Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Modelling of Wellhead Corrosion due to Operating Conditions: A Field Data Approach

The flow of crude oil, water, and gas from the reservoirs through the wellheads results in its deterioration. This deterioration which is due to the impact of turbulence, corrosion, and erosion significantly reduces the integrity of the wellheads. Effectively managing the wellheads, therefore, requires the knowledge of the extent to which these factors contribute to its degradation. In this paper, the contribution of some operating parameters (temperature, CO2 partial pressure, flow rate, and pH) on the corrosion rate of oil and gas wellheads was studied. Field data from onshore oil and gas fields were analysed with multiple linear regression model to determine the dependency of the corrosion rate on the operating parameters. ANOVA, value test, and multiple regression coefficients were used in the statistical analysis of the results, while in previous experimental results, de Waard-Milliams models and de Waard-Lotz model were used to validate the modelled wellhead corrosion rates. The study shows that the operating parameters contribute to about 26% of the wellhead corrosion rate. The predicted corrosion models also showed a good agreement with the field data and the de Waard-Lotz models but mixed results with the experimental results and the de Waard-Milliams models.
Analysis and Simulation of changes in EMG amplitude during high-level fatiguing contractions

Changes in surface electromyographic (EMG) amplitude during sustained, fatiguing contractions are commonly attributed to variations in muscle fiber conduction velocity (MFCV), motor unit firing rates, transmembrane action potentials and the synchronization or recruitment of motor units. However, the relative contribution of each factor remains unclear. Analytical relationships relating changes in MFCV and mean motor unit firing rates to the root mean square (RMS) and average rectified (AR) value of the surface EMG signal are derived. The relationships are then confirmed using model simulation. The simulations and analysis illustrate the different behaviors of the surface EMG RMS and AR value with changing MFCV and firing rate, as the level of motor unit superposition varies. Levels of firing rate modulation and short-term synchronization that, combined with variations in MFCV, could cause changes in EMG amplitude similar to those observed during sustained isometric contraction of the brachioradialis at 80% of maximum voluntary contraction were estimated. While it is not possible to draw conclusions about changes in neural control without further information about the underlying motor unit activation patterns, the examples presented illustrate how a combined analytical and simulation approach may provide insight into the manner in which different factors affect EMG amplitude during sustained isometric contractions.
Predicting Binding Poses and Affinities in the CSAR 2013-2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential

The 2013-2014 CSAR docking exercise was the opportunity to assess the performance of the novel knowledge-based potential we are developing, named Convex-PL. The data used to derive the potential consists only of structural information from protein-ligand interfaces found in the PDBBind database. As expected, our potential proved to be very efficient in the near-native pose detection exercises, where we correctly predicted two near-native poses in the 2013 exercise and also ranked 22 near-native poses first and 2 second in the 2014 exercise. Somewhat more surprisingly, we obtained a fair performance in some of the CSAR affinity ranking exercises, where the Spearman correlation coefficients between our predictions and the experiments are greater than 0.5 for several protein-ligand sets. Nonetheless, affinity prediction exercises turned out to be a challenge, and significant progress in the development of our method is needed before we can successfully predict binding constants.
Kernel Target Alignment Parameter: A New Modelability Measure for Regression Tasks

In this paper, we demonstrate that the kernel target alignment (KTA) parameter can efficiently be used to estimate the relevance of molecular descriptors for QSAR modeling on a given data set, i.e., as a modelability measure. The efficiency of KTA to assess modelability was demonstrated in two series of QSAR modeling studies, either varying different descriptor spaces for one same data set, or comparing various data sets within one same descriptor space. Considered data sets included 25 series of various GPCR binders with ChEMBL-reported pKi values, and a toxicity data set. Employed descriptor spaces covered more than 100 different ISIDA fragment descriptor types, and ChemAxon BCUT terms. Model performances (RMSE) were seen to anticorrelate consistently with the KTA parameter. Two other modelability measures were employed for benchmarking purposes: the Jaccard distance average over the data set (Div), and a measure related to the normalized mean absolute error (MAE) obtained in 1-nearest neighbors calculations on the training set (Sim = 1 - MAE). It has been demonstrated that both Div and Sim perform similarly to KTA. However, a consensus index combining KTA, Div and Sim provides a more robust correlation with RMSE than any of the individual modelability measures.
Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions

Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased "pocket optimization" simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its "exemplar": a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a "pocket-optimized" ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target.
ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting

The Caco-2 cell monolayer model is a popular surrogate in predicting the in vitro human intestinal permeability of a drug due to its morphological and functional similarity with human enterocytes. A quantitative structure-property relationship (QSPR) study was carried out to predict Caco-2 cell permeability of a large data set consisting of 1272 compounds. Four different methods including multivariate linear regression (MLR), partial least-squares (PLS), support vector machine (SVM) regression and Boosting were employed to build prediction models with 30 molecular descriptors selected by nondominated sorting genetic algorithm-II (NSGA-II). The best Boosting model was obtained finally with R(2) = 0.97, RMSEF = 0.12, Q(2) = 0.83, RMSECV = 0.31 for the training set and RT(2) = 0.81, RMSET = 0.31 for the test set. A series of validation methods were used to assess the robustness and predictive ability of our model according to the OECD principles and then define its applicability domain. Compared with the reported QSAR/QSPR models about Caco-2 cell permeability, our model exhibits certain advantage in database size and prediction accuracy to some extent. Finally, we found that the polar volume, the hydrogen bond donor, the surface area and some other descriptors can influence the Caco-2 permeability to some extent. These results suggest that the proposed model is a good tool for predicting the permeability of drug candidates and to perform virtual screening in the early stage of drug development.
A hypothesis: King Henry VIII’s (1491–1547) personality change: A case of lead poisoning?

Henry VIII (1491–1547) became King of England in 1509. He started out as a good monarch, sensible, reasonable and pleasant, but later his behaviour changed drastically. He became irascible, intolerant, violent and tyrannical. In January 1536, Henry had a serious jousting accident and was unconscious for 2 h. It is generally believed that this accident played a major role in his personality change. Letters of that time, however, indicate that the change began insidiously in 1534 and became most drastic in 1535, a year before the accident. Henry had suffered from leg ulcers before and after the accident and had been constantly treated for them for many years. Sloane MS1047, now in the British Library in London, contains the prescriptions for the medications used to treat these ulcers. Many of the medications contain a high proportion of lead in various forms. Lead can be absorbed through skin, especially damaged skin. Absorbed lead can affect the brain, causing psychiatric problems, especially those associated with violence. The author presents a hypothesis that absorbed lead from his medications might have been a major factor in King Henry’s personality change.
Face Recognition Despite Wearing Glasses

In this paper we address the challenge of performing face recognition on human faces that are wearing glasses. This is a common problem for face recognition and automatic identity checking at airports, as passengers frequently forget to remove their glasses when passing through customs. In order to solve this problem, we first propose an automatic glasses presence detection model based on the tree-pictorial-structured face detection model and such model can detect the presence of glasses and further assign landmarks on the rim, hinge, and bridge of the glasses on frontal faces. Experimental results show that the glasses detection rate is highly satisfactory for various face databases. Secondly, based on the landmarks on glasses, we apply the non-local colour total variation (CTV) inpainting approach in an attempt to remove the glasses; also, we apply the deep learning technique to further remove the traces of glasses and light reflection on lenses by regarding them as noises. Finally, experiments for face recognition after glasses removal are conducted by using some typical approaches and the results show that our glasses removal framework can improve face recognition accuracy significantly.
Improving EMG-based muscle force estimation by using a high-density EMG grid and principal component analysis

The accuracy of predictions of muscle force based on electromyography (EMG) is an important issue in biomechanics and kinesiology. Since human skeletal muscles show a high diversity and heterogeneity in their fiber architecture, it is difficult to properly align electrodes to the muscle fiber direction. Against this background, we analyzed the effect of different bipolar configuration directions on EMG-based force estimation. In addition, we investigated whether principal component analysis (PCA) can improve this estimation. High-density surface-EMG from the triceps brachii muscle and the extension force of the elbow were measured in 11 subjects. The root mean square difference (RMSD) between predicted and measured force was determined. We found the best bipolar configuration direction to cause a 13% lower RMSD relative to the worst direction. Optimal results were obtained with electrodes aligned with the expected main muscle fiber direction. We found that PCA reduced RMSD by about 40% compared to conventional bipolar electrodes and by about 12% compared to optimally aligned multiple bipolar electrodes. Thus, PCA contributes to the accuracy of EMG-based estimation of muscle force when using a high-density EMG grid.
Early CU size determination in HEVC intra prediction using Average Pixel Cost

The HEVC (H.265) standard has brought in significant improvement in terms of coding efficiency. However, this reduction in bit rate (almost half) comes along with an increase in complexity resulting in a very high compression time. This paper proposes a novel method to reduce the complexity in the intra-mode encoding process. We have defined a new term called the Average Pixel Cost (APC) for a CU. When the APC of a CU is below a specified threshold, the splitting process is stopped and therefore the encoder no longer needs to address the smaller CUs in the quad-tree. This results in considerable time saving for the encoder with negligible deterioration in quality and bitrate. Experimental results, based on this method alone, produced an average reduction of 31.3% in compression time in HM 10.0 with an increase of 0.7% in bitrate along with a negligible decrease of PSNR (-0.03 dB) using an APC threshold of 20.
Finite-Element Modeling of Bones From CT Data: Sensitivity to Geometry and Material Uncertainties

The aim of this paper is to analyze how the uncertainties in modelling the geometry and the material properties of a human bone affect the predictions of a finite-element model derived from computed tomography (CT) data. A sensitivity analysis, based on a Monte Carlo method, was performed using three femur models generated from in vivo CT datasets, each subjected to two different loading conditions. The geometry, the density and the mechanical properties of the bone tissue were considered as random input variables. Finite-element results typically used in biomechanics research were considered as statistical output variables, and their sensitivity to the inputs variability assessed. The results showed that it is not possible to define a priori the influence of the errors related to the geometry definition process and to the material assignment process on the finite-element analysis results. The errors in the geometric representation of the bone are always the dominant variables for the stresses, as was expected. However, for all the variables, the results seemed to be dependent on the loading condition and to vary from subject to subject. The most interesting result is, however, that using the proposed method to build a finite-element model of a femur from a CT dataset of the quality typically achievable in the clinical practice, the coefficients of variation of the output variables never exceed the 9%. The presented method is hence robust enough to be used for investigating the mechanical behavior of bones with subject-specific finite-element models derived from CT data taken in vivo
What's What: The (Nearly) Definitive Guide to Reaction Role Assignment

When analyzing chemical reactions it is essential to know which molecules are actively involved in the reaction and which educts will form the product molecules. Assigning reaction roles, like reactant, reagent, or product, to the molecules of a chemical reaction might be a trivial problem for hand-curated reaction schemes but it is more difficult to automate, an essential step when handling large amounts of reaction data. Here, we describe a new fingerprint-based and data-driven approach to assign reaction roles which is also applicable to rather unbalanced and noisy reaction schemes. Given a set of molecules involved and knowing the product(s) of a reaction we assign the most probable reactants and sort out the remaining reagents. Our approach was validated using two different data sets: one hand-curated data set comprising about 680 diverse reactions extracted from patents which span more than 200 different reaction types and include up to 18 different reactants. A second set consists of 50 000 randomly picked reactions from US patents. The results of the second data set were compared to results obtained using two different atom-to-atom mapping algorithms. For both data sets our method assigns the reaction roles correctly for the vast majority of the reactions, achieving an accuracy of 88% and 97% respectively. The median time needed, about 8 ms, indicates that the algorithm is fast enough to be applied to large collections. The new method is available as part of the RDKit toolkit and the data sets and Jupyter notebooks used for evaluation of the new method are available in the Supporting Information of this publication.
Low-Noise Two-Wired Buffer Electrodes for Bioelectric Amplifiers

Active buffer electrodes are known to improve the immunity of bioelectric recordings against power line interferences. A survey of published work reveals that buffer electrodes are almost exclusively designed using operational amplifiers (opamps). In this paper, we discuss the advantage of utilizing a single transistor instead. This allows for a simple electrode, which is small and requires only two wires. In addition, a single transistor adds considerably less noise when compared to an opamp with the same power consumption. We then discuss output resistance and gain as well as their respective effect on the common mode rejection ratio (CMRR). Finally, we demonstrate the use of two-wired buffer electrodes for a bioelectric amplifier.
Rituals, Death and the Moral Practice of Medical Futility

Medical futility is often defined as providing inappropriate treatments that will not improve disease prognosis, alleviate physiological symptoms, or prolong survival. This understanding of medical futility is problematic because it rests on the final outcomes of procedures that are narrow and medically defined. In this article, Walker's `expressivecollaborative' model of morality is used to examine how certain critical care interventions that are considered futile actually have broader social functions surrounding death and dying. By examining cardiopulmonary resuscitation and life-sustaining intensive care measures as moral practices, we show how so-called futile interventions offer ritualistic benefit to patients, families, and health care providers, helping to facilitate the process of dying. This work offers a new perspective on the ethical debate concerning medical futility and provides a means to explore how the social value of treatments may be as important in determining futility as medical scientific criteria.
Adapting a supply-chain simulation for HLA

Supply-chain management covers the planning and management of material and information from the manufacturer through the distributors, and finally to the customer. With the globalization of markets, the optimization of supply-chain management becomes more and more important. Simulation of supply-chains can help in the optimization process by evaluating the impact of alternative policies. To support the reusability of existing simulation models in a supply-chain simulation, a common standard is required for enabling the interoperability amongst different simulation programs through a well-defined interface. The High Level Architecture (HLA) is an architecture for reuse and interoperation of simulations. We report our experiences on employing the HLA to support reusability and interoperability in semiconductor supply-chain simulation. Our experiments show that by fine-tuning the integration of the application with the HLA Run-Time Infrastructure (RTI), considerable performance improvements can be achieved.
Morfología del Jugador de Élite de Pádel: Comparación con Otros Deportes de Raqueta

El rendimiento deportivo se encuentra asociado a una optima morfologia del deportista. El padel que es un deporte que posee gran numero de adeptos, necesita de mayor informacion acerca de los jugadores que lo practican. El objetivo del presente estudio fue describir el somatotipo antropometrico de 48 jugadores de alto rendimiento en padel. Un analisis de la varianza fue aplicado para hallar diferencias entre categorias de juego y la pruena T-Student para aquellas en funcion del sexo y el rendimiento deportivo (ganadores y perdedores). Las variables de peso, talla e Indice de Masa Corporal son similares entre las diferentes categorias de jugadores analizados, excepto entre hombres y mujeres. El somatotipo dominante de los jugadores fue meso-endomorfico (3,6 - 6,9 - 1,9 y 4,1 - 5,4 - 3,0; en hombres y mujeres, respectivamente). Las distancias de dispersion del somatotipo muestran diferencias significativas con otros deportes como el squash, badminton y tenis. Los resultados aportan informacion relevante para determinar la morfologia ideal de los jugadores de padel, hasta la fecha desconocido.
The ethics of care: Role obligations and moderate partiality in health care

This article contends that an ethics of care has a particular moral ontology that makes it suitable to argue for the normative significance of relational responsibilities within professional health care. This ontology is relational. It means that moral choices always have to account for the web of relationships, the relational networks and responsibilities that are an essential part of particular moral circumstances. Given this ontology, the article investigates the conditions for health care professionals to be partial and to act on the basis of particular responsibilities to their patients. We will argue that priorities could be partial in three ways: first, because there may be exceptional circumstances that allow for giving priority to one patient over another; second, because the integrity of the patient and a health care worker may be connected in special ways; and, finally, even if impartiality is essential, the institutional basis of health care must always give ample space for an ethically qualified individual and personal care for patients. Even if difficult priorities may be necessary, the conditions of institutional health care should always seek to create the prerequisites for nurses and doctors to administer proper care.
Diversified ranking on graphs from the influence maximization viewpoint

To characterize the relationship between items, graph-based ranking algorithms are widely used in various applications, such as information retrieval, recommender system, and natural language processing. Many ranking approaches tackle the dilemma between relevance and diversity. Diversity is considered as a critical objective of reducing redundancy and retrieving prestige information that has high coverage. However, the traditional evaluation of diversification is found to be deficient. In this paper, we address the coverage problem from a viewpoint of influence diffusion. Firstly, we transform the coverage problem into the diffusion problem and propose a novel measure called essential influence that combines relevance and diversity into a single function. Next, we propose a reinforced random walk, InfRank, of which the heuristic function is based on the essential influence. We applied InfRank on two applications, ranking in networks and tag recommendation. Our approach outperforms existing network-based ranking methods.