Miracle-dz/UniFAR-FTUs huggingface dataset:
A double‐blind randomised feasibility trial of angiotensin‐2 in cardiac surgery*

Acute kidney injury is common after cardiac surgery. Vasoplegic hypotension may contribute to kidney injury, and different vasopressors may have variable effects on kidney function. We conducted a double‐blind, randomised feasibility trial comparing peri‐operative angiotensin‐2 with noradrenaline. We randomly allocated 60 patients at two centres to a blinded equipotent angiotensin‐2 or noradrenaline infusion intra‐operatively and for up to 48 h postoperatively, titrated to mean arterial pressure of 70–80 mmHg. Primary feasibility outcomes included consent rate, protocol adherence, infusion duration, mean arterial pressure maintenance in the target range and major adverse outcomes. Secondary outcomes included kidney injury rate. The consent rate was 47%. Protocol adherence was 100% in the angiotensin‐2 group and 94% in the noradrenaline group. Study drug duration was median (IQR [range]) 217 (160–270 [30–315]) vs. 185 (135–301 [0–480]) min (p = 0.78) min intra‐operatively, and 5 (0–16 [0–48]) vs. 14.5 (4.8–29 [0–48]) hours (p = 0.075) postoperatively for angiotensin‐2 and noradrenaline, respectively. The mean arterial pressure target was achieved postoperatively in 25 of 28 (89%) of the angiotensin‐2 group and 27 of 32 (84%) of the noradrenaline group. One participant had a stroke, one required extracorporeal support and three required renal replacement therapy, all in the noradrenaline group (p = 0.99, p = 0.99 and p = 0.1). Acute kidney injury occurred in 7 of 28 in the angiotensin‐2 group vs. 12 of 32 patients in the noradrenaline group (p = 0.31). This pilot study suggests that a trial comparing angiotensin‐2 with noradrenaline is feasible. Its findings justify further investigations of angiotensin‐2 in cardiac surgery.
Evaluation of hourly forecasts of the European Centre for Medium‐Range Weather Forecasts over the southeastern extension of the Tibetan Plateau

The accuracy of rainfall and low‐level atmospheric circulation forecasts of the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) is evaluated through comparison with gauge observations during the warm seasons (May–September) from 2019 to 2020 over the southeastern extension of the Tibetan Plateau (SETP). The IFS can suitably capture the spatial distribution of the rainfall frequency over the SETP but generally overestimates the rainfall amount and frequency and underestimates the rainfall intensity. The IFS can hardly simulate the early‐morning and morning peaks in the gauge observations. In two regions along the southern edges of the SETP, exhibiting distinct diurnal features, the difference in the peak time of the rainfall frequency between these two key regions cannot be reproduced in IFS simulations. The simulated rainfall occurs earlier at night, which can be related to the earlier strengthening of southerlies and water vapour flux in the IFS simulations. In the afternoon, the low‐elevation atmosphere is warmer and wetter, in addition to more unstable energy in the IFS simulations, which can contribute to the forecast deviation in the afternoon rainfall frequency.
Quantitative proteomics analysis of FFPE tumor samples reveals the influences of novel targeting nanobubbles conjugated with NET-1 siRNA on tetraspanin protein involved in HCC CURRENT STATUS: UNDER REVIEW

Background: Hepatocellular carcinoma (HCC) poses a severe threat to human health. The NET-1 protein has been proved to be strongly associated with HCC proliferation and metastasis in our previous study. Methods: Here, we developed a label-free proteome mass spectrometry workflow to analyze formalin-fixed and paraffin-embedded HCC xenograft samples collected in our previous study. Results: The result showed that 78 proteins were differentially expressed after NET-1 protein inhibited. Among them, the expression of 61 proteins up-regulated and the expression of 17 proteins were significantly down-regulated. Of the differentially expressed proteins, the vast majority of Gene Ontology enrichment terms belong to the biological process. The KEGG pathway enrichment analysis showed that the 78 differentially expressed proteins significantly enriched in 45 pathways. We concluded that the function of the NET-1 gene is not only to regulate HCC but also to participate in a variety of biochemical metabolic pathways in the human body. Furthermore, the protein-protein interaction analysis indicated that the interactions of differentially expressed proteins are incredibly sophisticated. All the protein-protein interactions happened after the NET-1 gene has been silenced. Conclusions: Finally, our study also provides a useful proposal for targeted therapy based on tetraspanin proteins to treat HCC, and further mechanism investigations are needed to reveal a more detailed mechanism of action for NET-1 protein regulation of HCC.
Numerical Simulation of Flow around an Elliptical Cylinder at High Reynolds Numbers

High Reynolds number flows (Re= 0.5 ×106, 1 ×106, 2×106 and 3.6×106, based on the free stream velocity and cylinder diameter) covering the supercritical to upper-transition flow regimes around a two dimensional (2D) elliptical cylinder, have been investigated numerically using 2D Unsteady Reynolds-Averaged Navier-Stokes( URANS) equations with a standard high Reynolds number k- e turbulence model. The objective of the present study is to evaluate the coefficient of drag along the outer surface of elliptical cylinder with minor-to-major axis ratios of 0.4, 0.6, and 0.8 for different types of velocity input e.g. uniform, parabolic, triangular and plain shear velocity profiles. The results are compared with published data for circular cylinder in cross flow having uniform velocity. Drag of the elliptical cylinders is lower than a circular cylinder. Reductions in drag may be increased by making the streamlined cylinders more slender. Over the range of Reynolds number considered, an elliptical cylinder with an axis ratio equal to 0.6 reduces drag coefficient by 40 to 45 percent compared to that of a circular cylinder. Although the k- e model is known to yield less accurate predictions of flows with strong anisotropic turbulence, satisfactory results for engineering design purposes are obtained for high Reynolds number flows around an elliptical cylinder in the supercritical and upper-transition flow regimes.
First detailed analysis of multiple system V2083 Cyg

The main aim of this paper is the first detailed analysis of multiple system V2083 Cyg, to reveal its basic physical properties. The system was studied using the methods of light-curve and radial-velocity curve analysis, together with interferometric data from the visual pair obtained during the last century. It was found that the close subsystem contains two very similar stars of spectral type A7–8. Moreover, a third body is orbiting around this pair with a period of about 177 yr. Due to the discrepancy in the total mass derived with the two methods, the possibility arises that the third body is perhaps also a binary, or some object with lower luminosity but higher mass than a normal main-sequence star. Another explanation is that the Hipparcos value of parallax is incorrect and the system is much closer to the Sun.
Introduction to Classical Mechanics

CLASSICAL MECHANICS is a lucid introduction to Vectors, Classical Mechanics and Special Theory of Relativity. It give the student a firm grounding for advanced courses on Quantum Mechanics, Nuclear Physics, Electrodynamics, etc. Apart from covering basic topic like vector Algebra and Analysis, Mechanics of Single adn Systems of Particles, adn Motin of Rigid Body, it comprehensively discusses Langrangian Formulation, Hamilton's Equations, Collisions of Particles and Theroy of small Oscillations. It contains a systematic treatment of Actions of Paricle under different types of Forces and gives a unified approach to Fields and Potentials in inverse Square Law Force Field. Included is an appendix on coordinate System and an extensive Bibliography.The book meets complete requirements of undergraduate and post graduate students. It contains numerous questions and problems at the end of each chapter which should provide an invigorating exercise for the student and teacher.
Outage Constrained Robust Beamforming Optimization for Multiuser IRS-Assisted Anti-Jamming Communications With Incomplete Information

Malicious jamming attacks have been regarded as a serious threat to Internet of Things (IoT) networks, which can significantly degrade the Quality of Service (QoS) of users. This article utilizes an intelligent reflecting surface (IRS) to enhance anti-jamming performance due to its capability in reconfiguring the wireless propagation environment via dynamically adjusting each IRS reflecting elements. To enhance the communication performance against jamming attacks, a robust beamforming optimization problem is formulated in a multiuser IRS-assisted anti-jamming communications scenario with or without imperfect jammer’s channel state information (CSI). In addition, we further consider the fact that the jammer’s transmit beamforming can not be known at BS. Specifically, with no knowledge of jammers transmit beamforming, the total transmit power minimization problems are formulated subject to the outage probability requirements of legitimate users with the jammer’s statistical CSI, and signal-to-interference-plus-noise ratio requirements of legitimate users without the jammer’s CSI, respectively. By applying the decomposition-based large deviation inequality, Bernstein-type inequality, Cauchy–Schwarz inequality, and penalty nonsmooth optimization method, we efficiently solve the initial intractable and nonconvex problems. Numerical simulations demonstrate that the proposed anti-jamming approaches achieve superior anti-jamming performance and lower power-consumption compared to the non-IRS scheme and reveal the impact of key parameters on the achievable system performance.
Multi-Dimensional Data Publishing With Local Differential Privacy

This paper studies the publication of multi-dimensional data with local differential privacy (LDP). This problem raises tremendous challenges in terms of both computational efficiency and data utility. The state-of-the-art solution addresses this problem by first constructing a junction tree (a kind of probabilistic graphical model, PGM) to generate a set of noisy low-dimensional marginals of the input data and then using them to approximate the distribution of the input dataset for synthetic data generation. However, there are two severe limitations in the existing solution, i.e., calculating a large number of attribute pairs’ marginals to construct the PGM and not solving well in calculating the marginal distribution of large cliques in the PGM, which degrade the quality of synthetic data. To address the above deficiencies, based on the sparseness of the constructed PGM and the divisibility of LDP, we first propose an incremental learning-based PGM construction method. In this method, we gradually prune the edges (attribute pairs) with weak correlation and allocate more data and privacy budgets to the useful edges, thereby improving the model’s accuracy. In this method, we introduce a high-precision data accumulation technique and a low-error edge pruning technique. Second, based on joint distribution decomposition and redundancy elimination, we propose a novel marginal calculation method for the large cliques in the context of LDP. Extensive experiments on real datasets demonstrate that our solution offers desirable data utility.
KWIQ: Answering k-core Window Queries in Temporal Networks

Understanding the evolution of communities and the factors that contribute to their development, stability and disappearance over time is a fundamental problem in the study of temporal networks. The concept of 𝑘 -core is one of the most popular metrics to detect communities. Since the 𝑘 -core of a temporal network changes with time, an important question arises: Are there nodes that always remain within the 𝑘 -core? In this paper, we explore this question by introducing the notion of core-invariant nodes . Given a temporal window ∆ and a parameter K , the core-invariant nodes are those that are part of the K -core throughout ∆. Core-invariant nodes have been shown to dictate the stability of networks, while being also useful in detecting anomalous behavior. The complexity of finding core-invariant nodes is 𝑂 ( | ∆ |×| 𝐸 | ), which is exorbitantly high for million-scale networks. We overcome this computational bottleneck by designing an algorithm called Kwiq. Kwiq efficiently processes the cascading impact of network updates through a novel data structure called orientation graph. Through extensive experiments on real temporal networks containing millions of nodes, we establish that the proposed pruning strategies are more than 5 times faster than baseline strategies.
Example-Driven Exploratory Analytics over Knowledge Graphs

Due to their expressive power, Knowledge Graphs (KGs) have received increasing interest not only as means to structure and integrate heterogeneous information but also as a native storage format for large amounts of knowledge and statistical data. Therefore, analytical queries over KG data, typically stored as RDF, have become increasingly important. Yet, formulating such queries represents a difficult task for users that are not familiar with the query language (typically SPARQL) and the structure of the dataset at hand. To overcome this limitation, we propose Re2xOLAP: the first comprehensive interactive approach that allows to reverse-engineer and refine RDF exploratory OLAP queries over KGs containing statistical data. Thus, Re2xOLAP enables to perform KG exploratory analytics without requiring the user to write any query at all. We achieve this goal by first reverse-engineering analytical SPARQL queries from a small set of user-provided examples and then, given the reverse-engineered query, we propose intuitive and explainable exploratory query refinements to iteratively help the user obtain the desired information. Our experiments on real-world large-scale KGs show that Re2xOLAP can efficiently reverse-engineer analytical SPARQL queries solely based on a small set of input examples. Additionally, we demonstrate the expressive power of our interactive refinement methods by showing that Re2xOLAP allows users to navigate hundreds of thousands of different exploration paths with just a few interactions.
Multi-Task Processing in Vertex-Centric Graph Systems: Evaluations and Insights

Vertex-centric (VC) graph systems are at the core of large-scale distributed graph processing. For such systems, a common usage pattern is the concurrent processing of multiple tasks ( multi-processing for short), which aims to execute a large number of unit tasks in parallel. In this paper, we point out that multi-processing has not been sufficiently studied or evaluated in previous work; hence, we fill this critical gap with three major contributions. First, we examine the tradeoff between two important measures in VC-systems: the number of communication rounds and message congestion . We show that this tradeoff is crucial to system performance; yet, existing approaches fail to achieve an optimal tradeoff, leading to poor performance. Second, based on extensive experimental evaluations on mainstream VC systems (e.g., Giraph, Pregel+, GraphD) and benchmark multi-processing tasks (e.g., Batch Personalized PageRanks, Multiple Source Shortest Paths), we present several important insights on the correlation between system performance and configurations, which is valuable to practitioners in optimizing system performance. Third, based on the insights drawn from our experimental evaluations, we present a cost-based tuning framework that optimizes the performance of a representative VC-system. This demonstrates the usefulness of the insights.
Learning with Surprise - Theory and Applications

Everybody knows what it feels to be surprised. Surprise raises our attention and is crucial for learning. It is a ubiquitous concept whose traces have been found in both neuroscience and machine learning. However, a comprehensive theory has not yet been developed that addresses fundamental problems about surprise : (1) surprise is difficult to quantify. How should we measure the level of surprise when we encounter an unexpected event ? What is the link between surprise and startle responses in behavioral biology ? (2) the key role of surprise in learning is somewhat unclear. We believe that surprise drives attention and modifies learning ; but, how should surprise be incorporated, in general paradigms of learning ? and (3) can we develop a biologically plausible theory that explains how surprise can be neurally calculated and implemented in the brain ? I propose a theoretical framework to address the above issues about surprise. There are three components to this framework : (1) a subjective confidence-adjusted measure of surprise, that can be used for quantification purposes, (2) a surprise-minimization learning rule that models the role of surprise in learning by balancing the relative contribution of new and old data for inference about the world, and (3) a surprisemodulated Hebbian plasticity rule that can be implemented in both artificial and spiking neural networks. The proposed online rule links surprise to the activity of the neuromodulatory system in the brain, and belongs to the class of neo-Hebbian plasticity rules. My work on the foundations of surprise provides a suitable framework for future studies on learning with surprise. Reinforcement learning methods can be enhanced by incorporating the proposed theory of surprise. The theory could ultimately become interesting for the analysis of fMRI and EEG data. It may also inspire new synaptic plasticity rules that are under the simultaneous control of reward and surprise. Moreover, the proposed theory can be used to make testable predictions about the time course of the neural substrate of surprise (e.g., noradrenaline), and suggests behavioral experiments that can be performed on real animals for studying surprise-related neural activity.
Progressive Entity Resolution over Incremental Data

Entity Resolution (ER) algorithms identify entity profiles corresponding to the same real-world entity among one or multiple data sets. Modern challenges for ER are posed by volume, variety, and velocity that characterize Big Data. While progressive ER aims to efficiently solve the problem under time constraints by prioritizing useful work over superfluous work, incremental ER aims to incrementally produce results as new data increments come in. This paper presents algorithms that combine these two approaches in the context of streaming and heterogeneous data. The overall goal is to maximize the chances to spot duplicates to a given entity profile in a moment closest to its arrival time (early quality), without relying on any schema information, while being sufficiently efficient to process large volumes of fast streaming data without compromising the eventual quality (by cutting too many corners for efficiency). Experiments validate that our algorithms are the first to support incremental and progressive ER and, compared to state-of-the-art incremental approaches, improve early quality, eventual quality, and system efficiency by progressively and adaptively performing the unexecuted comparisons that are more likely to match when waiting for the next stream input increment.
A Cyber-Physical Intelligent Container for Industry 4.0

Title : smaRTI - Manufacturing Abstract: Cyber-Physical Systems (CPSs) are becoming a significant research focus resulting from advancements in technologies such as the Internet of Things (IoT), Cloud Manufacturing and Intelligent Products. Successful deployment of CPSs has the potential to provide a step change in manufacturing efficiency, flexibility and production yield as envisaged by the fourth industrial revolution or Industry 4.0 paradigm. The realisation of intelligent products and services is in the provisioning of predictive, risk preventative and high-performance manufacturing systems. As part of these manufacturing systems, Returnable Transit Items (RTIs) play a critical role in provisioning robust and efficient means of component (e.g. Work in Progress and finished items) protection and logistics. The research outlined in this paper details how a Returnable Transit Item (RTI) can become an integral part of the Industrie 4.0 vision as an intelligent container that can interact with components, machines and other cyber-physical manufacturing services. This paper discusses a CPS reference architecture for the integration of intelligent containers and presents a hardware and software proof of concept solution suitable for industrial deployments. The paper concludes with feasibility studies utilising the intelligent container for context determination services including the identification of intelligent components and monitoring of logistical handling process (i.e. the detection of collisions, lifting and turns). been discussed within the freight industry and cold food supply chain, this research has implemented a proof of concept device for creating intelligent RTIs that have the ability to interact with intelligent components, its environment and an intelligent manufacturing system within the harsh automotive manufacturing domain. Through the identification of intelligent product definitions and requirements, a CPS reference architecture has been presented that identifies the need to be adaptable, scalable, extendable and secure. The instantiation of the CPS reference architecture has been presented in the form of an MQTT publish and subscribe service bus integrated within a series of Node-Red services for the rapid deployment of intelligent container services and user interfaces for investigation purposes.
CAN OF SPINACH, a novel long non-coding RNA, affects iron deficiency responses in Arabidopsis thaliana

Long non-coding RNAs (lncRNAs) are RNA molecules with functions independent of any protein-coding potential. A whole transcriptome (RNA-seq) study of Arabidopsis shoots under iron sufficient and deficient conditions was carried out to determine the genes that are iron-regulated in the shoots. We identified two previously unannotated transcripts on chromosome 1 that are significantly iron-regulated. We have called this iron-regulated lncRNA, CAN OF SPINACH (COS). cos mutants have altered iron levels in leaves and seeds. Despite the low iron levels in the leaves, cos mutants have higher chlorophyll levels than WT plants. Moreover, cos mutants have abnormal development during iron deficiency. Roots of cos mutants are longer than those of WT plants, when grown on iron deficient medium. In addition, cos mutant plants accumulate singlet oxygen during iron deficiency. The mechanism through which COS affects iron deficiency responses is unclear, but small regions of sequence similarity to several genes involved in iron deficiency responses occur in COS, and small RNAs from these regions have been detected. We hypothesize that COS is required for normal adaptation to iron deficiency conditions.