Predicting sex from brain rhythms with deep learning

We have excellent skills to extract sex from visual assessment of human faces, but assessing sex from human brain rhythms seems impossible. Using deep convolutional neural networks, with unique potential to find subtle differences in apparent similar patterns, we explore if brain rhythms from either sex contain sex specific information. Here we show, in a ground truth scenario, that a deep neural net can predict sex from scalp electroencephalograms with an accuracy of >80% (p < 10-5), revealing that brain rhythms are sex specific. Further, we extracted sex-specific features from the deep net filter layers, showing that fast beta activity (20-25 Hz) and its spatial distribution is a main distinctive attribute. This demonstrates the ability of deep nets to detect features in spatiotemporal data unnoticed by visual assessment, and to assist in knowledge discovery. We anticipate that this approach may also be successfully applied to other specialties where spatiotemporal data is abundant, including neurology, cardiology and neuropsychology

Adubação e arranjo de plantas no consórcio milho e braquiária.

A otimização do manejo de milho consorciado com braquiária depende da fertilidade do solo, da densidade de plantas de braquiária e do arranjo de plantas do milho. Avaliaram-se as interações desses fatores e sua influência na produtividade de grãos e da pastagem, em experimento conduzido por duas safras, num fatorial 2x2x3+1, combinando-se duas condições de adubação do milho (para produtividade de 6 ou > 10 t ha-1 de grãos), densidade média ou alta de braquiária ( 30 plantas m-2), três arranjos de plantas de milho (A = 90 cm entre linhas, 5 plantas m-1; B = 45 cm entre linhas, 2,5 plantas m-1; e C = 45 cm entre linhas, 3 plantas m-1) e um tratamento adicional (maior adubação, sem braquiária e arranjo A do milho). A disponibilidade hídrica influenciou as respostas aos tratamentos. Em ano chuvoso, maior adubação resultou em expressivo incremento da produtividade de grãos, independentemente do arranjo de plantas de milho e da presença de braquiária. Em ano com veranico, o ganho do milho, devido à adubação, foi menor e houve efeito prejudicial da maior densidade de braquiária. A produção de matéria seca pela braquiária não apresentou relação direta com a variação na densidade de plantas

On-the-fly Uniformization of Time-Inhomogeneous Infinite Markov Population Models

This paper presents an on-the-fly uniformization technique for the analysis of time-inhomogeneous Markov population models. This technique is applicable to models with infinite state spaces and unbounded rates, which are, for instance, encountered in the realm of biochemical reaction networks. To deal with the infinite state space, we dynamically maintain a finite subset of the states where most of the probability mass is located. This approach yields an underapproximation of the original, infinite system. We present experimental results to show the applicability of our technique

Allogeneic NK cells induce the <i>in vitro</i> activation of monocyte-derived and conventional type-2 dendritic cells and trigger an inflammatory response under cancer-associated conditions

Natural killer (NK) cells are innate lymphocytes capable to recognize and kill virus-infected and cancer cells. In the past years, the use of allogeneic NK cells as anti-cancer therapy gained interest due to their ability to induce graft-versus-cancer responses without causing graft-versus-host disease and multiple protocols have been developed to produce high numbers of activated NK cells. While the ability of these cells to mediate tumor kill has been extensively studied, less is known about their capacity to influence the activity of other immune cells that may contribute to a concerted anti-tumor response in the tumor microenvironment (TME). In this study, we analyzed how an allogeneic off-the-shelf cord blood stem cell-derived NK-cell product influenced the activation of dendritic cells (DC). Crosstalk between NK cells and healthy donor monocyte-derived DC (MoDC) resulted in the release of IFNγ and TNF, MoDC activation, and the release of the T-cell-recruiting chemokines CXCL9 and CXCL10. Moreover, in the presence of prostaglandin-E2, NK cell/MoDC crosstalk antagonized the detrimental effect of IL-10 on MoDC maturation leading to higher expression of multiple (co-)stimulatory markers. The NK cells also induced activation of conventional DC2 (cDC2) and CD8 + T cells, and the release of TNF, GM-CSF, and CXCL9/10 in peripheral blood mononuclear cells of patients with metastatic colorectal cancer. The activated phenotype of MoDC/cDC2 and the increased release of pro-inflammatory cytokines and T-cell-recruiting chemokines resulting from NK cell/DC crosstalk should contribute to a more inflamed TME and may thus enhance the efficacy of T-cell-based therapies.</p

The exceptional influence of storm ‘Xaver’ on design water levels in the German Bight

Design water levels for coastal structures are usually estimated based on extreme value statistics. Since their robustness depends heavily on the sample size of observations, regular statistical updates are needed, especially after extreme events. Here, we demonstrate the exceptional influence of such an event based on storm 'Xaver', which caused record breaking water levels for large parts of the southwestern German North Sea coastline on 6 December 2013. We show that the water level estimates for a 1 in 200 years event increased by up to 40 cm due to the update after 'Xaver', a value twice as large as the estimated regional sea level rise for the entire 20th century. However, a thorough analysis of different independent meteorological (winds and pressure) and oceanographic components (tides, surges, mean sea level (MSL) anomalies) driving the event reveals that their observed combination does not yet represent the physically possible worst case scenario. Neither tides, nor surges nor MSL anomalies were at their observational maximum, suggesting that there is a realistic risk of a storm like 'Xaver' to cause even higher extreme water levels by a few decimetres under current climate conditions. Our results question purely statistical design approaches of coastal structures, which neglect the physical boundary conditions of individual extreme events

Bayesian extreme value analysis of extreme sea levels along the German Baltic coast using historical information

Developed coastlines require considerable investments into coastal protection measures to mitigate the effects of flooding caused by extreme sea levels (ESLs). To maximize the effectiveness of these measures, accurate estimates of the underlying hazard are needed. These estimates are typically determined by performing extreme value analysis on a sample of events taken from tide-gauge observations. However, such records are often limited in duration, and the resulting estimates may be highly uncertain. Furthermore, short records make it difficult to assess whether exceptionally large events within the record are appropriate for analysis or should be disregarded as outliers. In this study, we explore how historical information can be used to address both of these issues for the case of the German Baltic coast. We apply a Bayesian Markov chain Monte Carlo approach to assess ESLs using both systematic tide-gauge observations and historical information at seven locations. Apart from the benefits provided by incorporating historical information in extreme value analysis, which include reduced estimate uncertainties and the reclassification of outliers into useful samples, we find that the current tide-gauge records in the region alone are insufficient for providing accurate estimates of ESLs for the planning of coastal protection. We find long-range dependence in the series of ESLs at the site of Travemünde, which suggests the presence of some long-term variability affecting events in the region. We show that ESL activity over the full period of systematic observation has been relatively low. Consequently, analyses which consider only these data are prone to underestimations.</p

Quantitative properties of complex porous materials calculated from X-ray μCT images

A microcomputed tomography (μCT) facility and computational infrastructure developed at the Department of Applied Mathematics at the Australian National University is described. The current experimental facility is capable of acquiring 3D images made up of 20003 voxels on porous specimens up to 60 mm diameter with resolutions down to 2 μm. This allows the three-dimensional (3D) pore-space of porous specimens to be imaged over several orders of magnitude. The computational infrastructure includes the establishment of optimised and distributed memory parallel algorithms for image reconstruction, novel phase identification, 3D visualisation, structural characterisation and prediction of mechanical and transport properties directly from digitised tomographic images. To date over 300 porous specimens exhibiting a wide variety of microstructure have been imaged and analysed. In this paper, analysis of a small set of porous rock specimens with structure ranging from unconsolidated sands to complex carbonates are illustrated. Computations made directly on the digitised tomographic images have been compared to laboratory measurements. The results are in excellent agreement. Additionally, local flow, diffusive and mechanical properties can be numerically derived from solutions of the relevant physical equations on the complex geometries; an experimentally intractable problem. Structural analysis of data sets includes grain and pore partitioning of the images. Local granular partitioning yields over 70,000 grains from a single image. Conventional grain size, shape and connectivity parameters are derived. The 3D organisation of grains can help in correlating grain size, shape and orientation to resultant physical properties. Pore network models generated from 3D images yield over 100000 pores and 200000 throats; comparing the pore structure for the different specimens illustrates the varied topology and geometry observed in porous rocks. This development foreshadows a new numerical laboratory approach to the study of complex porous materials