Pattern Features Research Articles

Differential Effects of Sequence-Local versus Nonlocal Charge Patterns on Phase Separation and Conformational Dimensions of Polyampholytes as Model Intrinsically Disordered Proteins.

Conformational properties of intrinsically disordered proteins (IDPs) are governed by a sequence-ensemble relationship. To differentiate the impact of sequence-local versus sequence-nonlocal features of an IDP's charge pattern on its conformational dimensions and its phase-separation propensity, the charge "blockiness" κ and the nonlocality-weighted sequence charge decoration (SCD) parameters are compared for their correlations with isolated-chain radii of gyration (Rgs) and upper critical solution temperatures (UCSTs) of polyampholytes modeled by random phase approximation, field-theoretic simulation, and coarse-grained molecular dynamics. SCD is superior to κ in predicting Rg because SCD accounts for effects of contact order, i.e., nonlocality, on dimensions of isolated chains. In contrast, κ and SCD are comparably good, though nonideal, predictors of UCST because frequencies of interchain contacts in the multiple-chain condensed phase are less sensitive to sequence positions than frequencies of intrachain contacts of an isolated chain, as reflected by κ correlating better with condensed-phase interaction energy than SCD.

Identification and validation of tryptophan-related gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma reveals a critical role for PTTG1.

Tryptophan metabolism is strongly associated with immunosuppression and may influence lung adenocarcinoma prognosis as well as tumor microenvironment alterations. Sequencing datasets were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Two different clusters were identified by consensus clustering, and prognostic models were established based on differentially expressed genes (DEGs) in the two clusters. We investigated differences in mutational landscapes, enrichment pathways, immune cell infiltration, and immunotherapy between high- and low-risk scoring groups. Single-cell sequencing data from Bischoff etal. were used to identify and quantify tryptophan metabolism, and model genes were comprehensively analyzed. Finally, PTTG1 was analyzed at the pan-cancer level by the pan-TCGA cohort. Risk score was defined as an independent prognostic factor for lung adenocarcinoma and was effective in predicting immunotherapy response in patients with lung adenocarcinoma. PTTG1 is one of the key genes, and knockdown of PTTG1 in vitro decreases lung adenocarcinoma cell proliferation and migration and promotes apoptosis and down-regulation of tryptophan metabolism regulators in lung adenocarcinoma cells. Our study revealed the pattern and molecular features of tryptophan metabolism in lung adenocarcinoma patients, established a model of tryptophan metabolism-associated lung adenocarcinoma prognosis, and explored the roles of PTTG1 in lung adenocarcinoma progression, EMT process, and tryptophan metabolism.

Teaching as a Profession in Albania

This paper aims to analyse teaching as a profession in Albania. Also, the paper aims to answer two research questions: 1. Is the teaching profession gender based in Albania? 2. What are some of the common features in patterns of students attending a study program in teaching in preschool? The first findings show that it is very difficult to state that teaching profession has achieved the full status of a regulated one, at least in practice, in Albania. Second, based on data collecting through a questionnaire, with a sample of 21 teacher-students attending a preschool teaching program study, 9 (42.9%) of them declare that this program study is a second one, listing as reason of choosing the actual status of employing - this training is a condition to change the status, from assistant teacher to teacher; the others evaluate the short time of entering in the labour market and the no need for a licence; also, the average age of entering the labour market is 31.1 years old (the students were between 21-54 years old). Third, the teaching profession in Albania remains dominated by women. Data from four Albanian universities, across 14 study programs that graduate teachers, indicate that only 37 students are male; also, findings reveals a pattern in the teaching workforce: it is gender-based, not a priority in student’s decision making after the graduation from upper secondary school, average age of entering the profession is relatively high. Received: 2 June 2024 / Accepted: 25 July 2024 / Published: 29 July 2024

Rare radiological manifestation of enchondromatosis in children: Columnar pattern: A retrospective cohort study.

The columnar cartilage pattern is characterized by parallel aligned cartilage tissue columns related to the physis without matrix calcification separated by the surrounding osseous tissue. Usually, it is seen in patients with multiple enchondromas. The objective of this study was to elucidate the clinical and radiological features of this rare radiological pattern in the physis, which remains unfamiliar to most physician. We retrospectively evaluated the clinical features and imaging findings of 15 patients (9 men and 6 women) who have a columnar pattern with varied spectrum of enchondromatosis. On X-ray and computed tomography (CT) examination, all these lesions were seen as vertical or oblique oriented tubular zones, which have relatively low radiologic density compared with normal bone. The lesions have similar signal characteristics relative to epiphyseal cartilage plates, on T1W and T2W magnetic resonance images. Columnar pattern was observed in different appearances from one single column in one physis to multiple columns in multiple physis. The mean follow-up was 62 months (range: 36-96 months). The mean age was 9.7 (range: 4-14) years at the initial admission. Eight patients had 3 or less affected physis. Five patients had only one affected physis. We defined these patients' group who had up to 3 affected physis as "limited enchondromatosis with columnar pattern (LE-CP)." We observed that most of the columnar cartilage was turning into the normal bone via endochondral ossification. Based on our observations, the columnar pattern is a rare manifestation of the enchondromas. Columnar pattern, along with the related physis, acts as a normal endochondral ossification process, and surgery is not necessary unless there is a risk of fracture or severe deformity. Further awareness of this unique subset of patients may improve our understanding of the disease and lead to better patient outcomes. We have modified non-hereditarily enchondromatosis into 2 categories: limited enchondromatosis with the columnar pattern and multiple enchondromatosis. We believe that LE-CM reflects a developmental anomaly of the physis rather than a true neoplasia, and it acts as a normal endochondral ossification process. Level IV (case series).

Prevalence of rare earth elements in natural waters of Khanty-Mansiysk

Relevance. New data on the content of lanthanides in the water–rock system are important for understanding the behavior of rare-earth elements in the environment, open up the possibility of using them as indicators of geochemical processes and anthropogenic factor, taking into account the specialities of their migration and fractionation. Aim. Assessment of the quantitative content and distribution of rare-earth elements (La, Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu) in the surface and underground waters of Khanty-Mansiysk to identify the main patterns of their behavior. Objects. Surface and underground waters of the natural reserve Samarovsky Chugas, as well as ground deposits and products of secondary mineral formation. Methods. The bulk analysis of water is performed by a standard set of spectral and electrochemical methods. The rare-earth content was determined by inductively coupled plasma mass spectrometry. Chemical composition of ground deposits and secondary mineral formation products – by neutron activation analysis. Results. The paper demonstrates rare-earth elements behavior in aqueous medium, ground deposits and products of secondary mineral formation. Different approaches to rare-earth elements rationing in waters and solid sediments showed comparability of lanthanide distribution spectra to each other and made it possible to identify some features of the rare-earth elements distribution pattern in different environments. In the waters, there are positive anomalies of europium and gadolinium and negative anomaly of cesium. Gadolinium anomalies were detected in ground deposits, and europium in secondary mineral formation products. The high migration activity of the entire considered series of rare-earth elements in the solution coming from the holding deposits was established. During following geochemical processes, mainly middle lanthanides (Sm, Eu, Gd, and Tb) fall out of the solution in the form of secondary mineral formation products.

The robust SmartEnsembleNet: A game changer in finger knuckle biometrics

SmartEnsembleNet represents a major leap forward in the domain of biometric identification, focusing on the relatively underexplored area of finger knuckle biometrics. This research enhances the effectiveness and uniqueness of biometric systems by leveraging the distinct and secure features of finger knuckle patterns. As a complement to existing biometric techniques like fingerprints and facial recognition, it adds an additional layer of security and reliability. Addressing the challenges faced by finger knuckle identification, such as image acquisition, environmental effects, and aging, SmartEnsembleNet introduces a pioneering multi-layered ensemble learning strategy that combines eight diverse machine learning models. These models, trained via a 5-fold cross-validation on datasets from IIT Delhi and PolyU, demonstrate significant improvements in accuracy, precision, recall, and F1-score. A distinctive feature of SmartEnsembleNet is its innovative meta-model, streamlining the creation of a meta-dataset. This dataset is created by applying bilinear interpolation to the predicted probabilities from the top-performing model, and it is further refined by subtracting these interpolated probabilities from the validation data. This iterative process generates ‘smart data’, which serves as the basis for training the ultimate ‘smart model’. Logistic regression is chosen for its outstanding performance. This method, applied uniformly to both training and testing phases, considerably enhances classification accuracy in finger knuckle biometrics, establishing new benchmarks. SmartEnsembleNet’s performance undergoes a detailed evaluation using the Friedman test and Nemenyi post-hoc analysis, guaranteeing a selection process grounded in statistical rigor. With its resilience to noise and adaptability under various conditions, SmartEnsembleNet not only addresses existing gaps in finger knuckle biometric research but also pioneers future advancements, providing a versatile and secure solution impactful for both academia and high-security scenarios.

Monitoring of the Weld Pool, Keyhole Morphology and Material Penetration State in Near-Infrared and Blue Composite Laser Welding of Magnesium Alloy

The laser welding of magnesium alloys presents challenges attributed to their low laser-absorbing efficiency, resulting in instabilities during the welding process and substandard welding quality. Furthermore, the complexity of signals during laser welding processes makes it difficult to accurately monitor the molten state of magnesium alloys. In this study, magnesium alloys were welded using near-infrared and blue lasers. By varying the power of the near-infrared laser, the energy absorption pattern of magnesium alloys toward the composite laser was investigated. The U-Net model was employed for the segmentation of welding images to accurately extract the features of the melt pool and keyhole. Subsequently, the penetrating states were predicted using the convolutional neural network (CNN), and the novel approach employing Local Binary Pattern (LBP) features + a backpropagation (BP) neural network was applied for comparison. The extracted images achieved MPA and MIoU values of 89.54% and 81.81%, and the prediction accuracy of the model can reach up to 100%. The applicability of the two monitoring approaches in different scenarios was discussed, providing guidance for the quality of magnesium welding.

Abnormal breathing pattern features in chronic refractory cough

Abnormal breathing pattern features in chronic refractory cough

Patterning strategies in grade 1 students with low and high number sense proficiency

AbstractRepeating patterning proficiency predicts students’ later mathematical proficiency. A comparative multi-case design enabled the present study to compare patterning success and strategy use for repeating patterns of 75 Norwegian 6-year-old grade 1 students. We provided the students with duplicate, extend, transfer, and unit isolation activities in a semi-structured, task-based interview that was video-recorded. The students’ number sense was also measured using a digital assessment. In-depth analysis of 52 students (divided evenly into two groups based on their number sense scores, lowest and highest) was performed. A statistically significant group difference was found for extend activities. Multimodal video analysis of strategies employed revealed that both student groups used recursive and different strategies and only students with high number sense used relational strategies. The students with low number sense used more variants of recursive patterning strategies. Our findings also demonstrated that attention to pattern features may both hinder identification of a pattern’s underlying structure and reflect advanced strategy use rather than not showing pattern awareness. We discuss group differences in terms of decomposition and awareness of mathematical pattern and structure and emphasise the need for scaffolding awareness of mathematical pattern and structure through communication.

The promotion of sustainable land use planning for the enhancement of ecosystem service capacity: Based on the FLUS-INVEST-RUSLE-CASA model.

Land Use/Land Cover (LULC) is one of the most significant human variables influencing the efficiency of Ecosystem Services (ESs) in terrestrial ecosystems. Theoretical and technical assistance for regional sustainable land use planning and management, as well as ecosystem conservation and restoration, is provided by investigating the influence of changes in the LULC pattern on the efficiency of ESs. This research focuses on the interactions between socioeconomic activities and natural ecological processes in the Three Gorges Reservoir Area (TGRA). We use LULC data from the TGRA for the years 1990, 2000, 2010, and 2020. The study includes the analysis and calculation of the spatiotemporal evolution features of the current LULC pattern and the efficiency of ESs, including their spatiotemporal distribution. Considering the TGRA's national development orientation and guidance, three potential LULC patterns are constructed under various develop-ment scenarios. To calculate the efficiency of ESs, the GeoSOS-FLUS future LULC simulation model is linked, and several methodologies such as INVEST, RUSLE, and CASA are used. The goal is to investigate the influence of future changes in LULC patterns on ESs efficiency. The findings show the following: (1) From 1990 to 2020, the values of water conservation services in the TGRA decreased and subsequently increased. High-value areas are primarily located in the reservoir's centre and eastern sections, whereas low-value areas are mostly found in the western section. Soil conservation service values initially declined and later climbed. The TGRA's carbon storage services have in-creased yearly, from 552.64 g/m2 in 2000 to 615.92 g/m2 in 2020. (2) In the ecological protection scenario, carbon storage and soil erosion increased compared to the ecosystem services in 2020. The ecological system service benefits are greater when compared to the natural development scenario. (3) The four ESs show positive spatial correlations across all three scenarios, and local spatial au-tocorrelation analysis findings demonstrate that carbon storage, water yield, and habitat quality have comparable spatial distributions across all three scenarios. To some extent, high-value areas for water conservation, soil retention, carbon storage, and habitat quality overlap.

Long-term dynamics of epidemiological characteristics of some chronic leukemia and lymphoma in Moscow before, during and after the pandemic of the new coronavirus infection COVID-19. Epidemiologic study

Background. The COVID-19 pandemic, which swept the world in 2020–2021, had a significant impact on the epidemiological picture of hematological malignancies. Aim. To study the dynamics of epidemiological indicators of five chronic hemoblastoses in Moscow over a 12-year period, and also to assess the impact of the COVID-19 pandemic on these indicators. Materials and methods. A population-based study was conducted for five types of oncohematological diseases: multiple myeloma, Hodgkin's lymphoma, follicular lymphoma, chronic lymphocytic leukemia, chronic myeloid leukemia. The annual rates of morbidity, mortality, prevalence of these nosologies, overall survival, and expected overall survival were calculated. Gender and age characteristics for each disease were determined. Results and discussion. The present study showed a uniform increase in the incidence and prevalence of five hematological malignancies between 2012 and 2019. Convincing evidence of an increase in mortality and a decrease in the number of diagnosed cases in comparison with the expected figures for these hematological malignancies in 2020–2021 was presented. A greater reduction was shown for cases diagnosed at early stages compared to later ones. During the pandemic, a significant increase in mortality was recorded among patients with chronic lymphocytic leukemia and multiple myeloma, while in cohorts of patients with chronic myeloid leukemia and Hodgkin lymphoma it increased slightly. The return of morbidity, mortality and prevalence rates to the original dynamics in 2022–2023 has been demonstrated. Conclusion. This study demonstrated the features of the regional pattern of incidence, mortality, prevalence and gender-age characteristics of five hematological malignancies and provided the changes in the indicators dynamics of different hematological malignancies during the COVID-19 pandemic.

The analysis of colour and pattern in Romanian folk dress: protecting past legacies in an uncertain future

ABSTRACT Dress may show the characteristics of an era, a country, a community or a person. As in other folk arts, clothing is one of the most vibrant documents of cultures. Since folk clothing, which is a cultural product, is directly related to people, it is an indicator of the people’s modes of living. The colours, patterns and motifs especially used in folk clothing provide important information on such matters. This article deals with the colours and patterns of the folk clothing in the regions within Romania. For this purpose, samples of folk clothing belonging to the regions considered as the most traditional are illustrated and the elements of folk clothing examined in terms of colour and pattern features. The article lists various colours and patterns and shows how their configurations serve as identifying markers for the various regional locations of their wearers.

Identification of Distinct, Quantitative Pattern Classes from Emergent Tissue-Scale hiPSC Bioelectric Properties.

Bioelectric signals possess the ability to robustly control and manipulate patterning during embryogenesis and tissue-level regeneration. Endogenous local and global electric fields function as a spatial 'pre-pattern', controlling cell fates and tissue-scale anatomical boundaries; however, the mechanisms facilitating these robust multiscale outcomes are poorly characterized. Computational modeling addresses the need to predict in vitro patterning behavior and further elucidate the roles of cellular bioelectric signaling components in patterning outcomes. Here, we modified a previously designed image pattern recognition algorithm to distinguish unique spatial features of simulated non-excitable bioelectric patterns under distinct cell culture conditions. This algorithm was applied to comparisons between simulated patterns and experimental microscopy images of membrane potential (Vmem) across cultured human iPSC colonies. Furthermore, we extended the prediction to a novel co-culture condition in which cell sub-populations possessing different ionic fluxes were simulated; the defining spatial features were recapitulated in vitro with genetically modified colonies. These results collectively inform strategies for modeling multiscale spatial characteristics that emerge in multicellular systems, characterizing the molecular contributions to heterogeneity of membrane potential in non-excitable cells, and enabling downstream engineered bioelectrical tissue design.

Structure and Content of Forensic Characteristics of High-Tech Crimes

The paper identifies the main classifying characteristics of the forensic group of high-tech crimes. The problems of developing a forensic characterization of crimes included in this group are considered. The most important structural elements of the forensic characteristics under consideration are described. The determining role of the high-tech method of committing crimes has been established and its features have been analyzed. Debating points related to the object and subject of high-tech criminal encroachment are noted. With regard to the subject of the crime, the author concludes that such is a group, while the accomplices of the crime have personal characteristics that prevent the construction of a single typical portrait. The specificity of the crime situation is considered, because the main cause-and-effect relationships are formed in cyberspace. Important features of the trace pattern, manifested in the formation of electronic digital traces, are noted. It is shown that the means of committing high-tech crimes are special software, hardware and software-hardware, the peculiarity of which is their novelty. The role of counteraction to the investigation of high-tech criminal acts on the part of criminals and persons associated with them is revealed. For the first time in criminological theory, the glocalization factor is noted, and the relevance of this element of criminological characteristics for the study of high-tech crimes is shown. The cause-and-effect and correlation relationships between the elements of the forensic characteristics are analyzed.

Explainable rumor detection based on grey clustering: Fusion of manual features and deep learning features

The importance of rumor detection on social media is self-evident. However, many existing studies have focused on exploring potential features in text content and propagation patterns, while neglecting a key aspect—the explainability of the model. The comment content can provide support for the credibility of the detection. Nevertheless, most studies that use comments encode them into specific models, rarely considering their semantic attitudes and standpoints, making it difficult for models to explain why a post is a rumor. In this study, we propose an Explainable rumor detection model based on Grey clustering called MDE-Grey, which combines Manual features and Deep learning features. In terms of manual features, we constructed a relevant vocabulary based on the specific comment environment of rumors to capture comment standpoints. In terms of deep learning features, we have designed a GCN sub network that includes two attention mechanisms to capture noteworthy content in posts and comments. Finally, we constructed a new grey clustering model to fuse the two types of features and obtain the final prediction. In the grey clustering model, we designed new whitening functions to capture the intrinsic relationship between features and rumor categories, ensuring the traceability of prediction results. The experiments on three datasets and case studies have demonstrated the effectiveness of the MDE-Grey model in detecting rumors and explaining the results.

Research on the Design of Zhuang Brocade Patterns Based on Automatic Pattern Generation

To promote the inheritance of Zhuang brocade culture and the rapid extraction of features and automatic generation of patterns, this paper constructs a feature dataset of Zhuang brocade patterns and proposes an automatic generation technology using relative coordinates and regional content replacement. Firstly, by sorting through a large number of cases, a feature dataset of Zhuang brocade patterns is constructed. For the significant features of Zhuang brocade patterns, intelligent extraction algorithms and processes are used to effectively extract the color matching, patterns, and organizational forms of the patterns into the feature dataset. Secondly, to generate Zhuang brocade patterns quickly, an automatic generation technology based on genotype encoding and regional replacement algorithms is proposed, which encodes these pattern elements into a format that can be interpreted by computer algorithms. Finally, through similarity evaluation, the method’s feasibility for rapid extraction and generation of Zhuang brocade patterns is effectively verified. This method is significant for the inheritance of Zhuang brocade patterns and the development of the intangible cultural heritage industry.

CC-GAIN: Clustering and classification-based generative adversarial imputation network for missing electricity consumption data imputation

The widespread use of data across various fields has made missing data imputation technology a crucial tool. High-quality data is essential for effective energy management in smart grid environments, but essential data may be absent during collection. Despite the development of various imputation techniques for electricity consumption data, previous studies have made limited efforts to address the distinctive characteristics of this domain adequately. To overcome this limitation, a high-performance imputation model must effectively leverage time-series and pattern features of the data. This study proposes a novel missing data imputation model based on unsupervised clustering and classification-based generative adversarial imputation network(CC-GAIN), which excels in pattern classification and feature extraction. The CC-GAIN model demonstrates superior performance across all types and rates of missing data, outperforming alternative models.

Comparison of the Impact of Different Economic Patterns on Population Inflows: Evidence from China’s Guangdong, Jiangsu, and Zhejiang Provinces

Between 2000 and 2022, China’s top three highest GDP provinces, Guangdong, Zhejiang, and Jiangsu, each having distinct economic structures, displayed different paths of development in their net population inflows. This prompts us to ponder how the economic patterns of the most economically developed regions impact population inflows. To answer the question, we first examine each economic pattern and use the entropy weight method to construct a comprehensive index to capture the features of each economic pattern in different regions. Then, we employ a two-way fixed effects model with panel data from the three provinces to conduct the empirical analysis. Moving forward, we expand the sample size to 10 provinces, including China’s eastern metropolitan areas, to extend the analysis beyond the previously selected regions and corroborate the consistency and robustness of our model. The results show that the Wenzhou pattern, featured primarily by the private sector, has the most impact on population inflows, followed by the Pearl River pattern, driven by an export-oriented economy. In contrast, the Sunan pattern, characterized by the collective economy, has an insignificant impact. We further dissect and determine the essential factors influencing population inflows within the three economic patterns and estimate the sustainability of the economic pattern via net population inflows. Our findings can provide insights for policy-makers to understand and utilize economic patterns in order to impact population inflows effectively. Specifically, we propose that the observable net population inflows can serve as an indicator to evaluate the sustainability of local economic patterns, thus providing another perspective on assessing the region’s economic development.

Polarimeters for the Detection of Anisotropy from Reflectance.

Polarimetry is used to determine the Stokes parameters of a laser beam. Once all four S0,1,2,3 parameters are determined, the state of polarisation is established. Upon reflection of a laser beam with the defined S polarisation state, the directly measured S parameters can be used to determine the optical properties of the surface, which modify the S-state upon reflection. Here, we use polarimetry for the determination of surface anisotropies related to the birefringence and dichroism of different materials, which have a common feature of linear patterns with different alignments and scales. It is shown that polarimetry in the back-reflected light is complementary to ellipsometry and four-polarisation camera imaging; experiments were carried out using a microscope.

Protection of Guizhou Miao batik culture based on knowledge graph and deep learning

In the globalization trend, China’s cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their categories can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 94.46%, 94.47%, 93.62%, and 93.8%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection.