Descriptive Ability Research Articles

Preparation of human serum albumin nanoparticles using a chemometric technique

Human serum albumin (HSA), a versatile protein carrier for drug delivery, is an ideal material to fabricate nanoparticles for drug delivery systems. These nanoparticles can accumulate in tumor interstitium due to the enhanced permeability and retention effect. The most important characteristics of HSA nanoparticles are particle size, shape, and zeta potential. A chemometric approach was applied for HSA nanoparticles’ size optimization in this study. The effects of three experimental parameters; pressure (P) or power, organic solvent volume (V), and time (T), were investigated under sonication and high-pressure homogenization, using multivariate analysis. The trials were performed based on the Box–Behnken experimental design. The criteria for the appraisal of the descriptive ability of a multinomial were R2 = 0.819, standard error = 20.420, and F-ratio = 19.550. The method was optimized with respect to the nanoparticles’ size as a response. The Box–Behnken experimental design was applied to optimize and trial the robustness of the HSA nanoparticle preparation method.

Stacking Characteristics of Close Packed Materials.

It is shown that the enthalpy of any close packed structure for a given element can be characterized as a linear expansion in a set of continuous variables α_{n}, which describe the stacking configuration. This enables us to represent the infinite, discrete set of stacking sequences within a finite, continuous space of the expansion parameters H_{n}. These H_{n} determine the stable structure and vary continuously in the thermodynamic space of pressure, temperature, or composition. The continuity of both spaces means that only transformations between stable structures adjacent in the H_{n} space are possible, giving the model predictive as well as descriptive ability. We calculate the H_{n} using density functional theory (DFT)and interatomic potentials for a range of materials. Some striking results are found: e.g., the Lennard-Jones potential model has 11 possible stable structures and over 50 phase transitions as a function of cutoff range. The very different phase diagrams of Sc, Tl, Y, and the lanthanides are understood within a single theory. We find that the widely reported 9R-fcc transition is not allowed in equilibrium thermodynamics, and in cases where it has been reported in experiments (Li, Na), we show that DFT theory is also unable to predict it.

Annular flow stability within small-sized channels

Abstract An analytical study based on a variational thermodynamic principle is presented to evaluate the influence of surface tension on the stability of annular flow within small-sized channels. The model introduces phenomenological assumptions in the interfacial structure of the flow regime and theoretically draws the equilibrium transition line from an annular regime to the initiation of the partial wetting condition on the inner surface. By including surface tension, this model expands previous theories and identifies the stable flow configuration in terms of void fraction and interfacial extension. The significant influence of a higher surface tension and smaller diameter (i.e. lower Weber number) are responsible for a lower stable void fraction and higher slip ratio. A complete screening of the main influential parameters is conducted to explore the descriptive ability of the model. This analysis aims at contributing to the understanding of the stability of two-phase flow regimes and can be extended to the transition between other neighbouring regimes, including wall friction as well as liquid entrainment phenomena.

A Randomized Controlled Study of Art Observation Training to Improve Medical Student Ophthalmology Skills

Observation and description are critical to the practice of medicine, and to ophthalmology in particular. However, medical education does not provide explicit training in these areas, and medical students are often criticized for deficiencies in these skills. We sought to evaluate the effects of formal observation training in the visual arts on the general and ophthalmologic observational skills of medical students. Randomized, single-masked, controlled trial. Thirty-six first-year medical students, randomized 1:1 into art-training and control groups. Students in the art-training group were taught by professional art educators at the Philadelphia Museum of Art, during 6 custom-designed, 1.5-hour art observation sessions over a 3-month period. All subjects completed pre- and posttesting, in which they described works of art, retinal pathology images, and external photographs of eye diseases. Grading of written descriptions for observational and descriptive abilities by reviewers using an a priori rubric and masked to group assignment and pretesting/posttesting status. Observational skills, as measured by description testing, improved significantly in the training group (mean change+19.1 points) compared with the control group (mean change-13.5 points), P= 0.001. There were significant improvements in the training vs. control group for each of the test subscores. In a poststudy questionnaire, students reported applying the skills they learned in the museum in clinically meaningful ways at medical school. Art observation training for first-year medical students can improve clinical ophthalmology observational skills. Principles from the field of visual arts, which is reputed to excel in teaching observation and descriptive abilities, can be successfully applied to medical training. Further studies can examine the impact of such training on clinical care.

RRV: A Spatiotemporal Descriptor for Rigid Body Motion Recognition.

The motion behaviors of a rigid body can be characterized by a six degrees of freedom motion trajectory, which contains the 3-D position vectors of a reference point on the rigid body and 3-D rotations of this rigid body over time. This paper devises a rotation and relative velocity (RRV) descriptor by exploring the local translational and rotational invariants of rigid body motion trajectories, which is insensitive to noise, invariant to rigid transformation and scale. The RRV descriptor is then applied to characterize motions of a human body skeleton modeled as articulated interconnections of multiple rigid bodies. To show the descriptive ability of our RRV descriptor, we explore its potentials and applications in different rigid body motion recognition tasks. The experimental results on benchmark datasets demonstrate that our RRV descriptor learning discriminative motion patterns can achieve superior results for various recognition tasks.

Non-linear parameters of specific resistance loops to characterise obstructive airways diseases

BackgroundSpecific resistance loops appear in different shapes influenced by different resistive properties of the airways, yet their descriptive ability is compressed to a single parameter - its slope. We aimed to develop new parameters reflecting the various shapes of the loop and to explore their potential in the characterisation of obstructive airways diseases.MethodsOur study included 134 subjects: Healthy controls (N = 22), Asthma with non-obstructive lung function (N = 22) and COPD of all disease stages (N = 90). Different shapes were described by geometrical and second-order transfer function parameters.ResultsOur parameters demonstrated no difference between asthma and healthy controls groups, but were significantly different (p < 0.0001) from the patients with COPD. Grouping mild COPD subjects by an open or not-open shape of the resistance loop revealed significant differences of loop parameters and classical lung function parameters. Multiple logistic regression indicated RV/TLC as the only predictor of loop opening with OR = 1.157, 95% CI (1.064–1.267), p-value = 0.0006 and R2 = 0.35. Inducing airway narrowing in asthma gave equal shape measures as in COPD non-openers, but with a decreased slope (p < 0.0001).ConclusionThis study introduces new parameters calculated from the resistance loops which may correlate with different phenotypes of obstructive airways diseases.

Robust 3D keypoint detection method based on double Gaussian weighted dissimilarity measure

This paper proposes a novel multiscale 3D keypoint detection method via the double Gaussian weighted dissimilarity measure. At each scale, the shape index value and the double Gaussian weighted dissimilarity measure value of each 3D point are firstly computed. Then the candidate keypoints with local maximum dissimilarity measure values are determined. Finally the final 3D keypoints are detected under our proposed multiscale detection scheme. As the dissimilarity measure used in this paper has better robust descriptive ability and is rotation and translation transformation invariant, the proposed detection method is robust to noise, rotation and translation transformation. Extensive experimental results have shown that using our proposed multiscale detection method, we can detect the keypoints with higher repeatability under different noise levels.

Odorant Normative Data for Use in Olfactory Memory Experiments: Dimension Selection and Analysis of Individual Differences.

The present study reports normative ratings for 200 food and non-food odors. One hundred participants rated odors across measures of verbalisability, perceived descriptive ability, context availability, pleasantness, irritability, intensity, familiarity, frequency, age of acquisition, and complexity. Analysis of the agreement between raters revealed that four dimensions, those of familiarity, intensity, pleasantness, and irritability, have the strongest utility as normative data. The ratings for the remaining dimensions exhibited reduced discriminability across the odor set and should therefore be used with caution. Indeed, these dimensions showed a larger difference between individuals in the ratings of the odors. Familiarity was shown to be related to pleasantness, and a non-linear relationship between pleasantness and intensity was observed which reflects greater intensity for odors that elicit a strong hedonic response. The suitability of these data for use in future olfactory study is considered, and effective implementation of the data for controlling stimuli is discussed.

A comparative study between nonlinear regression and nonparametric approaches for modelling Phalaris paradoxa seedling emergence

SummaryParametric nonlinear regression (PNR) models are used widely to fit weed seedling emergence patterns to soil microclimatic indices. However, such approximation has been questioned, mainly due to several statistical limitations. Alternatively, nonparametric approaches can be used to overcome the problems presented by PNR models. Here, we used an emergence data set of Phalaris paradoxa to compare both approaches. Mean squared error and correlation results indicated higher accuracy for the descriptive ability but similar poor performance for predictive ability of the nonparametric approach in comparison with the PNR approach. These results suggest that our nonparametric cumulative distribution function approach is a valuable alternative to the classical parametric nonlinear regression models to describe complex emergence patterns for P. paradoxa, but not to predict them.

Children's science learning: A core skills approach.

Research has identified the core skills that predict success during primary school in reading and arithmetic, and this knowledge increasingly informs teaching. However, there has been no comparable work that pinpoints the core skills that underlie success in science. The present paper attempts to redress this by examining candidate skills and considering what is known about the way in which they emerge, how they relate to each other and to other abilities, how they change with age, and how their growth may vary between topic areas. There is growing evidence that early-emerging tacit awareness of causal associations is initially separated from language-based causal knowledge, which is acquired in part from everyday conversation and shows inaccuracies not evident in tacit knowledge. Mapping of descriptive and explanatory language onto causal awareness appears therefore to be a key development, which promotes unified conceptual and procedural understanding. This account suggests that the core components of initial science learning are (1) accurate observation, (2) the ability to extract and reason explicitly about causal connections, and (3) knowledge of mechanisms that explain these connections. Observational ability is educationally inaccessible until integrated with verbal description and explanation, for instance, via collaborative group work tasks that require explicit reasoning with respect to joint observations. Descriptive ability and explanatory ability are further promoted by managed exposure to scientific vocabulary and use of scientific language. Scientific reasoning and hypothesis testing are later acquisitions that depend on this integration of systems and improved executive control.

The Effect of Project Based Learning to the Students’writing Ability in Descriptive Text

This study aims to know the effect of Project Based Learning to the students’ writing ability in descriptive text in the eighth grade students of SMPN 8 Kediri in which the experimental research design was applied . The sample consisted of 16 males and 14 females students. The essay test of writing descriptive text was used to collect the data about the students’ writing ability before and after the treatments about teaching writing using project based learning.Then the data were analyzed using statistical formula of t-test computation. The result shown that the students’total score of post-test is 2460, and then total score of p re-test is 2217. It means that students’ score increased. Based on the data report of the table of t-test based on signification 5%, the result of this research showed that T-score (14, 54) ˃ T-table 5% (1, 699). It means that was significant so the Null Hypothesis (Ho) was rejected and Alternative Hypothesis (Ha) was accepted. Besides, in analyze t-test, t-score is higher than t-table. The score of t-test is 14, 54 and the t-table is 1, 699 in the level of significant 5% (0, 05). Based on the t-test result which obtain that Project Based Learning has significant effect t-score 14.54 and the table is 1.699 in the level of significant 5%. From the data analysis, it can be seen that Project Based Learning is effective to the students’ writing descriptive text ability.

Towards optimal VLAD for human action recognition from still images

Human action recognition from still image has recently drawn increasing attention in human behavior analysis and also poses great challenges due to the huge inter ambiguity and intra variability. Vector of locally aggregated descriptors (VLAD) has achieved state-of-the-art performance in many image classification tasks based on local features. The great success of VLAD is largely due to its high descriptive ability and computational efficiency. In this paper, towards optimal VLAD representations for human action recognition from still images, we improve VLAD by tackling three important issues including empty cavity, ambiguity and pooling strategies. The empty cavity limits the performance of VLAD and has long been overlooked. We investigate the empty cavity and provide an effective solution to deal with it, which improves the performance of VLAD; we enhance the codewords with middle level of assignments which are more reliable and can provide more useful information for realistic activity; we propose incorporating the generalized max pooling to replace sum pooling in VLAD, which is more reliable for the final representation. We have conducted extensive experiments on four widely-used benchmarks to validate the proposed method for human action recognition from still images. Our method produces competitive performance with state-of-the-art algorithms. Tackle the empty cavity issue by properly selecting reference points.Reveal codeword ambiguity by imbalance assignment and enhance the reliable codeword.Incorporate GMP into VLAD.

Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.

A Comparison of Two Methods for Generating Descriptive Attributes with Trained Assessors: Check‐All‐That‐Apply (CATA) vs. Free Choice Profiling (FCP)

AbstractThe quality and reliability of descriptive profiles are closely linked to an accurate selection of the attributes included therein. Descriptive profiles with trained assessors often face challenges stemming from consensual procedures and the risk of forgetting some relevant descriptors. To overcome these problems, the suitability of two nonconsensual methods performed by trained assessors, namely free choice profiling (FCP) and check‐all‐that‐apply (CATA), are examined and compared when building a sensory profile for fish.Eighteen trained panelists having similar training were randomly split into two groups of nine assessors each. One group evaluated different fish species using CATA and the other using FCP, with both groups adopting the same experimental design.Although both methodologies generated an important number of sensory descriptors for the tested products, noticeable differences among methods were observed. CATA performed better than FCP in terms of the descriptive ability and slightly better regarding the discriminant capacity. Both methods provided similar product location in the multidimensional space. The RV coefficient was significantly different from zero for all the sensory modalities except for odor and texture. However, noticeable differences were observed in product description. The main limitations of the study were also discussed.Practical ApplicationsSensory descriptive analysis is normally the first step in the characterization of a food product, thus providing valuable information for food companies when designing and/or improving a product. This study shows and compares the usefulness of two methods, namely CATA and FCP, to obtain descriptive profiles, thus avoiding some of the bias linked to consensual procedures. Both methods are suitable for product discrimination, although they provide different sensory characterization for the different samples. The use of trained assessors, both with CATA and FCP, might increase the quality of a descriptive profile by avoiding useless terms and, especially in the case of CATA, ensuring that the most relevant descriptors are included.

Remembering the past and imagining the future: Selective effects of an episodic specificity induction on detail generation

According to the constructive episodic simulation hypothesis, remembering past experiences and imagining future experiences both rely heavily on episodic memory. However, recent research indicates that nonepisodic processes such as descriptive ability also influence memory and imagination. We recently found that an episodic specificity induction--brief training in recollecting details of past experiences--enhanced detail generation on memory and imagination tasks but not a picture description task and thereby concluded that the induction can dissociate episodic processes involved in remembering the past and imagining the future from those nonepisodic processes involved in description. To evaluate the generality of our previous findings and to examine the role of generative search in producing those findings, we modified our paradigm so that word cues replaced picture cues, and a word comparison task that requires generation of sentences and word definitions replaced picture description. Young adult participants received either a specificity induction or one of two control inductions before completing the memory, imagination, and word comparison tasks. Replicating and extending our previous work, we found that the specificity induction increased detail generation in memory and imagination without having an effect on word comparison. The induction's selective effect on memory and imagination stemmed from an increase in internal (i.e., on-topic and episodic) details and had no effect on external (e.g., off-topic or semantic) details. The results point to the efficacy of the specificity induction for isolating episodic processes involved in remembering the past and imagining the future even when a nonepisodic task requires generative search.

An at-site flood estimation method in the context of nonstationarity II. Statistical analysis of floods in Quebec

Summary This paper, the second of a two-part paper, investigates the nonstationary behaviour of flood peaks in Quebec (Canada) by analyzing the annual maximum flow series (AMS) available for the common 1966–2001 period from a network of 32 watersheds. Temporal trends in the mean of flood peaks were examined by the nonparametric Mann–Kendall test. The significance of the detected trends over the whole province is also assessed by a bootstrap test that preserves the cross-correlation structure of the network. Furthermore, The LM–NS method (introduced in the first part) is used to parametrically model the AMS, investigating its applicability to real data, to account for temporal trends in the moments of the time series. In this study two probability distributions (GEV & Gumbel) were selected to model four different types of time-varying moments of the historical time series considered, comprising eight competing models. The selected models are: two stationary models (GEV0 & Gumbel0), two nonstationary models in the mean as a linear function of time (GEV1 & Gumbel1), two nonstationary models in the mean as a parabolic function of time (GEV2 & Gumbel2), and two nonstationary models in the mean and the log standard deviation as linear functions of time (GEV11 & Gumbel11). The eight models were applied to flood data available for each watershed and their performance was compared to identify the best model for each location. The comparative methodology involves two phases: (1) a descriptive ability based on likelihood-based optimality criteria such as the Bayesian Information Criterion (BIC) and the deviance statistic; and (2) a predictive ability based on the residual bootstrap. According to the Mann–Kendall test and the LM–NS method, a quarter of the analyzed stations show significant trends in the AMS. All of the significant trends are negative, indicating decreasing flood magnitudes in Quebec. It was found that the LM–NS method could provide accurate flood estimates in the context of nonstationarity. The results have indicated the importance of taking into consideration the nonstationary behaviour of the flood series in order to improve the quality of flood estimation. The results also provided a general impression on the possible impacts of climate change on flood estimation in the Quebec province.

Center Symmetric Local Multilevel Pattern Based Descriptor and Its Application in Image Matching

This paper presents an effective local image region description method, called CS-LMP (Center Symmetric Local Multilevel Pattern) descriptor, and its application in image matching. The CS-LMP operator has no exponential computations, so the CS-LMP descriptor can encode the differences of the local intensity values using multiply quantization levels without increasing the dimension of the descriptor. Compared with the binary/ternary pattern based descriptors, the CS-LMP descriptor has better descriptive ability and computational efficiency. Extensive image matching experimental results testified the effectiveness of the proposed CS-LMP descriptor compared with other existing state-of-the-art descriptors.

Are all runoff processes the same? Numerical experiments comparing a Darcy‐Richards solver to an overland flow‐based approach for subsurface storm runoff simulation

AbstractHillslope runoff theory is based largely on the differentiation between infiltration excess overland flow, saturation excess overland flow, and subsurface stormflow. Here we explore to what extent a 2‐D friction‐based overland flow model is useful for predicting hillslope‐scale subsurface stormflow, posited here as phenomenologically the same as infiltration excess at depth. We compare our results to a 3‐D variably saturated Darcy‐Richards subsurface solver for individual rainfall runoff events. We use field data from the well‐studied Panola Mountain Experimental hillslope in Georgia USA. Our results show that the two models are largely indistinguishable in terms of their ability to simulate the hillslope hydrograph magnitude and timing for a range of slopes and rainfall depths. Furthermore, we find that the descriptive ability of the overland flow model is comparable to the variably saturated subsurface flow model in terms of its ability to represent the spatial distribution of subsurface stormflow and infiltration across the soil‐bedrock interface. More importantly, these results imply that the physics of infiltration excess subsurface stormflow at the soil‐bedrock interface is similar to infiltration excess overland flow at the soil surface, in terms of detention storage, loss along the lower boundary, and threshold‐like activation at the larger hillslope scale. Given the phenomenological similarity of overland flow and subsurface stormflow and the fact that overland flow model predictions are considerably faster to run (particularly as slope and rainfall depth increase), these findings imply that new forms of hillslope‐scale subsurface storm runoff predictions may be possible with the knowledge of bedrock permeability and limited soil information. Finally, this work suggests that the role of soil mantle vis‐à‐vis subsurface stormflow is mainly as a filter that delays the development of patches of saturation along the bedrock surface. Our model results show that simple realizations of soil based on a few soil depth measurements can possibly be enough to characterize this filtering effect.

Application of Sigmoidal Transformation Functions in Optimization of Micellar Liquid Chromatographic Separation of Six Quinolone Antibiotics.

A chemometrics approach has been used to optimize the separation of six quinolone compounds by micellar liquid chromatography (MLC). A Derringer's desirability function, a multicriteria decision-making (MCDM) method, was tested for evaluation of two different measures of chromatographic performance (resolution and analysis time). The effect of three experimental parameters on a chromatographic response function (CRF) expressed as a product of two sigmoidal desirability functions was investigated. The sigmoidal functions were used to transform the optimization criteria, resolution and analysis time into the desirability values. The factors studied were the concentration of sodium dodecyl sulfate, butanol content and pH of the mobile phase. The experiments were done according to the face-centered cube central composite design, and the calculated CRF values were fitted to a polynomial model to correlate the CRF values with the variables and their interactions. The developed regression model showed good descriptive and predictive ability (R(2) = 0.815, F = 6.919, SE = 0.038, [Formula: see text]) and used, by a grid search algorithm, to optimize the chromatographic conditions for the separation of the mixture. The efficiency of prediction of polynomial model was confirmed by performing the experiment under the optimal conditions.

Agricultural image target segmentation based on fuzzy set

Abstract Target objects are direct characteristics of image description, but there are significant limitations, including the image being sensitive to changes in global illumination, etc., which lead to lack direct descriptive ability for objects in an image. Image target segmentation is one of the primary image processing operations, which means it is the first operation of the processing of an image. For the segmentation of a target area from the image, there are two kinds of existing methods. One method is to manually remove the obstructions to be a uniform color fill or outline its contours; and another way is to use the shortest path algorithm or the expansion of biology technique to extract optimal edges. For an image with unclear target boundaries and background, it is very necessary to eliminate the interfering information by transforming the interference information into fuzzy sets. In this paper, to solve the complex background images and the image target segmentation error, considering the dominance on image processing of fuzzy sets, the maximum fuzzy correlation criterion is proposed. Image is converted to a fuzzy domain, and then the maximum fuzzy correlation criterion is calculated. It is important to optimize the image maximum fuzzy membership function. Finally, experiment on leaf and fruit agricultural images, the results show that more edge details can be detected by fuzzy sets.