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In the recent years, short-term heart rate variability (HRV) describing complex variations of beat-to-beat interval series that are mainly controlled by the autonomic nervous system (ANS) has been increasingly analyzed to assess the ANS activity in different diseases and under various conditions. In contrast to long-term HRV analysis, short-term investigations (<30 min) provide a test result almost immediately. Thus, short-term HRV analysis is suitable for ambulatory care, patient monitoring and all those applications where the result is urgently needed. In a previous study, we could show significant variations of 5-min HRV indices according to age in almost all domains (linear and nonlinear) in 1906 healthy subjects from the KORA S4 cohort. Based on the same group of subjects, general gender-related influences on HRV indices are to be determined in this study. Short-term 5-min HRV indices from linear time and frequency domain and from nonlinear methods (compression entropy, detrended fluctuation analysis, traditional and segmented Poincaré plot analysis, irreversibility analysis, symbolic dynamics, correlation and mutual information analysis) were determined from 782 females and 1124 males. First, we examined the gender differences in two age clusters (25-49 years and 50-74 years). Secondly, we investigated the gender-specific development of HRV indices in five age decade categories, namely for ages 25-34, 35-44, 45-54, 55-64 and 65-74 years. In this study, significant modifications of the indices according to gender could be obtained, especially in the frequency domain and correlation analyses. Furthermore, there were significant modifications according to age in nearly all of the domains. The gender differences disappeared within the last two age decades and the age dependencies disappeared in the last decade. To summarize gender and age influences need to be considered when performing HRV studies even if these influences only partly differ.

The diffusion model (Ratcliff, Psychol Rev 85(2):59–108, 1978) is a stochastic model that is applied to response time (RT) data from binary decision tasks. The model is often used to disentangle different cognitive processes. The validity of the diffusion model parameters has, however, rarely been examined. Only few experimental paradigms have been analyzed with those being restricted to fast response time paradigms. This is attributable to a recommendation stated repeatedly in the diffusion model literature to restrict applications to fast RT paradigms (more specifically, to tasks with mean RTs below 1.5 s per trial). We conducted experimental validation studies in which we challenged the necessity of this restriction. We used a binary task that features RTs of several seconds per trial and experimentally examined the convergent and discriminant validity of the four main diffusion model parameters. More precisely, in three experiments, we selectively manipulated these parameters, using a difficulty manipulation (drift rate), speed-accuracy instructions (threshold separation), a more complex motoric task (non-decision time), and an asymmetric payoff matrix (starting point). The results were similar to the findings from experimental validation studies based on fast RT paradigms. Thus, our experiments support the validity of the parameters of the diffusion model and speak in favor of an extension of the model to paradigms based on slower RTs.

In the present article, a flexible and fast computer program, called fast-dm, for diffusion model data analysis is introduced. Fast-dm is free software that can be downloaded from the authors' websites. The program allows estimating all parameters of Ratcliff's (1978) diffusion model from the empirical response time distributions of any binary classification task. Fast-dm is easy to use: it reads input data from simple text files, while program settings are specified by commands in a control file. With fast-dm, complex models can be fitted, where some parameters may vary between experimental conditions, while other parameters are constrained to be equal across conditions. Detailed directions for use of fast-dm are presented, as well as results from three short simulation studies exemplifying the utility of fast-dm.

Methods from nonlinear dynamics (NLD) have shown new insights into heart rate (HR) variability changes under various physiological and pathological conditions, providing additional prognostic information and complementing traditional time- and frequency-domain analyses. In this review, some of the most prominent indices of nonlinear and fractal dynamics are summarized and their algorithmic implementations and applications in clinical trials are discussed. Several of those indices have been proven to be of diagnostic relevance or have contributed to risk stratification. In particular, techniques based on mono- and multifractal analyses and symbolic dynamics have been successfully applied to clinical studies. Further advances in HR variability analysis are expected through multidimensional and multivariate assessments. Today, the question is no longer about whether or not methods from NLD should be applied; however, it is relevant to ask which of the methods should be selected and under which basic and standardized conditions should they be applied.

Background There exists a vast number of surgical treatment options for acromioclavicular (AC) joint injuries, and the current literature has yet to determine an equivocally superior treatment. AC joint repair has a long history and dates back to the beginning of the twentieth century. Main body Since then, over 150 different techniques have been described, covering open and closed techniques. Low grade injuries such as Type I-II according to the modified Rockwood classification should be treated conservatively, while high-grade injuries (types IV-VI) may be indicated for operative treatment. However, controversy exists if operative treatment is superior to nonoperative treatment, especially in grade III injuries, as functional impairment due to scapular dyskinesia or chronic pain remains concerning following non-operative treatment. Patients with a stable AC joint without overriding of the clavicle and without significant scapular dysfunction (Type IIIA) may benefit from non-interventional approaches, in contrast to patients with overriding of the clavicle and therapy-resistant scapular dysfunction (Type IIIB). If these patients are considered non-responders to a conservative approach, an anatomic AC joint reconstruction using a hybrid technique should be considered. In chronic AC joint injuries, surgery is indicated after failed nonoperative treatment of 3 to 6 months. Anatomic AC joint reconstruction techniques along with biologic augmentation (e.g. Hybrid techniques, suture fixation) should be considered for chronic high-grade instabilities, accounting for the lack of intrinsic healing and scar-forming potential of the ligamentous tissue in the chronic setting. However, complication and clinical failure rates remain high, which may be a result of technical failures or persistent horizontal and rotational instability. Conclusion Future research should focus on addressing horizontal and rotational instability, to restore native physiological and biomechanical properties of the AC joint.

ABSTRACT Germany is among the leading countries regarding the promotion of renewable energy towards a sustainable energy system transition. In this paper, we investigate the German pilot auction scheme for solar photovoltaics introduced in the Renewable Energies Act 2014 (EEG 2014) that serves as a pilot for the auctionbased promotion of the three major large-scale renewable electricity generation technologies (wind, solar, biomass) as of 2017. A strategic bidding model is used to determine the optimal bidding strategy and to determine the resulting project value. We consider pay-as-bid and uniform pricing and single and multiple bids. Moreover, we investigate the impact of investment cost uncertainty. In a sensitivity analysis we show how bid strategy adjustments affect the outcome. Specifically, higher uncertainty regarding the market clearing price increases the project value, as this additional uncertainty can be used to raise the probability of obtaining a higher level of remuneration by an adjusted auction strategy. The first- price auction can generate additional profits by placing a second, higher bid with a low probability of success. Investment cost uncertainty can have either a positive or negative impact on the project value, depending on the auction parameter values chosen.

Older adults typically show slower response times in basic cognitive tasks than younger adults. A diffusion model analysis allows the clarification of why older adults react more slowly by estimating parameters that map distinct cognitive components of decision making. The main components of the diffusion model are the speed of information uptake (drift rate), the degree of conservatism regarding the decision criterion (boundary separation), and the time taken up by non-decisional processes (i.e., encoding and motoric response execution; non-decision time). While the literature shows consistent results regarding higher boundary separation and longer non-decision time for older adults, results are more complex when it comes to age differences in drift rates. We conducted a multi-level meta-analysis to identify possible sources of this variance. As possible moderators, we included task difficulty and task type. We found that age differences in drift rate are moderated both by task type and task difficulty. Older adults were inferior in drift rate in perceptual and memory tasks, but information accumulation was even increased in lexical decision tasks for the older participants. Additionally, in perceptual and lexical decision tasks, older individuals benefitted from high task difficulty. In the memory tasks, task difficulty did not moderate the negative impact of age on drift. The finding of higher boundary separation and longer non-decision time in older than younger adults generalized over task type and task difficulty. The results of our meta-analysis are consistent with recent findings of a more pronounced age-related decline in memory than in vocabulary performance.

Since their discovery last century, antibiotics have served society well by saving tens of millions of lives. [...] carbapenems, an antibiotic class that represents the last available weapon against many gram-negative bacilli, are being used increasingly for empirical therapy.

Mathematical models of cognition are often memoryless and ignore potential fluctuations of their parameters. However, human cognition is inherently dynamic. Thus, we propose to augment mechanistic cognitive models with a temporal dimension and estimate the resulting dynamics from a superstatistics perspective. Such a model entails a hierarchy between a low-level observation model and a high-level transition model. The observation model describes the local behavior of a system, and the transition model specifies how the parameters of the observation model evolve over time. To overcome the estimation challenges resulting from the complexity of superstatistical models, we develop and validate a simulation-based deep learning method for Bayesian inference, which can recover both time-varying and time-invariant parameters. We first benchmark our method against two existing frameworks capable of estimating time-varying parameters. We then apply our method to fit a dynamic version of the diffusion decision model to long time series of human response times data. Our results show that the deep learning approach is very efficient in capturing the temporal dynamics of the model. Furthermore, we show that the erroneous assumption of static or homogeneous parameters will hide important temporal information.

Off-label hypomethylating agents and venetoclax (HMA/VEN) are often used for relapsed and refractory (R/R) AML patients. However, predictors of outcome are elusive. The objective of the current retrospective observational multicenter study of 240 adult patients (median age 68.6 years) with R/R AML was to establish a prognostic risk score. Overall response was documented in 106 (44%) patients. With a median follow-up of 31.5 months, 179 deaths were recorded. Median overall survival (mOS) was 7.9 months. In multivariate analysis of the subgroup with molecular information ( n  = 174), risk factors for inferior survival included the presence of extramedullary disease, HMA pretreatment and mutations in NF1 , PTPN11 , FLT3 , and TP53 , whereas mutated SF3B1 was identified as favorable risk factor. These risk factors were subsequently applied to construct an HR-weighted risk model that allocated patients to one of three risk groups with significantly different survival outcomes: favorable ( n  = 46; mOS 21.4 months), intermediate ( n  = 75; mOS 7.5 months), and adverse ( n  = 53; mOS 4.6 months; p  < 0.001). The model was validated in 189 AML patients treated with HMA/VEN in first line. This clinical-molecular, 3-tiered ven etoclax p rognostic r isk s core (VEN-PRS) for HMA/VEN treatment outcomes in R/R AML patients will support the selection of appropriate treatment options in this high-risk population.