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In this paper we present a new approach to deterministic modelling of COVID-19 epidemic. Our model dynamics is expressed by a single prognostic variable which satisfies an integro-differential equation. All unknown parameters are described with a single, time-dependent variable R(t). We show that our model has similarities to classic compartmental models, such as SIR, and that the variable R(t) can be interpreted as a generalized effective reproduction number. The advantages of our approach are the simplicity of having only one equation, the numerical stability due to an integral formulation and the reliability since the model is formulated in terms of the most trustable statistical data variable: the number of cumulative diagnosed positive cases of COVID-19. Once this dynamic variable is calculated, other non-dynamic variables, such as the number of heavy cases (hospital beds), the number of intensive-care cases (ICUs) and the fatalities, can be derived from it using a similarly stable, integral approach. The formulation with a single equation allows us to calculate from real data the values of the sample effective reproduction number, which can then be fitted. Extrapolated values of R(t) can be used in the model to make reliable forecasts, though under the assumption that measures for reducing infections are maintained. We have applied our model to more than 15 countries and the ongoing results are available on a web-based platform [1]. In this paper, we focus on the data for two exemplary countries, Italy and Germany, and show that the model is capable of reproducing the course of the epidemic in the past and forecasting its course for a period of four to five weeks with a reasonable numerical stability.
Climatic seasonality drives ecosystem processes (e.g. productivity) and influences plant species distribution. However, it is poorly understood how different aspects of seasonality (especially regarding temperature and precipitation) affect growth continuity of trees in climates with low seasonality because seasonality is often only crudely measured. On islands, exceptionally wide elevational species distribution ranges allow the use of tree rings to identify how growth continuity and climate–growth relationships change with elevation. Here, we present a novel dendroecological method to measure stem growth continuity based on annual density fluctuations (ADFs) in tree rings of Pinus canariensis to indicate low climatic seasonality. The species ranges from 300 to >2000 m a.s.l. on the trade wind-influenced island of La Palma (Canary Islands), where we measured three decades of tree-ring data of 100 individuals distributed over 10 sites along the entire elevational range. The successfully implemented ADF approach revealed a major shift of stem growth continuity across the elevational gradient. In a remarkably clear pattern, stem growth continuity (percentage of ADFs) showed a hump-shaped relationship with elevation reaching a maximum at around 1000 m a.s.l. Low- to mid-elevation tree growth was positively correlated with the Palmer Drought Severity Index (PDSI; indicating aridity) and sea surface temperature (indicating trade wind-influenced moderation of water supply), while high-elevation tree growth was positively correlated with winter temperature (indicating a cold-induced dormancy period). We conclude that ADFs are a useful method to measure stem growth continuity in low-seasonality climates. Growth of P. canariensis on the Canary Islands is more frequently interrupted by winter cold at high elevations and by summer drought at low elevations than in the trade wind-influenced mid elevations, where growth sometimes continues throughout the year. Climate change-associated alterations in trade wind cloud formation might cause non-analogue growth limitations for many unique island species.
The transitional nucleus 154Gd was investigated using a combination of a photon scattering experiment and a γγ-coincidence study following the β decay of 154Tb. A novel decay channel from the scissors mode to the band head of the β-band was observed. Its transition strength B(M1; 1sc+ → 0β+) was determined. An IBM-2 calculation reveals a correlation of this decay channel and the shape phase transition between spherical and deformed nuclei.
Mobilization of hematopoietic stem cells (HSCs) from the bone marrow to the peripheral blood is a complex mechanism that involves adhesive and chemotactic interactions of HSCs as well as their bone marrow microenvironment. In addition to a number of non-genetic factors, genetic susceptibilities also contribute to the mobilization outcome. Identification of genetic factors associated with HSC yield is important to better understand the mechanism behind HSC mobilization. In the present study, we enrolled 148 Korean participants (56 healthy donors and 92 patients) undergoing HSC mobilization for allogeneic or autologous HSC transplantation. Among a total of 53 polymorphisms in 33 candidate genes, one polymorphism (rs11264422) in relaxin/insulin-like family peptide receptor 4 (RXFP4) gene was significantly associated with a higher HSC yield after mobilization in Koreans. However, in a set of 101 Europeans, no association was found between circulating CD34+ cell counts and rs11264422 genotype. Therefore, we suggest that the ethnic differences in subjects’ genetic background may be related to HSC mobilization. In conclusion, the relaxin—relaxin receptor axis may play an important role in HSC mobilization. We believe that the results of the current study could provide new insights for therapies that use relaxin and HSC populations, as well as a better understanding of HSC regulation and mobilization at the molecular level.
Pattern recognition approaches to the analysis of neuroimaging data have brought new applications such as the classification of patients and healthy controls within reach. In our view, the reliance on expensive neuroimaging techniques which are not well tolerated by many patient groups and the inability of most current biomarker algorithms to accommodate information about prior class frequencies (such as a disorder's prevalence in the general population) are key factors limiting practical application. To overcome both limitations, we propose a probabilistic pattern recognition approach based on cheap and easy-to-use multi-channel near-infrared spectroscopy (fNIRS) measurements. We show the validity of our method by applying it to data from healthy controls (n = 14) enabling differentiation between the conditions of a visual checkerboard task. Second, we show that high-accuracy single subject classification of patients with schizophrenia (n = 40) and healthy controls (n = 40) is possible based on temporal patterns of fNIRS data measured during a working memory task. For classification, we integrate spatial and temporal information at each channel to estimate overall classification accuracy. This yields an overall accuracy of 76% which is comparable to the highest ever achieved in biomarker-based classification of patients with schizophrenia. In summary, the proposed algorithm in combination with fNIRS measurements enables the analysis of sub-second, multivariate temporal patterns of BOLD responses and high-accuracy predictions based on low-cost, easy-to-use fNIRS patterns. In addition, our approach can easily compensate for variable class priors, which is highly advantageous in making predictions in a wide range of clinical neuroimaging applications. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc.
A novel approach to measure brain-to-brain spatial and temporal alignment during positive empathy
(2022)
Empathy is defined as the ability to vicariously experience others’ suffering (vicarious pain) or feeling their joy (vicarious reward). While most neuroimaging studies have focused on vicarious pain and describe similar neural responses during the observed and the personal negative affective involvement, only initial evidence has been reported for the neural responses to others’ rewards and positive empathy. Here, we propose a novel approach, based on the simultaneous recording of multi-subject EEG signals and exploiting the wavelet coherence decomposition to measure the temporal alignment between ERPs in a dyad of interacting subjects. We used the Third-Party Punishment (TPP) paradigm to elicit the personal and vicarious experiences. During a positive experience, we observed the simultaneous presence in both agents of the Late Positive Potential (LPP), an ERP component related to emotion processing, as well as the existence of an inter-subject ERPs synchronization in the related time window. Moreover, the amplitude of the LPP synchronization was modulated by the presence of a human-agent. Finally, the localized brain circuits subtending the ERP-synchronization correspond to key-regions of personal and vicarious reward. Our findings suggest that the temporal and spatial ERPs alignment might be a novel and direct proxy measure of empathy.
Autism spectrum disorder (ASD) is a highly heritable disorder of complex and heterogeneous aetiology. It is primarily characterized by altered cognitive ability including impaired language and communication skills and fundamental deficits in social reciprocity. Despite some notable successes in neuropsychiatric genetics, overall, the high heritability of ASD (~90%) remains poorly explained by common genetic risk variants. However, recent studies suggest that rare genomic variation, in particular copy number variation, may account for a significant proportion of the genetic basis of ASD. We present a large scale analysis to identify candidate genes which may contain low-frequency recessive variation contributing to ASD while taking into account the potential contribution of population differences to the genetic heterogeneity of ASD. Our strategy, homozygous haplotype (HH) mapping, aims to detect homozygous segments of identical haplotype structure that are shared at a higher frequency amongst ASD patients compared to parental controls. The analysis was performed on 1,402 Autism Genome Project trios genotyped for 1 million single nucleotide polymorphisms (SNPs). We identified 25 known and 1,218 novel ASD candidate genes in the discovery analysis including CADM2, ABHD14A, CHRFAM7A, GRIK2, GRM3, EPHA3, FGF10, KCND2, PDZK1, IMMP2L and FOXP2. Furthermore, 10 of the previously reported ASD genes and 300 of the novel candidates identified in the discovery analysis were replicated in an independent sample of 1,182 trios. Our results demonstrate that regions of HH are significantly enriched for previously reported ASD candidate genes and the observed association is independent of gene size (odds ratio 2.10). Our findings highlight the applicability of HH mapping in complex disorders such as ASD and offer an alternative approach to the analysis of genome-wide association data.
(1) Background: Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) who are biologically at high risk for the development of loco–regional recurrences after postoperative radiotherapy (PORT) but at intermediate risk according to clinical risk factors may benefit from additional concurrent chemotherapy. In this matched-pair study, we aimed to identify a corresponding predictive gene signature. (2) Methods: Gene expression analysis was performed on a multicenter retrospective cohort of 221 patients that were treated with postoperative radiochemotherapy (PORT-C) and 283 patients who were treated with PORT alone. Propensity score analysis was used to identify matched patient pairs from both cohorts. From differential gene expression analysis and Cox regression, a predictive gene signature was identified. (3) Results: 108 matched patient pairs were selected. We identified a 2-metagene signature that stratified patients into risk groups in both cohorts. The comparison of the high-risk patients between the two types of treatment showed higher loco–regional control (LRC) after treatment with PORT-C (p < 0.001), which was confirmed by a significant interaction term in Cox regression (p = 0.027), i.e., the 2-metagene signature was indicative for the type of treatment. (4) Conclusion: We have identified a novel gene signature that may be helpful to identify patients with high-risk HNSCC amongst those at intermediate clinical risk treated with PORT, who may benefit from additional concurrent chemotherapy.
This paper presents a comprehensive extension of pricing two-dimensional derivatives depending on two barrier constraints. We assume randomness on the covariance matrix as a way of generalizing. We analyse common barrier derivatives, enabling us to study parameter uncertainty and the risk related to the estimation procedure (estimation risk). In particular, we use the distribution of empirical parameters from IBM and EURO STOXX50. The evidence suggests that estimation risk should not be neglected in the context of multidimensional barrier derivatives, as it could cause price differences of up to 70%.
This article shows that there exist two particular linear orders such that first-order logic with these two linear orders has the same expressive power as first-order logic with the Bit-predicate FO(Bit). As a corollary we obtain that there also exists a built-in permutation such that first-order logic with a linear order and this permutation is as expressive as FO(Bit).