Pattern Features Research Articles

Association of genetic and sulcal traits with executive function in congenital heart disease.

Persons with congenital heart disease (CHD) are at increased risk of neurodevelopmental disabilities, including impairments to executive function. Sulcal pattern features correlate with executive function in adolescents with single-ventricle heart disease and tetralogy of Fallot. However, the interaction of sulcal pattern features with genetic and participant factors in predicting executive dysfunction is unknown. We studied sulcal pattern features, participant factors, and genetic risk for executive function impairment in a cohort with multiple CHD types using stepwise linear regression and machine learning. Genetic factors, including predicted damaging de novo or rare inherited variants in neurodevelopmental disabilitiesrisk genes, apolipoprotein E genotype, and principal components of sulcal pattern features were associated with executive function measures after adjusting for age at testing, sex, mother's education, and biventricular versus single-ventricle CHD in a linear regression model. Using regression trees and bootstrap validation, younger participant age and larger alterations in sulcal pattern features were consistently identified as important predictors of decreased cognitive flexibility with left hemisphere graph topology often selected as the most important predictor. Inclusion of both sulcal pattern and genetic factors improved model fit compared to either alone. We conclude that sulcal measures remain important predictors of cognitive flexibility, and the model predicting executive outcomes is improved by inclusion of potential genetic sources of neurodevelopmental risk. If confirmed, measures of sulcal patterning may serve as early imaging biomarkers to identify those at heightened risk for future neurodevelopmental disabilities.

Strain and Strain Recovery of Human Hair from the Nano- to the Macroscale.

In this study, in operandi SAXS experiments were conducted on samples of human hair with a varying degree of strain (2% within the elastic region and 10% beyond). Four different features in the SAXS patterns were evaluated: The intermediate filament distance perpendicular to and the distance from the meridional arc in the load direction, as well as the distances of the lipid bilayer peak in and perpendicular to the load direction. From the literature, one concludes that polar lipids in the cuticle are the origin of the lipid peak in the SAXS pattern, and this study shows that the observed strain in the lipids is much lower than in the intermediate filaments. We support these findings with SEM micrographs, which show that the scales in the cuticle deform much less than the cortex. The observed deformation of the intermediate filaments is very high, about 70% of the macrostrain, and the ratio of the transverse strain to the longitudinal strain at the nanoscale gives a Poisson ratio of νnano = 0.44, which is typical for soft matter. This work also finds that by varying the time period between two strain cycles, the typical strain recovery time is about 1000 min, i.e., one day. After this period, the structure is nearly identical to the initial structure, which suggests an interpretation that this is the typical time for the self-healing of hair after mechanical treatment.

Dynamics of Atmospheric-Driven Surface Currents on The Gulf of Cadiz Continental Shelf and its link with The Strait of Gibraltar and The Western Alboran Sea

Several studies about the coastal circulation over the continental shelf of the Gulf of Cadiz have been published in the last twenty years. One of the most studied features in these papers are the so-called Coastal Counter Currents (CCCs) which are located roughly between the 100 m isobath and the coastline and are directed westwards. Despite the considerable number of articles devoted to the analysis of this phenomenon, the actual along-coast extension and origin of these CCCs currently remain unclear.In the present study, different mechanisms are proposed to explain the development of these CCCs. They have been revised and discussed taking into account the latest findings from a recent analysis that utilized surface currents derived from High Frequency Radar (HFR) as well as numerical model simulations. The most interesting finding is that the most intense CCCs events are initiated on the coastal margin of the westernmost side of the Strait of Gibraltar, and they are extended further west of Cape Santa Maria. Furthermore, these intense and extensive CCCs seem to be triggered by the joint action of local winds over the Gulf of Cadiz and additional remote effects which are coming from (i) the wind forcing over the Alboran Sea and the easternmost side of the Strait of Gibraltar, and (ii) the Sea Level Atmospheric Pressure forcing over the Ligurian Sea (western Mediterranean Sea). In addition, the same forcing mechanisms are valid to explain the eastward intensification of the coastal currents.The most relevant feature of the hydrodynamic pattern originated by this combination of forcing agents is its quasi-geostrophic adjustment against the coast in the whole spatial dominion. In addition, these results offer a clear example of how important the effects of forcing in rather remote regions may be in defining the hydrodynamic patterns of certain oceanographic regions.

Extracting gait and balance pattern features from skeleton data to diagnose attention deficit/hyperactivity disorder in children

Extracting gait and balance pattern features from skeleton data to diagnose attention deficit/hyperactivity disorder in children

Analytical study of spirograph using parametric equations and pattern design by parametric-graphic interactions

Perfectly symmetric curves always have a high degree of aesthetic value. While it is difficult to draw them by hand, Spirograph is an old and popular drawing toy that produces fascinating symmetric patterns. In playing with drawing software, the author explores types of Spirograph patterns, the type identification method, and the parametric equations to express Spirograph mathematically. This paper also discusses the key parameters of a Spirograph pattern and how they affect the pattern shape. Finally, Spirograph pattern design is carried out by analyzing the features of a random pattern and estimating its different parameters. These results to some extent demystify and predict the seemingly infinite Spirograph patterns, as the corresponding parameters of a given Spirograph pattern can be found so that the similar image can be drawn by hand through a physical Spirograph set.

Evolution of Plumage Patterns in a Pattern Morphospace: A Phylogenetic Analysis of Melanerpine Woodpeckers.

AbstractPlumage patterns of melanerpine (Melanerpes-Sphyrapicus) woodpeckers are strikingly diverse. Understanding the evolution and function of this diversity is challenging because of the difficulty of quantifying plumage patterns. We use a three-dimensional space to characterize the evolution of melanerpine achromatic plumage patterns. The axes of the space are three pattern features (spatial frequency, orientation, and contrast) quantified using two-dimensional fast Fourier transformation of museum specimen images. Mapping plumage in pattern space reveals differences in how species and subclades occupy the space. To quantify these differences, we derive two new measures of pattern: pattern diversity (diversity across plumage patches within a species) and pattern uniqueness (divergence of patterns from those of other species). We estimate that the melanerpine ancestor had mottled plumage and find that pattern traits across patches and subclades evolve at different rates. We also find that smaller species are more likely to display horizontal face patterning. We promote pattern spaces as powerful tools for investigating animal pattern evolution.

Classification and endoscopic diagnosis of colorectal polyps

Background: Colorectal cancer is a common malignancy and a major health concern in Korea. The increased use of screening colonoscopies has led to improved survival of patients with colorectal cancer despite its high prevalence; this highlights the importance of early detection and removal of neoplastic polyps such as adenoma and sessile serrated lesions. Therefore, optical diagnosis of colorectal polyps is crucial in deciding whether the lesion requires removal and if endoscopic procedures can be used for its removal.Current Concepts: Several image-enhanced endoscopy (IEE) techniques and classifications can be used to improve the optical diagnosis of different polyps that match the histopathologic findings. Kudo’s pit pattern classification is based on features of pit patterns observed from different types of polyps via chromoendoscopy. The Narrow-Band Imaging (NBI) International Colorectal Endoscopic (NICE) classification is an international classification based on patterns observed from NBI findings. The Japan NBI Expert Team (JNET) classification identifies different patterns observed from magnified images with NBI. The Workgroup serrAted polypS and Polyposis (WASP) classification helps to differentiate hyperplastic polyps from sessile serrated lesions. Endoscopy manufacturers have also developed IEE techniques such as I-scan, flexible spectral imaging color enhancement, and blue laser imaging/linked color imaging to highlight, delineate, and differentiate various polyps.Discussion and Conclusion: Optical diagnosis of colorectal polyps using IEE such as NBI with different classifications such as NICE, JNET, and WASP is important for differentiating neoplastic polyps and removing them with an appropriate method.

Facies conditional simulation based on VAE-GAN model and image quilting algorithm

Characterization of complex reservoir structures by using limited observations is challenging in geosciences because it requires to reproduce geological realism. We propose a novel method to reconstruct complex structures by combining variational autoencoder and generative adversarial networks (VAE-GAN) with conditional image quilting algorithm. It improves the stability of traditional GAN-based simulation method without decreasing the quality of patterns. Firstly, we construct a VAE-GAN model to extract the high-dimensional features of facies patterns and to create abundant new patterns. The VAE-GAN-based learning method has a good ability in feature learning from training patches and accurately reproduce new facies patterns. Finally, these new patterns are spliced together to reconstruct a complex geological structure by employing the conditional image quilting algorithm, in which patterns are pasted to the simulated areas based on the calculated minimum cost path. During this process, conditioning is also considered. Since the pattern is generated by using the deep learning method rather than directly extracted from the training image, the diversity of realizations is enhanced without losing reproducibility. Based on synthetic training image, several examples are described and analyzed in detail, demonstrating the effectiveness and reliability of the present method. In addition, the new method is applied to a real training image. The complex heterogeneous structures are well reproduced by our method, indicating its practicability.

Development and verification of an unresolved CFD-DEM method applicable to different-sized grids

The traditional unresolved CFD-DEM method is designed for fluid grid sizes larger than particle sizes, and improved methods are imperative when encountering cases with fluid grid sizes comparable to particle sizes. This study attempts to develop an unresolved CFD-DEM method that applies to different-sized grids and can be easily implemented on commercial platforms using unstructured grids, aiming to greatly help engineers solve engineering problems without investing much effort into choosing appropriate simulation methods and designing fluid grids. To this end, the new method considers interphase force correction at the particle level and field smoothing at the cell level by constructing a spherical search area whose size is determined by particle sizes. Mass conservation is strictly guaranteed in the new method, which is essential to simulations involving chemical reactions. The accuracy and universality of the new method are comprehensively evaluated by two test cases. The first test case is conducted in a spouted bed discretized by unstructured grids of three sizes. The results demonstrate that the new method not only applies to comparable-sized grids but can yield the same predictions as the traditional method in large-sized grids. In other words, the new method is universal as long as grid sizes lie at the unresolved level. Thanks to the simplicity of the algorithm, the new method does not compromise calculation efficiency. The second test case is conducted in a draft tube-type feeder discretized by non-uniform unstructured grids. By comparing with experimental measurements, the main features of flow patterns, pressure drop characteristics, and mass flow rates are reasonably predicted, confirming the capability of the new method to simulate complex gas-solid flows with non-uniform unstructured grids. Users do not need to make any adjustments to the new method once it is implemented on commercial platforms. In the future, the new method can potentially become a universal method to simulate gas-solid flows and greatly help engineers solve engineering problems.

The notable relatedness between ESBL producing Enterobacteriaceae isolated from clinical samples and asymptomatic fecal carriers

BackgroundThe investigation of the presence of extended-spectrum beta-lactamase (ESBL) within Enterobacteriaceae in both fecal carriers and patients is an essential matter. Furthermore, the assessment of distinct characteristics exhibited by resistant bacteria obtained from fecal carriers and patients, as well as the comparison of these characteristics between the two groups, could provide a deeper understanding of how the resistant isolates can remain concealed within a dormant reservoir and intensify antimicrobial resistance. The aim of the present study was to concentrate on the comparison of the antimicrobial resistance pattern and molecular features between strains obtained from clinical and carrier sources.Material and methodsA total of 142 clinical samples and 120 rectal swabs were collected from June to October 2016. ESBL screening was performed using the double-disk synergy test. PCR was done for the detection of ESBL genes. Assessment of biofilm formation, virulence factor genes, and MLVA was performed for K. pneumonae isolates. Phylogroup typing was performed for E. coli isolates.ResultsOf 146 samples, 67.6% were E. coli, and 32.4% were K. pneumoniae. The rate of blaCTXM-15 was 89.4%. In K. pneumoniae type D, ompk35 and fimH were the highest. All the K. pneumoniae isolates were classified into 12 mini clusters and the clinical isolates were characterized into 7 mini clusters. The phylogroup B2 in ESBL-EC was the highest (56.2%).DiscussionComparison of molecular characteristics and clonal relatedness between fecal carriers and patients showed noticeable relatedness and similarity which may indicate that ESBL-KP can be colonized with the same profiles in different settings and, therefore, may be widely distributed in both community and hospital settings. Therefore, implementation of control protocols, including surveillance of the fecal carriers, could impressively reduce silent reservoirs without clinical symptoms as well as patient rates.

Dual-feature and multi-scale fusion using U2-net deep learning model for ECG biometric recognition

Aiming at the challenges that the traditional photoplethysmography (PPG) biometrics is not robust and precision of recognition, this paper proposes a dual-feature and multi-scale fusion using U2-net deep learning model (DMFUDM). First, to obtain complementary information of different features, we extract the local and global features of one-dimensional multi-resolution local binary patterns (1DMRLBP) and multi-scale differential feature (MSDF). Then, to extract robust discriminant feature information from the 1DMRLBP and MSDF features, a novel two-branch U2-net framework is constructed. In addition, a multi-scale extraction module is designed to capture the transition information. It consists of multiple convolution layers with different receptive fields for capturing multi-scale transition information. At last, a two-level attention module is used to adaptively capture valuable information for ECG biometrics. DMFUDM can obtain the average subject recognition rates of 99.76%, 98.31%, 98.97% and 98.87% on four databases, respectively, and experiment results show that it performs competitively with state-of-the-art methods on all four databases.

Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics.

AI-assisted ultrasound diagnosis is considered a fast and accurate new method that can reduce the subjective and experience-dependent nature of handheld ultrasound. In order to meet clinical diagnostic needs better, we first proposed a breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics (hereafter, Auto BI-RADS). In this study, we prospectively verify its performance. In this study, the model development was based on retrospective data including 480 ultrasound dynamic videos equivalent to 18122 static images of pathologically proven breast lesions from 420 patients. A total of 292 breast lesions ultrasound dynamic videos from the internal and external hospital were prospectively tested by Auto BI-RADS. The performance of Auto BI-RADS was compared with both experienced and junior radiologists using the DeLong method, Kappa test, and McNemar test. The Auto BI-RADS achieved an accuracy, sensitivity, and specificity of 0.87, 0.93, and 0.81, respectively. The consistency of the BI-RADS category between Auto BI-RADS and the experienced group (Kappa:0.82) was higher than that of the juniors (Kappa:0.60). The consistency rates between Auto BI-RADS and the experienced group were higher than those between Auto BI-RADS and the junior group for shape (93% vs. 80%; P = .01), orientation (90% vs. 84%; P = .02), margin (84% vs. 71%; P = .01), echo pattern (69% vs. 56%; P = .001) and posterior features (76% vs. 71%; P = .0046), While the difference of calcification was not significantly different. In this study, we aimed to prospectively verify a novel AI tool based on ultrasound dynamic videos and ACR BI-RADS characteristics. The prospective assessment suggested that the AI tool not only meets the clinical needs better but also reaches the diagnostic efficiency of experienced radiologists.

Detecting visual texture patterns in binary sequences through pattern features.

We measured human ability to detect texture patterns in a signal detection task. Observers viewed sequences of 20 blue or yellow tokens placed horizontally in a row. They attempted to discriminate sequences generated by a random generator ("a fair coin") from sequences produced by a disrupted Markov sequence (DMS) generator. The DMSs were generated in two stages: first a sequence was generated using a Markov chain with probability, pr = 0.9, that a token would be the same color as the token to its left. The Markov sequence was then disrupted by flipping each token from blue to yellow or vice versa with probability, pd-the probability of disruption. Disruption played the role of noise in signal detection terms. We can frame what observers are asked to do as detecting Markov texture patterns disrupted by noise. The experiment included three conditions differing in pd (0.1, 0.2, 0.3). Ninety-two observers participated, each in only one condition. Overall, human observers' sensitivities to texture patterns (d' values) were markedly less than those of an optimal Bayesian observer. We considered the possibility that observers based their judgments not on the entire texture sequence but on specific features of the sequences such as the length of the longest repeating subsequence. We compared human performance with that of multiple optimal Bayesian classifiers based on such features. We identify the single- and multiple-feature models that best match the performance of observers across conditions and develop a pattern feature pool model for the signal detection task considered.

Clinical Features of Cystinosis and Practice Patterns: A Report of the NAPRTCS Cystinosis Registry

Clinical Features of Cystinosis and Practice Patterns: A Report of the NAPRTCS Cystinosis Registry

Fraunhofer diffraction by a slit between a vacuum and a non-magnetic medium with the magneto-optical Faraday effect

Fraunhofer diffraction by a slit on an opaque screen between vacuum and a nonmagnetic dielectric medium in the presence of magneto-optical activity induced in the medium by an external magnetic field is considered. A number of features of the diffraction pattern are revealed for different directions of an external magnetic field that induces magneto-optical activity (Faraday magneto-optical effect).

BurstSketch: Finding Bursts in Data Streams

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Burst</i> is a common pattern in data streams which is characterized by a sudden increase in terms of arrival rate followed by a sudden decrease. Burst detection has attracted extensive attention from the research community. To detect bursts accurately in real time, we propose a novel sketch, namely BurstSketch, which consists of two stages. Stage 1 uses the technique Running Track to select potential burst items efficiently. Stage 2 monitors the potential burst items and captures the key features of burst pattern by a technique called Snapshotting. We further propose an optimization, namely Dynamic Buckets, which can improve the accuracy of BurstSketch. We provide theoretical error bounds for Stage 1, Stage 2 and the optimized version. Experimental results show that, compared with the strawman solution, Burstsketch achieves 2.00 to 11.63 times higher F1 score, and 1.56 times higher throughput. We also integrate BurstSketch into Apache Flink, and show that using BurstSketch can be faster than simply using the built-in APIs provided by Apache Flink.

Micropapillary Pattern in Invasive Mucinous Adenocarcinoma of the Lung: Comparison With Invasive Non-Mucinous Adenocarcinoma.

Background. The presence of a micropapillary pattern is associated with poor outcomes in lung adenocarcinoma. This study aimed to assess the clinicopathological features of micropapillary pattern in mucinous adenocarcinoma of the lung. Methods. The patients were stratified into three groups: the invasive mucinous adenocarcinoma group (60 patients), the mixed invasive mucinous adenocarcinoma group (33 patients), and the invasive non-mucinous adenocarcinoma group (237 patients). The presence of micropapillary pattern and its clinicopathological features were analyzed and compared between the three groups. Results. Compared to mixed invasive mucinous adenocarcinoma, invasive mucinous adenocarcinoma had lower frequencies of micropapillary pattern (28.3% vs 87.9%, respectively; P < .001) and lymph node metastasis (00.0% vs 15.1%, respectively; P = .005). The frequency of tumor spread through air space (STAS) in invasive mucinous adenocarcinoma (23.3%) was higher than that in invasive non-mucinous adenocarcinoma (6.3%; P < .001), while lower than that in mixed invasive mucinous adenocarcinoma (60.6%; P < .001). When the three groups were all accompanied by micropapillary pattern, there was no obvious difference in STAS between invasive mucinous adenocarcinoma with micropapillary pattern and mixed mucinous adenocarcinoma with micropapillary pattern (P > .05). No filigree pattern occurred in invasive mucinous adenocarcinoma with micropapillary pattern. Conclusions. The micropapillary pattern is frequently observed in invasive mucinous adenocarcinoma and has a better prognosis compared to mixed invasive mucinous adenocarcinoma and invasive non-mucinous adenocarcinoma. However, the malignancy of the micropapillary pattern in mixed mucinous adenocarcinoma was similar to that in invasive non-mucinous adenocarcinoma, even with the presence of mucus. These findings suggest that the development mechanisms of the micropapillary pattern in invasive mucinous adenocarcinoma and mixed mucinous adenocarcinoma may differ.

Pattern and Clinical Characteristics of Cystic Kidney Disease Patients in a Tertiary Centre, South-West Nigeria: A Retrospective Study

Background: Cystic kidney disease is a group of disorders that are characterized by the formation of cysts in the kidneys.It is a major cause of end-stage renal disease (ESRD) worldwide. Objectives: This study aimed to determine the pattern and clinical characteristics of patients with cystic kidney disease who presented at a tertiary hospital in South-West Nigeria. Methods and Materials: We reviewed the records of patients admitted with cystic kidney disease over an 8-year period at Ekiti State University Teaching Hospital. Data extracted included patients’ demographics, clinical presentations, and treatment outcomes. We compared the data between patients with ADPKD and other types of kidney cysts and summarised the treatment outcomes among the patients. Result: The prevalence of cystic kidney disease was 9.2% and the mean age of the patients was 52.87 years with a male predominance. The most common type of kidney cyst was simple renal cysts (53.2%), followed by ADPKD (40.4%). Incidental findings with no symptoms were the most common mode of presentation (61.7%), followed by abdominal pain (38.3%) and palpable abdominal swelling (36.2%). Nephrolithiasis was documented in 4.3% while the frequency of hypertension, haematuria and urinary tract infection was 55.3%, 14.9% and 8.5 %respectively. More than one in five ADPKD patients (21.1%) developed end-stage renal disease. Overall, the majority 41(87.2%) were managed conservatively. Conclusion: This study contributes valuable insights into the pattern and clinical features of cystic kidney disease in SouthWest Nigeria, further enriching the existing data on this condition within the region.

Intelligent terrain generation considering global information and terrain patterns

Simulated terrains can provide rich information for landform and terrain research, disaster prediction, rescue and disaster relief, and national security. Quickly generating an accurate simulated terrain for target areas is of great importance. However, for existing data-driven terrain generation methods, it is difficult to balance modeling accuracy and the amount of data required. To overcome this gap, this study proposes a deep learning method that integrates global information and pattern features of the local terrain (IGPN) to realize terrain generation. In our proposed IGPN method, we first apply both terrain patterns (ridge line and drainage line) and elevation points as input data; thus, the terrain features (i.e., channel and peak) and elevation of the target terrain can be provided. The local information extraction module (LIEM) and global information extraction module (GIEM) are then applied to generate local and global terrain features, respectively. Thus, global and local terrain features can be supplemented, and a more accurate terrain can be generated. Experiments show that the IGPN method performs state-of-the-art terrain-generation tasks. Specifically, compared with existing terrain generation methods (IETA, SRResNet, Bicubic, Kriging, FEN, and ERFFN), the terrain generated by IGPN is closer to the real terrain and can retain more local terrain features.

Growth, Structural Change and Plantation Crops: A Brief History of Rubber Cultivation in Travancore

Plantation crops are commodities worthy of serious attention because of the central and longstanding place it has held in the world economy and in the lives of millions of people. The introduction of it marked a distinct departure from the peasant agriculture of Travancore. Large scale mono-culture of these crops for the export market introduced certain new features of structure and pattern. In this chapter a product-oriented approach has been chosen and attention has been focused on a limited number of crops (Coffee, Tea, Rubber) which account for a major share in the plantation economy .One of the few holdovers from the era of the spice trade and colonialism, these crops had been a major traded commodity since the second half of the nineteenth century. Colonial plantations in Travancore had three phases. The first period started in the 1850s, and the first plantation crop was coffee. During the first thirty years after the planting of coffee, the principal form of ownership was proprietary. From the eighties, the ownership structure began to change and partnership firms became common. The main plantation crop of this period was Tea. The rise of rubber cultivation in 1900 introduced changes in the structure of ownership. Before going to the details it would be apt to discuss about the cultivation of these particular crops.