Miracle-dz/UniFAR-FTUs huggingface dataset:
The Cost of Good Intentions: The Ethics and Economics of the War on Conventional Energy

The pastoral call requires shepherding a congregation through difficult circumstances, including challenges from the spiritual message and economic consequences of environmentalism. It is difficult to develop the knowledge and wisdom necessary to give biblical counsel on such issues, especially in light of complex scientific problems and intense policy debates. Yet the church must evaluate alarms raised about the environment and policies to address them. This paper is intended to assist ministry leaders, policy makers, regulators, and the public in understanding and applying biblical worldview, theology, and ethics, coupled with excellent science and economics, to promote a free, prosperous, and just society in a fruitful, beautiful, and safe environment.
Authentication of biomedical images based on zero location watermarking

In this paper, a fragile watermarking method for authentication of biomedical images is proposed in the z-transform domain based on the zero locations. The z-transform is a convenient yet invaluable tool for representing, analyzing and designing discrete-time signals and system. Our watermarking method is designed by exploiting the sensitivity of the positions of z-transform zeros on the unit circle to any tampering made on the host image, such that the watermarking system can localize the portions of a watermarked image that have been tampered maliciously with high accuracy. Simulation results are presented to demonstrate the effectiveness and efficiency of the proposed scheme.
Pattern Adaptive and Finger Image-guided Keypad Interface for In-vehicle Information Systems

Abstract In this paper we propose a pattern adaptive keypad interface for in-vehicle information system. The keypad interface recommends the estimated input sequence to fit the user’s preference based on individual model of operation pattern. Pattern (shape) of button switches corresponding to the estimated input sequence is actively reformed. Button switches are displayed tactilely and visually. Finger image is shown on the monitor in real-time in order to guide input operation on the tactile input device. To confirm the effect of the keypad interface, experiments are performed comparing with a touch screen on which pattern (shape) of buttons switch is unchanged.
The Role of Intraorganellar Ca2+In Late Endosome–Lysosome Heterotypic Fusion and in the Reformation of Lysosomes from Hybrid Organelles

We have investigated the requirement for Ca2+ in the fusion and content mixing of rat hepatocyte late endosomes and lysosomes in a cell-free system. Fusion to form hybrid organelles was inhibited by 1,2-bis(2-aminophenoxy) ethane-N,N,N′,N′-tetraacetic acid (BAPTA), but not by EGTA, and this inhibition was reversed by adding additional Ca2+. Fusion was also inhibited by methyl ester of EGTA (EGTA-AM), a membrane permeable, hydrolyzable ester of EGTA, and pretreatment of organelles with EGTA-AM showed that the chelation of lumenal Ca2+ reduced the amount of fusion. The requirement for Ca2+ for fusion was a later event than the requirement for a rab protein since the system became resistant to inhibition by GDP dissociation inhibitor at earlier times than it became resistant to BAPTA. We have developed a cell-free assay to study the reformation of lysosomes from late endosome–lysosome hybrid organelles that were isolated from the rat liver. The recovery of electron dense lysosomes was shown to require ATP and was inhibited by bafilomycin and EGTA-AM. The data support a model in which endocytosed Ca2+ plays a role in the fusion of late endosomes and lysosomes, the reformation of lysosomes, and the dynamic equilibrium of organelles in the late endocytic pathway.
THE PRICE EQUATION, FISHER'S FUNDAMENTAL THEOREM, KIN SELECTION, AND CAUSAL ANALYSIS

A general framework is presented to unify diverse models of natural selection. This framework is based on the Price Equation, with two additional steps. First, characters are described by their multiple regression on a set of predictor variables. The most common predictors in genetics are alleles and their interactions, but any predictor may be used. The second step is to describe fitness by multiple regression on characters. Once again, characters may be chosen arbitrarily. This expanded Price Equation provides an exact description of total evolutionary change under all conditions, and for all systems of inheritance and selection. The model is first used for a new proof of Fisher's fundamental theorem of natural selection. The relations are then made clear among Fisher's theorem, Robertson's covariance theorem for quantitative genetics, the Lande‐Arnold model for the causal analysis of natural selection, and Hamilton's rule for kin selection. Each of these models is a partial analysis of total evolutionary change. The Price Equation extends each model to an exact, total analysis of evolutionary change for any system of inheritance and selection. This exact analysis is used to develop an expanded Hamilton's rule for total change. The expanded rule clarifies the distinction between two types of kin selection coefficients. The first measures components of selection caused by correlated phenotypes of social partners. The second measures components of heritability via transmission by direct and indirect components of fitness.
Different circuits for different pain: Patterns of functional connectivity reveal distinct networks for processing pain in self and others

Abstract The ability to empathize with the suffering of others is critical for maintaining relationships and engaging in prosocial behavior. Recently, a series of studies have demonstrated that while watching other people experience pain (other pain), participants engage the anterior insula (AI) and anterior cingulate cortex (ACC), brain regions involved in the direct experience of pain (self pain). Here we test the hypothesis that common activity in ACC and AI may reflect the operation of distinct but overlapping networks of regions that support perception of self or other pain. To address this possibility, we scanned participants using fMRI while they received noxious thermal stimulation (self pain) or watched short videos of other people sustaining painful injuries (other pain). We isolated overlapping regions for self and other pain in the ACC and AI and then used them as seed regions for two kinds of functional connectivity analyses. These analyses identified areas whose activity co-varied with ACC and AI activity during self or other pain either across time (intra-individual connectivity) or across participants (inter-individual connectivity). Both connectivity analyses identified clusters in the midbrain and periaqueductal gray with greater connectivity to the AI during self pain as opposed to other pain. The opposite pattern was found in the dorsal medial prefrontal cortex, that showed greater connectivity to the ACC and AI during other pain than during self pain using both types of analysis. Intra-individual connectivity analyses also revealed regions in the superior temporal sulcus, posterior cingulate, and precuneus that became more connected to ACC during other pain as compared to self pain. Together, these data demonstrated that regions showing similar activity during self and other pain may nonetheless be part of distinct functional networks. These networks could not have been detected in prior work that examined overlap between self and other pain in terms of average activity, but not connectivity.
MicroRNAs in skeletal muscle and their regulation with exercise, ageing, and disease

Skeletal muscle makes up approximately 40% of the total body mass, providing structural support and enabling the body to maintain posture, to control motor movements and to store energy. It therefore plays a vital role in whole body metabolism. Skeletal muscle displays remarkable plasticity and is able to alter its size, structure and function in response to various stimuli; an essential quality for healthy living across the lifespan. Exercise is an important stimulator of extracellular and intracellular stress signals that promote positive adaptations in skeletal muscle. These adaptations are controlled by changes in gene transcription and protein translation, with many of these molecules identified as potential therapeutic targets to pharmacologically improve muscle quality in patient groups too ill to exercise. MicroRNAs (miRNAs) are recently identified regulators of numerous gene networks and pathways and mainly exert their effect by binding to their target messenger RNAs (mRNAs), resulting in mRNA degradation or preventing protein translation. The role of exercise as a regulatory stimulus of skeletal muscle miRNAs is now starting to be investigated. This review highlights our current understanding of the regulation of skeletal muscle miRNAs with exercise and disease as well as how they may control skeletal muscle health.
Factors affecting the content of universities’ mission statements: an analysis of the United Kingdom higher education system

ABSTRACT This article explores the factors affecting the content of universities’ mission statements. We conceptualize missions as identity narratives, a type of symbolic representation of an organization. Based on the literature on organizational identity we argue that universities need to address two major challenges when crafting their mission statements, (i) to pursuit legitimacy in spite of multiple and competing expectations, and (ii) to properly balance similarity and distinctiveness from other universities. We identify factors that affect this strategic effort and develop hypotheses that we test in the empirical context of the United Kingdom’s higher education system. Results show that (i) among competing expectations, universities choose claims that are plausible to external constituents and consistent with the values of internal members, and that (ii) they adopt claims similar to universities belonging to the same organizational form while differentiating from geographically closer universities in order to reduce competitive overlap.
Sensitization of ovarian cancer cells to cisplatin by gold nanoparticles

Recently we reported that gold nanoparticles (AuNPs) inhibit ovarian tumor growth and metastasis in mice by reversing epithelial-mesenchymal transition (EMT). Since EMT is known to confer drug resistance to cancer cells, we wanted to investigate whether anti-EMT property of AuNP could be utilized to sensitize ovarian cancer cells to cisplatin. Herein, we report that AuNPs prevent cisplatin-induced acquired chemoresistance and stemness in ovarian cancer cells and sensitize them to cisplatin. AuNPs inhibit cisplatin induced EMT, decrease the side population cells and key stem cell markers such as ALDH1, CD44, CD133, Sox2, MDR1 and ABCG2 in ovarian cancer cells. Mechanistically, AuNPs prevent cisplatin-induced activation of Akt and NF-κB signaling axis in ovarian cancer cells that are critical for EMT, stem cell maintenance and drug resistance. In vivo, AuNPs sensitize orthotopically implanted ovarian tumor to a low dose of cisplatin and significantly inhibit tumor growth via facilitated delivery of both AuNP and cisplatin. These findings suggest that by depleting stem cell pools and inhibiting key molecular pathways gold nanoparticles sensitize ovarian cancer cells to cisplatin and may be used in combination to inhibit tumor growth and metastasis in ovarian cancer.
Using Graphics Processors for a High Performance Normalization of Gene Expressions

Ultra high density oligonucleotide micro arrays allow several millions of genetic markers in a single experiment to be observed. Current bioinformatics software for gene expression quantile data normalization is unable to process such huge datasets. In parallel with this perception, the huge volume of molecular data produced by current high-throughput technologies in modern molecular biology has increased at a similar pace the challenge in our capacity to process and understand data. On the other hand, the arrival of CUDA has unveiled the extraordinary power of Graphics Processors (GPUs) to accelerate data intensive general purpose computing more and more as times goes by. This work takes these two emerging trends to benefit side by side during the development of a high performance version for a biomedical application of growing popularity: gene expression normalization. A variety of experimental issues are analyzed for this execution, including cost, performance and scalability of the graphics architecture on three different platforms. Our study reveals advantages and drawbacks of using the GPU as target hardware, providing lessons to benefit a broad set of existing genetic applications, either based on those pillars or having similarities with their procedures.
Trx2p-dependent Regulation of Saccharomyces cerevisiae Oxidative Stress Response by the Skn7p Transcription Factor under Respiring Conditions

The whole genome analysis has demonstrated that wine yeasts undergo changes in promoter regions and variations in gene copy number, which make them different to lab strains and help them better adapt to stressful conditions during winemaking, where oxidative stress plays a critical role. Since cytoplasmic thioredoxin II, a small protein with thiol-disulphide oxidoreductase activity, has been seen to perform important functions under biomass propagation conditions of wine yeasts, we studied the involvement of Trx2p in the molecular regulation of the oxidative stress transcriptional response on these strains. In this study, we analyzed the expression levels of several oxidative stress-related genes regulated by either Yap1p or the co-operation between Yap1p and Skn7p. The results revealed a lowered expression for all the tested Skn7p dependent genes in a Trx2p-deficient strain and that Trx2p is essential for the oxidative stress response during respiratory metabolism in wine yeast. Additionally, activity of Yap1p and Skn7p dependent promoters by β-galactosidase assays clearly demonstrated that Skn7p-dependent promoter activation is affected by TRX2 gene deficiency. Finally we showed that deleting the TRX2 gene causes Skn7p hyperphosphorylation under oxidative stress conditions. We propose Trx2p to be a new positive efector in the regulation of the Skn7p transcription factor that controls phosphorylation events and, therefore, modulates the oxidative stress response in yeast.
Learning Inverse Dynamics: a Comparison

While it is well-known that model can enhance the control performance in terms of precision or energy eciency, the practical appli- cation has often been limited by the complexities of manually obtaining suciently accurate models. In the past, learning has proven a viable al- ternative to using a combination of rigid-body dynamics and handcrafted approximations of nonlinearities. However, a major open question is what nonparametric learning method is suited best for learning dynamics? Tra- ditionally, locally weighted projection regression (LWPR), has been the standard method as it is capable of online, real-time learning for very com- plex robots. However, while LWPR has had signicant impact on learning in robotics, alternative nonparametric regression methods such as support vector regression (SVR) and Gaussian processes regression (GPR) oer interesting alternatives with fewer open parameters and potentially higher accuracy. In this paper, we evaluate these three alternatives for model learning. Our comparison consists out of the evaluation of learning qual- ity for each regression method using original data from SARCOS robot arm, as well as the robot tracking performance employing learned models. The results show that GPR and SVR achieve a superior learning precision and can be applied for real-time control obtaining higher accuracy. How- ever, for the online learning LWPR presents the better method due to its lower computational requirements.
Inference of High-dimensional Autoregressive Generalized Linear Models

Vector autoregressive models characterize a variety of time series in which linear combinations of current and past observations can be used to accurately predict future observations. For instance, each element of an observation vector could correspond to a different node in a network, and the parameters of an autoregressive model would correspond to the impact of the network structure on the time series evolution. Often these models are used successfully in practice to learn the structure of social, epidemiological, financial, or biological neural networks. However, little is known about statistical guarantees on estimates of such models in non-Gaussian settings. This paper addresses the inference of the autoregressive parameters and associated network structure within a generalized linear model framework that includes Poisson and Bernoulli autoregressive processes. At the heart of this analysis is a sparsity-regularized maximum likelihood estimator. While sparsity-regularization is well-studied in the statistics and machine learning communities, those analysis methods cannot be applied to autoregressive generalized linear models because of the correlations and potential heteroscedasticity inherent in the observations. Sample complexity bounds are derived using a combination of martingale concentration inequalities and modern empirical process techniques for dependent random variables. These bounds, which are supported by several simulation studies, characterize the impact of various network parameters on estimator performance.
Accurate Spatial Resolution Estimates for Reactive Supersonic Flow with Detailed Chemistry

Ar obust method is developed and used to provide rational estimates of reaction zone thicknesses in onedimensional steady gas-phase detonations in mixtures of inviscid ideal reacting gases whose chemistry is described by detailed kinetics of the interactions of N molecular species constituted from L atomic elements. The conservation principles are cast as a set of algebraic relations giving pressure, temperature, density, velocity, and L species mass fractions as functions of the remaining N‐L species mass fractions. These are used to recast the N‐L species evolution equations as a self-contained system of nonlinear ordinary differential equations of the form dYi/dx = fi (Y1 ,., YN‐L). These equations are numerically integrated from a shock to an equilibrium end state. The eigenvalues of the Jacobian of fi are calculated at every point in space, and their reciprocals give local estimates of all length scales. Application of the method to the standard problem of a stoichiometric Chapman‐Jouguet hydrogen‐air detonation in a mixture with ambient pressure of 1 atm and temperature of 298 K reveals that the finest length scale is on the order of 10 −5 cm; this is orders of magnitude smaller than both the induction zone length, 10 −2 cm, and the overall reaction zone length, 10 0 cm. To achieve numerical stability and convergence of the solution at a rate consistent with the order of accuracy of the numerical method as the spatial grid is refined, it is shown that one must employ a grid with a finer spatial discretization than the smallest physical length scale. It is shown that published results of detonation structures predicted by models with detailed kinetics are typically underresolved by one to five orders of magnitude.
Emprendimiento como proceso de responsabilidad social en instituciones de educación superior en Colombia y Venezuela

El estudio esta orientado a caracterizar el emprendimiento como proceso de responsabilidad social en instituciones de educacion superior en Colombia y Venezuela, para alcanzar objetivos rentables que contribuyan a la generacion de bienestar y crecimiento social. La Investigacion fue de tipo descriptivo, no experimental, de campo, la poblacion estuvo conformada por cuarenta (40) docentes especialistas en emprendimiento. La tecnica de recoleccion de datos fueron dos cuestionarios para medir emprendimiento y responsabilidad social. Compuestos por (15) items cada uno, con diferentes alternativas tipo escala likert. La validez se obtuvo a traves del juicio de expertos. Para el calculo de la confiabilidad se utilizo el metodo de alfa Cronbach, donde se obtuvo un valor de 0.80 para cada variable, comprobando que el instrumento es confiable. Los datos obtenidos fueron interpretados estadisticamente mediante frecuencia absoluta y relativa, ademas agrupados por dimension e indicador. Se concluye que contar con herramientas para el emprendimiento radica en la posibilidad de desarrollar politicas de desarrollo y de responsabilidad social en un contexto en que este ha cambiado notablemente a partir del proceso educativo.
One class support vector machine based non-relevance feedback document retrieval

This paper reports a new document retrieval method using non-relevant documents. From a large data set of documents, we need to find documents that relate to human interesting in as few iterations of human testing or checking as possible. In each iteration, a comparatively small batch of documents is evaluated for relating to the human interesting. We applied active learning techniques based on support vector machine for evaluating successive hatches, which is called relevance feedback. Our proposed approach has been very useful for document retrieval with relevance feedback experimentally. The relevance feedback needs a set of relevant and non-relevant documents to work usefully. However, the initial retrieved documents, which are displayed to a user, sometimes don't include relevant documents. In order to solve this problem, we propose a new feedback method using information of non-relevant documents only. We named this method non-relevance feedback document retrieval. The non-relevance feedback document retrieval is based on one-class support vector machine. Our experimental results show that this method can retrieve relevant documents using information of non-relevant documents only.
Bag-of-Embeddings for Text Classification

Words are central to text classification. It has been shown that simple Naive Bayes models with word and bigram features can give highly competitive accuracies when compared to more sophisticated models with part-of-speech, syntax and semantic features. Embeddings offer distributional features about words. We study a conceptually simple classification model by exploiting multiprototype word embeddings based on text classes. The key assumption is that words exhibit different distributional characteristics under different text classes. Based on this assumption, we train multiprototype distributional word representations for different text classes. Given a new document, its text class is predicted by maximizing the probabilities of embedding vectors of its words under the class. In two standard classification benchmark datasets, one is balance and the other is imbalance, our model outperforms state-of-the-art systems, on both accuracy and macro-average F-1 score.