Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of Hospital Performance Measures of Acute Stroke Care Quality. Riksstroke, the Swedish Stroke Register

Background: Registers are increasingly used to monitor stroke care performance. Fair benchmarking requires sufficient data quality. We have validated acute care data in Riksstroke, the Swedish Stroke Register. Methods: Completeness was assessed by comparisons with diagnoses at hospital discharge recorded in the compulsory National Patient Register and content validity by comparisons with (a) key variables identified by European stroke experts, and (b) items recorded in other European stroke care performance registers. Five test cases recorded by 67 hospitals were used to estimate inter-hospital reliability. Results: All 72 Swedish hospitals admitting acute stroke patients participated in Riksstroke. The register was estimated to cover at least 90% of acute stroke patients. It includes 18 of 22 quality indicators identified by international stroke experts and 14 of 15 indicators used by at least 2 stroke performance registers in other European countries. Inter-hospital reliability was high (≥85%) in 77 of 81 Riksstroke items. Conclusions: A nationwide stroke care register can be maintained with sufficient data quality to permit between-hospital performance benchmarking. Our experiences may serve as a model for other stroke registers while evaluating data quality.
Structural insight into recognition of phosphorylated threonine‐4 of RNA polymerase II C‐terminal domain by Rtt103p

Phosphorylation patterns of the C‐terminal domain (CTD) of largest subunit of RNA polymerase II (called the CTD code) orchestrate the recruitment of RNA processing and transcription factors. Recent studies showed that not only serines and tyrosines but also threonines of the CTD can be phosphorylated with a number of functional consequences, including the interaction with yeast transcription termination factor, Rtt103p. Here, we report the solution structure of the Rtt103p CTD‐interacting domain (CID) bound to Thr4 phosphorylated CTD, a poorly understood letter of the CTD code. The structure reveals a direct recognition of the phospho‐Thr4 mark by Rtt103p CID and extensive interactions involving residues from three repeats of the CTD heptad. Intriguingly, Rtt103p's CID binds equally well Thr4 and Ser2 phosphorylated CTD. A doubly phosphorylated CTD at Ser2 and Thr4 diminishes its binding affinity due to electrostatic repulsion. Our structural data suggest that the recruitment of a CID‐containing CTD‐binding factor may be coded by more than one letter of the CTD code.
Game Theory based Incentive Mechanisms in Delay Tolerant Network

Delay Tolerant Networking is the art and science of moving bits around in bad situations, such as between distant endpoints in deep space, or among unknown mobile users at uncertain positions. It is an opportunistic network where there is lack of end to end connectivity between source and destination, so that the communications take place using store-carry-and-forward manner. The rational parties send the bundle to its destination using the basic store-carry-andforward method. DTN has a potential assumption that the nodes are willing to help other node for packet forwarding. But in reality, selfish behavior always exists. Many incentive schemes are proposed to address the selfish behavior of node. This paper analyzes a game theory based incentive mechanism to address the selfishness problem of node and stimulate the intermediate nodes to forward the bundles towards the destination node for better packet delivery.
The Webgraph framework II: codes for the World-Wide Web

This paper describes the Web graph framework that provides simple methods to manage very large graphs, specially tailored around Web graphs. A fundamental observation about compression of the web graph was made in the construction of the LINK database. Web graph contains a fully-documented implementation of various instantaneous codes, and of parameterisable compression algorithms that achieve the best compression rates and introducing a new technique called intervalisation. Web graph also contains algorithms for accessing a compressed graph.
LeanStore: In-Memory Data Management beyond Main Memory

Disk-based database systems use buffer managers in order to transparently manage data sets larger than main memory. This traditional approach is effective at minimizing the number of I/O operations, but is also the major source of overhead in comparison with in-memory systems. To avoid this overhead, in-memory database systems therefore abandon buffer management altogether, which makes handling data sets larger than main memory very difficult. In this work, we revisit this fundamental dichotomy and design a novel storage manager that is optimized for modern hardware. Our evaluation, which is based on TPC-C and micro benchmarks, shows that our approach has little overhead in comparison with a pure in-memory system when all data resides in main memory. At the same time, like a traditional buffer manager, it is fully transparent and can manage very large data sets effectively. Furthermore, due to low-overhead synchronization, our implementation is also highly scalable on multi-core CPUs.
CELL-MATRIX MECHANICAL INTERACTIONS: MODELLING AN ACTIVE, DEFORMABLE CELL ON A HOMOGENEOUS SUBSTRATE IN 3D

SUMMARY Based on a particle-based model used to describe initial, passive cell-spreading, a model for endothelial cell active spreading and migration has been developed. To describe the interaction of this cell-model with a homogeneous viscoelastic solid substrate, the latter has been implemented using the non-inertial smoothed-particle hydrodynamics method. The combination of these models is validated using traction force microscopy on individual cells in addition to geometrical measures such as contact area and eccentricity. This mechanistic validated model allows for a detailed computational investigation of cell-matrix interaction in both 2D and 3D environments.
BrainScope: interactive visual exploration of the spatial and temporal human brain transcriptome

Abstract Spatial and temporal brain transcriptomics has recently emerged as an invaluable data source for molecular neuroscience. The complexity of such data poses considerable challenges for analysis and visualization. We present BrainScope: a web portal for fast, interactive visual exploration of the Allen Atlases of the adult and developing human brain transcriptome. Through a novel methodology to explore high-dimensional data (dual t-SNE), BrainScope enables the linked, all-in-one visualization of genes and samples across the whole brain and genome, and across developmental stages. We show that densities in t-SNE scatter plots of the spatial samples coincide with anatomical regions, and that densities in t-SNE scatter plots of the genes represent gene co-expression modules that are significantly enriched for biological functions. We also show that the topography of the gene t-SNE maps reflect brain region-specific gene functions, enabling hypothesis and data driven research. We demonstrate the discovery potential of BrainScope through three examples: (i) analysis of cell type specific gene sets, (ii) analysis of a set of stable gene co-expression modules across the adult human donors and (iii) analysis of the evolution of co-expression of oligodendrocyte specific genes over developmental stages. BrainScope is publicly accessible at www.brainscope. nl.
A spectral/finite difference method for simulating large deformations of heterogeneous, viscoelastic materials

SUMMARY A numerical algorithm is presented that simulates large deformations of heterogeneous, viscoelastic materials in two dimensions. The algorithm is based on a spectral/finite difference method and uses the Eulerian formulation including objective derivatives of the stress tensor in the rheological equations. The viscoelastic rheology is described by the linear Maxwell model, which consists of an elastic and viscous element connected in series. The algorithm is especially suitable to simulate periodic instabilities. The derivatives in the direction of periodicity are approximated by spectral expansions, whereas the derivatives in the direction orthogonal to the periodicity are approximated by finite differences. The 1-D Eulerian finite difference grid consists of centre and nodal points and has variable grid spacing. Time derivatives are approximated with finite differences using an implicit strategy with a variable time step. The performance of the numerical code is demonstrated by calculation, for the first time, of the pressure field evolution during folding of viscoelastic multilayers. The algorithm is stable for viscosity contrasts up to 5r10 5 , which demonstrates that spectral methods can be used to simulate dynamical systems involving large material heterogeneities. The successful simulations show that combined spectral/finite difference methods using the Eulerian formulation are a promising tool to simulate mechanical processes that involve large deformations, viscoelastic rheologies and strong material heterogeneities.
Social Commerce In Retaling - Why You Use IT?

Social media has not only reshaped the way people make decisions, but also changed the norms of how people interact with others. Due to the popularity of social media, social commerce is becoming a new form of e-commerce approaches. This study attempts to investigate what motivates individuals’ use of social commerce in the context of grocery retailing service based on the theoretical framework of the Uses and Gratifications theory. The empirical data was collected from a Finnish social commerce website offering a social media environment for social commerce in grocery services. Based on both survey data and clickstream data, we found that i) individuals are motivated to use social commerce in grocery retailing service mainly due to their utilitarian gratification in using it as social commerce platform can meet their functional needs; ii) social gratification plays weak role in determining social commerce in grocery retailing service as individuals has less social needs compared to their functional needs in social commerce; and iii) hedonic gratification might be a potential reason as the hedonic needs seems to be very weak. The functions (searching for products and recipes, compiling shopping lists and online shopping, etc.) offered by the social commerce services meet individuals’ functional needs, and motivate them to use social commerce. Finally the limitations of the current research are discussed, and the directions for further research are also suggested.
Designing an Internet-Based Multidomain Intervention for the Prevention of Cardiovascular Disease and Cognitive Impairment in Older Adults: The HATICE Trial.

BACKGROUND Many dementia and cardiovascular disease (CVD) cases in older adults are attributable to modifiable vascular and lifestyle-related risk factors, providing opportunities for prevention. In the Healthy Aging Through Internet Counselling in the Elderly (HATICE) randomized controlled trial, an internet-based multidomain intervention is being tested to improve the cardiovascular risk (CVR) profile of older adults. OBJECTIVE To design a multidomain intervention to improve CVR, based on the guidelines for CVR management, and administered through a coach-supported, interactive, platform to over 2500 community-dwellers aged 65+ in three European countries. METHODS A comparative analysis of national and European guidelines for primary and secondary CVD prevention was performed. Results were used to define the content of the intervention. RESULTS The intervention design focused on promoting awareness and self-management of hypertension, dyslipidemia, diabetes mellitus, and overweight, and supporting smoking cessation, physical activity, and healthy diet. Overall, available guidelines lacked specific recommendations for CVR management in older adults. The comparative analysis of the guidelines showed general consistency for lifestyle-related recommendations. Key differences, identified mostly in methods used to assess the overall CVR, did not hamper the intervention design. Minor country-specific adaptations were implemented to maximize the intervention feasibility in each country. CONCLUSION Despite differences in CVR management within the countries considered, it was possible to design and implement the HATICE multidomain intervention. The study can help define preventative strategies for dementia and CVD that are applicable internationally.
Posterior population expansion for solving inverse problems

Solving inverse problems in a complex, geologically realistic, and discrete model space and from a sparse set of observations is a very challenging task. Extensive exploration by Markov chain Monte Carlo (McMC) methods often results in considerable computational efforts. Most optimization methods, on the other hand, are limited to linear (continuous) model spaces and the minimization of an objective function, what often proves to be insufficient. To overcome these problems, we propose a new ensemble‐based exploration scheme for geostatistical prior models generated by a multiple‐point statistics (MPS) tool. The principle of our method is to expand an existing set of models by using posterior facies information for conditioning new MPS realizations. The algorithm is independent of the physical parametrization. It is tested on a simple synthetic inverse problem. When compared to two existing McMC methods (iterative spatial resampling (ISR) and Interrupted Markov chain Monte Carlo (IMcMC)), the required number of forward model runs was divided by a factor of 8–12.
MERGENT C OMPLEXITY VIA M ULTI-A GENT

Reinforcement learning algorithms can train agents that solve problems in complex, interesting environments. Normally, the complexity of the trained agent is closely related to the complexity of the environment. This suggests that a highly capable agent requires a complex environment for training. In this paper, we point out that a competitive multi-agent environment trained with self-play can produce behaviors that are far more complex than the environment itself. We also point out that such environments come with a natural curriculum, because for any skill level, an environment full of agents of this level will have the right level of difficulty. This work introduces several competitive multi-agent environments where agents compete in a 3D world with simulated physics. The trained agents learn a wide variety of complex and interesting skills, even though the environment themselves are relatively simple. The skills include behaviors such as running, blocking, ducking, tackling, fooling opponents, kicking, and defending using both arms and legs. A highlight of the learned behaviors can be found here: https://goo.gl/eR7fbX.
Barium sulfate/regenerated cellulose composite fiber with X-ray radiation resistance

In this article, submicron barium sulfate particles, as the radiation-resistant component, were incorporated into regenerated cellulose spinning solution. Then a series of X-ray radiation-resistant fibers were fabricated via a primarily industrialized wet-spinning trail, and the resultant fibers were knitted into fabrics by knitting loom. The morphology and structure of the fibers were studied with the aid of scanning electron micrography, Fourier-transform infrared spectroscopy, and X-ray diffraction. The composite fibers exhibited reasonably good properties, which met the criteria of mechanical requirements of commercial textiles—dry breaking strength and elongation (>1.5 cN/dtex and 26%) and wet breaking strength and elongation (>1.4 cN/dtex and 22%) and permanent laundry-resistant abilities even after being washed 20 times. An effective and feasible X-ray radiation-resistant method, the medical digital X-ray photography system, was proposed to evaluate the radiation resistance of the composite fiber and its fabric. The X-ray attenuation ratio of the sample tended to increase with increasing barium sulfate content and finally reached a dose of a 0.1 mmPb lead equivalent. Therefore, these fibers and fabrics can be utilized as the base materials for X-ray radiation-resistant lightweight apparel and detective surgical yarn.
Balancing Weighted Substreams in MIMO Interference Channels

Substreams refer to the streams of each user in a system. Substream weighting, where the weights determine the prioritization order, can be important in multiple-input multiple-output interference channels. In this letter, a distributed algorithm is proposed for the problem of power minimization subject to weighted SINR constraint. The algorithm is based on two basic features, the well-known distributed power control algorithm by Yates in 1995 and a simple linear search to find feasible SINR targets. The power control law used in the proposed algorithm is proven to linearly converge to a unique fixed point.
Information Theory of Data Privacy

By combining Shannon's cryptography model with an assumption to the lower bound of adversaries' uncertainty to the queried dataset, we develop a secure Bayesian inference-based privacy model and then in some extent answer Dwork et al.'s question [1]: "why Bayesian risk factors are the right measure for privacy loss". This model ensures an adversary can only obtain little information of each individual from the model's output if the adversary's uncertainty to the queried dataset is larger than the lower bound. Importantly, the assumption to the lower bound almost always holds, especially for big datasets. Furthermore, this model is flexible enough to balance privacy and utility: by using four parameters to characterize the assumption, there are many approaches to balance privacy and utility and to discuss the group privacy and the composition privacy properties of this model.
A new sub-multilevel inverter with reduced number of components

During a few decades many multilevel topologies have been proposed. Contemporary research has engaged for new multilevel inverter topology by unique circuits. This paper presents a new sub-module inverter for cascaded multilevel inverters with reduced number of count. The recommended inverter generates 17 step by four DC links (only two DC link are unequal) and 8 power switches. The suggested sub-module inverter generates all voltage levels (positive, negative and zero) at the output without any additional circuit. Also, a cascaded connection for suggested structure is proposed. The advantages of the suggested cascaded connection are reduction number of components such as semiconductor switches, driver, IGBTs, the variety of the value of DC links and DC links in comparison with classical cascaded structures and other new structures which presented in literatures. For specifying the magnitudes of the DC links three new methods are calculated in order to produce more voltage steps, too. PSCAD/EMTDC simulation outcome are demonstrated to confirm the suggested structure excellent operation.
Turbo Compressed Sensing with Partial DFT Sensing Matrix

In this letter, we propose a turbo compressed sensing algorithm with partial discrete Fourier transform (DFT) sensing matrices. Interestingly, the state evolution of the proposed algorithm is shown to be consistent with that derived using the replica method. Numerical results demonstrate that the proposed algorithm outperforms the well-known approximate message passing (AMP) algorithm when a partial DFT sensing matrix is involved.
An equivalent synchronous generator model for current-controlled voltage source converters considering the dynamic of phase-locked-loop

This paper presents an equivalent synchronous generator model (ESGM) of current-controlled (CC) voltage source converters (VSCs) for small signal stability analysis, addressing the pivotal dynamic of the universal phase-locked-loop (PLL). The virtual electromechanical swing process of PLL-based CC-VSC is revealed, which is utilized to emulates the behavior of traditional SG, and the ESGM (including inertia constant, synchronizing coefficient, damping coefficient, and power-angle curve) is developed accordingly. It provides the foundation of migrating the well-developed theories and methodologies of traditional power system to investigate the frequency and power angle stability of the VSC-based system or the hybrid system containing several VSCs and traditional SGs.
C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

Recently, significant accuracy improvement has been achieved for acoustic recognition systems by increasing the model size of Long Short-Term Memory (LSTM) networks. Unfortunately, the ever-increasing size of LSTM model leads to inefficient designs on FPGAs due to the limited on-chip resources. The previous work proposes to use a pruning based compression technique to reduce the model size and thus speedups the inference on FPGAs. However, the random nature of the pruning technique transforms the dense matrices of the model to highly unstructured sparse ones, which leads to unbalanced computation and irregular memory accesses and thus hurts the overall performance and energy efficiency. In contrast, we propose to use a structured compression technique which could not only reduce the LSTM model size but also eliminate the irregularities of computation and memory accesses. This approach employs block-circulant instead of sparse matrices to compress weight matrices and reduces the storage requirement from $\mathcalO (k^2)$ to $\mathcalO (k)$. Fast Fourier Transform algorithm is utilized to further accelerate the inference by reducing the computational complexity from $\mathcalO (k^2)$ to $\mathcalO (k\textlog k)$. The datapath and activation functions are quantized as 16-bit to improve the resource utilization. More importantly, we propose a comprehensive framework called C-LSTM to automatically optimize and implement a wide range of LSTM variants on FPGAs. According to the experimental results, C-LSTM achieves up to 18.8X and 33.5X gains for performance and energy efficiency compared with the state-of-the-art LSTM implementation under the same experimental setup, and the accuracy degradation is very small.
Characterizing disease-associated changes in post-translational modifications by mass spectrometry

ABSTRACT Introduction: Exploring post-translational modifications (PTMs) with the use of mass spectrometry (PTMomics) is a rapidly developing area, with methods for discovery/quantification being developed and advanced on a regular basis. PTMs are highly important for the regulation of protein function, interaction and activity, both in physiological and disease states. Changes in PTMs can either cause, or be the result of a disease, making them central for biomarker studies and studies of disease pathogenesis. Recently, it became possible to study multiple PTMs simultaneously from low amount of sample material, thereby increasing coverage of the PTMome obtainable from a single sample. Thus, quantitative PTMomics holds great potential to discover biomarkers from tissue and body fluids as well as elucidating disease mechanisms through characterization of signaling pathways. Areas covered: Recent mass spectrometry-based methods for assessment of the PTMome, with focus on the most studied PTMs, are highlighted. Furthermore, both data dependent and data independent acquisition methods are evaluated. Finally, current challenges in the field are discussed. Expert commentary: PTMomics holds great potential for clinical and biomedical research, especially with the generation of spectral libraries of peptides and PTMs from individual patients (permanent PTM maps) for use in personalized medicine.