Miracle-dz/UniFAR-FTUs huggingface dataset:
Does phosphorylation of the cap‐binding protein eIF4E play a role in translation initiation?

Eukaryotic initiation factor 4E (eIF4E) plays an important role in mRNA translation by binding the 5′‐cap structure of the mRNA and facilitating the recruitment to the mRNA of other translation factors and the 40S ribosomal subunit. eIF4E can interact either with the scaffold protein eIF4G or with repressor proteins termed eIF4E‐binding proteins (4E‐BPs). High levels of expression can disrupt cellular growth control and are associated with human cancers. A fraction of the cellular eIF4E is found in the nucleus where it may play a role in the transport of certain mRNAs to the cytoplasm. eIF4E undergoes regulated phosphorylation (at Ser209) by members of the Mnk group of kinases, which are activated by multiple MAP kinases (hence Mnk = MAP‐kinase signal integrating kinase). The functional significance of its phosphorylation has been the subject of considerable interest. Recent genetic studies in Drosophila point to a key role for phosphorylation of eIF4E in growth and viability. Initial structural data suggested that phosphorylation of Ser209 might allow formation of a salt bridge with a basic residue (Lys159) that would clamp eIF4E onto the mRNA and increase its affinity for ligand. However, more recent structural data place Ser209 too far away from Lys159 to form such an interaction, and biophysical studies indicate that phosphorylation actually decreases the affinity of eIF4E for cap or capped RNA. The implications of these studies are discussed in the light of other, in vitro and in vivo, investigations designed to address the role of eIF4E phosphorylation in mRNA translation or its control.
Short-Term Plasticity Shapes the Response to Simulated Normal and Parkinsonian Input Patterns in the Globus Pallidus

Basal ganglia structures show strong activity modulation during movement and synchronous bursting in Parkinson's disease. Recent work has shown that short-term synaptic plasticity (STP) can play an important role in the effect of temporal activity patterns on postsynaptic targets. To determine the role of STP in the subthalamic nucleus (STN) to globus pallidus (GP) connection, which has been suggested to underlie rhythmical bursting in Parkinson's disease, we first measured STP using trains of electrical input stimulationin vitro. We found that STN inputs to GP typically show both facilitation and depression with input frequencies of 10–100 Hz and that facilitation is dominant for the first few inputs in a train but that depression takes over subsequently. We quantified the strength and time course of facilitation and depression using a computational model of STP. Using the STP model, we constructed synaptic conductance patterns of normal and Parkinsonian STN activity and applied these conductances to GP neurons in vitro using the technique of dynamic clamping. We show that STP controls the slope and shape of the function describing the steady-state level of GP neuron firing in response to different levels of STN input. In addition, we show that STP modulates responses of GP neurons to bursts and pauses in the input pattern. These findings indicate that STP plays an important role in modulating both spike rates and temporal patterns of GP activity in the normal state, as well as in Parkinson's disease.
Phylogenetic relationship and species delimitation of matsutake and allied species based on multilocus phylogeny and haplotype analyses

Tricholoma matsutake (S. Ito & S. Imai) Singer and its allied species are referred to as matsutake worldwide and are the most economically important edible mushrooms in Japan. They are widely distributed in the northern hemisphere and established an ectomycorrhizal relationship with conifer and broadleaf trees. To clarify relationships among T. matsutake and its allies, and to delimit phylogenetic species, we analyzed multilocus datasets (ITS, megB1, tef, gpd) with samples that were correctly identified based on morphological characteristics. Phylogenetic analyses clearly identified four major groups: matsutake, T. bakamatsutake, T. fulvocastaneum and T. caligatum; the latter three species were outside the matsutake group. The haplotype analyses and median-joining haplotype network analyses showed that the matsutake group included four closely related but clearly distinct taxa (T. matsutake, T. anatolicum, Tricholoma sp. from Mexico and T. magnivelare) from different geographical regions; these were considered to be distinct phylogenetic species.
Constructional tools as the origin of cognitive capacities

It is argued that cognitive capacities can be understood as the outcome of the collective action of a set of agents created by tools that explore possible behaviors and train the agents to behave in such appropriate ways as may be discovered. The coherence of the whole system is assured by a combination of vetting the performance of new agents and dealing appropriately with any faults that the whole system may develop. This picture is shown to account for a range of cognitive capacities, including language.
Entrepreneurial and Intrapreneurial Attitudes: Conceptualization, Measure Development, Measure Test and Model Fit

The extant literature recognized the significance of attitudes in understanding an entrepreneur/intrapreneur. The present study contributes to the value addition in the extant literature in the form of developing and validating standard measures and models of entrepreneurial attitudes (EA)/intrapreneurial attitudes (IA). Our models suggest that the six attitudes, namely, achievement orientation, risk-taking propensity, internal locus of control, innovativeness, pro-activeness and market orientation (MO), have a positive impact on venture performance of both entrepreneurs and intrapreneurs. Our well-tested models and the measures of EA/IA attitudes provide a concrete foundation for future research studies of EA/IA across the historical, temporal, institutional, spatial, social and cultural contexts.
Long term follow up of the early repolarization pattern in participants in the West of Scotland Coronary Prevention study

The West of Scotland Coronary Prevention Study was a primary prevention trial aimed at assessing the effects of pravastatin in middle aged males with hypercholesterolemia but no history of myocardial infarction. As part of that study, baseline 12 lead ECGs were recorded in digital form around 1990. The aim of the present study was to analyse these ECGs to look for the early repolarization pattern (ERP) and to see if there was any adverse outcome associated with ERP during 15 years of follow up. At baseline, 6595 men aged 45-64 (mean age 55.2 ± 5.5) were recruited. Subsequent hospitalizations and/or death were recorded centrally. Twelve lead ECGs were recorded and stored on a Mingocare system. 6575 ECGs were recently retrieved and analysed with the University of Glasgow ECG Analysis Program, which incorporates criteria for the detection of ERP. A health census of participants was taken 15 years after entry into the study. Analysis was undertaken of the diference in outcome for those with versus those without ERP. ERP was found in 1398 cases. Kaplan-Meier analysis of the J 5 year outcomes data showed no significant diference between those with and without ERP for the outcome of i) non-fatal myocardial infarction or coronary heart disease death, and ii) all cause mortality (P=0.65).
Opening a Pandora's Box: Modeling World Trade Patterns at the 2035 Horizon

Economic projections for the world economy, particularly in relation to the construction of Computable General Equilibrium (CGE) baselines, are generally rather conservative and take scant account of the wide range of possible evolutions authorized by the underlying economic mechanisms considered. Against this background, we adopt an ‘open mind’ to the projection of world trade trajectories. Taking a 2035 horizon, we examine how world trade patterns will be shaped by the changing comparative advantages, demand, and capabilities of different regions. We combine a convergence model fitting three production factors (capital, labor and energy) and two factor-specific productivities, alongside a dynamic CGE model of the world economy calibrated to reproduce observed elasticity of trade to income. Each scenario involves three steps. First, we project growth at country level based on factor accumulation, educational attainment and efficiency gains, and discuss uncertainties related to our main drivers. Second, we impose this framework (demographics, gross domestic product, saving rates, factors and current account trajectories) on the CGE baseline. Third, we implement trade policy scenarios (tariffs as well as non-tariff measures in goods and services), in order to get factor allocation across sectors from the model as well as demand and trade patterns. We show that the impact of changing baselines is greater than the impact of a policy shock on the order of magnitude of changes in world trade patterns, which points to the need for care when designing CGE baselines.
Effect of climatic variables on production and reproduction traits of colored broiler breeder poultry

Aim: The present study was conducted to investigate the important climatic variables affecting production and reproduction in a broiler breeder flock. Materials and Methods: The experiment was conducted for a period of 1 year on colored synthetic female line male and female poultry birds. 630 female progeny and 194 male progenies from 69 sires and 552 dams produced in four consecutive hatches at an interval of 10 days were used for the present study. Each of the seven, body weight and reproduction traits were regressed with nine environmental variables. Initially, the data were subjected to hatch effect and sire effect corrections through best linear unbiased estimator (BLUE) method and, then, multiple linear regressions of environmental variables on each trait were applied. Result: The overall regression was significant (p<0.01) in all traits except 20 week age body weight of females. The R2 value ranged from 0.12 to 0.90 for the traits. Regression coefficient values (b values) for maximum temperature and minimum temperature were significant (p<0.05) on 5th week age body weight of males. Similarly, evaporation and morning relative humidity (RH) was significant (p<0.05) for 5th week age body weight of females. Almost all b values were significant (p<0.05) for egg production up to 40 week age. The b values representing rainfall, morning RH, afternoon RH, sunshine hours, and rainy days were significant (p<0.05) on bodyweight at 20 week age. All environmental variables except maximum temperature and minimum temperature were significant (p<0.05) on body weight of females at 20 weeks of age. Age at sexual maturity was regressed significantly (p<0.05) with evaporation, afternoon RH whereas, egg shape index was regressed significantly (p<0.05) with a maximum temperature, evaporation and afternoon RH. Conclusion: The result indicated that various environmental variables play a significant role in production and reproduction of breeder broiler poultry. Controlling these variables in adverse weathers may increase production.
Development of a perimeter odor monitoring system for landfill sites

A need exists for instrumentation capable of continuous measurement of gas and odor emission from the perimeter of a landfill site. Current practice is to manually use a flame ionization detector. Instruments based on an array of MOS gas sensors were developed and placed on the perimeter of a landfill site, functioning as point monitoring systems, using methane as a surrogate for odor emission. The single-point instrument (SPEMMI) proved to be sensitive to methane, with the sensors showing a change in resistance of about 4% for a methane concentration of 50 ppm. On-site measurements showed that, while there was some sensitivity to temperature, it was possible to identify methane emission events. Data analysis techniques using a Savitsky-Golay second order filter show that event detection was possible. The system was operational in the field and was reliable. The use of complementary, n-type and p-type gas sensors, allowed discrimination of gas events from ambient temperature fluctuations.
Arguments for and against planning

Planning is increasingly under attack around the world in the political arena, the popular press, and academic literature. Responding to these critiques, this article examines four major types of argument which have been made for and against planning in a modern democratic 'free market' society economic arguments based on the advantages and deficiencies of competitive markets, pluralist arguments based on the benefits and limitations of pluralist group interactions, the traditional arguments used by the early planning profession, and recent neoMarxist' arguments for and against planning. Together these perspectives suggest that planning can be defended on theoretical grounds as performing four essential social functions: promoting the collective interests of the community; considering the external effects of individual and group action; improving the information base for public and private decision-making; and protecting the interests of society's most needy members. Ultimately, however, planning can only be judged on the practical grounds of how well it performs these functions.
On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's

We discuss the use of non-stationary penalty functions to solve general nonlinear programming problems (NP) using real-valued GAs. The non-stationary penalty is a function of the generation number; as the number of generations increases so does the penalty. Therefore, as the penalty increases it puts more and more selective pressure on the GA to find a feasible solution. The ideas presented in this paper come from two basic areas: calculus-based nonlinear programming and simulated annealing. The non-stationary penalty methods are tested on four NP test cases and the effectiveness of these methods are reported. <<ETX>>
Distributed optimisation in dynamic wireless sensor networks

Over the past decade, Wireless Sensor Networks (WSNs) have evolved into a hot interdisciplinary research area. WSNs are generally considered to be statically deployed, but in reality they are dynamic in nature due to a variety of characteristics including fluctuating wireless link quality and clock drift. Furthermore, new WSN topologies and applications have introduced more dynamics, such as time-varying power resources, data traffic patterns, and mobile sensing. These dynamics pose challenges to the theoretical understanding of WSN behaviours and the design of practical algorithms. This thesis investigates distributed network optimisation in three types of dynamic WSNs: WSNs powered by time-varying solar energy, WSNs with fluctuating wireless channel quality, and WSNs with mobile relays and mobile sinks. In distributed optimisation, sensor nodes communicate with each other to collaboratively solve the overall network optimisation problem. Realistic models are established for these dynamic WSNs, and efficient distributed algorithms are developed to optimise network performance, including power management, duty cycling, wireless link scheduling, data routing and forwarding, sensing rate control, and network resource allocation and pricing. Considering the limited capacity of typical sensor nodes, this thesis also aims to understand and balance the tradeoff between system performance and complexity, bridging the gap between optimisation theory and practical algorithm design in dynamic WSNs. The proposed algorithms are shown to outperform state-of-the-art schemes through theoretical analysis, simulations, and real testbed experiments. The work presented in this thesis should be of interest to researchers in the areas of general embedded networked systems and wireless networks. It should also prove useful in emerging research areas including the Internet of Things (IoTs), Cyber-Physical Systems (CPS), and Information and Communications Technology (ICT) for smart sustainable cites.
Nonlinear ChangeModels in Populations with Unobserved Heterogeneity Ken Kelley

When unobserved heterogeneity exists in populations where the phenomenon of interest is governed by a functional form of change linear in its parameters, the growth mixture model (GMM) is useful for modeling change conditional on latent class. However, when the functional form of interest is nonlinear in its parameters, the GMM is not very useful because it is based on a system of equations linear in its parameters. The nonlinear change mixture model (NCMM) is proposed, which explicitly addresses unobserved heterogeneity in situations where change follows a nonlinear functional form. Due to the integration of nonlinear multilevel models and finite mixture models, neither of which generally have closed form solutions, analytic solutions do not generally exist for the NCMM. Five methods of parameter estimation are developed and evaluated with a comprehensive Monte Carlo simulation study. The simulation showed that the parameters of the NCMM can be accurately estimated with several of the proposed methods, and that the method of choice depends on the precise question of interest.
Generalized immune activation and innate immune responses in simian immunodeficiency virus infection.

PURPOSE OF REVIEW Chronic immune activation is a key factor driving the immunopathogenesis of AIDS. During pathogenic HIV/simian immunodeficiency virus (SIV) infections, innate and adaptive antiviral immune responses contribute to chronic immune activation. In contrast, nonpathogenic SIV infections of natural hosts such as sooty mangabeys and African green monkeys (AGMs) are characterized by low immune activation despite similarly high viremia. This review focuses on the role of innate immune responses in SIV infection. RECENT FINDINGS Several studies have examined the role of innate immune responses to SIV as potential drivers of immune activation. The key result of these studies is that both pathogenic SIV infection of macaques and nonpathogenic SIV infections of natural hosts are associated with strong innate immune responses to the virus, high production of type I interferons by plasmacytoid dendritic cells, and upregulation of interferon-stimulated genes (ISGs). However, SIV-infected sooty mangabeys and AGMs (but not SIV-infected macaques) rapidly downmodulate the interferon response within 4-6 weeks of infection, thus resulting in a state of limited immune activation during chronic infection. SUMMARY Studies in nonhuman primates suggest that chronic innate/interferon responses may contribute to AIDS pathogenesis. Further, the ability of natural host species to resolve innate immune responses after infection provides a novel avenue for potential immunotherapy.
A Review of EEG Biofeedback Treatment of Anxiety Disorders

Alpha, theta and alpha-theta enhancements are effective treatments of the anxiety disorders (Table 1). Alpha suppression is also effective, but less so (Table 2). Perceived success in carrying out the task plays an important role in clinical improvement. Research is needed to find out how much more effective they are than placebo, and which variables are important for efficacy. Variables needing study are: duration of treatment, type and severity of anxiety, number and type of EEG waveforms used, pretreatment with other kinds of feedback, position and number of electrodes, and presence of concomitant medication.
A new approach for multi-label classification based on default hierarchies and organizational learning

Learning Classifier Systems (LCSs) are a class of expert systems that use a knowledge base of decision rules and a genetic algorithm (GA) [9] as a discovery mechanism. The set of decision rules allows the LCS to represent and learn control strategies, while the robust search ability of the GA allows it to search for new rules based on the performance of existing rules. LCS were first designed to solve machine learning problems, especially classification problems. Classification problems are problems where instances of a data set belong to a set of classes, and the system needs to infer, based on past experience, the correct class (or classes) of new, previously unseen, instances. However, the features of LCSs are also very useful for solving reinforcement learning problems, a class of problems where the system should learn to operate in the environment based only on performance feedback. This paper considers LCSs as an approach to classification problems, more specifically a more complex kind of classification called multi-label classification. This paper analyses the default hierarchy formation theory presented by [14] as a way of favoring the hierarchical arrangement of rules, and also the organizational learning theory [17] for adjusting the degree of individual and collective behaviors. We suggest a new method, combining both organizational learning and default hierarchy formation, for solving multi-label problems. The preliminary results with a simple multi-label problem show the potential of this method. Final discussion presents the conclusions and directions for further research.
Serotonin-transporter polymorphism modulates anterior cingulate cortex activation during working memory tasks – an fMRI study

Serotonergic influences on anterior cingulate cortex (ACC) functions have been described. In addition, functional polymorphisms in the promoter region of the serotonin transporter gene 5-HTTLPR are associated with increased vulnerability for major depressive disorder (MDD). Here we investigated whether 5-HTTLPR risk variants are related to abnormalities in ACC function observed in MDD, i.e. overactivation of the ACC during the performance of a working-memory task. Fifty-six healthy subjects and remitted MDD patients were investigated with a verbal n-back task using functional magnetic resonance imaging at 3 Tesla. The impact of genotype and group were analyzed in a full-factorial design. Carriers of risk variants and remitted MDD patients revealed ACC hyperactivation. The effect of genotype remained significant for healthy participants alone. Presence of MDD and presence of 5HTTLPR risk variants are associated with ACC dysregulation during the performance of a working-memory task. In healthy participants, presence of risk variants leads to a pattern of hyperactivation similar to overactivation in MDD, indicating that 5HTTLPR effects on ACC function may mediate vulnerability to depression.
FLEXBAR—Flexible Barcode and Adapter Processing for Next-Generation Sequencing Platforms

Quantitative and systems biology approaches benefit from the unprecedented depth of next-generation sequencing. A typical experiment yields millions of short reads, which oftentimes carry particular sequence tags. These tags may be: (a) specific to the sequencing platform and library construction method (e.g., adapter sequences); (b) have been introduced by experimental design (e.g., sample barcodes); or (c) constitute some biological signal (e.g., splice leader sequences in nematodes). Our software FLEXBAR enables accurate recognition, sorting and trimming of sequence tags with maximal flexibility, based on exact overlap sequence alignment. The software supports data formats from all current sequencing platforms, including color-space reads. FLEXBAR maintains read pairings and processes separate barcode reads on demand. Our software facilitates the fine-grained adjustment of sequence tag detection parameters and search regions. FLEXBAR is a multi-threaded software and combines speed with precision. Even complex read processing scenarios might be executed with a single command line call. We demonstrate the utility of the software in terms of read mapping applications, library demultiplexing and splice leader detection. FLEXBAR and additional information is available for academic use from the website: http://sourceforge.net/projects/flexbar/.