Machine Learning in Falls Prediction; A cognition-based predictor of falls for the acute neurological in-patient population

Background Information: Falls are associated with high direct and indirect costs, and significant morbidity and mortality for patients. Pathological falls are usually a result of a compromised motor system, and/or cognition. Very little research has been conducted on predicting falls based on this premise. Aims: To demonstrate that cognitive and motor tests can be used to create a robust predictive tool for falls. Methods: Three tests of attention and executive function (Stroop, Trail Making, and Semantic Fluency), a measure of physical function (Walk-12), a series of questions (concerning recent falls, surgery and physical function) and demographic information were collected from a cohort of 323 patients at a tertiary neurological center. The principal outcome was a fall during the in-patient stay (n = 54). Data-driven, predictive modelling was employed to identify the statistical modelling strategies which are most accurate in predicting falls, and which yield the most parsimonious models of clinical relevance. Results: The Trail test was identified as the best predictor of falls. Moreover, addition of any others variables, to the results of the Trail test did not improve the prediction (Wilcoxon signed-rank p < .001). The best statistical strategy for predicting falls was the random forest (Wilcoxon signed-rank p < .001), based solely on results of the Trail test. Tuning of the model results in the following optimized values: 68% (+- 7.7) sensitivity, 90% (+- 2.3) specificity, with a positive predictive value of 60%, when the relevant data is available. Conclusion: Predictive modelling has identified a simple yet powerful machine learning prediction strategy based on a single clinical test, the Trail test. Predictive evaluation shows this strategy to be robust, suggesting predictive modelling and machine learning as the standard for future predictive tools

Deep brain stimulation for movement disorder treatment: Exploring frequency-dependent efficacy in a computational network model

A large scale computational model of the basal ganglia (BG) network is proposed to describes movement disorder including deep brain stimulation (DBS). The model of this complex network considers four areas of the basal ganglia network: the subthalamic nucleus (STN) as target area of DBS, globus pallidus, both pars externa and pars interna (GPe-GPi), and the thalamus (THA). Parkinsonian conditions are simulated by assuming reduced dopaminergic input and corresponding pronounced inhibitory or disinhibited projections to GPe and GPi. Macroscopic quantities can be derived which correlate closely to thalamic responses and hence motor programme fidelity. It can be demonstrated that depending on different levels of striatal projections to the GPe and GPi, the dynamics of these macroscopic quantities switch from normal conditions to parkinsonian. Simulating DBS on the STN affects the dynamics of the entire network, increasing the thalamic activity to levels close to normal, while differing from both normal and parkinsonian dynamics. Using the mentioned macroscopic quantities, the model proposes optimal DBS frequency ranges above 130 Hz.Comment: 40 pages, 16 figure

The Asco meteorite (1805): New petrographic description, chemical data, and classification

Abstract— We present magnetic measurements, chemical analyses, and petrographic observations of the poorly studied Asco historical meteorite fall (1805). These new data indicate that this meteorite has been previously misclassified as an L6 ordinary chondrite. Asco is reclassified as an H6 ordinary chondrite with shock stage S3. An interesting feature of this meteorite is the presence of chromite‐plagioclase assemblages with variable textures

The Trail Making test : a study of its ability to predict falls in the acute neurological in-patient population

Objective: To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls. Design: Prospective cohort study. Setting: Tertiary neurological and neurosurgical center. Subjects: In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care. Main Measures: Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function). Results: The principal outcome was a fall during the in-patient stay (n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity. Conclusion: This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test

Functional Metaplasticity of Hippocampal Schaffer Collateral-CA1 Synapses Is Reversed in Chronically Epileptic Rats

Spatial learning and associating spatial information with individual experience are crucial for rodents and higher mammals. Hence, studying the cellular and molecular cascades involved in the key mechanism of information storage in the brain, synaptic plasticity, has led to enormous knowledge in this field. A major open question applies to the interdependence between synaptic plasticity and its behavioral correlates. In this context, it has become clear that behavioral aspects may impact subsequent synaptic plasticity, a phenomenon termed behavioral metaplasticity. Here, we trained control and pilocarpine-treated chronically epileptic rats of two different age groups (adolescent and adult) in a spatial memory task and subsequently tested long-term potentiation (LTP) in vitro at Schaffer collateral—CA1 synapses. As expected, memory acquisition in the behavioral task was significantly impaired both in pilocarpine-treated animals and in adult controls. Accordingly, these groups, without being tested in the behavioral training task, showed reduced CA1-LTP levels compared to untrained young controls. Spatial memory training significantly reduced subsequent CA1-LTP in vitro in the adolescent control group yet enhanced CA1-LTP in the adult pilocarpine-treated group. Such training in the adolescent pilocarpine-treated and adult control groups resulted in intermediate changes. Our study demonstrates age-dependent functional metaplasticity following a spatial memory training task and its reversal under pathological conditions

Legal situation and current practice of waste incineration bottom ash utilisation in Europe

Almost 500 municipal solid waste incineration plants in the EU, Norway, and Switzerland generate about 17.6 Mt/a of incinerator bottom ash (IBA). IBA contains minerals and metals. Metals are mostly separated and sold to the scrap market and minerals are either disposed of in landfills or utilised in the construction sector. Since there is no uniform regulation for IBA utilisation at EU level, countries developed own rules with varying requirements for utilisation. As a result from a cooperation network between European experts an up-to-date overview of documents regulating IBA utilisation is presented. Furthermore, this work highlights the different requirements that have to be considered. Overall, 51 different parameters for the total content and 36 different parameters for the emission by leaching are defined. An analysis of the defined parameter reveals that leaching parameters are significantly more to be considered compared to total content parameters. In order to assess the leaching behaviour nine different leaching tests, including batch tests, up-flow percolation tests and one diffusion test (monolithic materials) are in place. A further discussion of leaching parameters showed that certain countries took over limit values initially defined for landfills for inert waste and adopted them for IBA utilisation. The overall utilisation rate of IBA in construction works is approximately 54 wt.%. It is revealed that the rate of utilisation does not necessarily depend on how well regulated IBA utilisation is, but rather seems to be a result of political commitment for IBA recycling and economically interesting circumstances

ROS- and Radiation Source-Dependent Modulation of Leukocyte Adhesion to Primary Microvascular Endothelial Cells

Anti-inflammatory effects of low-dose irradiation often follow a non-linear dose–effect relationship. These characteristics were also described for the modulation of leukocyte adhesion to endothelial cells. Previous results further revealed a contribution of reactive oxygen species (ROS) and anti-oxidative factors to a reduced leukocyte adhesion. Here, we evaluated the expression of anti-oxidative enzymes and the transcription factor Nrf2 (Nuclear factor-erythroid-2-related factor 2), intracellular ROS content, and leukocyte adhesion in primary human microvascular endothelial cells (HMVEC) upon low-dose irradiation under physiological laminar shear stress or static conditions after irradiation with X-ray or Carbon (C)-ions (0–2 Gy). Laminar conditions contributed to increased mRNA expression of anti-oxidative factors and reduced ROS in HMVEC following a 0.1 Gy X-ray and 0.5 Gy C-ion exposure, corresponding to reduced leukocyte adhesion and expression of adhesion molecules. By contrast, mRNA expression of anti-oxidative markers and adhesion molecules, ROS, and leukocyte adhesion were not altered by irradiation under static conditions. In conclusion, irradiation of endothelial cells with low doses under physiological laminar conditions modulates the mRNA expression of key factors of the anti-oxidative system, the intracellular ROS contents of which contribute at least in part to leucocyte adhesion, dependent on the radiation source

Digitalisierung in der Erwachsenenbildung

Aus dem Inhalt: Digitalisierung in der Erwachsenenbildung - zur Einleitung in den Themenschwerpunkt; Erwachsenenbildung in der digitalen Welt: Handlungsebenen der digitalen Transformation; Digitalisierung und Mediatisierung in der Erwachsenenbildung/Weiterbildung; Digitale Lernumwelten in produzierenden Betrieben; Zur Modellierung einer Kultur der Digitalität; Bildungsberatung in Beschäftigung und Weiterbildung im Kontext der Digitalisierung; Best-practice-Beispiele für digitale Weiterbildungsangebote

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Evaluation of the Neutron Data Standards

With the need for improving existing nuclear data evaluations, (e.g., ENDF/B-VIII.0 and JEFF-3.3 releases) the first step was to evaluate the standards for use in such a library. This new standards evaluation made use of improved experimental data and some developments in the methodology of analysis and evaluation. In addition to the work on the traditional standards, this work produced the extension of some energy ranges and includes new reactions that are called reference cross sections. Since the effort extends beyond the traditional standards, it is called the neutron data standards evaluation. This international effort has produced new evaluations of the following cross section standards: the H(n,n), 6Li(n,t), 10B(n,α), 10B(n,), natC(n,n), Au(n,γ), 235U(n,f) and 238U(n,f). Also in the evaluation process the 238U(n,γ) and 239Pu(n,f) cross sections that are not standards were evaluated. Evaluations were also obtained for data that are not traditional standards: the Maxwellian spectrum averaged cross section for the Au(n,γ) cross section at 30 keV; reference cross sections for prompt γ-ray production in fast neutron-induced reactions; reference cross sections for very high energy fission cross sections; the 252Cf spontaneous fission neutron spectrum and the 235U prompt fission neutron spectrum induced by thermal incident neutrons; and the thermal neutron constants. The data and covariance matrices of the uncertainties were obtained directly from the evaluation procedure