Miracle-dz/UniFAR-FTUs huggingface dataset:
Task switching in mild cognitive impairment: Switch and nonswitch costs

Abstract The ability to switch rapidly and fluidly between tasks is an important component of many everyday activities. In this study, we used a predictable, externally cued task-switching paradigm to investigate executive control processes in individuals with mild cognitive impairment (MCI). Participants were 26 individuals with amnestic MCI and 26 healthy older adult (OA) controls. In the mixed-task trials, participants switched between classifying whether a digit was odd/even or a letter was a consonant/vowel on every fourth trial. In the single-task trials, participants completed only the digit task or letter task throughout the entire block. Task switching costs were decomposed into nonswitch costs, which reflect the dual nature of the task, and switch costs, which reflect set-shifting abilities. The results revealed that the MCI group was not affected more than the healthy OAs by the requirement of keeping two tasks sets active in working memory (nonswitch costs). In contrast, the cost of switching between the two tasks was significantly greater for the MCI group compared with the OA controls (switch costs). Future research is needed to better understand the nature and implications for daily living of the greater switch costs found for individuals with MCI. (JINS, 2009, 15, 103–111.)
Regulated Expression of the Streptococcus mutans dltGenes Correlates with Intracellular Polysaccharide Accumulation

ABSTRACT Intracellular polysaccharides (IPS) are glycogen-like storage polymers which contribute significantly to Streptococcus mutans-induced cariogenesis. We previously identified and cloned a locus from the S. mutans chromosome which is required for the accumulation of IPS. Sequencing of this locus revealed at least four contiguous open reading frames, all of which are preceded by a common promoter region and are transcribed in the same direction. Analysis of the amino acid sequence deduced from the first of these open reading frames (ORF1) revealed domains which are highly conserved among d-alanine-activating enzymes (DltA) inLactobacillus rhamnosus (formerlyLactobacillus casei) and Bacillus subtilis. The deduced amino acid sequences derived from ORF2, -3, and -4 also exhibit extensive similarity to DltB, -C, and -D, respectively, in these microorganisms. However, Southern hybridization experiments indicate that this operon maps to a locus on the S. mutanschromosome which is separate from the glgP,glgA, and glgD genes, whose products are known mediators of bacterial IPS accumulation. We therefore assigned a newdlt designation to the locus which we had formerly calledglg. We maintain that the dlt genes are involved in S. mutans IPS accumulation, however, since they complement a mutation in trans which otherwise rendersS. mutans IPS deficient. In this study, we found that expression of the S. mutans dlt genes is growth phase dependent and is modulated by carbohydrates internalized via the phosphoenolpyruvate phosphotransferase system (PTS). We demonstrated that the S. mutans dlt genes are expressed constitutively when non-PTS sugars are provided as the sole source of carbohydrate. Consistent with a role for the PTS in dltexpression is a similar constitutive expression of the dltgenes in an S. mutans PTS mutant grown in a chemically defined medium supplemented with glucose. In summary, these findings support a novel role for the dlt gene products inS. mutans IPS accumulation and suggest thatdlt expression in this oral pathogen is subject to complex mechanisms of control imposed by growth phase, dietary carbohydrate, and other factors present in the plaque environment.
Improve the generalization of emotional classifiers across time by using training samples from different days

Electroencephalographic (EEG)-based emotion recognition has attracted increasing attention from the field of human-computer interaction (HCI). However, there are a number of challenges for machines to correctly recognize human emotional states. One problem is how to generalize the emotion model across time, since the brain may show different patterns of EEG for the same emotion experience at different time. This study investigated the feasibility of adding samples from different days to the training set to improve the generalization of the emotion classifier. Eight subjects participated in this experiment, and they were asked to watch different kinds of movie clips to produce neutral, positive or negative emotional states for different trials. Five sessions in five different days were conducted for each subject. EEG signals were recorded throughout the experiment. Support vector machine was used to perform a classification of the three emotional states, in which the training samples may come from 1, 2, 3 or 4 days' sessions but have a same number. The results showed that three categories were classified with average accuracies of 64.9%, 68.7%, 70.9%, and 73.0% respectively for 1-day, 2-day, 3-day and 4-day conditions. N-day condition represented the case in which data from N days were sent to train the SVM and the remaining 5-N days were used to form the testing set. Importantly, the accuracy had an increasing trend with the number of days in the training set for all subjects. Compared with 1-day condition, the accuracy of 4-day condition has about 10% improvement with a peak of 81.2% By analyzing the selected features in 4-day condition, we found the distributions of the selected features were relatively stable across days. The classifier did select emotion-relevant features and reject time-relevant features. These results suggested that incorporation of samples from different days to the training set could improve the generalization of an emotion classifier across time.
Targeted BMI1 inhibition impairs tumor growth in lung adenocarcinomas with low CEBPα expression

In lung cancers with low expression of C/EBPα, BMI1 expression correlates with worse prognosis but can be targeted with a drug. The right drug for the right tumor The expression of a tumor suppressor called C/EBPα is often lost in non–small cell lung cancer, as well as in other cancer types. Yong et al. discovered that lung tumors deficient in C/EBPα often overexpress a particular oncogenic protein, BMI1, and that higher expression of BMI1 correlates with worse prognosis in this group of patients. The authors characterized the role of these two proteins and their interaction in lung cancer development, then used cell lines and a genetic mouse model to test a therapeutic approach, showing that a pharmaceutical inhibitor of BMI1 is effective against non–small cell lung cancer with low C/EBPα and high BMI1. Lung cancer is the most common cause of cancer deaths. The expression of the transcription factor C/EBPα (CCAAT/enhancer binding protein α) is frequently lost in non–small cell lung cancer, but the mechanisms by which C/EBPα suppresses tumor formation are not fully understood. In addition, no pharmacological therapy is available to specifically target C/EBPα expression. We discovered a subset of pulmonary adenocarcinoma patients in whom negative/low C/EBPα expression and positive expression of the oncogenic protein BMI1 (B lymphoma Mo-MLV insertion region 1 homolog) have prognostic value. We also generated a lung-specific mouse model of C/EBPα deletion that develops lung adenocarcinomas, which are prevented by Bmi1 haploinsufficiency. BMI1 activity is required for both tumor initiation and maintenance in the C/EBPα-null background, and pharmacological inhibition of BMI1 exhibits antitumor effects in both murine and human adenocarcinoma lines. Overall, we show that C/EBPα is a tumor suppressor in lung cancer and that BMI1 is required for the oncogenic process downstream of C/EBPα loss. Therefore, anti-BMI1 pharmacological inhibition may offer a therapeutic benefit for lung cancer patients with low expression of C/EBPα and high BMI1.
‘Hen's teeth and horse's toes’: the adult onset offender in criminology

Several scholars have argued that the prevalence of an adult onset of offending necessitates research and theoretical explanation, whereas other scholars remain skeptical about the existence of offenders who onset in adulthood. The reconciliation of this debate holds significant implications for the sort of criminological theory the field should value and pursue. For instance, persuasive empirical evidence of adult onset offenders supports symmetrical theories of crime, such as Sampson and Laub's age-graded theory, which allow for both positive and negative behavioral change in adulthood. In contrast, the lack of such evidence supports asymmetrical theories of crime, such as Gottfredson and Hirschi's general theory of crime, which argue that change is one way, as crime uniformly declines after an initial peak in adolescence. Using the Cambridge Study in Delinquency Development and the Rochester Youth Development Study, as well as between and within-subjects analysis, we find no evidence of true adult onset offenders, thus supporting asymmetrical theories of crime and questioning the utility of continued research and discussion on this supposed ‘type’ of offender
Randomized trial of effects of continuous combined HRT on markers of lipids and coagulation in women with acute coronary syndromes: WHISP Pilot Study.

AIMS Randomized trials have not demonstrated coronary heart disease benefit from hormone replacement therapy (HRT). We hypothesized that low-dose HRT may avoid harm. METHODS AND RESULTS We studied the effects of HRT on lipids and coagulation in women with acute coronary syndromes. A total of 100 post-menopausal women >55 years were enrolled between 2 and 28 days after an acute coronary syndrome and randomized to oral oestradiol-17beta 1 mg plus norethisterone acetate 0.5 mg daily, or matching placebo, and followed for up to 12 months. Levels of lipids, lipoproteins, and haemostasis markers were measured at baseline, 3, and 6 months. There were no significant differences in lipid levels between the two groups, probably due to concomitant statin use. Antithrombin and factor VII levels were significantly lower in the HRT group, whereas fibrinogen was significantly decreased in the placebo group. No evidence of increased coagulation activation was observed, nor of adverse cardiovascular outcomes [odds ratio (OR) 0.63 (95% confidence intervals 0.31-1.31)]. CONCLUSION Low-dose HRT may give cardiovascular benefit. These findings require confirmation in a full clinical trial with evaluation of cardiovascular outcomes as the primary objective.
Comparison of Techniques to Learn Agent Strategies in Adversarial Games

The focus of this project is to develop methodologies for using machine learning techniques in adversarial robot situations. In particular, we are using multiple robots to play a version of the wumpus world game. In this game, one robot represents the agent and a second robot represents the wumpus. Our goal is for the agent robot to make autonomous decisions that allow it to elude the wumpus, grab the gold and win the game. To achieve this goal, we consult several supervised machine learning algorithms to decide the agent’s move. Agent moves are learned from training examples encoding characteristics of the world, the game state, and the predicted wumpus move. In this paper we will compare the performance of a decision tree learner, a naive Bayesian classifier, a backpropagation neural network, and a learning-based belief network on actual wumpus world games.
Probing protein oligomerization in living cells with fluorescence fluctuation spectroscopy

Fluorescence fluctuation spectroscopy provides information about protein interactions in the intercellular environment from naturally occurring equilibrium fluctuations. We determine the molecular brightness of fluorescent proteins from the fluctuations by analyzing the photon counting histogram (PCH) or its moments and demonstrate the use of molecular brightness in probing the oligomerization state of proteins. We report fluorescence fluctuation measurements of enhanced GFP (EGFP) in cells up to concentrations of 10 μM by using an improved PCH theory. The molecular brightness of EGFP is constant in the concentration range studied. The brightness of a tandem EGFP construct, which carries two fluorophores, increases by a factor of two compared with EGFP alone, demonstrating the sensitivity of molecular brightness as a probe for protein complex formation. Oligomerization of nuclear receptors plays a crucial role in the regulation of gene expression. We probe the oligomerization state of the testicular receptor 4 and the ligand-binding domains of retinoid X receptor and retinoic acid receptor by observing molecular brightness changes as a function of protein concentration. The large concentration range accessible by experiment allows us to perform titration experiments on EGFP fusion proteins. An increase in the molecular brightness with protein concentration indicates the formation of homocomplexes. We observe the formation of homodimers of retinoid X receptor ligand binding domain upon addition of ligand. Resolving protein interactions in a cell is an important step in understanding cellular function on a molecular level. Brightness analysis promises to develop into an important tool for determining protein complex formation in cells.
A multi-task learning model for malware classification with useful file access pattern from API call sequence

Based on API call sequences, semantic-aware and machine learning (ML) based malware classifiers can be built for malware detection or classification. Previous works concentrate on crafting and extracting various features from malware binaries, disassembled binaries or API calls via static or dynamic analysis and resorting to ML to build classifiers. However, they tend to involve too much feature engineering and fail to provide interpretability. We solve these two problems with the recent advances in deep learning: 1) RNN-based autoencoders (RNN-AEs) can automatically learn low-dimensional representation of a malware from its raw API call sequence. 2) Multiple decoders can be trained under different supervisions to give more information, other than the class or family label of a malware. Inspired by the works of document classification and automatic sentence summarization, each API call sequence can be regarded as a sentence. In this paper, we make the first attempt to build a multi-task malware learning model based on API call sequences. The model consists of two decoders, one for malware classification and one for $\emph{file access pattern}$ (FAP) generation given the API call sequence of a malware. We base our model on the general seq2seq framework. Experiments show that our model can give competitive classification results as well as insightful FAP information.
Genomics of Fungal Disease Resistance in Tomato

Tomato (Solanum lycopersicum) is an important vegetable crop worldwide. Often times, its production is hindered by fungal diseases. Important fungal diseases limiting tomato production are late blight, caused by Phytophthora infestans, early blight, caused by Alternaria solanii, and septoria leaf spot, caused by Septoria lycopersici, fusarium wilt caused by Fusarium oxysporium fsp. oxysporium, and verticilium wilt caused by Verticilium dahlea. The Phytophthora infestans is the same fungus that caused the devastating loss of potato in Europe in 1845. A similar magnitude of crop loss in tomato has not occurred but Phytophthora infestans has caused the complete loss of tomato crops around the world on a small scale. Several attempts have been made through conventional breeding and the molecular biological approaches to understand the biology of host-pathogen interaction so that the disease can be managed and crop loss prevented. In this review, we present a comprehensive analysis of information produced by molecular genetic and genomic experiments on host-pathogen interactions of late blight, early blight, septoria leaf spot, verticilim wilt and fusarium wilt in tomato. Furthermore, approaches adopted to manage these diseases in tomato including genetic transformation are presented. Attempts made to link molecular markers with putative genes and their use in crop improvement are discussed.
Why teach primary science? Influences on beginning teachers' practices

The state of primary science teaching has consistently been of concern for many years. While preservice primary teachers' feelings of inadequacy in teaching science have been partially addressed in some teacher education programmes, there is growing evidence to suggest that many of the problems about primary science teaching may arise from school contexts. The study reported here was a pilot investigation of the perceptions a small number of graduate teachers about their science teaching during their first eighteen months of teaching. Nine teachers were interviewed about their teaching of science, and field notes compiled regarding their classroom circumstances. The emergent themes related to their perceived self-confidence in teaching science and to school influences such as collegial support, resources, and the perceived importance of science in the curriculum.
Shrinkage estimator generalizations of Proximal Support Vector Machines

We give a statistical interpretation of Proximal Support Vector Machines (PSVM) proposed at KDD2001 as linear approximaters to (nonlinear) Support Vector Machines (SVM). We prove that PSVM using a linear kernel is identical to ridge regression, a biased-regression method known in the statistical community for more than thirty years. Techniques from the statistical literature to estimate the tuning constant that appears in the SVM and PSVM framework are discussed. Better shrinkage strategies that incorporate more than one tuning constant are suggested. For nonlinear kernels, the minimization problem posed in the PSVM framework is equivalent to finding the posterior mode of a Bayesian model defined through a Gaussian process on the predictor space. Apart from providing new insights, these interpretations help us attach an estimate of uncertainty to our predictions and enable us to build richer classes of models. In particular, we propose a new algorithm called PSVMMIX which is a combination of ridge regression and a Gaussian process model. Extension to the case of continuous response is straightforward and illustrated with example datasets.
Comparison of Women’s Diet Assessed by FFQs and 24-Hour Recalls with and without Underreporters: Associations with Biomarkers

Background/Aims: Women’s diet can be especially difficult to assess, as women tend to underreport their intakes more often than men and are more likely to do so if they think they are overweight or obese. The aim was to compare two methods to assess women’s diet and how well they associate with biomarkers. The influence and frequency of underreporting was also investigated. Methods: Diet of 53 women was assessed by two 24-hour recalls and a food frequency questionnaire (FFQ). Blood was analyzed for retinol, β-carotene, vitamin C and serum ferritin, and 24-hour urine for nitrogen, potassium and sodium. Underreporting was evaluated with nitrogen excretion vs. intake, and energy intake vs. basal metabolic rate. Results: Energy percent (E%) from macronutrients was similar from FFQ and 24-hour recalls, but total intake was higher from 24-hour recalls (9,516 ± 2,080 vs. 8,183 ± 2,893 kJ, p < 0.01). Intakes of vitamin C and potassium from both methods correlated with their respective biomarkers (r = 0.316–0.393). Underreporters had higher body mass index (BMI) than others (27.7 ± 5.5 vs. 23.8 ± 3.7 kg/m2, p < 0.05). They reported lower E% total fat (32 ± 5 vs. 38 ± 6 E%, p < 0.01) and higher E% carbohydrate (49 ± 4 vs. 45 ± 7 E%, p < 0.05). Correlation between intake and biomarkers increased after exclusion of underreporters. Conclusion: For women, FFQ and 24-hour recalls give similar E% and most nutrients correlate, but FFQ gives lower intake. Underreporters have higher BMI and diminish the correlation between calculated intake and biomarkers. This has to be considered when intake data are associated with weight management, disease and lifestyle factors.
Eurographics/siggraph Symposium on Computer Animation (2003) Particle-based Fluid Simulation for Interactive Applications

Realistically animated fluids can add substantial realism to interactive applications such as virtual surgery sim-ulators or computer games. In this paper we propose an interactive method based on Smoothed Particle Hydro-dynamics (SPH) to simulate fluids with free surfaces. The method is an extension of the SPH-based technique by Desbrun to animate highly deformable bodies. We gear the method towards fluid simulation by deriving the force density fields directly from the Navier-Stokes equation and by adding a term to model surface tension effects. In contrast to Eulerian grid-based approaches, the particle-based approach makes mass conservation equations and convection terms dispensable which reduces the complexity of the simulation. In addition, the particles can directly be used to render the surface of the fluid. We propose methods to track and visualize the free surface using point splatting and marching cubes-based surface reconstruction. Our animation method is fast enough to be used in interactive systems and to allow for user interaction with models consisting of up to 5000 particles.
Speed and Accuracy of Gaze Gestures

We conducted an experiment where participants carried out six gaze gesture tasks. The gaze paths were analyzed to find out the speed and accuracy of the gaze gestures. As a result, the gaze gestures took more time than we anticipated and only the very fastest participants got close to what was expected. There was not much difference in performance times between small and large gaze gestures, because the participants reached significantly faster speed when making large gestures than small gestures. Curved shapes were found difficult to follow and time-consuming when followed properly. In general, the accuracy in following shapes was sometimes very poor. We believe that to improve the speed and accuracy of gaze gestures, proper feedback must be used more than in our experiment.