A decision-making framework to maximise the evolutionary potential of populations - Genetic and genomic insights from the common midwife toad (Alytes obstetricans) at its range limits
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB77624 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR13366417.fastq.gz | Fastqsanger.gz | |||
| ERR13366418.fastq.gz | Fastqsanger.gz | |||
| ERR13366419.fastq.gz | Fastqsanger.gz | |||
| ERR13366420.fastq.gz | Fastqsanger.gz | |||
| ERR13366421.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 70 |
Similar Datasets
Project description:Some decisions, such as predicting the winner of a baseball game, are challenging in part because outcomes are probabilistic. When making such decisions, one view is that humans stochastically and selectively retrieve a small set of relevant memories that provides evidence for competing options. We show that optimal performance at test is impossible when retrieving information in this fashion, no matter how extensive training is, because limited retrieval introduces noise into the decision process that cannot be overcome. One implication is that people should be more accurate in predicting future events when trained on idealized rather than on the actual distributions of items. In other words, we predict the best way to convey information to people is to present it in a distorted, idealized form. Idealization of training distributions is predicted to reduce the harmful noise induced by immutable bottlenecks in people's memory retrieval processes. In contrast, machine learning systems that selectively weight (i.e., retrieve) all training examples at test should not benefit from idealization. These conjectures are strongly supported by several studies and supporting analyses. Unlike machine systems, people's test performance on a target distribution is higher when they are trained on an idealized version of the distribution rather than on the actual target distribution. Optimal machine classifiers modified to selectively and stochastically sample from memory match the pattern of human performance. These results suggest firm limits on human rationality and have broad implications for how to train humans tasked with important classification decisions, such as radiologists, baggage screeners, intelligence analysts, and gamblers.
| S-EPMC3651451 | biostudies-literature
Project description:ObjectiveCaregivers of children with medical complexity (CMC) face decisions about life-sustaining treatments (LST) like tracheostomy. We sought to develop a clinically relevant and realistic model for decision-making about tracheostomy placement that might apply to other LST in CMC.DesignThis qualitative study, conducted between 2013 and 2015, consisted of 41 interviews with 56 caregivers of CMC who had received tracheostomies and 5 focus groups of 33 healthcare providers (HCPs) at a tertiary-care children's hospital in North Carolina. Participants were asked about their perspectives on the tracheostomy decision-making process. Data were transcribed, and coded. Using thematic content analysis, we inductively developed a tracheostomy decision-making framework and process.ResultsMany factors influenced caregivers' decisions, including children's well-being and caregivers' values, faith, knowledge, experience, emotional state, and social factors; preserving the child's life was the most important. HCPs consider many clinical and nonclinical factors; recommending tracheostomy for children with limited survival, perceived poor functioning and quality of life, and progressive conditions is ethically difficult. The framework of tracheostomy decision-making has inter-related caregiver- and HCP-level factors that influence the process. The framework contains elements not captured in a shared decision-making model, but better fits a collaborative decision-making (CDM) model. The tracheostomy CDM process that emerged from the data has two nonsequential components that HCPs could use: (1) gaining understanding and (2) holding decision-making conversations.ConclusionsCDM could be a useful model for clinicians guiding families about tracheostomy for CMC. The applicability of CDM for decision-making about other LSTs needs further exploration.
| S-EPMC9825978 | biostudies-literature
Project description:Adolescence is a period of life during which peers play a pivotal role in decision-making. The narrative of social influence during adolescence often revolves around risky and maladaptive decisions, like driving under the influence, and using illegal substances (Steinberg, 2005). However, research has also shown that social influence can lead to increased prosocial behaviors (Van Hoorn et al., 2017) and a reduction in risk-taking (Braams et al., 2019). While many studies support the notion that adolescents are more sensitive to peer influence than children or adults, the developmental processes that underlie this sensitivity remain poorly understood. We argue that one important reason for this lack of understanding is the absence of precisely formulated models. To make a first step toward formal models of social influence during adolescence, we first identify three prominent verbal models of social influence in the literature: (1) social motivation, (2) reward sensitivity, and (3) distraction. We then illustrate how these can be translated into formal models, and how such formal models can inform experimental design and help identify developmental processes. Finally, by applying our formal models to existing datasets, we demonstrate the usefulness of formalization by synthesizing different studies with seemingly disparate results. We conclude with a discussion on how formal modeling can be utilized to better investigate the development of peer influence in adolescence.
| S-EPMC6727856 | biostudies-literature
Project description:Heuristics and the application of fast-and-frugal trees may play a role in establishing a clinical decision-making framework for value-based oncology. We determined whether clinical decision-making in oncology can be structured heuristically based on the timeline of the patient's treatment, clinical intuition, and evidence-based medicine. A group of 20 patients with advanced non-small cell lung cancer (NSCLC) were enrolled into the study for extensive treatment analysis and sequential decision-making. The extensive clinical and genomic data allowed us to evaluate the methodology and efficacy of fast-and-frugal trees as a way to quantify clinical decision-making. The results of the small cohort will be used to further advance the heuristic framework as a way of evaluating a large number of patients within registries. Among the cohort whose data was analyzed, substitution and amplification mutations occurred most frequently. The top five most prevalent genomic alterations were TP53 (45%), ALK (40%), LRP1B (30%), CDKN2A (25%), and MYC (25%). These 20 cases were analyzed by this clinical decision-making process and separated into two distinctions: 10 straightforward cases that represented a clearer decision-making path and 10 complex cases that represented a more intricate treatment pathway. The myriad of information from each case and their distinct pathways was applied to create the foundation of a framework for lung cancer decision-making as an aid for oncologists. In late-stage lung cancer patients, the fast-and-frugal heuristics can be utilized as a strategy of quantifying proper decision-making with limited information.
| S-EPMC6057456 | biostudies-literature
Project description:BackgroundScientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation.ResultsSidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick.ConclusionsSidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to approach genomic analysis that traditional single gene lists do not, particularly in areas such as interaction discovery.
| S-EPMC3022632 | biostudies-literature
Project description:BACKGROUND:Of the 50,000 men in the US who elect for radical prostatectomy for prostate cancer, 24% to 40% will have a prostate-specific antigen (PSA) recurrence (PSA-R) within 10 years. Deciding whether to administer salvage therapy (ST) at PSA-R presents challenges, as treatment reduces the risk of progression to clinical metastasis but incurs unnecessary side effects should the man die before metastasis. We have developed a new harm-benefit framework using a clinical cohort to inform shared decision making between patients and physicians at PSA-R. METHODS:Records of 1,045 Johns Hopkins University Hospital patients diagnosed between 1984 and 2013 who had PSA-R following radical prostatectomy were analyzed using marginal structural models to estimate the baseline risk of metastasis and the effect of ST (radiation therapy with or without hormone therapy) while accounting for selection into ST on the basis of PSA growth. The estimated model predicts the harm-benefit tradeoffs of ST within patient subgroups. The benefit of ST is the absolute reduction in the risk of metastasis within 10 years; the harm is the frequency of cancers that would not have metastasized in the patient's lifetime in the absence of ST (overtreatment). RESULTS:The adjusted hazard ratio associated with ST was 0.41 (95% CI, 0.31 to 0.55). Providing ST to all men at PSA-R reduced the risk of metastasis from 43% to 23% but led to 31% of men being overtreated (harm/benefit = 31/(43-23) = 1.6). Providing ST to men with Gleason score >7 reduced the risk of metastasis from 67% to 39%, with 13% of men being overtreated (harm/benefit = 13/(67-39) = 0.5). CONCLUSIONS:A quantitative framework that evaluates primary harms and benefits of ST after PSA-R will facilitate informed decision making. Immediate ST may be more appropriate in patient subgroups at elevated risk of metastasis.
| S-EPMC5632112 | biostudies-literature
Project description:In older patients, radiation treatment plays a vital role in curative and palliative cancer therapy. Radiation treatment recommendations should be informed by a comprehensive, personalized risk-benefit assessment that evaluates treatment efficacy and toxicity. We review several clinical factors that distinctly affect efficacy and toxicity of radiation treatment in older patients. First, locoregional tumor behavior may be more indolent in older patients for some disease sites but more aggressive for other sites. Assessment of expected locoregional relapse risk informs the magnitude and timeframe of expected radiation treatment benefits. Second, assessment of the competing cancer versus noncancer mortality and morbidity risks contextualizes cancer treatment priorities holistically within patients' entire spectrum and time course of health needs. Third, assessment of functional reserve helps predict patients' acute treatment tolerance, differentiating those patients who are unlikely to benefit from treatment or who are at high risk for treatment complications. Potential radiation treatment options include immediate curative treatment, delayed curative treatment, and no treatment, with additional consideration given to altered radiation target, dose, or sequencing with chemotherapy and/or surgery. Finally, when cure is not feasible, palliative radiation therapy remains valuable for managing symptoms and achieving meaningful quality-of-life improvements. Our proposed decision-making framework integrates these factors to help radiation oncologists formulate strategic treatment recommendations within a multidisciplinary context. Future research is still needed to identify how advanced technologies can be judiciously applied in curative and palliative settings to enhance risk-benefit profiles of radiation treatment in older patients and more accurately quantify treatment efficacy in this group.
| S-EPMC4876341 | biostudies-other
Project description:The development of a new drug is an extremely high-risk enterprise. The attrition rates of development projects and the average costs for each launched product are daunting, and the completion of a development program requires a very long time horizon. These facts imply that there are huge potential gains, should one be able to improve efficiency and enhance decision-making capabilities. In this paper, we argue that substantial gains can be achieved by adapting a holistic view of drug development. Historically, too much planning, design and decision-making in the pharmaceutical development has been based on locally optimising separate parts of the development program, and too often important sources of uncertainty are ignored. We propose instead a model-based approach built on two essential pillars; (1) an integrated holistic view of the development program, including post-launch marketing and sales, with all parts evaluated simultaneously; (2) an explicit appreciation of all relevant sources of uncertainty. Computer simulations are utilised to evaluate the properties of the program options at hand, and to provide valuable quantitative decision support. Applications of this modelling approach have proven to add large value to development projects in terms of better program options being generated and more value-adding decisions taken.
| S-EPMC6713335 | biostudies-literature
Project description:Aims and methodThe aim of the study is to improve patient safety by identifying factors influencing gatekeeping decisions by crisis resolution and home treatment teams. A theoretical sampling method was used to recruit clinicians. Semi-structured interviews to elicit various aspects of clinical decision-making were carried out. The transcripts were thematically analysed using a grounded theory approach. RESULTS: Patient needs (safety and treatment) was the primary driver behind decisions. The research also revealed that information gathered was processed using heuristics. We identified five key themes (anxiety, weighting, agenda, resource and experience), which were constructed into an acronym 'AWARE'.Clinical implicationsAWARE provides a framework to make explicit drivers for decision-making that are often implicit. Incorporating these drivers into reflective practice will help staff be more mindful of undue influences and result in improved clinical decisions.Declaration of interestNone.
| S-EPMC6472319 | biostudies-literature
Project description:IntroductionThe vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues.ObjectivesThis research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods.MethodsThe methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM.Results(1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values.ConclusionThe findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.
| S-EPMC8378994 | biostudies-literature