Miracle-dz/UniFAR-FTUs huggingface dataset:
Complexity Analysis and Parameter Estimation of Dynamic Metabolic Systems

A metabolic system consists of a number of reactions transforming molecules of one kind into another to provide the energy that living cells need. Based on the biochemical reaction principles, dynamic metabolic systems can be modeled by a group of coupled differential equations which consists of parameters, states (concentration of molecules involved), and reaction rates. Reaction rates are typically either polynomials or rational functions in states and constant parameters. As a result, dynamic metabolic systems are a group of differential equations nonlinear and coupled in both parameters and states. Therefore, it is challenging to estimate parameters in complex dynamic metabolic systems. In this paper, we propose a method to analyze the complexity of dynamic metabolic systems for parameter estimation. As a result, the estimation of parameters in dynamic metabolic systems is reduced to the estimation of parameters in a group of decoupled rational functions plus polynomials (which we call improper rational functions) or in polynomials. Furthermore, by taking its special structure of improper rational functions, we develop an efficient algorithm to estimate parameters in improper rational functions. The proposed method is applied to the estimation of parameters in a dynamic metabolic system. The simulation results show the superior performance of the proposed method.
Accuracy of Three Diagnostic Tests for Determining Mycobacterium Bovis Infection Status in Live-Sampled Wild Meerkats (Suricata Suricatta)

Accurate diagnosis of Mycobacterium bovis infection (bovine tuberculosis [bTB]) in live animals is notoriously problematic. The aim of the present study was to evaluate the diagnostic potential of 2 new serologic tests (multiantigen print immunoassay [MAPIA] and lateral flow immunoassay rapid test [RT]) in comparison with mycobacterial culture of tracheal washes for determining M. bovis infection status in a free-ranging population of wild meerkats (Suricata suricatta). During a longitudinal study lasting 2.5 years, 240 individually identifiable meerkats were each sampled up to 8 times under anesthesia every 3 months. Diagnostic accuracy was determined through Bayesian and maximum likelihood estimations of sensitivity, specificity, and likelihood ratios for each diagnostic test when used independently and in parallel to classify the disease status of individual meerkats in the absence of a gold standard. Culture of tracheal washes was highly specific (0.99; 95% confidence interval [CI] = 0.77, 1.00) but of low sensitivity (0.36; 95% CI = 0.24, 0.50) for diagnosing M. bovis-infected individuals. The longitudinal nature of the study with repeated sampling of the same individual animals served simultaneously to improve chances of detecting infection and increase confidence in a negative result in individual animals repeatedly testing negative. Although MAPIA and RT were individually of limited diagnostic use, interpreting the results of these 2 tests in parallel produced estimates of sensitivity (0.83; 95% CI = 0.67, 0.93) and specificity (0.73; 95% CI = 0.62, 0.82) high enough to usefully inform decision making when determining exposure to bTB in wild meerkats and potentially other species in which bTB poses a diagnostic challenge.
Density‐dependent diel activity in stream‐dwelling Arctic charr Salvelinus alpinus

Abstract Intraspecific competition plays a significant role in shaping how animals use and share habitats in space and time. However, the way individuals may modify their diel activity in response to increased competition has received limited attention. We used juvenile (age 1+) Arctic charr Salvelinus alpinus to test the prediction that individuals at high population density are more active and distribute their foraging activity over a greater portion of the 24‐h cycle than individuals at low population density. Individually tagged fish were stocked in seminatural stream enclosures at low (2 fish/m2) and high (6 fish/m2) density. During each of two 2‐week experimental rounds, activity of all fish within each enclosure was recorded every 3 h over seven 24‐h cycles. At high density, fish were more active and distributed their activity over a greater portion of the 24‐h cycle, with increased activity particularly at crepuscular times. Fluctuations in ecological conditions (e.g., water temperature and light intensity) also affected activity. Fish at high density grew as fast as fish at low density. This study demonstrates that individuals exhibit a degree of behavioral flexibility in their response to changes in ecological conditions and suggests that intraspecific competition can cause animals to modify temporal aspects of their activity to gain access to resources and maintain growth.
An Evaluation of Synapse Independence

If, as is widely believed, information is stored in the brain as distributed modifications of synaptic efficacy, it can be argued that the storage capacity of the brain will be maximized if the number of synapses that operate independently is as large as possible. The majority of synapses in the brain are glutamatergic; their independence will be compromised if glutamate released at one synapse can significantly activate receptors at neighboring synapses. There is currently no agreement on whether “spillover” after the liberation of a vesicle will significantly activate receptors at neighboring synapses. To evaluate the independence of central synapses, it is necessary to compare synaptic responses with those generated at neighboring synapses by glutamate spillover. Here, synaptic activation and spillover responses are simulated in a model, based on data for hippocampal synapses, that includes an approximate representation of the extrasynaptic space. Recently-published data on glutamate transporter distribution and properties are incorporated. Factors likely to influence synaptic or spillover responses are investigated. For release of one vesicle, it is estimated that the mean response at the nearest neighboring synapse will be <5% of the synaptic response. It is concluded that synapses can operate independently.
SRL TOOL: Heuristics-based Semantic Role Labeling through natural language processing

The Semantic Role Labeling (SRL Tool) is developed to label the semantic roles that exist in English sentences. This paper proposed a set of new heuristics to assist the semantic role labeling using natural language processing. The preliminary result shows that the use of heuristics can improve the process of assigning the correct semantic roles. This application tool is useful for researchers in Natural Language processing field and also for experts or students in Linguistics.
A novel Iris recognition method based on the Contourlet Transform and Biomimetic Pattern Recognition Algorithm

Iris recognition has been a hot research topic in these years. In this paper, an iris recognition method based on the the Contourlet Transform (CT) and Biomimetic Pattern Recognition (BPR) has been proposed. In proposed method, the Contourlet Transform was used to extract the significant features of the preprocessed iris image, and the Biomimetic Pattern Recognition algorithm was used to construct the high-dimensional covering hypersurface in order to recognize the iris images. Experiments on the CASIA iris image database show that the proposed iris recognition method based on the CT and BPR can achieve promising results compared with other methods.
Measuring outcomes from a peer-led social communication skills intervention for adults following acquired brain injury

Background: Reduced social competence and social integration following acquired brain injury (ABI) is well-documented. There is evidence that group social communication interventions for people with ABI and training for neuro-typical communication partners can be more effective than training the person with ABI alone. This study explores the effectiveness of a peer-led group intervention based on claims that peer models are a more powerful mechanism for learning and behaviour change than interventions led by a clinician. A peer-led training model for social communication has not previously been tested in ABI. Method: Twenty-four participants with severe ABI were recruited from a residential post-acute neurorehabilitation centre. An experimental parallel group design was used to compare a peer-led group intervention to a social activity group (usual care). A pilot study tested the feasibility of the approach followed by a main study. The groups ran for 8 weeks. A peer facilitator was trained in sixteen individual sessions over 4 weeks with a clinician. Behaviour was measured twice at baseline, after intervention and at maintenance. Four primary outcome measures, including the Adapted Measure of Participation in Conversation (MPC), and a newly devised measure of conversational interaction evaluated change in group communication behaviours. Results: Groups did not differ in baseline behaviour. There were significant differences in the treated group on the MPC and the measure of conversational interaction post-intervention. The treated group showed a more balanced interaction post-intervention and at follow-up. However, outcome measures showed differential sensitivity. Conclusion: There is preliminary evidence of advantage for peer-led groups in ABI intervention. The new conversational measure shows promise as a method to detect change in group communication behaviour.
CCTIS: An. Expert ansaetions recessing System

CCTIS, the Cargo Container Targeting Information System, was developed for the U.S, Customs Service to help monitor and control goods imported by ship. As an expert system, CCTIS has a combination of features which make it of interest to the applied A.I. community. First of all, CCTIS interacts with a large database - but unlike most data-oriented expert systems CCTIS is used in a transactions-oriented environment and needs the speed of such a system. Secondly, there exists no single cognitive model for the domain of import control, and it is unlikely that such a model can be developed in the near future. To address this problem CCTIS includes the ability for users to weigh and parameterise rules. And thirdly, the information CCTIS uses is often derived from free text of Bow quality tRat must be corrected and analyzed through natural language analysis techniques, The system uses a logic-based approach to solving these problems, defining explicit algorithms to extract data from text, and logical rules to analyze transactions. OuE experience shows that this approacb can produce a robust system. CCTIS has been used every business day for over a year in the two largest ports in the country, and has aided in the seizure of a number of illicit goods. Design is underway to merge CCTIS with another Customs A.H. system and to deploy the resulting system nationally, processing every sea-based import into the U.S.’
Lawrence Berkeley Laboratory UNIVERSITY OF CALIFORNIA Physics Division Determination of the Neutron Spin Structure Function

The spin structure function of the neutron gj' has been determined over the range 0.03 < x < 0.6 at an average Q 2 of 2 (GeV /c) 2 by measuring the asymmetry in deep inelastic scattering of polarized electrons from a polarized 3 He target at energies between 19 and 26 GeV. The integral of the ueutron spiu structure function is fouud to be Jd g\'(x) dx = -0.022 ± 0.011. Earlier reported proton results t.ogether
A Wrapper Approach for Constructive Induction

Inductive algorithms rely strongly on their representational biases. Representational inadequacy can be mitigated by constructive induction. This paper introduces the notion of a relative gain measure and describes a new constructive induction algorithm (GALA) which is independent of the learning algorithm. GALA generates a small number of new boolean attributes from existing boolean, nominal or real-valued attributes. Unlike most previous research on constructive induction, our methods are designed as preprocessing step before standard machine learning algorithms are applied. We present results which demonstrate the e ectiveness of GALA on both arti cial and real domains for both symbolic and subsymbolic learners. For symbolic learners, we used C4.5 and CN2. For subsymbolic learners, we used perceptron and backpropagation. In all cases, the GALA preprocessor increased the performance of the learning algorithm.
Body image and body satisfaction differ by race in overweight postpartum mothers.

BACKGROUND Body image (BI) and body satisfaction may be important in understanding weight loss behaviors, particularly during the postpartum period. We assessed these constructs among African American and white overweight postpartum women. METHODS The sample included 162 women (73 African American and 89 white) in the intervention arm 6 months into the Active Mothers Postpartum (AMP) Study, a nutritional and physical activity weight loss intervention. BIs, self-reported using the Stunkard figure rating scale, were compared assessing mean values by race. Body satisfaction was measured using body discrepancy (BD), calculated as perceived current image minus ideal image (BD<0: desire to be heavier; BD>0: desire to be lighter). BD was assessed by race for: BD(Ideal) (current image minus the ideal image) and BD(Ideal Mother) (current image minus ideal mother image). RESULTS Compared with white women, African American women were younger and were less likely to report being married, having any college education, or residing in households with annual incomes >$30,000 (all p < 0.01). They also had a higher mean body mass index (BMI) (p = 0.04), although perceived current BI did not differ by race (p = 0.21). African Americans had higher mean ideal (p = 0.07) and ideal mother (p = 0.001) BIs compared with whites. African Americans' mean BDs (adjusting for age, BMI, education, income, marital status, and interaction terms) were significantly lower than those of whites, indicating greater body satisfaction among African Americans (BD(Ideal): 1.7 vs. 2.3, p = 0.005; BD(Ideal Mother): 1.1 vs. 1.8, p = 0.0002). CONCLUSIONS Racial differences exist in postpartum weight, ideal images, and body satisfaction. Healthcare providers should consider tailored messaging that accounts for these racially different perceptions and factors when designing weight loss programs for overweight mothers.
TRAF6 Mediates IL-1β/LPS-Induced Suppression of TGF-β Signaling through Its Interaction with the Type III TGF-β Receptor

Transforming growth factor-β1 (TGF-β1) is an important anti-inflammatory cytokine that modulates and resolves inflammatory responses. Recent studies have demonstrated that inflammation enhances neoplastic risk and potentiates tumor progression. In the evolution of cancer, pro-inflammatory cytokines such as IL-1β must overcome the anti-inflammatory effects of TGF-β to boost pro-inflammatory responses in epithelial cells. Here we show that IL-1β or Lipopolysaccharide (LPS) suppresses TGF-β-induced anti-inflammatory signaling in a NF-κB-independent manner. TRAF6, a key molecule in IL-1β signaling, mediates this suppressive effect through interaction with the type III TGF-β receptor (TβRIII), which is TGF-β-dependent and requires type I TGF-β receptor (TβRI) kinase activity. TβRI phosphorylates TβRIII at residue S829, which promotes the TRAF6/TβRIII interaction and consequent sequestration of TβRIII from the TβRII/TβRI complex. Our data indicate that IL-1β enhances the pro-inflammatory response by suppressing TGF-βsignaling through TRAF6-mediated sequestration of TβRIII, which may be an important contributor to the early stages of tumor progression.
The Video Genome

Fast evolution of Internet technologies has led to an explosive growth of video data available in the public domain and created unprecedented challenges in the analysis, organization, management, and control of such content. The problems encountered in video analysis such as identifying a video in a large database (e.g. detecting pirated content in YouTube), putting together video fragments, finding similarities and common ancestry between different versions of a video, have analogous counterpart problems in genetic research and analysis of DNA and protein sequences. In this paper, we exploit the analogy between genetic sequences and videos and propose an approach to video analysis motivated by genomic research. Representing video information as video DNA sequences and applying bioinformatic algorithms allows to search, match, and compare videos in large-scale databases. We show an application for content-based metadata mapping between versions of annotated video.
Multi Feature Space Combination for Authorship Clustering

The Author Identification task for PAN 2016 consisted of three different Sub-tasks: authorship clustering, authorship links and author diarization. We developed a machine learning approaches for two of three of these tasks. For the two authorship related tasks we created various sets of feature spaces. The challenge was to combine these feature spaces to enable the machine learning algorithms to detect these difference authors across multiple feature spaces. In the case of authorship clustering we combine these feature spaces and use a two-step approach for clustering. Then we use results of the clustering, and employ new feature space to determine links between documents in given problems.
The Impact of Screen Trading on the Link between Stock Index and Stock Index Futures Prices: Evidence from UK Markets

In this paper, we consider the impact of the introduction of LIFFE CONNECT on the lead-lag relationship between the FTSE100 index and its futures. In general, the results of this study suggest that the move to screen trading strengthens the simultaneity of price discovery in the cash and futures markets and lessens the existence of a lead-lag relationship. This evidence differs to that of the previous literature which has generally found a strengthening of the lead of the futures market to the cash market. The reason for this difference in results is most likely a reflection of the fact that the cash market was generally floor traded in the previous literature, while in this study the FTSE100 was screen traded.
Hierarchical Cascade of Classifiers for Efficient Poselet Evaluation

Poselets have been used in a variety of computer vision tasks, such as detection, segmentation, action classification, pose estimation and action recognition, often achieving state-of-the-art performance. Poselet evaluation, however, is computationally intensive as it involves running thousands of scanning window classifiers. We present an algorithm for training a hierarchical cascade of part-based detectors and apply it to speed up poselet evaluation. Our cascade hierarchy leverages common components shared across poselets. We generate a family of cascade hierarchies, including trees that grow logarithmically on the number of poselet classifiers. Our algorithm, under some reasonable assumptions, finds the optimal tree structure that maximizes speed for a given target detection rate. We test our system on the PASCAL dataset and show an order of magnitude speedup at less than 1% loss in AP.
Efficient maximum likelihood classification for imaging spectrometer data sets

A simplified maximum likelihood classification technique for handling remotely sensed image data is proposed which reduces, significantly, the processing time associated with traditional maximum likelihood classification when applied to imaging spectrometer data, and copes with the training of geographically small classes. Several wavelength subgroups are formed from the complete set of spectral bands in the data, based on properties of the global correlation among the bands. Discriminant values are computed for each subgroup separately and the sum of discriminants is used for pixel labeling. Several subgrouping methods are investigated and the results show that a compromise among classification accuracy, processing time, and available training pixels can be achieved by using appropriate subgroup sizes. >
Molecular dissection of TatC defines critical regions essential for protein transport and a TatB–TatC contact site

The twin arginine transport (Tat) system transports folded proteins across the prokaryotic cytoplasmic membrane and the plant thylakoid membrane. TatC is the largest and most conserved component of the Tat machinery. It forms a multisubunit complex with TatB and binds the signal peptides of Tat substrates. Here we have taken a random mutagenesis approach to identify substitutions in Escherichia coli TatC that inactivate protein transport. We identify 32 individual amino acid substitutions that abolish or severely compromise TatC activity. The majority of the inactivating substitutions fall within the first two periplasmic loops of TatC. These regions are predicted to have conserved secondary structure and results of extensive amino acid insertion and deletion mutagenesis are consistent with these conserved elements being essential for TatC function. Three inactivating substitutions were identified in the fifth transmembrane helix of TatC. The inactive M205R variant could be suppressed by mutations affecting amino acids in the transmembrane helix of TatB. A physical interaction between TatC helix 5 and the TatB transmembrane helix was confirmed by the formation of a site‐specific disulphide bond between TatC M205C and TatB L9C variants. This is the first molecular contact site mapped to single amino acid level between these two proteins.
Efficient semiparametric estimator for heteroscedastic partially linear models

We study the heteroscedastic partially linear model with an unspecified partial baseline component and a nonparametric variance function. An interesting finding is that the performance of a naive weighted version of the existing estimator could deteriorate when the smooth baseline component is badly estimated. To avoid this, we propose a family of consistent estimators and investigate their asymptotic properties. We show that the optimal semiparametric efficiency bound can be reached by a semiparametric kernel estimator in this family. Building upon our theoretical findings and heuristic arguments about the equivalence between kernel and spline smoothing, we conjecture that a weighted partial-spline estimator could also be semiparametric efficient. Properties of the proposed estimators are presented through theoretical illustration and numerical simulations. Copyright 2006, Oxford University Press.
A Survey of Constraint HandlingTechniques used with Evolutionary

Despite the extended applicability of evolutionary algorithms to a wide range of domains, the fact that these algorithms are unconstrained optimization techniques leaves open the issue regarding how to incorporate constraints of any kind (linear, non-linear, equality and inequality) into the tness function as to search eeciently. The main goal of this paper is to provide a detailed and comprehensive survey of the many constraint handling approaches that have been proposed for evolutionary algorithms, analyzing in each case their advantages and disadvantages, and concluding with some of the most promising paths of research.