Miracle-dz/UniFAR-FTUs huggingface dataset:
Different Characteristics and Nucleotide Binding Properties of Inosine Monophosphate Dehydrogenase (IMPDH) Isoforms

We recently reported that Inosine Monophosphate Dehydrogenase (IMPDH), a rate-limiting enzyme in de novo guanine nucleotide biosynthesis, clustered into macrostructures in response to decreased nucleotide levels and that there were differences between the IMPDH isoforms, IMPDH1 and IMPDH2. We hypothesised that the Bateman domains, which are present in both isoforms and serve as energy-sensing/allosteric modules in unrelated proteins, would contribute to isoform-specific differences and that mutations situated in and around this domain in IMPDH1 which give rise to retinitis pigmentosa (RP) would compromise regulation. We employed immuno-electron microscopy to investigate the ultrastructure of IMPDH macrostructures and live-cell imaging to follow clustering of an IMPDH2-GFP chimera in real-time. Using a series of IMPDH1/IMPDH2 chimera we demonstrated that the propensity to cluster was conferred by the N-terminal 244 amino acids, which includes the Bateman domain. A protease protection assay suggested isoform-specific purine nucleotide binding characteristics, with ATP protecting IMPDH1 and AMP protecting IMPDH2, via a mechanism involving conformational changes upon nucleotide binding to the Bateman domain without affecting IMPDH catalytic activity. ATP binding to IMPDH1 was confirmed in a nucleotide binding assay. The RP-causing mutation, R224P, abolished ATP binding and nucleotide protection and this correlated with an altered propensity to cluster. Collectively these data demonstrate that (i) the isoforms are differentially regulated by AMP and ATP by a mechanism involving the Bateman domain, (ii) communication occurs between the Bateman and catalytic domains and (iii) the RP-causing mutations compromise such regulation. These findings support the idea that the IMPDH isoforms are subject to distinct regulation and that regulatory defects contribute to human disease.
Computing with Continuous Attractors: Stability and Online Aspects

Two issues concerning the application of continuous attractors in neural systems are investigated: the computational robustness of continuous attractors with respect to input noises and the implementation of Bayesian online decoding. In a perfect mathematical model for continuous attractors, decoding results for stimuli are highly sensitive to input noises, and this sensitivity is the inevitable consequence of the system's neutral stability. To overcome this shortcoming, we modify the conventional network model by including extra dynamical interactions between neurons. These interactions vary according to the biologically plausible Hebbian learning rule and have the computational role of memorizing and propagating stimulus information accumulated with time. As a result, the new network model responds to the history of external inputs over a period of time, and hence becomes insensitive to short-term fluctuations. Also, since dynamical interactions provide a mechanism to convey the prior knowledge of stimulus, that is, the information of the stimulus presented previously, the network effectively implements online Bayesian inference. This study also reveals some interesting behavior in neural population coding, such as the trade-off between decoding stability and the speed of tracking time-varying stimuli, and the relationship between neural tuning width and the tracking speed.
Analysis of third-order methods for secular equations

Third-order numerical methods are analyzed for secular equations. These equations arise in several matrix problems and numerical linear algebra applications. A closer look at an existing method shows that it can be considered as a classical method for an equivalent problem. This not only leads to other third-order methods, it also provides the means for a unifying convergence analysis of these methods and for their comparisons. Finally, we consider approximated versions of the aforementioned methods.
Simple high-order approximations for unsteady-state diffusion, adsorption and reaction in catalyst: A unified method by a continued fraction

For unsteady-state diffusion, adsorption and a first-order reaction in a slab, cylinder and sphere catalyst, high-order approximations are developed. A continued fraction is developed as a function of the shape factor and the Thiele modulus in Laplace domain to unify the exact transfer functions of the three catalyst geometries. Inversion of the continued fraction to the time domain results in the approximation, the accuracy of which depends on the order of approximation and improves very quickly with each increment in the order. Construction of the proposed time-domain approximation is easy and systematic for any approximation order.
Spatial network structure and amphibian persistence in stochastic environments

In the past few years, the framework of complex networks has provided new insight into the organization and function of biological systems. However, in spite of its potential, spatial ecology has not yet fully incorporated tools and concepts from network theory. In the present study, we identify a large spatial network of temporary ponds, which are used as breeding sites by several amphibian species. We investigate how the structural properties of the spatial network change as a function of the amphibian dispersal distance and the hydric conditions. Our measures of network topology suggest that the observed spatial structure of ponds is robust to drought (compared with similar random structures), allowing the movement of amphibians to and between flooded ponds, and hence, increasing the probability of reproduction even in dry seasons.
Finite Element Approximations for Stokes–darcy Flow with Beavers–joseph Interface Conditions *

Numerical solutions using finite element methods are considered for transient flow in a porous medium coupled to free flow in embedded conduits. Such situations arise, for example, for groundwater flows in karst aquifers. The coupled flow is modeled by the Darcy equation in a porous medium and the Stokes equations in the conduit domain. On the interface between the matrix and conduit, Beavers–Joseph interface conditions, instead of the simplified Beavers–Joseph–Saffman conditions, are imposed. Convergence and error estimates for finite element approximations are obtained. Numerical experiments illustrate the validity of the theoretical results. 1. Introduction. The purpose of this paper is to investigate finite element approximations of the time-dependent Stokes–Darcy system with the Beavers–Joseph interface condition. Our motivation is the modeling and numerical simulation of groundwater flows in karst aquifers. Karst aquifers represent a very significant source of water for public and private use. For instance, aquifers supply 90% of the water used for domestic and public purposes in the state of Florida. However, in comparison with large amounts of studies of groundwater in porous and fractured media, studies about karst aquifers are still very limited and inaccurate. One of the difficulties in the modeling of karst aquifers is that, in addition to a porous limestone matrix, a typical karst aquifer also has large cavernous conduits that are known to largely control groundwater flows within the aquifer. We refer to such a system as a conduit-matrix system. In this study, we develop a new modeling approach for water flows in conduit-matrix systems.
Learning-centred parental engagement: Freire reimagined

Abstract This article builds on the seminal work of Paulo Freire, whose critique of the “banking model” of education has inspired educators to look beyond mechanistic, didactic means of teaching, toward more constructivist, engaging methods. In this article, I argue that, although the teaching of children has changed as a result of work such as Freire’s, school staff often revert to a “banking model” when seeking to engage parents. This paper utilises Freire’s characteristics of “banking education” as a lens to look at relationships between schools and families. Based on this, the paper then suggests a way of moving toward a more equitable, sustainable and fruitful partnership between all those involved in schooling and learning (with the former being a formalised subset of the much larger latter).
Significant Predictors for Resilience among a Sample of Undergraduate Students: Acceptance, Forgiveness and Gratitude

Acceptance, forgiveness and gratitude are three protective factors and appear to be as three personal characteristics that have been found to be closely related to psychological well-being. Greater acceptances show better psychological functioning in life. Forgiveness fosters people's ability to deal with psychological stressors higher level of gratitude demonstrated higher level of perceived social support, lower level of stress and depression. The personal qualities that allow one to thrive in the face of adversity, is termed as resilience. This study aimed to examine whether acceptance, forgiveness, and gratitude are significant predictors for resilience among undergraduate students. 150 students are selected for study in one of the college in Hapur. Schedules are used as a research tool, which includes demographic information, acceptance and action Questionnaire-II, Heartland Forgiveness Scale, The Gratitude Questionnaire and Connor-Davidson Resilience Scale. Results supported the hypothesis and revealed that, acceptance, forgiveness, and gratitude appear to be significant predictors for resilience among undergraduate students. The three predictors contributed 66% of the total variance of student's resilience model, whereby gratitude illustrates the highest predictive value for resilience, followed by forgiveness and acceptance. The result implies that forgiveness is the key factor to resilience in which forgiveness is essential in order for acceptance to be statistically correlated to resilience.
Best practice recommendations for data screening

Summary Survey respondents differ in their levels of attention and effort when responding to items. There are a number of methods researchers may use to identify respondents who fail to exert sufficient effort in order to increase the rigor of analysis and enhance the trustworthiness of study results. Screening techniques are organized into three general categories, which differ in impact on survey design and potential respondent awareness. Assumptions and considerations regarding appropriate use of screening techniques are discussed along with descriptions of each technique. The utility of each screening technique is a function of survey design and administration. Each technique has the potential to identify different types of insufficient effort. An example dataset is provided to illustrate these differences and familiarize readers with the computation and implementation of the screening techniques. Researchers are encouraged to consider data screening when designing a survey, select screening techniques on the basis of theoretical considerations (or empirical considerations when pilot testing is an option), and report the results of an analysis both before and after employing data screening techniques. Copyright © 2014 John Wiley & Sons, Ltd.
Workplace civility training: understanding drivers of motivation to learn

Abstract Training is recommended as an important human resource management (HRM) practice to prevent mistreatment and enhance civility, but little is known about what influences the effectiveness of civility training. The central aim of this study was to address how workgroup conditions influence employees' attitudes about civility training and motivation to learn, which previous research shows is a predictor of training outcomes. Predictors were posited to include psychological and workgroup climate for civility, and personal and ambient mistreatment experiences. These predictors were hypothesized to drive positive (training discrepancy) and negative (training skepticism) pre‐training attitudes, which in turn were expected to influence motivation to learn. Results suggest the influence of climate for civility and mistreatment experiences on motivation to learn is largely indirect via pre‐training attitudes. Training skepticism and training discrepancy have conflicting influences on motivation to learn. Findings provide an empirical basis for HRM professionals to maximize employee motivation to learn in their own civility interventions.
Photovoltaic panel characterization by using artificial neural networks and comparison with classical models

In this paper, a characterization methodology for PV panels by using artificial neural networks (ANN) is proposed. Data typically provided by the manufacturer in the datasheet, or data obtained from environmental measurements taken at the place where the photovoltaic (PV) generation system is installed, is used for the characterization of the PV panels. The results obtained for the representation of the PV panel using ANN are presented, and then compared with results found by classical models. The results of the simulation of two PV panels using ANN show the accuracy and effectiveness of the proposed approach.
Runtime prediction models for Web-based system resources

Several activities of Web-based architectures are managed by algorithms that take runtime decisions on the basis of continuous information about the state of the internal system resources. The problem is that in this extremely dynamic context the observed data points are characterized by high variability, dispersion and noise at different time scales to the extent that existing models cannot guarantee accurate predictions at runtime. In this paper, we evaluate the predictability of the internal resource state and point out the necessity to filter the noise of raw data measures. We then verify that more accurate prediction models are required which take into account the non stationary effects of the data sets, the time series trends and the runtime constraints. To these purposes, we propose a new prediction model, called trend-aware regression. It is specifically designed to deal with on the fly and short-term forecast of time series which originate from filtered data points belonging to internal resources of Web system. The experiment evaluation for different workload scenarios shows that the proposed trend-aware regression model improves the prediction accuracy with respect to popular algorithms based on auto-regressive and linear models, while satisfying the computational constraints of runtime prediction.
A systematic study of the delayed column generation method for optimizing wireless networks

The main-thread approach for optimizing the throughput capacity over a multihop wireless network is to apply a multi-commodity flow (MCF) formulation, augmented with a scheduling constraint derived from the conflict graph associated with the network. A fundamental issue with the conflict graph based MCF formulation is that finding all independent sets (ISs) for scheduling is NP-hard in general. If we express the MCF formulation in a matrix format, the constraint matrix will contain a very large number of columns, with each IS being associated with one column. According to the linear programming theorem, such a type of problem can be addressed with the delayed column generation (DCG) method. Unfortunately, applications of the DCG in wireless networks have not received much attention. To the best of knowledge, none of the existing work conducted theoretical studies of the performance of DCG in wireless networks. In this paper, we study the DCG method in the context of a general network flow problem. With a protocol interference model, we rigorously prove that searching an entering column in the DCG operation is equivalent to a maximum weighted independent set (MWIS) problem. A prominent theoretical contribution of this paper is the theorem that: if an MWIS approximation algorithm with the approximation ratio β (<1) is applied in the DCG method, the maximum flow solved will be at least β of the optimal solution. Furthermore, the DCG method is also applied to the multi-radio multi-channel (MR-MC) networks. With extensive numerical results comparing to the existing methods, we show that the DCG method achieves the most preferred tradeoff between computation complexity and network capacity and maintains good scalability when addressing large-scale networks, particularly in the complex MR-MC context.
Iterative solution of general sparse linear systems on clusters of workstations

Solving sparse irregularly structured linear systems on parallel platforms poses several challenges. First, sparsity makes it difficult to exploit data locality, whether in a distributed or shared memory environment. A second, perhaps more serious challenge, is to find efficient ways to precondition the system. Preconditioning techniques which have a large degree of parallelism, such as multicolor SSOR, often have a slower rate of convergence than their sequential counterparts. Finally, a number of other computational kernels such as inner products could ruin any gains gained from parallel speed-ups, and this is especially true on workstation clusters where start-up times may be high. In this paper we discuss these issues and report on our experience with PSPARSLIB, an on-going project for building a library of parallel iterative sparse matrix solvers.
Weighted-Codon-Usage Based Phylogeny In Ectocarpales

We analyse forty seven chloroplastid genes of the large subunit of RuBisCO, from the Algal order Ectocarpales, sourced from GenBank. Codon-usage weighted by the nucleotide base bias defines our score called the Codon-Impact-Parameter. This score is used to obtain phylogenetic relations amongst the 47 Ectocarpales. We compare our classification with the ones done earlier.
Dialogue, inquiry, and encounter: Critical geographies of online higher education

The rapid expansion of online education compels debate over what accessible higher education should be, how it should be delivered, and whom it should serve. While geographers remain relatively marginal to this debate, they have engaged the question of the neoliberal university, where online education is sometimes characterized as another instantiation of the neoliberal turn. This paper draws geographies of education scholarship into productive conversation with online teaching and learning, critical pedagogy, and public geographies literatures to argue that geographers can reframe the debate over online education and reposition it as a productive space of critical dialogue, inquiry, and encounter.
The professional identity of Czech journalists in an international perspective

This article, placed in the field of comparative journalism studies, explores the extent to which Czech journalists share their professional identity and compares the congruence of their professional identity with selected Western journalistic communities working in the liberal, democratic-corporatist, and polarized pluralism media model defined by Hallin and Mancini. The results show that the professional identity structure of Czech journalists does not, in principle, differ from that of foreign journalists. This implies that they have cut themselves from the Soviet journalistic model. Czech journalists do not significantly differ in the intensity of the selected professional values, and the measure of their professional identity split is comparable to that of their foreign counterparts.
Effects of tetrathiomolybdate in a mouse model of retinal neovascularization.

PURPOSE To determine the effects of tetrathiomolybdate (TM), a copper-chelating agent, on retinal angiogenesis and vascular endothelial growth factor (VEGF) in a mouse model of retinal neovascularization. METHODS Postnatal day (P)7 C57BL/6N mice were exposed to 75% +/- 2% oxygen for 5 days (P7-P11) and then returned to room air for 5 days (P12-P17) to induce retinal neovascularization. Beginning on P10 or P12, mice received daily intraperitoneal injections of TM or phosphate-buffered saline (PBS; control) through P17. Retinal neovascularization was examined by fluorescein dextran angiography after 5 days in room air and was quantitated histologically by counting the neovascular endothelial cell nuclei anterior to the inner limiting membrane. TM's effects on VEGF expression were measured by ELISA. RESULTS TM-treated and control animals demonstrated comparable regions of retinal nonperfusion. Retinas from control mice at P17 contained neovascular tufts at the junction between perfused and nonperfused retina. The tufts contained numerous neovascular nuclei. Retinas from mice treated with TM beginning on P10 (2 days before returning to room air), but not P12, demonstrated a 41% reduction in neovascular cell nuclei compared with control mice (P <0.01). The P10-treated mice also demonstrated a 24% reduction of VEGF compared with control animals (P=0.01). CONCLUSIONS TM significantly inhibits retinal neovascularization and VEGF production in a mouse model of retinal neovascularization.
A planar parallel manipulator - dynamics revisited and controller design

In this paper, the dynamic modelling and control design of a planar parallel manipulator used as a pick-and-place machine, is addressed. First, in a departure from standard modelling techniques utilized for planar parallel mechanisms, it is demonstrated that since the translational axes of the manipulator are driven by DC motors through industry standard ball screws, the nonlinear dynamics and coupling effects of the nonlinear dynamics of the manipulator are greatly reduced by a very large effective gear ratio factor, in this case, 1.097 xlO6. The dynamics of the driving motors thus become the dominant dynamics in the system. Hence, the dynamics of the entire system can be approximated as a set of three identical linear dynamic equations, each of which represents the dynamics of one kinematic chain, with constraints representing the coupling of these axes. Then a robust closed-loop controller designed with a Convex Integrated Design (CID) method is determined, such that multiple closed-loop performance specifications, together with a robustness specification, are simultaneously satisfied. The robustness of the closed-loop controller thus guarantees that the controller, although determined based on a simplified linear model, performs as expected on the practical system, i.e., the manipulator, hence results in satisfactory closed-loop performance. Both simulation and experiments conducted demonstrate that the multiple simultaneous closed-loop performance specifications are satisfied thus validating the simplified modeling strategy and verifying the effectiveness of the control design approach.
Energy-based rate adaptation for 802.11n

Rate adaptation (RA) has been used to achieve high goodput. In this work, we explore to use RA for energy efficiency in 802.11n NICs. We show that current MIMO RA algorithms are not energy efficient for NICs despite ensuring high throughput. The fundamental problem is that, the high-throughput setting is not equivalent to the energy-efficient one. Marginal throughput gain may be realized at high energy cost. We propose EERA, an energy-based RA solution that trades off goodput for energy savings at NICs. Our experiments have confirmed its energy savings at NICs while keeping the cost at the device level and across clients acceptable.