Miracle-dz/UniFAR-FTUs huggingface dataset:
Atrial natriuretic peptide and C-type natriuretic peptide in spontaneously hypertensive rats and their vasorelaxing actions in vitro.

The present study determined circulating concentrations of atrial natriuretic peptide (ANP) and C-type natriuretic peptide (CNP) in Wistar-Kyoto (WKY) rats and spontaneously hypertensive rats (SHR) and also investigated the vasorelaxing action of ANP and CNP on isolated contracted aorta. We also defined the vasorelaxing action of a novel and newly synthesized 27-amino acid chimera of ANP and CNP termed vasonatrin peptide (VNP), which we compared with ANP and CNP in WKY rats and SHR. Plasma and urinary cyclic GMP and sodium excretion were also investigated. Plasma ANP was increased in SHR in contrast to no change in circulating CNP. Plasma and urinary cyclic GMP and sodium excretion were no different between WKY rats and SHR. In WKY rats maximal relaxations to VNP in aortic rings without endothelium were greater than those to ANP and CNP. In SHR aortic rings the potency of VNP relaxation was preserved, the actions of ANP were enhanced, and the actions of CNP were markedly impaired. In association with these vasorelaxing actions, these data suggest that (1) circulating CNP is not different in SHR and WKY rats, but the aortic relaxing action of CNP is markedly impaired in SHR; (2) endogenous plasma ANP is significantly increased in SHR without associated increases in plasma or urinary cyclic GMP; (3) there is an increase in aortic relaxation to exogenous ANP in SHR; and (4) VNP has a potent endothelium-independent aortic relaxing action in both WKY rats and SHR. (ABSTRACT TRUNCATED AT 250 WORDS)
Fermihedral: On the Optimal Compilation for Fermion-to-Qubit Encoding

This paper introduces Fermihedral, a compiler framework focusing on discovering the optimal Fermion-to-qubit encoding for targeted Fermionic Hamiltonians. Fermion-to-qubit encoding is a crucial step in harnessing quantum computing for efficient simulation of Fermionic quantum systems. Utilizing Pauli algebra, Fermihedral redefines complex constraints and objectives of Fermion-to-qubit encoding into a Boolean Satisfiability problem which can then be solved with high-performance solvers. To accommodate larger-scale scenarios, this paper proposed two new strategies that yield approximate optimal solutions mitigating the overhead from the exponentially large number of clauses. Evaluation across diverse Fermionic systems highlights the superiority of Fermihedral, showcasing substantial reductions in implementation costs, gate counts, and circuit depth in the compiled circuits. Real-system experiments on IonQ's device affirm its effectiveness, notably enhancing simulation accuracy.
Green HPC: An analysis of the domain based on Top500

The demand in computing power has never stopped growing over the years. Today, the performance of the most powerful systems exceeds the exascale and the number of petascale systems continues to grow. Unfortunately, this growth also goes hand in hand with ever-increasing energy costs, which in turn means a significant carbon footprint. In view of the environmental crisis, this paper intents to look at the often hidden issue of energy consumption of HPC systems. As it is not easy to access the data of the constructors, we then consider the Top500 as the tip of the iceberg to identify the trends of the whole domain.The objective of this work is to analyze Top500 and Green500 data from several perspectives in order to identify the dynamic of the domain regarding its environmental impact. The contributions are to take stock of the empirical laws governing the evolution of HPC computing systems both from the performance and energy perspectives, to analyze the most relevant data for developing the performance and energy efficiency of large-scale computing systems, to put these analyses into perspective with effects and impacts (lifespan of the HPC systems) and finally to derive a predictive model for the weight of HPC sector within the horizon 2030.
MODIS ocean color product validation around the Yellow Sea and East China Sea

Ocean color product validation is vital to improve the algorithms and to secure subsequent applications. China coastal region is one of the most highly turbid waters in the world. The precision of NASA’s standard MODIS ocean color products are poor for this region. However, the validations have not been made due to the shortage of high quality in-situ data. In this paper, in-situ data collected in the Yellow Sea and the East China Sea in the spring and autumn of 2003 are used to validate the main parameters of MODIS Aqua standard ocean color product, i.e., the normalized water-leaving radiance LwN(λ) (converted to remote sensing reflectance Rrs(λ)) and chlorophyll-a concentration (Chl.a). The resolution of 3×3 pixel box and ±0.5h are used as the sampling window, and spatial homogeneity criteria are used to screen data. Based on the analysis of match-ups (in low and middle turbid waters), primary conclusions are obtained: (1) MODIS-derived Rrs(λ) can assure certain precisions but can not satisfy the expected error of 10%, and has obviously poor results in 412nm bands. There is good linear relationship between satellite images and modeled images. The overall root mean square error is 0.0030sr-1 and the median of absolute values over the relative error is about 25%, respectively. Derived Rrs(λ) is similar with in-situ ones in spectrum shape, and is mostly underestimated especially in high value region. Besides, Rrs(λ) at 412 and 667nm bands are mostly underestimated with even negative values in low value region. The reasons for this phenomenon are due to the influences of water-leaving radiance in the near-infrared bands and improper aerosol models used * 国家自然科学基金项目(40606043)、国家863计划项目(2007AA12Z145)和国家重点基础研究发展计划项目(2006CB40370) 联合资助. 2008-10-29收稿; 2008-12-03收修改稿. 孙凌, 女, 1977年生, 博士, 副研究员; E-mail: sunling@cma.gov. cn. 孙 凌等: MODIS水色产品在黄东海域的真实性检验 299 in the atmospheric correction process. (2) MODIS derived Chl.a is very poor. The satellite derived values are systematically overestimated. And there is no obvious linear relationship between satellite derived and in-situ Chl.a, which the root mean square error is about 1.56mg/m3, the median absolute relative error about 103.25%, and the median satellite to in-situ ratio about 2.03. The major reason for this phenomenon is the OC3M model (coefficients) unsuitability.
Empowering Data Mesh with Federated Learning

The evolution of data architecture has seen the rise of data lakes, aiming to solve the bottlenecks of data management and promote intelligent decision-making. However, this centralized architecture is limited by the proliferation of data sources and the growing demand for timely analysis and processing. A new data paradigm, Data Mesh, is proposed to overcome these challenges. Data Mesh treats domains as a first-class concern by distributing the data ownership from the central team to each data domain, while keeping the federated governance to monitor domains and their data products. Many multi-million dollar organizations like Paypal, Netflix, and Zalando have already transformed their data analysis pipelines based on this new architecture. In this decentralized architecture where data is locally preserved by each domain team, traditional centralized machine learning is incapable of conducting effective analysis across multiple domains, especially for security-sensitive organizations. To this end, we introduce a pioneering approach that incorporates Federated Learning into Data Mesh. To the best of our knowledge, this is the first open-source applied work that represents a critical advancement toward the integration of federated learning methods into the Data Mesh paradigm, underscoring the promising prospects for privacy-preserving and decentralized data analysis strategies within Data Mesh architecture.
2D Gaussian Splatting for Geometrically Accurate Radiance Fields

3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking. However, 3DGS fails to accurately represent surfaces due to the multi-view inconsistent nature of 3D Gaussians. We present 2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images. Our key idea is to collapse the 3D volume into a set of 2D oriented planar Gaussian disks. Unlike 3D Gaussians, 2D Gaussians provide view-consistent geometry while modeling surfaces intrinsically. To accurately recover thin surfaces and achieve stable optimization, we introduce a perspective-accurate 2D splatting process utilizing ray-splat intersection and rasterization. Additionally, we incorporate depth distortion and normal consistency terms to further enhance the quality of the reconstructions. We demonstrate that our differentiable renderer allows for noise-free and detailed geometry reconstruction while maintaining competitive appearance quality, fast training speed, and real-time rendering. Our code will be made publicly available.
Root foraging response to gradients of calcium and magnesium, essential bivalent cations with low mobility in the soil

Plants forage for nutrients by root proliferation in nutrient-rich patches. While foraging for nitrogen and phosphorus has been repeatedly confirmed, foraging for calcium and magnesium, which are essential for plant growth and form much more stable patches in the soil, has never been examined. We examined preferential root placement into dolomite-limestone-rich patches in a pot experiment with 17 species, and compared it with foraging for a nitrogen, phosphorus and potassium mixture (NPK). About one half of the species showed root proliferation in dolomite-rich patches. It was less pronounced than foraging for NPK and did not show any relationship to species field preferences to soil reaction, or dicots-grass difference, but it showed clear negative relationship to species-specific Ca+Mg tissue concentrations. While foraging for NPK shows the potential of species to change their root systems by proliferation, only some species use this potential to respond to the Ca+Mg gradient. The negative correlation of this response to Ca+Mg tissue concentrations implies that nonresponding species compensate for it by physiological mechanisms. The response to Ca+Mg also implies that in contrast to nitrogen, which never shows stable patches in the soil, Ca+Mg-rich patches, which are much more stable, can be exploited by root proliferation.
Analytic Variability in Immunohistochemistry Biomarker Studies

Background: Despite the widespread use of immunohistochemistry (IHC), there are no standardization guidelines that control for antibody probe variability. Here we describe the effect of variable antibody reagents in the assessment of cancer-related biomarkers by IHC. Methods: Estrogen receptor (ER), epidermal growth factor receptor (EGFR) 1, and human epidermal growth factor receptor 3 (HER3) were evaluated by quantitative immunofluorescence. Correlations between ER clones 1D5, SP1, F10, and ER60c, and EGFR monoclonal 31G7, 2-18C9, H11, and 15F8, and polyclonal 2232 antibodies were assessed in 642 breast cancer patients. HER3 was measured by RTJ1, RTJ2, SGP1, M7297, RB-9211, and C-17 antibodies in 42 lung cancer patients. Survival analysis was done with the use of multiple cutoff points to reveal any prognostic classification. Results: All ER antibodies were tightly correlated (Pearson's r2 = 0.94-0.96; P < 0.0001) and western blotting confirmed their specificity in MCF-7 and BT474 cells. All EGFR antibodies but 2232 yielded specific results in western blotting; however, only 31G7 and 2-18C9 were strongly associated (Pearson's r2 = 0.61; P < 0.0001). HER3 staining was nonspecific and nonreproducible. High EGFR–expressing patients had a worse prognosis when EGFR was measured with H11 or 31G7 (log rank P = 0.015 and P = 0.06). There was no statistically significant correlation between survival and EGFR detected by 2-18C9, 15F8, or polyclonal 2232 antibodies. Conclusions: Antibody validation is a critical analytic factor that regulates IHC readings in biomarker studies. Evaluation of IHC proficiency and quality control are key components toward IHC standardization. Impact: This work highlights the importance of IHC standardization and could result in the improvement of clinically relevant IHC protocols. Cancer Epidemiol Biomarkers Prev; 19(4); 982–91. ©2010 AACR.
Pseudo-pac site sequences used by phage P22 in generalized transduction of Salmonella

Salmonella enterica Serovar Typhimurium (Salmonella) and its bacteriophage P22 are a model system for the study of horizontal gene transfer by generalized transduction. Typically, the P22 DNA packaging machinery initiates packaging when a short sequence of DNA, known as the pac site, is recognized on the P22 genome. However, sequences similar to the pac site in the host genome, called pseudo-pac sites, lead to erroneous packaging and subsequent generalized transduction of Salmonella DNA. While the general genomic locations of the Salmonella pseudo-pac sites are known, the sequences themselves have not been determined. We used visualization of P22 sequencing reads mapped to host Salmonella genomes to define regions of generalized transduction initiation and the likely locations of pseudo-pac sites. We searched each genome region for the sequence with the highest similarity to the P22 pac site and aligned the resulting sequences. We built a regular expression (sequence match pattern) from the alignment and used it to search the genomes of two P22-susceptible Salmonella strains-LT2 and 14028S- for sequence matches. The final regular expression successfully identified pseudo-pac sites in both LT2 and 14028S that correspond with generalized transduction initiation sites in mapped read coverages. The pseudo-pac site sequences identified in this study can be used to predict locations of generalized transduction in other P22-susceptible hosts or to initiate generalized transduction at specific locations in P22-susceptible hosts with genetic engineering. Furthermore, the bioinformatics approach used to identify the Salmonella pseudo-pac sites in this study could be applied to other phage-host systems. Importance Bacteriophage P22 has been a genetic tool and a key model for the study of generalized transduction in Salmonella since the 1950s, yet certain components of the generalized transduction molecular mechanism remain unknown. Specifically, the locations and sequences of pseudo-pac sites, hypothesized to facilitate packaging of Salmonella DNA by P22, to date have not been determined. In this study, we identified the specific locations and sequences of the pseudo-pac sites frequently recognized by P22 in Salmonella genomes. The identification of highly efficient pseudo-pac sites in Salmonella provides fundamental insights into the sequence specificity necessary for P22 pac site recognition and opens the door to more targeted use of generalized transduction with P22.
Defining the constituents and functions of a lipid-based jumbo phage compartment

Viruses are vulnerable to cellular defenses at the start of the infection when a single copy genome has not yet initiated de novo viral protein synthesis. A family of jumbo phages related to phage ΦKZ, which infects Pseudomonas aeruginosa, uses a protein-based phage nucleus to protect the DNA during middle and late stages of infection, but how it is protected prior to phage nucleus assembly is unclear. To reveal the environment surrounding injected phage DNA, we determine which proteins interact with phage proteins injected with the phage genome. Here, we surprisingly identify host proteins related to membrane and lipid biology co-purifying with the injected phage protein. Staining of infected cells with lipophilic dyes revealed a clustering of host lipids co-localized with phage DNA and protein in an early phage infection (EPI) vesicle. Early gene expression is induced by the co-injected virion RNAP (vRNAP), with specific early proteins then associating with the EPI vesicle and ribosomes, likely to facilitate localized translation. As the infection progresses, the phage genome separates from the EPI vesicle and vRNAP, moving into the nascent proteinaceous phage nucleus. Enzymes involved in DNA replication and CRISPR/restriction immune nucleases notably do not localize to the EPI vesicle, supporting that DNA is not exposed at this early time point. We therefore propose that the EPI vesicle is rapidly constructed together with injected phage proteins, phage DNA, host lipids, and host membrane proteins to enable genome protection, early transcription, localized translation, and to ensure the faithful transfer of the single copy phage genome to the proteinaceous nucleus.
Statistical methods for multivariate interval‐censored recurrent events

Multi‐type recurrent event data arise when two or more different kinds of events may occur repeatedly over a period of observation. The scientific objectives in such settings are often to describe features of the marginal processes and to study the association between the different types of events. Interval‐censored multi‐type recurrent event data arise when the precise event times are unobserved, but intervals are available during which the events are known to have occurred. This type of data is common in studies of patients with advanced cancer, for example, where the events may represent the development of different types of metastatic lesions which are only detectable by conducting bone scans of the entire skeleton. In this setting it is of interest to characterize the incidence of the various types of bone lesions, to estimate the impact of treatment and other covariate effects on the development of new lesions, and to understand the relationship between the processes generating the bone lesions. We develop joint models for multi‐type interval‐censored recurrent events which accommodate dependencies between different types of events and enable one to examine the covariate effects via regression. However, since the marginal likelihood resulting from the multivariate random effect model is intractable, we describe a Gibbs sampling algorithm to facilitate model fitting and inference. We use generalized estimating equations for estimation and inference based on marginal models. The finite sample properties of the marginal approach are studied via simulation. The estimates of both the regression coefficients and the variance–covariance parameters are shown to have negligible bias and 95 per cent confidence intervals based on the asymptotic variance formula are shown to have excellent empirical coverage probabilities in all of the settings considered. The application of these methods to data from a trial of women with advanced breast cancer provides insight into the clinical course of bone metastases in this population. Copyright © 2004 John Wiley & Sons, Ltd.
Using Virtualization to Improve Software Rejuvenation

In this paper, we present an approach for software rejuvenation based on automated self-healing techniques that can be easily applied to off-the-shelf application servers. Software aging and transient failures are detected through continuous monitoring of system data and performability metrics of the application server. If some anomalous behavior is identified, the system triggers an automatic rejuvenation action. This self-healing scheme is meant to disrupt the running service for a minimal amount of time, achieving zero downtime in most cases. In our scheme, we exploit the usage of virtualization to optimize the self-recovery actions. The techniques described in this paper have been tested with a set of open-source Linux tools and the XEN virtualization middleware. We conducted an experimental study with two application benchmarks (Tomcat/Axis and TPC-W). Our results demonstrate that virtualization can be extremely helpful for failover and software rejuvenation in the occurrence of transient failures and software aging.
Locally Planar Patch Features for Real-Time Structure from Motion

The performance of sequential structure from motion systems, where scene mapping is sparse to permit real-time operation, depends greatly on the ability to repeatedly measure the same visual features from a wide range of viewpoints. While previous systems have tracked features as 2D templates in image space, we show that long-term tracking is improved by treating salient feature patches as observations of locally planar regions on 3D world surfaces. Within a SLAM framework for motion and structure estimation, a gradient-based image alignment method is used to deduce estimates feature surface normal estimates, enabling pre-warping of templates for matching. As an added benet these normals provide a richer description of the scene.
MorphVAE: Advancing Morphological Design of Voxel-Based Soft Robots with Variational Autoencoders

Soft robot design is an intricate field with unique challenges due to its complex and vast search space. In the past literature, evolutionary computation algorithms, including novel probabilistic generative models (PGMs), have shown potential in this realm. However, these methods are sample inefficient and predominantly focus on rigid robots in locomotion tasks, which limit their performance and application in robot design automation. In this work, we propose MorphVAE, an innovative PGM that incorporates a multi-task training scheme and a meticulously crafted sampling technique termed ``continuous natural selection'', aimed at bolstering sample efficiency. This method empowers us to gain insights from assessed samples across diverse tasks and temporal evolutionary stages, while simultaneously maintaining a delicate balance between optimization efficiency and biodiversity. Through extensive experiments in various locomotion and manipulation tasks, we substantiate the efficiency of MorphVAE in generating high-performing and diverse designs, surpassing the performance of competitive baselines.
Enhancing Multi-Scale Diffusion Prediction via Sequential Hypergraphs and Adversarial Learning

Information diffusion prediction plays a crucial role in understanding the propagation of information in social networks, encompassing both macroscopic and microscopic prediction tasks. Macroscopic prediction estimates the overall impact of information diffusion, while microscopic prediction focuses on identifying the next user to be influenced. While prior research often concentrates on one of these aspects, a few tackle both concurrently. These two tasks provide complementary insights into the diffusion process at different levels, revealing common traits and unique attributes. The exploration of leveraging common features across these tasks to enhance information prediction remains an underexplored avenue. In this paper, we propose an intuitive and effective model that addresses both macroscopic and microscopic prediction tasks. Our approach considers the interactions and dynamics among cascades at the macro level and incorporates the social homophily of users in social networks at the micro level. Additionally, we introduce adversarial training and orthogonality constraints to ensure the integrity of shared features. Experimental results on four datasets demonstrate that our model significantly outperforms state-of-the-art methods.