Basic Features Research Articles

Effect of SARS-CoV-2 shedding rate distribution of individuals during their disease days on the estimation of the number of infected people. Application of wastewater-based epidemiology to the city of Thessaloniki, Greece

During the COVID-19 pandemic, wastewater-based epidemiology has proved to be an important tool for monitoring the spread of a disease in a population. Indeed, wastewater surveillance was successfully used as a complementary approach to support public health monitoring schemes and decision-making policies. An essential feature for the estimation of a disease transmission using wastewater data is the distribution of viral shedding rate of individuals in their personal human wastes as a function of the days of their infection. Several candidate shapes for this function have been proposed in literature for SARS-CoV-2. The purpose of the present work is to explore the proposed function shapes and examine their significance on analyzing wastewater SARS-CoV-2 shedding rate data. For this purpose, a simple model is employed applying to medical surveillance and wastewater data of the city of Thessaloniki during a period of Omicron variant domination in 2022. The distribution shapes are normalized with respect to the total virus shedding and then their basic features are investigated. Detailed analysis reveals that the main parameter determining the results of the model is the difference between the day of maximum shedding rate and the day of infection reporting. Since the latter is not part of the distribution shape, the major feature of the distribution affecting the estimation of the number of infected people is the day of maximum shedding rate with respect to the initial infection day. On the contrary, the duration of shedding (total number of disease days) as well as the exact shape of the distribution are by far less important. The incorporation of such wastewater surveillance models in conventional epidemiological models - based on recorded disease transmission data- may improve predictions for disease spread during outbreaks.

Tuning acid and basic features on MgxAlOy-SiO2 impacted ethanol upgrading to 1,3-butadiene

Ethanol has captured a lot of interest from the industry in recent years due to its sustainable appeal, being obtained through the process of biomass fermentation. Not only its application in the energy sector as a potent fuel, but its conversion to higher added value products has been a commercial highlight. In this work, we sought to synthesize a new mixed catalyst MgxAlOy-SiO2 to investigate its performance in the upgrading of ethanol to a product of significant relevance, particularly for the plastics and polymers industry, namely1,3-butadiene. The molar ratio of Mg:Al:Si in the catalyst was tuned, revealing a remarkable impact on the selectivity of the reaction products. For the production of 1,3-butadiene, the optimal catalyst composition was determined to be 3:1:1, exhibiting a selectivity of 23 % at 723 K. This composition offered a favorable amount of acid and basic active sites, indicated by the acid to basic sites density ratio of 0.109, with medium basic sites predominantly represented. This arrangement is believed to have facilitated dehydrogenation, condensation, and dehydration reactions, each playing a crucial role in the overall process.

Structure of anellovirus-like particles reveal a mechanism for immune evasion

Anelloviruses are nonpathogenic viruses that comprise a major portion of the human virome. Despite being ubiquitous in the human population, anelloviruses (ANVs) remain poorly understood. Basic features of the virus, such as the identity of its capsid protein and the structure of the viral particle, have been unclear until now. Here, we use cryogenic electron microscopy to describe the first structure of an ANV-like particle. The particle, formed by 60 jelly roll domain-containing ANV capsid proteins, forms an icosahedral particle core from which spike domains extend to form a salient part of the particle surface. The spike domains come together around the 5-fold symmetry axis to form crown-like features. The base of the spike domain, the P1 subdomain, shares some sequence conservation between ANV strains while a hypervariable region, forming the P2 subdomain, is at the spike domain apex. We propose that this structure renders the particle less susceptible to antibody neutralization by hiding vulnerable conserved domains while exposing highly diverse epitopes as immunological decoys, thereby contributing to the immune evasion properties of anelloviruses. These results shed light on the structure of anelloviruses and provide a framework to understand their interactions with the immune system.

Lightweight pyramid attention residual network for intelligent fault diagnosis of machine under sharp speed variation

Deep learning based diagnostic methods have obtained great success in intelligent fault diagnosis of machines. However, most of the existing methods are developed for fault diagnosis task under stable working conditions, and cannot handle the data generated under sharp speed variation favorably. Besides, the large model parameters and high computing costs of these methods fail to meet the requirement of small memory and fast-precise prediction in practical applications. To address these challenges, a lightweight pyramid attention residual network (PARNet) is proposed in this paper. First, an efficient pyramid squeeze convolution module is proposed to capture multi-scale information from raw signals. Then, a soft squeeze-and-excitation attention is designed to establish long-range channel dependency, and fuse features of different scales. Based on the above improvements, a pyramid attention residual block is proposed as basic feature learning unit, and a concise CNN-based model PARNet is built using PAR block. Extensive experiments in two case studies are conducted to validate the effectiveness and adaptability of PARNet under different speed variation conditions. The test results demonstrate the superiority of the proposed method for machine fault diagnosis and reliability assessment in terms of recognition accuracy and model parameter complexity, compared with other advanced diagnostic methods.

Video stalling identification for web live streaming under HTTP-FLV

Live broadcasts have become one of the most popular forms of entertainment. Quality of user Experience (QoE) is a vital quantitative criterion for evaluating user satisfaction while watching live broadcasts, and it is positively correlated with the increase in the income of Internet Service Providers (ISPs). Video stalling identification plays a crucial role in the evaluation of QoE. However, encrypted live streaming hides video content, which makes identifying video stalling challenging. Existing studies primarily detect video stalling in a fixed time interval and focus on high-dimensional features. However, the capacity of the client byte buffer is dynamic, resulting in the stalling and non-stalling existing in a certain and fixed stalling time. In addition, the handling time of abundant features causes further latency. We propose Truncation of Dynamic Bytes and non-linear Integrated Modification based on Double Buffers (DB2) to identify video stalling under HTTP-FLV protocol in various network conditions and live types. We pull real-time video to get client buffer parameters and build a dynamic mapping based on the double buffer between network packets and the video playing states. This allows a more objective and precise evaluation of video stalling. We design a new network feature by creating a non-linear relationship between network packets and the client buffer. This is achieved by combining non-linear convergent distribution with basic traffic features. The feature is fed into a lightweight machine learning model to train the classifier, achieving low processing latency and high identification accuracy. The experimental results show that DB2 can achieve 98.91% stalling identification accuracy with 1.256 ms operation time in a mixture of live video types, outperforming state-of-the-art techniques.

Mechanical force modulates inflammation and immunomodulation in periodontal ligament cells

Abstract Mechanical forces control a multitude of biological responses in various cells and tissues. The periodontal ligament, located between the tooth’s root and alveolar bone, is a major tissue compartment that is incessantly subjected to such mechanical stimulation through either normal or abnormal oral functionality. It is now known that mechanical stimulation activates periodontal ligament stem cells (PDLSCs) to modulate periodontal immunity and regulate inflammation – a basic feature of periodontal disease that affects virtually every human during their lifetime. For instance, shear stress induces the expression of immunomodulatory-related gene, indoleamine 2,3-dioxygenase (IDO). IDO cleaves l-tryptophan, resulting in increased l-kynurenine levels that, in turn, further promote regulatory T-cell differentiation and inhibit T cell proliferation. These and other related data reinforce the notion that mechanical stimulation plays a crucial role in controlling inflammation and immunomodulation of periodontal tissues. Further investigations, however, are warranted to evaluate the immunomodulatory features of PDLSCs so as to understand the pathological basis of periodontal disease and translate these into clinical interventions.

Electronic structure of noncentrosymmetric B20 compound HfSn and tuning of multifold band-crossing points

We present a detailed theoretical study of the electronic structure of hafnium tin HfSn crystallizing in a B20 structure, renowned for the diversity of physical and peculiar topological properties. The chiral crystal structure of these materials protects multifold band crossings located at high-symmetry points. We employ density functional methods to reveal basic features of the band structure and Fermi surface topology of HfSn, on top of which a tight-binding model is built. The compound exhibits a fourfold band crossing pinned at the Γ point. We investigate routes that can shift such crossings towards the Fermi level, offering a way to possibly tune the compound's properties. Specifically, we show that the energy position of the fourfold crossing can be easily manipulated via external perturbations such as strain and pressure. Considering that this point carries a topological charge larger than 1, such tuning is of great importance. We anticipate that the approach presented in the current study can be utilized to investigate symmetry-protected crossings in a wide class of materials. Published by the American Physical Society 2024

Evaluating Existing Mobile Apps for Toddler Parenting: Recommendations for Future App Developers

Background: As the number of smartphones in use rises, more parents are turning to mobile applications for parenting guidance. However, identifying quality apps for guidance on child development can be challenging for parents. Objective: This study evaluated existing mobile applications to disseminate important parenting information among Indian parents using a structured analysis method. Methodology: This app analysis study aimed to document the basic and special features of toddler parenting apps in India available to parents as they promote their child's development and health. To understand the accessibility and quality of apps for toddler parents, we conducted an analysis of apps in the Google Play Store. The Google Play Store was searched for available parenting apps using different search terms such as mom, mother, mommy, father, dad, papa, daddy, baby, kid, toddler, child, children, parent, and parenting. We screened 138 apps and evaluated 40 that met the criteria for their features and usability. Results: We found that a great number of toddler parenting apps are available, but most of the apps had foreign regions of origin (50%). The information is in English language (65%) and the apps are mostly non-interactive (60%). Conclusion: The study addresses a crucial gap in the availability and quality of mobile applications tailored for toddler parenting, specifically in the Indian context. By evaluating and highlighting the shortcomings of existing apps, the study provides valuable insights that can inform the development of more effective, culturally relevant, and interactive parenting tools. The findings also emphasize the need for localized content, which is vital for enhancing parenting practices and child development.

DeepOnto: A Python package for ontology engineering with deep learning

Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms. Although packages such as OWL API and Jena offer robust support for basic ontology processing features, they lack the capability to transform various types of information within ontologies into formats suitable for downstream deep learning-based applications. Moreover, widely-used ontology APIs are primarily Java-based while deep learning frameworks like PyTorch and Tensorflow are mainly for Python programming. To address the needs, we present DeepOnto, a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more “Pythonic” manner and extending its capabilities to incorporate other essential components including reasoning, verbalisation, normalisation, taxonomy, projection, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of DeepOnto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI).

Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds

The cluster analysis of materials categorizes them according to similarities based on the features of materials, providing insight into the relationship between the materials. Conventional cluster analyses typically use basic features derived from the chemical composition and crystal structure without considering target material properties such as the bandgap and dielectric constant. However, such approaches do not meet demands for grading materials according to properties of interest simultaneously with chemical and structural similarities. Herein, a clustering method grouping similar materials in terms of both the target properties and basic features is proposed. The clustering is compared considering the cohesive energy with that considering the bandgap of metal oxides, showing that their categorizations are clearly different. Further, several clusters classified by the bandgap are analyzed, and coordination environments related to each range of the bandgap are revealed. The clustering for the electronic static dielectric constant identifies a cluster involving several perovskite‐type oxides and balancing with the bandgap near the Pareto front. The method enables analyses with different viewpoints from those of the conventional clustering and feature importance analyses by taking the relationship between the target property and the basic features into account.

Prediction of remaining useful life for rolling bearing based on ISOMAP and multi-head self-attention with gated recurrent unit

The remaining useful life of rolling element bearing affects the reliability and stability of the machine. Accurately predicting the remaining useful life of rolling element bearings is always necessary to make maintenance decisions in practical engineering. However, the remaining useful life prediction methods using traditional machine learning are incompetent for the task of training without labels, which consumes computational cost, financial resources, and labor. Therefore, a gated recurrent unit model based on feature dimensionality reduction and the multi-head self-attention mechanism (MHGRU) is established, and a homology algorithm is proposed to train the model and make the prediction. The isometric feature mapping algorithm and a homology algorithm are used in the model training, which incorporates the advantages of appreciable computational efficiency and preservation of automatic labeling for training in engineering. First, 24 basic features are extracted from the life-cycle vibration signals of rolling element bearings to reconstruct fusion features by the isometric feature mapping algorithm, which aims to reduce the feature dimension and improve computational efficiency. Since the multi-head self-attention mechanism in the MHGRU has the ability to comprehensively highlight the attention coefficient for long-term fusion features at different moments, it gives the gated recurrent unit considerable performance in reducing the computational complexity of extremely long time series and improving the accuracy of prediction results. In addition, two open-source IEEE PHM Challenge and XJTU-SY bearing datasets sampled by the sensor are adopted to assess the prediction performance of the MHGRU with homology algorithm. Finally, some existing remaining useful life prediction approaches of rolling element bearings are used for comparison. The experimental results show that MHGRU is suitable for the remaining useful life prediction of rolling element bearings and is superior to other models.

A Novel Multi-modal Sentiment Analysis Based on Multiple Kernel Learning with Margin-Dimension Constraint

Most of the sentiment analysis studies focus on the sentimental classification of pictures in the video, ignoring the spatio-temporal information of the sequence of picture frames as well as text and audio information. The multiple kernel learning is a new hotspot in the field of nuclear machine learning, capable of handling multiple modalities. For multiple kernel learning, it is easy to ignore the basic features that are not discriminative, and cannot make full use of the base features of different modes. This paper puts forward a novel multi-modal fusion model for sentiment analysis, in which a multiple kernel learning algorithm based on convolution margin-dimension constraint is proposed for feature fusion. Moreover, the 3D convolutional neural network is used to extract the features of visual information, and the multiple kernel learning algorithm based on margin-dimension constraint is used to fuse visual, text and audio sentiment features. Experiments conducted on the MOUD and IEMOCAP sentiment databases show that the proposed model outperforms existing models in the field of multi-modal sentiment analysis research.

Searching for Semantic Distance Effects

Language processing relies on memory. There exists a considerable body of literature on retrieval in sentence processing and, in particular, on cases involving recall of syntax-relevant information. There is no reason to doubt, however, that memory is involved in semantic aspects of language processing as well. In this work, we look at the case of additive presuppositions, such as those involved in interpreting the additive particle too. When one hears Mary went to the party, too, one should recall that someone other than Mary went to the party. We make the case that, as a starting hypothesis, it would be expected that the retrieval of this kind of information should share basic features of memory processes in language with the better-known cases of recall involved in syntactic parsing. In particular, we argue that, given certain assumptions and linking hypotheses, all prominent retrieval theories predict the existence of distance effects for the recall of previous information, independent of whether the recall is driven by syntactic or semantic, sentential or inter-sentential, considerations. As the distance increases, so does the difficulty of processing. We test this prediction in four experiments that investigate the role of retrieval in interpreting too. Using the Bayesian hierarchical modelling paradigm, we find evidence in two self-paced reading experiments that it takes more time to read sentences with too when the distance between the trigger and its antecedent is greater, compared to a baseline that lacks the presupposition trigger. This result shows that theories of the role of memory in language are just as relevant to the domain of discourse interpretation as they are to syntactic parsing. The fact that our evidence was relatively hard to find, however, suggests additionally that there are interesting differences to explore in the future.

Systematic investigation on the rational design and optimization of bi-based metal oxide semiconductors in photocatalytic applications

To address the global energy shortage and mitigate greenhouse gas emissions on a massive scale, it is critical to explore novel and efficient photocatalysts for the utilization of renewable resources. Bi-based metal oxide (Bi x MO y ) semiconductors composed of bismuth, transition metal, and oxygen atoms have demonstrated improved photocatalytic activity and product selectivity. The vast number of element combinations available for Bi x MO y materials provides a huge compositional space for the rational design and isolation of promising photocatalysts for specific applications. In this study, we have systematically investigated the electronic and optical properties over Bi2O3 and a series of selected Bi x MO y group materials (BiVO4, BiFeO3, BiCoO3, and BiCrO3) by calculating band structure, basic optical property features, mobility and separation of charge carriers. It is clearly noted that the band gap and band edge position of the Bi x MO y group materials can be tuned in a wide range in comparison to Bi2O3. Similarly, the light response of Bi x MO y also can be broadened from the ultraviolet to the visible light region by adjusting the selection of transition metals. Additionally, the analysis of the effective mass of charge carriers of these materials further confirms their possibility in photocatalytic reaction applications because of the appropriate separation efficiency and mobility of carriers. A selection of experimental investigations on the crystal structure, composition, and optical properties of Bi2O3, BiVO4, and BiFeO3 as well as their catalytic performance in the degradation of methylene blue over was also conducted, which agree well with the theoretical predictions.

Unraveling the binding problem in working memory: insights from the hierarchical binding model.

The binding problem is a crucial issue in the study of working memory (WM) and remains a central topic of debate among various WM models. Over the past decade, we have explored feature binding within WM, guided by the Hierarchical Binding Model (HBM). This model suggests that WM binding occurs in two stages: an initial implicit binding involving rapid, coarse feature processing, followed by explicit binding where focused attention refines these features via a reentry process. We found that implicit binding is closely related to the attentional processing of features during the perceptual stage. Basic features that can be rapidly and coarsely processed in parallel through spread attention are involuntarily extracted into WM along with the target features, forming a rough bound representation. For explicit binding, we examined the role of attention in retaining explicit binding in WM, emphasizing the unique role of reentry in the HBM. Our findings indicate that WM binding requires additional object attention through the reentry process. These results demonstrate that both implicit and explicit bindings are integral to WM and that the HBM is effective in elucidating the binding mechanisms within WM.

Intuitive network topology.

Topology is the branch of mathematics that seeks to understand and describe spatial relations. A number of studies have examined the human perception of topology-in particular, whether adults and young children perceive and differentiate objects based on features like closure, boundedness, and emptiness. Topology is about more than "wholes and holes," however; it also offers an efficient language for representing network structure. Topological maps, common for subway systems across the world, are an example of how effective this language can be. Inspired by this idea, here we examine "intuitive network topology." We first show that people readily differentiate objects based on several different features of topological networks. We then show that people both remember and match objects in accordance with their topology, over and above substantial variation in their surface features. These results demonstrate that humans possess an intuitive understanding for the basic topological features of networks, and hint at the possibility that topology may serve as a format for representing relations in the mind. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Association of obesity and the clinical course of hospitalised COVID-19 survivors.

Obesity is a risk factor for chronic inflammation and severe pulmonary infections. This study aimed to assess the association between obesity and the clinical courses of hospitalised COVID-19 survivors. This cross-sectional study used the Isfahan COVID Cohort (ICC) baseline data. The ICC is an ongoing, 5-year prospective, longitudinal cohort study conducted on hospitalised COVID-19 survivors in affiliated hospitals of the Medical University of Isfahan (MUI), Iran. Patients aged 19 and over throughout Isfahan County were recruited using a consecutive sampling method 1 month after discharge. Demographic and basic characteristics, symptoms and clinical features of these patients were collected and analysed. A total of 3843 hospitalised patients with COVID-19 were included in this study. Regarding the body mass index classification in the general obesity group, the patients with overweight and obesity had more extended hospitalisation and a higher frequency of low O2 saturation compared to the normal weight patients, and the highest frequency of low O2 saturation and more extended hospitalisation was observed in patients with obesity (5.9 ± 3.8 vs. 6.8 ± 5.4 vs. 7.1 ± 5.3, respectively; p = .001 and 59% vs. 64.5% vs. 65.5%; p < .001). Furthermore, individuals with abdominal obesity had a significantly longer duration of hospitalisation compared to the non-abdominal obesity group (6.3 ± 4.6 vs. 7.0 ± 5.3; p < .001). In the fully adjusted model, a significant association was observed between abdominal obesity and an increased occurrence of low oxygen saturation compared to general obesity (odds ratio: 1.25, 95% confidence interval: 1.03-1.44). Obesity was associated with more extended hospitalisation and hypoxia in patients with COVID-19. However, no significant relationship was found between obesity and other clinical courses.

Enhancing rural transportation services for older adults: Bridging customer-provider perspectives

Rural older adults reliant on transportation services represent an historically underserved demographic, yet their specific service needs remain inadequately understood. This study aims to explore important rural transportation service features for senior customers while identifying service gaps and potential misconceptions between senior customers and service providers. Utilizing data from a cross-sectional service survey in rural Wisconsin, 13 important service features were identified by both senior customers and service providers, and grouped into three categories—basic, technological, and special—via exploratory factor analysis. While consensus existed between seniors and providers on the primary importance of basic features, seniors attributed greater significance to technological aspects than service providers anticipated. Differing preferences surfaced between seniors and providers regarding special features. Statistical tests revealed the substantial importance of basic features, particularly among older females, individuals with mobility constraints, and limited leisure activity. Moreover, technological features received higher ratings from older females, younger individuals, and those with internet access. Varied attention towards special features aligned with diverse older adult needs. Service providers, regardless of their characteristics, exhibited consistent prioritization of service features. Our findings emphasize opportunities for rural transport service providers to better serve senior users by enhancing basic service provisions, embracing age-friendly technologies, and tailoring specialized services to meet distinct user requirements within local contexts.

Cortical tracking of language structures: Modality-dependent and independent responses

ObjectivesThe mental parsing of linguistic hierarchy is crucial for language comprehension, and while there is growing interest in the cortical tracking of auditory speech, the neurophysiological substrates for tracking written language are still unclear. MethodsWe recorded electroencephalographic (EEG) responses from participants exposed to auditory and visual streams of either random syllables or tri-syllabic real words. Using a frequency-tagging approach, we analyzed the neural representations of physically presented (i.e., syllables) and mentally constructed (i.e., words) linguistic units and compared them between the two sensory modalities. ResultsWe found that tracking syllables is partially modality dependent, with anterior and posterior scalp regions more involved in the tracking of spoken and written syllables, respectively. The cortical tracking of spoken and written words instead was found to involve a shared anterior region to a similar degree, suggesting a modality-independent process for word tracking. ConclusionOur study suggests that basic linguistic features are represented in a sensory modality-specific manner, while more abstract ones are modality-unspecific during the online processing of continuous language input. SignificanceThe current methodology may be utilized in future research to examine the development of reading skills, especially the deficiencies in fluent reading among those with dyslexia.

HGLA: Biomolecular Interaction Prediction based on Mixed High-Order Graph Convolution with Filter Network via LSTM and Channel Attention.

Predicting biomolecular interactions is significant for understanding biological systems. Most existing methods for link prediction are based on graph convolution. Although graph convolution methods are advantageous in extracting structure information of biomolecular interactions, two key challenges still remain. One is how to consider both the immediate and highorder neighbors. Another is how to reduce noise when aggregating high-order neighbors. To address these challenges, we propose a novel method, called mixed high-order graph convolution with filter network via LSTM and channel attention (HGLA), to predict biomolecular interactions. Firstly, the basic and high-order features are extracted respectively through the traditional graph convolutional network (GCN) and the two-layer Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing (MixHop). Secondly, these features are mixed and input into the filter network composed of LayerNorm, SENet and LSTM to generate filtered features, which are concatenated and used for link prediction. The advantages of HGLA are: 1) HGLA processes high-order features separately, rather than simply concatenating them; 2) HGLA better balances the basic features and high-order features; 3) HGLA effectively filters the noise from high-order neighbors. It outperforms state-ofthe-art networks on four benchmark datasets. The codes are available at https://github.com/zznb123/HGLA.