Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Pre-Transplant Creatinine on the Percent Improvement in Kidney Function after Kidney Transplantation

With 108,335 people on kidney replacement therapy in the United States, the need for kidney transplantation has increased. In 1998 there were 40,825 on the national waiting list for a kidney transplant in comparison to 71,862 in 2007 with a median wait time of over three years. At the same time the number of kidneys transplanted went from 9,761 to 13,156. One of the ways that people in need of a kidney transplant have found to fill the need is to look to living donors for a preemptive transplant as a way to stay off dialysis. The purpose of this study is to test the hypothesis that the percent improvement in kidney function in patients with similar MDRD GFR’s across a spectrum of serum creatinine values is relatively constant across the entire range of pre-transplant creatinine values. A retrospective chart review study was conducted in a Kidney/ Kidney-Pancreas Transplant Center at a large university affiliated medical center with 350 beds in southern California. Medical records of patients who received a kidney from January 2005 through December 2006 were selected for this study. These findings support the hypothesis that the improvement in kidney function (as calculated from pre-and post-transplant MDRD GFR: MDRD Delta) is independent of pre-transplant MDRD. With the information obtained from this study it is hoped that the nursing staff will interact with the participants in a reflective and mindful manner when connecting with them and generate feelings of trust and safety while counseling them on the best timing of the kidney transplant so maximum utilization of the native and transplanted kidneys can be achieved.
University of Groningen On Interconnections of Infinite-dimensional Port-Hamiltonian Systems

Network modeling of complex physical systems leads to a class of nonlinear systems called port-Hamiltonian systems, which are defined with respect to a Dirac structure (a geometric structure which formalizes the power-conserving interconnection structure of the system). A power conserving interconnection of Dirac structures is again a Dirac structure. In this paper we study interconnection properties of mixed finite and infinite dimensional port-Hamiltonian systems and show that this interconnection again defines a port-Hamiltonian system. We also investigate which closed-loop port-Hamiltonian systems can be achieved by power conserving interconnections of finite and infinite dimensional port-Hamiltonian systems. Finally we study these results with particular reference to the transmission line.
Lawrence Berkeley National Laboratory Recent Work Title Transport and deposition of functionalized CdTe nanoparticles in saturated porous media Permalink

Comprehensive understanding of the transport and deposition of engineered nanoparticles (NPs) in subsurface is required to assess their potential negative impact on the environment. We studied the deposition behavior of functionalized quantum dot (QD) NPs (CdTe) in different types of sands (Accusand, ultrapure quartz, and iron-coated sand) at various solution ionic strengths (IS). The observed transport behavior in ultrapure quartz and iron-coated sand was consistent with conventional colloid deposition theories. However, our results from the Accusand column showed that deposition was minimal at the lowest IS (1 mM) and increased significantly as the IS increased. The effluent breakthrough occurred with a delay, followed by a rapid rise to the maximum normalized concentration of unity. Negligible deposition in the column packed with ultrapure quartz sand (100 mM) and Accusand (1 mM) rules out the effect of straining and suggests the importance of surface charge heterogeneity in QD deposition in Accusand at higher IS. Data analyses further show that only a small fraction of sand surface area contributed in QD deposition even at the highest IS (100 mM) tested. The observed delay in breakthrough curves of QDs was attributed to the fast diffusive mass transfer rate of QDs from bulk solution to the sand surface and QD mass transfer on the solid phase. Scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX) analysis were used to examine the morphology and elemental composition of sand grains. It was observed that there were regions on the sand covered with layers of clay particles. EDX spectra collected from these regions revealed that Si and Al were the major elements suggesting that the clay particles were kaolinite. Additional batch experiments using gold NPs and SEM analysis were performed and it was observed that the gold NPs were only deposited on clay particles originally on the Accusand surface. After removing the clays from the sand surface, we observed negligible QD deposition even at 100 mM IS. We proposed that nanoscale charge heterogeneities on clay particles on Accusand surface played a key role in QD deposition. It was shown that the value of solution IS determined the extent to which the local heterogeneities participated in particle deposition.
Effects of NaCl Levels on Germination of Some Confectionary Sunflower ( Helianthus annuus L . ) Genotypes

This study was conducted to investigate the effects of NaCl levels on germination of confectionary sunflower genotypes in 2007. Seeds of Opal, Beyaz, Suriye Nevadasi, Elazig, Kıbrıs, 01M007, 01M008, 01M012 and 01M013 were used as material. Electrical conductivity (EC) values of NaCl solutions were 5, 10 and 20 dS/m. Distilled water (0 dS/m) served as control. Germination percentage (%), mean germination time (day), root length (cm), shoot length (cm), seedling fresh and dry weight (mg) were measured at 10 days after sowing. As a result of the research, genotypes showed different response to NaCl. Increased NaCl levels resulted in both decreasing in germination and inhibiting of seedling growth but increasing in mean germination time. The least affected genotype by NaCl concentrations was Beyaz. Germination and seedling growth were inhibited at 10 dS/m and above. It was observed that NaCl depressed seedling growth of the genotypes rather than germination because five genotypes germinated but did not have root and shoot to be measured. Moreover, it was concluded that hull ratio of the seed was an effective factor for germination regardless of NaCl levels. It was evaluated that a positive and significant correlation (r=0.692**) between hull ratio and water uptake is an important clue for delaying germination.
Contribution of anthropogenic land cover change emissions to pre-industrial atmospheric CO 2

A B S T R A C T Based on a recent reconstruction of anthropogenic land cover change (ALCC), we derive the associated CO2 emissions since 800 AD by two independent methods: a bookkeeping approach and a process model. The results are compared with the pre-industrial development of atmospheric CO2 known from antarctic ice cores. Our results show that pre-industrial CO2 emissions from ALCC have been relevant for the pre-industrial carbon cycle, although before 1750 AD their trace in atmospheric CO2 is obscured by other processes of similar magnitude. After 1750 AD, the situation is different: the steep increase in atmospheric CO2 until 1850 AD—this is before fossil fuel emissions rose to significant values—is to a substantial part explained by growing emissions from ALCC.
Title Canonical models of geophysical and astrophysical flows : Turbulent convection experiments in liquid metals Permalink

Planets and stars are often capable of generating their own magnetic fields. This occurs through dynamo processes occurring via turbulent convective stirring of their respective molten metal-rich cores and plasma-based convection zones. Present-day numerical models of planetary and stellar dynamo action are not carried out using fluids properties that mimic the essential properties of liquid metals and plasmas (e.g., using fluids with thermal Prandtl numbers Pr < 1 and magnetic Prandtl numbers Pm 1). Metal dynamo simulations should become possible, though, within the next decade. In order then to understand the turbulent convection phenomena occurring in geophysical or astrophysical fluids and next-generation numerical models thereof, we present here canonical, end-member examples of thermally-driven convection in liquid gallium, first with no magnetic field or rotation present, then with the inclusion of a background magnetic field and then in a rotating system (without an imposed magnetic field). In doing so, we demonstrate the essential behaviors of convecting liquid metals that are necessary for building, as well as benchmarking, accurate, robust models of magnetohydrodynamic processes in Pm Pr < 1 geophysical and astrophysical systems. Our study results also show strong agreement between laboratory and numerical experiments, demonstrating that high resolution numerical simulations can be made capable of modeling the liquid metal convective turbulence needed in accurate next-generation dynamo models.
Active Players or Rubber-Stamps ? An Evaluation of the Policy-Making Role of Latin American Legislatures

Legislatures are critical institutions in the effective functioning of a democratic system and in the policymaking process. They are expected to represent the needs and wishes of citizens; identify problems and adopt statutes to address them; and oversee the implementation of policies. However, the extents to which Latin American legislatures fulfill these roles vary greatly from country to country. Despite the recent proliferation of research on executive-legislative relations in Latin America, comparative knowledge of how legislative institutions operate in these countries is still quite limited. In an effort to improve understanding in this area, I evaluate the extent and nature of the policymaking role played by 18 Latin American legislatures. Specifically, I use multidimensional scaling (MDS) techniques to test if and how the dimensions by which these legislatures have been previously distinguished correspond to observed similarities among them. The analysis reveals that four types of legislatures can be identified. The findings also confirm that those legislatures with greater capabilities are the ones that play more active roles in the policy making process
0 Statistical Analysis of Chemical Sensor Data

Chemical sensors measure and quantify substances via their associated chemical or physical response, thus providing data that can be analyzed to address a scientific question of interest (Eggins; 2002). Used in a variety of applications from monitoring to medicine, chemical sensors vary vastly by construction, style, format, size and dimension, and complexity. The common, underlying feature of these sensors lies in the associated data, which are abundant with technical and structural complexities, making statistical analysis a difficult task. These data further share a common need to be measured, analyzed, and interpreted properly so that the resulting inference is accurate. There are many image analysis algorithms available and amenable to a myriad of chemical analysis problems, thus potentially applicable to chemical sensor data problems in particular. By applying these tools to chemical sensor data, we can optimize and evaluate a chemical sensor’s ability to perform its intended tasks. This chapter is designed to give an overview of the modern statistical algorithms that are commonly used when designing and analyzing chemical sensor experiments. Without focusing the discussion around a specific chemical sensor platform, our goal is to provide a general framework that will be applicable, to some degree, for all chemical sensor data. From the beginning to the end of an experiment, various statistical methods can be employed to improve or understand the current scientific analysis. We decompose a general experiment into several facets and provide the motivation for potential statistical methods that can be applied within each component. Section 2 describes the pre-processing techniques that are available for summarizing the low-level image or signal data so that the subsequent scientific questions can be properly addressed. In this section, we particularly focus on removing background noise in order to isolate the chemical sensor signal data, quantifying this data, and normalizing it so that the resulting data are scalable across conditions. Section 3 introduces the higher-level statistical approaches that are used to analyze the pre-processed data, and Section 4 describes the statistical computational tools available for use to perform the analyses. Finally, Section 5 demonstrates and motivates the significance of these methods via a chemical sensor case study, and Section 6 concludes the chapter with discussion and summary.
Accounting Choices : The Brazilian Case Of Real Estate Investment

The objective in this study is to show that the business model of publicly traded real estate management companies in the Brazilian market explains their accounting choices to measure the investment properties. Therefore, the five main Brazilian investment property managers were studied, based on a method that triangulated documentary research, semistructured interviews and consistency analysis. The results of the analysis of the secondary data from the documentary research of the two main Brazilian companies in the real estate exploitation sector (one of which chose the fair value and the other the cost), together with the results of the interviews with the companies’ main internal and external agents direct or indirectly involved in the measuring choices of investment properties, indicated that the companies’ distinguished business model explains their distinct accounting choices to measure the investment properties. To enhance the consistency of these findings, next, the business models of the three consecutive largest Brazilian investment property managers were analyzed (two of which chose the fair value and one the cost), confronting them with their accounting choices; consistency was observed between the business models and the accounting choices, in accordance with earlier results. Although generalization of the results is not possible due to the small number of companies studied and the specific sector, evidence indicates that an integrated set of factors, which are considered in isolation in the literature, can explain the accounting choices. Several mutually interacting factors that explain the accounting choices, to the detriment of isolated factors, can contribute to expand the knowledge on this theme.
Title A neural mechanism of preference shifting under zero pricecondition

Right © 2016 Votinov, Aso, Fukuyama and Mima. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Type Journal Article
Pharmacological treatments in ARDS

Despite its high incidence and devastating outcomes, acute respiratory distress syndrome (ARDS) has no specific treatment, with effective therapy currently limited to minimizing potentially harmful ventilation and avoiding a positive fluid balance. Many pharmacological therapies have been investigated with limited success to date. In this review article we provide a state-of-the-art update on recent and ongoing trials, as well as reviewing promising future pharmacological therapies in ARDS.
The soluble guanylate cyclase stimulator praliciguat attenuates inflammation, fibrosis, and end-organ damage in the Dahl model of cardiorenal failure.

Reduced nitric oxide (NO) and a decrease in cyclic guanosine monophosphate (cGMP) signaling mediated by soluble guanylate cyclase (sGC) has been linked to development of several cardiorenal diseases. Stimulating sGC is a potential means for enhancing cGMP production in conditions of reduced NO bioavailability. The purpose of our studies was to determine the effects of praliciguat, a clinical-stage sGC stimulator, in a model of cardiorenal failure. Dahl salt-sensitive (DSS) rats fed a high-salt diet to induce hypertension and organ damage were treated with the sGC stimulator praliciguat to determine its effects on hemodynamics, biomarkers of inflammation, fibrosis, tissue function, and organ damage. Praliciguat treatment reduced blood pressure, improved cardiorenal damage, and attenuated an increase in circulating markers of inflammation and fibrosis. Notably, praliciguat affected markers of renal damage at a dose that had minimal effect on blood pressure. In addition, liver fibrosis and circulating markers of tissue damage were attenuated in praliciguat-treated rats. Stimulation of the NO-sGC-cGMP pathway by praliciguat attenuated or normalized indicators of chronic inflammation, fibrosis, and tissue dysfunction in the DSS rat model. Stimulation of sGC by praliciguat may present an effective mechanism for treating diseases linked to NO deficiency, particularly those associated with cardiac and renal failure. Praliciguat is currently being evaluated in patients with diabetic nephropathy and heart failure with preserved ejection fraction.
Bidirectional Inference Networks with Application to Health Profiling

We consider the problem of inferring the values of an arbitrary set of variables (e.g., risk of diseases) given other observed variables (e.g., symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images or EEG). This is a common problem in healthcare since variables of interest often differ for different patients. Existing methods including Bayesian networks and structured prediction either do not incorporate high-dimensional signals or fail to model conditional dependencies among variables. To address these issues, we propose bidirectional inference networks (BIN), which stich together multiple probabilistic neural networks, each modeling a conditional dependency. Predictions are then made via iteratively updating variables using backpropagation (BP) to maximize corresponding posterior probability. Furthermore, we extend BIN to composite BIN (CBIN), which involves the iterative prediction process in the training stage and improves both accuracy and computational efficiency by adaptively smoothing the optimization landscape. Experiments on synthetic and real-world datasets (a sleep study and a dermatology dataset) show that CBIN is a single model that can achieve state-of-the-art performance and obtain better accuracy in most inference tasks than multiple models each specifically trained for a different task.
Lenvatinib for thyroid cancer treatment: discovery, pre-clinical development and clinical application

ABSTRACT Introduction: About one third of patients affected with thyroid cancer present with recurrent disease. Unresectability, advanced disease and radioiodine refractoriness are considered poor prognostic factors. Treatment with small molecules inhibiting molecular signaling can be considered for patients with progressive disease, when other therapeutic strategies cannot be applied. Lenvatinib is a tyrosine kinase inhibitor targeting multiple molecular factors involved in angiogenesis and tumor progression. Preclinical studies have demonstrated the utility of lenvatinib as a targeted therapy for different tumors, including both differentiated and anaplastic thyroid cancer. Areas covered: The authors provide an overview of the preclinical development of lenvatinib in the treatment of thyroid cancer and review its clinical application. They also provide their expert opinion on its development. Expert opinion: Preclinical studies have helped in the understanding of the mechanisms of thyroid carcinogenesis and in the development of a targeted therapy. These findings have represented the rationale for the use of lenvatinib in clinical trials, which have confirmed its utility but yet failed to prove a clear benefit in overall survival. The decision to start a systemic treatment with lenvatinib must be personalized for each patient evaluating the risk/benefits ratio. Treatment emergent adverse events must be considered and reasonably managed by a multidisciplinary approach.
Deposited in DRO : 08 June 2018 Version of attached le : Published Version Peer-review status of attached

CdTe wires have been fabricated via a catalyst free method using the industrially scalable physical vapor deposition technique close space sublimation. Wire growth was shown to be highly dependent on surface roughness and deposition pressure, with only low roughness surfaces being capable of producing wires. Growth of wires is highly (111) oriented and is inferred to occur via a vapor-solid-solid growth mechanism, wherein a CdTe seed particle acts to template the growth. Such seed particles are visible as wire caps and have been characterized via energy dispersive X-ray analysis to establish they are single phase CdTe, hence validating the self-catalysation route. Cathodoluminescence analysis demonstrates that CdTe wires exhibited a much lower level of recombination when compared to a planar CdTe film, which is highly beneficial for semiconductor applications.
Conscience as a Regulatory Function: An Integrative Theory Put to the Test

The subject of this study is an integrative theory of the conscience. According to this theory, conscience is operationalised as a regulatory function of one’s own behaviour and identity, resulting from an interplay of empathy, self-conscious emotions such as guilt and shame, and moral reasoning. This study aimed to evaluate conscience in an adult forensic psychiatric sample by assessing the underlying factors proposed by Schalkwijk. Offenders (n = 48) appeared to show less affective but not less cognitive empathy, less identification with others, less personal distress in seeing others’ suffering, less shame and shame-proneness, and lower levels of moral reasoning than non-offenders (n = 50). In coping with self-conscious emotions, offenders used the same amount of externalising coping strategies, but fewer internalising coping strategies.
Mapping language with resting‐state functional magnetic resonance imaging: A study on the functional profile of the language network

Resting‐state functional magnetic resonance imaging (rsfMRI) is a promising technique for language mapping that does not require task‐execution. This can be an advantage when language mapping is limited by poor task performance, as is common in clinical settings. Previous studies have shown that language maps extracted with rsfMRI spatially match their task‐based homologs, but no study has yet demonstrated the direct participation of the rsfMRI language network in language processes. This demonstration is critically important because spatial similarity can be influenced by the overlap of domain‐general regions that are recruited during task‐execution. Furthermore, it is unclear which processes are captured by the language network: does it map rather low‐level or high‐level (e.g., syntactic and lexico‐semantic) language processes? We first identified the rsfMRI language network and then investigated task‐based responses within its regions when processing stimuli of increasing linguistic content: symbols, pseudowords, words, pseudosentences and sentences. The language network responded only to language stimuli (not to symbols), and higher linguistic content elicited larger brain responses. The left fronto‐parietal, the default mode, and the dorsal attention networks were examined and yet none showed language involvement. These findings demonstrate for the first time that the language network extracted through rsfMRI is able to map language in the brain, including regions subtending higher‐level syntactic and semantic processes.