Higher physical fitness levels are associated with less language decline in healthy ageing

Healthy ageing is associated with decline in cognitive abilities such as language. Aerobic fitness has been shown to ameliorate decline in some cognitive domains, but the potential benefits for language have not been examined. In a cross-sectional sample, we investigated the relationship between aerobic fitness and tip-of-the-tongue states. These are among the most frequent cognitive failures in healthy older adults and occur when a speaker knows a word but is unable to produce it. We found that healthy older adults indeed experience more tip-of-the-tongue states than young adults. Importantly, higher aerobic fitness levels decrease the probability of experiencing tip-of-the-tongue states in healthy older adults. Fitness-related differences in word finding abilities are observed over and above effects of age. This is the first demonstration of a link between aerobic fitness and language functioning in healthy older adults

Etanercept, improved dosage schedules and combinations in the treatment of psoriasis: an update

Etanercept, a subcutaneously administered fully human soluble tumor necrosis factor (TNF) receptor, was initially approved for the treatment of psoriasis at a dose of 25 mg twice weekly in repeated 24-week cycles with the possibility to double the dose in the first 12 weeks of the first cycle. During intermittent treatment, patients retain their ability to respond to etanercept. Recently, a new dosing schedule of etanercept 50 mg once weekly was approved, based on a study in which PASI-75 (75% improvement of Psoriasis Area and Severity Index) was achieved by 37% and 71% of patients at week 12 and 24. Another study demonstrated a PASI-75 of 57% and 69% in pediatric psoriasis patients receiving etanercept 0.8 mg/kg (up to 50 mg) once weekly for 12 and 24 weeks respectively, resulting in European approval from age 8. Based on recent clinical trials, the antipsoriatic effect of etanercept can be markedly increased in combination with acitretin, methotrexate or UVB. The combination with acitretin appears attractive because of its non-immunosuppressive and chemopreventive properties. Etanercept–methotrexate combination therapy is well established in rheumatologic patients. From a long-term perspective, the combination of TNF-inhibitors with phototherapy (photocarcinogenesis) or cyclosporine (carcinogenesis, infections) warrants great caution however. Finally, combination with topical calcipotriol–betamethasone ointment may increase the speed of response to TNF-inhibitors in the first 4 weeks of treatment

Robust bootstrap procedures for the chain-ladder method

Insurers are faced with the challenge of estimating the future reserves needed to handle historic and outstanding claims that are not fully settled. A well-known and widely used technique is the chain-ladder method, which is a deterministic algorithm. To include a stochastic component one may apply generalized linear models to the run-off triangles based on past claims data. Analytical expressions for the standard deviation of the resulting reserve estimates are typically difficult to derive. A popular alternative approach to obtain inference is to use the bootstrap technique. However, the standard procedures are very sensitive to the possible presence of outliers. These atypical observations, deviating from the pattern of the majority of the data, may both inflate or deflate traditional reserve estimates and corresponding inference such as their standard errors. Even when paired with a robust chain-ladder method, classical bootstrap inference may break down. Therefore, we discuss and implement several robust bootstrap procedures in the claims reserving framework and we investigate and compare their performance on both simulated and real data. We also illustrate their use for obtaining the distribution of one year risk measures

Finding Outliers in Surface Data and Video

Surface, image and video data can be considered as functional data with a bivariate domain. To detect outlying surfaces or images, a new method is proposed based on the mean and the variability of the degree of outlyingness at each grid point. A rule is constructed to flag the outliers in the resulting functional outlier map. Heatmaps of their outlyingness indicate the regions which are most deviating from the regular surfaces. The method is applied to fluorescence excitation-emission spectra after fitting a PARAFAC model, to MRI image data which are augmented with their gradients, and to video surveillance data

Memory encoding of syntactic information involves domain-general attentional resources:evidence from dual-task studies

We investigate the type of attention (domain-general or language-specific) used during syntactic processing. We focus on syntactic priming: In this task, participants listen to a sentence that describes a picture (prime sentence), followed by a picture the participants need to describe (target sentence). We measure the proportion of times participants use the syntactic structure they heard in the prime sentence to describe the current target sentence as a measure of syntactic processing. Participants simultaneously conducted a motion-object tracking (MOT) task, a task commonly used to tax domain-general attentional resources. We manipulated the number of objects the participant had to track; we thus measured participants’ ability to process syntax while their attention is not-, slightly-, or overly-taxed. Performance in the MOT task was significantly worse when conducted as a dual-task compared to as a single task. We observed an inverted U-shaped curve on priming magnitude when conducting the MOT task concurrently with prime sentences (i.e., memory encoding), but no effect when conducted with target sentences (i.e., memory retrieval). Our results illustrate how, during the encoding of syntactic information, domain-general attention differentially affects syntactic processing, whereas during the retrieval of syntactic information domain-general attention does not influence syntactic processin

Robust Identification of Target Genes and Outliers in Triple-negative Breast Cancer Data

Correct classification of breast cancer sub-types is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer (TNBC) which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma (BRCA) transcriptomic data publicly available from The Cancer Genome Atlas (TCGA) data portal. Our analysis identifies statistical outliers that may correspond to misdiagnosed patients. Furthermore, it is illustrated that classical statistical methods may fail in the presence of these outliers, prompting the need for robust statistics. Using robust sparse logistic regression we obtain 36 relevant genes, of which ca. 60\% have been previously reported as biologically relevant to TNBC, reinforcing the validity of the method. The remaining 14 genes identified are new potential biomarkers for TNBC. Out of these, JAM3, SFT2D2 and PAPSS1 were previously associated to breast tumors or other types of cancer. The relevance of these genes is confirmed by the new DetectDeviatingCells (DDC) outlier detection technique. A comparison of gene networks on the selected genes showed significant differences between TNBC and non-TNBC data. The individual role of FOXA1 in TNBC and non-TNBC, and the strong FOXA1-AGR2 connection in TNBC stand out. Not only will our results contribute to the breast cancer/TNBC understanding and ultimately its management, they also show that robust regression and outlier detection constitute key strategies to cope with high-dimensional clinical data such as omics data

Higher physical fitness levels are associated with less language decline in healthy ageing

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In dialogue with an avatar, language behaviour is identical compared to dialogue with a human partner.

The use of virtual reality (VR) as a methodological tool is becoming increasingly popular in behavioral research as its flexibility allows for a wide range of applications. This new method has not been as widely accepted in the field of psycholinguistics, however, possibly due to the assumption that language processing during human-computer interactions does not accurately reflect human-human interactions. Yet at the same time there is a growing need to study human-human language interactions in a tightly controlled context, which has not been possible using existing methods. VR, however, offers experimental control over parameters that cannot be (as finely) controlled in the real world. As such, in this study we aim to show that human-computer language interaction is comparable to human-human language interaction in virtual reality. In the current study we compare participants’ language behavior in a syntactic priming task with human versus computer partners: we used a human partner, a human-like avatar with human-like facial expressions and verbal behavior, and a computer-like avatar which had this humanness removed. As predicted, our study shows comparable priming effects between the human and human-like avatar suggesting that participants attributed human-like agency to the human-like avatar. Indeed, when interacting with the computer-like avatar, the priming effect was significantly decreased. This suggests that when interacting with a human-like avatar, sentence processing is comparable to interacting with a human partner. Our study therefore shows that VR is a valid platform for conducting language research and studying dialogue interactions in an ecologically valid manner