Bowlby’s (1969/1982) attachment theory has been employed as a broad and integrative framework to explore human wellness across a range of disciplines. Attachment theory has even been labelled one of the last surviving “grand theories” not to have been completely dismissed, replaced, or extensively reworked (e.g., Carr, 2012; Mercer, 2011). However, despite the ubiquitous nature of some of the theory’s fundamental tenets, there are always possibilities for new conceptual development, extension, and revision. In this paper, we critically explore the idea of “context-specific” attachment within parent-child relationships. We briefly outline critical assumptions and key areas of attachment and articulate potential rationale, conceptualisation, and relevance of contextual attachment.

In an open and dynamic IoT (the Internet of Things) environment, a common context information model is essential for active things to share common knowledge, reason their situations, and support adaptive interoperability with each other. There have been many studies on the IoT context information models based on semantic technology, but most of them have assumed a static situation based on a service-oriented information model suitable for specific applications of the IoT. In the case of applying their models to an open and dynamic IoT environment, two issues have been observed: Most of the models ignore (a) the mashup of the open-world semantics of context information generated by multiple context sources and (b) the reconciliation of the semantic relationships between multiple context entities under dynamic situation changes. Therefore, in this paper, we propose a context information model that is flexible enough to express complex and diverse semantic relationships between context information generated from a variety of context information sources in the IoT. The main background of this proposal is to propose an adaptive context model that can effectively mash up various context classes that use ontology in open and dynamic IoT environments. In this paper, we also show the effectiveness of the proposed model through an adequate verification model and a practical example.

No research to date has explored the possibility of context-specific, within-relationship fluctuation in attachment security. In this present article, two cross-sectional studies were designed (1) to develop and validate context-specific attachment scales in Traditional-Chinese, and (2) to explore fluctuations in within-parent attachment security between the contexts of sport and academics, in relation to global attachment patterns and indicators of psychological wellbeing. Results indicated that youth can and do perceive within-parent attachment patterns differently depending upon context but that the relationship of such differences to context-specific outcomes is complex. Of particular interest was that the degree of within-parent attachment variability between contexts was clearly and negatively related to indices of psychological wellbeing. This suggests that contextual variation may be a meaningful and useful way to explore within-parent attachment fluctuation.

Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-D camera, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robotic context query language (ST-RCQL) and spatio-temporal robotic context query-processing system (ST-RCQP), for service robots. We designed them based on the spatio-temporal context ontology. ST-RCQL can query not only the current context knowledge but also the past. In addition, ST-RCQL includes a variety of time operators and time constants, and thus queries can be written very efficiently. The ST-RCQP is a query-processing system equipped with a perception handler, working memory, and backward reasoner for real-time query-processing. Moreover, ST-RCQP accelerates query-processing speed by building a spatio-temporal index in the working memory, where percepts are stored. Through various qualitative and quantitative experiments, we demonstrate the high efficiency and performance of the proposed context query-processing framework.

The new Internet of Things (IoT) applications are enabling the development of projects that help monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s Disease (AD). This paper presents an ontology-based computational model which receives physiological data from external IoT applications, allowing to identify of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of Context Histories and Context Prediction, which considering the state of the art, it is the only one that uses analysis of Context Histories to perform predictions. The research also proposes a simulator to generate activities of the daily life of patients allowing the creation of datasets. These datasets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1025 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical and methodological aspects of DCARE that are recorded in this article.

The advent of the era of big data will bring more convenience to people and greater development to society. But at the same time, it will also bring people the problem of 'information overload', i.e., when people are faced with huge information data, there are many redundant and worthless data. The redundant and worthless data information seriously interferes with the accurate selection of information data. Even though people can use Internet search engines to access information data, they cannot meet the individual needs of specific users in specific contexts. The personalized needs of a particular user in a particular context. Therefore, how to find useful and valuable information quickly has become one of the key issues in the development of big data. With the advent of the era of big data, recommendation systems, as an important technology to alleviate information overload, have been widely used in the field of e-commerce. Recommender systems suffer from a key problem: data sparsity. The sparsity of user history rating data causes insufficient training of collaborative filtering recommendation models, which leads to a significant decrease in the accuracy of recommendations. In fact, traditional recommendation systems tend to focus on scoring information and ignore the contextual context in which users interact. There are various contextual modal information in people's real life, which also plays an important role in the recommendation process. In this paper we achieve data degradation and feature extraction, solving the problem of sparse data in the recommendation process. An interaction context-aware sub-model is constructed based on a tensor decomposition model with interaction context information to model the specific influence of interaction context in the recommendation process. Then an attribute context-aware sub-model is constructed based on the matrix decomposition model and using attribute context information to model the influence of user attribute contexts and item attribute contexts on recommendations. In the process of building the model, the method not only utilizes the explicit feedback rating information of users in the original dataset, but also utilizes the interaction context and attribute context information of the implicit feedback and the unlabeled rating data. We evaluate our model by extensive experiments. The results illustrate the effectiveness of our recommender model.

During recent decades, contextual computing applications have emerged in the field of healthcare and particularly in the field of telemonitoring of patients suffering from chronic obstructive pulmonary disease (COPD). According to WHO rankings, chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Various research works are therefore carried out to improve the health of patients and the monitoring of patients in the comfort of their home environment. To this end, several telemonitoring systems are designed to COPD patients. These systems are connected to health center. Emergency physicians follow the patients subscribed to these systems remotely. These systems focus mainly on prediction, decision-making and the requirements of the healthcare profession, and do not address the quality control aspects of services or QoS based on service level agreements (SLAs). This situation can be dangerous for patients in case of extreme exacerbation of COPD patients. For example, the unavailability of the monitoring system can lead to the death of the patient because the emergency physician could not have access to the patient's data in real time in the context of COPD Patient Monitoring. In addition, Remote medical monitoring platforms are manipulating large volumes of data and the risks of data lost or data quality are real. It is therefore important to have the mechanisms to continuously improve the quality of service of these monitoring platforms in general and COPD patients particularly. In this article, we propose an ontology that uses SLA information from COPD monitoring platforms with dynamic data from the patient context. The purpose of this article is to propose a dynamic mechanism model for evaluating SLA violations. This solution allows retrieving knowledge from the main items of the SLA document based on XML and the COPD patient context data dynamically from a COPD SLA ontology. These data retrieved in real time allow the calculation of SLO-based metrics and display a SLA template available on the supplier and consumer interfaces. The information of the SLA violation control Interface changes dynamically depending the context-aware system and SLA document data. The SLA parties can dynamically control their Key Performance Indicators (KPI) Target.

Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.

Public participation in the decision-making process in Urban Interventions is the key to the success of the project for improving the quality of life of its citizens. The citizen has the democratic right to express his needs and aspiration; he is the final user who experiences the outcomes of the policy decisions. Non involvement of the citizens in the planning process can bring about the misinterpretation of the intention of political leadership and lead to opposition and protest. The inadequate understanding of citizens of the urban context makes public participation ineffective. In this context, the decision-makers are often faced with the challenges of the level of confidence of the citizens about their ideas and responses being incorporated in the project and the confidence of the citizens in the local urban authority in its ability to carry out the project. However, the decision-makers base their decision on the assumption that the citizens have a general understanding of the urban issues. This research work investigates the basis of this assumption. 1. Do the citizens have confidence that the local urban authority considers their choices and responses in the course of decision making 2. Do the citizens have the confidence that the local urban authority can undertake the Urban Regeneration project 3. Whether in the decision-making process of urban regeneration intervention, citizen's responses are backed by a general understanding of urban issues. The case study taken up is of Hassan city. Five areas of crucial importance have been selected based on the development plan report of the city. The integrated approach aims to find the most appropriate area for proposing the Urban Regeneration project. The framework adopted includes 1. Questionnaire survey: to collect citizens’ responses 2. Analysis of variance (ANNOVA) for analysis of the data collected.

This paper proposes a method for recognizing audio events in urban environments that combines handcrafted audio features with a deep learning architectural scheme (Convolutional Neural Networks, CNNs), which has been trained to distinguish between different audio context classes. The core idea is to use the CNNs as a method to extract context-aware deep audio features that can offer supplementary feature representations to any soundscape analysis classification task. Towards this end, the CNN is trained on a database of audio samples which are annotated in terms of their respective "scene" (e.g. train, street, park), and then it is combined with handcrafted audio features in an early fusion approach, in order to recognize the audio event of an unknown audio recording. Detailed experimentation proves that the proposed context-aware deep learning scheme, when combined with the typical handcrafted features, leads to a significant performance boosting in terms of classification accuracy. The main contribution of this work is the demonstration that transferring audio contextual knowledge using CNNs as feature extractors can significantly improve the performance of the audio classifier, without need for CNN training (a rather demanding process that requires huge datasets and complex data augmentation procedures).

Semantic segmentation of remote sensing urban scene imagery is a pixel-wise prediction, which is applied to identify the land-cover or land-use category. However, semantic segmentation demands huge computation cost. In order to reduce the huge computation cost, a common method is to introduce transformer and CNN hybrid method to have a good trade-off between accuracy and computation cost. However, recent CNN-transformer hybrid methods often capture local-global context and cross-window interaction information simultaneously. Then they introduce the local-global context to fuse the local feature, which repeat fuse the local and global feature and result in the additional computation cost. And previous methods often ignore to filter the local and global feature. To fix the problem, we design a lightweight decoder-EDDformer, which is Efficient Global Value Transformer with Dynamic Gatefusion module. Efficient global value transformer only response for extracting global feature. Dynamic Gatefusion module filter the local and global semantic feature and fuse them to capture the local-global context in one time. Extensive experiments reveal that our method not only runs faster but also achieves higher accuracy compared with other state-of-the-art lightweight models.

Carbapenems are considered for treating Klebsiella pneumoniae and other Enterobacteriaceae infections, especially if they are not susceptible to other generally prescribed antibiotics, i.e., if they show resistance. In such cases, antibiotic activity decreases, and most patients succumb to the infection. A better understanding of the disease pattern and resistance mechanisms could be gained by magnifying the genes that confer resistance to antibiotics. Therefore, studying the genes that confer resistance to carbapenems and any other antibiotics for that matter is indispensable for coming up with improved treatment options. This study included the analyses of co-resistance patterns between resistance genes-between drug classes and within the carbapenem-resistant genes, genomic context analysis of highly expressed carbapenem-resistant genes, and phylogenetic study of OXA-producing genes, plasmid incompatibility identification, and sequence type identification using MLST. The presence of ESBLs, MBLs, and SBLs across the downloaded genomes was studied. SHV-producing genes were found to co-occur with most of the resistant genes belonging to different drug classes. The plasmid incompatibility type IncFIB was found to be common among the highly expressed genes, and most of these genes were flanked by different families of insertion sequence (IS) elements. MLST study suggested that the presence of sequence types ST-11, ST-14, and ST-147 was common in the downloaded set of genomes.

Background: Colorectal cancer (CRC) is one of the most prevalent diseases and the second leading cause of death worldwide. However, the relationship between CRC and cerebrovascular-specific mortality (CVSM) remains elusive and less is known about the influencing factors associated with CVSM in CRC. Here, we aimed to analyze the incidence as well as the risk factors of CVSM in CRC. Methods: Patients with a primary CRC diagnosed between 1973 and 2015 were identified from Surveillance Epidemiology and End Results database with follow-up data available until 31 December 2016. Conditional standardized mortality ratios were calculated to compare the incidence of CVSM between CRC patients and the general US population. Univariate and multivariate survival analyses with a competing risk model were used to interrogate the risk factors for CVSM. Results: A total of 563298 CRC individuals were included. The CVSM in CRC patients was significantly higher than the general population in all age subgroups. Among competing causes of death in patients, the cumulative mortality caused by cerebrovascular-specific diseases steadily increased during study period. While age and surgery positively influenced CVSM on both univariate and multivariate analyses, male patients and those who had radiotherapy, chemotherapy, more recent year (2001-2015) of diagnosis as well as multiple primary or distant tumors experienced a lower risk of CVSM. Interpretation: Our data suggest a potential role for CRC in the incidence of CVSM and also identify several significant predictors of CVSM, which may be helpful for risk stratification and therapeutic optimization of cerebrovascular-specific diseases in CRC patients.

This paper shares research results of an explorative study investigating a Malta context university lectureship experience. The qualitative research findings, based on thematic analysis of 10 interview transcripts, describe the experience of being a university academic as an ever-changing journey of time passages in space and time. The journey is characterised by the strong theme of teaching along with the two other primary themes labelled identity and un/belonging. Identity feeds on teaching and the sense of un/belonging, but not only. Profession/Practice and Research/Publication are two other themes that in this study emerge as feeding the primary themes. These research findings unsettle privilege portrayals of tenure track university lectureship. They confirm the emphasis on teaching in this local context and increased administration obligations. Distinctively, they expose another identity dimension additional to the teacher and researcher attributions highlighted in mainstream literature. These findings suggest that university academics need to be supported for keeping alive differentiated identity dimensions which are not in opposition to each other yet in competition for time. While the limitations of the study are acknowledged, several recommendations deriving from this study for the local context are shared.

Investments in digital infrastructure in marginalised communities are set to increase in the next decade. These are premised on the potential of digital technologies to contribute towards solving societal problems, including the fragility of food value chains in rural areas. Although there are mixed empirical findings on the impact of these digital infrastructure investments, huge investments are continuing amid changing ICT policies in most developing countries. This paper, using a case study of a local livestock value chain in a rural community in Zimbabwe, argues for the application of non-conventional approaches towards digital infrastructure transformation impact assessment. Using selected theories and frameworks (socio-ecological systems framework, choice framework and technology affordances theory) as well as empirical data from a project in a rural community, the paper shows that real-time impact assessment using context-specific metrics may reveal hidden digital infrastructure transformation impacts, positive and negative, that are often overlooked when traditional impact assessment approaches are employed. The findings of this study contribute towards improving approaches towards ICT impact assessment. Practitioners engaging in impact assessment are challenged to move beyond dependence on traditional metrics (e.g. access) to the adoption of participatory processes to decipher context-appropriate metrics.

This article analyzes local level normative regulations aimed at directly or indirectly conserving the urban landscape in rural areas. Using a discursive analysis methodology on regulatory documents being enforced in a series of localities assigned to a tourism promotion program, evidence suggests that promotional activity retroactively influences the phraseology of these municipal regulations. The results obtained point to the existence of perpetuating historicist approaches within the current regulations, which appear to largely derive from the search for success in the tourism market and the resulting benefits to the local economy.

Malware analysis is fundamental for defending against prevalent cyber security threats, and requires a means to deploy and study behavioural software traits as more sophisticated malware is developed. Traditionally, virtual machines are used to provide an environment that is isolated from production systems so as to not cause any adverse impact on existing infrastructure. Malware developers are fully aware of this and so will often develop evasion techniques to avoid detection within sandbox environments. In this paper, we conduct an investigation of anti-evasion malware triggers for uncovering malware that may attempt to conceal itself when deployed in a traditional sandbox environment. To facilitate our investigation, we developed a tool called MORRIGU that couples together both automated and human-driven analysis for systematic testing of anti-evasion methods using dynamic sandbox reconfiguration techniques. This is further supported by visualisation methods for performing comparative analysis of system activity when malware is deployed under different sandbox configurations. Our study reveals a variety of anti-evasion traits that are shared amongst different malware families, such as sandbox `wear-and-tear', and Reverse Turing Tests (RTT), as well as more sophisticated malware samples that require multiple anti-evasion checks to be deployed. We also perform a comparative study using Cuckoo sandbox to demonstrate the limitations of adopting only automated analysis tools, to justify the exploratory analysis provided by MORRIGU. By adopting a clearer systematic process for uncovering anti-evasion malware triggers, as supported by tools like MORRIGU, this study helps to further the research of evasive malware analysis so that we can better defend against such future attacks.

Aim: The paper aims at defining the role of abductive reasoning in the reader’s interpretation of English fiction narrative text. Three research questions are defined as follows: (1) what is the nature of sign interpretation in its application to textual analysis? (2) what linguistic factors determine the use of abduction in the interpretation of signs? (3) how to apply abductive reasoning in the process of reading and interpretation in EFL teaching practice? Abduction is viewed here as a type of reasoning in the three-componential semiotic model of argument and as a deductive hypothesis, responsible for implicit meaning processing (Charles Peirce). Materials and Methods: The paper states the four-stage process of abduction to be a basic inquiry method of the reader on his way to fiction world interpretation. By providing a step-by-step analysis of patterns of abductive reasoning in a short story “Happy Endings” by Margaret Atwood, the paper conducts a textual analysis of narratives in terms of subjectivity theory of communication, reflecting the mechanisms of reader’s manipulation with information as a dynamic semiotic process of interpretation, limited by habit (final interpretant). Results: of the research of the mental operations employed by the reader while processing textual information proved a strong interrelation of reading with writing, and mental sub-processes and operations. As the empirical research shows, the process of conceptualization demands a higher level of cognitive maturity on the part of the reader/writer, as it presupposes “knowledge transforming” operations as opposed to “knowledge telling” strategy (Paltridge et. al. 2009: 20). To represent this process schematically, scholars assign the reader/interpretant the central role in the process of triadic sign interpretation, as he makes the further interpretation possible by a reference to the environment (Scheibmayer 2004: 305). The interpretant (I) and Representamen (R1) refer to the same object (O); as Representamen (R2) stands in the same relation to object, represented by Representamen (R1) and to the system (O2), where it acquires the functions of the observer (Sonnenhauser, 2008: 331). Conclusions: The conclusions coming from this research lead to the recognition of the second-level (or third level) observer as a source of subjectivity. And subjectivity, in its turn, arises from the difference in interpretation of signs recognized and established by the observer (Maturana & Varela, 1980). Thus, the process of differentiation by the observer is expected to fix the possible existence of other meanings, produced by the relations of the interpretant to the environment. This is the notion of thirdness. And, therefore, “sign situation”, plays the role of marking the pairs of differentiation in semiotic interpretation of signs. And it is this differential potential of indexical components of signs, and not their relatedness of meaning, which makes communication dynamic.

Although prior research has extensively examined the association of emotional intelligence (EI) with various job attitudes (e.g., job satisfaction), the empirical and systematic investigation of this link within military institutions has captured considerably less research attention. The present research analyzed the relationship between EI, teamwork communication, and job satisfaction among Spanish military cadets. We tested the potential unique contribution of EI to job satisfaction over and above demographics (i.e., gender and age), proactive personality, and resilience. Moreover, we also examined whether EI indirectly affects job satisfaction via its relationship with teamwork communication. A sample of 363 cadet officers of the Spanish General Military Academy completed questionnaires assessing EI, teamwork communication, proactive personality, resilience, and job satisfaction. Our results revealed that EI exhibited incremental variance in predicting job satisfaction even after accounting for demographics, proactive personality, and resilience. Additionally, we found that the effect of EI on job satisfaction was partially driven by enhanced teamwork communication. This research provides empirical evidence suggesting a pathway (i.e., effective teamwork communication) through which EI helps military cadets to experience higher job satisfaction. Implications for future academic programs including EI and teamwork communication to promote positive job attitudes among military personnel are discussed.

The eating behaviors of young people underwent substantial changes due to the COVID-9 19 pandemic. This study addresses the understanding of pandemic impacts on perceived psycho-10 logical and physical well-being exploring the connection between self-perceived eating behavior 11 quality and chronic stress. A sample of Portuguese young people aged 15-26 years (N=951), com-12 pleted a self-report questionnaire assessing socio-demographic data, perception of eating quality 13 behavior, appetite, stress, well-being and optimism. The results reveal gender differences, with 14 males scoring higher in well-being and optimism, while females exhibit elevated appetite and 15 chronic stress scores, and also reveals negative links between age group and eating quality and 16 chronic stress, and positive links between eating quality and well-being. Well-being emerges as a 17 significant predictor of stress in middle and oldest young adults that maintain or increase quality of 18 eating behaviors, while optimism plays a paradoxical role in predicting higher stress in younger 19 adults with reduced eating quality. These results emphasize the need to consider eating behaviors 20 and stress among young adults as an investment in global public health, given the significant rep-21 resentation of young adults in the population, to mitigate long-term health risks. Effectively reduc-22 ing detrimental eating behaviors and stress in these demographic groups can enhance overall well-23 being and longevity.

This article obtains data through questionnaire survey, and then uses the Multiple Indicators Multiple Causes (MIMIC) Model to establish the Taoyuan City’s quality of life quantifying framework in a city-level community context. Statistical test results show that the reliability of overall model is high and is suitable for policy analysis. There are thirteen variables in the structural model that significantly affect the quality of life as well as eleven indicators in the measurement model that can significantly measure the quality of life. Then, half of the observations are substituted back to calculate the quality of life. Via ArcGis operation, it finds that the quality of life of Longtan District is the best as well as Dayuan District is the worst one. LISA cluster of quality of life illustrates hot spots areas are distributed in Longtan, Daxi, and Pingzhen Districts.

This paper presents the goals and components of a quantitative energy balance assessment framework to define PEDs flexibly in three important contexts: the context of the district's density and RES potential, the context of a district's location, induced mobility and the context of the dis-trict's future environment and its decarbonized energy demand or supply. It starts by introducing the practical goals of this definition approach: achievable, yet sufficiently ambitious to be inline with Paris 2050 for most urban and rural Austrian district typologies. It goes on to identify the main design parts of the definition: system boundaries, balancing weights and balance targets and argue how they can be linked to the definition goals in detail. In particular we specify three levels of system boundaries and argue their individual necessity: operation, including everyday mobili-ty, including embodied energy and emissions. It argues that all three pillars of PEDs, energy effi-ciency, onsite renewables and energy flexibility can be assessed with the single metric of a prima-ry energy balance when using carefully designed, time-dependent conversion factors. Finally, it is discussed how balance targets can be interpreted as information and requirements from the sur-rounding energy system, which we identify as a "context factor". Three examples of such context factors, each corresponding to the balance target of one of the previously defined system bounda-ries operation, mobility and embodied emissions are presented: Density (as a context of opera-tion), sectoral energy balances and location (as a context for mobility) and an outlook of a person-al emission budgets (as a context for embodied emissions). Finally, the proposed definition framework is applied to seven distinct district typologies in Austria and discussed in terms of its design goals.

It is widely acknowledged that adolescent females are particularly at risk of low body-esteem. Low body-esteem is associated with poor mental health and other negative outcomes. Interventions to help raise body-image could have considerable impact, especially if the intervention is low-cost, easy to implement and scalable. We investigated the efficacy of an intervention where participnants could chose their own clothes to wear during a Physical Education (PE lesson) on changes in body-esteem. We hypothesized that body-esteem would improve with choice. To show that body-esteem is not a transient construct, we tested its stability when assessed in a test-retest design when completed in a classroom setting, hypothesizing body-esteem would be stable. Participants (N =110; Mage =14.9; SDage = 0.68) females completed a 14-item body-esteem scale 4 times; a) wearing school uniform in an assembly, b) during a PE lesson separated by a 2-week gap. The intervention was implemented where students got a choice of PE kit and could wear their own clothes. Findings indicate that body-esteem was stable in the classroom setting where clothes and context were stable, but improved significantly when participants were given a free choice of kit to wear during PE. We argue that this low-cost and scalable intervention represents a useful start point for helping support low body-esteem among a potentially vulnerable population.

This paper describes an image processing method that makes use of image parts instead of neural parts. Neural networks excel at image or pattern recognition and they do this by constructing complex networks of weighted values that can cover the complexity of the pattern data. These features however are integrated holistically into the network, which means that they can be difficult to use in an individual sense. A different method might scan individual images and use a more local method to try to recognise the features in it. This paper suggests such a method, where a trick during the scan process can not only recognise separate image parts, as features, but it can also produce an overlap between the parts. It is therefore able to produce image parts with real meaning and also place them into a positional context. Tests show that it can be quite accurate, on some handwritten digit datasets, but not as accurate as a neural network, for example. The fact that it offers an explainable interface could make it interesting however. It also fits well with an earlier cognitive model, and an ensemble-hierarchy structure in particular.

With the widespread of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose to augment the time series signals from inertia sensors with signals from ambient sensing to train deep convolutional neural networks (DCNN) models. DCNN provides the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertia and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors such as environment noise level and illumination, with an overall improvement of 5.3% accuracy.

This paper presents a system based on pedestrian dead reckoning for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a cooperative system to estimate the direction of motion based on a machine learning approach for perturbation detection and filtering, combined with a consensus algorithm for performance augmentation by cooperative data fusion at multiple device. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth's magnetic field. A second algorithm aggregates groups of users walking in the same direction, while a third one combines the measurements of the aggregated users in a distributed way to extract a more accurate heading estimate. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance.

Parasite control strategies such as blanket anthelmintic treatment of all horses in a herd or interval treatment regimen based on egg reappearance remain widely followed practices for the last several decades. These practices have resulted in the fast emergence of resistance in equine strongyles to all major classes of drugs on the market. In this regard, the guidelines set by the American Association of Equine Practitioners advocate evidence-based strongyle control in horses. It recommends targeted treatment of all heavy egg shedders [> 500 eggs per gram (EPG) of feces] while the low shedders (0-200 EPG) are left untreated to maintain susceptible parasite refugia in a herd. As 50-75% of adult horses in a herd are low shedders, preventing them from unnecessary anthelmintic exposure is critical to tackling resistance. There are various fecal egg count (FEC) techniques with many modifications and variations in use, but none is identified as a gold standard and methods comparison studies are lacking. We hypothesized that the diagnostic performance of commonly used quantitation methodologies differs and the FEC methods must be evaluated to determine the gold standard. In this regard, we performed methods comparison studies using polystyrene beads with size and specific gravity comparable to strongyle eggs, as proxy. The linearity of 12 commonly used FEC methodologies (3 techniques with 4 different solutions) was studied using bead standards in the clinically applicable range (63 to 1,000) to determine their suitability for strongyle egg counts. Deming regression analysis identified Mini-FLOTAC with sugar (specific gravity 1.33) as the test with the greatest coefficient of determination. All tests underestimated the true bead count, and we determined a correction factor (CF) for each test to estimate the true count. CF negates the preference for a gold standard test. Finally, we analyzed the validity of CF for 5 tests with R2>0.95 to accurately quantify strongyle eggs from 40 different horses. Overall, this study identified methodologies with the highest diagnostic performance and the limitations to standardizing routine FEC tests to promote uniformity in implementing AAEP parasite control guidelines.

Obtaining a proper position for THA’s components is a crucial aspect for the implant perfor-mance and consequently patient outcomes. Patient specific kinematic seems to be a key factor to reach the optimal implant positioning. The aim of this study was to assess the accuracy and safety of a computed dynamic analysis system which through patient specific guides tries to improve implant positioning and functional orientation according to patient’s spinopelvic mobility and anatomy.A total of 100 consecutive patients were prospectively enrolled. All patients received an OPS dynamic hip preoperative planning. A TC scan protocol follow up analysis has done at 6 month after surgery.The mean deviation from the planned acetabular inclination and anteversion was 4.3° e 3.8° respectively. The 98% of cases was within +/- 10° of Lewinnek safe zone both for inclination and anterversion. The height of osteotomy deviated on the average of 1.6mm. The 100% cases were included within 4mm of osteotomy. Patient specific and laser guided instru-mentation is safe and accurately reproduce the dynamic planning in terms of components orien-tation, osteotomy level, leg length and offset.

This paper aims to provide an overview of the complete process in the development of a Domain-Specific Language (DSL). It explains the construction steps such as preliminary research, language implementation, and evaluation. Moreover, it provides details for different key components which are commonly found in the DSLs such as the abstraction layer, DSL metamodel, and the applications. It also explains the general limitations related to the Domain-Specific Languages for Workflows.

The purpose of this study is to check out the involvement of entropy in Mpemba effect. Provided that preheating of the water the cooling duration is reduced, we theoretically show that water gains more entropy when warmed and re-cooled to the original temperature.

Nanoparticles (NPs) are widely used in diverse disciplines, including biology, medicine science. The central question that need to be answered is whether NPs have toxic effects on biological cells and molecules or are they safe. The safety of NPs including targeted drug delivery is critical and so is their toxicity in the environment. In recent years, in vitro and in vivo research on animals has generated abundant information about the toxicity of NPs. However, due to varying laboratory conditions, the comparison of the results from ensuing studies is somewhat unreliable. It should be noted that, depending on the type of production, NPs can enter the body through inhalation, skin and via digestive routes. Due to the diversity of NPs and their properties, there is paucity of accurate information on their toxicological effects; particle size, shape, surface area and the chemical levels are considered as key factors in creating health and toxicological effects. Consequently, there is a need for reliable information about their effects on various organs so as to deal with NPs effectively and their impact on health and the environment. This review covers the existing knowledge base on the subject that hopefully prepares us better to address these challenges.

Neisseria gonorrhoeae is a strict human pathogen responsible for more than 100 million new sexually transmitted infections worldwide each year. Due to the global emergence of antibiotic resistance, the CDC recently listed N. gonorrhoeae as an urgent threat to public health. No vaccine is available in spite of the huge disease burden and the possibility of untreatable gonorrhea. The aim of this study is to investigate the immunogenicity of a novel whole-cell based inactivated gonococcal microparticle vaccine formulation loaded in dissolvable microneedles for transdermal administration. The nanotechnology-based vaccine formulation consists of inactivated whole-cell gonococci strain CDC-F62, spray dried and encapsulated into biodegradable cross-linked albumin matrix with sustained slow antigen release. The dry vaccine nanoparticles were then loaded in a dissolvable microneedle skin patch for transdermal delivery. The efficacy of the whole-cell microparticles vaccine formulation loaded in microneedles was assessed in vitro using dendritic ,cells and macrophages as well as in vivo mouse model. Antibody titers were measured using an ELISA and antigen-specific T lymphocytes were assessed in spleens and lymph nodes. Here we report that whole-cell based gonococcal microparticle vaccine loaded in dissolvable microneedles for transdermal administration induced significant increase in antigen-specific IgG antibody titers and antigen-specific CD4 and CD8 T lymphocytes in mice compared to gonococcal antigens in solution or empty microneedles. Significant increase in antigen-specific IgG antibody levels was observed at end of week 2 in groups that received the vaccine compared to the group receiving empty nanoparticles. The advantages of using formalin-fixed whole-cell gonococci that all immunogenic epitopes are covered and preserved from degradation. The spherical shaped micro and nanoparticles are biological mimics of gonococci, therefore present to the immune system as invaders but without the ability to suppress adaptive immunity. In conclusion, the transdermal delivery of microparticles vaccine via a microneedle patch was shown to be an effective system for vaccine delivery. The novel gonorrhea nanovaccine is cheap to produce in a stable dry powder and can be delivered in microneedle skin patch obviating the need for needle use or the cold chain.

Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization; the scale of the “village terroir” seems to be a better option. This is the hypothesis put forward by the Global Collaboration for Resilient Food Systems program. By analyzing the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri), this study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. It is based on data collected through participatory mapping and surveys. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agro-socio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio-temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.

Detecting rotational objects in remote sensing imagery is a significant challenge. These images typically encompass a broad field of view, featuring diverse and intricate backgrounds, with ground objects of various sizes densely scattered. As a result, identifying objects of interest within these images is a daunting task. While the integration of Convolutional Neural Networks (CNN) and Transformer networks leads to some advancements in rotational object detection, there is still room for improvement, particularly in enhancing the extraction and utilization of information related to smaller objects. To address this, our paper presents a multi-scale feature fusion module and a global feature context aggregation module. Initially, we fuse original, shallow, and deep features to reduce the loss of shallow feature information, thereby improving the detection performance of small objects in complex backgrounds. Subsequently, we compute the correlation of contextual information within feature maps to extract valuable insights. We name the newly proposed model the "Multiscale Feature Context Aggregation Module" (MFCA). We evaluate our proposed methodology on three challenging remote sensing datasets: DIOR-R, HRSC, and MAR20. Comprehensive experimental results show that our approach surpasses baseline models by 2.07\% mAP, 1.02\% mAP, and 1.98\% mAP on the DIOR-R, HRSC2016, and MAR20 datasets, respectively.

The intravenous cocaine self-administration model is widely used to characterize the neurobiology of cocaine seeking. When studies are aimed at understanding relapse to cocaine-seeking, a post-cocaine abstinence period is imposed, followed by “relapse” tests to assess the ability of drug-related stimuli (“primes”) to evoke the resumption of the instrumental response previously made to obtain cocaine. Here we review this literature on the impact of post-cocaine abstinence procedures on neurobiology, finding that the prelimbic and infralimbic regions of the prefrontal cortex are recruited by extinction training, and are not part of the relapse circuitry when extinction training does not occur. Pairing cocaine infusions with discrete cues recruits the involvement of the NA which, together with the dorsal striatum, is a key part of the relapse circuit regardless of abstinence procedures. Differences in molecular adaptations in the NA core include increased expression of GluN1 and glutamate receptor signaling partners after extinction training. AMPA receptors and glutamate transporters are similarly affected by abstinence and extinction. Glutamate receptor antagonists show efficacy at reducing relapse following extinction and abstinence, with a modest increase in efficacy of compounds which restore glutamate homeostasis after extinction training. Imaging studies in humans reveal cocaine-induced adaptations that are similar to those produced after extinction training. Thus, while instrumental extinction training does not have face validity, its use does not produce adaptations distinct from human cocaine users.

This paper investigates the problem of the optimal arrangement for both UAVs’ caching contents and service locations in UAV-assisted networks based on the context awareness, which considers the influence between users and environment. In the existing work, users within the coverage of UAVs are considered to be served perfectly, which ignores the communication probability caused by line-of-sight (LOS) and non- line-of-sight (NLOS) links. However, the links are related to UAV deployment. Moreover, the transmission overhead should be taken into account. To balance the tradeoff between these two factors, we design the ratio of users’ probability and transmission overhead as the performance measure mechanism to evaluate the performance of UAV-assisted networks. Then, we formulate the objective for maximizing the performance of UAV-assisted networks as a UAV-assisted caching game. It is proved that the game is an exact potential game with the performance of UAV-assisted networks serving as the potential function. Next, we propose the log-linear caching algorithm (LCA) to achieve the Nash equilibrium (NE). Finally, related simulation results reflect the great performance of the proposed algorithm.

Work context is essential to understand in relation to handle the stress at work that ultimately creates a feeling of satisfaction or dissatisfaction among health professionals. The current study was conducted to investigate the relationship of work context and work stress among sonographers (n=153) in Riyadh, Saudi Arabia. Additionally, the study provided a gender-based comparison of both variables among sonographers. Work context was measured by administering subscale of work context derived from Work Design Questionnaire. Whereas, work stress was measured by Job Stress Scale. In addition, relationship of lifestyle was explored with work context and work stress. Data was collected through survey research forms. Results revealed the significant relationship of work context and work stress (r=.251, p=.002). Among lifestyle variables, perceived good health (r= .214, p=.008) and sleep (r=.242. p=.003) were found positively related with satisfaction toward work. Whereas, the strong positive correlation was found between work context and frequency of physical activity (r=.255, p=.005). No significant difference was found among male and female sonographers. The findings of this study contributed to evaluating the working condition of sonographers in relation to work stress. Effective strategies for better working settings as well as strategies for achieving satisfaction in work will be discussed to enhance the performance of sonographers.

This study focused on the precise detection and concordant analysis of circulating tumor cells (CTCs) to verify the usefulness of CTCs as promising clinical markers. CTCs are traditionally achieved using immune-fluorescence and morphological techniques. By combining two prostate cancer-specific genes (PSA and PSMA) and two epithelial-specific genes (EpCAM and KRT19), the CTC detection efficiency can be enhanced. The detection rates of localized prostate cancer (LPC), locally advanced prostate cancer (LAPC), metastatic hormone-sensitive prostate cancer (mHSPC), and metastatic castration-resistive prostate cancer (mCRPC) were 63.04 % (29/46), 70.59% (34/51), 94.34% (50/53), and 90% (126/140), respectively. Subsequent correlation analysis between the four genes, baseline PSA, and CTC count revealed that only the CTC count correlated with baseline PSA in the localized stages of prostate cancer, whereas all four genes were correlated with baseline PSA and CTC count in the metastatic stages of prostate cancer. The precise detection of CTCs using both numerical and transcriptomic analyses can improve the accuracy of biomarker investigations. This study provides valuable insights into the limitations of serum PSA testing and offers a promising alternative to diagnose and manage prostate cancer via the CTC number and specific genes for prostate cancer.

To increase the prediction accuracy of positive oral food challenge (OFC) outcome during stepwise slow oral immunotherapy (SS-OIT) in children with hen’s egg (HE) allergy, we evaluated the predictive value of the combination of antigen-specific IgE (sIgE) with antigen binding-avidity and sIgG4 values. Sixty-three children with HE allergy undergoing SS-OIT were subjected to repeated OFC with HE. We measured the ovomucoid (OVM)-sIgE by ImmunoCAP or densely carboxylated protein (DCP) microarray, sIgG4 by DCP microarray, and the binding-avidity of OVM-sIgE defined as the level of 1/ IC50 (nM) measured by competitive binding inhibition assays. The OFC was positive in 37 (59%) patients undergoing SS-OIT. Significant differences in DCP-OVM-sIgE, CAP-OVM-sIgE, I/ IC50, DCP-OVM-sIgG4, the multiplication products of DCP-OVM-sIgE and binding-avidity of DCP-OVM-sIgE (DCP-OVM-sIgE/IC50) and DCP-OVM-sIgE/sIgG4 were compared between the negative and positive groups (p<0.01). Among them, the variable with greatest area under the receiver operating characteristic curve was DCP-OVM-sIgE/IC50 (0.84), followed by DCP-OVM-sIgE/sIgG4 (0.81). DCP-OVM-sIgE/IC50 and DCP-OVM-sIgE/sIgG4 are potentially useful markers for the prediction of positive OFC during HE-SS-OIT and may allow proper evaluation of the current allergic status in the healing process during HE-SS-OIT

Basketball playing entails the repetitive performance of short intense actions using lower limb explosive power. As such, it is important to measure this capability in basketball players, especially among young players, and to optimize training programs and game plans. After presenting an in-depth understanding of the specific physiological requirements when playing basketball, as well as the type of movements required, The aim of this review is to better understand the importance of the physiological energy systems in basketball, to examine the contribution of each energy system and, accordingly, to heighten awareness of the importance and dominance of the alactic anaerobic physiological system in basketball, for actions requiring high level explosive power. This review of the literature depicts the horizontal and vertical physical movements and physiological requirements entailed in playing basketball, and presents eight standardized anaerobic alactic measurement tools relevant to the game. As some of these tests suit a number of ball games, the findings of this review article are important for making order of the elements unique to basketball as well as additional parameters to consider when testing basketball players. By reliably and validly testing the anaerobic alactic capabilities of basketball players, test results can be used for training purposes and for improving game outcomes. Despite the fact that much of the information in this review is familiar to coaches, highlighting the specific needs of basketball may help them to choose the most suitable tools, and may also shed light on new directions for developing basketball-specific assessment tests.

Profitability is an underestimated concept in precision agriculture. In this research, a new module is developed within a pre-existing farm management system to assess the profitability of precision agriculture applications in extended crops. The module is regulated on a 5-meter spatial resolution, thus allowing scaling up of original and processed data on a zone-, field-, cultivar-, and farm-scale. A bottom-up approach, taking advantage of the full functionality of the farm management system, together with a flexible architecture and an easy-to-use interface, renders the new module an innovative commercial application.

The mutation databases have vital importance in detecting genetic mutation and their corresponding phenotypes. The emergence of next generation sequencing genetic technologies has accelerated this process, and the growth of genetic disease identification has created a need for quality control and documentation of mutation-related information. Ethnic-specific mutation databases offer valuable information for researchers, and the article evaluates the homogeneity and effectiveness of these databases. The aim to raise awareness of NEMDBs to healthcare professionals, the general public, and researchers studying genetic disorders. The article also recommendations to improve the effectiveness of these databases.

Nowadays, some coffee production centers are still classification manually, so it requires a very long time, a lot of labor, and expensive operational costs. Therefore, the purpose of this research was to design and performance of the coffee bean classifier that can accelerate the process of classification beans. The classifier used consists of three main parts, namely the frame, driving force, and sieves. Research parameters include classifier work capacity, power, specific energy, classification distribution and effectiveness, and efficiency. The results showed that the best operating conditions of the coffee bean classifier was found at a rotational speed of 91.07 rpm and a 16° sieves angle with a classifier working capacity of 38.27 kg/h, the distribution of the seeds retained in the first sieve was 56.77 %, the second sieves was 28.12%, and the third sieves was 15.11%. The efficiency of using a classifier was found at a rotating speed of 91.07 rpm and a sieves angle of 16°. This classifier was simple in design, easy to operate, and can sort coffee beans into three classification, namely small, medium, and large.

Neuronal timing is critical for many brain functions such as perception and learning. At the level of the chemical synapse, the synaptic delay is determined by the presynaptic release probability (Pr) and the waveform of the presynaptic action potential (AP). Depolarization-induced analogue-digital facilitation (d-ADF) is a form of context-dependent synaptic facilitation that is induced by constant depolarization of the presynaptic neuron and is mediated by voltage inactivation of presynaptic Kv1 channels. Whether synaptic delay is modulated during d-ADF remains unclear. We show here that despite Pr is elevated during d-ADF at L5-L5 synapses, the synaptic delay is surprisingly unchanged, suggesting that both Pr- and AP-dependent changes in synaptic delay compensate for each other. We conclude that in contrast with other short- or long-term modulation of presynaptic release, timing is not affected during d-ADF because of the opposite interaction of Pr- and AP-dependent modulations of synaptic delay.

Detecting rotated objects in remote sensing images poses a substantial challenge. These images usually cover a wide field of view, containing diverse and complex backgrounds with variously sized ground objects densely distributed. Therefore, identifying objects of interest in remote-sensing images is challenging. While integrating Convolutional Neural Networks (CNNs) and Transformer networks has exhibited progress in detecting rotated objects, there is still scope for enhancing feature extraction and fusion. To address this issue, we propose a feature extraction module, a feature context aggregation module, and a multi-scale feature fusion module. Initially, we substitute the Spatial Pyramid Pooling Bottleneck (SPPFBottleneck) with a new module aimed at extracting multi-scale features, thereby enhancing the detection of small objects in complex backgrounds. Next, we develop a novel module for the multi-scale fusion of contextual information within feature maps, extracting valuable information. Finally, we combine the original features with the fused ones to prevent the loss of specific features in the fusion process. We refer to our newly proposed model as the "Multi-scale Feature Context Aggregation Network" (MFCANet). We assess our approach on three challenging remote sensing datasets: MAR20, SRSDD, and HRSC. Comprehensive experimental results show that our method surpasses baseline models by 2.13%, 10.28%, and 1.46% mAP on the MAR20, SRSDD, and HRSC datasets, respectively.

A major challenge of augmented and mixed reality applications is identifying the context and semantics of the real environment. Studies on object and action recognition were developed based on the improvement of machine learning techniques, allowing them to be annotated and recognized. This study aims to characterize current knowledge on the use of machine learning for recognizing objects and actions in augmented and mixed reality environments, increasing context awareness. Therefore, a systematic literature review of works related to these topics was made, using the Scopus and Web of Science knowledge bases. We searched articles and conference reviews or papers published between 2018 and 2022 and selected fifteen studies to be reviewed. The results indicate that there is a great demand for using machine learning to immersive technologies in factories, engineering, entertainment, education, health, among other application domains. However, these real-time interactive systems still have challenges and limitations to be solved, involving network communication, prediction time and the creation of a model that recognize objects and actions in broad contexts. Furthermore, additional research is needed to investigate how object and action recognition can increase context awareness in augmented reality applications.

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

Considering the worldwide impact of heart failure, it is crucial to develop approaches that can help us comprehend its root cause and make accurate predictions about its outcome. This is essential for lowering the suffering and death rates connected with this widespread illness. Cardiomyopathies frequently result from genetic factors, and the study of heart failure genetics is advancing quickly. Dilated cardiomyopathy (DCM) is the most prevalent kind of cardiomyopathy, encompassing both hereditary and nongenetic abnormalities. It is distinguished by the enlargement of the left ventricle or both ventricles, accompanied by reduced contractility. The discovery of the molecular origins and subsequent awareness of the molecular mechanism is broadening our knowledge of DCM development. Additionally, it emphasizes the complicated nature of DCM and the necessity to formulate several different strategies to address the diverse underlying factors contributing to this disease. Genetic variations that can be transmitted from one generation to another can be a significant contributor to causing family or isolated DCM. Genetic variation also plays a significant role in determining susceptibility for acquired triggers for DCM. The genetic causes of DCM can have a diverse range of phenotypic expressions. It is crucial to select the most probable patients to have advantages from genetic testing. The purpose of this research is to emphasize the significance of identifying genetic DCM, the crucial relationships between genotype and phenotype, and the usefulness of genetic testing in the therapeutic care of both those affected and their relatives. This approach is expected to gain importance once treatment is guided by genotype-specific advice and disease-modifying medications.

Oak woodlands are a keystone ecosystem that may not be sustainable under ongoing fire management regimes and climate change. For example, in 2014, sassafras saplings started to emerge in degraded woodlands at the 1200 ha Drury Conservation Area as part of research at the Bull Shoals Field Station in Taney County, Missouri where burning was initiated in 1999. After Spring (March) 2021 burnt, we examined trend in the potential community and ecosystem effects, we assessed sapling density, height, and specific leave weigh (SLW) of white oak and sassafras (<1.0 m) from fall 2021 through fall 2022. In fall 2021, sassafras density was 560/0.1ha with mean height of 25.42cm and SLW of 0.00436g/cm² while white Oak density was 171/0.1ha with mean height of 10.2cm and SLW of 0.00680g/cm. However, in fall 2022, Sassafras density was 582/0.1ha with mean height of 71.689cm and SLW of 0.00366 g/cm² meanwhile, white Oak density was 213/0.1ha with mean height of 50.544cm and SLW of 0.00563 g/cm². We analyzed mass loss for sassafras and white oak after buried for 198 days. The weight loss for sassafras was 0.857g while oak was 0.790g for 61 days of buried. Subsequent sassafras weight loss was 1.942g, 3.343g, 4.222g and 4.82g while that of white Oak was 1.877g, 2.862g, 3.344g and 4.28g for day 105, 153 and 198 respectively. The nutrients composition and carbon sequestration of sassafras and white oak species need to be determined to better understand the effect of their dominance in fire management regime of degraded oak woodland in Southwest Missouri Ozarks.

African Swine Fever (ASF) poses a significant threat to domestic pig populations worldwide. This study investigates the histopathological alterations in confirmed ASF-positive domestic pigs, focusing on over 100 cases from 2018 to 2021. Biological material from 118 pigs, confirmed ASF-positive via PCR examination, was histopathologically examined. Tissue samples from various organs were processed, embedded, and analyzed using hematoxylin and eosin staining. Immunohistochemistry (IHC) was employed to detect the major capsid protein (p72) of the ASF virus.Histological examination revealed characteristic ASF lesions, including severe hemorrhages, lymphoid depletion, and inflammatory infiltrates. Vascular anomalies leading to hemorrhagic events were widespread. Microthromboses were prevalent in lymph nodes and kidneys. IHC detected p72 in various organs, with tonsils consistently showing the highest viral presence.This comprehensive study underscores the pivotal role of lymph nodes, spleen, and kidneys in ASF pathogenesis, highlighting prominent vascular involvement and hemorrhagic manifestations. The study establishes the efficacy of IHC in detecting ASF virus, even in autolyzed tissue samples. Tonsils emerge as a consistent epicenter of viral presence, shedding light on the intricate pathogenesis of ASFV.

High prevalence of allergic disorders in Indian pediatric patients demands early diagnosis of allergens so that appropriate allergen avoidance methods, pharmacotherapy or immunotherapy can be provided in time to avoid the worsening of symptoms due to triggers. In this context, identification of the most prevalent aeroallergens in a given area is critical for diagnosis and treatment of various allergic reactions especially in case of rhino-conjunctivitis and asthma. The specific diagnosis of IgE mediated allergy is usually based on the correlation between the clinical history and signs and symptoms endorsed by the diagnostic tests. Skin prick tests are the gold standard however has some limitation. In view of this, the aim of this study was to assess the significance of ImmunoCAP assay for assessing serum specific IgE levels, available in India, against skin prick test by standardized allergens, for the diagnosis of allergic sensitization. A single-center observational study was conducted at tertiary care corporate hospitals in North India from 2017-2020. Patients with self-reported allergic symptoms or whose symptoms were suspected for allergies were tested for the type of sensitization using skin prick test (SPT) after their consents followed by ImmunoCAP based quantification of serum specific IgE levels. A total of 256 (95.9%) pediatric patients were analyzed in the study and were analysed. The patients were most commonly (30% patients) sensitized to D. farinae (173 [67.6%]), D. pteronyssinus (174 [68.0%]), Acarus siro (93 [36.3%]), Lepidoglyphus destructor (96 [37.5%]), Plantago lanceolata (Engl.). The ROC curve of Bermuda grass shows that the AUC of serum-specific IgE is larger than that of SPT (1.000 / 0.881). The receiver operating characteristic (ROC) analysis of serum-specific IgE reference method resulted in the largest AUC of all the allergens taken into consideration (0.711 / 1.000) compared to those of SPT (0.589 / 1.000). These results revealed that serum-specific IgE is more reliable method over prick test for accessing the sensitivity of patient for various allergens. This study may be helpful in performing early diagnosis of allergies as well as in taking appropriate preventive measures for managing life threatening allergic reactions

The most effective method of treating allergic diseases, aimed not at relieving symptoms, but at eliminating the cause of the disease, is allergen-specific immunotherapy (AIT). To reduce the risk of side effects and improve the delivery of allergens to the mucosa, various delivery systems, such as liposomes, dendrimers, nanoparticles, etc., can be used. To date, there are data on the creation of delivery systems based on glycyrrhizic acid (GA) and its derivatives, but such a delivery system has not been used for allergen-specific therapy until now. At the same time, it is known that GA has an anti-inflammatory effect, shifts the balance towards Th1, and increases the number of Treg cells, which means that in the future it can enhance the anti-allergic effect of AIT and reduce the risk of unwanted side effects. Thus, the study of the immunomodulatory effect of supramolecular complexes (micelles) of GA with extracts of allergens seems to be very promising for the development of new drugs for AIT.

Several studies have demonstrated estrogen’s cardioprotective abilities in decreasing the fibrotic response of cardiac fibroblasts (CFs). However, the majority of these studies are not sex-specific, and those at the cellular level utilize tissue culture plastic, a substrate that has a stiffness much higher than physiological conditions. Understanding the intrinsic differences between male and female CFs under more physiologically “healthy” conditions will help to elucidate the divergences in their complex signaling networks. We aimed to do this by conducting sex-disaggregated analysis of changes in cellular morphology and relative concentrations of profibrotic signaling proteins in CFs cultured on 8kPa stiffness plates with and without 17-β estradiol (E2). Cyclic immunofluorescent analysis indicated that there is a negligible change in cellular morphology due to sex and E2 treatment and that the differences between male and female CFs are occurring at a biochemical rather than structural level. Several proteins corresponding to profibrotic activity had various sex-specific responses with and without E2 treatment. Single-cell correlation analysis exhibited varied protein-protein interaction across experimental conditions. These findings demonstrate the need for further research into the dimorphisms of male and female CFs to develop better tailored, sex-informed prevention and treatment interventions of cardiac fibrosis.

This paper focuses on quantifying the uncertainty in the specific absorption rate values of brain induced by the uncertain positions of the electroencephalography electrodes placed on patient's scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.

This paper proposes the utilization of large language models as recommendations systems for museums. Since the aforementioned models lack the notion of context, they can’t work with temporal information that is often present in recommendations for cultural environments (e.g. special exhibitions or events). In this respect, the current work aims at enhancing the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations, aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-ware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.

Address parsing is a crucial task in natural language processing, particularly for Chinese addresses. The complex structure and semantic features of Chinese addresses present challenges due to their inherent ambiguity. Additionally, different task scenarios require varying levels of granularity in address components, further complicating the parsing process. To address these challenges and adapt to low-resource environments, we propose CapICL, a novel Chinese address parsing model based on the In-Context Learning (ICL) framework. CapICL leverages a sequence generator, regular expression matching, BERT semantic similarity computation, and GPT modeling to enhance parsing accuracy by incorporating contextual information. We construct the sequence generator using a small annotated dataset, capturing distribution patterns and boundary features of address types to model address structure and semantics, mitigating interference from unnecessary variations. We introduce the REB-KNN algorithm, which selects similar samples for ICL-based parsing using regular expression matching and BERT semantic similarity computation. The selected samples, raw text, and explanatory text are combined to form prompts, and inputted into the GPT model for prediction and address parsing. Experimental results demonstrate significant achievements of CapICL in low-resource environments, reducing dependency on annotated data and computational resources. Our model's effectiveness, adaptability, and broad application potential are validated, showcasing its positive impact in natural language processing and geographical information systems.

The paper describes an algorithm for semantic representation of behavioral contexts relative to a dichotomic decision alternative. The contexts are represented as quantum qubit states in two-dimensional Hilbert space visualized as points on the Bloch sphere. The azimuthal coordinate of this sphere functions as a one-dimensional semantic space in which the contexts are accommodated according to their subjective relevance to the considered uncertainty. The contexts are processed in triples defined by knowledge of a subject about a binary situational factor. The obtained triads of context representations function as stable cognitive structure at the same time allowing a subject to model probabilistically-variative behavior. The developed algorithm illustrates an approach for quantitative subjectively-semantic modeling of behavior based on conceptual and mathematical apparatus of quantum theory.

We present a new unsupervised method aimed to obtain a partition of a complex urban system in homogenous urban areas, called urban contexts. The area of study is initially partitioned in microzones, homogeneous portion of the urban system, that are the atomic reference elements for the census data. With the contribution of domain experts, we identify the physical, morphological, environmental and socio-economic indicators need to identify synthetic characteristics of urban contexts and create the fuzzy rule set necessary to determine the type of urban context. We implement the set of spatial analysis processes necessary to calculate the indicators for microzone and apply a Mamdani fuzzy rule system to classify the microzones. Finally, the partition of the area of study in urban contexts is obtained by dissolving continuous microzones belonging to the same type of urban context. Tests are performed on the Municipality of Pozzuoli (Naples - Italy); the reliability of out model is measured by comparing the results with the ones obtained by detailed analysis.

Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to various parameters, e.g. efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach. Thereby the Cyber-Physical Systems play an important role as sources of information to achieve context sensitivity. In this paper it is demonstrated how context sensitivity can be used to realize a holistic solution for (self-) optimization of discrete flexible manufacturing systems, by making use of Cyber-Physical System integrated in manufacturing systems/processes. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a various manufacturing systems. The new solution is propos encompassing run-time context extractor and optimizer. Based on the self-learning module both context extraction and optimizer are continuously learning and improving their performance. The solution is following Service Oriented Architecture principles. The generic solution is developed and then applied to two very different manufacturing processes. This paper proposes a holistic solution to achieve context sensitivity for Flexible Manufacturing Systems, whereby the knowledge created by applying the context sensitivity approach can be used for (self-) optimization of manufacturing processes.

Thorough and precise gene structure annotations are essential for maximizing the benefits of genomic data and unveiling valuable genetic insights. The cucumber genome was first released in 2009 and updated in 2019. To increase the accuracy of predicted gene models, 64 published RNA-seq data and 9 new strand-specific RNA-seq data from multiple tissues were used for manual comparison with the gene models. The updated annotation file (version 2.0) contains increased 24,145 predicted genes from previous version (24317 genes) with a higher BUSCO value of 96.9%. 6231 and 1490 transcripts were adjusted and newly added, respectively, accounting for 31.99% of the overall gene tally. These newly added and adjust genes were renamed (CsaV3.1_XGXXXXX), while genes remaining unaltered preserve their original des ignations. A random selection of 21 modified/added genes were validated using RT-PCR analyses. Additionally, tissue-specific patterns of gene expression were examined using newly obtained transcriptome data with the revised gene prediction model. This improved annotation of cucumber genome will provide essential and accurate resources for studies in cucumber.

Overexpression of human epidermal growth factor receptor 2 (HER2) in breast and gastric cancers is an important target for monoclonal antibody (mAb) therapy. All therapeutic mAbs, including anti-HER2 mAbs, exhibit adverse effects probably due to the recognition of antigens expressed in normal cells. Therefore, tumor-selective or specific mAbs can be beneficial in reducing the adverse effects. In this study, we established a novel cancer-specific anti-HER2 antibody, named H₂Mab-250/H₂CasMab-2 (IgG₁, kappa). H₂Mab-250 reacted with HER2-positive breast cancer BT-474 and SK-BR-3 cells. Importantly, H₂Mab-250 did not react with non-transformed normal epithelial cells (HaCaT and MCF 10A) and immortalized normal epithelial cells in flow cytometry. In contrast, most anti-HER2 mAbs including H₂Mab-119 (IgG₁, kappa) reacted with both cancer and normal epithelial cells. Furthermore, a core-fucose deleted IgG_2a-type H₂Mab-250 (H₂Mab-250-mG_2a-f) could trigger the antibody-dependent cellular cytotoxicity activity to BT-474, but not to HaCaT cells. Furthermore, H₂Mab-250-mG_2a-f exhibited an in vivo antitumor effect against BT-474 xenograft. Immunohistochemical analysis demonstrated that H₂Mab-250 possesses much higher reactivity to the HER2-positive breast cancer tissues compared to H₂Mab-119, and did not react with normal tissues, including heart, breast, stomach, lung, colon, kidney, and esophagus. The epitope mapping demonstrated that the Trp614 of HER2 domain IV mainly contributes to the recognition by H₂Mab-250. H₂Mab-250 could contribute to the development of chimeric antigen receptor-T or antibody-drug conjugates without adverse effects for breast cancer therapy.

Insulin icodec of Novo Nordisk is a novel long-acting insulin analogue that exhibits an extended duration of action, providing a promising treatment option for individuals with diabetes. It has been reported that the incorporation of fatty acid moieties into insulin icodec plays a crucial role in its prolonged action, as these fatty acid chains facilitate the formation of stable hexameric structures, thereby delaying insulin absorption and promoting sustained release. Yet, the underlying biophysics still is elusive of the roles of the three site-specific mutations (Y14A\_E, Y37B\_H, F46B\_H) of insulin icodec in its prolonged activity. Thus, through a comprehensive structural and biophysical analysis of the insulin (both native and icodec) structures bound to its receptor, this article delves deep into the biophysics underlying the molecular design of insulin icodec, and identified a delicate biophysical mechanism through which two missense mutations of insulin icodec (Y37B\_H and F46B\_H) contribute to its prolonged duration of action. Overall, this structural and biophysical investigation provides valuable insights into the mechanisms underlying the relationship between three site-specific mutations and prolonged duration of action of insulin icodec, while understanding these modifications at a structural and biophysical level can aid in the rational design of future long-acting insulin analogues, offering further enhanced therapeutic options for diabetic patients.

Tomato (Solanum lycopersicum L.) is a high value horticultural crop in Kenya. Nutritionally, the crop is rich in niacin, carotene, thiamine, and vitamin C. Mwea in Kirinyaga County is one of the major tomato growing areas in Kenya. Tomato production in Kenya is hindered by losses due to diseases caused by pathogens that include plant parasitic nematodes (PPN). Among the plant parasitic nematodes, the root-knot nematode (RKN) is the most predominant in Mwea. This study investigated the soil parameters that influence the distribution of the RKN for the purpose of mapping their distribution. This is important since it ensures that nematicide application is only to specific sites where these nematodes are found thereby saving on input costs as well as protecting the environment. The study consisted of surveys conducted in geo-referenced tomato production fields in seven (7) tomato production sites in Tebere and Mwea. Sampling was done in a stratified random manner in both rainfed and irrigated tomato production fields in both dry and rainy seasons for determination of the spatial and temporal distribution of nematodes, respectively. Nematode extraction was done using the centrifugal floatation technique and identification done to genera level using morphological features. Soil characteristics determining PPN distribution patterns in tomato production fields of Mwea were measured using standard methods. Soil parameters measured included soil pH, electrical conductivity, elevation and soil texture. The PPN incidence and diversity was determined using the Shannon-Weiner species diversity index. Detrended canonical correspondence analysis (DCCA) was performed to interpret and summarize major patterns of variation within the soil variable data and to estimate the ability of each soil variable to reflect variance in the entire PPN data set. Fourteen (14) genera of nematodes were identified with the diversity between 0.6 and 1.2. RKN distribution differed significantly (p<0.05) among the sites. Among the soil samples analyzed, 81% were positive for RKN infestation. There was an insignificant difference (p>0.05) between nematode densities in the dry and rainy seasons. Rainfed fields exhibited a significantly higher (p<0.05) RKN population densities compared to irrigated fields. The study established a great variability in the soil parameters in the area. The RKN distribution pattern, density and abundance were inversely correlated (p<0.05) with the soil pH and positively correlated (p<0.05) with soil EC. Based on the inverse relationship between soil pH and RKN distribution in the Mwea ecosystem, maps of nematode distribution and soil pH were developed. This enables the possibility of a site specific system for management of RKN

Virtual human body models contribute to designing safe and user-friendly products through virtual prototyping. Anthropometric biomechanical models address different physiques using average dimensions. In designing personal protective equipment, biomechanical models with the correct geometry and shape shall play a role. The presented study shows the variations of subject-specific anthropometric dimensions from the average for the different population groups in the Czech Republic and China as a background for the need for personalized human body models. The study measures a set of clothing industry dimensions of Czech children, Czech teens, Czech adults and Chinese adults and compares them to the corresponding age average, which is represented by a scaled anthropometric human body model. The cumulative variation of clothing industry dimensions increases the farer is the population group from the average. It is smallest for the Czech adults 7.54% ± 6.63%, Czech teens report 7.93% ± 6.25% and Czech children differ 9.52% ± 6.08%. Chinese adults report 10.86% ± 11.11%. As the variations of the particular clothing industry dimensions from the average prove the necessity of having personalized subject-specific models, the personalization of particular body segments using the measured clothing industry dimensions leading to a subject-specific virtual model is addressed. The developed personalization algorithm results in the continuous body surface desired for contact applications for assessing body behavior and injury risk under impact loading.

Candida auris is an emerging multidrug resistant yeast causing nosocomial infections and associated with high mortality in immunocompromised patients. Rapid identification and characterisation is necessary for its diagnosis and containing spread. In this study, we present a selective culture medium for all C. auris clades. This medium is sensitive with a limit of detection of 102 CFU/ml. The 100% specificity of SCA (Specific C. auris) medium is confirmed on a set of 134 Candida strains, 50 bacterial species and 200 human stool samples. Thus, this medium specifically selects for C. auris isolation from clinical samples, and allows studying its phenotypic profile.

Abstract Optimal targeting of nanoparticles (NP) to dendritic cells (DCs) receptors to deliver cancer-specific antigens is key to an efficient induction of anti-tumor immune responses. Poly (lactic-co-glycolic acid) (PLGA) nanoparticles containing tètanus toxoid and gp100 melanoma-associated antigen, toll-like receptor adjuvants were targeted to the DC-SIGN receptor in DCs by specific humanized antibodies or by ICAM3-Fc fusion proteins mimicking natural ligand. Despite higher binding and uptake efficacy of anti-DC-SIGN antibody-targeted NP vaccines than ICAM3-Fc ligand, no difference were observed in DC activation markers CD80, CD83, CD86 and CCR7 induced. DCs loaded with NP coated with ICAM3-Fc appeared more potent in activating T cells via cross-presentation than antibody-coated NP vaccines. This fact could be very crucial in the design of new cancer vaccines.

The effects of psychological pressure on perceiving the height of a jump bar just before starting a high jump run was investigated. University students (N = 14) training for a high jump event performed 15 trials (3 practice, 6 pressure, and 6 non-pressure) in counterbalanced order in their daily practice environment. The height of the bar was judged as significantly higher on pressure trials compared to non-pressure trials. A regression analysis indicated that participants who reported increased subjective perceived pressure tended to judge the bar to be higher. There was no significant difference between pressure and non-pressure trials for the performance index, defined as the success rate. This study provides the first evidence that environmental perceptions prior to executing a motor task under pressure may make performance of the task appear to be more difficult.

IgE-mediated wheat allergy is a gluten-related disorder. Wheat is one of the five most common food allergens in children. However, the natural history of IgE-mediated wheat allergy has seldom been described in the research literature. This study presents the current state of knowledge about the IgE-mediated wheat allergy in children.

Prostate cancer (PC) is the second most diagnosed cancer among men. It was observed that early diagnosis of disease is highly beneficial for survival of cancer patients. Therefore, extension and increasing quality of life of PC patients can be achieved by broadening the Cancer Screening programs that are aimed at the identification of cancer manifestation in patients at earlier stages before they demonstrate well-understood signs of the disease. Therefore, there is an urgent need in standard, sensitive, robust, and commonly available screening and diagnosis tools for identification of early signs of cancer pathologies. In this respect, “Holy Grail” of cancer researchers and bioengineers for decades have been molecular sensing probes that would allow diagnosis, prognosis, and monitoring of cancer diseases by their interaction with cell-secreted and cellassociated PC biomarkers, e.g. PSA and PSMA correspondinly. At present, most PSA tests are performed at centralized laboratories using high throughput Total PSA immune analyzers which are suitable for dedicated laboratories and are not readily available for the broad health screening. Therefore, the current trend in detection of PC is developments of the portable biosensors for mobile laboratories and individual use. Phage display since its conception by George Smith in 1985 has emerged as a premier tool in molecular biology with widespread applications. This review describes how the paradigm of molecular evolution and phage display revolutionized the methods of early diagnosis and monitoring of PC.

Our brain continuously interacts with the body as we engage with the world. Although we are mostly unaware of internal bodily processes, such as our heartbeats, they may be influenced by and in turn influence our perception and emotional feelings. While there is a recent focus on understanding cardiac interoceptive activity and interaction with brain activity during emotion processing, the investigation of cardiac-brain interactions with more ecologically valid naturalistic emotional stimuli is still very limited. We also do not understand how an essential aspect of emotions like context familiarity influences affective feelings and is linked to cardiac-brain interactions. Hence to answer these questions, we designed an exploratory study by recording ECG and EEG signals for the emotional events while participants were watching emotional movie clips. Participants also rated their familiarity with the stimulus on the familiarity scale. Linear mixed effect modelling was performed in which the ECG power and familiarity were considered as predictors of EEG power. We focused on three brain regions, including prefrontal (PF), frontocentral (FC) and parietooccipital (PO). The analyses showed that cardiac-brain interaction is dependent on familiarity such that the interaction is stronger with high familiarity. In addition, the results indicate that arousal is predicted by cardiac-brain interaction, which also depends on familiarity. The results support emotional theories that emphasize context dependency and interoception. Multimodal studies with more realistic stimuli would further enable us to understand and predict different aspects of emotional experience.

The issues concerning the development of the smart systems for the management of freight transport are related to many factors which are influencing by properties of such a complex processes and ICT development. We are concerned with the recognition of a wide spectrum of services and provision specifics under conditions of wireless communication networks. Also, we are investigated for the adequate provision of context data with problematic of a definition of such context data and with possibilities to apply formalized artificial intelligence methods for recognition of this context information needs for transportation. In this stage of application of the smart system, we are solving the problem of priority of provision of possible providing services, ensuring of quite optimal quality of data supply channels and restriction of flooding of wireless communication channels. The proposed methodology is based on methods of indication of con-text-aware situations and integration of such data into the situation recognition algorithms. The constructions of smart service provision system are developed for more safety management of transportation. The experimental results are demonstrated on analysis of heterogeneity of smart services, construction of schemas for service provision priorities, and extension of potential of intelligent transport with intellectual recognition possibilities of context-aware information in the transportation process.

Language learners often face communication problems when they need to express themselves and do not have this ability. On the other hand, continuous advances in technology create new opportunities to improve second language (L2) acquisition through context-aware ubiquitous learning (CAUL) technology. Since vocabulary is the foundation of all language acquisition, this article presents the ULearnEnglish, an open-source system to allow ubiquitous English learning focused on incidental vocabulary acquisition. To evaluate the proposal, 15 learners used the system developed, and 10 answered a survey based on the Technology Acceptance Model (TAM). Results indicate a favorable response to the use of the learner context to assist them in their learning. The ULearnEnglish achieved an acceptance of 78.66% for the perception of the utility, 96% for the perception of ease of use, 86% for user context assessment, and 88% for ubiquity. This study presented a positive response in using the location of users to assist their learning. Among the main contributions, this study demonstrates an opportunity for ubiquity use in future research in language learning. Also, furthers studies can use the source available to evolve the model and system.

Ubiquitous computing comprises scenarios where networks, devices within the network, and software components change frequently. Market demand and cost-effectiveness are forcing device manufacturers to introduce new-age devices. Also, the Internet of Things (IoT) is transitioning rapidly from the IoT to the Internet of Everything (IoE). Due to this enormous scale, effective management of these devices becomes vital to support trustworthy and high-quality applications. One of the key challenges of IoT device management is automatic device classification with the logically semantic type and using that as a parameter for device context management. This would enable smart security solutions. In this paper, a device classification approach is proposed for the context management of ubiquitous devices based on unsupervised machine learning. To classify unknown devices and to label them logically, a proactive device classification model is framed using a k-Means clustering algorithm. To group devices, it uses the information of network parameters such as Received Signal Strength Indicator (rssi), packet_size, number_of_nodes in the network, throughput, etc. Experimental analysis suggests that the well-formedness of clusters can be used to derive cluster labels as a logically semantic device type which would be a context for resource management and authorization of resources. This paper fulfills an identified need of proactive device classification for device management.

The worldwide interconnected objects, called Internet of Things (IoTs), have been increasingly growing in the last several years. Different social media platforms and devices are continuously generating data about individuals and facilitate the technological and the social convergence of their Internet-based data and services with globalized users. These social and device-related IoTs create rooms for data breaches as such platforms provide ability to collect private and sensitive data. We assert that data breaches are fundamentally failures of access control - most users are too busy or technically ill-equipped to understand access control policy expressions and decisions. We argue that this is symptomatic of globalised societies structured by the conditions of algorithmic modernity; an era in which our data is increasingly interdependent on, and enmeshed with, ever more complex systems and processes that are vulnerable to attack. Ethically managing data breaches is now too complex for current access control systems, such as Role-Based Access Control (RBAC) and Context-Aware Access Control (CAAC). These systems do not provide an explicit mechanism to engage in decision making processes, about who should have access to what data and when, that are involved in data breaches. We argue that a policy ontology will contribute towards the development of Ethical CAAC better suited to attributing accountability for data breaches in the context of algorithmic modernity. We interrogate our proposed Ethical CAAC as a theoretical construct with implications for future policy ontology models and data breach countermeasures. An experimental study on the performance of the proposed framework is carried out with respect to a more generic CAAC framework.

In the world's first pig-to-human cardiac xenotransplant, the elevated levels of porcine cytomeg-alovirus (PCMV) are considered key factors contributing to patient mortality. This has renewed attention to PCMV, a virus widely prevalent in pigs. Currently, there are no effective drugs or vaccines targeting PCMV, and its high detection difficulty poses challenges for prevention and control research. In this study, antiviral small hairpin RNA (shRNA) was selected and inserted into the Rosa26 and miR-17-92 loci of pigs via a CRISPR/Cas9-mediated knock-in strategy. Further in vitro viral challenge experiments demonstrated that these genetically edited pig cells could effec-tively limit PCMV replication. Through this process, we constructed a PCMV-infected cell model, validated partial viral interference sites, enhanced gene knock-in efficiency, performed gene editing at two different gene loci, and ultimately demonstrated that RNA interference (RNAi) technology combined with CRISPR/Cas9 has the potential to generate pig cells with enhanced antiviral infec-tion capabilities. This opens up possibilities for the future production of pig populations with anti-viral functionalities.

Increasingly, firms are expected to implement ESG (Environment, Social, and Governance) mandates to appeal to the global market. However, this requires overcoming practical challenges and capitalizing on emerging opportunities. This paper conducts an in-depth analysis of the ESG practices of leading Korean global firms. The study selects five firms for case studies representing industries such as electronics, automotive, steelmaking, and chemicals. It examines ESG reports, performance indicators, and corporate disclosures of these firms. The analysis reveals the adoption of industry-specific ESG strategies, highlighting how these firms tailor their approaches to address unique industry challenges while aligning with global sustainability trends. Key findings include a strong emphasis on innovative solutions to ESG challenges, the integration of ESG into core business operations, and a balanced approach to economic, social, and environmental responsibilities. This study contributes to a broader understanding of how ESG practices are implemented in various industrial contexts, particularly in the dynamic and evolving landscape of Korean global businesses. It offers valuable insights and benchmarks for global companies, underlining the importance of contextualized and industry-specific ESG strategies in achieving sustainable and ethical business practices

Study design: Creating and psychometric testing of a new QoL Questionnaire about Physiotherapeutic Specific Exercises of Scoliosis (Questionnaire of Physiotherapeutic Specific Exercises of Scoliosis -QPSSE). Purpose: The purpose of this study is to create a reliable and valid questionnaire for patients suffering mild and moderate adolescent idiopathic scoliosis (AIS) who have been treated with Physiotherapeutic Specific Exercises of Scoliosis (PSSE) in order to evaluate their quality of life. Materials and Methods: The developed questionnaire was based on a thorough literature review as well as on authors experience. It consists of 53 questions, of which 37 have a positive meaning, 15 have a negative meaning, and one is multiple choice question, additionally there are six “open” questions. Except for the multiple choice question, all other questions are answered on a Likert scale ranging from 1 to 5 scores. 5 represents a positive meaning or very much, whereas 1 stands for a negative meaning or not at all. Questions were developed by the authors who subsequently categorized the 53 questions into the following 8 domains: physical functioning, self-image, Physiotherapeutic Scoliosis-Specific Exercises (PSSEs), psychosocial functioning, cognitive functioning, compliance, motivation and pain. A pilot study was conducted so that we could calculate Cronbach’s Alpha based on the outcome. Due to the COVID-19 pandemic, the authors worked through the Zoom online platform to structure the questionnaire. Results: Pearson's correlation coefficient was used for all correlations evaluated. P values of less than 0.05 were considered to be significant. Internal consistency was evaluated with Cronbach's alpha. Although there were very few missing values, accounting for 0.78% of the total values of the questionnaire, expectation maximization likelihood algorithm was used to impute data. IBM® SPSS® Statistics Software v.25 was used for the analysis. Cronbach's alpha coefficients for the overall score was 0.84. Conclusion: This original QPSSE was found to be a reliable and valid tool for AIS treated conservatively with PSSE and for their clinicians.

Defects in low-density lipoprotein receptor (LDLR) are associated with familial hypercholesterolemia (FH), manifested by atherosclerosis and cardiovascular disease. Defective LDLR in hepatocytes leads to increased blood cholesterol level that damage vascular cells, especially endothelial cells, through oxidative stress and inflammation. However, the distinctions between endothelial cells from individuals with normal and defective LDLR are not yet fully comprehended. In this study, we obtained and examined endothelial derivatives of induced pluripotent stem cells (iPSC) generated previously from conditionally healthy donors and compound heterozygous FH patients carrying pathogenic LDLR alleles. In normal iPSC-derived endothelial cells (iPSC-EC), we detected the LDLR protein predominantly in its mature form, while iPSC-EC from patients with FH display reduced level of mature LDLR and show abolished low-density lipoprotein uptake. RNA-seq of iPSC-EC with mutant LDLR revealed a unique transcriptome profile comprising downregulated genes related to monocarboxylic acid transport, exocytosis and cell adhesion, as well as upregulated signaling pathways of cell secretion and leukocyte activation. Overall, these findings suggest that LDLR defects increases susceptibility of endothelial cells to inflammation and oxidative stress. This, combined with elevated extrinsic cholesterol levels, may result in accelerated endothelial dysfunction, contributing to early progression of atherosclerosis and other cardiovascular pathologies related to FH.

Podocalyxin (PODXL) overexpression is associated with poor clinical outcomes in various tumors. PODXL is involved in tumor malignant progression through the promotion of invasiveness and metastasis. Therefore, PODXL has been considered as a promising target of monoclonal antibody (mAb)-based therapy. However, PODXL also plays an essential role in normal cells such as vascular and lymphatic endothelial cells.Therefore, cancer-specificity or selectivity is required for the reduction of adverse effects on normal cells. Here, we developed an anti-PODXL cancer-specific mAb (CasMab), PcMab-6 (IgG₁, kappa), by immunizing mice with soluble PODXL ectodomain derived from a glioblastoma LN229 cell.PcMab-6 reacted with the PODXL-positive LN229 cells, but not with PODXL-knockout LN229 cells in flow cytometry. Importantly, PcMab-6 recognized pancreatic ductal adenocarcinoma (PDAC) cell lines (MIA PaCa-2, Capan-2, and PK-45H), but did not react with normal lymphatic endothelial cells (LECs). In contrast, one of the non-CasMabs, PcMab-47 showed high reactivity to both the PDAC cell lines and LECs. Next, we engineered PcMab-6 into a mouse IgG_2a type (PcMab-6-mG_2a) and a humanized IgG₁-type (humPcMab-6) mAbs, and further produced the core fucose-deficient types (PcMab-6-mG_2a-f and humPcMab-6-f, respectively) to potentiate the antibody-dependent cellular cytotoxicity (ADCC). Both PcMab-6-mG_2a-f and humPcMab-6-f exerted ADCC and complement-dependent cellular cytotoxicity in the presence of effector cells and complements, respectively. In the PDAC xenograft model, both PcMab-6-mG_2a-f and humPcMab-6-f exhibited potent antitumor effects. These results indicated that humPcMab-6-f could apply to antibody-based therapy against PODXL-expressing pancreatic cancers.

Domain-specific systems-on-chip (DSSoCs) aim to narrow the gap between general-purpose processors and application-specific designs. CPU clusters enable programmability, while hardware accelerators tailored to the target domain minimize task execution times and power consumption. Traditional operating system (OS) schedulers can diminish the potential of DSSoCs as their execution times can be orders of magnitude larger than the task execution time. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the advantages of a fast, low-overhead scheduler and a sophisticated, high-performance scheduler with a larger overhead. We present a novel runtime classifier that chooses the better scheduler type as a function of the system workload, leading to improved system performance and energy-delay product (EDP). Experiments with five real-world streaming applications indicate that DAS consistently outperforms fast, low-overhead, and slow, sophisticated schedulers. DAS achieves a 1.29x speedup and 45% lower EDP than the sophisticated scheduler under low data rates and a 1.28x speedup and 37% lower EDP than the fast scheduler when the workload complexity increases. Furthermore, we demonstrate that the superior performance of the DAS framework also applies to hardware platforms, with up to a 48% and 52% reduction in execution time and EDP, respectively.

The main issues of accounting reporting regarding goodwill are whether a firm’s management reliably conveys their private information about future earnings, and whether they disclose value-relevant and useful information to accounting users. In the current International Financial Reporting Standards (IFRS) regulations, the goodwill impairment test is based on management’s discretionary assessments. This study examines how goodwill impairment is reported under IFRS considering company- and industry-specific economic factors, proxies for earnings management, and macroeconomic crisis years. We extend previous research using Tobit and Logit regression, by employing a fixed-effects model. This approach is possible because of a panel data set comprising 449 of 600 active companies sampled from the STOXX Europe 600 index from 2005 to 2018. We find that goodwill impairments are largely concentrated in certain companies, industries, and years. The regression models show a significant negative correlation between companies’ return on total assets and goodwill impairments. Moreover, we discover that goodwill impairments have a significant positive correlation with goodwill intensity, debt ratio, and the proxy for reporting a one-off big bath charge. In addition, we find that the global financial crisis in 2008-2009 and the European debt crisis in 2011 differ significantly from other fiscal years

Somatic embryogenesis-mediated plant regeneration is essential for genetic manipulation of agronomically important traits in upland cotton. Genotype specific recalcitrance to regeneration is a primary challenge in deploying genome editing and incorporating useful transgenes into elite cotton germplasm. In this study, transcriptomes of a semi-recalcitrant cotton (Gossypium hirsutum L.) genotype ‘Coker312’ were analyzed at two critical stages of Somatic Embryogenesis that includes non-embryogenic callus (NEC) and embryogenic callus (EC) cells, and the results compared to a non-recalcitrant genotype ‘Jin668’. We discovered of 305 differentially expressed genes in Coker312, whereas, in Jin668, about 6-fold more genes (2,155) were differentially expressed. A total of 154 differentially expressed genes were common between the two genotypes. Gene enrichment analysis of upregulated genes identified functional categories such as lipid transport, embryo development, regulation of transcription, sugar transport, vitamin biosynthesis, among others. In Coker312 EC cells, five major transcription factors were highly upregulated: LEAFY COTYLEDON 1 (LEC1), WUS-related homeobox 5 (WOX5), ABSCISIC ACID INSENSITIVE3 (ABI3), FUSCA3 (FUS3), and WRKY2. In Jin668, LEC1, BABY BOOM (BBM), FUS3, and AGAMOUS-LIKE15 (AGL15) were highly expressed in EC cells. We also found that gene expression of these embryogenesis genes was typically higher in Jin668 when compared to Coker312. We conclude that significant differences in expression of the above genes between Coker312 and Jin668 may be a critical factor affecting the regenerative ability of these genotypes.

The aim of this article is to make a multi-criteria analysis of various exhibition spaces of an originally non-exhibition character and to determine how these spaces affect the selection of works and the exhibition concept. The analysis is based on the exhibitions of art objects at collective exhibitions in unconventional architectural spaces: commercial, i.e. the modern office building of PBG Gallery Skalar Office Centre in Poznań, post-industrial i.e. in the former Zakłady Przemysłu Ziemniaczanego Lubanta S.A. and in the historic interior of the "U Jezuitów" Gallery of the Cultural Integration Centre in Poznań. The multi-criteria comparative analysis shows a variety of features of the studied spaces as well as the relationship between architecture and art and their mutual interaction. The participatory role of the non-exhibition space in the process of creating an exhibition and selecting works has been proven. It has also been confirmed that the presentation of works of art in originally non-exhibition spaces creates a new quality of the artwork. Unconventional architectural space, when used for the exhibition of works of art, expands and strengthens the area of ​​their influence through the interaction between the work and the architectural space. The specificity of the space adapted for exhibition needs, the presence and type of architectural details in the interior, the quantity and quality of light and its distribution in space, the volume and colour of the interior determine the exhibition space and influence the shape of the exhibitions organised and the reception of the artworks. The only condition for the change of the original function of an architectural space into that of an exhibition space is a coherent artistic vision of the creator. This should take into account the appropriate selection of the exhibited objects, where the process of searching for the relationship between architecture and art determines the features of the architectural space as integral components influencing the realisation of the exhibition .

The aim of this study was to investigate the possible role of 99mTc-sestamibi in the regulation of cancer cell proliferation and apoptosis. To this end, Te in vivo values of 99mTc-sestamibi uptake have been associated to the in-situ expression of both Ki67 and caspase-3. For in vitro investiga-tions BT-474 cells were incubated with three different concentration of 99mTc-sestamibi: 10µg/ml –1µg/ml – 0,1µg/ml. Expression of caspase-3 and Ki67, as well as the ultrastructure of cancer cells, were evaluated at T0 and after 24, 48, 72 and 120 hours after 99mTc-sestamibi incubation. Ex vivo data strengthened the known association between the sestamibi uptake and the Ki67 expression. Linear regression analysis showed a significant association between the sestamibi uptake and the number of apoptotic cells evaluated as caspase-3 positive breast cancer cells. As concern the in vitro data, a significant decrease of the proliferation index was observed in breast cancer cells incubated with high concentration of 99mTc-sestamibi (10µg/ml). Amazingly, a significant increase in caspase-3 positive cells in cultures incubated with 10µg/ml 99mTc-sestamibi was observed. This study suggested the possible role of sestamibi in the regulation of pathophysiological process involved in breast cancer.

In the recent past, there has been a rise in Prostate Cancer (PCa) in Asia, particularly India. Although systematic reviews on PCa have dealt on the genetics, genomics and the environmental influence in causal of PCa, no predictive analytics in comparing the PCa from Caucasian, American to Asian population was attempted. In this review article, we have attempted to elaborate this aspect of PCa and deliberated on challenges related to next generation sequencing methods of PCa’s manifestation when compared to the west.

The barley (Hordeum vulgare) genome comprises over 32,000 genes, with differentiated cells expressing only a subset of genes; the remainder being silent. Mechanisms by which tissue-specific genes are regulated are not entirely understood, although DNA methylation is likely to be involved. DNA methylation patterns are not static during plant development, but it is still unclear whether different organs possess distinct methylation profiles. Methylation-sensitive GBS was used to generate DNA methylation profiles for roots, leaf-blades and leaf-sheaths from five barley varieties, using seedlings at the three-leaf stage. Differentially Methylated Markers (DMMs) were characterised by pairwise comparisons of roots, leaf-blades and leaf-sheaths of three different ages. While very many DMMs were found between roots and leaf parts, only a few existed between leaf-blades and leaf-sheaths, with differences decreasing with leaf rank. Organ-specific DMMs appeared to target mainly repeat regions, implying that organ differentiation partially relies on the spreading of DNA methylation from repeats to promoters of adjacent genes. Furthermore, the biological functions of differentially methylated genes in the different organs correlated with functional specialisation. Our results indicate that different organs do possess diagnostic methylation profiles and suggest that DNA methylation is important for both tissue development and differentiation and organ function.

This paper constitutes an original and new methodology for the determination of the surface properties of carbon fibers in the two oxidized and untreated forms by inverse gas chromatography technique at infinite dilution based on the effect of the temperature on the surface area of the various organic molecules adsorbed on the carbon fibers. The studied thermal effect proved a large deviation of the classical methods or models relative to the new determination of the surface properties of carbon fibers, such as the dispersive component of their surface energy, the free surface energy, the free specific energy, the enthalpy and entropy of adsorption of molecules on the carbon fibers. The obtained specific enthalpy and entropy of adsorption for the polar solvents led to the determination of the Lewis’s acid-base constants of carbon fibers. Different molecular models and chromatographic methods were used to quantify the surface thermodynamic properties of carbon fibers and compared to the thermal model. The obtained results proved that the oxidized carbon fibers gave more specific interaction energy and greater acid-base constants than that of the untreated carbon fibers and thus highlighting the important role of the oxidization on the acid-base of fibers. The determination of the specific acid-base surface energy of the two carbon fibers showed greater values of the oxidized carbon fibers relative to the untreated carbon fibers. An important basic character was highlighted for the two studied carbon fibers, larger than the acidic character. It was observed that the Lewis’s acid-base constants of the oxidized carbon fibers are larger than that of the untreated fibers with more accentuated basic charterer. This result was confirmed by all molecular models and chromatographic methods.

Petri nets are a graphical notation for describing a class of discrete event dynamic systems whose behaviours are charaterised by concurrency, synchronisation, mutual exclusion and conflict. They have been used over the years for the modelling of various distributed systems applications. With the advent of pervasive systems and the Internet of Things, the Calculus of Context-aware Ambients (CCA) emerged as a suitable formal notation for analysing the behaviours of these systems. In this paper, we are interested in comparing the expressive power of Petri nets to that of CCA. That is, can the class of systems represented by Petri nets be modelled in CCA? To answer this question, an algorithm is proposed that maps any Petri net onto a CCA process. We show that a Petri net and its corresponding CCA process behave the same way through experiments. It follows that CCA is at least as expressive as Petri nets, i.e. any system that can be specified in Petri nets can also be specified in CCA. Moreover, tools developed for CCA can also be used to analyse Petri nets.

Ship detection using synthetic aperture radar (SAR) has been extensively utilized in both the military and civilian fields. On account of complex backgrounds, large scale variations, small-scale targets, and other challenges, it is difficult for current SAR ship detection methods to strike a balance between detection accuracy and computation efficiency. To overcome those challenges, ESarDet, an efficient SAR ship detection method based on context information and large effective receptive field (ERF) is proposed. We introduce the anchor-free object detection method YOLOX-tiny as a baseline model and make several improvements on it. First, the CAA-Net, which has a large ERF, is proposed to better merge the context and semantic information of ships in SAR images to improve ship detection, particularly for small-scale ships with complex backgrounds. Further, to prevent the loss of semantic information regarding ship targets in SAR images, we redesign a new spatial pyramid pooling network, namely A2SPPF. Finally, in consideration of the challenge posed by the large variation in ship scale in SAR images, we design a novel convolution block, called A2CSPlayer, to enhance the fusion of feature maps from different scales. Extensive experiments are conducted on three publicly available SAR ship datasets, DSSDD, SSDD, and HRSID, to validate the effectiveness of the proposed ESarDet. The experimental results demonstrate that ESarDet has distinct advantages over current state-of-the-art (SOTA) detectors in terms of detection accuracy, generalization capability, computational complexity, and detection speed.

The relationship between Climate Change and Water is an obvious and key issue within the Sustainable Development Goals. This study aims to investigate the social representation created around this relationship in three different territorial contexts in order to evaluate the influence of the territory on the perception of the risk of Climate Change and its relationship with water. By means of a questionnaire completed by 1709 university students, the climatic literacy of the individual was evaluated in order to relate it to other dimensions on the relationship between Climate Change and Water (information, training previous on climate change and pro-environmental attitudes) in their different dimensions in three different territorial contexts. The results show that the socio-cultural context influences the social representation of Climate Change, but not from the climatological condition, so that it is reasonable to think that the social representation of this relationship is favoured by a common culture around this relationship.

Currently the majority of studies on vision-based measurement has been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect the measurement accuracy. This paper develops a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal context aspects. To validate the feasibility, stability and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination change and fog interference are simulated experimentally in the laboratory. The results of the proposed method are compared to conventional displacement sensor data and current vision-based method results. It is demonstrated that the proposed method gives better measurement results than the current ones under illumination change and fog interference.

Abstract: Extensive research shows nurses’ work environment to be particularly stressful. This study develops, explores, and psychometrically tests a new profession specific ques-tionnaire identifying generalized and specific resistance resources, that make it possible to measure resources to manage work-related stress. An exploratory study design was em-ployed. The questionnaire was developed inspired by the MEASURE approach and the salutogenic theory of health. Building on the results from a literature review of nursing re-search and salutogenesis, supplemented by twelve interviews with hospital nurses, an item pool was generated. A first version was pilot tested in a group of nurses who were studying to become specialist nurses. A second version of the questionnaire was psycho-metrically tested on nurses in close patient care (n = 475), analysed using Principal Com-ponent Analysis as the extracting technique to identify the underlying structure of the questionnaire. The Principal Component Analysis revealed a four-component model of 19 items. “Manageability as a resource for handling the workload” was the strongest compo-nent, accounting for 21 % of the explained variance. “Contacts with patients as resources for nurses’ job satisfaction” (17.8 %). “Professional attitudes are labelled as resources to nursing” (15 %). “Colleagues are important resources for remaining in the nursing profes-sion” (11 %). The structure of the questionnaire indicates its usefulness in clinical practice for measuring resistance resources.

Abstract: Dispersal strategies of a species can affect its invasion success. Investigation into the dispersal strategies of invasive species in relation to different factors helps our understanding of the invasion mechanisms and provides knowledge for population management and invasion evaluation. Tetranychus ludeni Zacher (Acari: Tetranychidae) is an invasive species, which is native to Europe but now cosmopolitan. Here, we examined the effects of age and density on dispersal in mated females. Our results show that older females with more eggs were more likely to disperse and moved longer distances than younger ones with fewer eggs. Older females spread most of their eggs out of the natal habitats and over longer distances, which reduced competition and increased offspring fitness. Females significantly increased dispersal probability and distance with the increase of population density to avoid crowding. The synchronization of dispersal and reproduction, along with the positive density-dependent dispersal strategy, may facilitate habitat colonization and invasion speed of T. ludeni.

Epithelial ovarian cancer (EOC) has not significantly benefited from advances in immunotherapy, mainly because of the lack of well-defined actionable antigen targets. Using proteogenomic analyses of primary EOC tumors, we previously identified 91 aberrantly expressed tumor-specific antigens (TSAs) originating from unmutated genomic sequences. Most of these TSAs derive from non-exonic regions, and their expression results from cancer-specific epigenetic changes. The present study aimed to evaluate the immunogenicity of 48 TSAs selected according to two criteria: presentation by highly prevalent HLA allotypes and expression in a significant fraction of EOC tumors. Using targeted mass spectrometry analyses, we found that pulsing with synthetic TSA peptides leads to a high-level presentation on dendritic cells. TSA abundance correlated with the predicted binding affinity to the HLA allotype. We stimulated naïve CD8 T cells from healthy blood donors with TSA-pulsed dendritic cells and assessed their expansion with two assays: MHC-peptide tetramer staining and TCR Vβ CDR3 sequencing. We report that these TSAs can expand sizeable populations of CD8 T cells and, therefore, represent attractive targets for EOC immunotherapy.

This study examines the capacity of a Site-Specific Fodder Management Decision Support System (SSFM-DSS) model to assist in successful cultivation of sericea lespedeza [SL; Lespedeza cuneata (Dum.-Cours.) G. Don.], with a specific focus on small-scale agricultural systems in the southeastern United States (U.S.). The study emphasizes incorporating advanced geospatial technologies, such as Geographic Information Systems (GIS), remote sensing, and Global Navigation Satellite Systems (GNSS), to improve fodder production. The research showcases the versatility of SL under varying environmental conditions, underscoring its significance in promoting sustainable livestock production. The methodology integrates empirical field data, geospatial analysis, and predictive modeling to formulate an SSFM strategy customized to address the distinct difficulties climate change presents, such as sudden and severe drought conditions. The study introduces an automated geospatial model for assessing the appropriateness of SL production throughout Alabama, Georgia, and South Carolina. The model uses many environmental criteria, including soil properties, climate fluctuations, and terrain. Furthermore, the research was instrumental in development of a WebGIS Dashboard, which offers farmers a decision assistance system to enhance the sustainable production of SL. The results highlight the significant impact that a SSFM-DSS can have on promoting sustainable agricultural practices, providing a solution to address the issues of food security and environmental concerns in agriculture.

Repurposing previously approved drugs may fast track the route to clinic for potential senotherapeutics and improves the inefficiency of the clinical drug development pipeline. We carried out a repurposing screen of 240 clinically approved molecules in human primary dermal fibroblasts for effects on CDKN2A expression. Molecules demonstrating effects on CDKN2A expression underwent secondary screening for senescence-associated beta galactosidase (SAB) activity, based on effect size, direction and/or molecule identity. Selected molecules then underwent a more detailed assessment of senescence phenotypes including proliferation, apoptosis, DNA damage, senescence-associated secretory phenotype (SASP) expression and regulators of alternative splicing. A selection of the molecules demonstrating effects on senescence were then used in a new bioinformatic structure-function screen to identify common structural motifs. 90 molecules displayed altered CDKN2A expression at one or other dose, of which 15 also displayed effects on SAB positivity in primary human dermal fibroblasts. Of these, three were associated with increased SAB activity, and 11 with reduced activity. The female synthetic sex hormones; diethylstilboestrol, ethynyl estradiol and levonorgestrel; were all associated with a reduction in aspects of the senescence phenotype in male cells, with no effects visible in female cells. Finally, we identified that the 30 compounds that decreased CDKN2A activity the most had a common substructure linked to this function. Our results suggest that several drugs licenced for other indications may warrant exploration as future senotherapies, but that different donors and potentially different sexes may respond differently to senotherapeutic compounds. This underlines the importance of consideration of donor-related characteristics when designing drug screening platforms.

Sexual selection involving female choice or female preference (‘inter-sexual’ selection) or male-male competition (‘intra-sexual’ selection) is one of the key mechanisms for evolutionary diversification and speciation. In particular, sexual selection is recently suggested to be an important mode to drive the evolution of “novel” phenotype (i. e. “evolutionary novelty”). Despite extensive studies performed on sexually selected traits or male-specific ornaments (or weapon-like structures) with respect to their evolutionary origin and history and fitness benefits, relatively little is known about the molecular genetic mechanisms underlying their developmental process. However, with advances of genomic technologies (including whole transcriptome analysis using Next Generation Sequencing [NGS] techniques; RNA-Seq), progresses have been made to unveil the genetic background underpinning diverse sexually selected traits in different animal taxa. In the present review, we compile empirical data on the genes, genetic mechanisms or regulatory pathways underlying various sexually selected traits to explore whether the “common” genetic architectures shape the development and evolution of those traits across evolutionarily distant animal lineages. We show that the recruitment of pre-existing genetic network for a new purpose (i.e. gene network “co-option”) is rather widespread in the development and evolution of sexually selected traits, indicating that particular genes or gene-sets are repeatedly involved in different sexually selected traits. Information of genes or genetic mechanisms regulating the development of sexually selected traits would be an essential piece to complete a whole picture of the origin and evolution of sexually selected traits.

According to the guidelines of the European Association of Urology (EAU), it is recommended to exclude PSA testing when the expected remaining lifespan is less than 10 years. In this study, we provide a comprehensive analysis of the impact of prostate-specific antigen (PSA) testing on the quality of life of elderly men who are frail. We conducted a comprehensive literature review to identify publications that examine the use of PSA as a screening and prognostic tool for prostatic illnesses in frail men. Our search included the following keywords: Prostate-specific antigen, Frailty, Prostate cancer screening, Hypogonadism, and Benign Prostatic Obstruction (BPO). Prostate-specific antigen (PSA) testing can be used to identify prostate cancer in its early and treatable stages in fragile men with a life expectancy of over 15 years. Additionally, PSA testing allows for the evaluation of the effectiveness of various prostate cancer treatments and the monitoring of testosterone replacement therapy for hypogonadism. PSA is also indicative of the clinical development of patients with benign prostatic obstruction (BPO). Nevertheless, PSA is being extensively provided without individualised patient assessment, contributing to the economic strain on healthcare systems. Conducting a frailty evaluation on men to determine the potential advantages of PSA tests on their quality of life will help minimise avoidable expenses.

Abstract Background: Small Cajal body-specific RNAs (scaRNAs) are a specific subset of small nucleolar RNAs (snoRNAs) that have recently been emerged as pivotal contributors in diverse physiological and pathological processes. However, their defined roles in carcinogenesis remain largely elusive. This study aims to explore the potential function and mechanism of SCARNA12 in bladder cancer (BLCA) and to provide a theoretical basis for further investigations into the biological functionalities of scaRNAs. Materials and Methods: TCGA, GEO and GTEx data sets were used to analyze the expression of SCARNA12 and its clinicopathological significance in BLCA. Quantitative real-time PCR (qPCR) and in situ hybridization were applied to validate the expression of SCARNA12 in both BLCA cell lines and tissues. RNA sequencing (RNA-seq) combined with bioinformatics analyses were conducted to reveal the changes of gene expression patterns and functional pathways in BLCA patients with different expression of SCARNA12 and T24 cell lines upon SCARNA12 knockdown. Single-cell mass cytometry (CyTOF) was then used to evaluate the tumor-related cell cluster affected by SCARNA12. Moreover, SCARNA12 was stably knocked down in T24 and UMUC3 cell lines by lentivirus-mediated CRISPR/Cas9 approach. The biological effects of SCARNA12 on the proliferation, clonogenic, migration, invasion, cell apoptosis, cell cycle and tumor growth were assessed by in vitro MTT, colony formation, wound healing, transwell, flow cytometry assays and in vivo nude mice xenograft models, respectively. Finally, chromatin isolation by RNA purification (ChIRP) experiment were further conducted to delineate the potential mechanisms of SCARNA12 in BLCA. Results: The expression of SCARNA12 was significantly up-regulated in both BLCA tissues and cell lines. RNA-seq data elucidated that SCARAN12 may play a potential role in cell adhesion and extracellular matrix (ECM) related signaling pathways. CyTOF results further showed that an ECM-related cell cluster with vimentin+, CD13+, CD44+, CD47+ was enriched in BLCA patients with high SCARNA12 expression. Additionally, SCARNA12 knockdown significantly inhibited the proliferation, colony formation, migration, and invasion abilities in T24 and UMUC3 cell lines. SCARNA12 knockdown prompted cell arrest in the G0/G1 and G2/M phase, and promoted the apoptosis in T24 and UMUC3 cell lines. Furthermore, SCARNA12 knockdown could suppress the in vivo tumor growth in nude mice. ChIRP experiment further suggested that SCARNA12 may combine transcription factors H2AFZ to modulate transcription program and then affect BLCA progression. Conclusion: Our study is the first to propose aberrant alteration of SCARNA12 and elucidate its potential oncogenic roles in BLCA via the modulation of ECM signaling. The interaction of SCARNA12 with the transcriptional factor H2AFZ emerges as a key contributor to the carcinogenesis and progression of BLCA. These findings suggest SCARNA12 may serve as a diagnostic biomarker and potential therapeutic target for the treatment of BLCA.

This study aims at the sustainable valorization of eggshells (ES) from different countries of origin in cement concrete as a cement replacement for Hong Kong. For this purpose, sixteen different eggs from different regions across the world were selected from the local market. Firstly, the extent of uniformity in the weight and mineral content of sampled eggshells were assessed by a specific gravity test and thermogravimetric analysis (TGA), respectively. It was found that specific gravity varies between 2.02 and 2.39, with an average value of 2.20 ± 0.01, whereas CaCO₃ varies between 94.65% and 97.23%, with an average value of 96.33%. These values were a bit lower than those of extra-pure limestone (LS) because of the organic part and the porous structure of eggshells. Secondly, three different eggshells were selected for cement replacement in cement concrete, both in the uncalcined and calcined states, based on having the highest, medium, and lowest CaCO₃ content, respectively. To get the calcined eggshells, the eggshells were calcined at 800^oC for three hours. It was found that the strength of eggshells varies with CaCO₃, but the variation was acceptable. The calcined eggshells showed comparatively more compressive strength and were close to the LS because of the free CaO and the absence of an organic part. In general, the variation in the basic properties of eggshells from different regions across the world is negligible and suitable to use as a cement replacement with acceptable variation in strength in Hong Kong.