Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelado farmacocinético de ciclosporina en pacientes con trasplante renal

Objetivo: El objetivo de este trabajo era caracterizar el comportamiento farmacocinetico de ciclosporina oral (CsA) en pacientes con trasplante renal, a partir de la concentracion sanguinea valle (C0) y, desarrollar y evaluar un metodo bayesiano para el ajuste individualizado de dosis diaria (DD). Material y metodos: Sesenta y siete pacientes (42 hombres y 25 mujeres) receptores de un injerto renal y subsidiarios de tratamiento con CsA (Sandimmun Neoral®) asociada a micofenolato de mofetilo (2g/dia) y prednisona (0,5-1 mg/kg/dia), fueron asignados aleatoriamente en dos grupos. El grupo A (n=48 pacientes) se utilizo para caracterizar el comportamiento farmacocinetico de CsA y el grupo B (n=19 pacientes) se utilizaron para evaluar la capacidad predictiva bayesiana del modelo farmacocinetico desarrollado. Se evaluaron distintas funciones para describir la relacion entre DD y C0 a traves de modelos de efectos mixtos, implementados en el software NONMEN. La exactitud y la precision se evaluo mediante la media y la desviacion estandar del error de prediccion estandarizado de la DD. Resultados: El modelo de Michaelis-Menten con los parametros constante de Michaelis-Menten (Km) y dosis maxima diaria (Dmax) dependientes del tiempo y, con terminos autorregresivos de primer orden de DD y C0 incorporados al modelo estructural de forma aditiva, fue el modelo con el que se obtuvo el mejor ajuste de los datos experimentales. En el modelo final, el parametro Dmax esta afectado por los valores de urea plasmatica y presenta un tiempo medio de estabilizacion de 90,90 dias (IC 95%: 52,60 a 250 dias). Para un valor de urea plasmatica de 50 mg/dL, el valor inicial de Dmax es de 3 mg/kg/dia (IC 95%: 1,81 a 4,19 mg/kg/dia) y disminuye de forma exponencial con el tiempo post-trasplante hasta alcanzar un valor constante de 2,16 mg/kg/dia (IC 95%: 1,41 a 2,91 mg/kg/dia). El parametro Km presenta un valor de tendencia central de 93,60 ng/mL (IC 95%: 28,60 a 158,60 ng/mL) y el tiempo medio necesario para su estabilizacion es de 12,70 dias (IC 95%: 9,80 a 17,90 dias). La variabilidad residual de este modelo ha sido de 8,2%. El calculo de la media de los errores de prediccion estandarizados poblacionales e individuales y su desviacion estandar, asi como sus intervalos de confianza 95%, confirma la adecuada exactitud y precision de las predicciones realizadas, a priori y a posteriori, con este modelo. Ademas, proporciona entre 70 y 100% de predicciones a posteriori realizadas de forma secuencial con error de prediccion por debajo del 10%. Conclusion: La caracterizacion del comportamiento farmacocinetico de CsA exige considerar los parametros Dmax y Km como funciones no lineales del tiempo y la adicion al modelo de los terminos autorregresivos de primer orden de DD y C0.
Characterizing cooling water source and usage patterns across US thermoelectric power plants: a comprehensive assessment of self-reported cooling water data

This study characterizes cooling water sources (by type and quality) and cooling water usage rates in thermoelectric power plants across the US based on data reported by power plant operators to the Energy Information Administration (EIA) for the year 2014. Geospatial distributions of water usage by specific cooling technologies and water sources confirm trends towards wet recirculating cooling systems, dry cooling and reclaimed water usage in the power sector, especially in more water constrained locations. Results include a database of water withdrawal and water consumption rates for 672 unique power plants organized by fuel, prime mover and cooling system classification, expanding available data records by an order of magnitude from previous analyses. While median calculated rates are generally comparable to values reported in the literature for most cooling technologies, results suggest that water usage rates at power plants with unique locations or operating conditions might not be accurately characterized by averages, especially in the case of once-through cooled facilities. Despite previous criticisms of EIA cooling water data, improvements in form instructions, reporting methods, and cooling system definitions have markedly improved the quality and usability of cooling water data records in recent years.
Schiff base of isoniazid and ketoprofen: synthesis, X-ray crystallographic, spectroscopic, antioxidant, and computational studies

Ketoprofen and isoniazid together are a potential combination of a nonsteroidal antiinflammatory drug and an antitubercular medicine to treat tuberculosis and associated symptoms like fever and fatigue. The Schiff base of isoniazid with ketoprofen is synthesized in this research. Infrared spectroscopy (IR) and X-ray diffraction (XRD) analysis of the crystal packing proved the formation of the Schiff base and the existence of N-H · · ·O hydrogen bonds between the hydrogen-bonded dimer of the Schiff base. The complete geometrical optimization of the monomer and hydrogen-bonded dimer of the Schiff base is performed utilizing M06-2X/6-31G(d,p) level theory and compared with the experimental data to optimize the molecular structure. The effect of 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity shows that the synthesized Schiff base has improved antioxidant activity. Molecular docking studies against Mycobacterium tuberculosis (Mtb) RNA polymerase-related targets with PDB codes 2M6O and 4KBJ and lung surfactant protein A (SP-A) with PDB ID 5FFT suggest that it can be screened as a potent drug against Mtb infection of the lungs. Frontier orbital theory analysis shows a high energy gap between the HOMO and LUMO, which suggests that the reported Schiff base might be a bioactive compound. Different experimental (XRD, IR, thermal gravimetric analysis, differential scanning calorimetry) and computational studies correlate and validate findings related to this novel Schiff base.
Social Entrepreneurship and Sustainable Development: The Nigerian Conservation Foundation Phenomenon

The study was designed to explore the relationship of social entrepreneurship and sustainable development in Nigeria, using the Nigerian Conservation Foundation as the point of analysis. Even though this type of study is very scanty in Nigeria, social entrepreneurship has gained significant recognition as an important phenomenon for social change and sustainable development. As a multidimensional movement social entrepreneurs through their social enterprises often perceive huge opportunities resulting from market failures, and whether operating as a profit or not for profit organizations aim at building and sustaining a social enterprise to enhance generational social change and sustainable development. Since the 1980s the Nigerian Conservation Foundation has been focusing on sustainable development indicators like poverty alleviation, halting environmental degradation, soil conservation and reforestation as efforts to reduce meaningless and unsustainable development activities in Nigeria. Case study research design was used for the study, and through data analysis the study found high positive association between the activity of the Nigerian Conservation Foundation and UN SDGs agenda. On the basis of the result, it was recommended among others that government should support SEOs in efforts to promote sustainable development in Nigeria.
IMPROVEMENT OF RABBIT PASTEURELLOSIS VACCINE USING MONTANIDE ISA50

Montanide ISA50 was used as an adjuvant for preparation of inactivated rabbit pasteurellosis vaccine, and compared with the commercially produced oil inactivated rabbit pasteurellosis vaccine adjuvanted with mineral oil. Two groups of rabbits were inoculated separately by first dose of these vaccines followed by second dose after 21 days from the first dose, the mineral oil used for first group and ISA50 used for the second group as adjuvant, meanwhile, the third group was left as non-vaccinated control group. Cellular immunity was evaluated by using cell proliferation test and macrophage activity test (phagocytic percentage, phagocytic index). Humoral immune response was evaluated by using indirect haemagglutination test (IHA) and enzyme linked immunosorbent assay (ELISA). The results of the aforementioned tests proved that the two types of adjuvant induced protective titre against the diseases, but Montanide ISA50 oil adjuvant gave higher and longer duration of antibody level than the mineral oil. Challenge test and protection test in rabbit and mice gave similar results.
ENHANCING TEACHER TRAINEES’ UNDERSTANDING ABOUT CHEMICAL REACTIONS AND EQUATIONS

Teacher trainees with misconceptions about chemical phenomena tend to pass this on to their students, thereby creating a vicious cycle of misconceptions which are often difficult to break among learners. This article presents the use of micro chemistry activities and worksheet activities in remediating identified alternative conceptions about types of chemical reactions and their diverse representations among 74 chemistry teacher trainees. The design adopted for the study was a case study which followed the pretest, post-test, delayed test approach in order to assess trainees’ retention of desired basic concepts about types of chemical reactions. The study which lasted for four weeks exposed the trainees’ weaknesses about the nature of matter, types of chemical reactions and their representations. Remediation was offered to enable the trainees to distinguish between types of chemical reactions, with a success rate of 85%. The tools used in this study were found to have great potential in uncovering, deconstructing, and remediating trainees’ misconceptions as well as equipping them with skills, such that authentic conceptions were built at the end of the study period.
Structural and functional learning for learning language use

One of the most ambitious goals of AI is to develop agents that are able to communicate with humans. While many existing systems are already capable of producing human-like utterances, they often focus on learning structural properties of language and miss the utilitarian and functional aspects of communication, i.e., that humans use words to coordinate with others and make things happen in the world. In this work, we investigate if and how we could use the multi-agent interactions (between an agent and a user simulator) as a building component for learning natural language use, and how to harness the structural knowledge of language, that is easily extractable from large collections of texts using language models . One of the most ambitious goals of AI is to develop intelligent agents that are able to communicate with humans. Thus, communication and interaction should be at the core of the language learning process of these agents. However, traditional machine learning approaches to language learning [14, 17, 18] are dissociated from communication but are based on static, passive, and mainly supervised (or self-supervised) regimes, focusing on learning from corpora about the structural properties of language. While this is a great way to learn general statistical single-modality associations between symbols (e.g., the fact that adjectives come before nouns and after determiners) or even multi-modal associations between symbols and things in the world (e.g, the fact that the word cat refers to the furry animal with the four legs) it misses the functional aspects of communication, i.e., that humans use words to coordinate with others and make things happen in the real world [1, 3, 20]. One way to add communication in the core learning of agents is to cast functional language learning (i.e., learning to communicate grounded in a goal) as a supervised learning task and collect language data grounded to a particular goal. However this would require us to collect data of all potential language usages that we would want our agent to be able to communicate about. Motivated by this, previous research
I Know What You Did Last Summer

Modern network telemetry systems collect and analyze massive amounts of raw data in a space efficient manner. These require advanced capabilities such as drill down queries that allow iterative refinement of the search space. We present a first integral solution that (i) enables multiple measurement tasks inside the same data structure, (ii) supports specifying the time frame of interest as part of its queries, and (iii) is sketch-based and thus space efficient. Namely, our approach allows the user to define both the measurement task (e.g., heavy hitters, entropy estimation, count distinct, etc.) and the time frame of relevance (e.g., 5PM-6PM) at query time. Our approach provides accuracy guarantees and is the only space-efficient solution that offers such capabilities. Finally, we demonstrate how our system can be used for accurately pinpointing the start of a realistic DDoS attack.
A Novel Methodology for Generating Thermal Scattering Cross Sections and Uncertainties

With the rise in interest of GEN-IV reactor systems, newer, and more accurate thermal scattering nuclear data are needed. Both GEN-IV reactors and current light water reactors applying for license extensions require high fidelity cross sections and uncertainties to better thermal margins and thereby maximizing energy production and improving economics and safety. Thermal moderator data play a key role in both reactor physics and nuclear criticality safety analyses. Due to the lack of uncertainties data for thermal scattering material, there is no way to quantify the effects of thermal scattering uncertainties in quantities of interest (e.g., keff) in reactor or criticality safety systems. The framework outlined here aims to alleviate these issues by providing a generalized method to generate thermal scattering double differential cross sections (DDCSs) and their respective uncertainties from model parameters. This is accomplished by fitting the computer-simulated scattering data against experimental data using the Unified Monte Carlo (UMC) method. This method had previously only been used on fast spectrum data [1]. The UMC method is tested on light water systems, as it is of significant importance to both reactor systems as well as criticality safety. These new cross sections are then validated against experimental benchmarks and other experimentally gathered cross section data.
CLOSURE: Assessing Systematic Generalization of CLEVR Models

The CLEVR dataset of natural-looking questions about 3D-rendered scenes has recently received much attention from the research community. A number of models have been proposed for this task, many of which achieved very high accuracies of around 97-99%. In this work, we study how systematic the generalization of such models is, that is to which extent they are capable of handling novel combinations of known linguistic constructs. To this end, we test models' understanding of referring expressions based on matching object properties (such as e.g. "the object that is the same size as the red ball") in novel contexts. Our experiments on the thereby constructed CLOSURE benchmark show that state-of-the-art models often do not exhibit systematicity after being trained on CLEVR. Surprisingly, we find that an explicitly compositional Neural Module Network model also generalizes badly on CLOSURE, even when it has access to the ground-truth programs at test time. We improve the NMN's systematic generalization by developing a novel Vector-NMN module architecture with vector-valued inputs and outputs. Lastly, we investigate the extent to which few-shot transfer learning can help models that are pretrained on CLEVR to adapt to CLOSURE. Our few-shot learning experiments contrast the adaptation behavior of the models with intermediate discrete programs with that of the end-to-end continuous models.
Lowrank one-step wave extrapolation for reverse-time migrationa

Reverse-time migration (RTM) relies on accurate wave extrapolation engines to image complex subsurface structures. To construct such operators with high efficiency and numerical stability, we propose an approach of one-step wave extrapolation using complex-valued lowrank decomposition to approximate the mixed-domain spacewavenumber wave extrapolation symbol. The lowrank one-step method involves a complex-valued phase function, which is more flexible than a real-valued phase function of two-step schemes, and thus is capable of modeling a wider variety of dispersion relations. Two novel designs of the phase function lead to desired properties in wave extrapolation. First, for wave propagation in inhomogeneous media, we include a velocity gradient term to implement a more accurate phase behavior, particularly when velocity variations are large. Second, we develop an absorbing boundary condition, which is propagation-direction-dependent and can be incorporated into the phase function as an anisotropic attenuation term. This term allows waves to travel parallel to the boundary without absorption, thus reducing artificial reflections at wide-incident angles. Using numerical experiments, we demonstrate the stability improvement of a one-step scheme in comparison with two-step schemes. We observe the lowrank one-step operator to be remarkably stable and capable of propagating waves using large time step sizes, even beyond the Nyquist limit. The stability property can help minimize the computational cost of seismic modeling or reverse-time migration. We also demonstrate that lowrank one-step wave extrapolation handles anisotropic wave propagation accurately and efficiently. When applied to RTM in anisotropic media, the proposed method generates high quality images.
Human umbilical cord derived mesenchymal stem cells suppress over-proliferation of pulmonary artery smooth muscle cells in a rat model of pulmonary hypertension

Inflammation and over-proliferation of pulmonary artery smooth muscle cells (PASMCs) have been considered as the major pathological features of pulmonary hypertension (PH). Even though transplantation of mesenchymal stem cells (MSCs) could improve PH in many experimental animal models, the exact mechanism of MSCs to suppress inflammation associated over-proliferation of PASMCs is not completely clear. Here we aimed to determine that MSCs suppress over-proliferation of PASMCs in PH via TNF-α/CaN/NFAT pathway. By transplantation of Human umbilical cord derived MSCs (UC-MSCs) into monocrotaline (MCT) induced PH rats, and establishing a co-culture system in vitro consist of MSCs, activated T cells, PASMCs, we assessed the therapeutic effects of MSCs on PH and the changes of correlate factors in TNF-α/CaN/NFAT pathway. The results indicated that transplantation of MSCs could improve the hemodynamics and histology in the progression of PH induced by MCT. Furthermore, in the co-culture system in vitro, MSCs could suppress the TNF-α production of T cells, and then reduce the intracellular calcium level in PASMCs and the expression of CaN and NFATc2, suppress the CaN activity and NFATc2 activation, and could finally achieve the suppressive effect on proliferation of PASMCs. Similar regulatory effects with MSCs could be observed with the presence of infliximab in vitro, and recombinant TNF-α could still be able to promote the proliferation of PASMCs via CaN/NFAT pathway, even under the suppressive effect of MSCs. Taken together, our findings have suggested that MSCs suppress inflammatory associated over-proliferation of PASMCs and remodeling via TNF-α/CaN/NFAT pathway in PH.
Building the Case for Delivering Health Promotion Services Within the Vocational Rehabilitation System

Research studies report a negative relationship between employment and secondary conditions. Access to health promotion programs to manage secondary conditions, however, is limited for people with disabilities due to employment, financial, and insurance barriers. Vocational rehabilitation (VR) is one possible delivery point to overcome these barriers and improve access. To build the case for VR delivery, this article compares VR clients to Living Well health promotion program participants, who successfully reduced their secondary conditions. At baseline,VR (n = 162) and Living Well (n = 188) participants reported similar health promoting lifestyle behavior ratings and similar severity ratings across many secondary conditions. In addition, the VR group experienced similar rates of limitation from secondary conditions over time, indicating that their health was not improving in the absence of a health promotion intervention. Because health promotion programs can reduce secondary conditions, this research supports continued exploration of health promotion delivery within the VR system.
Influence of an extended source on Goniopolarimetry (or Direction Finding) with Cassini and STEREO radio receivers

In a recent paper, Cecconi and Zarka (2005) provided analytical goniopolarimetric inversions — i.e. which allow us to retrieve the direction of arrival of an incoming electromagnetic wave, its flux, and its polarization state, also referred as direction finding inversions — to be used with measurements acquired with a system of electric dipole antennas on a three axis stabilized spacecraft such as the Cassini/RPWS/HFR or the STEREO/Waves receivers. In the present study, we establish the expressions of the measurements (autoand cross-correlations) in the case of an extended source . We also analyze the effect of an extended source on the outputs of the analytical inversions presented in the former paper, which are supposing a point radio source. We show that for a source with an angular half width smaller than 5◦, the induced biases are not significant.
Sensing-Based Dynamic Spectrum Sharing in Integrated Wireless Sensor and Cognitive Satellite Terrestrial Networks

This paper presents a cognitive satellite communication based wireless sensor network, which combines the wireless sensor network and the cognitive satellite terrestrial network. To address the conflict between the continuously increasing demand and the spectrum scarcity in the space network, the cognitive satellite terrestrial network becomes a promising candidate for future hybrid wireless networks. With the higher transmit capacity demand in satellite networks, explicit concerns on efficient resource allocation in the cognitive network have gained more attention. In this background, we propose a sensing-based dynamic spectrum sharing scheme for the cognitive satellite user, which is able to maximize the ergodic capacity of the satellite user with the interference of the primary terrestrial user below an acceptable average level. Firstly, the cognitive satellite user monitors the channel allocated to the terrestrial user through the wireless sensor network; then, it adjusts the transmit power based on the sensing results. If a terrestrial user is busy, the satellite user can access the channel with constrained power to avoid deteriorating the communication quality of the terrestrial user. Otherwise, if the terrestrial user is idle, the satellite user allocates the transmit power based on its benefit to enhance the capacity. Since the sensing-based dynamic spectrum sharing optimization problem can be modified into a nonlinear fraction programming problem in perfect/imperfect sensing conditions, respectively, we solve them by the Lagrange duality method. Computer simulations have shown that, compared with the opportunistic spectrum access, the proposed method can increase the channel capacity more than 20% for Pav=10dB in a perfect sensing scenario. In an imperfect sensing scenario, Pav=15 dB and Qav=5 dB, the optimal sensing time achieving the highest ergodic capacity is about 2.34 ms when the frame duration is 10 ms.