The Effects of Rearing Environment on Organization of The Olfactory System And Brain of Juvenile Sockeye Salmon, Oncorhynchus Nerka.

Pacific salmon (Oncorhynchus spp.) hatch and feed in freshwater habitats, migrate to sea to mature, and return to spawn at natal sites. The final, riverine stages of the return migrations are mediated by chemical properties of the natal stream that they learned as juveniles. Like some other fishes, salmon growth is asymptotic; they grow continuously throughout life toward a maximum size. The continued growth of the nervous system may be plastic in response to environmental variables. Due to the ecological, cultural, and economic importance of Pacific salmon, individuals are often reared in hatcheries and released into the wild as juveniles to supplement natural populations. However, hatchery-reared individuals display lower survivorship and may also stray (i.e., spawn in a non-natal stream) at higher rates than their wild counterparts. Hatchery environments may lack stimuli needed to promote normal development of the nervous system, thus leading to behavioral deficits and a higher incidence of straying. This study compared the peripheral olfactory system and brain organization of hatchery-reared and wild-origin sockeye salmon fry (O. nerka). Surface area of the olfactory rosette, diameter of the olfactory nerve, total brain size, and size of major brain regions were measured from histological sections and compared between wild and hatchery-origin individuals. Hatchery-origin fish had significantly larger optic tecta, and marginally insignificant, yet noteworthy trends, existed in the valvula cerebelli (hatchery > wild) and olfactory bulbs (hatchery < wild). We also found a putative difference in olfactory nerve diameter (dmin) (hatchery > wild), but the validity of this finding needs further analyses with higher resolution methods Overall, these results provide insight into the potential effects of hatchery rearing on nervous system development in salmonids, and may explain behavioral deficits displayed by hatchery-origin individuals post-release.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Comparison of triploid and diploid rainbow trout (Oncorhynchus mykiss) fine-scale movement, migration and catchability in lowland lakes of western Washington.

Fisheries managers stock triploid (i.e., infertile, artificially produced) rainbow trout Oncorhynchus mykiss in North American lakes to support sport fisheries while minimizing the risk of genetic introgression between hatchery and wild trout. In Washington State, the Washington Department of Fish and Wildlife (WDFW) allocates approximately US $3 million annually to stock hatchery-origin rainbow trout in > 600 lakes, yet only about 10% of them are triploids. Many lakes in Washington State drain into waters that support wild anadromous steelhead O. mykiss that are listed as threatened under the U.S. Endangered Species Act. As a result, there is a strong interest in understanding the costs and benefits associated with stocking sterile, triploid rainbow trout as an alternative to traditional diploids. The objectives of this study were to compare triploid and diploid rainbow trout in terms of: (1) contribution to the sport fishery catch, (2) fine-scale movements within the study lakes, (3) rate of emigration from the lake, and (4) natural mortality. Our results demonstrated that triploid and diploid trout had similar day-night distribution patterns, but triploid trout exhibited a lower emigration rate from the lake and lower catch rates in some lakes. Overall, triploid rainbow trout represent a viable alternative to stocking of diploids, especially in lakes draining to rivers, because they are sterile, have comparable home ranges, and less often migrate.

Two decades of change in sea star abundance at a subtidal site in Puget Sound, Washington.

Long-term datasets can reveal otherwise undetectable ecological trends, illuminating the historical context of contemporary ecosystem states. We used two decades (1997-2019) of scientific trawling data from a subtidal, benthic site in Puget Sound, Washington, USA to test for gradual trends and sudden shifts in total sea star abundance across 11 species. We specifically assessed whether this community responded to the sea star wasting disease (SSWD) epizootic, which began in 2013. We sampled at depths of 10, 25, 50 and 70 m near Port Madison, WA, and obtained long-term water temperature data. To account for species-level differences in SSWD susceptibility, we divided our sea star abundance data into two categories, depending on the extent to which the species is susceptible to SSWD, then conducted parallel analyses for high-susceptibility and moderate-susceptibility species. The abundance of high-susceptibility sea stars declined in 2014 across depths. In contrast, the abundance of moderate-susceptibility species trended downward throughout the years at the deepest depths- 50 and 70 m-and suddenly declined in 2006 across depths. Water temperature was positively correlated with the abundance of moderate-susceptibility species, and uncorrelated with high-susceptibility sea star abundance. The reported emergence of SSWD in Washington State in the summer of 2014 provides a plausible explanation for the subsequent decline in abundance of high-susceptibility species. However, no long-term stressors or mortality events affecting sea stars were reported in Washington State prior to these years, leaving the declines we observed in moderate-susceptibility species preceding the 2013-2015 SSWD epizootic unexplained. These results suggest that the subtidal sea star community in Port Madison is dynamic, and emphasizes the value of long-term datasets for evaluating patterns of change.

BrainGENIE: The Brain Gene Expression and Network Imputation Engine.

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

Ontogenetic shifts in olfactory rosette morphology of the sockeye salmon, Oncorhynchus nerka.

Sockeye salmon, Oncorhynchus nerka, are anadromous, semelparous fish that breed in freshwater-typically in streams, and juveniles in most populations feed in lakes for 1 or 2 years, then migrate to sea to feed for 2 or 3 additional years, before returning to their natal sites to spawn and die. This species undergoes important changes in behavior, habitat, and morphology through these multiple life history stages. However, the sensory systems that mediate these migratory patterns are not fully understood, and few studies have explored changes in sensory function and specialization throughout ontogeny. This study investigates changes in the olfactory rosette of sockeye salmon across four different life stages (fry, parr, smolt, and adult). Development of the olfactory rosette was assessed by comparing total rosette size (RS), lamellae number, and lamellae complexity from scanning electron microscopy images across life stages, as a proxy for olfactory capacity. Olfactory RS increased linearly with lamellae number and body size (p < .001). The complexity of the rosette, including the distribution of sensory and nonsensory epithelia and the appearance of secondary lamellar folding, varied between fry and adult life stages. These differences in epithelial structure may indicate variation in odor-processing capacity between juveniles imprinting on their natal stream and adults using those odor memories in the final stages of homing to natal breeding sites. These findings improve our understanding of the development of the olfactory system throughout life in this species, highlighting that ontogenetic shifts in behavior and habitat may coincide with shifts in nervous system development.

Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples.

Many high-performance DTA deep learning models have been proposed, but they are mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models more trustworthy, and allows to distill biological knowledge from the models. Counterfactual explanation is one popular approach to explaining the behaviour of a deep neural network, which works by systematically answering the question "How would the model output change if the inputs were changed in this way?". We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug-target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex. Our proposed framework provides human-interpretable counterfactual instances while optimizing both the input drug and target for counterfactual generation at the same time. We benchmark the proposed MACDA framework using the Davis and PDBBind dataset and find that our framework produces more parsimonious explanations with no loss in explanation validity, as measured by encoding similarity. We then present a case study involving ABL1 and Nilotinib to demonstrate how MACDA can explain the behaviour of a DTA model in the underlying substructure interaction between inputs in its prediction, revealing mechanisms that align with prior domain knowledge.

Maternal gut microbiota during pregnancy and the composition of immune cells in infancy.

Background: Preclinical studies have shown that maternal gut microbiota during pregnancy play a key role in prenatal immune development but the relevance of these findings to humans is unknown. The aim of this prebirth cohort study was to investigate the association between the maternal gut microbiota in pregnancy and the composition of the infant's cord and peripheral blood immune cells over the first year of life. Methods: The Barwon Infant Study cohort (n=1074 infants) was recruited using an unselected sampling frame. Maternal fecal samples were collected at 36 weeks of pregnancy and flow cytometry was conducted on cord/peripheral blood collected at birth, 6 and 12 months of age. Among a randomly selected sub-cohort with available samples (n=293), maternal gut microbiota was characterized by sequencing the 16S rRNA V4 region. Operational taxonomic units (OTUs) were clustered based on their abundance. Associations between maternal fecal microbiota clusters and infant granulocyte, monocyte and lymphocyte subsets were explored using compositional data analysis. Partial least squares (PLS) and regression models were used to investigate the relationships/associations between environmental, maternal and infant factors, and OTU clusters. Results: We identified six clusters of co-occurring OTUs. The first two components in the PLS regression explained 39% and 33% of the covariance between the maternal prenatal OTU clusters and immune cell populations in offspring at birth. A cluster in which Dialister, Escherichia, and Ruminococcus were predominant was associated with a lower proportion of granulocytes (p=0.002), and higher proportions of both central naïve CD4+ T cells (CD4+/CD45RA+/CD31-) (p<0.001) and naïve regulatory T cells (Treg) (CD4+/CD45RA+/FoxP3low) (p=0.02) in cord blood. The association with central naïve CD4+ T cells persisted to 12 months of age. Conclusion: This birth cohort study provides evidence consistent with past preclinical models that the maternal gut microbiota during pregnancy plays a role in shaping the composition of innate and adaptive elements of the infant's immune system following birth.

Group size affects predation risk and foraging success in Pacific salmon at sea.

Grouping is ubiquitous across animal taxa and environments. Safety in numbers is perhaps the most cited reason for grouping, yet this fundamental tenet of ecological theory has rarely been tested in wild populations. We analyzed a multidecadal dataset of Pacific salmon at sea and found that individuals in larger groups had lower predation risk; within groups of fish, size outliers (relatively small and large fish) had increased predation risk. For some species, grouping decreased foraging success, whereas for other species, grouping increased foraging success, indicating that safety competition trade-offs differed among species. These results indicate that survival and growth depend on group size; understanding the relationship between group size distributions and population size may be critical to unraveling ecology and population dynamics for marine fishes.

Implications of Large-Effect Loci for Conservation: A Review and Case Study with Pacific Salmon.

The increasing feasibility of assembling large genomic datasets for non-model species presents both opportunities and challenges for applied conservation and management. A popular theme in recent studies is the search for large-effect loci that explain substantial portions of phenotypic variance for a key trait(s). If such loci can be linked to adaptations, 2 important questions arise: 1) Should information from these loci be used to reconfigure conservation units (CUs), even if this conflicts with overall patterns of genetic differentiation? 2) How should this information be used in viability assessments of populations and larger CUs? In this review, we address these questions in the context of recent studies of Chinook salmon and steelhead (anadromous form of rainbow trout) that show strong associations between adult migration timing and specific alleles in one small genomic region. Based on the polygenic paradigm (most traits are controlled by many genes of small effect) and genetic data available at the time showing that early-migrating populations are most closely related to nearby late-migrating populations, adult migration differences in Pacific salmon and steelhead were considered to reflect diversity within CUs rather than separate CUs. Recent data, however, suggest that specific alleles are required for early migration, and that these alleles are lost in populations where conditions do not support early-migrating phenotypes. Contrasting determinations under the US Endangered Species Act and the State of California's equivalent legislation illustrate the complexities of incorporating genomics data into CU configuration decisions. Regardless how CUs are defined, viability assessments should consider that 1) early-migrating phenotypes experience disproportionate risks across large geographic areas, so it becomes important to identify early-migrating populations that can serve as reliable sources for these valuable genetic resources; and 2) genetic architecture, especially the existence of large-effect loci, can affect evolutionary potential and adaptability.

North American diadromous fishes: Drivers of decline and potential for recovery in the Anthropocene.

Diadromous fishes migrate between freshwater and marine habitats to complete their life cycle, a complexity that makes them vulnerable to the adverse effects of current and past human activities on land and in the oceans. Many North American species are critically endangered, and entire populations have been lost. Major factors driving declines include overfishing, pollution, water withdrawals, aquaculture, non-native species, habitat degradation, over-zealous application of hatcheries designed to mitigate effects of other factors, and effects of climate change. Perhaps, the most broadly tractable and effective factors affecting diadromous fishes are removals of the dams that prevent or hinder their migrations, alter their environment, and often favor non-native biotic communities. Future survival of many diadromous fish populations may depend on this.

The three ghosts of medical AI: Can the black-box present deliver?

Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in A Christmas Carol, who guide Ebenezer through the past, present, and future of Christmas holiday events. Similarly, our article takes readers through a journey of the past, present, and future of medical AI. In doing so, we focus on the crux of modern machine learning: the reliance on powerful but intrinsically opaque models. When applied to the healthcare domain, these models fail to meet the needs for transparency that their clinician and patient end-users require. We review the implications of this failure, and argue that opaque models (1) lack quality assurance, (2) fail to elicit trust, and (3) restrict physician-patient dialogue. We then discuss how upholding transparency in all aspects of model design and model validation can help ensure the reliability and success of medical AI.

Learning sparse log-ratios for high-throughput sequencing data.

MOTIVATION: The automatic discovery of sparse biomarkers that are associated with an outcome of interest is a central goal of bioinformatics. In the context of high-throughput sequencing (HTS) data, and compositional data (CoDa) more generally, an important class of biomarkers are the log-ratios between the input variables. However, identifying predictive log-ratio biomarkers from HTS data is a combinatorial optimization problem, which is computationally challenging. Existing methods are slow to run and scale poorly with the dimension of the input, which has limited their application to low- and moderate-dimensional metagenomic datasets. RESULTS: Building on recent advances from the field of deep learning, we present CoDaCoRe, a novel learning algorithm that identifies sparse, interpretable and predictive log-ratio biomarkers. Our algorithm exploits a continuous relaxation to approximate the underlying combinatorial optimization problem. This relaxation can then be optimized efficiently using the modern ML toolbox, in particular, gradient descent. As a result, CoDaCoRe runs several orders of magnitude faster than competing methods, all while achieving state-of-the-art performance in terms of predictive accuracy and sparsity. We verify the outperformance of CoDaCoRe across a wide range of microbiome, metabolite and microRNA benchmark datasets, as well as a particularly high-dimensional dataset that is outright computationally intractable for existing sparse log-ratio selection methods. AVAILABILITY AND IMPLEMENTATION: The CoDaCoRe package is available at https://github.com/egr95/R-codacore. Code and instructions for reproducing our results are available at https://github.com/cunningham-lab/codacore. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Photoinduced Covalent Irreversible Inactivation of Proline Dehydrogenase by S-Heterocycles.

Proline dehydrogenase (PRODH) is a flavoenzyme that catalyzes the first step of proline catabolism, the oxidation of l-proline to Δ1-pyrroline-5-carboxylate. PRODH has emerged as a cancer therapy target because of its involvement in the metabolic reprogramming of cancer cells. Here, we report the discovery of a new class of PRODH inactivator, which covalently and irreversibly modifies the FAD in a light-dependent manner. Two examples, 1,3-dithiolane-2-carboxylate and tetrahydrothiophene-2-carboxylate, have been characterized using X-ray crystallography (1.52-1.85 Å resolution), absorbance spectroscopy, and enzyme kinetics. The structures reveal that in the dark, these compounds function as classical reversible, proline analogue inhibitors. However, exposure of enzyme-inhibitor cocrystals to bright white light induces decarboxylation of the inhibitor and covalent attachment of the residual S-heterocycle to the FAD N5 atom, locking the cofactor into a reduced, inactive state. Spectroscopic measurements of the inactivation process in solution confirm the requirement for light and show that blue light is preferred. Enzyme activity assays show that the rate of inactivation is enhanced by light and that the inactivation is irreversible. We also demonstrate the photosensitivity of cancer cells to one of these compounds. A possible mechanism is proposed involving photoexcitation of the FAD, while the inhibitor is noncovalently bound in the active site, followed by electron transfer, decarboxylation, and radical combination steps. Our results could lead to the development of photopharmacological drugs targeting PRODH.

Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient.

BACKGROUND: The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient. METHODS: We take 2 approaches to benchmarking a "dual-channel" random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from individual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 individual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses. CONCLUSIONS: Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a "dual-channel" method to predict up-regulation and down-regulation separately. Second, we use individualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for individual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.

Gut Microbiome Diversity and Composition Are Associated with Habitual Dairy Intakes: A Cross-Sectional Study in Men.

BACKGROUND: At a population level, the relation between dairy consumption and gut microbiome composition is poorly understood. OBJECTIVES: We sought to study the cross-sectional associations between individual dairy foods (i.e., milk, yogurt, and cheese), as well as total dairy intake, and the gut microbiome composition in a large, representative sample of men living in south-eastern Australia. METHODS: Data on 474 men (mean ± SD: 64.5 ± 13.5 y old) from the Geelong Osteoporosis Study were used to assess the cross-sectional association between dairy consumption and gut microbiome. Information on dairy intake was self-reported. Men were categorized as consumers and nonconsumers of milk, yogurt, cheese, and high- and low-fat milk. Milk, yogurt, and cheese intakes were summed to calculate the total dairy consumed per day and categorized into either low (<2.5 servings/d) or high (≥2.5 servings/d) total dairy groups. Fecal samples were analyzed using bacterial 16S ribosomal RNA (rRNA) gene sequencing. After assessment of α and ß diversity, differential abundance analysis was performed to identify bacterial taxa associated with each of milk, yogurt, and cheese consumption compared with nonconsumption, low compared with high total dairy, and low- compared with high-fat milk consumption. All analyses were adjusted for potential confounders. RESULTS: α Diversity was not associated with consumption of any of the dairy groups. Differences in ß diversity were observed between milk and yogurt consumption compared with nonconsumption. Taxa belonging to the genera Ruminococcaceae UCG-010 and Bifidobacterium showed negative and weak positive associations with milk consumption, respectively. A taxon from the genus Streptococcus was positively associated with yogurt consumption, whereas a taxon from the genus Eisenbergiella was negatively associated with cheese consumption. No specific taxa were associated with low- compared with high-fat milk nor low compared with high total dairy consumption. CONCLUSIONS: In men, community-level microbiome differences were observed between consumers and nonconsumers of milk and yogurt. Bacterial taxon-level associations were detected with milk, yogurt, and cheese consumption. Total dairy consumption was not associated with any microbiome measures, suggesting that individual dairy foods may have differential roles in shaping the gut microbiome in men.

Evolutionary analysis of the Delta and Delta Plus variants of the SARS-CoV-2 viruses.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been rapidly evolving in the form of new variants. At least eleven known variants have been reported. The objective of this study was to delineate the differences in the mutational profile of Delta and Delta Plus variants. High-quality sequences (n = 1756) of Delta (B.1.617.2) and Delta Plus (AY.1 or B.1.617.2.1) variants were used to determine the prevalence of mutations (≥20 %) in the entire SARS-CoV-2 genome, their co-existence, and change in prevalence over a period of time. Structural analysis was conducted to get insights into the impact of mutations on antibody binding. A Sankey diagram was generated using phylogenetic analysis coupled with sequence-acquisition dates to infer the migration of the Delta Plus variant and its presence in the United States. The Delta Plus variant had a significant number of high-prevalence mutations (≥20 %) than in the Delta variant. Signature mutations in Spike (G142D, A222V, and T95I) existed at a more significant percentage in the Delta Plus variant than the Delta variant. Three mutations in Spike (K417N, V70F, and W258L) were exclusively present in the Delta Plus variant. A new mutation was identified in ORF1a (A1146T), which was only present in the Delta Plus variant with ~58 % prevalence. Furthermore, five key mutations (T95I, A222V, G142D, R158G, and K417N) were significantly more prevalent in the Delta Plus than in the Delta variant. Structural analyses revealed that mutations alter the sidechain conformation to weaken the interactions with antibodies. Delta Plus, which first emerged in India, reached the United States through England and Japan, followed by its spread to more than 20 the United States. Based on the results presented here, it is clear that the Delta and Delta Plus variants have unique mutation profiles, and the Delta Plus variant is not just a simple addition of K417N to the Delta variant. Highly correlated mutations may have emerged to keep the structural integrity of the virus.

The maternal gut microbiome during pregnancy and offspring allergy and asthma.

Environmental exposures during pregnancy that alter both the maternal gut microbiome and the infant's risk of allergic disease and asthma include a traditional farm environment and consumption of unpasteurized cow's milk, antibiotic use, dietary fiber, and psychosocial stress. Multiple mechanisms acting in concert may underpin these associations and prime the infant to acquire immune competence and homeostasis following exposure to the extrauterine environment. Cellular and metabolic products of the maternal gut microbiome can promote the expression of microbial pattern recognition receptors, as well as thymic and bone marrow hematopoiesis relevant to regulatory immunity. At birth, transmission of maternally derived bacteria likely leverages this in utero programming to accelerate postnatal transition from a TH2- to TH1- and TH17-dominant immune phenotype and maturation of regulatory immune mechanisms, which in turn reduce the child's risk of allergic disease and asthma. Although our understanding of these phenomena is rapidly evolving, the field is relatively nascent, and we are yet to translate existing knowledge into interventions that substantially reduce disease risk in humans. Here, we review evidence that the maternal gut microbiome impacts the offspring's risk of allergic disease and asthma, discuss challenges and future directions for the field, and propose the hypothesis that maternal carriage of Prevotella copri during pregnancy decreases the offspring's risk of allergic disease via production of succinate, which in turn promotes bone marrow myelopoiesis of dendritic cell precursors in the fetus.