gebhart/patrepeval huggingface dataset:
NaCl Potentiates Human Fibrocyte Differentiation (2012)

Excessive NaCl intake is associated with a variety of fibrosing diseases such as renal and cardiac fibrosis. This association has been attributed to increased blood pressure as the result of high NaCl intake. However, studies in patients with high NaCl intake and fibrosis reveal a connection between NaCl intake and fibrosis that is independent of blood pressure. We find that increasing the extracellular concentration of NaCl to levels that may occur in human blood after high-salt intake can potentiate, in serum-free culture conditions, the differentiation of freshly-isolated human monocytes into fibroblast-like cells called fibrocytes. NaCl affects the monocytes directly during their adhesion. Potassium chloride and sodium nitrate also potentiate fibrocyte differentiation. The plasma protein Serum Amyloid P (SAP) inhibits fibrocyte differentiation. High levels of extracellular NaCl change the SAP Hill coefficient from 1.7 to 0.8, and cause a four-fold increase in the concentration of SAP needed to inhibit fibrocyte differentiation by 95%. Together, our data suggest that NaCl potentiates fibrocyte differentiation. NaCl-increased fibrocyte differentiation may thus contribute to NaCl-increased renal and cardiac fibrosis.
ChemInform Abstract: The New Orthosilicate Li4SrCa(SiO4)2: Structure and Eu2+ Luminescence. (1995)

Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.
Learning to Model the Relationship Between Brain Structural and Functional Connectomes (2022)

Recent neuroimaging advances along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and thus facilitate revealing some of the brain's organizing principles at the system level. In this direction, we develop a supervised graph representation learning framework to model the relationship between brain structural connectivity (SC) and functional connectivity (FC) via a graph encoder-decoder system, where the SC is used as input to predict empirical FC. A trainable graph convolutional encoder captures direct and indirect interactions between brain regions-of-interest that mimic actual neural communications, as well as to integrate information from both the structural network topology and nodal (i.e., region-specific) attributes. The encoder learns node-level SC embeddings which are combined to generate (whole brain) graph-level representations for reconstructing empirical FC networks. The proposed end-to-end model utilizes a multi-objective loss function to jointly reconstruct FC networks and learn discriminative graph representations of the SC-to-FC mapping for downstream subject (i.e., graph-level) classification. Comprehensive experiments demonstrate that the learnt representations of said relationship capture valuable information from the intrinsic properties of the subject's brain networks and lead to improved accuracy in classifying a large population of heavy drinkers and non-drinkers from the Human Connectome Project. Our work offers new insights on the relationship between brain networks that support the promising prospect of using graph representation learning to discover more about brain function.
Tumor cell agglutination and not solely cytokine release as mechanism of adverse reactions during anti-CD20 monoclonal antibody (IDEC-C2B8, rituximab) treatment (2001)

The chimeric monoclonal anti-CD20 antibody has been widely used for the treatment of relapsed or refractory low-grade B-cell non-Hodgkin lymphoma (NHL). However, in patients with a high number of tumor cells in blood, severe infusion-related toxicities have been reported.[1][1] On the basis of
ChemInform Abstract: Porphyrin‐Sensitized Solar Cells (2013)

Abstract Review: 144 refs.
Current Filamentation in Large<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>Bi</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>Sr</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>CaCu</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mrow><mml:mn>8</mml:mn><mml:mo>+</mml:mo><mml:mi>δ</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>Mesa Devices Observed via Luminescent and Scanning Laser Thermal Microscopy (2015)

Stacked intrinsic Josephson junctions in the well studied superconductor Bi${}_{2}$Sr${}_{2}$CaCu${}_{2}$O${}_{8}$ are promising as a compact source of coherent terahertz radiation, which would have applications ranging from bioimaging to security screening. Understanding localized heating in such stacks is essential to optimizing their performance, and to this end the authors directly image the complex self-heating behaviors of these devices under bias conditions typical for THz emission. For good heat removal, narrow (compared to device size) filaments of current nucleate hot spots in asymmetric locations, which is consistent with theoretical predictions and suggests a means to enhance emission power.
Dynamic Task Allocation in Cooperative Robot Teams (2009)

In this paper a dynamic task allocation and controller design methodology for cooperative robot teams is presented. Fuzzy logic based utility functions are derived to quantify each robot's ability to perform a task. These utility functions are used to allocate tasks in real-time through a limited lookahead control methodology partially based on the basic principles of discrete event supervisory control theory. The proposed controller design methodology accommodates flexibility in task assignments, robot coordination, and tolerance to robot failures and repairs. Implementation details of the proposed methodology are demonstrated through a warehouse patrolling case study.
PARAMETRIC MODEL-ORDER REDUCTION FOR VISCO- ELASTIC FINITE ELEMENT MODELS: AN APPLICATION TO MATERIAL PARAMETER IDENTIFICATION (2015)

In many engineering applications, viscoelastic treatments are used to suppress vibrations of lightly damped structures. Computational methods provide powerful tools for the design and analysis of these structures. The most commonly used method to model the dynamics of complex structures is the finite element method. Its use, however, often results in very large and computationally demanding models, especially when viscoelastic material behaviour has to be taken into account. To alleviate this problem, model-order reduction (MOR) techniques have been developed to reduce the size of the finite element model while still maintaining an accurate description of the most important system dynamics. In order to apply these MOR schemes, the parameter values of the full-order model have to be fixed. As a consequence, the resulting reduced-order model is only valid for one specific set of viscoelastic material properties. Recently though, parametric model-order reduction (pMOR) techniques have been introduced. These methods allow the parameter dependency to be retained in the reduced-order models. This makes them a valuable tool for use in optimization procedures, where the system model has to be evaluated time and again for varying parameter values. This paper presents a Krylov subspace technique for reduced-order modelling, embedded in a recent pMOR framework, to create reduced-order models of viscoelastic finite element models in which the dependency on the viscoelastic material parameters is retained. The viscoelastic material properties are modelled using the Golla-Hughes-McTavish formulation. This procedure is then applied to a finite element model of a cantilever beam with viscoelastic treatment. The resulting reduced-order model is used to identify viscoelastic material properties from experiments through an inverse optimization procedure, demonstrating both the efficiency and accuracy of the obtained reduced-order model.
A value-oriented specification for weak sequencing validation (2014)

Weak sequencing is a UML2 concept which reduces to a parallel merge when the operands are on disjunct sets of participants. This semantics needs to be well defined based on a formal language that yields the fundamentals required for a rigorous validation of the specification. In this paper, we firstly provide this semantics through a new approach to translate the UML weak sequencing Combined Fragment into Colored Petri Nets, or CPNs. The derived specification is value-oriented, composed of identified objects and events, thus allowing a more precise analysis of the model behavior. The analysis deals with the CPNs good construction as well as their faithfulness to the real-world system. To verify that the CPN models preserve the system properties, we use OCL invariants. This use motivates our second contribution based on the association ends specification on the behavioral models. Such a modeling allows a deep exploitation of the OCL expressivity while validating the models consistency. The verification is performed on a predicate/transition net explored by model checking.
Expediting the transition from replacement medicine to tissue engineering (2016)

In this article, an expansive interpretation of "Tissue Engineering" is proposed which is in congruence with classical and recent published definitions. I further simplify the definition of tissue engineering as: "Exerting systematic control of the body's cells, matrices and fluids." As a consequence, many medical therapies not commonly considered tissue engineering are placed in this category because of their effect on the body's responses. While the progress of tissue engineering strategies is inexorable and generally positive, it has been subject to setbacks as have many important medical therapies. Medical practice is currently undergoing a transition on several fronts (academics, start-up companies, going concerns) from the era of "replacement medicine" where body parts and functions are replaced by mechanical, electrical or chemical therapies to the era of tissue engineering where health is restored by regeneration generation or limitation of the body's tissues and functions by exploiting our expanding knowledge of the body's biological processes to produce natural, healthy outcomes.
Spatial and temperature resolution of sub-Y type antenna array configuration for high resolution interferometric synthetic aperture radiometer (2003)

The sub-Y type configuration of antenna array is proposed to obtain a higher angular resolution than the case of Y-type array in this paper. The sub-Y type array consists of sub-arrays that are the four elements Y-type arrays. Using sub-Y-type array, one can obtain more wide visibility function than the case of conventional Y-type array keeping the hexagonal sampling characteristic. To analyze characteristics of the proposed configuration, the synthesized angular resolution is compared to resolution of Y-type with the same number of antennas through simulation. The temperature sensitivity of sub-Y type array is considered in view of the standard deviation of sampled visibility function. The performance evaluation by use of array factor simulation shows more than 40% improvement in average for angular resolution.
Collagen type X is essential for successful mesenchymal stem cell-mediated cartilage formation and subsequent endochondral ossification (2019)

in tissue engineering, endochondral ossification (EO) is often replicated by chondrogenically differentiating mesenchymal stromal cells (MSCs) in vitro and achieving bone formation through in vivo implantation. The resulting marrow-containing bone constructs are promising as a treatment for bone defects. However, limited bone formation capacity has prevented them from reaching their full potential. This is further complicated since it is not fully understood how this bone formation is achieved. Acellular grafts derived from chondrogenically differentiated MSCs can initiate bone formation; however, which component within these decellularised matrices contribute to bone formation has yet to be determined. Collagen type X (COLX), a hypertrophy-associated collagen found within these constructs, is involved in matrix organisation, calcium binding and matrix vesicle compartmentalisation. However, the importance of COLX during tissue-engineered chondrogenesis and subsequent bone formation is unknown. The present study investigated the importance of COLX by shRNA-mediated gene silencing in primary MSCs. A significant knock-down of COLX disrupted the production of extracellular matrix key components and the secretion profile of chondrogenically differentiated MSCs. Following in vivo implantation, disrupted bone formation in knock-down constructs was observed. The importance of COLX was confirmed during both chondrogenic differentiation and subsequent EO in this tissue engineered setting.
A Roadmap to Strengthen Geoscience Education for Sustainable Development in Kenya (2023)

Meeting the targets of the 17 United Nations (UN) Sustainable Development Goals (SDGs) requires contributions from geoscientists. Like most countries, Kenya is faced with the triple dimensional challenge of balancing economic, social and environmental sustainability. Through implementation of its &amp;#8216;Vision 2030&amp;#8217; strategy, Kenya aims to transform into a high-functioning, industrialised middle-income country, providing a high quality of life to all its citizens by 2030. The country&amp;#8217;s constitution harmonises with Vision 2030 and entitles every Kenyan to a clean and secure environment. This background provides an integrated roadmap entrenched in the SDGs. Kenya has also committed to implementing the African Union Agenda 2063 and the East African Community (EAC) Vision 2050, both aiming to build a more prosperous Africa. Eradicating extreme poverty, waste management, disaster preparedness, ensuring affordable, clean and sustainable energy, improving access to clean water, promoting sustainable consumption and production, environmental sustainability, building resilience to climate change impacts and managing Kenya's natural resources are some of the key elements of these policies.&amp;#160;Here, we highlight the importance of geoscience education in accelerating the critical goals of Kenya&amp;#8217;s Vision 2030 and the SDGs. We synthesise key policy documents to explore sustainable development priorities at the national and county level in the context of geoscience in Kenya and explore the relevant geoscience training and skills needed to help address these priorities. We demonstrate that geoscience will continue to be instrumental for achievement of the SDGs and overall performance on socio-economic development in Kenya. Baseline assessments of the geoscience courses available, including training and description of modules offered in Kenya's higher institutions, were compiled and analysed. The extent of the existing geoscience workforce and future workforce required to deliver on Kenya's Vision 2030 and the SDGs was identified through online surveys, focus group discussions, and interviews. We mapped the future needs against the training available and existing current workforce capacity to conduct a skills gap analysis. We identify a number of needs, including (1) to embed sustainability concepts into geoscience curriculum; (2) to expand provision of specialist postgraduate courses (e.g. MSc); (3) to strengthen access to field-based training in both undergraduate and postgraduate degrees; (4) to increase the number of trained geoscientists; (5) improved communication between geoscientists and policymakers. We conclude with a roadmap to address these gaps, embedding good practice from the UN Technology Facilitation Mechanism, and include transferable insights for other national settings.
Serum creatinine, cystatin C, and beta-trace protein in diagnostic staging and prediction of progression of primary non-diabetic chronic kidney disease (2010)

Background: Chronic kidney disease (CKD) is a growing public health problem with increasing incidence and prevalence rates, poor outcomes and high health care costs. Impaired baseline kidney function is a well defined risk factor for progression of CKD. Measured glomerular filtration rate (GFR) is widely accepted as the best measure of overall renal function. Alternatively, serum markers, such as serum creatinine, or the newer low-molecular weight markers, cystatin C and beta-trace protein (BTP), are used to assess kidney function. We compared the diagnostic performance of creatinine, cystatin C, and BTP for staging kidney impairment measured by GFR and for predicting progression of CKD. Methods: Serum marker concentrations were measured in 227 patients with primary non-diabetic CKD and various degrees of renal impairment. 177 patients were followed prospectively for up to 7 years to assess progression of CKD. Results: At baseline, creatinine, cystatin C, and BTP were strongly correlated with GFR. Concentrations of all three markers increased progressively with decreasing GFR. Their diagnostic performance for the detection of even minor deteriorations of renal function (indicated by a GFR&lt;90 mL/min/1.73m2) was similar. 65 patients experienced progression of CKD defined as doubling of baseline creatinine and/or terminal renal failure during follow-up. These patients had a lower GFR, higher serum creatinine, cystatin C, and BTP values at baseline (all P&lt;0.001) compared to the patients who did not reach the predefined renal endpoint. Cox proportional hazard regression analysis revealed that all three clearance markers were equally strong predictors of CKD progression, even after adjustment for age, sex, GFR, and proteinuria. Conclusions: The diagnostic performance of serum creatinine, cystatin C or BTP for detecting even minor degrees of deterioration of renal function is good and these markers provide reliable risk prediction for progression of kidney disease in patients with CKD beyond the gold standard marker GFR.
A Novel Technique for Determination of Residual Direct-Current Voltage of Liquid Crystal Cells with Vertical and In-Plane Electric Fields (2021)

Generation of residual direct-current (DC) voltage (VrDC) induces serious image sticking of liquid crystal displays (LCDs). In this study, a novel technique to determine the VrDC of LC cells is proposed. We found that the VrDC could be determined from a current-voltage (I-V) curve obtained by the application of triangular voltage. In the case of a vertically aligned twisted nematic (VTN) mode LC cell, where a vertical electric field is applied, the I-V curve shows maximum and minimum current peaks owing to rotation of an LC director, and the VrDC is able to be determined from an average value of the two peaks. On the other hand, in the case of a fringe field switching (FFS) mode LC cell, where an in-plane (lateral) electric field is applied from comb electrodes, the current peaks derived from the rotation of the LC director do not appear. Therefore, we could not adopt the same way with that of the VTN mode LC cell. However, we found that there were two minimum current peaks derived from minimum capacitances of the FFS mode LC cell, and could determine the VrDC by using these two current peaks. The proposed technique would be useful for the evaluation of the VrDC of the LCDs, where the electric field is applied both vertically and laterally.
Biomedical data privacy: problems, perspectives, and recent advances (2012)

The notion of privacy in the healthcare domain is at least as old as the ancient Greeks. Several decades ago, as electronic medical record (EMR) systems began to take hold, the necessity of patient privacy was recognized as a core principle, or even a right, that must be upheld.1 ,2 This belief was re-enforced as computers and EMRs became more common in clinical environments.3–5 However, the arrival of ultra-cheap data collection and processing technologies is fundamentally changing the face of healthcare. The traditional boundaries of primary and tertiary care environments are breaking down and health information is increasingly collected through mobile devices,6 in personal domains (eg, in one's home7), and from sensors attached on or in the human body (eg, body area networks8–10). At the same time, the detail and diversity of information collected in the context of healthcare and biomedical research is increasing at an unprecedented rate, with clinical and administrative health data being complemented with a range of *omics data, where genomics11 and proteomics12 are currently leading the charge, with other types of molecular data on the horizon.13 Healthcare organizations (HCOs) are adopting and adapting information technologies to support an expanding array of activities designed to derive value from these growing data archives, in terms of enhanced health outcomes.14

The ready availability of such large volumes of detailed data has also been accompanied by privacy invasions. Recent breach notification laws at the US federal and state levels have brought to the public's attention the scope and frequency of these invasions. For example, there are cases of healthcare provider snooping on the medical records of famous people, family, and friends, use of personal information for identity fraud, and millions of records disclosed through lost and … 

Correspondence to Dr Bradley Malin, Department of Biomedical Informatics, Vanderbilt University, 2525 West End Avenue, Suite 600, Nashville, TN 37203, USA; b.malin{at}vanderbilt.edu
Automatic phase calibration for RF cavities using beam-loading signals (2018)

Precise calibration of the cavity phase signals is necessary for the operation of any particle accelerator. For many systems this requires human in the loop adjustments based on measurements of the beam parameters downstream. Some recent work has developed a scheme for the calibration of the cavity phase using beam measurements and beam-loading however this scheme is still a multi-step process that requires heavy automation or human in the loop. In this paper we analyze a new scheme that uses only RF signals reacting to beam-loading to calculate the phase of the beam relative to the cavity. This technique could be used in slow control loops to provide real-time adjustment of the cavity phase calibration without human intervention thereby increasing the stability and reliability of the accelerator.
The Mont-Blanc Prototype: An Alternative Approach for HPC Systems (2016)

High-performance computing (HPC) is recognized as one of the pillars for further progress in science, industry, medicine, and education. Current HPC systems are being developed to overcome emerging architectural challenges in order to reach Exascale level of performance, projected for the year 2020. The much larger embedded and mobile market allows for rapid development of intellectual property (IP) blocks and provides more flexibility in designing an application-specific system-on-chip (SoC), in turn providing the possibility in balancing performance, energy-efficiency, and cost. In the Mont-Blanc project, we advocate for HPC systems being built from such commodity IP blocks, currently used in embedded and mobile SoCs. As a first demonstrator of such an approach, we present the Mont-Blanc prototype; the first HPC system built with commodity SoCs, memories, and network interface cards (NICs) from the embedded and mobile domain, and off-the-shelf HPC networking, storage, cooling, and integration solutions. We present the system's architecture and evaluate both performance and energy efficiency. Further, we compare the system's abilities against a production level supercomputer. At the end, we discuss parallel scalability and estimate the maximum scalability point of this approach across a set of applications.