Transport properties of continuous-time quantum walks on Sierpinski fractals

We model quantum transport, described by continuous-time quantum walks (CTQW), on deterministic Sierpinski fractals, differentiating between Sierpinski gaskets and Sierpinski carpets, along with their dual structures. The transport efficiencies are defined in terms of the exact and the average return probabilities, as well as by the mean survival probability when absorbing traps are present. In the case of gaskets, localization can be identified already for small networks (generations). For carpets, our numerical results indicate a trend towards localization, but only for relatively large structures. The comparison of gaskets and carpets further implies that, distinct from the corresponding classical continuous-time random walk, the spectral dimension does not fully determine the evolution of the CTQW.Comment: 10 pages, 6 figure

Little Ball of Fur: A Python Library for Graph Sampling

Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our goal is to make node, edge, and exploration-based network sampling techniques accessible to a large number of professionals, researchers, and students in a single streamlined framework. We created this framework with a focus on a coherent application public interface which has a convenient design, generic input data requirements, and reasonable baseline settings of algorithms. Here we overview these design foundations of the framework in detail with illustrative code snippets. We show the practical usability of the library by estimating various global statistics of social networks and web graphs. Experiments demonstrate that Little Ball of Fur can speed up node and whole graph embedding techniques considerably with mildly deteriorating the predictive value of distilled features.Comment: Code is available here: https://github.com/benedekrozemberczki/littleballoffu

Chickenpox Cases in Hungary: A Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks

Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing. Newly proposed graph neural network architectures are repetitively evaluated on standard tasks such as traffic or weather forecasting. In this paper, we propose the Chickenpox Cases in Hungary dataset as a new dataset for comparing graph neural network architectures. Our time series analysis and forecasting experiments demonstrate that the Chickenpox Cases in Hungary dataset is adequate for comparing the predictive performance and forecasting capabilities of novel recurrent graph neural network architectures

Impact of sarcopenia on daily functioning: a cross-sectional study among older inpatients.

BACKGROUND AND AIM Geriatric patients with sarcopenia are at increased risk for functional decline with loss of independence in daily living. This cross-sectional study aims to investigate the impact of sarcopenia on different domains of functional status in hospitalized geriatric patients. METHODS Sarcopenia was assessed at hospital admission using the recommendations of the European Working Group on Sarcopenia in Older People 2 (EWGSOP2). Body impedance analysis (BIA) was performed to determine muscle mass, and a pneumatic hand dynamometer was used to assess muscle strength. The functional independence measure (FIM) score, an 18-item tool exploring an individual's physical, cognitive and social functions, was used to measure functional status. RESULTS In 305 included inpatients with a median age of 84.0 years (65.6% female), prevalence of sarcopenia was 22.6%. Overall, sarcopenic patients had significant lower FIM scores compared to non-sarcopenic patients (p = 0.006). An association with sarcopenia was found for the FIM items bed/chair/wheelchair transfer (p = 0.047), transfer to toilet (p = 0.048), locomotion (p = 0.001), climbing stairs (p = 0.012), comprehension (p = 0.029), and social interaction (p = 0.028). CONCLUSION In hospitalized geriatric patients, sarcopenia was found to be associated with both cognitive and mobility domains, but not with self-care domains of the FIM score. Therefore, when addressing sarcopenia in inpatients, tailored and multi-dimensional training interventions mainly should focus on motor-cognitive abilities

Exon-phase symmetry and intrinsic structural disorder promote modular evolution in the human genome

A key signature of module exchange in the genome is phase symmetry of exons, suggestive of exon shuffling events that occurred without disrupting translation reading frame. At the protein level, intrinsic structural disorder may be another key element because disordered regions often serve as functional elements that can be effectively integrated into a protein structure. Therefore, we asked whether exon-phase symmetry in the human genome and structural disorder in the human proteome are connected, signalling such evolutionary mechanisms in the assembly of multi-exon genes. We found an elevated level of structural disorder of regions encoded by symmetric exons and a preferred symmetry of exons encoding for mostly disordered regions (>70% predicted disorder). Alternatively spliced symmetric exons tend to correspond to the most disordered regions. The genes of mostly disordered proteins (>70% predicted disorder) tend to be assembled from symmetric exons, which often arise by internal tandem duplications. Preponderance of certain types of short motifs (e.g. SH3-binding motif) and domains (e.g. high-mobility group domains) suggests that certain disordered modules have been particularly effective in exon-shuffling events. Our observations suggest that structural disorder has facilitated modular assembly of complex genes in evolution of the human genome. © 2013 The Author(s)

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of the library is to make temporal geometric deep learning available for researchers and machine learning practitioners in a unified easy-to-use framework. PyTorch Geometric Temporal was created with foundations on existing libraries in the PyTorch eco-system, streamlined neural network layer definitions, temporal snapshot generators for batching, and integrated benchmark datasets. These features are illustrated with a tutorial-like case study. Experiments demonstrate the predictive performance of the models implemented in the library on real world problems such as epidemiological forecasting, ridehail demand prediction and web-traffic management. Our sensitivity analysis of runtime shows that the framework can potentially operate on web-scale datasets with rich temporal features and spatial structure.Comment: Source code at: https://github.com/benedekrozemberczki/pytorch_geometric_tempora

Long-Term Socio-Ecological Research in Practice: Lessons from Inter- and Transdisciplinary Research in the Austrian Eisenwurzen

Long-Term Socio-Ecological Research (LTSER) is an inter- and transdisciplinary research field addressing socio-ecological change over time at various spatial and temporal scales. In the Austrian Eisenwurzen region, an LTSER platform was founded in 2004. It has fostered and documented research projects aiming at advancing LTSER scientifically and at providing regional stakeholders with relevant information for sustainable regional development. Since its establishment, a broad range of research activities has been pursued in the region, integrating information from long-term ecological monitoring sites with approaches from social sciences and the humanities, and in cooperation with regional stakeholders. Based on the experiences gained in the Eisenwurzen LTSER platform, this article presents current activities in the heterogeneous field of LTSER, identifying specific (inter-)disciplinary contributions of three research strands of LTSER: long-term ecological research, socio-ecological basic research, and transdisciplinary research. Given the broad array of diverse contributions to LTSER, we argue that the platform has become a relevant "boundary organization", linking research to its regional non-academic context, and ensuring interdisciplinary exchange among the variety of disciplines. We consider the diversity of LTSER approaches an important resource for future research. Major success criteria of LTSER face specific challenges: (1) existing loose, yet stable networks need to be maintained and extended; (2) continuous generation of and access to relevant data needs to be secured and more data need to be included; and (3) consecutive research projects that have allowed for capacity building in the past may be threatened in the future if national Austrian research funders cease to provide resources

EANM guideline on quality risk management for radiopharmaceuticals

This document is intended as a supplement to the EANM "Guidelines on current Good Radiopharmacy Practice (cGRPP)" issued by the Radiopharmacy Committee of the EANM (Gillings et al. in EJNMMI Radiopharm Chem. 6:8, 2021). The aim of the EANM Radiopharmacy Committee is to provide a document that describes how to manage risks associated with small-scale "in-house" preparation of radiopharmaceuticals, not intended for commercial purposes or distribution

Temporal development of unfavourable urodynamic parameters during the first year after spinal cord injury

Objectives: To describe the temporal development of and risk factors for the occurrence of unfavourable urodynamic parameters during the first year after spinal cord injury (SCI). Patients and methods: This population-based longitudinal study used data from 97 adult patients with a single-event traumatic or ischaemic SCI who underwent video-urodynamic investigation (UDI) at a university SCI centre. The first occurrences of unfavourable urodynamic parameters (detrusor overactivity combined with detrusor sphincter dyssynergia [DO-DSD], maximum storage detrusor pressure ≥40 cmH2 O, bladder compliance <20 mL/cmH2 O, vesico-ureteric reflux [VUR] and any unfavourable parameter [composite outcome]) were evaluated using time-to-event analysis. Results: The majority of the population (87/97 [90%]) had at least one unfavourable urodynamic parameter. Most unfavourable urodynamic parameters were initially identified during the 1- or 3-month UDI, including 92% of the DO-DSD (78/85), 82% of the maximum storage pressure ≥40 cmH2 O (31/38), and 100% of the VUR (seven of seven) observations. No low bladder compliance was observed. The risk of DO-DSD was elevated in patients with thoracic SCI compared to those with lumbar SCI (adjusted hazard ratio [aHR] 2.38, 95% confidence interval [CI] 1.16-4.89). Risk of maximum storage detrusor pressure ≥40 cmH2 O was higher in males than females (aHR 8.33, 95% CI 2.51-27.66), in patients with a cervical SCI compared to those with lumbar SCI (aHR 14.89, 95% CI 3.28-67.55), and in patients with AIS Grade B or C compared to AIS Grade D SCI (aHR 6.17, 95% CI 1.78-21.39). No risk factors were identified for the composite outcome of any unfavourable urodynamic parameter. Conclusions: The first UDI should take place within 3 months after SCI as to facilitate early diagnosis of unfavourable urodynamic parameters and timely treatment. Neuro-urological guidelines and individualised management strategies for patients with SCI may be strengthened by considering sex and SCI characteristics in the scheduling of UDIs. Keywords: #Urology; longitudinal studies; spinal cord injuries; survival analysis; urinary bladder, neurogenic; urinary bladder, overactive; urodynamic

Urodynamics Are Essential to Predict the Risk for Upper Urinary Tract Damage after Acute Spinal Cord Injury

We used clinical parameters to develop a prediction model for the occurrence of urodynamic risk factors for upper urinary tract (UUT) damage during the first year after acute spinal cord injury (SCI). A total of 97 patients underwent urodynamic investigation at 1, 3, 6, and 12 months after acute SCI, within the framework of a population-based longitudinal study at a single university SCI center. Candidate predictors included demographic characteristics and neurological and functional statuses 1 month after SCI. Outcomes included urodynamic risk factors for UUT damage: detrusor overactivity combined with detrusor sphincter dyssynergia, maximum storage detrusor pressure (pDetmax) ≥ 40 cmH$_{2}$O, bladder compliance < 20 mL/cmH$_{2}$O, and vesicoureteral reflux. Multivariable logistic regression was used for the prediction model development and internal validation, using the area under the receiver operating curve (aROC) to assess model discrimination. Two models showed fair discrimination for pDetmax ≥ 40 cmH$_{2}$O: (i) upper extremity motor score and sex, aROC 0.79 (95% CI: 0.69-0.89), C-statistic 0.78 (95% CI: 0.69-0.87), and (ii) neurological level, American Spinal Injury Association Impairment Scale grade, and sex, aROC 0.78 (95% CI: 0.68-0.89), C-statistic 0.76 (95% CI: 0.68-0.85). We identified two models that provided fair predictive values for urodynamic risk factors of UUT damage during the first year after SCI. Pending external validation, these models may be useful for clinical trial planning, although less so for individual-level patient management. Therefore, urodynamics remains essential for reliably identifying patients at risk of UUT damage