Miracle-dz/UniFAR-FTUs huggingface dataset:
Inbreeding intensifies sex‐ and age‐dependent disease in a wild mammal

The mutation accumulation theory of senescence predicts that age-related deterioration of fitness can be exaggerated when inbreeding causes homozygosity for deleterious alleles. A vital component of fitness, in natural populations, is the incidence and progression of disease. Evidence is growing for natural links between inbreeding and ageing; between inbreeding and disease; between sex and ageing; and between sex and disease. However, there is scant evidence, to date, for links among age, disease, inbreeding and sex in a single natural population. Using ecological and epidemiological data from a long-term longitudinal field study, we show that in wild European badgers (Meles meles) exposed naturally to bovine tuberculosis (bTB), inbreeding (measured as multilocus homozygosity) intensifies a positive correlation between age and evidence of progressed infection (measured as an antibody response to bTB), but only among females. Male badgers suffer a steeper relationship between age and progressed infection than females, with no influence of inbred status. We found no link between inbreeding and the incidence of progressed infection during early life in either sex. Our findings highlight an age-related increase in the impact of inbreeding on a fitness-relevant trait (disease state) among females. This relationship is consistent with the predictions of the mutation accumulation theory of senescence, but other mechanisms could also play a role. For example, late-life declines in condition, arising through mechanisms other than mutation accumulation might have increased the magnitude of inbreeding depression in late life. Whichever mechanism causes the observed patterns, we have shown that inbreeding can influence age-dependent patterns of disease and, by extension, is likely to affect the magnitude and timing of the late-life declines in components of fitness that characterise senescence. Better understanding of sex-specific links between inbreeding, disease and ageing provides insights into population-level pathogen dynamics and could influence management strategies for wildlife reservoirs of zoonotic disease.
Experimental infection of Salmonella Enteritidis in quails submitted to forced molting by feed fasting

This study aimed at evaluating bacterial shedding, as detected in swabs, feces, and eggs of quails submitted to forced molting by feed fasting and experimentally infected with a Salmonella Enteritidis (SE) strain. In the experiment, 84 40-week-old Italian female quails were distributed in the following groups: FI (quails induced to molt by fasting and inoculated with Salmonella Enteritidis - SE); CI (quails fed with a laying diet and inoculated with SE); FNI (quails induced to molt by fasting and not inoculated with SE); and CNI (quails fed with a laying feed and not inoculated with SE). Feces, cloacal swabs, and eggs were collected on day 1, 3, 7 and 14 post-inoculation (dpi) and submitted to bacteriological analyses. All samples obtained from cloacal swabs were negative for SE. None of the quails of the non-inoculated groups (FNI and CNI) were positive for SE in the fecal samples. Among the inoculated quails, the FI group presented significantly higher (p< 0.05) SE shedding in the feces on 1 dpi than the CI group. On 4 dpi, no significant difference was observed (p< 0.05) in SE shedding between the inoculated quail groups. On 7 dpi, only the FI group shed SE in the feces, whereas on 14 dpi, none of the groups shed SE. According to the results, we concluded that quails submitted to molting by fasting have higher possibility of shedding SE in the feces.
Visualizing smart water meter dataset clustering with parametric t-distributed stochastic neighbour embedding

The proliferation and affordability of sensing systems, smart networks, data storage and transmission technologies means water utility companies are now able to collect larger datasets than ever before. This information revolution opens up hereto unseen possibilities for data mining. Existing (nonparametric) non-linear visualization techniques can struggle with the visualization of real world, high-dimensional data because generally they are not able of retaining both the local and the global data structure in a single map. t-SNE is a technique that can be used for human-intuitive (two dimensional) visualization of high-dimensional data. It converts similarities between data points to joint probabilities and then minimizes the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the original high-dimensional data. Consequently the clusters that are separated in high dimensional space that could be merged by PCA can still remain separate in low dimensional space with t-SNE. The parametric version of t-SNE uses deep neural networks. Results are provided for artificial data and for a smart water meter dataset.
The fuzzy neural network based haul truck driver fatigue detection in surface mining

Haul trucks are one of the most important transportations in the surface mining. The traffic accidents involved haul trucks result in serious damages of the vehicles, even the loss of the drivers' lives. Drive fatigue is one of the key features that cause accidents. This paper analyzed the possible driver fatigue reasons based on the special features of the haul trucks. Then, it proposed the fuzzy neural network based fatigue detection for haul truck drivers. The CCD camera was amounted in the cabin of the haul truck to capture the key features of the drivers. The eyes of the driver could show most important information of fatigue. The head nod frequency, yawn frequency could also reflect the driver's fatigue degree. Each of these methods has its own limitation and detection errors. The results shows the fuzzy neural network detection method has more accurate detection by combining with PERCLOS (Percentage of eyelid closure over the pupil over time), AECS (Average eye closure speed), NodFreq (Nod frequency) and YawnFreq (Yawn frequency). It has great significance in reducing the accidents rate caused by the drive fatigue.
A model to facilitate the mental health of psychiatric nurses in a forensic unit to manage mental health care users’ hostile behaviour constructively

Background Hostile behaviour by mental health care users (MHCUs) is prevalent in forensic units in South Africa, and this causes service providers distress and burnout. Psychiatric nurses (PNs) find it difficult to render quality care to MHCUs who are threatening them and also challenging their authority in a forensic unit. Forensic mental health care practitioners may be challenged to engage authentically with MHCUs who constitute a risk to their personal safety or who have committed acts the practitioner finds morally disturbing. There is a need to facilitate the mental health of PNs in a forensic unit to manage hostile behaviour constructively. Objective The objective of this article is to describe the process that was followed in developing, implementing and evaluating a model that could be used as a framework of reference to facilitate the mental health of PNs in a forensic unit to manage hostile behaviour constructively. Method A theory-generative, qualitative, exploratory descriptive and contextual study design was used to develop the model. The steps of the process entailed the identification of the central concept, the definition of the central concept and other essential criteria and the classification of the central and related concepts. The model was then described and evaluated. Results The central concept was identified as the ‘facilitation of empowerment of PNs to manage hostility in a constructive manner’, defined, classified and then described and evaluated. Conclusion The model as framework of reference could assist PNs in managing hostility in a forensic unit constructively.
Opinion Context Extraction for Aspect Sentiment Analysis

Sentiment analysis is the computational study of opinionated text and is becoming increasing important to online commercial applications. However, the majority of current approaches determine sentiment by attempting to detect the overall polarity of a sentence, paragraph, or text window, but without any knowledge about the entities mentioned (e.g. restaurant) and their aspects (e.g. price). Aspect-level sentiment analysis of customer feedback data when done accurately can be leveraged to understand strong and weak performance points of businesses and services, and can also support the formulation of critical action steps to improve performance. In this paper we focus on aspect-level sentiment classification, studying the role of opinion context extraction for a given aspect and the extent to which traditional and neural sentiment classifiers benefit when trained using the opinion context text. We propose four methods to aspect context extraction using lexical, syntactic and sentiment co-occurrence knowledge. Further, we evaluate the usefulness of the opinion contexts for aspect-sentiment analysis. Our experiments on benchmark data sets from SemEval and a real-world dataset from the insurance domain suggests that extracting the right opinion context is effective in improving classification performance.Specifically combining syntactical features with sentiment co-occurrence knowledge leads to the best aspect-sentiment classification performance.
A safety evaluation method of mine pressure based on incomplete labeled data stream classification

Mine pressure monitoring data is essentially a data stream, and the safety label of mine pressure is difficult to obtain. In this case, the safety evaluation of mine pressure can be regarded as the incomplete labeled data stream classification, and classification labels are safety and unsafety. The safety evaluation method of mine pressure used in this paper is a incomplete labeled data stream classification algorithm based on K means clustering, which uses K means clustering to label unlabeled data, and uses support vector machine as the base classifier, and uses Bayesian classifier to filter noise data, and uses the double thresholds determined by Hoeffding Bounds inequality to detect concept drifts. Experimental results show the method can better label unlabeled data, and better detect concept drifts in data stream, and it has better classification accuracy for data stream, and it can be applied to the safety evaluation of mine pressure.
EviDense: A Graph-Based Method for Finding Unique High-Impact Events with Succinct Keyword-Based Descriptions

Despite the significant efforts made by the research community in recent years, automatically acquiring valuable information about high impact-events from social media remains challenging. We present EVIDENSE, a graph-based approach for finding high-impact events (such as disaster events) in social media. Our evaluation shows that our method outperforms state-of-the-art approaches for the same problem, in terms of having higher precision, lower number of duplicates, while providing a keyword-based description that is succinct and informative.
In vitro cell migration quantification method for scratch assays

The scratch assay is an in vitro technique used to assess the contribution of molecular and cellular mechanisms to cell migration. The assay can also be used to evaluate therapeutic compounds before clinical use. Current quantification methods of scratch assays deal poorly with irregular cell-free areas and crooked leading edges which are features typically present in the experimental data. We introduce a new migration quantification method, called ‘monolayer edge velocimetry’, that permits analysis of low-quality experimental data and better statistical classification of migration rates than standard quantification methods. The new method relies on quantifying the horizontal component of the cell monolayer velocity across the leading edge. By performing a classification test on in silico data, we show that the method exhibits significantly lower statistical errors than standard methods. When applied to in vitro data, our method outperforms standard methods by detecting differences in the migration rates between different cell groups that the other methods could not detect. Application of this new method will enable quantification of migration rates from in vitro scratch assay data that cannot be analysed using existing methods.
Ambient Sensors for Elderly Care and Independent Living: A Survey

Elderly care at home is a matter of great concern if the elderly live alone, since unforeseen circumstances might occur that affect their well-being. Technologies that assist the elderly in independent living are essential for enhancing care in a cost-effective and reliable manner. Elderly care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the elderly care system in the literature to identify current practices for future research directions. Therefore, this work is aimed at a comprehensive survey of non-wearable (i.e., ambient) sensors for various elderly care systems. This research work is an effort to obtain insight into different types of ambient-sensor-based elderly monitoring technologies in the home. With the aim of adopting these technologies, research works, and their outcomes are reported. Publications have been included in this survey if they reported mostly ambient sensor-based monitoring technologies that detect elderly events (e.g., activities of daily living and falls) with the aim of facilitating independent living. Mostly, different types of non-contact sensor technologies were identified, such as motion, pressure, video, object contact, and sound sensors. Besides, multicomponent technologies (i.e., combinations of ambient sensors with wearable sensors) and smart technologies were identified. In addition to room-mounted ambient sensors, sensors in robot-based elderly care works are also reported. Research that is related to the use of elderly behavior monitoring technologies is widespread, but it is still in its infancy and consists mostly of limited-scale studies. Elderly behavior monitoring technology is a promising field, especially for long-term elderly care. However, monitoring technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of elderly people.
Machine learning-based fall characteristics monitoring system for strategic plan of falls prevention

The occurrence of fall increases with age and level of physical frailty. Due to the fact that falls are unexpected and inevitable events, the fall characteristics recording to collect the related circumstance and characteristics of occurred fall events are important for strategic plan of falls prevention. However, typical clinical recording approaches suffers issues in objective and continuous assessment. In this study, a fall characteristic monitoring system is proposed to support the clinical professionals to assess the causes of fall events for fall prevention strategies, which consists of high accuracy fall event detection algorithm and fall direction identification. Eight males are recruited in this experiment and asked to perform the seven types of fall and six types of ADL. A waist-based tri-axial accelerometer is used to measure motion acceleration with 128 Hz sampling rate. The sensitivity, specificity, precision, negative predictive value, and accuracy using the hierarchical fall event detection algorithm are 99.83%, 98.44%, 98.67%, 98.44, and 99.19%, respectively. Furthermore, the overall average performances of the sensitivity, precision, and accuracy using the fall direction identification are 98.52%, 97.49%, and 97.34%, respectively, the results demonstrated that the proposed approach is fulfilled the requirements of fall characteristics monitoring system.
The Million Tweets Fallacy: Activity and Feedback Are Uncorrelated

In this paper we point to a surprising phenomenon in Online Social Networks (OSNs), which we call "The Million Tweets Fallacy". Our hypothesis is that the measurements of activity of a user in an OSN are not correlated with the measurements of feedback that the user receives on that activity. For example, the number of tweets is uncorrelated with the number of retweets or likes. In other words, a voluminously tweeting user is not necessarily fuelled by the attention he gets from his followers. An innovative aspect of this work is that we treat "activity" and "feedback" as multidimensional axes, and do not reduce the problem to a one-dimensional pairwise correlation problem. We apply our methodology to six OSNs. For Twitter, Instagram, LinkedIn and Steam we gathered the data ourselves, collecting features that cover both users' activity and feedback in the OSN. For YouTube and Flickr we used existing data from the literature. In all OSNs, with the only exception of Steam, we confirmed our hypothesis.
Facilitating patient participation in physiotherapy: Symptom-talk during exercise therapy from an Asian context

ABSTRACT Background and purpose: Patient participation is the cornerstone for effective physiotherapy intervention. The aim was to analyze how patients and physiotherapists negotiate symptoms during exercise therapy and describe patients’ participation during this process. Methods: Nineteen consultations with sixteen patients and six physiotherapists were video-recorded in two Hong Kong outpatient settings. Conversation Analysis was used to uncover interactional aspects of symptom-talk, focusing on turn-taking, sequence organization, and vocabulary. Results: Physiotherapists explored patients’ symptoms only minimally and their frequent use of closed-ended questions allowed limited opportunity for participation. For patient-initiated symptom-talk, less than half elicited actions from physiotherapists, whose minimal acknowledgments were often accepted. Yet, some patients achieved a more substantial contribution through: (1) pausing the exercise-in-progress; (2) gazing at the physiotherapist; (3) pointing at the painful area; and (4) interrupting the physiotherapist, thereby challenging the social order. While discussion about symptoms was often initiated by physiotherapists, some patients participated actively by engaging in certain communicative strategies. Conclusions: Patient participation can be improved by physiotherapists offering a supportive environment (i.e., question design, responding to patients’ initiations, and promoting health literacy), and by patients embracing action-engendering communicative strategies. The fine details of interaction shed light onto the subtleties of symptom-talk initiated by patients or physiotherapists in physiotherapy.
Considerations for a PAP Smear Image Analysis System with CNN Features

It has been shown that for automated PAP-smear image classification, nucleus features can be very informative. Therefore, the primary step for automated screening can be cell-nuclei detection followed by segmentation of nuclei in the resulting single cell PAP-smear images. We propose a patch based approach using CNN for segmentation of nuclei in single cell images. We then pose the question of ion of segmentation for classification using representation learning with CNN, and whether low-level CNN features may be useful for classification. We suggest a CNN-based feature level analysis and a transfer learning based approach for classification using both segmented as well full single cell images. We also propose a decision-tree based approach for classification. Experimental results demonstrate the effectiveness of the proposed algorithms individually (with low-level CNN features), and simultaneously proving the sufficiency of cell-nuclei detection (rather than accurate segmentation) for classification. Thus, we propose a system for analysis of multi-cell PAP-smear images consisting of a simple nuclei detection algorithm followed by classification using transfer learning.
45-Degree polarized microstrip grid arrays for millimeter-wave micro base station

Micro base stations will play an important role in the future millimeter-wave mobile networks. This paper presents a new array design for millimeter-wave micro base stations. The array is a microstrip grid structure configured to radiate 45-degree polarized waves. It was implemented in a low-cost FR4 PCB technology and tested in the 28-GHz band. It is shown that the array with a size of 20×20×0.8 mm3 achieved the wide bandwidth of more than 28% and high gain of 11.4 dBi at 28.5 GHz. More importantly, it is found that the performance of the array is insensitive to the location at and size of the grounded substrate and there is a large isolation between ± 45-degree polarized arrays, indicating its suitability to micro base stations of various sizes and dual polarizations.
Genetic diversity of autochthonous pig breeds analyzed by microsatellite markers and mitochondrial DNA D-loop sequence polymorphism

Abstract The evaluation of the genetic structure of autochthonous pig breeds is very important for conservation of local pig breeds and preservation of diversity. In this study, 18 microsatellite loci were used to detect genetic relationship between autochthonous pig breeds [Black Slavonian (BS), Turopolje pig (TP), and Croatian wild boar] and to determine phylogenetic relationship among Croatian autochthonous pig breeds and certain Asian and European pigs using the mitochondrial DNA (mtDNA) D-loop sequence polymorphism. Relatively high degree of genetic variation was found between the observed populations. The analysis of mtDNA showed that haplotypes of the studied pig populations are different from the other European and Chinese haplotypes. BS pigs showed some similarities with Mangalitsa and Duroc breeds. The genetic distances of TP can be explained by high degree of inbreeding during the past century. Despite the European origin of Croatian pig breeds with some impact of Chinese breeds in the past, the results of present study show that genetic diversity is still pronounced within investigated breeds. Furthermore, the genetic diversity is even more pronounced between Croatian breeds and other European and Chinese pig breeds. Thus, conservation of Croatian pig breeds will contribute to overall genetic diversity preservation of pig breeds.
Providing security for NFC-based payment systems using a management authentication server

Recent models of mobile phones have been designed to offer many services such as mobile e-commerce, e-ticketing, e-payment, among many other services. Of a particular importance nowadays is the NFC-enabled mobiles in which the NFC technology allowed the integration of services from various applications into one single mobile. However, the EMV protocol, which is currently used to provide the required security for NFC-based payment systems, has two serious vulnerabilities between user payment devices and the merchants' point of sales which could lead to obvious risks for users. These two security vulnerabilities must be treated to secure NFC-based mobile payment transactions. In this paper a protocol is proposed to improve the security of EMV by adding a new security layer. The security provided by the protocol to EMV has been verified against major security attacks.