Types Of Features Research Articles

Clinicopathologic Features of Breast Tumors in Germline TP53 Variant-Associated Li-Fraumeni Syndrome.

We present one of the largest cohorts of TP53-pathogenic germline variants (PGVs) associated with patients with Li-Fraumeni syndrome (n = 82) with breast tumors (19 to 76y; median age: 35). Most had missense variants (77%), followed by large gene rearrangements (LGRs; 12%), truncating (6%), and splice-site (5%) variants. Twenty-one unique germline missense variants were found, with hotspots at codons 175, 181, 245, 248, 273, 334, and 337. Of 100 total breast tumors, 63% were invasive (mostly ductal), 30% pure ductal carcinoma in situ, 4% fibroepithelial lesions, and 3% with unknown histology. Unlike BRCA-associated tumors, approximately half of the breast cancers exhibited HER2-positivity, of which ~50% showed estrogen receptor coexpression. Pathology slides were available for review for 61 tumors (44 patients), and no significant correlation between the type of TP53 PGVs and histologic features was noted. High p53 immunohistochemistry expression (>50%) was seen in 67% of tumors tested (mostly missense variant). Null pattern (<1% cells) was seen in 2 (LGR and splicing variants carriers). Surprisingly, 2 tumors from patients with an LGR and 1 tumor from a patient with a truncating variant showed p53 overexpression (>50%). The subset of patients with the Brazilian p.R337H variant presented at a higher age than those with non-p.R337H variant (46 vs 35y) though statistically insignificant (P = 0.071) due to an imbalance in the sample size, and were uniquely negative for HER2-overexpressing tumors. To conclude, breast cancer in carriers of TP53 PGVs has some unique clinicopathological features that suggest differential mechanisms of tumor formation. p53 immunohistochemistry cannot be used as a surrogate marker to identify germline TP53-mutated breast cancers.

Model can be subtle: Two important mechanisms for Social Media Popularity Prediction

Social media popularity prediction is an important channel to explore content sharing and communication on social networks. It aims to capture informative cues by analyzing multi-type data (such as user profile, image, and text) to decide the popularity of a specified post. In this paper, we divide social network users into two categories (i.e., active and inactive users) and find a dilemma in existing models: If an active user publishes the low-popularity post, the model will habitually predict the high score. On the contrary, if an inactive user provides the high-popularity post, the model still gives the low score incorrectly. Therefore, how to make the model more subtle to users is important. Comparing to existing methods that directly leverage multi-modal features for regression training, this paper stresses more on two novel mechanisms. The first method aims to prevent the over-fitting on user IDs. We propose the attribute-sensitive interactive mechanism (M1) by incorporating explicit user-attribute and post-attribute interaction. It can analyze which type of features a user cares the most and weaken the model’s dependence on user IDs. The second method aims to strengthen the influence of post content. We propose the knowledge embedding mechanism (M2) to revise the popularity scores in existing models by fusing the statistical frequency over multi-type data. Note that both mechanisms are model-agnostic, which can be applicable in any popularity prediction model. Extensive experiments conducted on the Social Media Prediction Dataset further validate the effectiveness.

Eye Movement Classification using Feature Engineering and Ensemble Machine Learning

This paper explores the classification of gaze direction using electrooculography (EOG) signals, integrating signal processing, deep learning, and ensemble learning techniques to enhance accuracy and reliability. A complex technique is proposed in which several feature types are derived from EOG data. Spectral properties generated from power spectral density analysis augment basic statistical characteristics such as mean and standard deviation, revealing the frequency content of the signal. Skewness, kurtosis, and cross-channel correlations are also used to represent intricate nonlinear dynamics and inter-channel interactions. These characteristics are then reformatted into a two-dimensional array imitating picture data, enabling the use of the pre-trained ResNet50 model to extract deep and high-level characteristics. Using these deep features, an ensemble of bagging-trained decision trees classifies gaze directions, lowering model variance and increasing prediction accuracy. The results show that the ensemble deep learning model obtained outstanding performance metrics, with accuracy and sensitivity ratings exceeding 97% and F1-score of 98%. These results not only confirm the effectiveness of the proposed approach in managing challenging EOG signal classification tasks but also imply important consequences for the improvement of Human-Computer Interaction (HCI) systems, especially in assistive technologies where accurate gaze tracking is fundamental.

Multimodal computed tomography-guided mechanical thrombectomy for large vessel occlusion stroke after cardiac surgery

Post-operative large vessel occlusion (LVO) stroke is one of the most severe complications associated with cardiac surgery. Mechanical thrombectomy (MT) is the first-line treatment for LVO stroke. In this paper, we present cases of patients who underwent MT guided by multimodal computed tomography (CT) to treat LVO stroke after cardiac surgery at our institution to clarify the impact of MT on patient outcomes. We analyzed the in-hospital stroke database from July 1, 2023, to June 30, 2024, to identify patients who had undergone MT to treat LVO stroke after cardiac surgery in our institution. Demographic data, stroke severity, type of operation, imaging features, and 90-day modified Rankin scale (mRS) scores were assessed. Of the 21 patients receiving endovascular therapy, we included nine (42.9%) who had undergone multimodal CT-guided MT after cardiac surgery. The operations varied and comprised four coronary artery bypass grafts, four surgical valve replacements, and one extended Morrow myectomy. The median time from post-surgery to the onset of stroke symptoms was 4 days (interquartile range [IQR]: 2.5 &amp;ndash; 5 days), the median National Institutes of Health Stroke Scale score was 16 (IQR: 15 &amp;ndash; 18), and the median time between the onset of symptoms and recanalization was 320 min (IQR: 270 &amp;ndash; 465 min). One patient died of severe cerebral hemorrhage, while eight patients survived and were discharged to another hospital (n = 1), a rehabilitation facility (n = 3), or home (n = 4). The median 3‐month mRS was 2 (IQR: 0.5 &amp;ndash; 3). In summary, multimodal CT-guided MT can improve the prognosis of patients with LVO stroke, although severe neurological deficits and mortality were observed in some patients. A larger sample size is required to clarify the effects of MT.

Investigating the Spatiotemporal Development of Substorm Expansion Phase Aurora: Successive Onsets or Poleward Boundary Intensifications?

AbstractFollowing the auroral substorm onset, the active aurora undergoes expansion, which can vary in spatial and temporal extent. The spatiotemporal development of the expansion phase active aurora is controlled by new auroral intensifications that often follow the initial onset. Using seven examples, we investigate the nature of these new auroral intensifications and address a question: are they new auroral onsets, that is, “successive onsets” or poleward‐boundary intensifications (PBIs) and ensuing auroral streamers? We observed events that included both types of auroral features—successive onsets and PBIs—and their combinations. For multiple‐onset substorms, successive onsets may occur eastward, westward, and poleward of the initial onset, resulting in a diverse range of expansion phase spatial extent and durations. Single‐onset substorms show only one auroral onset, but their spatiotemporal development can resemble that of multiple‐onset substorms. However, the additional activations are mainly PBIs and subsequent streamers. In some cases, PBIs undergo explosion, leading to a rapid poleward and azimuthal expansion of the aurora, resembling the auroral substorm onset. A prolonged sequence of PBIs and its longitudinal extension can contribute significantly to the spatiotemporal development of substorms expansion phase. Results suggest that post‐onset flow channels drive the spatiotemporal development of the substorm expansion phase by (a) triggering successive onsets and (b) inducing bursts of PBIs and their prolonged sequence. We speculate that post‐onset flow channels likely originate from the polar cap, but more evaluation is required. Our findings highlight the significance of examining imager data before solely relying on magnetometers to identify substorm onsets.

Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis

In recent years, the research on abnormal events detection is a significant work in surveillance video. Many researchers have been attracted by this work for the past two decades. As a result, several abnormal event detection approaches have been developed. Though several approaches have been used in the field still many problems remain to get the abnormal events detection accuracy. Moreover, many feature representations have limited capability to describe the content since several research works applied hand craft features, this type of feature can work in limited problems. To overcome this problem, this paper introduced the novel feature descriptor namely STS-D (Spatial and Temporal Saliency - Descriptor), which includes spatial and temporal information of the objects. This feature descriptor efficiently describes the shape and speed of the object. To find the anomaly score, fuzzy representation is modeled to efficiently differentiate the normal and abnormal events using fuzzy membership degree. The benchmark datasets UMN, UCSD Ped1 and Ped2 and real time roadway surveillance dataset are used to evaluate the performance of the proposed approach. Also, several existing abnormal events detection approaches are used to compare with the proposed method to evaluate the effectiveness of the proposed work.

The value of hippocampal sub-region imaging features for the diagnosis and severity grading of ASD in children

BackgroundHippocampal structural changes in Autism Spectrum Disorder (ASD) are inconsistent. This study investigates hippocampal subregion changes in ASD patients to reveal intrinsic hippocampal anomalies. MethodsA retrospective study from Hainan Children’s Hospital database (2020–2023) included ASD patients and matched controls. We classified ASD participants based on severity, dividing all subjects into four groups: normal, mild, moderate, and severe. High-resolution T1-weighted MRI images were analyzed for hippocampal subregion segmentation and volume calculations using Freesurfer. Texture features were extracted via the Gray-Level Co-occurrence Matrix. The Receiver Operating Characteristic curve was used to evaluate seven random forest predictive models constructed from volume, subregion, and texture features, as well as their combinations following feature selection. ResultsThe study included 114 ASD patients (98 boys, 2–8 years; 16 girls, 2–6 years; 17 mild, 57 moderate, 40 severe) and 111 healthy controls (HCs). No significant differences in volumes were found between ASD patients and HCs (adjusted P-value >0.05). The seven random forest models showed that single volume and texture features performed poorly for ASD classification; however, integrating various feature types improved AUC values. Further selection of texture, subregion, and volume features enhanced AUC performance across normal and varying severity categories, demonstrating the potential value of specific subregions and integrated features in ASD diagnosis. ConclusionRandom forest models revealed that hippocampal volume, texture features, and subregion characteristics are crucial for diagnosing and assessing the severity of ASD. Integrating selected texture and subregion features optimized diagnostic efficacy, while combining texture, subregion, and volume features further improved severity grading effectiveness.

ReMamba: a hybrid CNN-Mamba aggregation network for visible-infrared person re-identification.

Visible-Infrared Person Re-identification (VI-ReID) has been consistently challenged by the significant intra-class variations and cross-modality differences between different cameras. Therefore, the key lies in how to extract discriminative modality-shared features. Existing VI-ReID methods based on Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have shortcomings in capturing global features and controlling computational complexity, respectively. To tackle these challenges, we propose a hybrid network framework called ReMamba. Specifically, we first use a CNN as the backbone network to extract multi-level features. Then, we introduce the Visual State Space (VSS) model, which is responsible for integrating the local features output by the CNN from lower to higher levels. These local features serve as a complement to global information and thereby enhancing the local details clarity of the global features. Considering the potential redundancy and semantic differences between local and global features, we design an adaptive feature aggregation module that automatically filters and effectively aggregates both types of features, incorporating an auxiliary aggregation loss to optimize the aggregation process. Furthermore, to better constrain cross-modality features and intra-modal features, we design a modal consistency identity constraint loss to alleviate cross-modality differences and extract modality-shared information.Extensive experiments conducted on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that our proposed ReMamba outperforms state-of-the-art VI-ReID methods.

A Review on Features and Techniques of Digital Data Security Using Elliptical Curve Based Cryptographic Approach

Digital world has increased the comfort by various means. Image in digital form is applicable in almost all field of technical and non-technical works. As image is use for the authentication, hence security of data is highly required. In this paper, Elliptical Curve based cryptographic approach has been discussed to provide security to digital images. Many security techniques of digital content proposed by researchers have been discussed for developing robustness. Different types of attacks, image features, and other additional assessment criteria of an image have been discussed for the analysis of digital image security.

Using Public Participation Geographic Information System to Study Social Cohesion and Its Relationship with Activities and Specific Landscape Characteristics in Shanghai’s Modern Historic Parks

Evaluating the perceptions of social cohesion provides an elaborated understanding of the commemorative values of modern heritage. However, little attention has been paid to China’s modern historic parks (MHPs) due to the difficulty of capturing spatially explicit information. Utilizing a public participation GIS, we quantified four types of social cohesion in Shanghai’s existing MHPs, and analyzed their relationships with different types of activities and specific landscape features. The results show that the MHPs are rich in social cohesion, especially in belonging and place attachment. Social cohesion is spatially associated with leisure and sightseeing activities. The perception of different types of social cohesion varies across spaces with different landscape features. Our findings validate the composition of commemorative values of MHPs, and offer a reference for balancing the preservation and regeneration of modern heritage.

Prediction of Post Traumatic Epilepsy Using MR-Based Imaging Markers.

Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Despite the high prevalence of PTE, current methods for predicting its occurrence remain limited. In this study, we aimed to identify imaging-based markers for the prediction of PTE using machine learning. Specifically, we examined three imaging features: Lesion volumes, resting-state fMRI-based measures of functional connectivity, and amplitude of low-frequency fluctuation (ALFF). We employed three machine-learning methods, namely, kernel support vector machine (KSVM), random forest, and an artificial neural network (NN), to develop predictive models. Our results showed that the KSVM classifier, with all three feature types as input, achieved the best prediction accuracy of 0.78 AUC (area under the receiver operating characteristic (ROC) curve) using nested cross-validation. Furthermore, we performed voxel-wise and lobe-wise group difference analyses to investigate the specific brain regions and features that the model found to be most helpful in distinguishing PTE from non-PTE populations. Our statistical analysis uncovered significant differences in bilateral temporal lobes and cerebellum between PTE and non-PTE groups. Overall, our findings demonstrate the complementary prognostic value of MR-based markers in PTE prediction and provide new insights into the underlying structural and functional alterations associated with PTE.

DeepDR: a deep learning library for drug response prediction.

Accurate drug response prediction is critical to advancing precision medicine and drug discovery. Recent advances in deep learning (DL) have shown promise in predicting drug response; however, the lack of convenient tools to support such modeling limits their widespread application. To address this, we introduce DeepDR, the first DL library specifically developed for drug response prediction. DeepDR simplifies the process by automating drug and cell featurization, model construction, training, and inference, all achievable with brief programming. The library incorporates three types of drug features along with nine drug encoders, four types of cell features along with nine cell encoders, and two fusion modules, enabling the implementation of up to 135 DL models for drug response prediction. We also explored benchmarking performance with DeepDR, and the optimal models are available on a user-friendly visual interface. DeepDR can be installed from PyPI (https://pypi.org/project/deepdr). The source code and experimental data are available on GitHub (https://github.com/user15632/DeepDR). Supplementary data are available at Bioinformatics online.

MuSE: A deep learning model based on multi-feature fusion for super-enhancer prediction

Although bioinformatics-based methods accurately identify SEs (Super-enhancers), the results depend on feature design. It is foundational to representing biological sequences and automatically extracting their key features for improving SE identification. We propose a deep learning model MuSE (Multi-Feature Fusion for Super-Enhancer), based on multi-feature fusion. This model utilizes two encoding methods, one-hot and DNA2Vec, to signify DNA sequences. Specifically, one-hot encoding reflects single nucleotide information, while k-mer representations based on DNA2Vec capture both local sequence fragment information and global sequence characteristics. These types of feature vectors are conducted and combined by neural networks, which aim at SE prediction. To validate the effectiveness of MuSE, we design extensive experiments on human and mouse species datasets. Compared to baselines such as SENet, MuSE improves the prediction of F1 score to a maximum improvement exceeding 0.05 on mouse species. The k-mer representations based on DNA2Vec among the given features have the most important impact on predictions. This feature effectively captures context semantic knowledge and positional information of DNA sequences. However, its representation of the individuality of each species negatively affects MuSE's generalization ability. Nevertheless, the cross-species prediction results of MuSE improve again to reach an AUC of nearly 0.8, after removing this type of feature. Source codes are available at https://github.com/15831959673/MuSE.

Lightweight image super-resolution network based on dynamic graph message passing and convolution mixer

The single image super-resolution (SISR) task much consideration in the last few years. There are many image SR models that are developed based on advanced deep learning methods. However, these models extract features in image space limiting the ability to extract short and long dependencies. To solve this limitation, a graph mixer block (GMB) is designed based on merging the dynamic graph message passing network (DGMN) with the convolution mixer (ConvMix) for the task of image super-resolution (SR). By utilizing the designed GMB block and the concept of residual learning, we developed a graph mixer network (GMN) for SISR. The idea of this model is the integration to benefit from both types of graph features and traditional convolution features. Combining these features allows the model to capture both global graph structure and local patterns, leading to more comprehensive and expressive representations. The function of the DGMN is used to capture long-range structured relationships with efficient weight modules. This DGMN is conditioned on the input to make adaptively sampling nodes in the graph used for message passing. Then, the ConvMix is used to extract additional features that not is extracted by DGMN modules based on channel and spatial mixing. The ConvMix is designed based on using two depthwise convolutions with one pointwise convolution. The result shows that our GMN attained state-of-the-art results in multiple SR benchmarks

Radiomics-Based Diagnosis in Dentomaxillofacial Radiology: A Systematic Review.

Radiomics is a quantitative tool for digital image analysis. This systematic review aims to investigate the scientific articles to evaluate the potential implications of Radiomics analysis in Dentomaxillofacial Radiology (DMFR). Studies regarding Radiomics applications in DMFR and human samples, in vivo study, a case reports/series if ≧5 samples were included, while case reports/series if < 5 samples, articles other than in English, abstracts without full text, and studies published before 2015 were excluded. Fifty-one articles were selected from 3789 literatures. The QUADAS-2 tool was used for risk of bias assessment. The accuracy of predicting dentomaxillofacial pathologies was considered as the primary outcome, and the modeling type of Radiomics was considered as the secondary outcome. A meta-analysis could not be performed due to the lack of information and standardization among the reported accuracies. The reported accuracies were found between 0.66 and 99.65%. Logistic regression (n = 6) was found to be the most common Radiomics modeling type, followed by Support Vector Machine and Decision Tree (n = 5). Second-order statistics (n = 38) was the most common type of Radiomics application, followed by first-order (n = 26), higher-order (n = 20), and shape-based (n = 15) statistics. Further work is needed to increase standardization in the Radiomics workflow. Quantitative image analysis is an alternative tool for conventional visual radiographic evaluation. Radiomics systems depend on elements such as imaging modality, feature type, data mining, or statistical method. Radiomics applications do not justify digital transformation on their own, but the potential of its integration into the digital workflow is considerable.

High-Order Multimodal Interaction Network for Efficient Prediction of Drug-Drug Interaction

The pharmaceutical industry places a lot of focus on preventing drug-drug interactions (DDIs). Medication interaction detection has been the primary emphasis of machine learning-based DDI prediction methods. Since studies have shown that DDIs can cause different future occurrences, it is more beneficial to predict DDI connected events while examining the mechanism behind combination medicine consumption or adverse reactions. The areas of medication development and illness diagnostics are seeing increased usage of a developing approach that predicts DDIs-associated occurrences. We examine potential interactions between the two medications as well as the kinds of interactions that can occur in this research. Additionally, we provide a strategy that relies on learning and use High-Order Multimodal Interaction Network (HOMIN) to forecast DDIs by learning feature representations. The purpose of this work is to provide HOMIN-DDI, a new method for drug-drug interaction predictions that we have developed utilizing a HOMIN architecture. First, we develop feature vectors from medication categories, targets, paths, and digestive enzymes, and we use the Jaccard similarity to evaluate how similar drugs appear. After that, we build a new HOMIN using the feature representation to predict DDIs. The results of the experiments show that the drug categories feature type, when used to the HOMIN -DDI approach, is effective. Furthermore, compared to employing only one feature, combining numerous features yields more useful and informative results. Compared to other algorithms, HOMIN-DDI performs better when it comes to predicting DDIs.

Method of bed exit intention based on the internal pressure features in array air spring mattress

With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment.

A probable ancient nearshore zone in southern Utopia on Mars unveiled from observations at the Zhurong landing area

The Chinese Mars rover Zhurong successfully landed in southern Utopia Planitia on Mars in May 2021. Previous research suggested a Hesperian ocean may have existed in the northern lowland on Mars. Recent research observed water-related features at the Zhurong landing site from in situ data. In this study, we conducted a comprehensive geomorphological analysis of the landing area using remote sensing data, supplemented by in situ observations, and unveiled features consistent with the existence of a nearshore zone in southern Utopia. Different types of water-related geomorphological features were separated by specific topographic contours, suggesting different types of marine environments. The area was subdivided into a foreshore highland-lowland transition unit, a shallow marine unit and a deep marine unit. In situ observations including sedimentary deposit rocks, water-related lamination features, and subsurface sedimentary layers, also indicate past water activities. Results suggested an evolution scenario of the nearshore zone in southern Utopia: (1) Flooding of the Utopia Planitia in Late Noachian around 3.65–3.68 Ga reached the foreshore unit; (2) formation of the shallow and deep marine units after the flooding was completed by about 3.5 Ga and 3.42 Ga in Early Hesperian, respectively; (3) gradual loss of subsurface volatiles during the Amazonian epoch.

UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation

To tackle the challenges of cold start and data sparsity in recommendation systems, an increasing number of researchers are integrating item features, resulting in the emergence of multimodal recommendation systems. Although graph convolutional network-based approaches have achieved significant success, they still face two limitations: (1) Users have different preferences for various types of features, but existing methods often treat these preferences equally or fail to specifically address this issue. (2) They do not effectively distinguish the similarity between different modality item features, overlook the unique characteristics of each type, and fail to fully exploit their complementarity. To solve these issues, we propose the user perception-guided graph convolutional network for multimodal recommendation (UPGCN). This model consists of two main parts: the user perception-guided representation enhancement module (UPEM) and the multimodal two-step enhanced fusion method, which are designed to capture user preferences for different modalities to enhance user representation. At the same time, by distinguishing the similarity between different modalities, the model filters out noise and fully leverages their complementarity to achieve more accurate item representations. We performed comprehensive experiments on the proposed model, and the results indicate that it outperforms other baseline models in recommendation performance, strongly demonstrating its effectiveness.

A Dual-Stream Fusion Network for Human Energy Expenditure Estimation with Wearable Sensor

With the increasing awareness of health, using wearable sensors to monitor individual activities and accurately estimate energy expenditure has become a current research focus. However, existing research encounters challenges including low estimation accuracy, a deficiency of frequency domain features, and difficulty in integrating time domain and frequency domain features. To address these issues, we propose an innovative framework called the Dual-Stream Fusion Network (DSFN). This framework combines the Time Domain Encoding (TDE) module, the Frequency Domain Hierarchical-Split Encoding (FDHSE) module, and a Two-Stage Feature Fusion (TSF) module. Specifically, the temporal stream of the framework employs the TDE module to capture deep temporal features that reflect the complex dynamic variations in time-series data. The frequency domain stream introduces the FDHSE module, which extracts frequency domain features using a multi-level, multi-scale approach, ensuring a comprehensive and diverse representation of frequency information. Through this dual-stream architecture, our model effectively learns both time and frequency domain features, addressing the limitations of frequency domain features observed in prior studies. Additionally, we propose the TSF module to fully integrate time and frequency domain features, effectively overcoming the challenge of fusing these two types of features. We conducted experiments on two public datasets, namely the GOTOV dataset (elderly people) and the JSI dataset (young people). Experimental results demonstrate that our method achieves excellent performance across different age groups. Compared to the baseline models, the proposed DSFN significantly improves the accuracy of human energy expenditure estimation.