Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining Protein Database using Machine Learning Techniques

With a large amount of information relating to proteins accumulating in databases widely available online, it is of interest to apply machine learning techniques that, by extracting underlying statistical regularities in the data, make predictions about the functional and evolutionary characteristics of unseen proteins. Such predictions can help in achieving a reduction in the space over which experiment designers need to search in order to improve our understanding of the biochemical properties. Previously it has been suggested that an integration of features computable by comparing a pair of proteins can be achieved by an artificial neural network, hence predicting the degree to which they may be evolutionary related and homologous. We compiled two datasets of pairs of proteins, each pair being characterised by seven distinct features. We performed an exhaustive search through all possible combinations of features, for the problem of separating remote homologous from analogous pairs, we note that significant performance gain was obtained by the inclusion of sequence and structure information. We find that the use of a linear classifier was enough to discriminate a protein pair at the family level. However, at the superfamily level, to detect remote homologous pairs was a relatively harder problem. We find that the use of nonlinear classifiers achieve significantly higher accuracies. In this paper, we compare three different pattern classification methods on two problems formulated as detecting evolutionary and functional relationships between pairs of proteins, and from extensive cross validation and feature selection based studies quantify the average limits and uncertainties with which such predictions may be made. Feature selection points to a \"knowledge gap\" in currently available functional annotations. We demonstrate how the scheme may be employed in a framework to associate an individual protein with an existing family of evolutionarily related proteins.
Capturing motions and forces of the human masticatory system to replicate chewing and to perform dental wear experiments

One way to evaluate the life-time performance of dental restorative materials is to use in vitro dental wear simulators, which generate accelerated artificial dental wear on dental restorative components outside of the human oral environment. However, the work of several researchers has questioned the reliability of these in vitro results as a consequence of significant result variations produced by different types of dental wear simulators testing identical dental specimens. Natural six degree of freedom (DOF) mandibular movements and other characteristics of the human masticatory system are not replicated by any of these available simulators. A simulator replicating and controlling 6 DOF mandibular movements and occlusal bite forces improves this situation. This paper presents a method by which accurate jaw motion data can be obtained using a conventional 6 DOF motion capturing system and a method of measuring occlusal bite forces. The data obtained have subsequently been used as input signals for a new 6 DOF dental wear simulator capable of generating single and multi-contact wear formations in dental wear studies.
Employing Run-time Static Analysis to Improve Concolic Execution

Dynamic symbolic execution, or concolic execution, is a program testing technique that systematically executes a program with the aim of exploring all feasible program paths, and locating and reporting all errors encountered in these paths. However, as the complexity of the program grows, the number of program paths explodes, making it infeasible for concolic testers to explore all of them. To reduce the number of paths to explore, several concolic testing tools have recently started employing static analysis to prune paths guaranteed by the static analysis to be safe. The concolic tester must then only focus on those paths that might contain an error, as reported by the analysis. However, due to imprecisions in the analysis’ result, the reported errors may just be false positives, and it is up to the tester to verify whether a reported alarm is an actual error or merely a false positive. In this position paper, we propose to increase the precision of these analyses by not only performing an initial static analysis before starting concolic testing of the program, but also by launching incremental static analyses over the program at run time, and incorporating into the analyses run-time information observed by the tester. The increased precision that results from incorporating such run-time information should enable further pruning of the program paths that must be explored by the concolic tester.
Contact mechanics and tribology of polymer composites

We review contact mechanics with emphasis on the rheological (time dependent) properties of polymers and their relations to surface roughness, material properties, and friction as well as wear behavior of rubbing polymer surfaces. The main concept of polymer mechanics related to tribology consists of three basic elements involved in friction: deformation resulting in the real area of contact of rough surfaces, contact adhesion, and shear and rupture of materials in the contact during the sliding friction. The results of classical work are included, which addresses the real contact area calculation and the description of adhesion interaction between rough surfaces. A brief review of experimental investigations concerning the surface characterization by means of bearing curves, the intermolecular force interaction using the adhesion parameter, the effect of temperature on the real contact area, the formation of transferred polymer film during friction, and tribological behavior of ultrathin polymer layers are presented and their implications discussed. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 39870.
Somatic Embryogenesis: Identified Factors that Lead to Embryogenic Repression. A Case of Species of the Same Genus

Somatic embryogenesis is a powerful biotechnological tool for the mass production of economically important cultivars. Due to the cellular totipotency of plants, somatic cells under appropriate conditions are able to develop a complete functional embryo. During the induction of somatic embryogenesis, there are different factors involved in the success or failure of the somatic embryogenesis response. Among these factors, the origin of the explant, the culture medium and the in vitro environmental conditions have been the most studied. However, the secretion of molecules into the media has not been fully addressed. We found that the somatic embryogenesis of Coffea canephora, a highly direct embryogenic species, is disrupted by the metabolites secreted from C. arabica, a poorly direct embryogenic species. These metabolites also affect DNA methylation. Our results show that the abundance of two major phenolic compounds, caffeine and chlorogenic acid, are responsible for inhibiting somatic embryogenesis in C. canephora.
Maternal obesity and neonatal Apgar scores

Objective. To determine whether maternal obesity in early pregnancy is associated with low neonatal 5-min Apgar scores while adjusting for confounders. Methods. Data were obtained from Maine State Birth Records Database. Analyses were restricted to information on 58,089 white women and their newborns. Maternal weight status was defined using the recorded early second trimester maternal body mass index (BMI) and defined as normal weight (BMI <25), overweight (BMI 25 to <30), obese (BMI 30 to <40), and morbidly obese (BMI ≥40). Logistic regression analysis was used to assess the association of maternal weight status with low Apgar score while adjusting for confounders. Results. Compared with newborns of normal weight women, the risk to receive low Apgar scores (4–6) is increased in newborns of obese (OR 1.4, 95% CI 1.1–1.7) and morbidly obese mothers (OR 2.0, 95% CI 1.5–2.7). The association did not achieve significance for newborns of overweight mothers (OR 1.2, 95% CI 0.99–1.4). No association was identified between maternal weight status and very low Apgar scores (0–3). Conclusions. Maternal obesity is associated with a significantly increased risk for decreased Apgar scores at birth. Further studies are needed to clarify the relationships among maternal obesity, complications of pregnancy, and neonatal outcome.
Statistics of Neuronal Identification with Open- and Closed-Loop Measures of Intrinsic Excitability

In complex nervous systems patterns of neuronal activity and measures of intrinsic neuronal excitability are often used as criteria for identifying and/or classifying neurons. We asked how well identification of neurons by conventional measures of intrinsic excitability compares with a measure of neuronal excitability derived from a neuron’s behavior in a dynamic clamp constructed two-cell network. We used four cell types from the crab stomatogastric ganglion: the pyloric dilator, lateral pyloric, gastric mill, and dorsal gastric neurons. Each neuron was evaluated for six conventional measures of intrinsic excitability (intrinsic properties, IPs). Additionally, each neuron was coupled by reciprocal inhibitory synapses made with the dynamic clamp to a Morris–Lecar model neuron and the resulting network was assayed for four measures of network activity (network activity properties, NAPs). We searched for linear combinations of IPs that correlated with each NAP, and combinations of NAPs that correlated with each IP. In the process we developed a method to correct for multiple correlations while searching for correlating features. When properly controlled for multiple correlations, four of the IPs were correlated with NAPs, and all four NAPs were correlated with IPs. Neurons were classified into cell types by training a linear classifier on sets of properties, or using k-medoids clustering. The IPs were modestly successful in classifying the neurons, and the NAPs were more successful. Combining the two measures did better than either measure alone, but not well enough to classify neurons with perfect accuracy, thus reiterating that electrophysiological measures of single-cell properties alone are not sufficient for reliable cell identification.
Finding Regions for Local Repair in Partial Csp Solutions | First Report

In contrast to standard constraint satisfaction problems (CSPs) practical applications often require both, the management of changing validity of constraints and variables. Based on experiences made with the development of a commercial nurse scheduling system this paper suggests the application of local optimization algorithms for knowledge-based connguration systems, which strongly rely on dynamic constraint management. The main contribution is a small theory on how to enumerate the regions (a set of assignments) in a partial solution to a constraint problem, which have to be changed in order to obtain a global solution. Such a theory enables the application of local repair mechanisms to knowledge-based systems.
Evaluation of the spatio-temporal of soil salinity variation using data mining approach

The issue of temporal and spatial variation in soil salinity is considered as a fundamental element in salinity monitoring. The aim of this study is to develop a framework which integrates image mining techniques with Fuzzy logic methodology to improve the evaluation of spatio-temporal variation of soil salinity in areas with lack of available ground observation. Intensity and duration of salinity was characterized in space by the deviation of the current NDVI at each location from its corresponding temporal mean value. Landsat and ASTER images data was used to provide frequent Normalized Difference Vegetation Index (NDVI) in cultivation phase for a period of 22 years. Evolution of salinity condition before planting season was assessed by applying stepwise regression method on image data for two available dataset. The regression equation was obtained between reflectance value of band three and the measured soil Electrical Conductivity (EC) from field. Validation of the developed algorithm was done by comparing the obtained outputs with 50 ground observations, available salinity reports, and previous soil salinity maps. The result revealed that the proposed framework can be considered as a cost and time effective tool for proper assessment of the spatio-temporal variation of soil salinity.
Combustion properties of asphalt binder containing flame retardant

Combustion characteristics of asphalt binder containing flame retardant (magnesium hydroxide, MH) were investigated using thermogravimetry and Fourier transform infrared spectrometer, and multistage combustion model of asphalt binder was developed. The results indicate that the combustion processes of asphalt binder and MH‐modified asphalt binder both include three main sequential stages. But with the MH concentration increase, the release amount of flammable volatiles is reduced, and the asphalt binder ignition time is delayed. Asphalt binder thermal stability is improved by incorporation of MH. At the same MH concentration, the kinetics parameters increase from the first to third stage. But in each stage, the kinetics parameters exhibit an increase trend in the first stage as the MH content increases, but in the second and third stages, the kinetics parameters show a trend of first increase and then decrease. Furthermore, the parameters reach the maximum at the concentration of around 20% by weight. This concentration may be appropriate for flame‐retarding asphalt binder. It is concluded that MH may be used as a new kind of environment‐friendly flame retardant for asphalt binder, and the developed thermal analysis kinetics model can be employed to evaluate the combustion characteristics of asphalt binder. Copyright © 2011 John Wiley & Sons, Ltd.
Numerical simulation study of the T‐peel behavior of coated fabric films used in inflatable structures

A simple and reliable finite element method was developed to analyze the T-peel behavior of fabric coated with thin films. The material model was constructed simply by a tensile test of the coating and the coated fabric. In addition, the influence of the welding parameters on the mechanical properties of the welded zone was considered. Although some material characters were neglected, the material model was found to be convenient for engineering applications. The reliability was examined by a comparison of the numerical results with the experiments. A T-peel test was conducted, and a camera configured with an optical microscope lens was used to observe the material flow from the thickness view. The load–displacement curve predicted by the simulation agreed well with the experimental results. Small voids were detected when the sample was near fracture. These places were consistent with the simulation results and showed where the maximum stress–strain was located. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 41299.
Computer Aided Technique for Finger Print Image Enhancement and Minutiae Extraction

Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, and unacceptable error rates. To represent the large amount of data in the biometric images an efficient feature extraction method is needed. This paper presents the feature extraction of fingerprint image processing stages such as image Preprocessing, Converting the image in to gray scale, Image Enhancement can be performed with the help of Discrete Fourier transformation, image Binarization, image segmentation, image thinning, Minutiae Extraction after Minutiae Extraction again segmentation technique is used . In finger print images there are foreground regions and background regions where foreground region show the ridges and valleys while the background regions are to be left out. The foreground regions have high value while the background regions have low values. Segmentation separates the foreground region from the background image for the reliable extraction of minutiae. The feature extraction is the first step used for matching used in biometric recognition. After feature extraction the next step is preprocessing of Minutiae. The extracted feature is called as templates, which is used in matching.
Socioeconomic position across the lifecourse and cognitive function in late middle age.

OBJECTIVES To examine the influence of childhood and adult socioeconomic position, socioeconomic mobility, and cumulative disadvantage across the lifecourse on cognitive function in late middle age. METHODS Cross-sectional population-based study of 486 men age 58 and 64 from eastern Finland. Respondent's socioeconomic position in childhood was measured using parent's education and occupation, and respondent's position in adulthood was indicated by attained education and personal income. Cognitive function was assessed using five neuropsychological tests: Trail Making, Selective Reminding, Verbal Fluency, Visual Reproduction, and the Mini-Mental State Exam. RESULTS Each indicator of socioeconomic position made statistically independent contributions to levels of cognitive function: Respondents from poor childhood backgrounds, and those who attained a limited education and earned a low income, performed worst on each test. Men who occupied a disadvantaged socioeconomic position in childhood and then experienced upward mobility over the lifecourse exhibited better cognitive performance than those with similar socioeconomic origins but limited or no upward mobility. Conversely, men from advantaged childhood backgrounds who later in life experienced downward mobility scored poorer on each cognitive test than their counterparts who remained in the most advantaged groups throughout the lifecourse. There was a strong, graded association between cumulative socioeconomic disadvantage and cognitive function: Men who occupied a low socioeconomic position during both childhood and adulthood scored worse on every test than those who occupied a high position at all points in their lives. DISCUSSION Socioeconomic conditions across all stages of the lifecourse appear to make unique contributions to cognitive function in late middle age. These results also suggest that in terms of cognitive function, origin is not necessarily destiny, as disadvantaged socioeconomic circumstances in childhood may be overcome to some extent by upward mobility later in life.
Development of a Mediterranean diet score adapted to Japan and its relation to obesity risk

Background The Mediterranean diet (MD) is well known as a healthy diet that protects against several chronic diseases. However, there is no appropriate and easy index to assess adherence to the MD pattern in Japan. Objective The aim of this study was to develop a novel instrument to measure MD adherence adapted to a Japanese diet and to examine its association with overweight/obesity risk. Methods A cross-sectional nutritional survey provided the data for construction of a novel MD score. In total, 1,048 subjects who were employees and university students, aged 18–68 years (645 men and 403 women), completed a 58-item brief-type self-administered dietary history questionnaire. We constructed a Japanese-adapted MD score (jMD score) focusing on 13 components. Adherence to the jMD was categorized as low (score 0–4), moderate (5–7), or high (8–13). Results Men had higher jMD scores than women, and adherence to the jMD score increased with age. Only 11.6% of subjects showed high adherence to the jMD, whereas 29.6% showed low adherence. A higher jMD adherence was associated with a higher intake of favorable nutrients with the exception of salt. The jMD adherence was significantly associated with a reduced likelihood of having overweight/obesity for the highest category compared with lowest category (odds ratio [OR] 0.50, 95% confidence interval [CI] 0.30–0.85, p-trend=0.017) after adjusting for age, sex, smoking, physical activity, alcohol intake, and hypertension. A two-point increment in jMD score was related to a reduced likelihood of having overweight/obesity with an odds ratio of 0.76 (95% CI 0.65–0.90, p=0.002). Conclusions Our novel jMD score confirmed reasonable associations with nutrient intakes, and higher MD adherence was associated with a lower prevalence of overweight/obesity.
The incentive sensitization theory of addiction: some current issues

We present a brief overview of the incentive sensitization theory of addiction. This posits that addiction is caused primarily by drug-induced sensitization in the brain mesocorticolimbic systems that attribute incentive salience to reward-associated stimuli. If rendered hypersensitive, these systems cause pathological incentive motivation (‘wanting’) for drugs. We address some current questions including: what is the role of learning in incentive sensitization and addiction? Does incentive sensitization occur in human addicts? Is the development of addiction-like behaviour in animals associated with sensitization? What is the best way to model addiction symptoms using animal models? And, finally, what are the roles of affective pleasure or withdrawal in addiction?
Piecewise planar segmentation for automatic scene modeling

In this paper, we investigate the problem of the automatic creation of 3D models of man-made environments that we represent as collections of textured planes. A typical approach is to automatically compute a sparse feature reconstruction and to manually give their plane-memberships as well as the delineation of the planes. Textures are then extracted from the images while optimizing the model, typically the disparity between marked and predicted edges. We propose a means to automatically estimate the model of the scene, in terms of the number of planes and their parameters from a point feature reconstruction. The method is based on random sampling of reconstructed points to generate plane hypotheses. Each of these is then evaluated using a measure of approximate photoconsistency while recovering the corresponding plane delineation. We then compute the maximum likelihood estimate of all scene parameters, i.e. the set of planes and reconstructed points as well as relative camera pose, with respect to actual images. The approach is validated on simulated data and real images.
Sentence-level MT evaluation without reference translations: beyond language modeling

In this paper we investigate the possibility of evaluating MT quality and fluency at the sentence level in the absence of reference translations. We measure the correlation between automatically-generated scores and human judgments, and we evaluate the per- formance of our system when used as a classifier for identifying highly dysfluent and ill- formed sentences. We show that we can substantially improve on the correlation between language model perplexity scores and human judgment by combining these perplexity scores with class probabilities from a machine-learned classifier. The classifier uses linguis- tic features and has been trained to distinguish human translations from machine transla- tions. We show that this approach also performs well in identifying dysfluent sentences.