Robust Definition Research Articles

Meningiomas with CNS invasion.

CNS invasion has been included as an independent criterion for the diagnosis of a high-grade (WHO and CNS grade 2 and 3) meningioma in the 2016 and more recently in the 2021 WHO classification. However, the prognostic role of brain invasion has recently been questioned. Also, surgical treatment for brain invasive meningiomas may pose specific challenges. We conducted a systematic review of the 2016-2022 literature on brain invasive meningiomas in Pubmed, Scopus, Web of Science and the Cochrane Library. The prognostic relevance of brain invasion as a stand-alone criterion is still unclear. Additional and larger studies using robust definitions of histological brain invasion and addressing the issue of sampling errors are clearly warranted. Although the necessity of molecular profiling in meningioma grading, prognostication and decision making in the future is obvious, specific markers for brain invasion are lacking for the time being. Advanced neuroimaging may predict CNS invasion preoperatively. The extent of resection (e.g., the Simpson grading) is an important predictor of tumor recurrence especially in higher grade meningiomas, but also - although likely to a lesser degree - in benign tumors, and therefore also in brain invasive meningiomas with and without other histological features of atypia or malignancy. Hence, surgery for brain invasive meningiomas should follow the principles of maximal but safe resections. There are some data to suggest that safety and functional outcomes in such cases may benefit from the armamentarium of surgical adjuncts commonly used for surgery of eloquent gliomas such as intraoperative monitoring, awake craniotomy, DTI tractography and further advanced intraoperative brain tumor visualization.

The carrying capacity of the seas and oceans for future sustainable food production: Current scientific knowledge gaps

AbstractThe expected increase in global food demand, as a consequence of a rising and wealthier world population, and an awareness of the limits and drawbacks of modern agriculture, has resulted in a growing attention to the potential of the seas and oceans to produce more food. The capture production of presently exploited marine fish stocks and other species has more or less reached its maximum and can only be slightly improved by better management. This leaves four alternative options open to increase marine food production: (1) manipulating the entire food web structure via removal of high trophic level species to allow an increasing exploitation of low trophic level species, (2) harvesting so far unexploited stocks, such as various fish species from the mesopelagic zone of the ocean or the larger zooplankton species from polar regions, (3) low‐trophic mariculture of seaweeds and herbivorous animals, and (4) restoration of impoverished coastal ecosystems or artificially increasing productivity by ecological engineering. In this paper, we discuss these four options and pay attention to missing scientific knowledge needed to assess their sustainability. To assess sustainability, it is a prerequisite to establish robust definitions and assessments of the biological carrying capacity of the systems, but it is also necessary to evaluate broader socio‐economic and governance sustainability.

Identification of low-acuity attendances in routine clinical information documented in German Emergency Departments

IntroductionIt has not yet been possible to ascertain the exact proportion, characterization or impact of low-acuity emergency department (ED) attendances on the German Health Care System since valid and robust definitions to be applied in German ED routine data are missing.MethodsInternationally used methods and parameters to identify low-acuity ED attendances were identified, analyzed and then applied to routine ED data from two EDs of the tertiary care hospitals Charité—Universitätsmedizin Berlin, Campus Mitte (CCM) and Campus Virchow (CVK).ResultsBased on the three routinely available parameters `disposition´, `transport to the ED´ and `triage´ 33.2% (n = 30 676) out of 92 477 presentations to the two EDs of Charité—Universitätsmedizin Berlin (CVK, CCM) in 2016 could be classified as low-acuity presentations.ConclusionThis study provides a reliable and replicable means of retrospective identification and quantification of low-acuity attendances in German ED routine data. This enables both intra-national and international comparisons of figures across future studies and health care monitoring.

Questions in Mild Asthma: An Official American Thoracic Society Research Statement.

Background: Patients with mild asthma are believed to represent the majority of patients with asthma. Disease-associated risks such as exacerbations, lung function decline, and death have been understudied in this patient population. There have been no prior efforts from major societies to describe research needs in mild asthma. Methods: A multidisciplinary, diverse group of 24 international experts reviewed the literature, identified knowledge gaps, and provided research recommendations relating to mild asthma definition, pathophysiology, and management across all age groups. Research needs were also investigated from a patient perspective, generated in conjunction with patients with asthma, caregivers, and stakeholders. Of note, this project is not a systematic review of the evidence and is not a clinical practice guideline. Results: There are multiple unmet needs in research on mild asthma driven by large knowledge gaps in all areas. Specifically, there is an immediate need for a robust mild asthma definition and an improved understanding of its pathophysiology and management strategies across all age groups. Future research must factor in patient perspectives. Conclusions: Despite significant advances in severe asthma, there remain innumerable research areas requiring urgent attention in mild asthma. An important first step is to determine a better definition that will accurately reflect the heterogeneity and risks noted in this group. This research statement highlights the topics of research that are of the highest priority. Furthermore, it firmly advocates the need for engagement with patient groups and for more support for research in this field.

Robustness, Scott continuity, and computability

AbstractRobustness is a property of system analyses, namely monotonic maps from the complete lattice of subsets of a (system’s state) space to the two-point lattice. The definition of robustness requires the space to be a metric space. Robust analyses cannot discriminate between a subset of the metric space and its closure; therefore, one can restrict to the complete lattice of closed subsets. When the metric space is compact, the complete lattice of closed subsets ordered by reverse inclusion is $\omega$ -continuous, and robust analyses are exactly the Scott-continuous maps. Thus, one can also ask whether a robust analysis is computable (with respect to a countable base). The main result of this paper establishes a relation between robustness and Scott continuity when the metric space is not compact. The key idea is to replace the metric space with a compact Hausdorff space, and relate robustness and Scott continuity by an adjunction between the complete lattice of closed subsets of the metric space and the $\omega$ -continuous lattice of closed subsets of the compact Hausdorff space. We demonstrate the applicability of this result with several examples involving Banach spaces.

The high school effect on students’ mobility choices

This paper looks into the relationship between students’ university choices and their secondary school background. The main aim is to assess the role of secondary schools in steering university applications toward local or non-local institutions, also in the light of the tertiary education supply available in students’ areas of residence. With this aim, we classify students’ mobility choices by using a robust definition of local and non-local universities that accounts for the uncertainty in the definition of students’ local areas and their characteristics. In this framework, we apply a multilevel model to jointly consider the high school effect on the probability of students belonging to one specific category of mobility (local, forced non-local, free non-local) conditional upon students’ macro areas of residence, their chosen university and field of study. The findings highlight that high schools have a relevant role in affecting students’ mobility choices, especially when considering local universities. The magnitude of the effect depends on students’ macro area of residence. In particular, this result highlights that schools may pursue specific guidance policies to address students’ choices toward local universities; furthermore, it suggests that their influence on students is stronger in areas hosting the most important universities.

Mapping warming reefs—An application of multibeam acoustic water column analysis to define threatened abalone habitat

Robust definition of the spatial extent of seafloor habitats and how they may be changing through time is a holy grail for ecosystem management, particularly if an ecosystem is approaching a tipping point beyond which irreversible changes may occur. Here we generate and explore a new data set for the management of warming reefs in eastern Tasmania, Australia that will significantly improve the baseline maps required for fine-scaled spatial modelling and management that is, both robust at regional scales and is highly resolved within the water column. This procedure enabled the relative density of kelp vegetation to be identified in a region that is being overwhelmed by the range extension of a destructive grazer, the Longspined Sea Urchin, Centrostephanus rodgersii. We present a new online tool to visualize multibeam water column acoustic data as surfaces of kelp density at high resolution (50 cm) scale over seafloor terrain maps (spanning a total straight-line distance of 594 km and a total area of 29.14 km2) to reveal the types of reef structure on the East Coast of Tasmania where abalone habitat is threatened by kelp loss.

An Optimization Approach Coupling Preprocessing with Model Regression for Enhanced Chemometrics.

Chemometric methods are broadly used in the chemical and biochemical sectors. Typically, derivation of a regression model follows data preprocessing in a sequential manner. Yet, preprocessing can significantly influence the regression model and eventually its predictive ability. In this work, we investigate the coupling of preprocessing and model parameter estimation by incorporating them simultaneously in an optimization step. Common model selection techniques rely almost exclusively on the performance of some accuracy metric, yet having a quantitative metric for model robustness can prolong model up-time. Our approach is applied to optimize for model accuracy and robustness. This requires the introduction of a novel mathematical definition for robustness. We test our method in a simulated set up and with industrial case studies from multivariate calibration. The results highlight the importance of both accuracy and robustness properties and illustrate the potential of the proposed optimization approach toward automating the generation of efficient chemometric models.

A systematic review of social equity in FEWS analyses

Integrating social equity considerations into analyses of the food-energy-water systems nexus (FEWS) could improve understanding of how to meet increasing resource demands without impacting social vulnerabilities. Effective integration requires a robust definition of equity and an enhanced understanding of reliable FEWS analysis methods. By exploring how equity has been incorporated into FEWS research in the United States and countries with similar national development, this systematic literature review builds a knowledge base to address a critical research need. Our objectives were to 1) catalog analysis methods and metrics relevant to assessing FEWS equity at varying scales; 2) characterize current studies and interpret shared themes; and 3) identify opportunities for future research and the advancement of equitable FEWS governance. FEWS equity definitions and metrics were categorized by respective system (food, energy, water, overall nexus) and common governance scales (local, regional, national, global). Two central issues were climate change, which increases FEWS risks for vulnerable populations, and sustainable development, which offers a promising framework for integrating equity and FEWS in policy-making contexts. Social equity in FEWS was integrated into studies through affordability, access, and sociocultural elements. This framework could support researchers and practitioners to include equity in FEWS analysis tools based on study scale, purpose, and resource availability. Research gaps identified during the review included a lack of studies effectively integrating all three systems, a need for publicly available datasets, omission of issues related to energy conversion facilities, and opportunities for integration of environmental justice modalities into FEWS research. This paper synthesized how social equity has previously been incorporated into FEWS and outlines pathways for further consideration of equity within nexus studies. Our findings suggested that continued exploration of connections between FEWS, equity, and policy development across scales could reduce social risks and vulnerabilities associated with these systems.

Detection of electromagnetic anomalies over seismic regions during two strong (MW > 5) earthquakes

Ionospheric disturbances (such as electromagnetic emissions) in connection to strong earthquakes have been reported in literature for over two decades. In order to be reliable, the identification of such disturbances requires a preliminary robust definition of the ionospheric background in the absence of both seismic activity and any other possible input (e.g., transient change in solar activity).In this work, we present a new technique for the assessment of the electromagnetic (EM) background in the ionosphere over seismic regions. The background is estimated via a multiscale statistical analysis that makes use of most of the electric- and magnetic-field datasets (2019–2021) from the China Seismo-Electromagnetic Satellite (CSES-01).The result is a map of the average relative energy in a 6° x 6° LAT-LON cell centered at the earthquake epicenter (EE). Only EM signals that statistically differ from the background should be considered as events suitable for investigation.The method is tested against two strong seismic events, the 14 August 2021 Haitian earthquake (7.2 MW) and the 27 September 2021 Cretan earthquake (6.0 MW). In the former case, a signal (with characteristic frequency of 250 Hz) can be identified, which emerges from the background. In the latter one, the concurrent strong geomagnetic activity does not allow to tell any distinct signal apart from the background.

Reassessment of weak parent-of-origin expression bias shows it rarely exists outside of known imprinted regions.

In mouse and human, genes subjected to genomic imprinting have been shown to function in development, behavior, and post-natal adaptations. Failure to correctly imprint genes in human is associated with developmental syndromes, adaptive, and metabolic disorders during life as well as numerous forms of cancer. In recent years researchers have turned to RNA-seq technologies applied to reciprocal hybrid strains of mice to identify novel imprinted genes, causing a threefold increase in genes reported as having a parental origin-specific expression bias. The functional relevance of parental origin-specific expression bias is not fully appreciated especially since many are reported with only minimal parental bias (e.g. 51:49). Here, we present an in-depth meta-analysis of previously generated RNA-seq data and show that the methods used to generate and analyze libraries greatly influence the calling of allele-specific expression. Validation experiments show that most novel genes called with parental-origin-specific allelic bias are artefactual, with the mouse strain contributing a larger effect on expression biases than parental origin. Of the weak novel genes that do validate, most are located at the periphery of known imprinted domains, suggesting they may be affected by local allele- and tissue-specific conformation. Together these findings highlight the need for robust tools, definitions, and validation of putative imprinted genes to provide meaningful information within imprinting databases and to understand the functional and mechanistic implications of the process.

Genomic, spatial and morphometric data for discrimination of four species in the Mediterranean Tamus clade of yams (Dioscorea, Dioscoreaceae).

Among the numerous pantropical species of the yam genus, Dioscorea, only a small group occurs in the Mediterranean basin, including two narrow Pyrenean endemics (Borderea clade) and two Mediterranean-wide species (D. communis and D. orientalis, Tamus clade). However, several currently unrecognized species and infraspecific taxa have been described in the Tamus clade due to significant morphological variation associated with D. communis. Our overarching aim was to investigate taxon delimitation in the Tamus clade using an integrative approach combining phylogenomic, spatial and morphological data. We analysed 76 herbarium samples using Hyb-Seq genomic capture to sequence 260 low-copy nuclear genes and plastomes, together with morphometric and environmental modelling approaches. Phylogenomic reconstructions confirmed that the two previously accepted species of the Tamus clade, D. communis and D. orientalis, are monophyletic and form sister clades. Three subclades showing distinctive geographic patterns were identified within D. communis. These subclades were also identifiable from morphometric and climatic data, and introgression patterns were inferred between subclades in the eastern part of the distribution of D. communis. We propose a taxonomy that maintains D. orientalis, endemic to the eastern Mediterranean region, and splits D. communis sensu lato into three species: D. edulis, endemic to Macaronesia (Canary Islands and Madeira); D. cretica, endemic to the eastern Mediterranean region; and D. communis sensu stricto, widespread across western and central Europe. Introgression inferred between D. communis s.s. and D. cretica is likely to be explained by their relatively recent speciation at the end of the Miocene, disjunct isolation in eastern and western Mediterranean glacial refugia and a subsequent westward recolonization of D. communis s.s. Our study shows that the use of integrated genomic, spatial and morphological approaches allows a more robust definition of species boundaries and the identification of species that previous systematic studies failed to uncover.

Sequence Design of Random Heteropolymers as Protein Mimics.

Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deactivation radical polymerization allowed simulated polymer sequences based on the well-established Mayo-Lewis equation to more accurately reflect ground-truth sequences that are experimentally synthesized. This led to opportunities to perform bioinformatics-inspired analysis on simulated sequences to guide the design, synthesis, and interpretation of RHPs. We compared batches on the order of 10000 simulated RHP sequences that vary by synthetically controllable and measurable RHP characteristics such as chemical heterogeneity and average degree of polymerization. Our analysis spans across 3 levels: segments along a single chain, sequences within a batch, and batch-averaged statistics. We discuss simulator fidelity and highlight the importance of robust segment definition. Examples are presented that demonstrate the use of simulated sequence analysis for in-silico iterative design to mimic protein hydrophobic/hydrophilic segment distributions in RHPs and compare RHP and protein sequence segments to explain experimental results of RHPs that mimic protein function. To facilitate the community use of this workflow, the simulator and analysis modules have been made available through an open source toolkit, the RHPapp.

An Input-to-State Stability Perspective on Robust Locomotion

Uneven terrain necessarily transforms periodic walking into a non-periodic motion. As such, traditional stability analysis tools no longer adequately capture the ability of a bipedal robot to locomote in the presence of such disturbances. This motivates the need for analytical tools aimed at generalized notions of stability – robustness. Towards this, we propose a novel definition of robustness, termed δ-robustness, to characterize the domain on which a nominal periodic orbit remains stable despite uncertain terrain. This definition is derived by treating perturbations in ground height as disturbances in the context of the input-to-state-stability (ISS) of the extended Poincaré map associated with an orbit. The main theoretic result is the formulation of robust Lyapunov functions that certify δ-robustness of periodic orbits. This yields an optimization framework for verifying δ-robustness, which is demonstrated in simulation with a bipedal robot walking on uneven terrain.

NIM-Nets: Noise-aware Incomplete Multi-view Learning Networks.

Data in real world are usually characterized in multiple views, including different types of features or different modalities. Multi-view learning has been popular in the past decades and achieved significant improvements. In this paper, we investigate three challenging problems in the field of incomplete multi-view representation learning, namely, i) how to reduce the influences produced by missing views in multi-view dataset, ii) how to learn a consistent and informative representation among different views and iii) how to alleviate the impacts of the inherent noise in multi-view data caused by high-dimensional features or varied quality for different data points. To address these challenges, we integrate these three tasks into a problem and propose a novel framework termed Noise-aware Incomplete Multi-view Learning Networks (NIM-Nets). NIM-Nets fully utilize incomplete data from different views to produce a multi-view shared representation which is consistent, informative and robust to noise. We model the inherent noise in data by defining the distribution Γ and assuming that each observation in the incomplete dataset is sampled from the distribution Γ. To the best of our knowledge, this is the first work to unify learning the consistent and informative representation, alleviating the impacts of noise in data and handling the view-missing patterns in multi-view learning into a framework. We also first give a definition of robustness and completeness for incomplete multi-view representation learning. Based on NIM-Nets, we present joint optimization models for classification and clustering, respectively. Extensive experiments on different datasets demonstrate the effectiveness of our method over the existing work based on classification and clustering tasks in terms of different metrics.

Backward bifurcation, basic reinfection number and robustness of an SEIRE epidemic model with reinfection

Recent evidences show that individuals who recovered from COVID-19 can be reinfected. However, this phenomenon has rarely been studied using mathematical models. In this paper, we propose an SEIRE epidemic model to describe the spread of the epidemic with reinfection. We obtain the important thresholds [Formula: see text] (the basic reproduction number) and [Formula: see text] (a threshold less than one). Our investigations show that when [Formula: see text], the system has an endemic equilibrium, which is globally asymptotically stable. When [Formula: see text], the epidemic system exhibits bistable dynamics. That is, the system has backward bifurcation and the disease cannot be eradicated. In order to eradicate the disease, we must ensure that the basic reproduction number [Formula: see text] is less than [Formula: see text]. The basic reinfection number is obtained to measure the reinfection force, which turns out to be a new tipping point for disease dynamics. We also give definition of robustness, a new concept to measure the difficulty of completely eliminating the disease for a bistable epidemic system. Numerical simulations are carried out to verify the conclusions.

On the robustness of sparse counterfactual explanations to adverse perturbations

Counterfactual explanations (CEs) are a powerful means for understanding how decisions made by algorithms can be changed. Researchers have proposed a number of desiderata that CEs should meet to be practically useful, such as requiring minimal effort to enact, or complying with causal models. In this paper, we consider the interplay between the desiderata of robustness (i.e., that enacting CEs remains feasible and cost-effective even if adverse events take place) and sparsity (i.e., that CEs require only a subset of the features to be changed). In particular, we study the effect of addressing robustness separately for the features that are recommended to be changed and those that are not. We provide definitions of robustness for sparse CEs that are workable in that they can be incorporated as penalty terms in the loss functions that are used for discovering CEs. To carry out our experiments, we create and release code where five data sets (commonly used in the field of fair and explainable machine learning) have been enriched with feature-specific annotations that can be used to sample meaningful perturbations. Our experiments show that CEs are often not robust and, if adverse perturbations take place (even if not worst-case), the intervention they prescribe may require a much larger cost than anticipated, or even become impossible. However, accounting for robustness in the search process, which can be done rather easily, allows discovering robust CEs systematically. Robust CEs make additional intervention to contrast perturbations much less costly than non-robust CEs. We also find that robustness is easier to achieve for the features to change, posing an important point of consideration for the choice of what counterfactual explanation is best for the user. Our code is available at: https://github.com/marcovirgolin/robust-counterfactuals.

Persistent depressive symptoms are associated with frontal regional atrophy in patients with Alzheimer's disease.

Depression in individuals with Alzheimer's disease (AD) is common, difficult to treat and inadequately understood. Previous studies have identified possible differences in regional brain atrophy in individuals with AD and depression, but the results have been inconsistent and some studies had less robust definitions of depression. We aimed to examine regional brain atrophy in two large dementia focused cohorts. We used data from Alzheimer's disease neuroimaging initiative (ADNI) and the National Alzheimer's Co-ordinating Center (NACC), for those with data from at least one MRI scan. Depression ratings were available using the Geriatric Depression Scale (GDS) and Neuropsychiatric Inventory (NPI). Intermittent depressive symptoms were defined as one episode above threshold (≥8 on GDS, ≥6 on NPI depression subscale and ≥2 on the Neuropsychiatric Inventory version Q depression sub-scale) and persistent as ≥2 episodes. Derived regional volumetric data was available from ADNI and the NACC. Data was available from 698 individuals with AD in NACC and from 666 individuals in ADNI. We found no evidence of between group differences in regional brain volume at baseline, or of differential atrophy in NACC. In ADNI we found evidence of increased brain atrophy in several frontal brain areas. Because this study was limited to those with MRI data, the numbers in some analyses were low. MRI parcellation differed between studies making direct comparison difficult. For some individuals only the NPI was used to rate depression. We have found mixed evidence of increased regional atrophy in depression in AD, mainly in frontal brain regions. We found no evidence to support a vascular basis for depression in AD.

Prodromal FTD ‐ what do we know about it and what do we still have to learn?

AbstractBackgroundFrontotemporal dementia (FTD) is a complex disease that can initially present with subtle symptoms across several domains including behavior, cognitive/language, psychiatric and motor. This heterogenous prodromal phase is preceded by a slowly progressive presymptomatic accumulation of biological changes. With the emergence of new potential therapeutic agents for genetic forms, there is a need for robust clinical characterization and diagnostic definitions of these disease phases.MethodWe performed a systematic literature review on the current state of the science on prodromal FTD and provide expert consensus recommendations on diagnostic definitions.ResultThe preclinical phase should extend from the first signs of protein misfolding to the first FTD symptoms, however we lack molecular biomarkers to characterize this stage, with maybe the exception of C9orf72 expansion cases. In genetic mutation carriers it is uncertain if a ‘no disease’ phase exists, or if there is a neurodevelopmental component. To account for all possible clinical presentations, the definition of the prodromal stage of FTD needs to include the possibility of symptoms emerging with behavioral/psychiatric, cognitive and/or motor features. We subsume these features under the term mild cognitive and/or behavioral and/or motor impairment due to FTD (MCBMI‐FTD). MCBMI‐FTD needs to include psychiatric symptoms outside of typical bvFTD criteria as potential initial prodromal manifestations of FTD in genetic mutation carriers, however this would not be applicable in sporadic cases given the lack of specificity of such symptoms.ConclusionCharacterizing the preclinical and prodromal stages of FTD is crucial for disease detection and patient stratification for future clinical trials. However, several issues remain including the need to develop molecular biomarkers to better characterize the preclinical phase and more accurate models to predict symptom onset.

Toward a Theory of Sportswashing: Mega-Events, Soft Power, and Political Conflict

Sportswashing has emerged full force in the 21st century, highlighting the gap between word and deed in the sports world. Yet, the term suffers from definitional imprecision and is often applied solely to autocratic hosts. This article offers a robust definition of sportswashing and—building from the soft-power approach to analyzing mega-events like the Olympics and World Cup—creates a sportswashing typology. This paper offers four advances in conceptualizing sportswashing: (a) the practice is not just the domain of autocrats, but can emerge in democracies as well; (b) domestic audiences are crucial to understanding the political complexities of sportswashing; (c) sportswashing often sets the stage for military intervention; and (d) new forms of sportswashing are emerging, with authoritarian regimes funding teams and events in democratic states.