Miracle-dz/UniFAR-FTUs huggingface dataset:
Exhaustive Exercise Alters Thinking Times in a Tower of London Task in a Time-Dependent Manner

Purpose: In contrast to other aspects of executive functions, acute exercise-induced alterations in planning are poorly investigated. While only few studies report improved planning performances after exercise, even less is known about their time course after exhaustive exercise. Methods: One hundred and nineteen healthy adults performed the Tower of London (ToL) task at baseline, followed by a graded exercise test (GXT). Participants were subsequently randomized into one of four groups (immediately, 30, 60, and 90 min after the GXT) to repeat the ToL. Main outcomes of the ToL were planning (number of tasks completed in the minimum number of moves), solutions (correct responses independent of the given number of moves) as well as thinking times (time between presentation of each problem and first action) for tasks with varying difficulty (four-, five,- and six-move problems). Blood lactate levels were analyzed as a potential mediator. Results: No effect of exercise on planning could be detected. In contrast to complex problem conditions, median thinking times deteriorated significantly in the immediately after GXT tested group in less challenging problem conditions (four-move problems: p = 0.001, F = 5.933, df = 3; five-move problems: p = 0.005, F = 4.548, df = 3). Decreased lactate elimination rates were associated with impaired median thinking times across all groups ΔMTT4-6 (p = 0.001, r = −0.309), ΔMTT4 (p < 0.001, r = −0.367), and ΔMTT5 (p = 0.001, r = −0.290). Conclusion: These results suggest that planning does not improve within 90 min after exhaustive exercise. In line with previous research, revealing a negative impact of exhaustive exercise on memory and attention, our study extends this knowledge of exercise-induced alterations in cognitive functioning as thinking times as subcomponents of planning are negatively affected immediately after exercise. This is further associated with peripheral lactate levels.
Knowledge Reuse: Its Role and Implications for Upper Level Ontologies

There have been many ambitious attempts to develop both domain and large-scale, upper level ontologies, in the belief that ontologies are the key to success in the Semantic Web, web services, and other applications. However, there has been little research conducted on how to assess the effectiveness of these ontologies and how to reuse them in different contexts. This paper analyzes a well-known upper level ontology, CYC, using its ResearchCyc version. Problems and challenges faced in using ResearchCyc are identified and suggestions proposed for overcoming them. The solutions are intended to be generalizable to other upper level ontologies. Finally, based on the investigations presented in this paper, an application to the domain of collaborative learning is provided.
Regulation of Geminin Functions by Cell Cycle-Dependent Nuclear-Cytoplasmic Shuttling

ABSTRACT The geminin protein functions both as a DNA rereplication inhibitor through association with Cdt1 and as a repressor of Hox gene transcription through the polycomb pathway. Here, we report that the functions of avian geminin are coordinated with and regulated by cell cycle-dependent nuclear-cytoplasmic shuttling. In S phase, geminin enters nuclei and inhibits both loading of the minichromosome maintenance (MCM) complex onto chromatin and Hox gene transcription. At the end of mitosis, geminin is exported from nuclei by the exportin protein Crm1 and is unavailable in the nucleus during the next G1 phase, thus ensuring proper chromatin loading of the MCM complex and Hox gene transcription. This mechanism for regulating the functions of geminin adds to distinct mechanisms, such as protein degradation and ubiquitination, applied in other vertebrates.
Genetic Association and Cellular Function of MC1R Variant Alleles in Human Pigmentation

Abstract: We have examined MC1R variant allele frequencies in the general population of South East Queensland and in a collection of adolescent dizygotic and monozygotic twins and family members to define statistical associations with hair and skin color, freckling, and mole count. Results of these studies are consistent with a linear recessive allelic model with multiplicative penetrance in the inheritance of red hair. Four alleles, D84E, R151C, R160W, and D294H, are strongly associated with red hair and fair skin with multinomial regression analysis showing odds ratios of 63, 118, 50, and 94, respectively. An additional three low‐penetrance alleles V60L, V92M, and R163Q have odds ratios 6, 5, and 2 relative to the wild‐type allele. To address the cellular effects of MC1R variant alleles in signal transduction, we expressed these receptors in permanently transfected HEK293 cells. Measurement of receptor activity via induction of a cAMP‐responsive luciferase reporter gene found that the R151C and R160W receptors were active in the presence of NDP‐MSH ligand, but at much reduced levels compared with that seen with the wild‐type receptor. The ability to stimulate phosphorylation of the cAMP response element binding protein (CREB) transcription factor was also apparent in all stimulated MC1R variant allele‐expressing HEK293 cell extracts as assessed by immunoblotting. In contrast, human melanoma cell lines showed wide variation in the their ability to undergo cAMP‐mediated CREB phosphorylation. Culture of human melanocytes of known MC1R genotype may provide the best experimental approach to examine the functional consequences for each MC1R variant allele. With this objective, we have established more than 300 melanocyte cell strains of defined MC1R genotype.
Classification of the electrocardiogram using selected wavelet coefficients and linear discriminants

Twenty-live wavelet coefficients were selected as inputs and cross-validation used to estimate the classifier performance. An overall accuracy of 72.3% was achieved using a database of 500 ECG records independently classified into seven classes. This compared well with published cardiologist classification rates. By introducing a no-classification state, the accuracy increased to 7.9% with 80% of ECG records classified. The method presented here is not specific to the ECG domain and may easily be applied to other classification problems.
Occult Form of Premature Ovarian Insufficiency in Women with Infertility and Oligomenorrhea as Assessed by Poor Ovarian Response Criteria

Background: The purpose of this study was to evaluate the ability of poor ovarian response criteria to classify women presenting with infertility and oligomenorrhea as having “occult” premature ovarian insufficiency. Methods: This was a cross sectional study conducted at Aziz Medical Center, Karachi, Pakistan from 1st August 2015 to 31st July 2016. Women with infertility and oligomenorrhea were included. All eligible women underwent day 2 FSH level and an early follicular phase transvaginal ultrasound to assess the antral follicular count (AFC). All women then underwent the confirmatory test, of Anti-Mullerian Hormone (AMH) level. The main outcome measure was assignment to occult premature ovarian insufficiency (POI) after screening that used the criteria set out in fertility guideline for predicting the likely ovarian response to gonadotrophin stimulation. Another measure was to compare the sensitivity and specificity of the two index criteria, of FSH and AFC, relative to the emerging reference standard, of the AMH criterion. Results: The three criteria together classified 59 (34.91%) women as occult POI in those with oligomenorrhea. The sensitivity, specificity, negative predictive value and positive predictive value of FSH relative to AMH for these women were 77.8%, 95.7%, 90.2% and 89.4%, respectively whereas the same values of AFC relative to AMH were 92.6%, 99.1 %, 96.6% and 98%, respectively. Conclusion: Women with menstrual irregularity and infertility are at a higher risk for satisfying criteria of poor ovarian response irrespective of age. A policy incorporating these surrogate markers can be used to screen these women for occult premature ovarian insufficiency.
Culture-Dependent and -Independent Methods To Investigate the Microbial Ecology of Italian Fermented Sausages

ABSTRACT In this study, the microbial ecology of three naturally fermented sausages produced in northeast Italy was studied by culture-dependent and -independent methods. By plating analysis, the predominance of lactic acid bacteria populations was pointed out, as well as the importance of coagulase-negative cocci. Also in the case of one fermentation, the fecal enterocci reached significant counts, highlighting their contribution to the particular transformation process. Yeast counts were higher than the detection limit (>100 CFU/g) in only one fermented sausage. Analysis of the denaturing gradient gel electrophoresis (DGGE) patterns and sequencing of the bands allowed profiling of the microbial populations present in the sausages during fermentation. The bacterial ecology was mainly characterized by the stable presence of Lactobacillus curvatus and Lactobacillus sakei, but Lactobacillus paracasei was also repeatedly detected. An important piece of evidence was the presence of Lactococcus garvieae, which clearly contributed in two fermentations. Several species of Staphylococcus were also detected. Regarding other bacterial groups, Bacillus sp. , Ruminococcus sp. , and Macrococcus caseolyticus were also identified at the beginning of the transformations. In addition, yeast species belonging to Debaryomyces hansenii, several Candida species, and Willopsis saturnus were observed in the DGGE gels. Finally, cluster analysis of the bacterial and yeast DGGE profiles highlighted the uniqueness of the fermentation processes studied.
Submanifold-Preserving Discriminant Analysis With an Auto-Optimized Graph

Due to the multimodality of non-Gaussian data, traditional globality-preserved dimensionality reduction (DR) methods, such as linear discriminant analysis (LDA) and principal component analysis (PCA) are difficult to deal with. In this paper, we present a novel local DR framework via auto-optimized graph embedding to extract the intrinsic submanifold structure of multimodal data. Specifically, the proposed model seeks to learn an embedding space which can preserve the local neighborhood structure by constructing a <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-nearest neighbors (<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>NNs) graph on data points. Different than previous works, our model employs the <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\ell }_{\boldsymbol {0}}$ </tex-math></inline-formula>-norm constraint and binary constraint on the similarity matrix to impose that there only be a <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> nonzero value in each row of the similarity matrix, which can ensure the <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-connectivity in graph. More important, as the high-dimensional data probably contains some noises and redundant features, calculating the similarity matrix in the original space by using a kernel function is inaccurate. As a result, a mechanism of an auto-optimized graph is derived in the proposed model. Concretely, we learn the embedding space and similarity matrix simultaneously. In other words, the selection of neighbors is automatically executed in the optimal subspace rather than in the original space when the algorithm reaches convergence, which can alleviate the affect of noises and improve the robustness of the proposed model. In addition, four supervised and semisupervised local DR methods are derived by the proposed framework which can extract the discriminative features while preserving the submanifold structure of data. Last but not least, since two variables need to be optimized simultaneously in the proposed methods, and the constraints on the similarity matrix are difficult to satisfy, which is an NP-hard problem. Consequently, an efficient iterative optimization algorithm is introduced to solve the proposed problems. Extensive experiments conducted on synthetic data and several real-world datasets have demonstrated the advantages of the proposed methods in robustness and recognition accuracy.
Stimulus-dependent modulation of [(3)H]norepinephrine release from rat neocortical slices by gabapentin and pregabalin.

Gabapentin (GBP; Neurontin) has proven efficacy in several neurological and psychiatric disorders yet its mechanism of action remains elusive. This drug, and the related compounds pregabalin [PGB; CI-1008, S-(+)-3-isobutylgaba] and its enantiomer R-(-)-3-isobutylgaba, were tested in an in vitro superfusion model of stimulation-evoked neurotransmitter release using rat neocortical slices prelabeled with [(3)H]norepinephrine ([(3)H]NE). The variables addressed were stimulus type (i.e., electrical, K(+), veratridine) and intensity, concentration dependence, onset and reversibility of action, and commonality of mechanism. Both GBP and PGB inhibited electrically and K(+)-evoked [(3)H]NE release, but not that induced by veratridine. Inhibition by these drugs was most pronounced with the K(+) stimulus, allowing determination of concentration-effect relationships (viz., 25 mM K(+) stimulus: GBP IC(50) = 8.9 microM, PGB IC(50) = 11.8 microM). R-(-)-3-Isobutylgaba was less effective than PGB to decrease stimulation-evoked [(3)H]NE release. Other experiments with GBP demonstrated the dependence of [(3)H]NE release inhibition on optimal stimulus intensity. The inhibitory effect of GBP increased with longer slice exposure time before stimulation, and reversed upon washout. Combination experiments with GBP and PGB indicated a similar mechanism of action to inhibit K(+)-evoked [(3)H]NE release. GBP and PGB are concluded to act in a comparable, if not identical, manner to preferentially attenuate [(3)H]NE release evoked by stimuli effecting mild and prolonged depolarizations. This type of modulation of neurotransmitter release may be integral to the clinical pharmacology of these drugs.
When What is Ethical May Not Be Right: A Reply to Dr Glass

Two implicit assumptions behind these and other arguments are that all studies will yield correct results, if only they were large enough; and all new drugs that are tested will be both safe and effective. Hence, in randomized controlled trials (RCTs) comparing a new drug to an existing one, all patients will benefit and none will suffer; whereas in a placebocontrolled study, the control patients will be denied any therapy. Were all RCTs perfectly carried out, and if all experimental drugs made it to the market place, this would be a cogent argument. However, in this imperfect world, neither situation occurs. As I indicated previously, many trials fail because they are less-than perfectly designed or executed, even by highly experienced groups. If the results of an active-arm RCT showed no difference, then it is impossible to determine if both drugs were equally effective or equally ineffective. Consequently, the participation of patients in both groups would have been for nought, which indeed would be a waste of resources.
Wind power forecast uncertainty in daily operation of wind park combined with storage

The inevitable wind power forecast errors result in differences between forecasted and observed wind power. To mitigate their economic impact, combining the wind power with pumped hydro energy storage may be used. In order to deliver a joint operational strategy for a wind power plant combined with storage, one requires reliable wind power forecasts. The forecasts commonly only consist of a single-value forecast (point forecast) for each look-ahead time. However, additional representative information concerning the forecast uncertainty may be required. In this paper, the influence of uncertainty representation daily operation for a wind power plant combined with pumped hydro storage is discussed. The paper illustrates how uncertainty representations such as Gaussian uncorrelated errors may also not be satisfactory. Based on a case study with a wind power plant combined with pumped hydro storage, organized for illustrative purposes, the paper demonstrates the need to have uncertainty representation including cross-period dependencies, in order to define a correct operation policy.
Spectral Unmixing via Data-Guided Sparsity

Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral analysis, visualization, and understanding. From an unsupervised learning perspective, this problem is very challenging-both the spectral bases and their composite percentages are unknown, making the solution space too large. To reduce the solution space, many approaches have been proposed by exploiting various priors. In practice, these priors would easily lead to some unsuitable solution. This is because they are achieved by applying an identical strength of constraints to all the factors, which does not hold in practice. To overcome this limitation, we propose a novel sparsity-based method by learning a data-guided map (DgMap) to describe the individual mixed level of each pixel. Through this DgMap, the ℓp (0 <; p <; 1) constraint is applied in an adaptive manner. Such implementation not only meets the practical situation, but also guides the spectral bases toward the pixels under highly sparse constraint. What is more, an elegant optimization scheme as well as its convergence proof have been provided in this paper. Extensive experiments on several datasets also demonstrate that the DgMap is feasible, and high quality unmixing results could be obtained by our method.
Emergence of Field Intelligence for Collective Block Agents

This paper describes the collective behavior of block agents existing in a spatially constrained environment. The difficulty of this problem is the determination of the behavior of a block agent within an environment requiring mutual action of many block agents. This difficulty is caused by dynamic obstacle avoidance problem resulting from physical collisions among the autonomous motion of block agents. The objective of this research is to build an adaptive decision mechanism of behavior for autonomous block agents when they are given a task. Specifically, this paper shows one case study of the proposed mechanism for the problem of removing blocks from a container. It is assumed that agents are block shaped, have changeable postures, and they are existing in the automated warehouse. Our approach is uses Classifier System based architecture. The results of simulation experiments are presented which indicate the possibility of this architecture in allowing block agents to adapt to dynamic environments.
Angiotensin II and Its Heptapeptide (2‐), Hexapeptide (3dash;) and Pentapeptide (4‐) Metabolites in Arterial and Venous Blood of Man

We made separate measurements of angiotensin II (A II) and of its immunoreactire metabolites (2–8 heptapeptide, 3–8 bexapeptide, and 4–8 pentapeptide) in arterial and venous plasma from subjects with widely different plasma levels of the peptides. A II and its three metabolites were extracted from blood, separated by paper chromatography, and measured by radioimmunoassay using an A II antisemm which had a 100% cross-reaction with each metabolite. In contrast to results of previous studies, A II was found to predominate in both arterial (60–100%) and venous (55–100%) blood. The biologically active 2–8 beptapeptide metabolite accounted for only 10% of the activity in arterial plasma. Radioimmunoassay of venous plasma extracts using an A II antisemm which had a low cross-reaction with the 3–8 hexapeptide and the 4–8 pentapeptide confirmed the results obtained using the antisemm which had a 100% cross-reaction with the metabolites. We conclude that radioimmunoassay methods for measuring A II in venous blood may be more accurate and relevant than has previously been recognized. The small difference between A II concentrations in arterial and venous plasma suggests further that there may be significant conversion of angiotensin I (A I) to A II in the limb vasculature.
Smart People Ask for (My) Advice: Seeking Advice Boosts Perceptions of Competence

Although individuals can derive substantial benefits from exchanging information and ideas, many individuals are reluctant to seek advice from others. We find that people are reticent to seek advice for fear of appearing incompetent. This fear, however, is misplaced. We demonstrate that individuals perceive those who seek advice as more competent than those who do not. This effect is moderated by task difficulty, advisor egocentrism, and advisor expertise. Individuals perceive those who seek advice as more competent when the task is difficult rather than when it is easy, when people seek advice from them personally rather than when they seek advice from others and when people seek advice from experts rather than from nonexperts or not at all. This paper was accepted by Yuval Rottenstreich, judgment and decision making.
Recent Animal Models of Alcoholism

Animal models on alcohol preference have a long-standing tradition in biomedical research on alcoholism. However, these models allow only limited conclusions regarding alcohol addiction. Therefore, during the past 15 years, researchers have developed new animal models that mimic different aspects of human alcohol addiction, such as craving, relapse, and loss of control over drinking. These models include the reinstatement model, the alcohol deprivation model, and the point-of-no-return model. Some of these models have been pharmacologically validated with anticraving compounds that are used clinically for treating alcoholics. The detailed behavioral characterization of these new models and their pharmacological validation also allow researchers to study the neurochemical and molecular bases of addictive behavior.
Rapid evidence assessment: increasing the transparency of an emerging methodology.

RATIONALE, AIMS AND OBJECTIVES Within the field of evidence-based practice, policy makers, health care professionals and consumers require timely reviews to inform decisions on efficacious health care and treatments. Rapid evidence assessment (REA), also known as rapid review, has emerged in recent years as a literature review methodology that fulfils this need. It highlights what is known in a clinical area to the target audience in a relatively short time frame. METHODS This article discusses the lack of transparency and limited critical appraisal that can occur in REA, and goes on to propose general principles for conducting a REA. The approach that we describe is consistent with the principles underlying systematic review methodology, but also makes allowances for the rapid delivery of information as required while utilizing explicit and reproducible methods at each stage. RESULTS Our method for conducting REA includes: developing an explicit research question in consultation with the end-users; clear definition of the components of the research question; development of a thorough and reproducible search strategy; development of explicit evidence selection criteria; and quality assessments and transparent decisions about the level of information to be obtained from each study. In addition, the REA may also include an assessment of the quality of the total body of evidence. CONCLUSIONS Transparent reporting of REA methodologies will provide greater clarity to end-users about how the information is obtained and about the trade-offs that are made between speed and rigour.