Tolerant mothers: aggression does not explain solitary living in the bush Karoo rat.

Many mammal species are thought to adopt solitary living owing to mothers becoming intolerant of adult offspring and the occurrence of social intolerance between adults. However, field studies on how solitary mammals interact are rare. Here we show that solitary living can occur without social intolerance. Over 3 years, we recorded interactions between free-living bush Karoo rats (Otomys unisulcatus) and conducted dyadic encounter experiments between kin and non-kin female neighbours, both in a neutral test arena and in field intruder experiments. Social interactions were rare (230/2062 observations), and they were aggressive in only 34% of cases. In dyadic encounters, mothers interacted amicably with young offspring. Aggression between mothers and offspring was almost absent. This mother-offspring relationship remained amicable even after adult offspring had dispersed. Aggression between neighbouring adult females was low in neutral arena tests, independent of kinship and season. However, in the field, females reacted more aggressively towards non-kin than kin intruders, especially during the breeding season. Tolerance between mothers and adult offspring indicates that aggression is not the mechanism leading to dispersal and solitary living. We found a solitary social system characterized by social tolerance, suggesting that dispersal and lack of social attraction rather than aggression can lead to solitary living.

mmWave-RM: A Respiration Monitoring and Pattern Classification System Based on mmWave Radar.

Breathing is one of the body's most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring.

A major genetic locus in neighbours controls changes of gene expression and susceptibility in intraspecific rice mixtures.

Reports indicate that intraspecific neighbours alter the physiology of focal plants, and with a few exceptions, their molecular responses to neighbours are unknown. Recently, changes in susceptibility to pathogen resulting from such interactions were demonstrated, a phenomenon called neighbour-modulated susceptibility (NMS). However, the genetics of NMS and the associated molecular responses are largely unexplored. Here, we analysed in rice the modification of biomass and susceptibility to the blast fungus pathogen in the Kitaake focal genotype in the presence of 280 different neighbours. Using genome-wide association studies, we identified the loci in the neighbour that determine the response in Kitaake. Using a targeted transcriptomic approach, we characterized the molecular responses in focal plants co-cultivated with various neighbours inducing a reduction in susceptibility. Our study demonstrates that NMS is controlled by one major locus in the rice genome of its neighbour. Furthermore, we show that this locus can be associated with characteristic patterns of gene expression in focal plant. Finally, we propose an hypothesis where Pi could play a role in explaining this case of NMS. Our study sheds light on how plants affect the physiology in their neighbourhood and opens perspectives for understanding plant-plant interactions.

Improving the quality of hospital sterilization process using failure modes and effects analysis, fuzzy logic, and machine learning: experience in tertiary dental centre.

Activities practiced in the hospital generate several types of risks. Therefore, performing the risk assessment is one of the quality improvement keys in the healthcare sector. For this reason, healthcare managers need to design and perform efficient risk assessment processes. Failure modes and effects analysis (FMEA) is one of the most used risk assessment methods. The FMEA is a proactive technique consisting of the evaluation of failure modes associated with a studied process using three factors: occurrence, non-detection, and severity, in order to obtain the risk priority number using fuzzy logic approach and machine learning algorithms, namely the support vector machine and the k-nearest neighbours. The proposed model is applied in the case of the central sterilization unit of a tertiary national reference centre of dental treatment, where its efficiency is evaluated compared to the classical approach. These comparisons are based on expert advice and machine learning performance metrics. Our developed model proved high effectiveness throughout the results of the expert's vote (she agrees with 96% fuzzy-FMEA results against 6% with classical FMEA results). Furthermore, the machine learning metrics show a high level of accuracy in both training data (best rate is 96%) and testing data (90%). This study represents the first study that aims to perform artificial intelligence approach to risk management in the Moroccan healthcare sector. The perspective of this study is to promote the application of the artificial intelligence in Moroccan health management, especially in the field of quality and safety management.

Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques.

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.

A deterministic approach for protecting privacy in sensitive personal data.

BACKGROUND: Data privacy is one of the biggest challenges for any organisation which processes personal data, especially in the area of medical research where data include sensitive information about patients and study participants. Sharing of data is therefore problematic, which is at odds with the principle of open data that is so important to the advancement of society and science. Several statistical methods and computational tools have been developed to help data custodians and analysts overcome this challenge. METHODS: In this paper, we propose a new deterministic approach for anonymising personal data. The method stratifies the underlying data by the categorical variables and re-distributes the continuous variables through a k nearest neighbours based algorithm. RESULTS: We demonstrate the use of the deterministic anonymisation on real data, including data from a sample of Titanic passengers, and data from participants in the 1958 Birth Cohort. CONCLUSIONS: The proposed procedure makes data re-identification difficult while minimising the loss of utility (by preserving the spatial properties of the underlying data); the latter means that informative statistical analysis can still be conducted.

Evaluating the Window Size's Role in Automatic EEG Epilepsy Detection.

Electroencephalography is one of the most commonly used methods for extracting information about the brain's condition and can be used for diagnosing epilepsy. The EEG signal's wave shape contains vital information about the brain's state, which can be challenging to analyse and interpret by a human observer. Moreover, the characteristic waveforms of epilepsy (sharp waves, spikes) can occur randomly through time. Considering all the above reasons, automatic EEG signal extraction and analysis using computers can significantly impact the successful diagnosis of epilepsy. This research explores the impact of different window sizes on EEG signals' classification accuracy using four machine learning classifiers. The machine learning methods included a neural network with ten hidden nodes trained using three different training algorithms and the k-nearest neighbours classifier. The neural network training methods included the Broyden-Fletcher-Goldfarb-Shanno algorithm, the multistart method for global optimization problems, and a genetic algorithm. The current research utilized the University of Bonn dataset containing EEG data, divided into epochs having 50% overlap and window lengths ranging from 1 to 24 s. Then, statistical and spectral features were extracted and used to train the above four classifiers. The outcome from the above experiments showed that large window sizes with a length of about 21 s could positively impact the classification accuracy between the compared methods.

Friendly neighbours: Hydraulic redistribution accounts for one quarter of water used by neighbouring drought stressed tree saplings.

Hydraulic redistribution (HR) can buffer drought events of tree individuals, however, its relevance for neighbouring trees remains unclear. Here, we quantified HR to neighbouring trees in single- and mixed-species combinations. We hypothesized that uptake of HR water positively correlates with root length, number of root tips and root xylem hydraulic conductivity and that neighbours in single-species combinations receive more HR water than in phylogenetic distant mixed-species combinations. In a split-root experiment, a sapling with its roots split between two pots redistributed deuterium labelled water from a moist to a dry pot with an additional tree each. We quantified HR water received by the sapling in the dry pot for six temperate tree species. After 7 days, one quarter of the water in roots (2.1 ± 0.4 ml), stems (0.8 ± 0.2 ml) and transpiration (1.0 ± 0.3 ml) of the drought stressed sapling originated from HR. The amount of HR water transpired by the receiving plant stayed constant throughout the experiment. While the uptake of HR water increased with root length, species identity did not affect HR as saplings of Picea abies ((L.) Karst) and Fagus sylvatica (L.) in single- and mixed-species combinations received the same amount of HR water.

Social trust and emotional health in rural older adults in China: the mediating and moderating role of subjective well-being and subjective social status.

BACKGROUND: China is becoming an aging society. The emotional health of the elderly is gaining importance. Social trust is an important factor affecting emotional health, but existing studies have rarely considered the various effects of different types of social trust on rural elderly emotional health. Few studies have analysed the role of subjective well-being and subjective social status in the relationship between social trust and elderly emotional health. METHODS: Using the data of the China Labor-force Dynamics Survey 2016 (CLDS 2016) and regression models, this study selected 2084 rural respondents aged 60 years and above to analyse the impact of social trust on their emotional health. Social trust was divided into three categories: trust in family members, trust in friends, and trust in neighbours. This study also examined the mediating and moderating effects of subjective well-being and subjective social status on the relationship between social trust and emotional health. RESULTS: Trust in family members was significantly and positively associated with emotional health (coefficient = 0.194, P < 0.01) and subjective well-being (coefficient = 0.177, P < 0.01). Trust in friends was significantly and positively associated with emotional health and subjective well-being (coefficient = 0.097, P < 0.01; coefficient = 0.174, P < 0.01, respectively). Trust in neighbours was significantly and positively associated with emotional health and subjective well-being (coefficient = 0.088, P < 0.01; coefficient = 0.177, P < 0.01; respectively). Subjective well-being effectively reduced the impact of social trust in family, friends, and neighbours on the emotional health of the elderly by 0.023, 0.022, and 0.023, respectively. Trust in friends and neighbours significantly and positively affected respondents' subjective social status (coefficient = 0.120, P < 0.05; coefficient = 0.090, P < 0.10; respectively). Subjective social status effectively reduced the impact of social trust in friends and neighbours on the emotional health of the elderly both by 0.004. The positive relationship between trust in family members and emotional health is weakened by subjective well-being. CONCLUSIONS: Social trust, especially family relationships, play an important role in maintaining the emotional health of the rural elderly. In response to population ageing, more social policies must be introduced to care for the rural elderly and help them lead a happy and satisfactory life.

Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump.

This paper presents a learning system with a K-nearest neighbour classifier to classify the wear condition of a multi-piston positive displacement pump. The first part reviews current built diagnostic methods and describes typical failures of multi-piston positive displacement pumps and their causes. Next is a description of a diagnostic experiment conducted to acquire a matrix of vibration signals from selected locations in the pump body. The measured signals were subjected to time-frequency analysis. The signal features calculated in the time and frequency domain were grouped in a table according to the wear condition of the pump. The next step was to create classification models of a pump wear condition and assess their accuracy. The selected model, which best met the set criteria for accuracy assessment, was verified with new measurement data. The article ends with a summary.

Do sequence neighbours of intrinsically disordered regions promote structural flexibility in intrinsically disordered proteins?

Intrinsically disordered proteins (IDPs) are crucial players in various cellular activities. Several experimental and computational analyses have been conducted to study structural pliability and functional potential of IDPs. In spite of active research in past few decades, what induces structural disorder in IDPs and how is still elusive. Many studies testify that sequential and spatial neighbours often play important roles in determining structural and functional behaviour of proteins. Considering this fact, we assessed sequence neighbours of intrinsically disordered regions (IDRs) to understand if they have any role to play in inducing structural flexibility in IDPs. Our analysis includes 97% eukaryotic IDPs and 3% from bacteria and viruses. Physicochemical and structural parameters including amino acid propensity, hydrophobicity, secondary structure propensity, relative solvent accessibility, B-factor and atomic packing density are used to characterise the neighbouring residues of IDRs (NRIs). We show that NRIs exhibit a unique nature, which makes them stand out from both ordered and disordered residues. They show correlative occurrences of residue pairs like Ser-Thr and Gln-Asn, indicating their tendency to avoid strong biases of order or disorder promoting amino acids. We also find differential preferences of amino acids between N- and C-terminal neighbours, which might indicate a plausible directional effect on the dynamics of adjacent IDRs. We designed an efficient prediction tool using Random Forest to distinguish the NRIs from the ordered residues. Our findings will contribute to understand the behaviour of IDPs, and may provide potential lead in deciphering the role of IDRs in protein folding and assembly.

Exploiting mutual information for the imputation of static and dynamic mixed-type clinical data with an adaptive k-nearest neighbours approach.

BACKGROUND: Clinical registers constitute an invaluable resource in the medical data-driven decision making context. Accurate machine learning and data mining approaches on these data can lead to faster diagnosis, definition of tailored interventions, and improved outcome prediction. A typical issue when implementing such approaches is the almost unavoidable presence of missing values in the collected data. In this work, we propose an imputation algorithm based on a mutual information-weighted k-nearest neighbours approach, able to handle the simultaneous presence of missing information in different types of variables. We developed and validated the method on a clinical register, constituted by the information collected over subsequent screening visits of a cohort of patients affected by amyotrophic lateral sclerosis. METHODS: For each subject with missing data to be imputed, we create a feature vector constituted by the information collected over his/her first three months of visits. This vector is used as sample in a k-nearest neighbours procedure, in order to select, among the other patients, the ones with the most similar temporal evolution of the disease over time. An ad hoc similarity metric was implemented for the sample comparison, capable of handling the different nature of the data, the presence of multiple missing values and include the cross-information among features captured by the mutual information statistic. RESULTS: We validated the proposed imputation method on an independent test set, comparing its performance with those of three state-of-the-art competitors, resulting in better performance. We further assessed the validity of our algorithm by comparing the performance of a survival classifier built on the data imputed with our method versus the one built on the data imputed with the best-performing competitor. CONCLUSIONS: Imputation of missing data is a crucial -and often mandatory- step when working with real-world datasets. The algorithm proposed in this work could effectively impute an amyotrophic lateral sclerosis clinical dataset, by handling the temporal and the mixed-type nature of the data and by exploiting the cross-information among features. We also showed how the imputation quality can affect a machine learning task.

The importance of growing up: juvenile environment influences dispersal of individuals and their neighbours.

Dispersal is a key ecological process that is strongly influenced by both phenotype and environment. Here, we show that juvenile environment influences dispersal not only by shaping individual phenotypes, but also by changing the phenotypes of neighbouring conspecifics, which influence how individuals disperse. We used a model system (Tribolium castaneum, red flour beetles) to test how the past environment of dispersing individuals and their neighbours influences how they disperse in their current environment. We found that individuals dispersed especially far when exposed to a poor environment as adults if their phenotype, or even one-third of their neighbours' phenotypes, were shaped by a poor environment as juveniles. Juvenile environment therefore shapes dispersal both directly, by influencing phenotype, as well as indirectly, by influencing the external social environment. Thus, the juvenile environment of even a minority of individuals in a group can influence the dispersal of the entire group.

Tight junction-associated protein GEF-H1 in the neighbours of dividing epithelial cells is essential for adaptation of cell-cell membrane during cytokinesis.

Animal cells divide by a process called cytokinesis which relies on the constriction of a contractile actomyosin ring leading to the production of two daughter cells. Cytokinesis is an intrinsic property of cells which occurs even for artificially isolated cells. During division, isolated cells undergo dramatic changes in shape such as rounding and membrane deformation as the division furrow ingresses. However, cells are often embedded in tissues and thus are surrounded by neighbouring cells. How these neighbours might influence, or might themselves be influenced by, the shape changes of cytokinesis is poorly understood in vertebrates. Here, we show that during cytokinesis of epithelial cells in the Xenopus embryo, lateral cell-cell contacts remain almost perpendicular to the epithelial plane. Depletion of the tight junction-associated protein GEF-H1 leads to a transient and stereotyped deformation of cell-cell contacts. Although, this deformation occurs only during cytokinesis, we show that it originates from immediate neighbours of the dividing cell. Moreover, we show that exocyst and recycling endosome regulation by GEF-H1 are involved in adaptation of cell-cell contacts to deformation. Our results highlight the crucial role of tight junctions and GEF-H1 in cell-cell contact adaptation when cells are exposed to a mechanical stress such as cytokinesis.

A close call: Interference from semantic neighbourhood density and similarity in language production.

The present study investigated how lexical selection is influenced by the number of semantically related representations (semantic neighbourhood density) and their similarity (semantic distance) to the target in a speeded picture-naming task. Semantic neighbourhood density and similarity as continuous variables were used to assess lexical selection for which competitive and noncompetitive mechanisms have been proposed. Previous studies found mixed effects of semantic neighbourhood variables, leaving this issue unresolved. Here, we demonstrate interference of semantic neighbourhood similarity with less accurate naming responses and a higher likelihood of producing semantic errors and omissions over accurate responses for words with semantically more similar (closer) neighbours. No main effect of semantic neighbourhood density and no interaction between semantic neighbourhood density and similarity was found. We assessed further whether semantic neighbourhood density can affect naming performance if semantic neighbours exceed a certain degree of semantic similarity. Semantic similarity between the target and each neighbour was used to split semantic neighbourhood density into two different density variables: The number of semantically close neighbours versus distant neighbours. The results showed a significant effect of close, but not of distant, semantic neighbourhood density: Naming pictures of targets with more close semantic neighbours led to longer naming latencies, less accurate responses, and a higher likelihood for the production of semantic errors and omissions over accurate responses. The results show that word inherent semantic attributes such as semantic neighbourhood similarity and the number of coactivated close semantic neighbours modulate lexical selection supporting theories of competitive lexical processing.

Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense).

The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers' expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an "ad hoc" designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours' algorithm and Partial Least Square-Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar's test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values (p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours' model is considered the most convenient strategy to assess meat and fish freshness.

How residents in multifamily housing cope with neighbour noise: The role of attitude towards the neighbours.

AIMS: Residents in multifamily housing are considerably exposed to neighbour noise, which can lead to neighbour disputes and even criminal violence. This study investigated how residents' attitudes towards noisy neighbours develop and the role of such attitudes in their reactions to neighbour noise. METHODS: In-depth interviews were carried out with 57 South Korean residents. The data were collected and analyzed using grounded theory methods. Concepts and categories were identified through open coding and axial coding, respectively. RESULTS: Residents' attitudes towards the neighbours (i.e., noise source) were grouped into friends, enemies, and strangers/acquaintances. Each attitude formation was influenced by the individual's past experience/history, the attitude shown by the neighbours, and the predictability/certainty of noise exposure. Different attitudes towards the neighbours resulted in different cognitive and behavioural copings. CONCLUSION: Given that the neighbour noise issue involves the interpersonal relationship between neighbours, the findings extend the scope of existing understanding. The paper suggests that further investigation into coping strategies would have practical implementations for reducing conflict arising from neighbour disputes.

UBO Detector - A cluster-based, fully automated pipeline for extracting white matter hyperintensities.

We present 'UBO Detector', a cluster-based, fully automated pipeline for extracting and calculating variables for regions of white matter hyperintensities (WMH) (available for download at https://cheba.unsw.edu.au/group/neuroimaging-pipeline). It takes T1-weighted and fluid attenuated inversion recovery (FLAIR) scans as input, and SPM12 and FSL functions are utilised for pre-processing. The candidate clusters are then generated by FMRIB's Automated Segmentation Tool (FAST). A supervised machine learning algorithm, k-nearest neighbor (k-NN), is applied to determine whether the candidate clusters are WMH or non-WMH. UBO Detector generates both image and text (volumes and the number of WMH clusters) outputs for whole brain, periventricular, deep, and lobar WMH, as well as WMH in arterial territories. The computation time for each brain is approximately 15 min. We validated the performance of UBO Detector by showing a) high segmentation (similarity index (SI) = 0.848) and volumetric (intraclass correlation coefficient (ICC) = 0.985) agreement between the UBO Detector-derived and manually traced WMH; b) highly correlated (r2 > 0.9) and a steady increase of WMH volumes over time; and c) significant associations of periventricular (t = 22.591, p < 0.001) and deep (t = 14.523, p < 0.001) WMH volumes generated by UBO Detector with Fazekas rating scores. With parallel computing enabled in UBO Detector, the processing can take advantage of multi-core CPU's that are commonly available on workstations. In conclusion, UBO Detector is a reliable, efficient and fully automated WMH segmentation pipeline.

[Reduction in the noise pollution within residential environments - what has been achieved so far?] / Verringerung der Lärmbelastungen am Wohnort ­ Was wurde bisher erreicht?

Surveys show that noise in its various forms is among the most severe environmental burdens. According to estimates of the World Health Organization (WHO), there is a loss of one million healthy life years per year in Europe due to the effects of environmental noise. This paper discusses the harmful effects of noise with the example of traffic noise, for which the highest number of environmental noise effect studies are available. An increasing number of studies show associations between traffic noise and vascular, metabolic and psychic diseases. Too little is still known about the causal mechanisms, even though a number of explanatory models are available. However, it is clear that today it is no longer a question of whether noise is responsible for extra-aural diseases, but under what conditions and to what extent are the risks.The high degree of noise nuisance shows that more action is needed with regard to noise control policy.Over the past few years, federal, regional and local governments have taken countless initiatives on traffic noise control - for instance, within the framework of action plans. Especially notable are the innovative measures, instruments and programs regarding railway noise. In the most important source of nuisance, road traffic noise, less progress has been made. In air traffic noise, too, the revision and development of important laws are still lacking. To promote inner urban development, the level of protection against industrial and sports noise has been reduced.Significant health-related reductions in noise pollution (except regarding the railways), therefore, cannot be expected in the near future.

Informal support for women and intimate partner violence: the crucial yet ambivalent role of neighbours in urban India.

Women who experience intimate partner violence often rely on informal support to mitigate intimate partner violence's health effects. Yet there is little known about who gives the support and how it is provided. This paper explores from whom and how low-income women experiencing domestic violence in urban India seek informal support. In South Asia, women's reliance on kin for support is culturally valued, yet the urban social context makes it more likely that they will access such support from non-kin when they experience intimate partner violence. The paper draws on observations and interviews with 10 families collected over 14 months of in-depth ethnographic research in one Delhi slum community. Using a case study approach to explore women's responses to violence longitudinally, it was possible to track how women drew on support. Results show that even as women sought emotional support and direct intervention from their neighbours to deal with their domestic violence, they restricted these relationships, faced stigma, and emphasised the need to protect their families. Understanding the informal, but deeply ambivalent, systems of social support that women engage to deal with intimate partner violence is a first step toward strengthening such networks, a key recommendation to stem the health impacts of domestic violence.