Pseudanthia are widespread and have long been postulated to be a key innovation responsible for some of the angiosperm radiations. The aim of our study was to analyze macroevolutionary patterns of these flower‐like inflorescences and their potential correlation with diversification rates in Apiaceae subfamily Apioideae. In particular, we were interested to investigate evolvability of pseudanthia and evaluate their potential association with changes in the size of floral display.The framework for our analyses consisted of a time‐calibrated phylogeny of 1734 representatives of Apioideae and a morphological matrix of inflorescence traits encoded for 847 species. Macroevolutionary patterns in pseudanthia were inferred using Markov models of discrete character evolution and stochastic character mapping, and a principal component analysis was used to visualize correlations in inflorescence architecture. The interdependence between net diversification rates and the occurrence of pseudocorollas was analyzed with trait‐independent and trait‐dependent approaches.Pseudanthia evolved in 10 major clades of Apioideae with at least 36 independent origins and 46 reversals. The morphospace analysis recovered differences in color and compactness between floral and hyperfloral pseudanthia. A correlation between pseudocorollas and size of inflorescence was also strongly supported. Contrary to our predictions, pseudanthia are not responsible for variation in diversification rates identified in this subfamily.Our results suggest that pseudocorollas evolve as an answer to the trade‐off between enlargement of floral display and costs associated with production of additional flowers. The high evolvability and architectural differences in apioid pseudanthia may be explained on the basis of adaptive wandering and evolutionary developmental biology. Pseudanthia are inflorescences that resemble a single flower and usually act as a single pollination unit. Although some are formed only with uniform, undifferentiated flowers , their most widespread and derived types bear pseudocorollas: perianth‐like structures composed of modified peripheral florets in floral pseudocorollas or of extrafloral organs such as bracts, leaves, entire shoots or stipular excrescences in hyperfloral pseudocorollas. Pseudocorollar pseudanthia  arose independently in various lineages of angiosperms, including magnoliids Tucker, , monocotContrary to other key floral traits involved in pollination biology such as floral symmetry  with ca. 400 genera and 3000 species constitute the core clade of the campanulid order Apiales and include economically important crops  and spices . Their flowers are usually organized into a distinctive, modular inflorescence Figure , known aThe exact number and phylogenetic placement of independent origins of pseudanthia in Apioideae has not been reconstructed so far. Fortunately, in the last 20 years much effort has been made to establish a well‐supported phylogeny for this subfamily  over the most species‐rich apioid phylogeny and the largest morphological matrix of inflorescence‐derived traits encoded for this group, we aimed to answer the following questions: (1) What are the macroevolutionary dynamics of pseudanthia? (2) Is the evolution of pseudanthia correlated with changes in size of floral display? (3) Are there any morphological correlations in different pseudanthial architectures? (4) What are the diversification patterns of Apioideae and can they be explained by the evolution of pseudocorollas?rpoC1, rps16, and rpl16 introns for the species representing Apiaceae subfamily Apioideae were downloaded from NCBI using MatPhylobi, a bioinformatic tool created by Hanna Kranas and Łukasz Banasiak (https://github.com/hansiu/MatPhylobi). The software automatically joins conspecific ITS1 and ITS2 sequences that were frequently obtained and published separately because of historical limitations of sequencing methods. It also allows for specification of a blacklist including accessions previously identified as erroneous or dubious  by various researchers. The tree was rooted with Sanicula epipactis E.H.L. Krause, a representative of subfamily Saniculoideae that constitutes the sister group of apioids , we included the taxon only if at least one plastid marker was additionally sampled. Resulting matrices were aligned separately using the G‐INS‐I algorithm implemented in MAFFT v.7.271  and six qualitative (discrete) floral and inflorescence traits was compiled for 847 species of apioid umbellifers Table . The choThe scoring scheme allowed for missing data (when the description was incomplete or imprecise) and polymorphisms (indicating variable state of a character in a single species). Average umbel size, a secondary continuous character used in some analyses, was calculated as [(Minimum number of umbellets × Minimum number of flowers) + (Maximum number of umbellets × Maximum number of flowers)]/2. This approach should give a good approximation of inflorescence size for apioids, as their flowers are relatively uniform in size.  analysis was performed to obtain an alternative measure of branch support, as pseudoreplication‐based methods may fail to recover particular nodes when data sets are highly incomplete. Prior to all subsequent analyses, the outgroup was removed from the tree.Phylogenetic inference was conducted with a maximum likelihood (ML) approach implemented in RAxML 8.2.4 with a GTR + G substitution model recommended by the author of the software  approach as implemented in the corHMM 2.4 package in R  method. The combination of our two three‐state characters resulted in a transition rate matrix of nine by nine possible state combinations, with 72 transition rate parameters. However, ordering both characters allowed for matrix to be reduced to 24 parameters in the most complex model. By editing the combination of ordered transition matrices for both traits, we fitted two variants of SYM and ARD models for the combination of the two traits: correlated (the state of one character affects the relative transition rates of a second) and uncorrelated (states of characters change independently from each other). The schematic diagrams of all transition matrices are listed in Appendix ψ and φ statistics on 100 replicates of unrandomized and randomized matrices , following Björklund . Specifically, we used a mixed PCA approach implemented in R package PCAmixdata 3.1  based on Gower distances Appendix , as implhttp://www.plantsoftheworldonline.org/) and—whenever necessary—corrected following the most recent taxonomic revisions , extinction (μ), and transition rate parameters (q), MuHISSE fits derived measures of diversification defined as turnover  and extinction fraction .To address the influence of different types of pseudanthial pseudocorollas on diversification rates within Apioideae, we used one of the most recent members of the ever‐expanding family of state speciation and extinction (SSE) models. MuHiSSE  have floral pseudocorollas, seven (21.2%) have hyperfloral pseudocorollas, while in 23 species (69.7%), pseudocorollas are absent. In polyphyletic genera (such as Heracleum), unsampled species were presumed to be distributed evenly among different clades. The genera that were monomorphic based on sampled species were also assumed to be monomorphic across all species.Using maximum likelihood, we fitted a set of 12 MuHiSSE models with increasing complexity Table  to a datε) across all states or with varying extinction fraction—and subsequently evaluated using AICc. First, we fitted MuSSE‐like models (without hidden traits) assuming that changes in diversification rates are driven only by the observed character. MuHiSSE (with hidden traits) models were then optimized with hidden state associated only with one of the observed states, i.e., separately for the absence of pseudocorollas, for floral pseudocorollas, and for hyperfloral pseudocorollas, and then with all these three states at once. MuCID (multistate character‐independent) models with diversification parameters unlinked to observed traits were used as a null hypothesis. Specifically, we optimized MuCID‐3, a “fair competitor” for MuSSE models sharing the same number of diversification parameters  on speciation and extinction rates. The fitted models were equivalent to those used in the multistate approach, although redesigned to account for a binary character .The results of our phylogenetic analyses Appendix  are consc < 2; Table The evolution of pseudocorollas is best explained by the ordered ARD model ΔAICc < ; Table 2Pleurospermopsis clade, and Pleurospermeae. In later‐diverging apioids, this architecture is rare and dispersed among several distantly related genera: Chaerophyllum (Scandiceae), Hyalolaena (Pyramidoptereae), Vesper (Selineae), and a part of the polyphyletic genus Hymenidium  explain 24% of the variance within the data set , Heracleum (Tordylieae), Chaerophyllum, and Ferula (both Scandiceae). The Cachrys clade, Annesorhizeae, and Bunium (Pyramidoptereae) were also frequently associated with such an increase.Character‐independent analyses performed with BAMM and MEDUSA point to a high variation in diversification rates within Apioideae, with BAMM reconstructing 26–31 shifts (highest posterior density range) for analyses with specified species richness and 19–22 shifts when this parameter was not provided Figure . MEDUSA c < 2; Table The analyses with character‐dependent models indicate that the heterogeneity of diversification parameters within Apioideae is independent of the presence or absence of pseudocorollas. The best‐fitting model was a MuCID‐3 ΔAICc < ; Table 2The presence of pseudanthia is evolutionary labile with 95% confidence interval for total number of transitions reconstructed as 115–117 and at least 36 independent origins in a moderately large clade like Apioideae Figure . AlthougGeneralist pollination systems are frequently considered suboptimal because flowers have to specialize to mitigate trade‐offs resulting from mutually exclusive demands and qualitative differences between various pollinators , making them more appealing to visually sensitive pollinators. Such coloration patterns, however, show some degree of polymorphism. For example, Hymenidium apiolens Lindley includes three color variants of involucel margins , probably in response to differences in pollination niches  R.L. Hartm. & G.L. Nesom.), which further mimic the appearance of flesh.The important question to ask is how specific pseudanthial architectures answer the demands of various pollinators? Our morphospace analysis Figure , S9 implHeracleum sphondylium  Simonk. evolving as a visual cue for lowland beetles, while compact, white‐flowered and pseudocorollar umbels of H. sphondylium subsp. sphondylium (L.) are a plesiomorphic adaptation to flies and wasps in ancestral montane habitats. Moreover, many apioids with white petals and floral pseudocorollas  develop so‐called “dark central florets”, which have been thought to attract certain species of coleopterans .The modular architecture of apioid pseudanthia allows for their compactness and promotion to be altered as a means of balancing between the attractiveness to pollinators  and offspring genetic diversity . In large‐sized umbels, pseudanthia frequently develop at the level of individual umbellets rather than the entire umbel, forming several distinct, smaller units that cannot be freely explored without considerable energetic expense . This situation is fairly common in tribe Tordylieae, which has a high prevalence of species with floral pseudocorollas Figure . ConversFerula L., Heracleum or Angelica) are monocarpic perennials, which produce spectacular blossoms made of tens of thousands of florets but only once in a lifetime, following several seasons of vegetative growth. In our study, small pseudanthia were reconstructed to evolve predominantly by reduction of larger inflorescences  occurring in Asteraceae subtribe Nassauvinae are also considered an adaptation to dry habitats , allowing paralogues to gain new or additional functions without losing the original one. On the contrary to ray flowers, which are restricted to Apioideae, hyperfloral pseudocorollas are relatively common in all subfamilies of Apiaceae . As we are not aware of any species with pseudanthia in other families within Apiales  must have taken place after the divergence.of umbellifers.An interesting question concerning pseudanthia in Apioideae is how can such complex structures be so easily “turned on” and “off” in response to selection pressures. The proximal causes underlying this macroevolutionary phenomenon may to some extent be explained on the grounds of evolutionary developmental biology. As the exact molecular mechanisms underlying the formation of different architectures in compound umbels are currently unknown, the inference in this matter must be made based on the data available for distantly related plants. In the case of CYCLOIDEA genes, which expanded their initial role in the establishment of floral symmetry  and coriander (Coriandreae) has so far revealed two paralogues, which must have been present in their last common ancestor . Knowing that some representatives of Oenantheae also develop ray flowers, it is probable that the duplication occurred in the common ancestor of these lineages. The subsequent repeated recruitment/de‐recruitment of already existing CYC paralogues could be the “molecular switch” responsible for numerous gains and losses of floral pseudocorollas in Apioideae. This explanation is especially tempting in the light of recent studies documenting exceptionally high number of transitions between actinomorphy and zygomorphy  is full of gaps, and it remains difficult to reliably identify major drivers of their diversification. The explosive radiation of some clades identified in our study could have arisen from hybridization/introgression events  used in corHMM and trait‐dependent diversification analyses with pseudocorollas encoded as a single state and results of the model‐fitting (C) and stochastic mapping (D) conducted for this variant of analyses.Click here for additional data file.Appendix S5. Graphs with transition rate matrices used in corHMM (A) and trait‐dependent diversification analyses (B) and marginal likelihood ancestral character estimation conducted separately for types of pseudocorollas (C) and size (D).Click here for additional data file.Appendix S6. Results of the betadisper (A) and NMDS (B) analyses.Click here for additional data file.Appendix S7. Four Excel sheets containing information about species richness provided for MEDUSA (A) or BAMM (B), calculations of sampling fraction for trait‐dependent analyses (C), and data on apioid biodiversity according to Plants of the World Online database (POWO) (D).Click here for additional data file.Appendix S8. Phylogenetic tree of apioids with mapped bootstrap/SH‐aLRT support. All major clades/tribes are collapsed into triangles.Click here for additional data file.Appendix S9. Plots of all possible two‐dimensional combinations for the first five principal components.Click here for additional data file.Appendix S10. Phylorate plots for all variants of MEDUSA (A–F) and BAMM (G–X) analyses.Click here for additional data file.


Solenopsis invicta provide an ideal scenario for examining this. Fire ant queens may found colonies individually or in groups of up to 30 queens, depending on key factors such as density of newly mated queens and availability of nesting sites. We artificially manipulated availability of nesting sites to test how the brain responds to social versus solitary colony founding at two key timepoints (early vs. late colony founding) and to group size . We adopted a powerful neurogenomic approach to identify even subtle differences of gene expression between treatment groups, and we built a global gene co‐expression network of the fire ant brain to identify gene modules specifically associated with the different components of the social environment. The difference between group and single founding queens involves only one gene when founding behavior is still plastic and queens can switch from one modality to another, while hundreds of genes are involved later in the process, when behaviors have lost the initial plasticity and are more canalized. Furthermore, we find that large groups are associated with greater changes in gene expression than small groups, showing that even potentially subtle differences in the social environment can be linked to different neurogenomic states.Social life and isolation pose a complex suite of challenges to organisms prompting significant changes in neural state. However, plasticity in how brains respond to social challenges remains largely unexplored. The fire ants  Exposure to social isolation and varying group size are associated with significant changes in the levels of expression of genes in the brain of fire ant queens. It is known, for example, that rearing‐group size during development can shape brain structure and functions in multiple ways.Solenopsis invicta can experience two drastically different social environments when setting up a new colony: total isolation, when a single queen relies exclusively on her own resources to produce the first generation of workers, or group‐founding, when multiple queens share the same nest.S. invicta is a dynamic process, characterized by (a) high plasticity at initiation, when queens normally move from nest to nest and can shift between single and group‐founding strategiesColony founding in fire ants represents an ideal scenario to address these questions. Newly mated queens of S. invicta queens exposed to different social environments. We hypothesized that differences in the social environment present different behavioral challenges to queens that can be quantified through the measure of differential gene expression in their brain. A growing body of research is showing the potential of this approach in a wide range of social insects.Here we used a powerful transcriptomic approach, characterized by high sequencing depth (42 million reads per sample on average), good biological replication and multiple time‐points, to explore global patterns of gene expression in the brains of 22.1S. invicta were sampled on 4 May 2014 in a parking lot in Gainesville  immediately after a big mating flight. This area is densely populated by monogyne colonies, as reported in the literature.Newly mated queens of S. invicta in the United StatesThe other queens were setup to adopt one of two modalities of colony founding that are both recurrent in populations of All 14 trays were transported to an environmental chamber where queens were reared in standard claustral conditions . For the first 2 days, nesting chambers were left open to allow queens to move from one chamber to another if they wanted . We recorded the numbers of SF and GF nests for both days Figure . At the 2.2Prior to allocating queens to experimental groups for RNA sequencing (RNAseq) we dissected abdomens to check the spermatheca for mating status and to look at ovary development. In fire ants, the spermatheca is easily visible upon dissections in the abdomens of mated queens, where it appears as a bean‐shaped white structure filled with sperm Figure ; when quWe used NMQ (N = 6) as a control group with baseline gene expression levels to compare against both time points that were analyzed; NMQ were randomly picked from the pool of queens that were frozen immediately after collection, after confirmation of their mating status. For the earlier stage of the founding process, that is, 3 days, we compared the following two groups of queens against NMQ: (a) single‐founding queens at 3 days post‐founding ; and (b) group‐founding queens at 3 days post‐founding , and we removed both eyes, mouthparts and associated glands.We isolated total RNA from individual brains as described in the Appendix S. invicta genome  using the intron‐aware STAR aligner, version 2.6.1a.RNAseq read files were aligned to the 33.1We performed a series of analyses to explore whether group‐founding queens (GFQ) differ from single‐founding queens (SFQ) in their overall neurogenomic state Figure . Both gr2 test from equal: X2 = 0.72, df = 1, P = 0.40). The two sets also showed very similar proportions of genes that were more highly expressed and with expression levels more than 2‐fold higher compared with NMQ . These genes are likely involved in the general patterns associated with founding per se and onset of reproduction, that all queens shared at this stage, and therefore are less relevant for our study. These results clearly indicate that the difference between the neurogenomic states of GFQ and SFQ is minimal at 3dpf. This was supported by the fact that only one gene was significantly different between GFQ and SFQ at this time point when we compared them directly . Despite being different in size, the two gene sets largely overlapped, similarly to what was reported for 3dpf: 1614 of the significantly differentially expressed genes were shared across the two groups, a 3.9‐fold higher proportion than expected by chance (hypergeometric test: P < 0.001). These common genes are likely associated with the general biological processes that all queens experience at this stage, such as egg‐laying, brood care and aging.Later in the founding process the scenario changed dramatically. In fact, at 25dpf, there were 2169 genes in the brain  whose expression was significantly different between GFQ and NMQ, while this was the case for 2763 genes  in SFQ: the difference between the sizes of the two gene sets is statistically significant . The two sets showed similar proportions of genes that were more highly expressed compared with NMQ and also the same proportion of genes with large fold changes compared with NMQ : both the 2208 genes that significantly differed between GFQlarge and NMQ, and the 1449 genes that significantly differed between GFQsmall and NMQ were smaller than the 2763 genes that significantly differed between SFQ and NMQ  that were significantly associated with GFQ or SFQ. Second, we performed direct pairwise comparisons between GFQ and SFQ at 3dpf and 25dpf, to characterize the key genes that were significantly differentially expressed in the two groups of queens at the two time points.3.2.1S. invicta G‐protein coupled receptors , a group of genes known to extend lifespan in Drosophila when less expressed.S. invicta,Mth‐like 1, 3, 5, and 10) showed significantly differential expression between single‐founding and pair‐founding queens 1 month after colony founding in a previous microarray studyMth‐like 10 was more highly expressed in single‐founding queens, while the other three were more highly expressed in pair‐founding queens. Our study supports the idea that social environment and aging are tightly linked in fire ant founding queens, and shows that the interaction is particularly evident in SFQ very early in the founding process, probably as a response to isolation.The fire ant brain gene network encompassed 11 modules Figure , rangingninaA (LOC105194667), ninaC (LOC105200050), Arr1 (LOC105199319), and Arr2 (LOC105202669) all showed patterns of upregulation in SFQ at 25dpf (black module). Interestingly, the regulation of vision‐related genes has been observed in other insects following mating and it has been linked to the switch from photophilic to photophobic behavior.Lsp1beta  was less expressed in SFQ at 25dpf. This gene is a close relative of Lsp2, involved in synapse formation in DrosophilaPogonomyrmex californicus.Two modules (black = 20 genes and pink = 16 genes) were both associated with SFQ at 25dpf but in opposite directions: therefore, they both represent sets of genes that respond to long‐term exposure to social isolation, but follow opposite patterns of expression , encoding the Vitellogenin receptor. Vitellogenin is an important reproductive protein in insects, responsible for the formation of the egg yolk,vitellogenin in the insect head and brain to important social behaviors, like parental care or social aggressionvitellogenins and Vg‐like genes might have expanded their functional repertoire following major duplication events.vitellogenin plays a role in the regulation of DNA functions in the insect brain, its expression in isolated queens could be the key mechanism of their behavioral response to social isolation. We looked at the expression patterns of the two S. invicta vitellogenins (Vg2 LOC105205782 and Vg3 LOC105205783) that are known to be preferentially expressed in queens.Vg3 followed this pattern also at 25dpf; no difference was observed between GFQ and SFQ , and three predicted odorant binding proteins . Finding that odorant receptors are expressed in an insect brain is puzzling, as expression of these genes is normally localized on sensory organs.Or71a and Or22c, although consistent across all queens analyzed, were rather low in our experiments (less than 10 reads per sample on average), brain expression of ORs has been reported before in social insects  showed opposite patterns in SFQ at the two time points: in fact, it was positively associated with SFQ at 3dpf and negatively associated with SFQ at 25dpf . Slit is associated with axon guidance, dendrite morphogenesis, and neuron differentiation and migration in Drosophila.Slit is the one gene that differs between GFQ and SFQ (being more highly expressed in SFQ) suggests that future studies should explore its role in the process of brain restructuring caused by the lack of social interactions during isolation.Expression of only 1 gene was significantly different between GFQ and SFQ at 3dpf and FDR < 0.001: egr‐1), was explained as a stimulation of the reward system in the fish brain because of the sight of conspecifics. A similar mechanism could be in place for GFQ in our study or, alternatively, increased gene expression could be explained by a release of inhibition in the regulation of large group of genes because of repeated social stimulation by nestmates. Further studies are needed in the future to test which, if any, of these hypotheses holds true.A much larger difference between GFQ and SFQ was observed at 25dpf, when 659 genes  significantly differed at FDR < 0.001 Figure . A largeI'm not dead yet (LOC105193770), the superoxide dismutase genes Sod (LOC105208009) and Sod2 (LOC105203964), and the peroxiredoxin genes Prx3 (LOC105205792) and Prx5 (LOC105195487), similar to peroxiredoxins 6005 and 5037 from the microarray study. The fact that the same longevity genes also respond to social environments in other speciesThe difference between GFQ and SFQ at 25dpf is also in line with a previous microarray study, where a large set of genes significantly differed between single‐founding queens and pair‐founding queens sampled at a later stage in the founding process, when the conflict phase had already started among paired queens . According to these observations, it seems that brain gene expression could be used as a proxy for estimating cognitive tasks associated with different social environments. Life in social groups of different size poses different cognitive challenges and it has been observed that members of large groups have more brain power, in particular when groups are stable .We compared gene expression in fire ant queens from large groups  and small groups  at 25dpf  is associated with significant proportions of genes that differ in their expression patterns. We show that this difference is minimal very early in the process of colony founding (only one gene at 3dpf), when fire ant queen behavior is typically plastic,These results clearly illustrate the power and high resolution of the neurogenomic approach, making it an ideal complement to regularly adopted approaches such as the analysis of brain allometry : first, we collected queens from a homogeneous population with low genetic diversity, as indicated by the population's history and genetic similarity as observed in previous years (see Methods) and also by the low rate of polyandry for fire ants colonies in the areaIt would be interesting in the future to further investigate the molecular basis for founding behavior in fire ants by comparing gene expression in different brain tissues, to test for example, whether more differences are observed in the mushroom bodies, the region associated with highly cognitive functions in insects,The authors declare no conflict of interest.Appendix S1: Supporting information. Detailed methods for molecular work. Comparison between large vs. small groups of queens. Comparative network analyses.Click here for additional data file.Dataset S1: Sample details for RNAseq experiments. Details on ant queens selected for RNAseq, and quantities and quality of RNA samples from queens' brains that were used to prepare libraries. Only samples labeled in red were used in the end.Click here for additional data file.Dataset S2: Pairwise comparisons at 3dpf. Details are provided about gene expression analyses  and Gene Ontology analyses  for all pairwise comparisons.Click here for additional data file.Dataset S3: Pairwise comparisons at 25dpf. Details are provided about gene expression analyses  and Gene Ontology analyses  for all pairwise comparisons.Click here for additional data file.Dataset S4: WGCNA modules significantly associated with phenotypes of interest. Details are provided of the gene to gene correlation for all genes included in the module.Click here for additional data file.Dataset S5: Eigenvalues and loadings for all PCs obtained at the two timepoints.Click here for additional data file.


Alces alces) population growth by integrating two independent sources of observations within a hierarchical Bayesian population model. We used one of the largest existing spatiotemporal data sets on ungulate population dynamics globally. We documented a 20% population decline over the period examined. There was negative density‐dependent population growth of moose. Although we could not determine the mechanisms producing density‐dependent suppression of population growth, the relatively low densities at which we documented moose populations suggested it could be due to density‐dependent predation. Predation primarily limited population growth, except at low density, where it was regulating. After we simulated several harvest scenarios, it appeared that harvest was largely additive and likely contributed to population declines. Our results highlight how population dynamics are context dependent and vary strongly across gradients in climate, forest type, and predator abundance. These results help clarify long‐standing questions in population ecology and highlight the complex relationships between natural and human‐caused mortality in driving ungulate population dynamics.The relative effect of top‐down versus bottom‐up forces in regulating and limiting wildlife populations is an important theme in ecology. Untangling these effects is critical for a basic understanding of trophic dynamics and effective management. We examined the drivers of moose ( Globally, large herbivores are important cultural and economic resources that shape the function and structure of landscapes  across nearly 1 million km2 and over 20 years. At the extremities of their range, moose are thought to be limited in their distribution to the north by severe winter conditions and to the south by heat stress  is thought to be the primary factor limiting population growth , meningeal worm (Parelaphostrongylus tenuis), and giant liver fluke (Fascioloides magna) appear to play a substantial role in the population dynamics of some populations , potentially leading to apparent competition and increases in the shared parasites that deer carry . We also hypothesized that intraspecific competition, parasites associated with white‐tailed deer, and hunting would all play smaller but significant roles in limiting population growth. We predicted that population growth would be negatively related to moose density due to competition for limited resources and deer density because of shared parasites. We further predicted that moose in areas with high black bear density would exhibit lower population growth rates. Finally, because moose are harvested, and because hunting is thought to be largely additive to other factors limiting population growth  present in the southern extent. White‐tailed deer also overlap with moose, with the highest densities in the Great Lakes‐St. Lawrence (GLSL) forest region in the southeastern and extreme western parts of the province.The study took place throughout most of the distribution of moose in Ontario, Canada Figure . Moose aTo examine factors driving moose population dynamics, we obtained data on moose, white‐tailed deer, predators (canids and bears), hunter effort, and harvest. In the 69 WMUs where moose were hunted, abundance was estimated from standardized aerial surveys undertaken from December to mid‐February at least once every 4 years since 1975 McLaren, . During Canis latrans]) seen. Hunters also reported whether they harvested a moose and the number of days hunted. We divided the counts of animals by the total number of days spent hunting to provide a standardized average number of animals seen each day over the entire hunting season during each year for each WMU. Lastly, we obtained information on black bear density from spatial genetic capture–recapture surveys  was given asμt−1j was the natural log of the real postharvest population size of the preceeding year, t the year, j the WMU, rmax the intrinsic rate of growth from low abundance (N = 1), b the term that controlled the level of bottom‐up density‐dependent processes, and E the number of moose harvested, here indexed to the preceding hunting season , as a result estimating it at a population level rather than for each WMU was more straightforward. We assumed b was influenced by local landscape productivity, accordingly we allowed it to vary across the different management units and assumed these WMU‐specific parameters arose from a normal distribution, with the parameters of this distribution representing the average effect of increasing density on growth across WMUs and the variance term representing the variance across WMUs.We considered the  Gimenez , this teE). We did, however, know how many moose were harvested from a subset of hunters that returned their questionnaires. Therefore, we estimated the remaining unknown number of bulls, cows, and calves harvested using a binomial submodel for each group:Eo was the total number of known moose harvested, k the sex and age class, n the number of tags issued minus the number of reporting hunters, s the success rate of hunters that sent back their questionnaires, and K the number of sex and age classes .Hunter survey response rates were not 100%; consequently, we did not know the true number of moose harvested from the population  or not (w = 0), N^ was the estimated number of moose counted by aerial survey, and σ^tj was the standard error during the aerial survey undertaken in WMU j during year t. We linearized these values using the delta method by log transforming the count and transformed the standard error by dividing it by the counts.We used the aerial moose counts as an approximation of the true postharvest number of moose found in a WMU that year, but when there were no aerial counts, we estimated the number of moose from the Gompertz model such thatIn general, the estimates from the moose aerial surveys served to scale the Gompertz population model to the appropriate magnitude of moose abundance, while allowing for a consistent data set across the entire time series. Also, process error was only estimated in years without moose aerial inventories, since we assumed that the aerial inventory had high detectability of moose.βj was considered a WMU‐specific vector of coefficients estimated for the matrix of covariates expected to influence the population growth rate of moose. Specifically, we included the number of canids and deer observed during the fall hunting season and an interaction between the number of canids and the number of moose. Like the density dependence term bj, we allowed these parameters to vary across the different WMUs because these factors vary across moose populations. These parameters were assumed to arise from normal distributions whose means represented the average effect of covariates in WMUs across the province. We also allowed the effect of canids to vary as a function of the density of black bears in the unit because there were no available temporal data of black bear density, making this term an overall predation term, such thatj during year t (ytj) with a Poisson model with observation covariates and an observation‐level random error term to account for overdispersion:j was the intercept for each WMU and represented the nature log of the average proportion of the true number of preharvested moose (exp(μt+1j)+Etj) observable in that year and unit, ϑj was a vector of coefficients estimated for each covariate in ztj, and ηtj was a normal observation error term added to account for possible overdispersion. We used the number of days hunted and the fall snow depth index as observation covariates. In addition, we allowed α and ϑ to vary across the different WMUs and estimated them hierarchically such that they arose from a normal distribution with the mean representing the average effect of covariates across WMUs.We then tested the influence of covariates by incorporating them as additive effects on the linearized Gompertz model, where each β, α, and ϑ. We also chose vague priors for σpro and σobs. We used an informative prior for rmax that was originally estimated by Ruprecht et al.  in Koons et al.  for each parameter was <1.1  simulations in JAGS (version 4.3.0) Plummer,  through We determined any significant changes in growth rate (λ) for each wildlife management unit by first summing the preharvest moose numbers for each management unit in 2017–2018 and 1999–2000 using the estimated posterior distributions. We then calculated the average growth rate for each management unit and used quantiles to determine whether there was strong evidence for population change by building a 95% credible interval and verifying whether it overlapped λ = 1. We also calculated the estimated average proportion of moose harvested from each management unit for each year between 1999 and 2018 using estimated harvest numbers and estimated moose population size.rmax, b, βcanids, βx Ncanids , Harvest rate, βbears, and βdeer) because most occurred on different scales, so we used elasticity to determine their relative importance , at most 7 times (4 units), but at a median rate of five times between 1999 and 2018. The hunter survey response rate was on average 74.1% and ranged between 50.8% and 80.3%, with >70% reporting in all but 2 years. We compared the per‐capita number of moose observed by hunters in the fall and moose counted during the aerial surveys in the subsequent winter using the raw numbers, and Pearson's correlation coefficient was 0.714. Using a log transformation of both, the Pearson's correlation coefficient was 0.772. During the period analyzed, we estimated that WMUs contained between 68,350 and 109,308 moose during the preharvest period, whereas the average proportion of animals harvested/year varied between 0.035 and 0.113 of the provincial population  than our informative prior . There was also significant negative density‐dependent population growth . For top‐down population‐wide parameters, population growth was negatively associated with the number of canids observed during the hunting season . We also found strong evidence of density dependence in this term , with the effect weakening with increasing numbers of moose regardless of the level of harvest  had the largest elasticity, followed by density dependence (b) and both canid terms (βcanids and βx Ncanids ). Other than rmax, the elasticities for these parameters were comparable in magnitude because their 95% Bayesian credible intervals overlapped. Density dependence (b) and the average effect of canids (μβcanids) were both negative, whereas canid density dependence was positive (μβcanids×N). The elasticity for moose harvest rate was smaller than for these other population drivers except at high harvest rates. When we calculated elasticities at an increased harvest rate, however, the elasticity for harvest was similar in magnitude to density dependence and greater than the density‐dependent effect of canid abundance. The deer (βdeer) and bear (βbears) terms induced no substantial change in the number of moose.We compared the relative influence of all bottom‐up and top‐down parameters by calculating their elasticities at three levels and three harvest regimes Table . We founbWMU), predators , deer (βdeerj), and canid density dependence (βcanids×Nj) because these parameters were also estimated at the WMU level  had significant positive density‐dependent predator terms (outlined in black), but there did not seem to be any apparent spatial pattern .Another limitation was that the information reported by hunters was reported on a voluntary basis. It is possible that hunters who did not harvest a moose were less likely to report their sightings. However, we think that the hierarchical model we used was able to reduce the impact of this bias, because predictors of moose population dynamics were estimated at the management unit level, whereas a population‐wide effect was simultaneously estimated, and we made our inference at the population level. Consequently, spatial variation in bias should have had limited impact on our results, and temporal variation within a unit should have been most important. Also, there was no reason to suspect that reporting biases within a unit would have changed substantially over time. In addition, the population dynamics in our model were matched to the aerial survey data, and these were substantially more influential on the estimated abundance in each unit. As a result, any impact of temporal variation in hunter bias  should also be diminished. Lastly, assessments of bias in Ontario hunter survey data suggested that nonreporting hunters harvest big game species at rates similar to those that report  across WMUs. As a result, our density‐dependence parameter was not strictly estimating forage competition and likely represented a mix of factors that were unmodeled, including habitat. Nevertheless, our inferences related to growth and predators are expected to be robust to potential bias in aerial survey estimates.Another limitation of this research was that imperfect detection has the potential to bias moose population estimates. This primarily occurs in aerial surveys because the probability of detecting moose monotonically declines with increasing distance and vegetation cover (Giudice et al., Lastly, we were not able to account for additional mortality sources that are known to be present but for which we had no data. Specifically, we did not have spatiotemporal measures of harvest by Indigenous peoples, mortality due to winter ticks, and poaching at the scale of our analysis.Conservation of wildlife populations requires knowledge of their general population trajectories through time in space along with insight into the factors driving these dynamics. However, this knowledge requires long‐term and spatially extensive data to account for uncertainty and to sample over the range of putative drivers. As such, long‐term data sets with consistent monitoring methods are crucial to understanding wildlife population dynamics because they provide the ability to detect changes, test general hypotheses, and monitor population recovery. Our robust design provided us with unprecedented power to understand the drivers of ungulate population dynamics and our results can be generalized to other species and geographies. This analysis provides some of the most robust evidence to date for large‐scale density‐dependent predation in ungulate–predator systems and suggests similar dynamics are likely widespread, particularly in northern ecosystems with intact predator systems. Our findings also suggest that in parts of the world where predator populations are recolonizing following broad‐scale persecution, past conservation and management strategies that aim for sustainable harvest will need to be adjusted. Furthermore, our results highlight the context dependency of drivers of ungulate population dynamics. As climate and land‐use changes alter ecosystems and trophic dynamics, this context dependency will become increasingly important, and conservation strategies for ungulates will need to be fine‐tuned locally to account for specific limiting and regulating factors.Robby R. Marrotte collected data, performed modeling work, and analyzed output data. Robby R. Marrotte, Brent R. Patterson, and Joseph M. Northrup conceived the ideas and wrote the first draft of the manuscript, and all authors contributed substantially to revisions.The authors declare no conflict of interest.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.Appendix S3Click here for additional data file.


Nicrophorus vespilloides, a species in which parents shift the cost of reproduction toward future offspring and provide elaborate parental care. We manipulated brood size to alter the costs females experienced in association with current reproduction and measured the level of parental care during a subsequent breeding attempt. Given that these beetles breed on carcasses of small vertebrates, it is important to consider confounding effects due to benefits associated with resource access during breeding. We, therefore, manipulated access to carrion and measured the level of parental care during a subsequent breeding attempt. We found that females provided the same level of care regardless of previous brood size and resource access, suggesting that neither affected future ability to provide care. This may reflect that parents feed on carrion during breeding, which may buffer against any costs of previous breeding attempts. Our results show that increased allocation to current reproduction is not necessarily associated with a reduction in future ability to provide care. Nevertheless, this may reflect unique aspects of our study system, and we encourage future work on systems where parents do not have access to a rich resource during breeding.Studies investigating the trade‐off between current and future reproduction often find that increased allocation to current reproduction is associated with a reduction in the number or quality of future offspring. In species that provide parental care, this effect on future offspring may be mediated through a reduced future ability to provide care. Here, we test this idea in the burying beetle  Nicrophorus vespilloides that invested more during a current breeding attempt were less able to provide care during a subsequent breeding attempt. Our results showed that females maintained the level of care they provided regardless of previous reproductive allocation. This may reflect that females feed on carrion during breeding, which may buffer against costs of previous breeding attempts.We investigated whether females of the burying beetle  We chose these brood sizes to represent a low and high level of reproductive allocation, respectively, since they are approximately half and double the average brood size for N. vespilloides . It is not possible experimentally to separate the benefit of feeding from the carcass from the costs of egg laying and pre‐hatching care. However, the cost of egg laying and providing pre‐hatching care is known to be minimal  to initiate mating. After 48 h, when the eggs had been laid but before the larvae had begun hatching, we moved the female and her carcass into a new container lined with fresh moist soil. At this point, we discarded the male since males sometimes assist with parental care, in which case the male might absorb some of the cost of reproduction if present. In addition, male involvement in providing care is highly variable  lined with 1 cm of moist soil containing a freshly thawed mouse carcass (Livefoods Direct Ltd) of a standardized size . Being provided with a carcass initiated pre‐hatching parental care (preparing the carcass for larvae) and the laying of unfertilized eggs. Non‐breeding females without access to a carcass were fed raw organic beef twice a week (approximately 0.3 g) to ensure they did not starve. Unlike the presence of a carcass, the presence of beef did not initiate egg laying or pre‐hatching care. We handled non‐breeding females an equal number of times as we handled the breeding females described above. Non‐breeding females that were provided access to a carcass had access to it for the same period as the breeding females. We then weighed the females at the end of stage one before they were transferred into individual clear plastic containers (12 cm × 8 cm × 2 cm) lined with moist soil and left undisturbed for 24 hours before proceeding to stage two.Non‐breeding females were also weighed before being transferred into clear plastic containers (17 cm × 12 cm × 6 cm) lined with 1 cm of moist soil. Non‐breeding females provided with access to a carcass were given a freshly thawed mouse carcass (Livefoods Direct Ltd) of a standardized size . We allowed pairs to mate for 48 h after which the male was removed. We removed the male to control for potential confounding effects due to variation in the contribution of males toward caring for offspring as described above for stage one of the experiment. At this point, we recorded the number of eggs each female laid by counting the number of eggs visible through the bottom of the clear container. We chose this method to avoid handling and thereby potentially damaging the eggs. Furthermore, it has been shown that the visible number of eggs is strongly correlated with the actual clutch size when the container is lined with a thin layer of soil as they were here  lined with 1 cm of moist soil containing a freshly thawed mouse carcass (Livefoods Direct Ltd) of a standardized size  lined with moist soil, maintained them using the same protocol applied to the stock population, and checked them twice a week until death. We did this to record their lifespan as the number of days from eclosion to the day the female was found dead.2.3All statistical analyses were conducted using R version 3.6.1  but there was no difference in larval survival between small and large broods .The average mass per larvae at dispersal was higher in small broods than in large broods .There was no evidence that the cost of increased allocation or the benefit of carcass access during stage one affected the number of eggs females laid during stage two since there was no difference in the number of eggs laid by females from the different treatment groups . We are confident that our treatment had the intended effect since we found that breeding females that had cared for a large brood gained less mass than non‐breeding females that had access to a carcass during stage one, while breeding females that had cared for a small brood gained an intermediate amount of mass. Our design also accounted for potential confounding effects due to the benefit associated with access to carrion by manipulating carcass access during stage one. This benefit was evident since non‐breeding females with access to a carcass gained more mass than non‐breeding females without access to a carcass. Finally, we measured effects on parental food provisioning to larvae and maintenance of the carcass, which are the most predominant parental care behaviors in this species and that are known to impact larval survival and growth  gained more mass when provided with a second carcass than females that were not previously provided with a carcass. This finding does not fit our predictions for the effects of the cost of increased allocation or the benefit of carcass access during stage one. Instead, this result suggests that females that previously had access to a carcass responded by shifting toward greater allocation on future reproduction since mass gain during breeding is a proxy for allocation to future reproduction in Second, breeding females that were allocated a small brood in stage one produced broods with a lower average larval mass in stage two than non‐breeding females that did not have access to a carcass in stage one. This finding appears to contrast with previous studies, which found that increased allocation to current reproduction is associated with a reduction in the size and mass of future broods ; formal analysis (supporting); methodology (supporting); supervision (lead); writing – review and editing . Georgia A. Lambert: Conceptualization (lead); data curation (lead); formal analysis (lead); methodology (lead); writing – original draft (lead); writing – review and editing .None.https://doi.org/10.5061/dryad.gtht76hqc.This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at


Dear Editor,(MMS) is a chronic and progressive occlusive cerebrovascular disease characterized by steno‐occlusive changes at or around terminal carotid arteries or the initial segments of the anterior/middle cerebral arteries accompanied by the development of fragile collateral network, which is often secondary to an underlying acquired or inherited condition. Paroxysmal nocturnal hemoglobinuria (PNH) is an acquired hematopoietic stem cell genetic mutation disease manifesting as hemolytic anemia, marrow failure, smooth muscle dystonia, and thrombosis.Moyamoya syndrome A 42‐year‐old woman was admitted to Henan Provincial People's Hospital with a 20‐day history of clumsy movement of her right hand and slurred speech for 1 day. Initially diffusion weighted imaging (DWI) of the brain showed acute ischemic stroke in left frontoparietal lobe when she showed clumsy movement of her right hand 20 days earlier Figure . In the However, we knew from a telephone follow‐up that she died suddenly at home 2 months later.We provide a vw‐MRI image that directly supports vasculitic moyamoya changes of intracranial arteries in the situation of PNH for the first time, as far as we know. In our case report, the patient was diagnosed with PNH 3 years before her cerebral artery thrombosis, and hemolytic attacks occurred from time to time. Except PNH, the young patient has no family history of cardiovascular diseases or cerebrovascular risk factors. The first cerebral ischemic attack and subsequent stroke progression were both accompanied with hemolytic events (a reduction of hemoglobin as well as an increase of LDH and indirect bilirubin), which suggest a possible causal relationship between PNH and cerebral infarction. Next, vw‐MRI showed homogeneous, concentric arterial wall thickening and enhancement in terminal portions of left ICA and proximal segment of MCA, which indicated vasculitic moyamoya vessels in the patient.Paroxysmal nocturnal hemoglobinuria is a rare acquired bone‐marrow disease featured by deficiency of glycosylphosphatidylinositol (GPI)‐anchored complement regulatory proteins such as CD55 and CD59 on membrane of erythrocytes, leading to an increased sensitivity of red blood cells to membrane attack complex. It characteristically presents as hemolytic anemia, hemoglobinuria and intravascular thrombosis. Arterial thrombosis is an infrequent complication of PNH, accounting for approximately 15% of total thromboembolism events, which located primarily in cerebral vasculature (90.7%).For the treatment of PNH associated vasculitic moyamoya syndrome, complement inhibitor such as Eculizumab to cope with PNH and reduce PNH relevant complications is the first choice,The authors declare no conflict of interest.Written informed consent was obtained from the patient for publication of this case report and any accompanying images. A copy of the written consent is available for review by the editor of this journal. All authors agreed to publication.


Rapid climate change is threatening biodiversity via habitat loss, range shifts, increases in invasive species, novel species interactions, and other unforeseen changes. Coastal and estuarine species are especially vulnerable to the impacts of climate change due to sea level rise and may be severely impacted in the next several decades. Species distribution modeling can project the potential future distributions of species under scenarios of climate change using bioclimatic data and georeferenced occurrence data. However, models projecting suitable habitat into the future are impossible to ground truth. One solution is to develop species distribution models for the present and project them to periods in the recent past where distributions are known to test model performance before making projections into the future. Here, we develop models using abiotic environmental variables to quantify the current suitable habitat available to eight Neotropical coastal species: four mangrove species and four salt marsh species. Using a novel model validation approach that leverages newly available monthly climatic data from 1960 to 2018, we project these niche models into two time periods in the recent past  when either mangrove or salt marsh dominance was documented via other data sources. Models were hindcast‐validated and then used to project the suitable habitat of all species at four time periods in the future under a model of climate change. For all future time periods, the projected suitable habitat of mangrove species decreased, and suitable habitat declined more severely in salt marsh species. Species distribution modeling can project the potential future distributions of species under scenarios of climate change using bioclimatic data and georeferenced occurrence data. However, models projecting suitable habitat into the future are impossible to ground truth. Here, we overcome this limitation by developing species distribution models for the present and projecting them to periods in the recent past where distributions are known to test model performance before making projections into the future. Next, using models of past  or future  is a powerful tool for projecting where suitable habitat may exist in the future by using layers of environmental data  and species occurrence data . SDM has been used to predict species range shifts, invasions, and novel species interactions in response to climate change  and on a large scale . In the present study, we leverage known periods of dominance  and GBIF  and supplemented these data with locality data from personal collections for three mangrove species . Four of the species included in the analysis are mangroves  or mangrove‐associated species . For simplicity, these four species will hereafter be collectively referred to as “mangroves,” even though Conocarpus erecuts is not considered a true mangrove  for analyses. These four species were selected because they occur in close proximity to one another—indicating the presence of salt marsh habitat—and because of their broad and exclusively coastal distributions in the Neotropics. We used SDM to investigate changes in suitable habitat for all eight species. The raw data were cleaned using standard approaches and R scripts , and all cleaned occurrence data, layers, and models were deposited in Dryad (https://doi.org/10.5061/dryad.08kprr55b). We included species that had exclusively coastal or estuarine distributions, and only species with at least 50 occurrence points (after cleaning) were used in the analyses. Given the complexities of the modeling approach, we focused on the Neotropics as opposed to a global analysis; only mangrove and salt marsh species with native ranges in the Americas were used . Certain species that inhabit salt marshes, but that have extensive inland distributions, including freshwater wetlands, were excluded .We obtained specimen‐based occurrence data for each species from iDigBio , only one of the layers was retained for subsequent analyses , and BIO1  was excluded even though it is frequently included in SDM analyses because BIO6 has been identified as an important variable shaping range limits of coastal species , which measures the ratio of the area under the receiver operating characteristic curve (AUC). AUC ranges from 0 to 1 and measures the model's ability to predict suitable habitat, with 1 indicating perfect discrimination between suitable and unsuitable habitat. The pROC is the ratio of the partial AUC divided by random expectation, and it can range from 0 to 2, with 1 representing random model performance , and the shared socioeconomic pathway was selected because it represents a central part of the range of plausible future pathways. SDMs for the full geographic study region were projected into both the past and future time periods. Future time periods were determined by availability of WorldClim 2.1 data.Once the SDMs were hindcast‐validated, the same approach was used to infer projected change in suitable habitat for the three time periods in the future. To project future values of environmental variables, we used a widely used and well‐validated climate model—the CNRM‐CM6‐1 model, which is a fully coupled atmosphere–ocean general circulation model developed by Centre National de Recherches Météorologiques (CNRM) for the sixth generation of the IPCC Coupled Model Intercomparison Project 6 (CMIP6), and with the shared socioeconomic pathway 245  as one of the two most important variables  Table . For alles Table . Model pes Table . The pROes Table .3.2Sesuvium portulacastrum and Sporobolus virginicus). In the larger Florida validation region, all results had the same qualitative trends as in the NE Florida hindcast validation region  versus the period of salt marsh dominance (1989–1984). Although there are species‐specific differences when hindcasting, nevertheless the averaged results by species type  all indicated proper model validation . When species have ample georeferenced data from different time periods between 1960 and 2018 and/or there are external data sources that can confirm species presence at specific times in the past, hindcast‐validated SDMs are a powerful tool for verifying models that will be used to project future suitable habitat. Researchers can follow our approach of constructing models for the present and hindcasting them, or if there are sufficient temporally diverse occurrence data, SDMs could be constructed for time periods in the past  and validated using time periods closer to the present  before being used to project future suitable habitat. Our methods will be easier to apply in certain species; the large availability of coastal photographs from many time periods and the relatively low species richness of mangrove and salt marsh communities compared with other communities facilitated our analyses. However, future studies on different species could leverage other data sources, such as flora and fauna checklists, to confirm species presence at certain time periods.While a strength of our approach is the magnitude of the geographic range investigated, this also adds associated limitations. We validated the models using data from previous decades in the NE Florida ecotone, but projected future suitable habitat on a geographic extent larger than the validation area. Ideally, there would be external data sources that could be used for hindcast validation from other geographic regions, but to our knowledge there are no such data sources on the same timescale as the northeastern Florida ecotone data. The future projection results from our study align well with predictions in a small region of the Northern Hemisphere . Therefore, we followed a standard assumption in SDM analyses that the present is any time after 1950  into a model. Especially at large scales, biotic interactions can have a significant effect on the predictive and explanatory power of models based on bioclimatic data  amount of coastal land; any range increases will come at the expense of other coastal plant species. Although SLR could drastically affect coastal and estuarine regions by creating new suitable habitat for mangrove and/or salt marsh species, it will be challenging for plant species to occupy newly available habitat as the climate changes rapidly ; data curation (lead); funding acquisition (supporting); methodology (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead). Douglas E. Soltis: Funding acquisition (supporting); methodology (supporting); supervision ; validation ; writing – review and editing (supporting). Pamela S. Soltis: Funding acquisition (lead); methodology (supporting); supervision ; validation ; writing – review and editing (supporting).The authors declare no conflict of interest.Appendix S1Click here for additional data file.


A process of knowledge erosion, that in this instance contributes to our separation from the natural world, makes us blind to the biodiversity crisis and inhibits our ability to restore it. We argue that neglecting the importance of plants within education threatens the foundations of industries and professions that rely on this knowledge. Furthermore, this extinction of botanical education creates an existential threat: Without the skills to fully comprehend the scale of and solutions to human‐induced global change, how do we as a society combat it? We present key research agendas that will enable us to reverse the extinction of botanical education and highlight the critical role plants play on the global stage.Civilization is dependent upon plants for survival. Plants permeate our every moment and our relationship with them will dictate how we will manage the threats of climate change and ecological collapse defining the Anthropocene. Yet, despite the significance of plants and the critical role they have played in shaping ecosystems, civilizations, and human cultures, many people are now disconnected from the botanical world. Students are presented with little plant content, particularly identification, compared with animal content. Consequently, we are producing few plant scientists and educating fewer scientists about plants. This drives a self‐accelerating cycle we term  Civilization is dependent upon plants for survival and our relationship with them will dictate how we will manage the global impact of humanity which defines the Anthropocene. We document and define a self‐perpetuating educational cycle that we term the extinction of botanical education and its impact on the science of botany and potential ramifications for society to reverse and stabilise human included global change.  For every 185 general students of the biological disciplines educated, only a single student of the botanical sciences is produced in the United Kingdom ,Key or central plant focus,Modules with some ID component , orModules with clear plant ID component .We additionally pulled out those modules which focused on animal or zoological content for comparison , Advanced Topics in Evolution, and Advanced Topics in Conservation Science. Six with dedicated animal content such as Animal Behavior, Organismal Evolution, and Parasitology. Eight of them had a key or central plant focus such as Applied Biology and Agriculture, How Plants Work, Sustainable Food Production, and Plant Development. One module with some ID component is the Mediterranean Ecology Field Course. And only two modules with a clear plant ID component, Exploring Whole Organism Biology in the Lab and Field, and Urban Ecology and Conservation Field Course. We excluded Cambridge and Oxford due to their unique degree pathways which did not fit into our analysis framework. There is a degree of uncertainty to this method as the delivery of teaching content may likely change periodically or may not be explicitly stated in module titles or descriptions. We recognize that this method does not take account of teaching on degrees outside of biology programmes and some plant content for modules may not have been included within module descriptions .For example, the University of Leeds offers a BSc (Hons) Biology degree which has a total of 65 modules. Ten of these modules focus on ecology and affiliated life sciences with an implied focus on plants or strong ecological contexts such as n = 63) had a clear plant biology emphasis and only 1% (n = 10) had a significant emphasis on plant identification  amounting to damages approximate to US$1.4 billion ; data curation (lead); investigation (lead); project administration (lead); writing – original draft (lead); writing – review and editing . Mark Fennell: Conceptualization ; writing – original draft (supporting); writing – review and editing . Jonathan Mitchley: Conceptualization ; writing – original draft (supporting); writing – review and editing . Susannah Lydon: Conceptualization ; writing – original draft (supporting); writing – review and editing . Julie Peacock: Conceptualization ; supervision ; writing – original draft (supporting); writing – review and editing . Karen Bacon: Conceptualization ; project administration (supporting); supervision ; writing – original draft ; writing – review and editing .The authors state no conflict of interests.Appendix S1 Supporting informationClick here for additional data file.


This paper merges new research topics in Industry 5.0 using the Business Process Modeling and Notation (BPMN) approach able to integrate Artificial Intelligence (AI) in production processes. The goal is to provide an innovative approach to model production management in industry, adopting a new “proof of concept” of advanced Process Mining (PM) automatizing decisions and optimizing machine setting and maintenance interventions. Advanced electronic sensing and actuation systems, integrating supervised and unsupervised AI algorithms, are embedded in the PM model as theoretical process workflows suggested by a Decision Support System (DSS) engine enabling an intelligent decision-making procedure. The paper discusses, as examples, two theoretical models applied to specific industry sectors, such as food processing and energy production. The proposed work provides important elements of engineering management related to the digitalization of production process matching with automated control systems setting production parameters, thus enabling the self-adapting of product quality supervision and production efficiency in modern industrial systems.  We provide a new theoretical model of PM integrating a DSS based on AI-supervised and unsupervised algorithms;We discuss an application field applying PM in food roasting process ;We discuss an application field applying PM in energy production ;We analyze different aspects concerning electronic/mechatronic implementations and procedures;We discuss PM and AI aspects as regards impacts in the supply chains and in organizational processes, thus providing an overview of different possible implementations of the proposed PM model.The model of process workflows is fundamental for process mapping in industry. The possibility of mapping “AS IS” and “TO BE” processes allows one to optimize production, product quality and organization management. In the new Industry 5.0 scenario, process mapping can be improved by applying Artificial Intelligence (AI) decision making algorithms mainly behaving as workflow checkpoint-defining processes: AI is able to process production data by suggesting optimized sub-processes and by defining risks related to production and product quality. The process workflow merging AI algorithms in decisional logics is named Process Mining (PM) ,2,3. PM The topics discussed in the paper’s sections are addressed in the context of an Industry 5.0 scenario. In The block diagram of DSS Main Process. This pool represents the main process of the PM model, and integrates the DSS enabling supervised and unsupervised AI algorithms. The choice of the algorithm is based on the available dataset typology. The decision to select AI data processing is established by means of the “Exclusive Event based” gateway: in the case of a positive check of the monitored variables the production process continues; besides this, in the case of negative check, the AI algorithm will be able to optimize the machine parameter setting or to decide the intervention to perform. According to the alerting level detected by the AI algorithms, a standard parameter setting  or a non-standard parameter setting  will be activated. Corrective actions include predictive maintenance, possible slowdowns in production, major control by enforcing human resource operation, etc.; AI Engine (Supervised Model). This pool concerns the application of the supervised AI algorithm in processing data by means of the training and testing phases. The outputs are predicted or classified data defining alerting risk maps . This pool represents the data clustering process defining the risk maps based on the alerting levels [g levels . The outThe BPMN approach is adopted to “explode” in The BPMN model of Algorithm 1: PM pseudocodeStartproduction process;Initial setting of production machine;IfThen continue production (and parameter checking) until production ends;check of machine is positive Else(negative check) choose the suitable idoneous algorithm;If the unsupervised algorithm is ‘True’ Then perform data clustering and   structure risk maps;   If a moderate alert is estimated then set the standard machine       parameter;       Else  introduce further corrective actions;      End If;      Else  selection of the supervised   algorithm providing data prediction/classification through training and    testing models;   If a moderate alert is estimated then set the standard machine        parameters;        Else  introduce further corrective actions;        End If;        End If;   End If.The UML-AD model and the BPMN provide information about the logic to implement in the monitoring process. The logic explaining the data flow indicated in The roasting process of a food product passing into five ovens;The energy generation process of a Combined Cycle Power Plant (CCPP).The DSS of For the first application field, data from  are refeFor the second application, we consider another open dataset  with 956An example of power energy trend is illustrated in The method applied in this work is used to show how it is possible to use AI results following the PM model to optimize production and quality. In this direction, The PM model is applied to two processes related to food roasting and energy production. For each case, we apply the theoretical scheme of The first example of the application of PM is described in The k-Means algorithm is able to provide a risk map by which it is possible to distinguish different regions. By considering for example of three clusters (k = 3), the region named cluster 2 of The second example of the BPMN PM model is illustrated in By observing the historical data, the alerting status is identified when the energy is under a threshold value during a sufficient observation period  transfer function forwarding gain, while H(s) is the feedback transfer function, and E(s) is the error signal equal to the difference between the input signal X(s) and the feedback signal H(s)Y(s).A possible evolution of the PM model is its implementation in automated production systems. Specifically, AI tools can be interfaced, by means of Human–Machine Interfaces (HMIs), with production machines, providing an automated actuation control driven by the AI alerting output (through a translation via Programmable Logic Controller (PLC) protocols of the output into an executable command driving machine). In Y(s) is provided by the equation:The output signal The error signal may be a gap measured between the desired value of the monitored parameter and the real measured one. The whole feedback system of tool speed regulation integrating AI intelligent control ;tool speed regulation integrating AI controlling an electrical current traveling in series resistors;tool speed regulation integrating AI controlling values of series of resistors;tool speed regulation integrating AI controlling the value of a single resistance;collaborative exoskeletons with auto-adaptive solutions controlling motion trajectory and torque;AI controller for current-source inverter circuits (control of the switching conditions) as for three-phase Current-Source Inverters (CSIs);intelligent converter control;wave rectifier control;control of voltage-source inverters;control of current-source inverters;robotic PID controller based on AI learning;AI controller adjusting tool trajectory;AI-based image processing selecting inline objects ;soft robotics for intelligent collaborative robotics reading disturbance measurements;additive manufacturing control ;AI image vision circuits implementing AND logic ports.AI circuits based on feedback control are suitable for different industry 5.0 applications and functions, such as :tool speThe discussed examples will help readers to understand how the PM model can be applied in different production scenarios: as the theoretical model is flexible, it is applicable in different cases also involving substantial changes in production approaches and in organizational models. The AI analysis affects the organizational model of industries, because it activates a series of corrective interventions depending on the detected machine parameters. In this scenario, the application of CM models ,29 is esIn In The proposed BPMN-PM approach is mainly characterized by its limited ability to automatically define the alerting thresholds for specific cases; this requires an initial accurate analysis of historical data with the continuous checking of product quality to choose the best set of machine parameters. The challenge of future works is to implement a totally automatic system able to auto-calibrate the thresholds as a function of the quality parameters using the feedback control of The proposed work provides a new concept of process mapping based on AI integration into automated decision-making processes. In particular, we discuss a new concept of the PM model activating AI unsupervised and supervised algorithms and improving production processes. In order to facilitate the compression of the proposed model, we have used the BPMN approach. Our paper shows two production cases to show how it is possible to apply the theoretical model to a specific case of production. The discussed PM approach highlights other correlated aspects, including the organizational impacts, the formulation of risk maps based on the prediction of production parameters, the self-adapting processes setting machine parameters, and the possible integrations of PM with HMI. The PM model represents the first step for the implementation of advanced Industry 5.0 processes in production systems. The PM approach can be adopted to design all the advanced electronic and mechatronic systems characterized by checkpoint logics and automated parameter setting.


Antisense oligonucleotides (ASOs) represent a new and highly promising class of drugs for personalized medicine. In the last decade, major chemical developments and improvements of the backbone structure of ASOs have transformed them into true approved and commercialized drugs. ASOs target both DNA and RNA, including pre‐mRNA, mRNA, and ncRDA, based on sequence complementary. They are designed to be specific for each identified molecular and genetic alteration to restore a normal, physiological situation. Thus, the characterization of the underpinning mechanisms and alterations that sustain pathology is critical for accurate ASO‐design. ASOs can be used to cure both rare and common diseases, such as orphan genetic alterations and cancer. Through pioneering examples, this review shows the versatility of the mechanisms of action that provide ASOs with the potential capacity to achieve custom treatment, revolutionizing personalized medicine.RNA in Disease and Development > RNA in DiseaseRNA Interactions with Proteins and Other Molecules > Small Molecule–RNA InteractionsThis article is categorized under: ASO functioning towards personalized medicine. Personalized and precision medicine is now becoming essential, alongside more general and sometimes only symptomatic treatment. Personalized medicine is adapted to the genetic, molecular, and phenotypic characteristics of the disease and the patient, by targeting specifically causative components. Importantly, one of the major challenges in the current management of many diseases is predicting the responses to treatment as a function of patient‐intrinsic characteristics. There is, therefore, a genuine revolution underway in patient care.BCR‐ABL). The resulting BCR‐ABL fusion protein has oncogenic properties due to its constitutive tyrosine kinase activity  fusion genes recently led to the development of specific inhibitors  for the management of NTRK‐positive cancers in adults and children  was driven by the understanding of the role of ER in breast cancer and the identification of mechanisms for its blockade. Tamoxifen is now a standard treatment for ER‐positive breast cancers. It acts by competitively inhibiting the binding of estradiol to its receptors Jordan, . MonocloIn some cases, very little or no protein is produced. This is true for insulin, an enzyme that is deficient in patients with insulin‐dependent diabetes (type I). Patients are treated by insulin therapy to faithfully reproduce the effects of the physiological secretion of insulin by administering a substitute protein. The release of the first human insulin protein onto the market in 1982 launched a new paradigm: it became possible to modify the sequence of the hormone protein to match the pharmacokinetic properties to the physiological needs of the patient  have been developed. As for proteins, the first therapeutic attempts were based on global alterations to DNA, through the use of alkylating agents, for example. These agents induce the creation of non‐specific covalent bonds, resulting in the production of DNA‐adducts. They disrupt replication and transcription, explaining their use in cancer therapeutics  in the United States of America and European Medicines Agency (EMA) in Europe). This is the result of many years of basic research and clinical trials. Throughout pioneering examples, this review aims to underscore the versatility of ASOs for the treatment of various diseases through the targeted and personalized RNA‐based approach they allow.1.1ASOs are short (about 12 to 25 nucleotides long) single‐stranded nucleic acid (DNA or RNA) sequences. In their naked form, they cannot permeate the plasma membrane and are highly sensitive to degradation by endonucleases and exonucleases. ASOs have been chemically modified to overcome these problems. On the basis of these modifications, ASOs can be broadly classified into three generations , methyl‐phosphonates, and phosphoramidates, respectively Figure . These mO‐methyl (2′‐OME) and 2′‐O‐methoxyethyl (2′‐MOE) nucleotides  have created new opportunities in chemical therapeutics. Tricyclo‐DNA (tcDNA) belongs to this class of conformationally constrained DNA analogs with enhanced binding properties. They do not elicit RNAse‐H activity but show increased stability and improved cellular uptake, giving them substantial therapeutic advantages over those of ASOs  and the target sequence. The choice of ASO is, therefore, guided by the required molecular effect , by sequence complementarity. This process was first described in plants  approved by the FDA in 1998 and the EMA in 1999 Figure . Fomivir1.2.2β‐thalassemia. They showed that 2′‐O‐methylribooligonucleotides targeting specific sequence elements in mutated human β‐globin pre‐mRNA could eliminate the aberrant splicing pattern of the β‐globin pre‐mRNA  gene. These genetic alterations lead to the absence of SMN protein production. Interestingly, SMN proteins are encoded by two paralogous genes: SMN1 and SMN2. The SMN1 and SMN2 genes are identical (99% homology), except for an 11‐nucleotide sequence in exon 7. This difference alters splicing. The splicing of the SMN2 pre‐mRNA leads to the exclusion of exon 7, generating a truncated, unstable protein that is rapidly degraded. SMN2 cannot, therefore, compensate for the loss of SMN1 expression in SMA. Nusinersen, discovered by Ionis Pharmaceuticals and co‐developed by Biogen (Spinraza®), was designed to correct SMN2 splicing, thus allowing SMN2 to compensate for the loss of expression caused by the SMN1 mutation. This 2′‐O‐methoxyethyl (2′‐OME) phosphorothioate (PS) 18‐mer is a second‐generation ASO, which cannot recruit RNAse H. It targets the intronic splicing silencer (ISS‐N1), promoting the inclusion of exon 7, thereby restoring the production of a stable SMN2 protein  non‐coding RNAs that specifically hybridize to mRNAs to fine‐tune their expression. miRNAs bind to their cognate target RNA through RNA–RNA base‐pairing. The miRNA binding sequence is referred to as a miRNA recognition element (MRE). MREs are complementary to miRNA seed regions Bartel, . PerfectThe overexpression of miRNA in certain diseases, including cancers, can lead to massive pathological degradation of their targets. ASOs binding to specific MREs could thus be used to block such mRNA degradation. In this case, ASOs are specific for mRNA and not miRNA. No therapeutic molecules based on this mode of action are currently available. However, the group of Prevot has demonstrated the efficacy of such an approach  gene , is a clinically heterogeneous neuromuscular disease. DM1 is a triplet‐repeat disease originating from progressive expansion of CTG repeats in the 3′ UTR of the DMPK mRNA with an ASO displaying RNAse H activity (MOE gapmers), the sequestered MBNL1 protein is released and accurate splicing is restored  competes with MBNL binding to the CUG‐repeats. This ASO has been shown to be effective in triggering the release of MBNL and restoring splicing function in mice  in exon 23 of the dystrophin gene, resulting in a premature translation termination codon. They obtained comparable results in a hemophilia mouse model, in which Upf3b‐ASO treatment restored the production of functional factor IX protein  is one of such quality‐control mechanisms for mRNAs. It eliminates mRNAs that include a premature stop codon. This allows the removal of abnormal proteins that are non‐functional or with impaired function, and prevents the production of proteins with dominant‐negative activity . NGD is triggered by obstacles that impede ribosome movement along the mRNA. These altered mRNA molecules are targeted for degradation, initiated by endonucleolytic cleavage in the vicinity of the stalled ribosome  that specifically hybridize to the start codon (uAUG) of such uORFs. By specifically blocking translation of the uORF, the ASO redirects the translation machinery to the pORF. The efficacy and specificity of this approach has been evaluated with several human and mouse model genes (including the human RNAseH1 and sideroflexin 3 [SFXN3] genes). This resulted in a significant increase in the level of the corresponding proteins  to specifically increase the synthesis of the main protein translated from a downstream primary ORF (pORF). They generated ASOs  and in vivo (mouse liver)   by targeting its regulator PSCK9  is one of the first miRNA therapies to be developed and undergo clinical testing. It targets miR‐122 for the treatment of hepatitis C virus infections. miR‐122 is a key player in liver development. This abundant miRNA is produced in a specific manner. It also plays an important role in the replication cycle of the hepatitis C virus (HCV), helping to stabilize the viral genome. Patients infected with HCV have a long‐term risk of developing liver cancer . Like miravirsen, RG101 is an anti‐miR‐122 agent. It differs from miravirsen in its chemical structure. It is an ASO phosphorothioate coupled to an N‐acetylgalactosamine group , to enhance uptake by hepatocytes via the asialoglycoprotein receptor. Despite these significant improvements and the therapeutic effects observed  complementary to the coding region of the ApoB100 mRNA leads to its degradation by RNAse H. Mipomersen use over several weeks induces a progressive decrease of LDL‐cholesterol levels  is an inhibitor of the synthesis of ApoB100   complementary to the non‐coding UBEA3‐ATS RNA caused its degradation after the recruitment of RNAse H. As the paternal copy of UBE3A is no longer repressed, the paternal UBE3A protein is produced, thereby restoring UBE3A function , which, like coding RNAs, may cause disease. Angelman syndrome is a genetic neurological disorder characterized by severe motor and intellectual disability , resulting in wide systemic distribution and possible uptake by target tissues and cells. Unfortunately, these interactions also contribute to the toxicity of ASOs. Chemical modifications designed to minimize these effects and increase the therapeutic index are currently being studied  administration should be favored for systemic effects with the most recent generation of ASOs. This administration route maximizes the bioavailability of ASOs and leads to rapid distribution to highly irrigated organs . By contrast, distribution is weak in the lungs, cardiac muscle, and skin, and negligible in adipose tissue. Nonetheless, iv administration is considered to be the optimal route for the treatment of Duchenne muscular dystrophy   gene. Approximately 100 mutations of this gene have been described, associated with varying degrees of disease severity due to amyloid deposits in tissues and nerves. Such deposits lead to the progressive onset of cardiomyopathy, nephropathy, polyneuropathy, and gastrointestinal disorders  route is an interesting alternative to the iv route, increasing the ease of administration, when possible. Inotersen (Tegsedi®), marketed by Ionis Pharmaceuticals and Akcea Therapeutics, has recently been licensed for sc administration in the United States, Canada, and Europe Figure  Keam, 2. This drhuntingtin gene. In the presence of abnormally expanded CAG repeats, toxic accumulation of the protein occurs  to the eye . This is true for eluforsen in cystic fibrosis patients homozygous for the F508del‐CFTR mutation (cystic fibrosis transmembrane conductance regulator). This molecule restores the activity of the CFTR channel and, therefore, the respiratory function of treated patients   moiety to the ASO. The GalNAc receptor ligand specifically binds to the Asialoglycoprotein Receptor (ASGR), which is highly expressed at the surface of hepatocytes, substantially increasing the targeting of GalNac‐ASO to the liver and its uptake  makes ASOs up to 30 times more potent non‐conjugated ASOs, supporting lower doses and increased safety  that targets lipoprotein(a) mRNA (LPA), showed a significant dose‐dependent reduction in lipoprotein(a) levels in patients with cardiovascular disease . This is a major breakthrough, as APO(a)‐LRX is the first and currently only drug in clinical development designed to selectively and robustly inhibit the production of Lp(a)  and very recently for neuronal ceroid lipofuscinosis 7 (CLN7).DMD is a rare genetic disorder linked to the X chromosome. It affects between 1 in 3,500 and 1 in 6,000 newborn boys worldwide each year  and PRO044  target exon 45 . This strategy is, however, limited by the intrinsic packaging capacity of these viruses, which can hold, at most, only one third of the dystrophin sequence. Recent studies have been conducted with lentiviruses  and an SVA (SINE‐VNTR‐ALU) retrotransposon insertion in the second allele. The SVA insertion affects splicing of the nearby gene, inducing mis‐splicing of exon 6 of CLN7. 22‐nucleotide ASOs  were designed and tested in patient fibroblasts to restore the altered splicing. The most effective were retained for further development. This patient‐customized ASO was named Milasen. The urgency of the patient's clinical state sped up toxicological evaluation. After obtaining FDA authorization and institutional review board approval, the investigators administered the compound intrathecally to the patient in ascending doses, as for nusinersen, which had been safely administrated to infants with SMA , a form of Batten's disease  in BRAF‐V600 mutated metastatic melanoma  was designed to reduce the production of STAT3 . STAT3 plays a key role in transducing signals that promote cell growth and survival, and is thus a potential drug target for several types of cancer  , a phosphorothioate ASO, targets the transforming growth factor beta (TGF‐β) pathway. Trabedersen specifically targets TGF‐β2, which plays a key role in the late stages of tumor development . Several phase I/II clinical trials have evaluated the utility of this ASO to treat high‐grade gliomas. Although unsuccessful, these trials made it possible to evaluate the safety of this ASO and its administration. Promising results have also been obtained for the treatment of advanced pancreatic cancer , a second‐generation ASO, targets the clusterin mRNA. Clusterin is a stress‐dependent chaperone protein. Its anti‐apoptotic activity promotes the development of resistance to anticancer treatments. Clusterin inhibition therefore potentiates the therapeutic activity of conventional chemotherapies , a second‐generation ASO, targets another chaperone protein, heat‐shock protein 27 (Hsp27). Hsp27 levels are very high in some cancers and this is associated with a poor prognosis. The inhibition of Hsp27 decreases tumor growth and sensitizes the tumor to cytotoxic chemotherapies. The preliminary results of a phase II trial assessing docetaxel alone or in combination with apatorsen for the treatment of metastatic urothelial carcinoma showed a slight increase in overall survival in the combination group. These results are promising and require confirmation . Decreasing MALAT‐1 levels with an ASO significantly inhibits tumor growth and metastasis in lung and breast cancers . In vivo, the potential therapeutic value of these gapmers has been demonstrated in PDX models (patient‐derived xenografts)  Figure .tyrosinase‐related protein 1 (TYRP1) mRNA. TYRP1 is specifically expressed in the melanocyte lineage and is highly expressed in melanoma. We identified three non‐canonical MREs for miR‐16 in the 3′ UTR of TYRP1. These non‐canonical MRE‐16s provide a non‐coding function to TYRP‐1 mRNA, which acts as a miRNA‐sponge. Indeed, such non‐canonical binding results in the sequestration of miR‐16, with delayed or no TYRP1 mRNA degradation. As miR‐16 has tumor suppressor activity in cutaneous melanoma, its sequestration promotes tumor growth. In this context, we designed ASOs to prevent such sequestration. They consist of target‐site blockers (TSB): a third‐generation ASO, consisting of a mixmer of DNA and LNA, with no RNAse H activity. These TSBs were designed to be specific for TYRP1 mRNA, hybridizing to TYRP1 in place of miR‐16. As they are perfectly complementary to the TYRP1 mRNA along their entire length, their affinity for TYRP1 mRNA is greater than that of miR‐16. TSB displaces miR‐16, allowing the redirection of miR‐16 to other targets. The tumor suppressor activity of miR‐16 is thus restored. These TSBs were shown to be effective in vivo in a PDX model. This specific and ground‐breaking treatment strategy can restore the activity of miR‐16 exclusively in cells expressing TYRP1. This innovation circumvents the need for a synthetic microRNA, which through its systemic effects, would have consequences throughout the body  and bioavailability and, importantly, their sequence can be adapted to genetic alterations. The fact that ASO can be customized in a sequence‐specific fashion to match patient disorders was recently strongly underscored with the splice‐switch ASO Milasen (J. Kim, Jo, et al., Important chemical developments have been made to diminish the toxicity of ASOs, and the most representative molecules of these chemical classes are well tolerated. However, substantial toxicity may still be observed when they are used chronically. This may be an important drawback that should be lessened by the development of conjugated‐ASOs that promote cell‐specific uptake.ASOs may be used in monotherapy or in combination with conventional approaches. They are therefore both an alternative for diseases with no effective available treatments and an option for use in patients with refractory diseases, as second‐ or third‐line treatments.In conclusion, the phrases “Targeting the Untargetable” and “Treating the Untreatable” summarize the potential of ASO strategies, thus revolutionizing personalized medicine.Anais M. Quemener: Conceptualization; writing‐review and editing. Laura Bachelot: Conceptualization; writing‐review and editing. Anne Forestier: Writing‐review and editing. Emmanuelle Donnou‐Fournet: Writing‐review and editing. David Gilot: Writing‐review and editing. Marie‐Dominique Galibert: Conceptualization; funding acquisition; writing‐review and editing.The authors have declared no conflicts of interest for this article.Natural antisense transcripts in diseases: From modes of action to targeted therapiesAlternative‐splicing defects in cancer: Splicing regulators and their downstream targets, guiding the way to novel cancer therapeuticsSplicing and neurodegeneration: Insights and mechanisms