Miracle-dz/UniFAR-FTUs huggingface dataset:
Empathic Features and Absorption in Fantasy Role-Playing

This study examined the levels of empathy and absorption of individuals who regularly play fantasy and science fiction role-playing games. A hypothesis was developed that higher levels of empathy would be found in individuals who fantasy role-play based upon previous research in hypnosis such as J. R. Hilgard’s (1970) imaginative involvement hypothesis, research into the “fantasy prone” personality type (Wilson & Barber, 1981), and the empathic involvement hypothesis (Wickramasekera II & Szlyk, 2003). The participants in the current study were 127 fantasy role-players who volunteered and completed the Davis Interpersonal Reactivity Index (empathy) and the Tellegen Absorption Scale (absorption). The results demonstrated that those who play fantasy role-playing games scored significantly higher than the comparison group on the IRI scale of empathy, confirming the hypothesis that fantasy role-players report experiencing higher levels of empathic involvement with others. Correlational analysis between the measures demonstrated a significant positive correlation between empathy and absorption (r = .43, p < .001). These results collectively suggest that fantasy role-players have a uniquely empathically-imaginative style. The results also confirm and extend previous findings on the relationship between empathy and absorption as predicted by the Empathic Involvement Hypothesis (Wickramasekera II & Szlyk, 2003).
Novel Dental Cement to Combat Biofilms and Reduce Acids for Orthodontic Applications to Avoid Enamel Demineralization

Orthodontic treatments often lead to biofilm buildup and white spot lesions due to enamel demineralization. The objectives of this study were to develop a novel bioactive orthodontic cement to prevent white spot lesions, and to determine the effects of cement compositions on biofilm growth and acid production. 2-methacryloyloxyethyl phosphorylcholine (MPC), nanoparticles of silver (NAg), and dimethylaminohexadecyl methacrylate (DMAHDM) were incorporated into a resin-modified glass ionomer cement (RMGI). Enamel shear bond strength (SBS) was determined. Protein adsorption was determined using a micro bicinchoninic acid method. A dental plaque microcosm biofilm model with human saliva as inoculum was used to investigate metabolic activity, colony-forming units (CFU) and lactic acid production. Incorporating 3% of MPC, 1.5% of DMAHDM, and 0.1% of NAg into RMGI, and immersing in distilled water at 37 °C for 30 days, did not decrease the SBS, compared to control (p > 0.1). RMGI with 3% MPC + 1.5% DMAHDM + 0.1% NAg had protein amount that was 1/10 that of control. RMGI with triple agents (MPC + DMAHDM + NAg) had much stronger antibacterial property than using a single agent or double agents (p < 0.05). Biofilm CFU on RMGI with triple agents was reduced by more than 3 orders of magnitude, compared to commercial control. Biofilm metabolic activity and acid production were also greatly reduced. In conclusion, adding MPC + DMAHDM + NAg in RMGI substantially inhibited biofilm viability and acid production, without compromising the orthodontic bracket bond strength to enamel. The novel bioactive cement is promising for orthodontic applications to hinder biofilms and plaque buildup and enamel demineralization.
Online least-squares policy iteration for reinforcement learning control

Reinforcement learning is a promising paradigm for learning optimal control. We consider policy iteration (PI) algorithms for reinforcement learning, which iteratively evaluate and improve control policies. State-of-the-art, least-squares techniques for policy evaluation are sample-efficient and have relaxed convergence requirements. However, they are typically used in offline PI, whereas a central goal of reinforcement learning is to develop online algorithms. Therefore, we propose an online PI algorithm that evaluates policies with the so-called least-squares temporal difference for Q-functions (LSTD-Q). The crucial difference between this online least-squares policy iteration (LSPI) algorithm and its offline counterpart is that, in the online case, policy improvements must be performed once every few state transitions, using only an incomplete evaluation of the current policy. In an extensive experimental evaluation, online LSPI is found to work well for a wide range of its parameters, and to learn successfully in a real-time example. Online LSPI also compares favorably with offline LSPI and with a different flavor of online PI, which instead of LSTD-Q employs another least-squares method for policy evaluation.
Compact Bilinear Pooling

Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. However, bilinear features are high dimensional, typically on the order of hundreds of thousands to a few million, which makes them impractical for subsequent analysis. We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions. Our compact representations allow back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system. The compact bilinear representations are derived through a novel kernelized analysis of bilinear pooling which provide insights into the discriminative power of bilinear pooling, and a platform for further research in compact pooling methods. Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets.
Dose-Levels and First Signs of Efficacy in Contemporary Oncology Phase 1 Clinical Trials

Purpose Phase 1 trials play a crucial role in oncology by translating laboratory science into efficient therapies. Molecular targeted agents (MTA) differ from traditional cytotoxics in terms of both efficacy and toxicity profiles. Recent reports suggest that higher doses are not essential to produce the optimal anti-tumor effect. This study aimed to assess if MTA could achieve clinical benefit at much lower dose than traditional cytotoxics in dose seeking phase 1 trials. Patients and Methods We reviewed 317 recent phase 1 oncology trials reported in the literature between January 1997 and January 2009. First sign of efficacy, maximum tolerated dose (MTD) and their associated dose level were recorded in each trial. Results Trials investigating conventional cytotoxics alone, MTA alone and combination of both represented respectively 63.0% (201/317), 23.3% (74/317) and 13.7% (42/317) of all trials. The MTD was reached in 65.9% (209/317) of all trials and was mostly observed at the fifth dose level. First sign of efficacy was less frequently observed at the first three dose-levels for MTA as compared to conventional cytotoxics or combinations regimens (48.3% versus 63.2% and 61.3%). Sign of efficacy was observed in the same proportion whatever the treatment type (73–82%). MTD was less frequently established in trials investigating MTA alone (51.3%) or combinations (42.8%) as compared to conventional cytotoxic agents (75.6%). Conclusion First sign of efficacy was less frequently reported at the early dose-levels and MTD was less frequently reached in trials investigating molecular targeted therapy alone. Similar proportion of trials reported clinical benefit.
QUANTITATIVE STRUCTURE-METABOLISM RELATIONSHIP MODELING OF METABOLIC N-DEALKYLATION REACTION RATES

It is widely recognized that preclinical drug discovery can be improved via the parallel assessment of bioactivity, absorption, distribution, metabolism, excretion, and toxicity properties of molecules. High-throughput computational methods may enable such assessment at the earliest, least expensive discovery stages, such as during screening compound libraries and the hit-to-lead process. As an attempt to predict drug metabolism and toxicity, we have developed an approach for evaluation of the rate of N-dealkylation mediated by two of the most important human cytochrome P450s (P450), namely CYP3A4 and CYP2D6. We have taken a novel approach by using descriptors generated for the whole molecule, the reaction centroid, and the leaving group, and then applying neural network computations and sensitivity analysis to generate quantitative structure-metabolism relationship models. The quality of these models was assessed by using the cross-validated correlation coefficients of 0.82 for CYP3A4 and 0.79 for CYP2D6 as well as external test molecules for each enzyme. The relative performance of different neural networks was also compared, and modular neural networks with two hidden layers provided the best predictive ability. Functional dependencies between the neural network input and output variables, generalization ability, and limitations of the described approach are also discussed. These models represent an initial approach to predicting the rate of P450-mediated metabolism and may be applied and integrated with other models for P450 binding to produce a systems-based approach for predicting drug metabolism.
Human Myosin Vc Is a Low Duty Ratio, Nonprocessive Molecular Motor*

Myosin Vc is the product of one of the three genes of the class V myosin found in vertebrates. It is widely found in secretory and glandular tissues, with a possible involvement in transferrin trafficking. Transient and steady-state kinetic studies of human myosin Vc were performed using a truncated, single-headed construct. Steady-state actin-activated ATPase measurements revealed a Vmax of 1.8 ± 0.3 s-1 and a KATPase of 43 ± 11 μm. Unlike previously studied vertebrate myosin Vs, the rate-limiting step in the actomyosin Vc ATPase pathway is the release of inorganic phosphate (∼1.5 s-1), rather than the ADP release step (∼12.0–16.0 s-1). Nevertheless, the ADP affinity of actomyosin Vc (Kd = 0.25 ± 0.02 μm) reflects a higher ADP affinity than seen in other myosin V isoforms. Using the measured kinetic rates, the calculated duty ratio of myosin Vc was ∼10%, indicating that myosin Vc spends the majority of the actomyosin ATPase cycle in weak actin-binding states, unlike the other vertebrate myosin V isoforms. Consistent with this, a fluorescently labeled double-headed heavy meromyosin form showed no processive movements along actin filaments in a single molecule assay, but it did move actin filaments at a velocity of ∼24 nm/s in ensemble assays. Kinetic simulations reveal that the high ADP affinity of actomyosin Vc may lead to elevations of the duty ratio of myosin Vc to as high as 64% under possible physiological ADP concentrations. This, in turn, may possibly imply a regulatory mechanism that may be sensitive to moderate changes in ADP concentration.
Analysis of agonist and antagonist activities of phenylglycine derivatives for different cloned metabotropic glutamate receptor subtypes

The metabotropic glutamate receptors (mGluRs) consist of at least seven different subtypes and are coupled to intracellular signal transduction via G proteins. However, the lack of specific antagonists for the mGluRs limited the precise characterization of the role of the individual mGluRs. In this study, we investigated the agonist and antagonist activities of a series of phenylglycine derivatives for the mGluRs by examining their effects on the signal transduction of representative mGluR1, mGluR2, and mGluR4 subtypes expressed individually in Chinese hamster ovary cells. The phenylglycine derivatives examined included (S)- and (R)-forms of 3- hydroxyphenylglycine (3HPG), 4-carboxy-phenylglycine (4CPG), 4-carboxy- 3-hydroxyphenylglycine (4C3HPG), 3-carboxy-4-hydroxyphenylglycine (3C4HPG), and (+)- and (-)-alpha-methyl-4-carboxyphenylglycine (alpha M4CPG). Among these 10 compounds, (S)-3HPG acted as an agonist for mGluR1, while (S)-4C3HPG, (S)-3C4HPG, and (S)-4CPG served as effective agonists for mGluR2. The rank order of agonist potencies for mGluR2 was L-glutamate > (S)-4C3HPG > (S)-3C4HPG > (S)-4CPG. No other phenylglycine derivatives showed any definite agonist activity on either mGluR1 or mGluR2. Among the phenylglycine derivatives with no mGluR1 agonist activity, (S)-4C3HPG, (S)-3C4HPG, (S)-4CPG, and (+)- alpha M4CPG effectively antagonized the action of L-glutamate on mGluR1. The rank order of antagonist potencies was (S)-4C3HPG > or = (S)-4CPG > or = (+)-alpha M4CPG > (S)-3C4HPG. The Schild plot analysis indicated that (RS)-4C3HPG, (S)-4CPG, and (+)-alpha M4CPG all act as competitive antagonists for mGluR1 with pA2 values of 4.38, 4.46, and 4.38, respectively. (ABSTRACT TRUNCATED AT 250 WORDS)
Cooperative Effects of Bacterial Mutations Affecting A N Gene

We report the isolation and initial characterization of a class of mutations, Snu, that map near the rif locus on the Escherichia coli chromosome. Snu mutations inhibit the growth of phage A, an effect primarily seen when Snu mutations are combined with another class of mutations, nus. Nus mutations have previously been shown to inhibit the expression of the N gene product of h, and the experiments reported here suggest that Snu mutations add to this inhibitory effect. One Snu mutation, Snu-9, was shown to cause bacterial growth to be temperaturesensitive. This suggests that, at least, some Snu mutations may be in genes coding for a function essential for bacterial growth. Since genes coding for the p and p’ subunits of RNA polymerase map in this region, we recognize that Snu mutations might alter either of these subunits of RNA polymerase. Complementation studies demonstrate that Snu+ is dominant to Snu, indicating that the mutant phenotype is due to the partial loss of a function necessary for full N expression. Although the hosts carrying Snu and nus mutations (called Supernus) severely restrict the growth of phage which express the N function of A, they do not show any increased inhibitory effect on the growth of himmP22 and Aimm21, phages which express N functions different from that of A. However, Supernus hosts do restrict the growth of a A variant that can grow well in bacteria carrying either component mutation, Snu or nus -. The restrictive effect of the Supernus strain is far greater than would be expected if the restriction was due to an additive effect of the two component mutations. This implies that there might be an interaction between Snu and nus products and that the Supernus phenotype results from an interference with this interaction.
Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning

Iterative methods that calculate their steps from approximate subgradient directions have proved to be useful for stochastic learning problems over large and streaming data sets. When the objective consists of a loss function plus a nonsmooth regularization term, the solution often lies on a low-dimensional manifold of parameter space along which the regularizer is smooth. (When an l1 regularizer is used to induce sparsity in the solution, for example, this manifold is defined by the set of nonzero components of the parameter vector.) This paper shows that a regularized dual averaging algorithm can identify this manifold, with high probability, before reaching the solution. This observation motivates an algorithmic strategy in which, once an iterate is suspected of lying on an optimal or near-optimal manifold, we switch to a "local phase" that searches in this manifold, thus converging rapidly to a near-optimal point. Computational results are presented to verify the identification property and to illustrate the effectiveness of this approach.
From Acoustic Segmentation to Language Processing: Evidence from Optical Imaging

During language acquisition in infancy and when learning a foreign language, the segmentation of the auditory stream into words and phrases is a complex process. Intuitively, learners use “anchors” to segment the acoustic speech stream into meaningful units like words and phrases. Regularities on a segmental (e.g., phonological) or suprasegmental (e.g., prosodic) level can provide such anchors. Regarding the neuronal processing of these two kinds of linguistic cues a left-hemispheric dominance for segmental and a right-hemispheric bias for suprasegmental information has been reported in adults. Though lateralization is common in a number of higher cognitive functions, its prominence in language may also be a key to understanding the rapid emergence of the language network in infants and the ease at which we master our language in adulthood. One question here is whether the hemispheric lateralization is driven by linguistic input per se or whether non-linguistic, especially acoustic factors, “guide” the lateralization process. Methodologically, functional magnetic resonance imaging provides unsurpassed anatomical detail for such an enquiry. However, instrumental noise, experimental constraints and interference with EEG assessment limit its applicability, pointedly in infants and also when investigating the link between auditory and linguistic processing. Optical methods have the potential to fill this gap. Here we review a number of recent studies using optical imaging to investigate hemispheric differences during segmentation and basic auditory feature analysis in language development.
Glycinergic Projection Neurons of the Cerebellum

The cerebellum funnels its entire output through a small number of presumed glutamatergic premotor projection neurons in the deep cerebellar nuclei and GABAergic neurons that feed back to the inferior olive. Here we use transgenic mice selectively expressing green fluorescent protein in glycinergic neurons to demonstrate that many premotor output neurons in the medial cerebellar (fastigial) nuclei are in fact glycinergic, not glutamatergic as previously thought. These neurons exhibit similar firing properties as neighboring glutamatergic neurons and receive direct input from both Purkinje cells and excitatory fibers. Glycinergic fastigial neurons make functional projections to vestibular and reticular neurons in the ipsilateral brainstem, whereas their glutamatergic counterparts project contralaterally. Together, these data suggest that the cerebellum can influence motor outputs via two distinct and complementary pathways.
A semantic space approach to the computational semantics of noun compounds

Abstract This study examines the ability of a semantic space model to represent the meaning of noun compounds such as ‘information gathering’ or ‘heart disease. ’ For a semantic space model to compute the meaning and the attributional similarity (or semantic relatedness) for unfamiliar noun compounds that do not occur in a corpus, the vector for a noun compound must be computed from the vectors of its constituent words using vector composition algorithms. Six composition algorithms (i.e., centroid, multiplication, circular convolution, predication, comparison, and dilation) are compared in terms of the quality of the computation of the attributional similarity for English and Japanese noun compounds. To evaluate the performance of the computation of the similarity, this study uses three tasks (i.e., related word ranking, similarity correlation, and semantic classification), and two types of semantic spaces (i.e., latent semantic analysis-based and positive pointwise mutual information-based spaces). The result of these tasks is that the dilation algorithm is generally most effective in computing the similarity of noun compounds, while the multiplication algorithm is best suited specifically for the positive pointwise mutual information-based space. In addition, the comparison algorithm works better for unfamiliar noun compounds that do not occur in the corpus. These findings indicate that in general a semantic space model, and in particular the dilation, multiplication, and comparison algorithms have sufficient ability to compute the attributional similarity for noun compounds.
A numerical implementation of Fokas boundary integral approach: Laplace's equation on a polygonal domain

A recently discovered transform approach allows a large class of PDEs to be solved in terms of boundary and/or contour integrals. We introduce here a spectrally accurate numerical discretization of this approach for the case of Laplace's equation on a polygonal domain, and compare it against an also spectrally accurate implementation of the traditional boundary integral formulation.
Comparative Evaluation of TRAIL, FGF-2 and VEGF-A-Induced Angiogenesis In Vitro and In Vivo

Tumor necrosis-factor-related apoptosis-inducing ligand (TRAIL) has been implicated in angiogenesis; the growth of new blood vessels from an existing vessel bed. Our aim was to compare pro-angiogenic responses of TRAIL, vascular endothelial growth-factor-A (VEGF-A) and fibroblast growth-factor-2 (FGF-2) either separately (10 ng/mL) or in combination, followed by the assessment of proliferation, migration and tubule formation using human microvascular endothelial-1 (HMEC-1) cells in vitro. Angiogenesis was also measured in vivo using the Matrigel plug assay. TRAIL and FGF-2 significantly augmented HMEC-1 cell proliferation and migration, with combination treatment having an enhanced effect on cell migration only. In contrast, VEGF-A did not stimulate HMEC-1 migration at 10 ng/mL. Tubule formation was induced by all three factors, with TRAIL more effective compared to VEGF-A, but not FGF-2. TRAIL at 400 ng/mL, but not VEGF-A, promoted CD31-positive staining into the Matrigel plug. However, FGF-2 was superior, stimulating cell infiltration and angiogenesis better than TRAIL and VEGF-A in vivo. These findings demonstrate that each growth factor is more effective at different processes of angiogenesis in vitro and in vivo. Understanding how these molecules stimulate different processes relating to angiogenesis may help identify new strategies and treatments aimed at inhibiting or promoting dysregulated angiogenesis in people.
Complete Chloroplast Genome Sequence of Omani Lime (Citrus aurantiifolia) and Comparative Analysis within the Rosids

The genus Citrus contains many economically important fruits that are grown worldwide for their high nutritional and medicinal value. Due to frequent hybridizations among species and cultivars, the exact number of natural species and the taxonomic relationships within this genus are unclear. To compare the differences between the Citrus chloroplast genomes and to develop useful genetic markers, we used a reference-assisted approach to assemble the complete chloroplast genome of Omani lime (C. aurantiifolia). The complete C. aurantiifolia chloroplast genome is 159,893 bp in length; the organization and gene content are similar to most of the rosids lineages characterized to date. Through comparison with the sweet orange (C. sinensis) chloroplast genome, we identified three intergenic regions and 94 simple sequence repeats (SSRs) that are potentially informative markers with resolution for interspecific relationships. These markers can be utilized to better understand the origin of cultivated Citrus. A comparison among 72 species belonging to 10 families of representative rosids lineages also provides new insights into their chloroplast genome evolution.