Theoretical predictions strongly support decision accuracy as a major driver of ecological specialization.
Ontology highlight
ABSTRACT:
SUBMITTER: Tosh CR
PROVIDER: S-EPMC2666988 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The optical properties of rylenes are extremely interesting because their emission colors can be tuned from blue to near-infrared by simply elongating the chain length. However, for conjugated chains, the dipole-allowed odd-parity 1B(u) excited state often lies above the dipole-forbidden even-parity 2A(g) state as the chain length increases, thus preventing any significant luminescence according to Kasha's rule. We systemically investigated the 1B(u)∕2A(g) crossover behaviors with respect to the elongating rylene chain length with various quantum chemistry approaches, such as time-depended density functional theory (TDDFT), complete active space self-consistent field theory (CASSCF∕CASPT2), multireference configuration interaction (MRCI)∕Zerner's intermediate neglect of diatomic overlap (ZINDO), and MRCI∕modified neglect of differential overlap. The calculated results by CASSCF∕CASPT2 and MRCI∕ZINDO are completely coherent: the optical active 1B(u) state lies below the dark B(3g) or 2A(g) state for perylene and terrylene, which results in strong fluorescence; while a crossover to S(1) = 2A(g) occurs and leads to much weaker fluorescence for quaterrylene. Then we put forward a molecular design rule on how to recover fluorescence for the longer rylenes by introducing heteroatom bridges. Several heteroatom-annulated rylenes are designed theoretically, which are predicted to be strongly emissive in the red and near-infrared ranges. These are further confirmed by theoretical emission spectra as well as radiative and nonradiative decay rate calculations by using the vibration correlation function formalisms we developed earlier coupled with TDDFT.
| S-EPMC3071303 | biostudies-other
Project description:Predicted effects of climate change include high extinction risk for many species, but confidence in these predictions is undermined by a perceived lack of empirical support. Many studies have now documented ecological responses to recent climate change, providing the opportunity to test whether the magnitude and nature of recent responses match predictions. Here, we perform a global and multitaxon metaanalysis to show that empirical evidence for the realized effects of climate change supports predictions of future extinction risk. We use International Union for Conservation of Nature (IUCN) Red List criteria as a common scale to estimate extinction risks from a wide range of climate impacts, ecological responses, and methods of analysis, and we compare predictions with observations. Mean extinction probability across studies making predictions of the future effects of climate change was 7% by 2100 compared with 15% based on observed responses. After taking account of possible bias in the type of climate change impact analyzed and the parts of the world and taxa studied, there was less discrepancy between the two approaches: predictions suggested a mean extinction probability of 10% across taxa and regions, whereas empirical evidence gave a mean probability of 14%. As well as mean overall extinction probability, observations also supported predictions in terms of variability in extinction risk and the relative risk associated with broad taxonomic groups and geographic regions. These results suggest that predictions are robust to methodological assumptions and provide strong empirical support for the assertion that anthropogenic climate change is now a major threat to global biodiversity.
| S-EPMC3145734 | biostudies-literature
Project description:BackgroundEffective clinical decision support systems require accurate translation of practice recommendations into machine-readable artifacts; developing code sets that represent clinical concepts are an important step in this process. Many clinical coding systems are currently used in electronic health records, and it is unclear whether all of these systems are capable of efficiently representing the clinical concepts required in executing clinical decision support systems.ObjectiveThe aim of this study was to evaluate which clinical coding systems are capable of efficiently representing clinical concepts that are necessary for translating artifacts into executable code for clinical decision support systems.MethodsTwo methods were used to evaluate a set of clinical coding systems. In a theoretical approach, we extracted all the clinical concepts from 3 preventive care recommendations and constructed a series of code sets containing codes from a single clinical coding system. In a practical approach using data from a real-world setting, we studied the content of 1890 code sets used in an internationally available clinical decision support system and compared the usage of various clinical coding systems.ResultsSNOMED CT and ICD-10 (International Classification of Diseases, Tenth Revision) proved to be the most accurate clinical coding systems for most concepts in our theoretical evaluation. In our practical evaluation, we found that International Classification of Diseases (Tenth Revision) was most often used to construct code sets. Some coding systems were very accurate in representing specific types of clinical concepts, for example, LOINC (Logical Observation Identifiers Names and Codes) for investigation results and ATC (Anatomical Therapeutic Chemical Classification) for drugs.ConclusionsNo single coding system seems to fulfill all the needs for representing clinical concepts for clinical decision support systems. Comprehensiveness of the coding systems seems to be offset by complexity and forms a barrier to usability for code set construction. Clinical vocabularies mapped to multiple clinical coding systems could facilitate clinical code set construction.
| S-EPMC7641774 | biostudies-literature
Project description:BackgroundIndividualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities.ResultsIn the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level.ConclusionsBy integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.
| S-EPMC7011438 | biostudies-literature
Project description:Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with probabilistic guidance. However, many CDSS deploy static, generic rule-based logic, resulting in inequitably distributed accuracy and inconsistent performance in evolving clinical environments. Data-driven models could resolve this issue by updating predictions according to the data collected. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often imperfectly interoperable (IIO) or unshareable. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, as well as being robust to the systematic missingness common to CDSS-derived data, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules that can be combined in any number or combination to make any number or combination of diagnostic predictions, updatable at each step of a consultation. The model is validated on a real-world CDSS-derived dataset, comprising 3,192 paediatric outpatients in Tanzania. MoDN significantly outperforms 'monolithic' baseline models (which take all features at once at the end of a consultation) with a mean macro F1 score across all diagnoses of 0.749 vs 0.651 for logistic regression and 0.620 for multilayer perceptron (p < 0.001). To test collaborative learning between IIO datasets, we create subsets with various percentages of feature overlap and port a MoDN model trained on one subset to another. Even with only 60% common features, fine-tuning a MoDN model on the new dataset or just making a composite model with MoDN modules matched the ideal scenario of sharing data in a perfectly interoperable setting. MoDN integrates into consultation logic by providing interpretable continuous feedback on the predictive potential of each question in a CDSS questionnaire. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data.
| S-EPMC10351690 | biostudies-literature
Project description:Accounting for ecosystem services in environmental decision making is an emerging research topic. Modern frameworks for ecosystem services assessment emphasize evaluating the social benefits of ecosystems, in terms of who benefits and by how much, to aid in comparing multiple courses of action. Structured methods that use decision analytic-approaches are emerging for the practice of ecological restoration. In this article, we combine ecosystem services assessment with structured decision making to estimate and evaluate measures of the potential benefits of ecological restoration with a case study in the Woonasquatucket River watershed, Rhode Island, USA. We partnered with a local watershed management organization to analyze dozens of candidate wetland restoration sites for their abilities to supply five ecosystem services-flood water retention, scenic landscapes, learning opportunities, recreational opportunities, and birds. We developed 22 benefit indicators related to the ecosystem services as well as indicators for social equity and reliability that benefits will sustain in the future. We applied conceptual modeling and spatial analysis to estimate indicator values for each candidate restoration site. Lastly, we developed a decision support tool to score and aggregate the values for the organization to screen the restoration sites. Results show that restoration sites in urban areas can provide greater social benefits than sites in less urban areas. Our research approach is general and can be used to investigate other restoration planning studies that perform ecosystem services assessment and fit into a decision-making process.
| S-EPMC9446163 | biostudies-literature
Project description:BackgroundThe bushbuck, Tragelaphus scriptus, is a widespread and ecologically diverse ungulate species complex within the spiral-horned antelopes. This species was recently found to consist of two genetically divergent but monophyletic lineages, which are paraphyletic at mitochondrial (mt)DNA owing to an ancient interspecific hybridization event. The Scriptus lineage (T. s. scriptus) inhabits the north-western half of the African continent while Sylvaticus (T. s. sylvaticus) is found in the south-eastern half. Here we test hypotheses of historical demography and adaptation in bushbuck using a higher-resolution framework, with four nuclear (MGF, PRKCI, SPTBN, and THY) and three new mitochondrial markers (cytochrome b, 12S rRNA, and 16S rRNA).MethodsGenealogies were reconstructed for the mitochondrial and nuclear data sets, with the latter dated using fossil calibration points. We also inferred the demographic history of Scriptus and Sylvaticus using coalescent-based methods. To obtain an overview of the origins and ancestral colonisation routes of ancestral bushbuck sequences across geographic space, we conducted discrete Bayesian phylogeographic and statistical dispersal-vicariance analyses on our nuclear DNA data set.ResultsBoth nuclear DNA and mtDNA support previous findings of two genetically divergent Sylvaticus and Scriptus lineages. The three mtDNA loci confirmed 15 of the previously defined haplogroups, including those with convergent phenotypes. However, the nuclear tree showed less phylogenetic resolution at the more derived parts of the genealogy, possibly due to incomplete lineage sorting of the slower evolving nuclear genome. The only exception to this was the montane Menelik's bushbuck (Sylvaticus) of the Ethiopian highlands, which formed a monophyletic group at three of four nuclear DNA loci. We dated the coalescence of the two lineages to a common ancestor ∼2.54 million years ago. Both marker sets revealed similar demographic histories of constant population size over time. We show that the bushbuck likely originated in East Africa, with Scriptus dispersing to colonise suitable habitats west of the African Rift and Sylvaticus radiating from east of the Rift into southern Africa via a series of mainly vicariance events.DiscussionDespite lower levels of genetic structure at nuclear loci, we confirmed the independent evolution of the Menelik's bushbuck relative to the phenotypically similar montane bushbuck in East Africa, adding further weight to previous suggestions of convergent evolution within the bushbuck complex. Perhaps the most surprising result of our analysis was that both Scriptus and Sylvaticus populations remained relatively constant throughout the Pleistocene, which is remarkable given that this was a period of major climatic and tectonic change in Africa, and responsible for driving the evolution of much of the continent's extant large mammalian diversity.
| S-EPMC6476403 | biostudies-literature
Project description:Species' traits influence how populations respond to land-use change. However, even in well-characterized groups such as birds, widely studied traits explain only a modest proportion of the variance in response across species. Here, we show that associations with particular forest types strongly predict the sensitivity of forest-dwelling Amazonian birds to agriculture. Incorporating these fine-scale habitat associations into models of population response dramatically improves predictive performance and markedly outperforms the functional traits that commonly appear in similar analyses. Moreover, by identifying habitat features that support assemblages of unusually sensitive habitat-specialist species, our model furnishes straightforward conservation recommendations. In Amazonia, species that specialize on forests along a soil-nutrient gradient (i.e. both rich-soil specialists and poor-soil specialists) are exceptionally sensitive to agriculture, whereas species that specialize on floodplain forests are unusually insensitive. Thus, habitat specialization per se does not predict disturbance sensitivity, but particular habitat associations do. A focus on conserving specific habitats that harbour highly sensitive avifaunas (e.g. poor-soil forest) would protect a critically threatened component of regional biodiversity. We present a conceptual model to explain the divergent responses of habitat specialists in the different habitats, and we suggest that similar patterns and conservation opportunities probably exist for other taxa and regions.
| S-EPMC6834059 | biostudies-literature
Project description:BackgroundA clinical diagnostic support system (CDSS) can support medical students and physicians in providing evidence-based care. In this study, we investigate diagnostic accuracy based on the history of present illness between groups of medical students using a CDSS, Google, and neither (control). Further, the degree of diagnostic accuracy of medical students using a CDSS is compared with that of residents using neither a CDSS nor Google.MethodsThis study is a randomized educational trial. The participants comprised 64 medical students and 13 residents who rotated in the Department of General Medicine at Chiba University Hospital from May to December 2020. The medical students were randomly divided into the CDSS group (n = 22), Google group (n = 22), and control group (n = 20). Participants were asked to provide the three most likely diagnoses for 20 cases, mainly a history of a present illness (10 common and 10 emergent diseases). Each correct diagnosis was awarded 1 point (maximum 20 points). The mean scores of the three medical student groups were compared using a one-way analysis of variance. Furthermore, the mean scores of the CDSS, Google, and residents' (without CDSS or Google) groups were compared.ResultsThe mean scores of the CDSS (12.0 ± 1.3) and Google (11.9 ± 1.1) groups were significantly higher than those of the control group (9.5 ± 1.7; p = 0.02 and p = 0.03, respectively). The residents' group's mean score (14.7 ± 1.4) was higher than the mean scores of the CDSS and Google groups (p = 0.01). Regarding common disease cases, the mean scores were 7.4 ± 0.7, 7.1 ± 0.7, and 8.2 ± 0.7 for the CDSS, Google, and residents' groups, respectively. There were no significant differences in mean scores (p = 0.1).ConclusionsMedical students who used the CDSS and Google were able to list differential diagnoses more accurately than those using neither. Furthermore, they could make the same level of differential diagnoses as residents in the context of common diseases.Trial registrationThis study was retrospectively registered with the University Hospital Medical Information Network Clinical Trials Registry on 24/12/2020 (unique trial number: UMIN000042831).
| S-EPMC10214648 | biostudies-literature
Project description:During the Miocene, Hyaenidae was a highly diverse family of Carnivora that has since been severely reduced to four species: the bone-cracking spotted, striped, and brown hyenas, and the specialized insectivorous aardwolf. Previous studies investigated the evolutionary histories of the spotted and brown hyenas, but little is known about the remaining two species. Moreover, the genomic underpinnings of scavenging and insectivory, defining traits of the extant species, remain elusive. Here, we generated an aardwolf genome and analyzed it together with the remaining three species to reveal their evolutionary relationships, genomic underpinnings of their scavenging and insectivorous lifestyles, and their respective genetic diversities and demographic histories. High levels of phylogenetic discordance suggest gene flow between the aardwolf lineage and the ancestral brown/striped hyena lineage. Genes related to immunity and digestion in the bone-cracking hyenas and craniofacial development in the aardwolf showed the strongest signals of selection, suggesting putative key adaptations to carrion and termite feeding, respectively. A family-wide expansion in olfactory receptor genes suggests that an acute sense of smell was a key early adaptation. Finally, we report very low levels of genetic diversity within the brown and striped hyenas despite no signs of inbreeding, putatively linked to their similarly slow decline in effective population size over the last ∼2 million years. High levels of genetic diversity and more stable population sizes through time are seen in the spotted hyena and aardwolf. Taken together, our findings highlight how ecological specialization can impact the evolutionary history, demographics, and adaptive genetic changes of an evolutionary lineage.
| S-EPMC8382907 | biostudies-literature