Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Coarse-Grained Model for E. coli Cytoplasm: Accurate Calculation of the Diffusion Coefficient of Proteins and Observation of Anomalous Diffusion

A new coarse-grained model of the E. coli cytoplasm is developed by describing the proteins of the cytoplasm as flexible units consisting of one or more spheres that follow Brownian dynamics (BD), with hydrodynamic interactions (HI) accounted for by a mean-field approach. Extensive BD simulations were performed to calculate the diffusion coefficients of three different proteins in the cellular environment. The results are in close agreement with experimental or previously simulated values, where available. Control simulations without HI showed that use of HI is essential to obtain accurate diffusion coefficients. Anomalous diffusion inside the crowded cellular medium was investigated with Fractional Brownian motion analysis, and found to be present in this model. By running a series of control simulations in which various forces were removed systematically, it was found that repulsive interactions (volume exclusion) are the main cause for anomalous diffusion, with a secondary contribution from HI.
Symbol-rate estimation based on filter bank

In non-cooperative communication applications, such as automatic modulation recognition, it is necessary to estimate blindly some parameters of the received signal. We address the symbol-rate estimation of single-tone (i.e., single-carrier) digitally modulated signals. In the proposed algorithm, we first filter the received signal by using a lowpass filter bank, and then pass each of the filtered outputs through a fourth-order nonlinear unit. By proper design, several nonlinear outputs have spectral peaks at the frequency of the symbol-rate. The proposed algorithm extracts several symbol-rate candidates from each nonlinearity output and assigns a confidence metric to each of these candidates. Based on the symbol-rate candidates and their associated metrics, a majority vote scheme is then employed to determine the final estimate of the symbol-rate. The validity of the proposed algorithm has been verified through simulations.
An All-in-One Network for Dehazing and Beyond

This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated atmospheric scattering model. Instead of estimating the transmission matrix and the atmospheric light separately as most previous models did, AOD-Net directly generates the clean image through a light-weight CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level task performance on hazy images. Experimental results on both synthesized and natural hazy image datasets demonstrate our superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality. Furthermore, when concatenating AOD-Net with Faster R-CNN and training the joint pipeline from end to end, we witness a large improvement of the object detection performance on hazy images.
Market Discipline and Internal Governance in the Mutual Fund Industry

We develop a continuous-time model in which a portfolio manager is hired by a management company. On the basis of observed portfolio returns, all agents update their beliefs about the manager’s skills. In response, investors can move capital into or out of the mutual fund, and the management company can fire the manager. Introducing firing rationalizes several empirically documented findings, such as the positive relation between manager tenure and fund size or the increase of portfolio risk before a manager replacement and the following risk decrease. The analysis predicts that the critical performance threshold that triggers firing increases significantly over a manager’s tenure and that management replacements are accompanied by capital inflows when a young manager is replaced but may be accompanied by capital outflows when a manager with a long tenure is fired. Our model yields much lower valuation levels for management companies than simple applications of discounted cash flow (DCF) methods and is thus more consistent with empirical observations.
Cortical Contribution to Linear, Non-linear and Frequency Components of Motor Variability Control during Standing

Motor variability is an inherent feature of all human movements and reflects the quality of functional task performance. Depending on the requirements of the motor task, the human sensory-motor system is thought to be able to flexibly govern the appropriate level of variability. However, it remains unclear which neurophysiological structures are responsible for the control of motor variability. In this study, we tested the contribution of cortical cognitive resources on the control of motor variability (in this case postural sway) using a dual-task paradigm and furthermore observed potential changes in control strategy by evaluating Ia-afferent integration (H-reflex). Twenty healthy subjects were instructed to stand relaxed on a force plate with eyes open and closed, as well as while trying to minimize sway magnitude and performing a “subtracting-sevens” cognitive task. In total 25 linear and non-linear parameters were used to evaluate postural sway, which were combined using a Principal Components procedure. Neurophysiological response of Ia-afferent reflex loop was quantified using the Hoffman reflex. In order to assess the contribution of the H-reflex on the sway outcome in the different standing conditions multiple mixed-model ANCOVAs were performed. The results suggest that subjects were unable to further minimize their sway, despite actively focusing to do so. The dual-task had a destabilizing effect on PS, which could partly (by 4%) be counter-balanced by increasing reliance on Ia-afferent information. The effect of the dual-task was larger than the protective mechanism of increasing Ia-afferent information. We, therefore, conclude that cortical structures, as compared to peripheral reflex loops, play a dominant role in the control of motor variability.
The Finger in Flight: Real-Time Motor Control by Visually Masked Color Stimuli

Current theories of dual visual systems suggest that color is processed in a ventral cortical stream that eventually gives rise to visual awareness but is only indirectly involved in visuomotor control mediated by the dorsal stream. If the dorsal stream is indeed less sensitive to color than the ventral stream, color stimuli blocked from awareness by visual masking should also be blocked from guiding fast motor responses. In this study, pointing movements to one of two isoluminant color targets were preceded by consistent or inconsistent color primes. Trajectories were strongly affected by priming, with kinematics implying a continuous flow of color information into executive brain areas while the finger was already moving. Motor effects were more sensitive to color of the primes than were deliberate attempts to identify the primes in forced-choice tasks based on visual awareness. Priming was observed even when masking was complete.
Second Language Listening: Theory and Practice

Listening has received increasing attention from ESL/EFL professionals in recent years. The digital revolution has made spoken language much easier to record, edit, and share. Digital communications and travel raise the chances of needing to communicate in a second language, making that language much less "foreign. " Still, despite being increasingly important, understanding what someone says in a second language remains a challenge. John Flowerdew and Lindsay Miller in Second Language Listening: Theory and Practice offer guidance to teachers who want to help their students meet this challenge. Their book can help teachers apply current listening theory.
Exemplar-Based Face Parsing

Figures 1 and 2 supplement Figure 4 in our paper. In all cases, the input images come from our Helen [1] test set. We note that our algorithm generally produces accurate results, as shown in Figures 1. However, our algorithm is not perfect and makes mistakes on especially challenging input images, as shown in Figure 2. In our view, the mouth is the most challenging region of the face to segment: the shape and appearance of the lips vary widely from subject to subject, mouths deform significantly, and the overall appearance of the mouth region changes depending on whether the inside of the mouth is visible or not. Unusual mouth expressions, like those shown in Figure 2, are not represented well in the exemplar images, which results in poor label transfer from the top exemplars to the test image. Despite these challenges, our algorithm generally performs well on the mouth, with large segmentation errors occurring infrequently.
Towards better performance with heterogeneous training data in acoustic modeling using deep neural networks

Modeling heterogeneous data sources remains a fundamental challenge of acoustic modeling in speech recognition. We call this the multi-condition problem because the speech data come from many different conditions. In this paper, we introduce the fundamental confusability problem in multi-condition learning, then discuss the problem formalization, the taxonomy, and the architectures for multi-condition learning. While the ideas presented are applicable to all classifiers, we focus our attention in this work on acoustic models based on deep neural networks (DNN). We propose four different strategies for multi-condition learning of a DNN that we refer to as a mixed-condition model, a condition-dependent model, a condition-normalizing model, and a condition-aware model. Based on the experimental results on the voice search and short message dictation task and the Aurora 4 task, we show that the confusability introduced when modeling heterogeneous data depends on the source of acoustic distortion itself, the front-end feature extractor, and the classifier. We also demonstrate the best approach for dealing with heterogeneous data may not be to let the model sort it out blindly, even with a classifier as sophisticated as a DNN.
Differential adhesion leads to segregation and exclusion of N-cadherin-deficient cells in chimeric embryos.

Cadherin-mediated cell-cell interactions are thought to be critical in controlling cell sorting during embryogenesis. Here, we report that chimeric embryos generated with N-cadherin-deficient (N-cadherin(-/-)) embryonic stem cells develop further than embryos completely lacking N-cadherin only when the myocardium consists of N-cadherin-positive cells. Initially, the N-cadherin-negative and -positive cells mix together to form chimeric tissues; however, by embryonic day 9.5, the N-cadherin(-/-) cells segregate from the wild-type cells forming distinct aggregates. The chimeric embryos have large aggregates of N-cadherin(-/-) myocardial cells in the heart lumen, indicating that the cells are unable to maintain cell-cell contacts with N-cadherin-positive myocytes. This sorting-out phenomenon also is apparent in somites, neural tube, and developing brain where N-cadherin(-/-) cells form distinct lumenal structures. These studies demonstrate that N-cadherin-mediated adhesion is critical for maintaining cell-cell interactions in tissues undergoing active cellular rearrangements and increased mechanical stress associated with morphogenesis.
LexiPers: An ontology based sentiment lexicon for Persian

Sentiment analysis refers to the use of natural language processing to identify and extract subjective information from textual resources. One approach for sentiment extraction is using a sentiment lexicon. A sentiment lexicon is a set of words associated with the sentiment orientation that they express. In this paper, we describe the process of generating a general purpose sentiment lexicon for Persian. A new graph-based method is introduced for seed selection and expansion based on an ontology. Sentiment lexicon generation is then mapped to a document classification problem. We used the K-nearest neighbors and nearest centroid methods for classification. These classifiers have been evaluated based on a set of hand labeled synsets. The final sentiment lexicon has been generated by the best classifier. The results show an acceptable performance in terms of accuracy and F-measure in the generated sentiment lexicon.
Narrative Organization Deficit in Lewy Body Disorders Is Related to Alzheimer Pathology

Background: Day-to-day interactions depend on conversational narrative, and we examine here the neurobiological basis for difficulty organizing narrative discourse in patients with Lewy body disorders (LBD). Method: Narrative organization was examined in 56 non-aphasic LBD patients, including a non-demented cohort (n = 30) with Parkinson's disease (PD) or PD-Mild Cognitive Impairment PD-MCI,) and a cohort with mild dementia (n = 26) including PD-dementia (PDD) and dementia with Lewy bodies (DLB), with similar age and education but differing in MMSE (p < 0.001). We used a previously reported procedure that probes patients' judgments of the organization of brief, familiar narratives (e.g., going fishing, wrapping a present). A subgroup of 24 patients had MRI assessment of regional gray matter (GM) atrophy and cerebrospinal fluid (CSF) levels of biomarkers for Alzheimer's disease (AD) pathology, including beta amyloid (Aβ), total-tau (t-tau), and phosphorylated-tau (p-tau). Results: Mildly demented LBD patients had a significant deficit judging narratives compared to non-demented patients, but this deficit was not correlated with MMSE. Regression analyses instead related narrative organization to regions of frontal GM atrophy, and CSF levels of Aβ and t-tau associated with presumed AD pathology in these frontal regions. Conclusion: These findings are consistent with the hypothesis that CSF markers of AD pathology associated with frontal regions play a role in difficulty organizing narratives in LBD.
Mutagenic probability estimation of chemical compounds by a novel molecular electrophilicity vector and support vector machine

MOTIVATION Mutagenicity is among the toxicological end points that pose the highest concern. The accelerated pace of drug discovery has heightened the need for efficient prediction methods. Currently, most available tools fall short of the desired degree of accuracy, and can only provide a binary classification. It is of significance to develop a discriminative and informative model for the mutagenicity prediction. RESULTS Here we developed a mutagenic probability prediction model addressing the problem, based on datasets covering a large chemical space. A novel molecular electrophilicity vector (MEV) is first devised to represent the structure profile of chemical compounds. An extended support vector machine (SVM) method is then used to derive the posterior probabilistic estimation of mutagenicity from the MEVs of the training set. The results show that our model gives a better performance than TOPKAT (http://www.accelrys.com) and other previously published methods. In addition, a confidence level related to the prediction can be provided, which may help people make more flexible decisions on chemical ordering or synthesis. AVAILABILITY The binary program (ZGTOX_1.1) based on our model and samples of input datasets on Windows PC are available at http://dddc.ac. cn/adme upon request from the authors.
Word2vec semantic representation in multilabel classification for Indonesian news article

Mutilabel text classification is task to categorize a text into one or more categories. Like other supervised learning, performance of multilabel classification is limited when there are small labeled data and it leads to the difficulty of capturing semantic relationship. The previous research of multilabel classification for Indonesian news article focused on implementing multilabel classification using lexical feature that employed bag of words and TF-IDF term weighting, and there is no work yet that uses semantic features. The purpose of this paper is to present an implementation of multilabel classification using semantic feature based on Word2vec. Word2vec is an unsupervised task that is capable of utilizing unlabeled data to convert a word into its vector representation that can also find the semantic relationship between words by counting their distance. The experiment shows that the result using this semantic feature improves the previous result that used traditional bag of words and TF-IDF method. It escalates the testing F-measure value from 76.73% to 80.17%.
Addressing Test Anxiety in a High-Stakes Environment: Strategies for Classrooms and Schools

Preface About the Authors 1. Testing in a High-Stakes Environment 2. What Test Anxiety Is.And Isn't 3. The Effects of Test Anxiety on Students and Teachers 4. The Causes of Test Anxiety 5. Measuring Test Anxiety 6. Tips and Strategies for Reducing Test Anxiety 7. Conclusions and Next Steps Resource A: Glossary Resource B: General Resources Resource C: Resources for Measuring Test Anxiety References Subject Index Author Index
Robust Design of Power System Stabilizer Using Taguchi Technique and Particle Swarm Optimization

The tuning of PSS parameters for satisfactory power system response over a wide range of operating conditions requires an efficient robust optimization technique. This paper presents a novel concept of integrating the Taguchi robust design principle with particle swarm optimization (PSO) for the PSS design. The objective is to build an intrinsic robustness in the design of PSS over a wide range of power system operating conditions. The proposed approach employs signal to noise ratio and orthogonal array concepts of Taguchi design to determine the robust optimal setting of PSS parameters using PSO. The robustness of the PSS designed is tested through No-Way analysis of variance. Further, the effectiveness of the robust PSS is illustrated through time-domain simulations as it is applied to a single machine infinite bus system under wide range of loading conditions.
Phosphorylation of actopaxin regulates cell spreading and migration

Actopaxin is an actin and paxillin binding protein that localizes to focal adhesions. It regulates cell spreading and is phosphorylated during mitosis. Herein, we identify a role for actopaxin phosphorylation in cell spreading and migration. Stable clones of U2OS cells expressing actopaxin wild-type (WT), nonphosphorylatable, and phosphomimetic mutants were developed to evaluate actopaxin function. All proteins targeted to focal adhesions, however the nonphosphorylatable mutant inhibited spreading whereas the phosphomimetic mutant cells spread more efficiently than WT cells. Endogenous and WT actopaxin, but not the nonphosphorylatable mutant, were phosphorylated in vivo during cell adhesion/spreading. Expression of the nonphosphorylatable actopaxin mutant significantly reduced cell migration, whereas expression of the phosphomimetic increased cell migration in scrape wound and Boyden chamber migration assays. In vitro kinase assays demonstrate that extracellular signal-regulated protein kinase phosphorylates actopaxin, and treatment of U2OS cells with the MEK1 inhibitor UO126 inhibited adhesion-induced phosphorylation of actopaxin and also inhibited cell migration.
Optimisation of inulinase production by Kluyveromyces bulgaricus

Present work is based on observation of effects of pH and temperature of fermentation on the production of microbial enzyme inulinase by Kluyveromyces bulgaricus (former Kluyveromyces marxianus). Inulinase hydrolyses inulin, an oligosaccharide which can be isolated from plants such as Jerusalem artichoke, chicory or dahlia, into pure fructose (1). Fructooligosaccharides have great potential in food industry because they can be used as calorie-reduced and noncariogenic sweeteners. Fructose formation from inulin is a single step enzymatic reaction and yields are up to 95 % fructose. On contrary, conventional fructose production from starch needs at least three enzymatic steps, yielding only 45 % fructose (2). Process of inulinase production was optimised by using experimental design method. pH value of the cultivation medium showed to be the most significant variable and it should be maintained at optimum value, 3.6. The effect of temperature was slightly lower and optimal values are between 30 and 33 o C. At a low pH value of the cultivation medium, the microorganism was not able to produce enough enzyme and enzyme activities were low. Similar effect was caused by high temperature. Highest values of enzyme activities were achieved at optimal fermentation conditions and the values were: 100.16-124.36 IU/ml (with sucrose as substrate for determination of enzyme activity) or 8.6 -11.6 IU/ml (with inulin as substrate), respectively. The method of factorial design and response surface analysis makes it possible to study several factors simultaneously, to quantify the individual effect of each factor and to investigate their possible interactions (3). The model based on physiological assumptions is also applied. Assumed is a single enzyme rate determing growth (Monod kinetics) with proportional inulinase production rate. Applied are the models of reversible temperature and acidity inhibition based on thermodynamic equilibrium between active and inhibited enzyme states. Predictions by the two models are compared by ANOVA.
Value of apparent diffusion coefficient for predicting malignancy of intraductal papillary mucinous neoplasms of the pancreas.

PURPOSE We aimed to explore the potential value of the whole tumor apparent diffusion coefficient (ADC) for discriminating between benign and malignant intraductal papillary mucinous neoplasms (IPMNs) of the pancreas. METHODS Forty-two patients underwent 1.5 T magnetic resonance imaging that included diffusion-weighted imaging (DWI, b=0.500 s/mm2). The mean, minimum, and maximum ADC values were measured for the whole tumor. The differences between benign and malignant IPMNs were calculated for the mean ADC, ADC-min, and ADC-max values. Receiver operating characteristics (ROC) analysis was conducted to evaluate their potential diagnostic performance. RESULTS Fifteen of 25 benign IPMNs demonstrated low or iso-signal intensity on DWI with a b value of 500 s/mm2 compared with normal pancreatic parenchyma, whereas all malignant IPMNs demonstrated high signal intensity. The mean value of ADC was significantly higher in benign IPMNs compared with malignant IPMNs (3.39×10-3 mm2/s vs. 2.39×10-3 mm2/s, P < 0.001), with an area under the ROC curve (AUC) of 0.92 (95% confidence interval [CI], 0.79-0.98). The ADC-min value of malignant IPMNs was also significantly lower than that of benign IPMNs (1.24×10-3 mm2/s vs. 2.58×10-3 mm2/s, P < 0.001), with an AUC of 0.94 (95% CI, 0.82-0.99). No marked difference was found between benign and malignant IPMNs for the ADC-max value (3.89×10-3 mm2/s vs. 3.78×10-3 mm2/s, P = 0.299). CONCLUSION Lower mean and minimum ADC values of the whole tumor might be potential predictors of malignant IPMNs of the pancreas.