Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognostic Value of Urinary Calprotectin, NGAL and KIM-1 in Chronic Kidney Disease

Background/Aims: Urinary biomarkers like neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) do not only allow an early diagnosis of acute kidney injury, but also provide prognostic information in this setting. The present prospective study investigates, whether the urinary biomarkers NGAL, KIM-1 and calprotectin have prognostic information in chronic kidney disease (CKD) as well. Methods: Urinary calprotectin, NGAL and KIM-1 concentrations were assessed in a study population of 143 patients with stable CKD comprising diabetic and hypertensive nephropathy, glomerulonephritis/vasculitis, and autosomal dominant polycystic kidney disease. An eGFR fluctuation > 5ml/min/1.73m2 in the past 12 months was defined as an exclusion criterion in order to exclude cases with acute on chronic kidney injury. Renal function was monitored for a median follow-up of 37 months. Results: In the overall study population, all the three biomarkers failed to predict DeGFR and DACR from baseline to follow-up in linear regression analysis adjusted for age, gender, and baseline eGFR. Contrarily, baseline ACR was significantly associated with DeGFR (p< 0.001). In the subgroup of patients with vasculitis and glomerulonephritis, all the three biomarkers were significantly associated with DeGFR, with calprotectin having the highest regression coefficient. Conclusion: In contrast to the traditional biomarker “albuminuria”, neither the inflammatory biomarker calprotectin, nor the tubular biomarkers NGAL and KIM-1, provide robust prognostic information on the loss or renal function in a heterogeneous CKD population. All of them, however, are candidate prognostic biomarkers in primarily inflammatory renal diseases.
Ecosystem function in predator–prey food webs—confronting dynamic models with empirical data

Most ecosystem functions and related services involve species interactions across trophic levels, for example, pollination and biological pest control. Despite this, our understanding of ecosystem function in multitrophic communities is poor, and research has been limited to either manipulation in small communities or statistical descriptions in larger ones. Recent advances in food web ecology may allow us to overcome the trade-off between mechanistic insight and ecological realism. Molecular tools now simplify the detection of feeding interactions, and trait-based approaches allow the application of dynamic food web models to real ecosystems. We performed the first test of an allometric food web model's ability to replicate temporally nonaggregated abundance data from the field and to provide mechanistic insight into the function of predation. We aimed to reproduce and explore the drivers of the population dynamics of the aphid herbivore Rhopalosiphum padi observed in ten Swedish barley fields. We used a dynamic food web model, taking observed interactions and abundances of predators and alternative prey as input data, allowing us to examine the role of predation in aphid population control. The inverse problem methods were used for simultaneous model fit optimization and model parameterization. The model captured >70% of the variation in aphid abundance in five of ten fields, supporting the model-embodied hypothesis that body size can be an important determinant of predation in the arthropod community. We further demonstrate how in-depth model analysis can disentangle the likely drivers of function, such as the community's abundance and trait composition. Analysing the variability in model performance revealed knowledge gaps, such as the source of episodic aphid mortality, and general method development needs that, if addressed, would further increase model success and enable stronger inference about ecosystem function. The results demonstrate that confronting dynamic food web models with abundance data from the field is a viable approach to evaluate ecological theory and to aid our understanding of function in real ecosystems. However, to realize the full potential of food web models, in ecosystem function research and beyond, trait-based parameterization must be refined and extended to include more traits than body size.
On Generalizing Collective Spatial Keyword Queries

With the proliferation of spatial-textual data such as location-based services and geo-tagged websites, spatial keyword queries are ubiquitous in real life. One example of spatial-keyword query is the so-called <italic>collective spatial keyword query</italic> (CoSKQ) which is to find for a given query consisting a query location and several query keywords a set of objects which <italic>covers</italic> the query keywords collectively and has the smallest <italic>cost</italic> wrt the query location. In the literature, many different functions were proposed for defining the <inline-formula><tex-math notation="LaTeX">${cost}$</tex-math><alternatives> <inline-graphic xlink:href="chan-ieq1-2800746.gif"/></alternatives></inline-formula> and correspondingly, many different approaches were developed for the CoSKQ problem. In this paper, we study the CoSKQ problem systematically by proposing <italic>a unified cost function</italic> and <italic>a unified approach</italic> for the CoSKQ problem (with the unified cost function). The unified cost function includes all existing cost functions as special cases and the unified approach solves the CoSKQ problem with the unified cost function in a unified way. Experiments were conducted on both real and synthetic datasets which verified our proposed approach.
Effect of Relative Versus Absolute Quantitative Messages Regarding Suicide on the Perceived Severity of the Phenomenon

Presentation of epidemiological information about the absolute number of suicides has been used occasionally to increase awareness of the suicide phenomenon. This study investigated the effectiveness of absolute quantitative messages versus relative messages. An example of relative messages would be comparing the number of suicide deaths with the number of deaths by car accidents. Participants were randomly presented with absolute or relative messages regarding suicide. They were then requested to indicate the degree of severity they attributed to the suicide phenomenon, being the level to which they view the phenomenon as sufficiently important to mandate national intervention and resource allocation. Relative messages proved more effective than absolute ones in yielding greater perceived severity of suicide. Increasing public and policymakers’ awareness of the suicide phenomenon could be aided by presenting information in relative terms.
Wideband Techniques for Outphasing Power Amplifiers

In this paper a wideband, high-power amplifier that achieves an output power of 20 W with a bandwidth greater than one octave in the L and S bands is presented. Two ~10 W Class AB PAs are implemented with Gallium Nitride-high electron mobility transistor devices and a low loaded-Q matching network to achieve wideband performance. High power added efficiency (PAE) is achieved by combining outphased saturated power amplifiers using a wideband isolated combiner. We introduce new analysis detailing proper selection of the isolation resistance in isolated outphasing combiners. The impedance transforming Wilkinson combiner is designed to interface the system with a 50-<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> load. The L-S band (1.2~2.5GHz) amplifier was simulated, fabricated, and characterized. The fabricated HPA provides a peak output power of 43 dBm, gain of >15 dB with a peak PAE of >40% across the band. A 10 MHz, 64 QAM, long-term evolution signal is measured and achieves ACLR < −30 dBc, EVM = 4.8%-rms at an average output power and efficiency of 39.3 dBm and 33.1%, respectively.
Digital Twins: The Convergence of Multimedia Technologies

Originally developed to improve manufacturing processes, digital twins are being redefined as digital replications of living as well as nonliving entities that enable data to be seamlessly transmitted between the physical and virtual worlds. Digital twins facilitate the means to monitor, understand, and optimize the functions of all physical entities and for humans provide continuous feedback to improve quality of life and well-being.
Image-Guided Development of Heterocyclic Sulfoxides as Ligands for Tau Neurofibrillary Tangles: From First-in-Man to Second-Generation Ligands

Positron emission tomography (PET) imaging of misfolded protein aggregates that form in neurodegenerative processes of the brain is key to providing a robust marker for improved diagnosis and evaluation of treatments. We report the development of advanced radiotracer candidates based on the sulfoxide scaffold found in proton pump inhibitors (lansoprazole, prevacid) with inherent affinity to neurofibrillary tangles in Alzheimer’s disease and related disorders (e.g., dementia with Lewy bodies and the frontotemporal degeneration syndrome). First-in-man results obtained with [18F]lansoprazole and N-methyl-[18F]lansoprazole were used to guide the design of a set of 24 novel molecules with suitable properties for neuroimaging with PET. Compounds were synthesized and characterized pharmacologically, and the binding affinity of the compounds to synthetic human tau-441 fibrils was determined. Selectivity of binding was assessed using α-synuclein and β-amyloid fibrils to address the key misfolded proteins of relevance in dementia. To complete the pharmacokinetic profiling in vitro, plasma protein binding and lipophilicity were investigated. Highly potent and selective new radiotracer candidates were identified for further study.
Convex Properties of Center-of-Mass Trajectories for Locomotion Based on Divergent Component of Motion

This letter presents an in-depth analysis of the convex properties of center-of-mass (CoM) trajectories for legged robot locomotion based on the concept of Divergent Component of Motion (DCM). In particular, we show that the union of all possible trajectories forms a bounded convex set under appropriate boundary conditions. Additionally, we describe in detail our approach of generating closed-form CoM trajectories through piecewise interpolation over a sequence of waypoints and show how to compute the CoM trajectory efficiently through equations given in a matrix form. Applying the convex properties to our trajectory-generation approach, we present an algorithm for computing convex overapproximations of the CoM waypoints. Finally, we provide an example of usage in placing waypoints that lead to feasible CoM trajectories with respect to kinematic and dynamic constraints. The approach is validated with a multi-contact scenario in simulation with the humanoid robot TORO.
Three-Year Clinical Outcomes Associated With Warfarin vs. Direct Oral Anticoagulant Use Among Japanese Patients With Atrial Fibrillation　- Findings From the SAKURA AF Registry.

BACKGROUND Although direct oral anticoagulants (DOACs) are widely used in Japanese patients with atrial fibrillation (AF), large-scale investigations into their use, with suitable follow-up times and rates, are lacking. Methods and Results: The SAKURA AF Registry is a prospective multicenter registry created to investigate therapeutic outcomes of oral anticoagulant (OAC) use in Japanese AF patients. We conducted a study involving 3,237 enrollees from 63 institutions in the Tokyo area being treated with any of 4 DOACs (n=1,676) or warfarin (n=1,561) and followed-up for a median of 39.3 months (range 28.5-43.6 months). Analyses of 1- and 2-year follow-up data available for 3,157 (97.5%) and 2,952 (91.2%) patients, respectively, showed no significant differences in rates of stroke or systemic embolism (SE), major bleeding, and all-cause mortality for DOAC vs. warfarin users (1.2 vs. 1.8%/year, 0.5 vs. 1.2%/year, and 2.1 vs. 1.7%/year, respectively). Under propensity score matching, the incidence of stroke or SE (P=0.679) and all-cause death (P=0.864) remained equivalent, but the incidence of major bleeding was significantly lower (P=0.014) among DOAC than warfarin users. CONCLUSIONS A high follow-up rate allowed us to obtain reliable data on the status of OAC use and therapeutic outcomes among AF patients in Japan. Warfarin and DOACs appear to yield equivalent 3-year stroke and all-cause mortality rates, but DOACs appear to reduce the risk of major bleeding.
An Efficient and Robust Framework for SAR Target Recognition by Hierarchically Fusing Global and Local Features

Automatic target recognition (ATR) of synthetic aperture radar (SAR) images is performed on either global or local features. The global features can be extracted and classified with high efficiency. However, they lack the reasoning capability thus can hardly work well under the extended operation conditions (EOCs). The local features are often more difficult to extract and classify but they provide reasoning capability for EOC target recognition. To combine the efficiency and robustness in an ATR system, a hierarchical fusion of the global and local features is proposed for SAR ATR in this paper. As the global features, the random projection features can be efficiently extracted and effectively classified by sparse representation-based classification (SRC). The physically relevant local descriptors, i.e., attributed scattering centers (ASCs), are employed for local reasoning to handle various EOCs like noise corruption, resolution variance, and partial occlusion. A one-to-one correspondence between the test and template ASC sets is built by the Hungarian algorithm. Then, the local reasoning is performed by evaluating individual matched pairs as well as the false alarms and missing alarms. For the test image to be recognized, it is first classified by the global classifier, i.e., SRC. Once a reliable decision is made, the whole recognition process terminates. When the decision is not reliable enough, it is passed to the local classifier, where a further classification by ASC matching is carried out. Therefore, by the hierarchical fusion strategy, the efficiency of global features and the robustness of local descriptors to various EOCs can be maintained jointly in the ATR system. Extensive experiments on the moving and stationary target acquisition and recognition data set demonstrate that the proposed method achieves superior effectiveness and robustness under both SOC and typical EOCs, i.e., noise corruption, resolution variance, and partial occlusion, compared with some other SAR ATR methods.
A Data Set for User Request Trace-Oriented Monitoring and its Applications

User request trace-oriented monitoring is an effective method to improve the reliability of cloud services. However, there are some difficulties in getting useful traces in practice, which hinder the development of trace-oriented monitoring research. In this paper, we release a fine-grained user request-centric open trace data set, called TraceBench, which is collected in a real-world cloud storage service deployed in a real environment. When collecting, we consider different scenarios, involving multiple scales of clusters, different kinds of user requests, various speeds of workloads, many types of injected faults, etc. To validate the usability and authenticity, we have employed TraceBench in several trace-oriented monitoring topics, such as anomaly detection, performance problem diagnosis, and temporal invariant mining. The results show that TraceBench well supports these research topics. In addition, we have also carried out an extensive data analysis based on TraceBench, which validates the high quality of the data set.
PyGB: GraphBLAS DSL in Python with Dynamic Compilation Into Efficient C++

We present PyGB, a high-level Python domainspecific language (DSL) for GraphBLAS building blocks for graph algorithms. GraphBLAS is based on a small number of linear algebra operations, and it is described using mathematical notation and concepts. However, the concrete realizations of GraphBLAS concentrate on efficiency and widely usable programming interfaces. The GraphBLAS C language API standardized by the GraphBLAS Forum is the major example of such a realization, and our GraphBLAS Template Library (GBTL) implemented in the C++ language is another. PyGB exposes the \cpp interface of GBTL through a DSL that leverages Python's mature scientific computing ecosystem and high-level syntax. The syntax of PyGB more closely resembles the GraphBLAS mathematical notation than GBTL. The DSL dispatches to dynamically compiled templated classes to achieve comparable performance to the native GBTL code. We highlight the features of PyGB through code examples and discuss how Python's syntax and dynamic execution enable us to provide the high-level abstraction with minimal performance penalty. We demonstrate the stages of execution, from type inference to template compilation, to dynamic linking and invocation from the Python interpreter. Our experimental evaluation shows that for sufficiently large inputs, the overhead of PyGB is negligible and compilation times are not worse than for native GBTL implementation. Last, we outline concrete features we plan to implement in the future to extend PyGB.
T Cell Receptor-Independent, CD31/IL-17A-Driven Inflammatory Axis Shapes Synovitis in Juvenile Idiopathic Arthritis

T cells are considered autoimmune effectors in juvenile idiopathic arthritis (JIA), but the antigenic cause of arthritis remains elusive. Since T cells comprise a significant proportion of joint-infiltrating cells, we examined whether the environment in the joint could be shaped through the inflammatory activation by T cells that is independent of conventional TCR signaling. We focused on the analysis of synovial fluid (SF) collected from children with oligoarticular and rheumatoid factor-negative polyarticular JIA. Cytokine profiling of SF showed dominance of five molecules including IL-17A. Cytometric analysis of the same SF samples showed enrichment of αβT cells that lacked both CD4 and CD8 co-receptors [herein called double negative (DN) T cells] and also lacked the CD28 costimulatory receptor. However, these synovial αβT cells expressed high levels of CD31, an adhesion molecule that is normally employed by granulocytes when they transit to sites of injury. In receptor crosslinking assays, ligation of CD31 alone on synovial CD28nullCD31+ DN αβT cells effectively and sufficiently induced phosphorylation of signaling substrates and increased intracytoplasmic stores of cytokines including IL-17A. CD31 ligation was also sufficient to induce RORγT expression and trans-activation of the IL-17A promoter. In addition to T cells, SF contained fibrocyte-like cells (FLC) expressing IL-17 receptor A (IL-17RA) and CD38, a known ligand for CD31. Stimulation of FLC with IL-17A led to CD38 upregulation, and to production of cytokines and tissue-destructive molecules. Addition of an oxidoreductase analog to the bioassays suppressed the CD31-driven IL-17A production by T cells. It also suppressed the downstream IL-17A-mediated production of effectors by FLC. The levels of suppression of FLC effector activities by the oxidoreductase analog were comparable to those seen with corticosteroid and/or biologic inhibitors to IL-6 and TNFα. Collectively, our data suggest that activation of a CD31-driven, αβTCR-independent, IL-17A-mediated T cell-FLC inflammatory circuit drives and/or perpetuates synovitis. With the notable finding that the oxidoreductase mimic suppresses the effector activities of synovial CD31+CD28null αβT cells and IL-17RA+CD38+ FLC, this small molecule could be used to probe further the intricacies of this inflammatory circuit. Such bioactivities of this small molecule also provide rationale for new translational avenue(s) to potentially modulate JIA synovitis.
Querying a Collection of Continuous Functions

We introduce a new query primitive called Function Query (FQ). An FQ operates on a set of math functions and retrieves the functions whose output with a given input satisfies a query condition (e.g., being among top k, within a given range). While FQ finds its natural uses in querying a database of math functions, it can also be applied on a database of discrete values. We show that by interpreting the database as a set of user-defined functions, FQ can achieve the same functionality as existing analytic queries such as top-k query and scalar product query. We address the challenge of efficient execution of FQ. The core of our solution is a novel data structure called Intersection-tree. Our research takes advantage of the fact that 1) the intersections of a set of continuous functions partition their domain into a number of subdomains, and 2) in each of these subdomains, the functions can be sorted based on their output. We evaluate the performance of the proposed techniques through analysis, prototyping, and experiments using both synthetic and real-world data. When querying a database of functions, our techniques scale well. When applied on a database of discrete values, our techniques are more versatile and outperform existing techniques in terms of various performance metrics.
Inverse Nonnegative Local Coordinate Factorization for Visual Tracking

Recently, nonnegative matrix factorization (NMF) with part-based representation has been widely used for appearance modeling in visual tracking. Unfortunately, not all the targets can be successfully decomposed as “parts” unless some rigorous conditions are satisfied. To avoid this problem, this paper introduces NMF’s variants into the visual tracking framework in the view of data clustering for appearance modeling. First, an initial target appearance model based on NMF is proposed to describe the target’s appearance with the incorporated local coordinate factorization constraint, orthogonality of the bases, and $L_{1,1}$ norm regularized sparse residual error constraint. Second, an inverse NMF model is proposed in which each learned base vector is regarded as a clustering center in a low-dimensional subspace. Potential target samples (from the foreground) will be clustered around base vectors, while the candidate samples (from the background) are very likely to spread irregularly over the entire clustering space. Such differences can be fully exploited by the inverse NMF model to produce more discriminative encoding vectors than the conventional NMF method. Furthermore, incremental updating model is introduced into the tracking framework for online updating the initial appearance model. Experiments on object tracking benchmark suggest that our tracker is able to achieve promising performance when compared with some state-of-the-art methods in deformation, occlusion, and other challenging situations.
Person Reidentification Using Attribute-Restricted Projection Metric Learning

Person reidentification matches person observations captured by nonoverlapping cameras at different times and locations. Due to the appearance variations caused by view angle, pose, lighting, background, and occlusion, person reidentification is a challenging task. To improve the accuracy of person reidentification, this paper proposes a metric learning method. The main idea of our method is to learn a low-dimensional metric space, in which the features extracted from different observations of the same person are pulled, and the features extracted from observations of different persons but in the neighborhood of original feature space are pushed. Then all the features are projected to this metric space for matching. Compared with other metric learning methods, our method differentiates negative samples according to their importances in constructing the metric space, and pays more attention to restrain the more confusable negative pairs. Moreover, our method can simultaneously learn a uniform metric space for multiple features that is practical for multifeature fusion, an effective way to improve reidentification performance. Experimental results on three popular data sets show that our method is more robust against appearance changes and it outperforms other state-of-the-art reidentification methods.