Dimension of gradient measures

We prove that if pure derivatives with respect to all coordinates of a function on $\mathbb{R}^n$ are signed measures, then their lower Hausdorff dimension is at least $n-1$. The derivatives with respect to different coordinates may be of different order.Comment: 8 page

Protein alignment algorithms with an efficient backtracking routine on multiple GPUs

<p>Abstract</p> <p>Background</p> <p>Pairwise sequence alignment methods are widely used in biological research. The increasing number of sequences is perceived as one of the upcoming challenges for sequence alignment methods in the nearest future. To overcome this challenge several GPU (Graphics Processing Unit) computing approaches have been proposed lately. These solutions show a great potential of a GPU platform but in most cases address the problem of sequence database scanning and computing only the alignment score whereas the alignment itself is omitted. Thus, the need arose to implement the global and semiglobal Needleman-Wunsch, and Smith-Waterman algorithms with a backtracking procedure which is needed to construct the alignment.</p> <p>Results</p> <p>In this paper we present the solution that performs the alignment of every given sequence pair, which is a required step for progressive multiple sequence alignment methods, as well as for DNA recognition at the DNA assembly stage. Performed tests show that the implementation, with performance up to 6.3 GCUPS on a single GPU for affine gap penalties, is very efficient in comparison to other CPU and GPU-based solutions. Moreover, multiple GPUs support with load balancing makes the application very scalable.</p> <p>Conclusions</p> <p>The article shows that the backtracking procedure of the sequence alignment algorithms may be designed to fit in with the GPU architecture. Therefore, our algorithm, apart from scores, is able to compute pairwise alignments. This opens a wide range of new possibilities, allowing other methods from the area of molecular biology to take advantage of the new computational architecture. Performed tests show that the efficiency of the implementation is excellent. Moreover, the speed of our GPU-based algorithms can be almost linearly increased when using more than one graphics card.</p

ToFFi – Toolbox for frequency-based fingerprinting of brain signals

Spectral fingerprints (SFs) are unique power spectra signatures of human brain regions of interest (ROIs, Keitel &amp; Gross, 2016). SFs allow for accurate ROI identification and can serve as biomarkers of differences exhibited by non-neurotypical groups. At present, there are no open-source, versatile tools to calculate spectral fingerprints. We have filled this gap by creating a modular, highly-configurable MATLAB Toolbox for Frequency-based Fingerprinting (ToFFi). It can transform magnetoencephalographic and electroencephalographic signals into unique spectral representations using ROIs provided by anatomical (AAL, Desikan-Killiany), functional (Schaefer), or other custom volumetric brain parcellations. Toolbox design supports reproducibility and parallel computations

ToFFi - Toolbox for Frequency-based Fingerprinting of Brain Signals

Spectral fingerprints (SFs) are unique power spectra signatures of human brain regions of interest (ROIs, Keitel & Gross, 2016). SFs allow for accurate ROI identification and can serve as biomarkers of differences exhibited by non-neurotypical groups. At present, there are no open-source, versatile tools to calculate spectral fingerprints. We have filled this gap by creating a modular, highly-configurable MATLAB Toolbox for Frequency-based Fingerprinting (ToFFi). It can transform MEG/EEG signals into unique spectral representations using ROIs provided by anatomical (AAL, Desikan-Killiany), functional (Schaefer), or other custom volumetric brain parcellations. Toolbox design supports reproducibility and parallel computations.Comment: 21 pages, 10 figure

Evaluation of serum levels of soluble (s)L- and (s)P-selectins in endometrial cancer

Objectives: A number of reports on the role of selectin in the process of carcinogenesis, at the stage of proliferation and metastasis, have been available. The aim of the study was to analyze (s)P- and (s)L-selectin serum concentrations in women with EC and to compare these concentrations to clinical/pathological parameters and disease progression using surgical-pathological staging data. Material and methods: A total of 46 patients with EC and 50 healthy controls were included in the study. Serum concentrations of sL- and sP-selectins were measured in all participants. The oncologic protocol was implemented in all women from the study group. Results: Significantly higher serum concentrations were found in EC women as compared to controls. No statistically significant differences were found between the concentrations of the soluble forms of selectins and the following parameters: histologic type of EC, histologic tumor differentiation, depth of myometrial infiltration, cervical involvement, distant metastases, vascular space invasion, and disease advancement. Slightly higher (s)P-selectin concentrations were observed in serous carcinoma, in women with cervical involvement, in the sera of women with vascular space invasion and with advanced stages of the disease. Slightly higher mean (s)P-selectin concentrations correlated with lower differentiation of the tumor. Slightly higher mean (s)P-selectin concentration was detected in the sera of women with lymph node metastases and with the serosal and/or adnexal involvement. The results were statistically insignificant, but they almost reached statistical significance. Conclusions: L- and P-selectins play a role in the biology of EC. The absence of an unambiguous relationship between differences in (s)L- and (s)P-selectin levels and disease advancement suggests that they do not play a vital role in tumor progression in endometrial cancer

An oversimplification of physiological principles leads to flawed macroecological analyses

In light of the rapidly changing climate, there is an urgent need to develop a mechanistic understanding of how physiological functioning mediates ecological patterns. Recently, there has been a spate of papers using analyses that scale up from a standard physiological model, the Scholander–Irving model, to make predictions about range constraints on endothermic vertebrates (Buckley, Khaliq, Swanson, and Hof, 2018; Fristoe et al., 2015; Khaliq, Böhning‐Gaese, Prinzinger, Pfenninger, and Hof, 2017; Khaliq, Hof, Prinzinger, Böhning‐Gaese, and Pfenninger, 2014). Here, we argue that oversimplifications of the Scholander–Irving model and the use of questionable datasets lead to questionable macrophysiological analyses. Many of these problems have been addressed elsewhere, directly and indirectly (eg, McKechnie, Coe, Gerson, and Wolf, 2017; Mitchell et al., 2018), although the focus has largely been on the applicability of the Scholander–Irving model to warm environmental temperatures, which are often seen as more relevant to climate change. However, one specific aspect of the Scholander–Irving model, the assumption that energy expenditure of an endotherm below the thermoneutral zone (TNZ) can be described by basic Newtonian physics, has been used incorrectly in several papers. While not the only paper based on this assumption, the recent work by Buckley et al.(2018) reinvigorated discussions among physiologists about improper interpretations of the Scholander–Irving model. Our concerns are not new and have been voiced repeatedly in the past (Calder and Schmidt‐Nielsen, 1967; King, 1964; Tracy, 1972), but many of these ideas seem to have been buried by time