Background:
The course of multiple sclerosis (MS) appears to be milder in recent decades.
Objective:
To investigate how time from onset to disability milestones and how demographic and clinical ...characteristics have changed through subsequent onset cohorts of patients with MS.
Methods:
In the nationwide Danish Multiple Sclerosis Registry, we have registered all 13,562 Danish patients with onset of MS or clinically isolated syndrome from 1996 through 2020. For the analyses of prognosis, we used all cases with relapsing onset (N = 11,669). After stratification into 5-year onset cohorts, we computed the hazard ratios for disability endpoints for all cohorts having at least 10 years of follow-up and the oldest 1996–2000 onset cohort as reference.
Results:
Patients in more recent MS onset cohorts have a shorter diagnostic delay and more of them start disease-modifying treatment within 1 year since diagnosis. The prognosis was better for later onset cohorts. For the 2001–2005 cohort, the hazard ratio for confirmed Expanded Disability Status Scale (EDSS) 4 was 0.85 (95% confidence interval (CI), 0.76–0.95) and for confirmed EDSS 6: 0.76 (95% CI, 0.65–0.88). For the more recent 2006–2010 cohort, the corresponding hazard ratios were 0.70 (95% CI, 0.62–0.79) and 0.60 (95% CI, 0.50–0.71).
Conclusion:
We observed a considerable improvement of the prognosis in recent onset cohorts of relapsing-onset MS.
The ever-increasing attention of process mining (PM) research to the logs of low structured processes and of non-process-aware systems (e.g., ERP, IoT systems) poses a number of challenges. Indeed, ...in such cases, the risk of obtaining low-quality results is rather high, and great effort is needed to carry out a PM project, most of which is usually spent in trying different ways to select and prepare the input data for PM tasks. Two general AI-based strategies are discussed in this paper, which can improve and ease the execution of PM tasks in such settings: (a) using explicit domain knowledge and (b) exploiting auxiliary AI tasks. After introducing some specific data quality issues that complicate the application of PM techniques in the above-mentioned settings, the paper illustrates these two strategies and the results of a systematic review of relevant literature on the topic. Finally, the paper presents a taxonomical scheme of the works reviewed and discusses some major trends, open issues and opportunities in this field of research.
Multiple sclerosis (MS) leads to physical and cognitive disability, which in turn impacts the socioeconomic status of the individual. The altered socioeconomic trajectory combined with the critical ...role of aging in MS progression could potentially lead to pronounced differences between MS patients and the general population. Few nations have the ability to connect long-term clinical and socioeconomic data at the individual level, and Denmark's robust population-based registries offer unique insights. This study aimed to examine the socioeconomic aspects of elderly Danish MS patients in comparison to matched controls from the general population.
A nationwide population-based study in Denmark was conducted, comprising all living MS patients aged 50 years or older as of 1 January 2021. Patients were matched 1:10 based on sex, age, ethnicity, and residence with a 25% sample of the total Danish population. Demographic and clinical information was sourced from the Danish Multiple Sclerosis Registry, while socioeconomic data were derived from national population-based registries containing details on education, employment, social services, and household characteristics. Univariate comparisons between MS patients and matched controls were then carried out.
The study included 8,215 MS patients and 82,150 matched individuals, with a mean age of 63.4 years (SD: 8.9) and a 2:1 female-to-male ratio. For those aged 50-64 years, MS patients demonstrated lower educational attainment (high education: 28.3 vs. 34.4%,
< 0.001) and fewer received income from employment (46.0 vs. 78.9%,
< 0.001), and working individuals had a lower annual income (48,500 vs. 53,500€,
< 0.001) in comparison to the controls. Additionally, MS patients within this age group were more likely to receive publicly funded practical assistance (14.3 vs. 1.6%,
< 0.001) and personal care (10.5 vs. 0.8%,
< 0.001). Across the entire population, MS patients were more likely to live alone (38.7 vs. 33.8%,
< 0.001) and less likely to have one or more children (84.2 vs. 87.0%,
< 0.001).
MS presents significant socioeconomic challenges among the elderly population, such as unemployment, reduced income, and increased dependence on social care. These findings underscore the pervasive impact of MS on an individual's life course, extending beyond the clinical symptoms of cognitive and physical impairment.
Reproductive division of labor in eusocial insects is a striking example of a shared genetic background giving rise to alternative phenotypes, namely queen and worker castes. Queen and worker ...phenotypes play major roles in the evolution of eusocial insects. Their behavior, morphology and physiology underpin many ecologically relevant colony-level traits, which evolved in parallel in multiple species.
Using queen and worker transcriptomic data from 16 ant species we tested the hypothesis that conserved sets of genes are involved in ant reproductive division of labor. We further hypothesized that such sets of genes should also be involved in the parallel evolution of other key traits. We applied weighted gene co-expression network analysis, which clusters co-expressed genes into modules, whose expression levels can be summarized by their 'eigengenes'. Eigengenes of most modules were correlated with phenotypic differentiation between queens and workers. Furthermore, eigengenes of some modules were correlated with repeated evolution of key phenotypes such as complete worker sterility, the number of queens per colony, and even invasiveness. Finally, connectivity and expression levels of genes within the co-expressed network were strongly associated with the strength of selection. Although caste-associated sets of genes evolve faster than non-caste-associated, we found no evidence for queen- or worker-associated co-expressed genes evolving faster than one another.
These results identify conserved functionally important genomic units that likely serve as building blocks of phenotypic innovation, and allow the remarkable breadth of parallel evolution seen in ants, and possibly other eusocial insects as well.
In this paper, a framework based on a sparse Mixture of Experts (MoE) architecture is proposed for the federated learning and application of a distributed classification model in domains (like ...cybersecurity and healthcare) where different parties of the federation store different subsets of features for a number of data instances. The framework is designed to limit the risk of information leakage and computation/communication costs in both model training (through data sampling) and application (leveraging the conditional-computation abilities of sparse MoEs). Experiments on real data have shown the proposed approach to ensure a better balance between efficiency and model accuracy, compared to other VFL-based solutions. Notably, in a real-life cybersecurity case study focused on malware classification (the KronoDroid dataset), the proposed method surpasses competitors even though it utilizes only 50% and 75% of the training set, which is fully utilized by the other approaches in the competition. This method achieves reductions in the rate of false positives by 16.9% and 18.2%, respectively, and also delivers satisfactory results on the other evaluation metrics. These results showcase our framework’s potential to significantly enhance cybersecurity threat detection and prevention in a collaborative yet secure manner.
During colony relocation, the selection of a new nest involves exploration and assessment of potential sites followed by colony movement on the basis of a collective decision making process. Hygiene ...and pathogen load of the potential nest sites are factors worker scouts might evaluate, given the high risk of epidemics in group-living animals. Choosing nest sites free of pathogens is hypothesized to be highly efficient in invasive ants as each of their introduced populations is often an open network of nests exchanging individuals (unicolonial) with frequent relocation into new nest sites and low genetic diversity, likely making these species particularly vulnerable to parasites and diseases. We investigated the nest site preference of the invasive pharaoh ant, Monomorium pharaonis, through binary choice tests between three nest types: nests containing dead nestmates overgrown with sporulating mycelium of the entomopathogenic fungus Metarhizium brunneum (infected nests), nests containing nestmates killed by freezing (uninfected nests), and empty nests. In contrast to the expectation pharaoh ant colonies preferentially (84%) moved into the infected nest when presented with the choice of an infected and an uninfected nest. The ants had an intermediate preference for empty nests. Pharaoh ants display an overall preference for infected nests during colony relocation. While we cannot rule out that the ants are actually manipulated by the pathogen, we propose that this preference might be an adaptive strategy by the host to "immunize" the colony against future exposure to the same pathogenic fungus.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Introduction:
Prescribing guidance for disease-modifying treatment (DMT) in multiple sclerosis (MS) is centred on a clinical diagnosis of relapsing–remitting MS (RRMS). DMT prescription guidelines ...and monitoring vary across countries. Standardising the approach to diagnosis of disease course, for example, assigning RRMS or secondary progressive MS (SPMS) diagnoses, allows examination of the impact of health system characteristics on the stated clinical diagnosis and treatment access.
Methods:
We analysed registry data from six cohorts in five countries (Czech Republic, Denmark, Germany, Sweden and United Kingdom) on patients with an initial diagnosis of RRMS. We standardised our approach utilising a pre-existing algorithm (DecisionTree, DT) to determine patient diagnoses of RRMS or secondary progressive MS (SPMS). We identified five global drivers of DMT prescribing: Provision, Availability, Funding, Monitoring and Audit, data were analysed against these concepts using meta-analysis and univariate meta-regression.
Results:
In 64,235 patients, we found variations in DMT use between countries, with higher usage in RRMS and lower usage in SPMS, with correspondingly lower usage in the UK compared to other registers. Factors such as female gender (p = 0.041), increasing disability via Expanded Disability Status Scale (EDSS) score (p = 0.004), and the presence of monitoring (p = 0.029) in SPMS influenced the likelihood of receiving DMTs. Standardising the diagnosis revealed differences in reclassification rates from clinical RRMS to DT-SPMS, with Sweden having the lowest rate Sweden (Sweden 0.009, range: Denmark 0.103 – UK portal 0.311). Those with higher EDSS at index (p < 0.03) and female gender (p < 0.049) were more likely to be reclassified from RRMS to DT-SPMS. The study also explored the impact of diagnosis on DMT usage in clinical SPMS, finding that the prescribing environment and auditing practices affected access to treatment.
Discussion:
This highlights the importance of a healthcare system’s approach to verifying the clinical label of MS course in facilitating appropriate prescribing, with some flexibility allowed in uncertain cases to ensure continued access to treatment.
Background
To assign a course of secondary progressive multiple sclerosis (MS) (SPMS) may be difficult and the proportion of persons with SPMS varies between reports. An objective method for disease ...course classification may give a better estimation of the relative proportions of relapsing–remitting MS (RRMS) and SPMS and may identify situations where SPMS is under reported.
Materials and methods
Data were obtained for 61,900 MS patients from MS registries in the Czech Republic, Denmark, Germany, Sweden, and the United Kingdom (UK), including date of birth, sex, SP conversion year, visits with an Expanded Disability Status Scale (EDSS) score, MS onset and diagnosis date, relapses, and disease-modifying treatment (DMT) use. We included RRMS or SPMS patients with at least one visit between January 2017 and December 2019 if ≥ 18 years of age. We applied three objective methods: A set of SPMS clinical trial inclusion criteria (“EXPAND criteria”) modified for a real-world evidence setting, a modified version of the MSBase algorithm, and a decision tree-based algorithm recently published.
Results
The clinically assigned proportion of SPMS varied from 8.7% (Czechia) to 34.3% (UK). Objective classifiers estimated the proportion of SPMS from 15.1% (Germany by the EXPAND criteria) to 58.0% (UK by the decision tree method). Due to different requirements of number of EDSS scores, classifiers varied in the proportion they were able to classify; from 18% (UK by the MSBase algorithm) to 100% (the decision tree algorithm for all registries). Objectively classified SPMS patients were older, converted to SPMS later, had higher EDSS at index date and higher EDSS at conversion. More objectively classified SPMS were on DMTs compared to the clinically assigned.
Conclusion
SPMS appears to be systematically underdiagnosed in MS registries. Reclassified patients were more commonly on DMTs.
Intrusion detection tools have largely benefitted from the usage of supervised classification methods developed in the field of data mining. However, the data produced by modern system/network logs ...pose many problems, such as the streaming and non-stationary nature of such data, their volume and velocity, and the presence of imbalanced classes. Classifier ensembles look a valid solution for this scenario, owing to their flexibility and scalability. In particular, data-driven schemes for combining the predictions of multiple classifiers have been shown superior to traditional fixed aggregation criteria (e.g., predictions’ averaging and weighted voting). In intrusion detection settings, however, such schemes must be devised in an efficient way, since (part of) the ensemble may need to be re-trained frequently. A novel ensemble-based framework is proposed here for the online intrusion detection, where the ensemble is updated through an incremental stream-oriented learning scheme, correspondingly to the detection of concept drifts. Differently from mainstream ensemble-based approaches in the field, our proposal relies on deriving, though an efficient genetic programming (GP) method, an expressive kind of combiner function defined in terms of (non-trainable) aggregation functions. This approach is supported by a system architecture, which integrates different kinds of functionalities, ranging from the drift detection, to the induction and replacement of base classifiers, up to the distributed computation of GP-based combiners. Experiments on both artificial and real-life datasets confirmed the validity of the approach.
PDF
