP discovery. High-quality reads of each individual were aligned to the reference genome using SOAP2  [The paired-end reads were merged by Pear software (version 0.9.6)  . The merMD, USA)    program MD, USA)  was applp value (HW-P) and nucleotide diversity (Pi) were calculated by VCFtools  [The analysis of indicators of genetic diversity was performed as follows: the values of expected heterozygosity (He), observed heterozygosity (Ho), polymorphism information content (PIC), and effective number of alleles (Ne) were calculated using a custom Perl script with the following formulas: dge, UK)  softwareST) and population genetic distance (DR) were calculated by the R package genepop (version 1.0.5). A neighbour-joining tree was constructed with TreeBeST  [The genetic differentiation between populations was analysed using the following methods. The population differentiation fixation index    . Principdge, UK) .Based on the selection of 75 individuals in five populations of golden-backed carp, 44,896 SNPs were finally screened for genetic diversity analysis, and the results are shown in Based on the 44,896 SNP loci screened, the genetic structure of five golden-backed carp populations was analysed. The cross-validation error results showed that when K = 1 (identifying five populations as one population), the error rate was the lowest, and the error rate increased as the K value increased A. At theThe neighbour-joining tree showed that the LP population and the CJ population were mixed together and formed a single branch, and the HP group was mixed with an individual from the CJ and LP populations. In addition, the DY and JP populations each clustered independently into a branch C. The PCThe genetic differentiation index and genetic distance between the populations were between 0.007 and 0.094 and 0.007 and 0.099, respectively . The genMonitoring the genetic diversity and genetic structure of small biological populations is crucial for protecting their genetic diversity and the uniqueness of the gene pool . Golden-p value (d) is to 0, the closer the genotype distribution is to equilibrium, while d > 0 indicates excess heterozygosity [p values ranged from 0.882 to 0.900. Related studies have shown that if a population bottleneck occurs, the population size decreases, and the degree of heterozygosity will be excessive [The mean heterozygosity of the population is a common parameter for measuring the genetic variation in a species, and the higher the average heterozygosity of the population is, the lower the genetic consistency of the population, the higher the genetic variation, and the richer the genetic diversity . The expzygosity . Based oxcessive . Thus, aHabitat fragmentation is one of the most important factors affecting the genetic diversity of animals and plants . In tradIn addition, due to habitat fragmentation and population reduction, an increasing number of local farmers need to purchase fry from external sources, which continues to trigger a founder effect. The founder effect refers to the fact that when a small number of individuals or fertilised mothers in a large population move into a new environment to establish a new population, they often carry only a small part of the gene pool of the parent population, and the gradual loss of genetic diversity is caused by a series of founder events that occur as a result of the group entering a previously uninhabited area . TherefoBiodiversity is closely related to environmental differences, and the greater the pressure of natural selection is, the richer the diversity . In GuizST < 0.05 reflects a low level of equal differentiation, and 0.05 < FST < 0.15 reflects a moderate differentiation level [ST) of the DY population and the remaining four populations was greater than 0.05. Genetic distances also showed that the genetic differentiation of the DY population was the greatest. Genetic distance was calculated at the genus, species, and population levels according to Shaklee et al. [The degree of genetic differentiation between the DY population and the remaining populations was the greatest. Quan YC et al. proposed the following criteria for genetic differentiation between populations: 0 < Fon level . The gene et al. . The genST), genetic distance (DR), neighbour-joining tree and PCA results, we believe that the DY population has developed more obvious genetic differentiation from the remaining four populations. In our research, we found that compared with other populations, the DY population was extremely small. At present, the golden-backed carp is cultivated at a small scale in only one village, resulting in long-term inbreeding in the DY population. Therefore, inbreeding and a reduction in the number of breeders are the key factors leading to population differentiation. This is similar to the findings of Santos S. [The K value cross-validation error box plot and the pong cluster plot showed that when K = 1, the cross-validation error rate was the lowest. By combining the genetic differentiation coefficients (Fantos S. . Each frCompared with the other four populations, the DY population also showed obvious differentiation in morphology. The body colour of the DY population was dark red C,D, whilPurify the germplasm of the golden-backed carp and use pure-breeding parents for body colour for artificial reproduction.For the restoration of the germplasm of the golden-backed carp, the scope of breeding should be expanded as much as possible, inbreeding should be avoided, and good seeds should be regularly introduced from foreign breeding farms as breeding parents to improve the genetic diversity of offspring.Make full use of the globally important agricultural cultural heritage of the Congjiang Dongxiang Rice-fish-Duck Composite System area to carry out protection work.According to existing reports, golden-backed carp are distributed only in the border areas of Guizhou, Guangxi and Hunan Provinces in China, and the current distribution range and population size are very small. Rice field fish farming is a green, sustainable method of ecological breeding, and golden-backed carp have good rice field adaptability. They are a precious rice field fish germplasm resource. The genetic diversity of the current golden-backed carp population is very low, and production has shown a slow growth rate, body colour changes, and other disadvantages. The selection and protection of golden-backed carp is important; therefore, the following recommendations are made:In summary, we used 2b-RAD technology to molecularly label the genomes of five existing rice-reared golden-backed carp populations in Guizhou Province, China. Based on the screening of 45,789 SNP loci, we found that the genetic diversity level of the rice-farmed golden-backed carp population in Guizhou Province was very low, except for the DY population. The degree of interpopulation differentiation was very small, and the protection of golden-backed carp germplasm resources is important. This study could provide basic data for future breeding of golden-backed carp in paddy fields.


Breeding programs in developing countries still cannot afford the new genotyping technologies, hindering their research. We aimed to assemble an Association Mapping panel to serve as CGIAR Barley Breeding Toolbox (CBBT), especially for the Developing World. The germplasm had to be representative of the one grown in the Developing World; with high genetic variability and be of public domain. For it, we genotyped with the Infinium iSelect 50K chip, a Global Barley Panel (GBP) of 530 genotypes representing a wide range of row-types, end-uses, growth habits, geographical origins and environments. 40,342 markers were polymorphic with an average polymorphism information content of 0.35 and 66% of them exceeding 0.25. The analysis of the population structure identified 8 subpopulations mostly linked to geographical origin, four of them with significant ICARDA origin. The 16 allele combinations at 4 major flowering genes  explained 11.07% genetic variation and were linked to the geographic origins of the lines. ICARDA material showed the widest diversity as revealed by the highest number of polymorphic loci , number of private alleles and the fact that ICARDA lines were present in all 8 subpopulations and carried all 16 allelic combinations. Due to their genetic diversity and their representativity of the germplasm adapted to the Developing World, ICARDA-derived lines and cultivated landraces were pre-selected to form the CBBT. Using the Mean of Transformed Kinships method, we assembled a panel capturing most of the allelic diversity in the GBP. The CBBT (N=250) preserves good balance between row-types and good representation of both phenology allelic combinations and subpopulations of the GBP. The CBBT and its genotypic data is available to researchers worldwide as a collaborative tool to underpin the genetic mechanisms of traits of interest for barley cultivation. The new advances in genotyping technologies and genomic research applied to breeding (Genome Wide Association Studies and more recently Genomic Selection) have the potential to bring the largest yield productivity increase and stability improvement since the Green Revolution. The efficient use of these new tools is important to attain the production needed to feed the increasing population in a scenario of climate instability. These new approaches have been widely adopted thanks to new low-cost and high throughput genotyping technologies . HoweverHordeum vulgare L.) covers 50Mha worldwide, ca. 20Mha of them in developing countries  of 530 barley entries. This panel includes entries representing the different barley uses , morphophysiological traits and phenology patterns and local and regional preferences. It is using the information gathered from this Global Barley Panel that we aim to constitute a barley breeding toolbox that represents the global diversity in the GBP and: i) is representative of the germplasm grown in the Developing World; ii) covers a wide range of genetic variability, morphophysiological traits and phenology groups and iii) is of public domain or international public goods to facilitate germplasm exchange.A Global Barley Panel (GBP) of 530 genotypes mainly considered of spring-type, including elite lines, cultivars, and landraces from different geographic origins was assembled. The GBP includes a significant number of elite lines and cultivars from the ICARDA Global Barley Breeding Program of the CGIAR, both from the ICARDA program previously based in Syria, now in Morocco and Lebanon, and from the ICARDA/CIMMYT Latin-America program based in Mexico, merged during the first decade of the 2000s. The set represents a worldwide spectrum of barley genetic variance with diversity of row-types, end-uses, growth habits and geographical and environmental distribution. Out of the 530 genotypes (292 two-row and 238 six-row types), 288 are from the ICARDA Global Barley Breeding Program, 61 from the United States of America (USA), 48 from Europe, 40 from India, 23 from Australia, 19 from Canada, 12 from Africa, 6 from Latin America and 16 from other countries across the globe. The Panel also includes 17 landraces from different countries Figure 1CMplot package (The genotyping work was done in two events. The first 266 entries were genotyped as described in  package  for R st package .R2) and significant corresponding p-values ≤ 0.01. The R Statistical Software .From the set of 40,342 SNP markers, a subset of 40,256 polymorphic markers with known physical positions was selected to perform the linkage disequilibrium (LD) analysis for each chromosome using TASSEL 5.0 software  and a slSoftware  was usedpoppr  was conducted with the filtered SNP marker set using TASSEL version 5.0 , and thepoppr  to inves  gene, BK_14 (A/G) associated with HvPPD-H1 gene, BK_17 (C/G) associated to HvVRN-H1 gene, and BOPA2_12_30265 (A/G) associated with HvCEN gene (poppr (R2) calculated by TASSEL software  associated to CEN gene . AMOVA te  with any ancestry in the model  algorithm implemented in the R package LEA . This mefunction  to test R package GeneticSubsetter  which measures the kinship of genotypes in a subset and identifies the most dissimilar set of genotypes, a favorable trait for GWAS studies.To select a subset that represents the diversity of the population and at the same time provides an optimized set of entries for association mapping studies we used the ubsetter . For it,2 values for the whole genome decreased with increasing pairwise distance. In general, LD showed a fast rate of decline, and the decay distance was approximately 0.3 Mb  were considered for analysis. The cultivated landraces were considered as a separate group to avoid distorting the genetic integrity of the geographic groups, as the rest of the lines are either released varieties or advanced breeding lines. The AMOVA of this grouping explained up to 12.32% of the total genetic variation Table 2.Population subgrouping using sNMF, cross-validation and the cross-entropy criterion approaches gave further information regarding the genotypic structure of the GBP. The cross-entropy criterion did not exhibit a minimum value or a clear plateau and steadily decreased at higher k values Figure 4At K=7 (HvVRN-H3 (FT1), HvPPD-H1 HvVRN-H1 and HvCEN) and generating sixteen allelic combinations (AC) was studied. The AMOVA of the genetic differentiation between and within each flowering locus and their allelic combinations was conducted . The distribution of different allelic combinations within germplasm origin groups is shown in All sixteen allelic combinations were present in the 275 genotypes from ICARDA origin. Thirteen allelic combinations were detected in USA group, eight in European, Australian and “Other” barleys, and seven in Indian genotypes. The most frequent allelic combinations in the germplasm were the early flowering AC15 (21.17%), AC12 (12.62%) and AC16 (12.43%). AC11 and AC13 were less frequent and unique to the ICARDA and Landrace populations.  and the landraces. The reason for this was to fulfill the three objectives of the present study, that is, that the panel developed is of public domain and free to use; that is relevant to the developing world and that it represents a wide genetic diversity Figure 6The subset continues to preserve a good balance between the row type with a slightly larger proportion of 6-row types (55% of the 250 entries) over the 2-rowed types as compared with the Global population (46% 6-row and 54% 2-row) Figure 6The characterization and dissection of genetic diversity and structure present in current germplasm collections help the breeders to meet breeding goals through the identification of beneficial alleles for traits of interest associated to molecular markers . With thOut of the 43,461 scorable SNPs markers of the 50k iSelect SNP array , 40,342 This result is similar and often higher than other reports of 39,733 SNPs ; 33,818 Principal components analysis (PCA) and the NJ clustering partitioned the 530 entries into two clearly defined groups based on row-type (2-row vs 6-row) and highlighted the presence of subgroups characterized by common or closely related origins. Furthermore, AMOVA analysis indicated that germplasm origin could also explain a significant part of genetic diversity. The variation explained by geographic origin was higher than the value reported by Row type in barley has been shown in the past to be one of the main determinants of the genetic structure, both due to the inherent nature of the trait which is associated to major regional preferences and its association to the different barley end-uses . Most EuThe group of ICARDA germplasm showed high genetic diversity as revealed by the number of polymorphic loci, number of unique alleles and Nei’s unbiased gene diversity. Moreover, 99.72% of the 40,342 polymorphic SNPs in the panel were so in the ICARDA population too. Interestingly, the lower differentiation between ICARDA germplasm and most of the other origins evidenced the long-lasting collaborations between the Global Program and national and regional breeding programs. In fact, more than 250 spring and winter 2-row, 6-row and naked barley varieties of ICARDA origin have been released in 46 countries since 1979, including USA (5 varieties), Canada (15 varieties), Australia (4), India (8 varieties) and some African (70) and Latin American (27) countries among others. ICARDA germplasm has in addition being extensively used in crosses in the National programs. Interestingly, the Indian germplasm is closely related to the groups of Landrace, ICARDA and Africa, while distinct from the other groups. Landrace group exhibited the lowest Npl, MAF and PIC values which may be explained by the under-representation of Landrace genotypes during the development of the 50K SNP array .Latin American germplasm exhibited the highest He, MAF, PIC although having the lowest number of individuals (5) as compared to other origins. This could be due to the diverse source of genotypes originating from different countries . Despite American malting varieties being obtained through hybridization with European accessions, the national industrial standards for malting and brewing have led to the development of germplasm with specific characteristics . The EurHvCEN, HvVrn-H3, HvPpd and HvVrn-H1) and their additive effect on the phenology and adaptation of the panel studied. These highly polymorphic (PIC > 0.4) loci were not in LD . This subpopulation assembled almost exclusively the 6-row ICARDA lines issued from the ICARDA/CIMMYT Latin America program, notably the lines derived from the cultivars PETUNIA-1 and, especially, ‘DOÑA JOSEFA’ (aka V-MORALES) a widely adapted 6-row malt barley line. The last subpopulation consisted of genotypes mostly issued from crosses between entries from the Syrian and Mexican programs. Despite this differentiation, the PCA showed some level of overlap in Clusters 5, 6 and 8 suggesting that these subpopulations may have some genetic overlapping that goes beyond the origin.Cluster 5, 6 and 8 assembled most of the 6-row barley lines of the panel. Although in this case, the phylogenetic clustering did not completely overlap with the subpopulations at K=4, at larger K these three clusters were evident. Cluster 5 grouped two subpopulations at K=8. The first subpopulation consisted of USA and Canada 6-row feed barley entries. The second grouped mostly 6-row Indian varieties. Cluster 6 instead consisted of a distinct subpopulation from ICARDA origin (SubPop7.7 and SubPop8.5). Genotypes of this group were mostly derived from crosses with the cultivar Admixed genotypes at K=8 belonged to nine geographical origins and 57.6% of them were ICARDA breeding lines (47% two-row and 53% six-row). The higher number of admixed genotypes among the ICARDA lines can be explained by their largest population size (53.6% of genotypes present in the collection) and its extensive use of two-by-six row crosses for germplasm improvement . LikewisThe main objective of the present study was to identify and assemble an Association Mapping panel of barley genotypes that represented the diversity of the Global Barley Panel to serve as a CGIAR barley breeding toolbox (CBBT) especially for the Developing World. For it, the main criteria to select among the lines in the Global panel was: i) to be representative of the germplasm grown in the Developing World; ii) to cover a wide range of genetic variability, morphophysiological relevant traits and phenology groups and iii) be of public domain or international public goods to facilitate germplasm exchange.In order to meet these criteria and due to their large diversity, the CGIAR entries, including landraces hosted in the ICARDA genebank were pre-selected for a total of 241 lines. The Developing World representativity requirement was met due to the global target of the CGIAR Global Barley Breeding program especially in terms of adaptation and end-uses. In fact, most of the CGIAR lines included in the panel have been shared with and selected by National Partners as part of the Barley International Nurseries. These sets of diverse and elite barley genotypes distributed upon request to more than 70 collaborators in 20 countries in America, Europe, Africa and Asia  annuallyHowever, to facilitate future phenotyping studies and reduce redundancies a core subset of 250 entries was selected using Mean of Transformed Kinships method. The use of this method differs from others like the PIC based one used in other studies such as Germinate Database (https://germinateplatform.github.io/get-germinate/). All the data generated on this population will be freely available in Germinate for the global barley community to use.The genotypic data of the population is available in The main objective of the present study was to assemble a collection of diverse barley germplasm representative of the germplasm grown worldwide and with particular emphasis on Developing World. The results showed that the Global panel used in this study is highly diverse and covers a wide range of both genetic variability of cultivated barley and phenology groups. The CBBT assembled represents with fidelity the genetic diversity and key morphophysiological traits of the Global panel. This collection will be made available, together with the genotypic data, to breeders and researchers worldwide to serve as a collaborative tool to underpin the genetic mechanisms of traits of interest for barley cultivation. In addition, the collaborative approach to gene discovery will make this collection a toolbox for breeders that will be able to use the germplasm and the markers identified by researchers worldwide in their own breeding programs.https://germinateplatform.github.io/get-germinate/). All the data generated on this population will be freely available in Germinate for the global barley community to use.The genotypic data of the population is available in S-GM, VA, and BO formulated the research problem and designed the approaches. S-GM, VRPS, GS, and CF selected and obtained the germplasm and arranged the genotyping. BO, S-GM and VA performed statistical analysis of data, BO wrote the initial draft of the paper, VA and S-GM revised the manuscript. All authors contributed to the final draft of the paper and approved the final version of the manuscript. All authors reviewed the article and approved the submitted version.Modernization of crop breeding programs at ICARDA - building on past AFESD support to prepare for the future project funded by the Arab Fund for Economic and Social Development (AFESD).The present work was supported by the Accelerated Breeding Initiative (ABI) and the Sustainable Animal Productivity for Livelihoods, Nutrition and Gender inclusion (SAPLING) to the outcome of this research.The authors would like to thank the USDA small grains genotyping lab in Fargo  and Dr. Shiaoman Chao for the genotyping of 266 genotypes used in the present study. The authors acknowledge the support and commitment of the CGIAR Author SG was employed by Sakata Seed America Inc.The remaining 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.