Miracle-dz/UniFAR-FTUs huggingface dataset:
Adult Attachment, God Attachment and Gender in Relation to Perceived Stress

This study examines whether adult attachment, God attachment and gender are related to perceived stress. Based on the literature on these variables, it was expected that adult and God attachment would predict perceived stress, that God attachment would have incremental validity over adult attachment in predicting perceived stress and that gender would be a moderator in the relationship between attachment and perceived stress. Two hundred seventy-six participants from a private, Christian university in Southern California completed questionnaires assessing these variables. Multivariate regression analyses indicated that adult and God attachment anxiety as well as adult attachment avoidance significantly predicted perceived stress. Furthermore, God attachment anxiety had incremental validity over adult attachment. Interestingly, gender was a suppressor variable in the relationship between attachment anxiety and perceived stress. Therefore, attachment relationships with one's partner and God are both important in explaining perceived stress level. Gender may also play an indirect role in this relationship, though this concept should be further validated with future research.
EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a “reach/saccade to spatial target” cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI.
A novel EcoRII PCR-RFLP detecting genetic variation of goat NF1-C2 gene and its association with milk yield (Brief Report)

Abstract. The transcription factor nuclear factor1-C2 ( NF1-C2 ) mediates the action of prolactin in the mammary gland. Research on the molecular genetic mechanism of model system have indicated that the NF1-C2 gene plays an important role for the activation of several mammary gland specific genes (Nilsson et al. 2006). Moreover, the effects of prolactin on milk production traits have been reported in ruminant (Seriwatanachai et al. 2008). NF1-C2 may have a similar function in goat. To our knowledge polymorphisms of NF1-C2 gene have not been described in animals. Here a SNP of the caprine NF1-C2 was detected and a EcoRII PCR-RFLP was derived, that allowed to clearly detect NF1-C2 genotypes. Further investigation was conduct to evaluate the association between polymorphisms and milk yield at different lactation stages.
Broad-coverage model of prediction in human sentence processing

The aim of this thesis is to design and implement a cognitively plausible theory of sentence processing which incorporates a mechanism for modeling a prediction and verification process in human language understanding, and to evaluate the validity of this model on specific psycholinguistic phenomena as well as on broad-coverage, naturally occurring text. Modeling prediction is a timely and relevant contribution to the field because recent experimental evidence suggests that humans predict upcoming structure or lexemes during sentence processing. However, none of the current sentence processing theories capture prediction explicitly. This thesis proposes a novel model of incremental sentence processing that offers an explicit prediction and verification mechanism. In evaluating the proposed model, this thesis also makes a methodological contribution. The design and evaluation of current sentence processing theories are usually based exclusively on experimental results from individual psycholinguistic experiments on specific linguistic structures. However, a theory of language processing in humans should not only work in an experimentally designed environment, but should also have explanatory power for naturally occurring language. This thesis first shows that the Dundee corpus, an eye-tracking corpus of newspaper text, constitutes a valuable additional resource for testing sentence processing theories. I demonstrate that a benchmark processing effect (the subject/object relative clause asymmetry) can be detected in this data set (Chapter 4). I then evaluate two existing theories of sentence processing, Surprisal and Dependency Locality Theory (DLT), on the full Dundee corpus. This constitutes the first broad-coverage comparison of sentence processing theories on naturalistic text. I find that both theories can explain some of the variance in the eye-movement data, and that they capture different aspects of sentence processing (Chapter 5). In Chapter 6, I propose a new theory of sentence processing, which explicitly models prediction and verification processes, and aims to unify the complementary aspects of Surprisal and DLT. The proposed theory implements key cognitive concepts such as incrementality, full connectedness, and memory decay. The underlying grammar formalism is a strictly incremental version of Tree-adjoining Grammar (TAG), Psycholinguistically motivated TAG (PLTAG), which is introduced in Chapter 7. I then describe how the Penn Treebank can be converted into PLTAG format and define an incremental, fully connected broad-coverage parsing algorithm with associated probability model for PLTAG. Evaluation of the PLTAG model shows that it achieves the
Inverted-F/Slot Integrated Dual-Band Four-Antenna System for WLAN Access Points

A four-antenna system applicable to compact access points of wireless local area network is proposed. The antenna topology integrates an inverted-F antenna and a folded slot antenna in the same space to reduce sizes to 15.5 mm × 7.5 mm. The inverted-F antenna operates in the 5 GHz HiperLAN band and the slot functions in the 2.4 GHz ISM band. Antenna tuning can be done without difficulty because the two are of different antenna types. Four antenna assemblies are placed at corners of a 50 mm × 50 mm board to meet the multi-input multi-output operation needs. Since the element antenna exhibits moderate directivity and the four antennas are arranged in a rotational symmetry, decent isolations are achieved in both operation bands without additional decoupling measures. The antenna structure is fully planar and can be integrated within the printed circuit board, which makes it an attractive candidate for compact access point uses.
Advances and Future Challenges in Adenoviral Vector Pharmacology and Targeting

Adenovirus is a robust vector for therapeutic applications, but its use is limited by our understanding of its complex in vivo pharmacology. In this review we describe the necessity of identifying its natural, widespread, and multifaceted interactions with the host since this information will be crucial for efficiently redirecting virus into target cells. In the rational design of vectors, the notion of overcoming a sequence of viral “sinks” must be combined with re-targeting to target populations with capsid as well as shielding the vectors from pre-existing or toxic immune responses. It must also be noted that most known adenoviral pharmacology is deduced from the most commonly used serotypes, Ad5 and Ad2. However, these serotypes may not represent all adenoviruses, and may not even represent the most useful vectors for all purposes. Chimeras between Ad serotypes may become useful in engineering vectors that can selectively evade substantial viral traps, such as Kupffer cells, while retaining the robust qualities of Ad5. Similarly, vectorizing other Ad serotypes may become useful in avoiding immunity against Ad5 altogether. Taken together, this research on basic adenovirus biology will be necessary in developing vectors that interact more strategically with the host for the most optimal therapeutic effect.
Automatic assamese text categorization using WordNet

The increasing rate of Assamese text contents in digital format encourages us to generate a system that automatically categorizes them. This paper discusses a system that will perform the categorization of texts automatically based on the knowledge from Assamese WordNet. In WordNet, synset correspond to the words which implies the same concept and words having more than one sense in a particular text content is disambiguated in this approach. This approach extracts words occurred in the document and uses them to create a synset vector with union to its corresponding synsets from WordNet. To increase our performance, we present a process where it increases the weight of not only the terms but also that of the synsets corresponding to the terms. We later count the occurrences of the senses that help in disambiguation tasks by propagating the relationship between synsets. The proposed method outcomes with a reasonable state of art accuracy when measured with Precision and Recall.
FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model

We present an overview of FAB-MAP, an algorithm for place recognition and mapping developed for infrastructure-free mobile robot navigation in large environments. The system allows a robot to identify when it is revisiting a previously seen location, on the basis of imagery captured by the robot's camera. We outline a complete probabilistic framework for the task, which is applicable even in visually repetitive environments where many locations may appear identical. Our work introduces a number of technical innovations - notably we demonstrate that place recognition performance can be improved by learning an approximation to the joint distribution over visual elements. We also investigate several principled approaches to making the system robust in visually repetitive environments, and define an efficient bail-out strategy for multi-hypothesis testing to improve system speed. Our model has been shown to substantially outperform standard tf-idf ranking on our task of interest. We demonstrate the system performing reliable online appearance mapping and loop closure detection over a 1,000 km trajectory, with mean filter update times of 14 ms.
Combinatorial channel signature modulation for wireless ad-hoc networks

In this paper we introduce a novel modulation and multiplexing method which facilitates highly efficient and simultaneous communication between multiple terminals in wireless ad-hoc networks. We term this method Combinatorial Channel Signature Modulation (CCSM). The CCSM method is particularly efficient in situations where communicating nodes operate in highly time dispersive environments. This is all achieved with a minimal MAC layer overhead, since all users are allowed to transmit and receive at the same time/frequency (full simultaneous duplex). The CCSM method has its roots in sparse modelling and the receiver is based on compressive sampling techniques. Towards this end, we develop a new low complexity algorithm termed Group Subspace Pursuit. Our analysis suggests that CCSM at least doubles the throughput when compared to the state-of-the art.
Decolonisation of MRSA, S. aureus and E. coli by Cold-Atmospheric Plasma Using a Porcine Skin Model In Vitro

In the last twenty years new antibacterial agents approved by the U.S. FDA decreased whereas in parallel the resistance situation of multi-resistant bacteria increased. Thus, community and nosocomial acquired infections of resistant bacteria led to a decrease in the efficacy of standard therapy, prolonging treatment time and increasing healthcare costs. Therefore, the aim of this work was to demonstrate the applicability of cold atmospheric plasma for decolonisation of Gram-positive (Methicillin-resistant Staphylococcus aureus (MRSA), Methicillin-sensitive Staphylococcus aureus) and Gram-negative bacteria (E. coli) using an ex vivo pig skin model. Freshly excised skin samples were taken from six month old female pigs (breed: Pietrain). After application of pure bacteria on the surface of the explants these were treated with cold atmospheric plasma for up to 15 min. Two different plasma devices were evaluated. A decolonisation efficacy of 3 log10 steps was achieved already after 6 min of plasma treatment. Longer plasma treatment times achieved a killing rate of 5 log10 steps independently from the applied bacteria strains. Histological evaluations of untreated and treated skin areas upon cold atmospheric plasma treatment within 24 h showed no morphological changes as well as no significant degree of necrosis or apoptosis determined by the TUNEL-assay indicating that the porcine skin is still vital. This study demonstrates for the first time that cold atmospheric plasma is able to very efficiently kill bacteria applied to an intact skin surface using an ex vivo porcine skin model. The results emphasize the potential of cold atmospheric plasma as a new possible treatment option for decolonisation of human skin from bacteria in patients in the future without harming the surrounding tissue.
The Study of Religion: An Introduction to Key Ideas and Methods

1. What is Religion? 2. Levels of Religion 3. History of Scholarship 4. 'World Religion' 5. Phenomenology and its Critics 6. Authenticity and Diversity 7. Gender and Religion 8. Field Work 9. Quantitative Data in RS 10. The Question of Truth 11. Current Trends 12. Summary and Conclusions.
Sarcasm Detection Method to Improve Review Analysis

Currently, classifying sarcastic sentences into positive and negative sentiments has been a difficult problem and an important task. The sarcastic sentences could indicate negative meaning by using positive expressions, or positive meaning by using negative expressions.Sarcasm is a special kind of sentiment that comprise of words which mean the opposite of what you really want to say, especially in order to insult or wit someone, to show irritation, or to be funny.Therefore, determining sarcasm is an important task in order to correctly classify the sentence. In this paper, we propose an approach to detect sarcasm.First, we apply dependency parsing to amazon review data. After that, we classify phrases in the sentence into the proposed phrase based on the sequence of part-of-speech as proposed by Bharti et al. After being classified into either one of the phrase types, it is determined whether each phrase is positive or negative.If the emotions of the situation phrases and the sentiment phrases are different, the sentence is determined to be a “sarcasm”. Using the above method , the experimental result shows the effectiveness of our method as compared with the the existing research.
Hybrid Routing Protocol For wireless sensor networks

Increasing network (WSN) lifetime is a challenge in routing protocol design for ad hoc network, especially in such a network each node (sensor) is powered by batteries, characterized ability calculation and limited storage. Hierarchical routing protocols are best known in regard to energy efficiency. By using a clustering technique hierarchical routing protocols greatly minimize energy consumed in collecting and disseminating data. In this paper we discuss the problem of routing in networks with unidirectional links. We proposed Hybrid routing protocol (HRP) to decrease probability of failure nodes and to prolong the time interval before the death of the first node (stability period) and increasing the lifetime in heterogeneous WSNs, which is crucial for many applications. HRP (Hybrid routing protocol) is a hierarchical protocol based on GPS (Global positioning system). It is similar to Zone-based Hierarchical Link State.
Structural basis for single-stranded RNA recognition and cleavage by C3PO

Translin and translin-associated factor-x are highly conserved in eukaroytes; they can form heteromeric complexes (known as C3POs) and participate in various nucleic acid metabolism pathways. In humans and Drosophila, C3POs cleave the fragmented siRNA passenger strands and facilitate the activation of RNA-induced silencing complex, the effector complex of RNA interference (RNAi). Here, we report three crystal structures of Nanoarchaeum equitans (Ne) C3PO. The apo-NeC3PO structure adopts an open form and unravels a potential substrates entryway for the first time. The NeC3PO:ssRNA and NeC3PO:ssDNA complexes fold like closed football with the substrates captured at the inner cavities. The NeC3PO:ssRNA structure represents the only catalytic form C3PO complex available to date; with mutagenesis and in vitro cleavage assays, the structure provides critical insights into the substrate binding and the two-cation-assisted catalytic mechanisms that are shared by eukaryotic C3POs. The work presented here further advances our understanding on the RNAi pathway.
Particulate Air Pollution and Risk of ST-Segment Depression During Repeated Submaximal Exercise Tests Among Subjects With Coronary Heart Disease: The Exposure and Risk Assessment for Fine and Ultrafine Particles in Ambient Air (ULTRA) Study

Background—Daily variations in ambient particulate air pollution have been associated with cardiovascular mortality and morbidity. We therefore assessed the associations between levels of the 3 main modes of urban aerosol distribution and the occurrence of ST-segment depressions during repeated exercise tests. Methods and Results—Repeated biweekly submaximal exercise tests were performed during 6 months among adult subjects with stable coronary heart disease in Helsinki, Finland. Seventy-two exercise-induced ST-segment depressions >0.1 mV occurred during 342 exercise tests among 45 subjects. Simultaneously, particle mass <2.5 &mgr;m (PM2.5) and the number concentrations of ultrafine particles (particle diameter 10 to 100 nm [NC0.01–0.1]) and accumulation mode particles (100 to 1000 nm [NC0.1–1]) were monitored at a central site. Levels of particulate air pollution 2 days before the clinic visit were significantly associated with increased risk of ST-segment depression during exercise test. The association was most consistent for measures of particles reflecting accumulation mode particles (odds ratio 3.29; 95% CI, 1.57 to 6.92 for NC0.1–1 and 2.84; 95% CI, 1.42 to 5.66 for PM2.5), but ultrafine particles also had an effect (odds ratio 3.14; 95% CI, 1.56 to 6.32), which was independent of PM2.5. Also, gaseous pollutants NO2 and CO were associated with an increased risk for ST-segment depressions. No consistent association was observed for coarse particles. The associations tended to be stronger among subjects who did not use &bgr;-blockers. Conclusions—The present results suggest that the effect of particulate air pollution on cardiovascular morbidity is at least partly mediated through increased susceptibility to myocardial ischemia.
Weak temperature dependence of the free energy surface and folding pathways of structured peptides

The thermodynamics and energetics of a 20‐residue synthetic peptide with a stable three‐stranded antiparallel β‐sheet fold are investigated by implicit solvent molecular dynamics (MD) at 330 K (slightly above the melting temperature in the model) and compared with previous simulation results at 360 K. At both temperature values, the peptide folds reversibly to the NMR solution conformation, irrespective of the starting conformation. The sampling of the conformational space (2.3 μs and 25 folding events at 330 K, and 3 μs and 50 folding events at 360 K) is sufficient to obtain a thermodynamic description of minima and transition states on the free energy surface, which is determined near equilibrium by counting populations. The free energy surface, plotted as a function of two‐order parameters that monitor formation of either of the β‐hairpins, is similar at both temperature values. The statistically predominant folding pathway and its frequency (about two‐thirds of the folding events) are the same at 330 K and 360 K. Furthermore, the main unfolding route is the reverse of the predominant folding pathway. The effective energy and its electrostatic and van der Waals contributions show a downhill profile at both temperatures, implying that the free energy barrier is of entropic origin and corresponds to the freezing of about two‐thirds of the chain into a β‐hairpin conformation. The average folding rate is nearly the same at 330 K and 360 K, while the unfolding rate is about four times slower at 330 K than at 360 K. Taken together with previous MD analysis of α‐helices and β‐hairpins, the present simulation results indicate that the free energy surface and folding mechanism of structured peptides have a weak temperature dependence. Proteins 2002;47:305–314. © 2002 Wiley‐Liss, Inc.