Normal Behavior Patterns Research Articles

Subglottal ambulatory monitoring of vocal function to improve voice disorder assessment

Many common voice disorders are chronic or recurring conditions that are likely to result from inefficient and/or abusive patterns of vocal behavior, referred to as vocal hyperfunction. The clinical management of hyperfunctional disorders would be greatly enhanced by the ability to monitor and quantify detrimental vocal behaviors during an individual’s activities of daily life. This presentation will provide an update about ongoing work that is using a miniature accelerometer on the subglottal neck surface to collect a large set of ambulatory data on patients with hyperfunctional voice disorders (before and after treatment) and matched control subjects. Three types of analysis approaches are being employed in an effort to identify the best set of measures for differentiating among hyperfunctional and normal patterns of vocal behavior: (1) previously developed ambulatory measures of vocal function that include vocal dosages; (2) measures based on estimates of glottal airflow that are extracted from the accelerometer signal using a vocal system model, and (3) classification based on machine learning approaches that have been used successfully in analyzing long-term recordings of other physiologic signals (e.g., electrocardiograms).

Motricidad fetal durante el segundo trimestre de gestación: estudio ecográfico longitudinal

IntroductionThe aim of this research is to contribute to knowledge of the normal spontaneous motor behavior of the human fetus during the second trimester of pregnancy. This study focuses on five patterns of spontaneous fetal movement: startle (S), axo-rhizomelic rhythmia (ARR), axial stretching (AS), general movement (GM), and diaphragmatic contraction (DC). MethodsA cohort of 13 subjects was followed up using 2D obstetrical ultrasound images at 12, 16, 20, and 24 weeks of gestation. As inclusion criteria, neonatal neurological examination and general movements after eutocic delivery at term were normal in all of the subjects, and their neuromotor and cognitive development until the end of pre-school age were also normal. ResultsAll these five motor patterns are present at the beginning of the 2nd gestational trimester, but their quantitative and qualitative traits are diverse according to gestational ages. The phasic, isolated or rhythmically repeated movements, S and ARR, are prominent at 12 and 16 weeks of gestation, and then their presence gradually diminishes. By contrast, tonic and complex AS and GM movements increase their presence and quality at 20 and 24 weeks. RAR constitute a particular periodic motor pattern not described in previous literature. Moreover, the incidence of DC is progressive throughout the trimester, in clusters of 2-6 arrhythmic and irregular beats. Fetal heart rate increases during fetal motor active periods. ConclusionsAll five normal behavioral patterns observed in the ultrasounds reflect the progressive tuning of motor generators in human nervous system during mid-pregnancy.

User Activities Outliers Detection; Integration of Statistical and Computational Intelligence Techniques

In this article, a hybrid technique for user activities outliers detection is introduced. The hybrid technique consists of a two‐stage integration of principal component analysis and fuzzy rule‐based systems. In the first stage, the Hamming distance is used to measure the differences between different activities. Principal component analysis is then applied to the distance measures to find two indices of Hotelling's T2 and squared prediction error. In the second stage of the process, the calculated indices are provided as inputs to the fuzzy rule‐based systems to model them heuristically. The model is used to identify the outliers and classify them. The proposed system is tested in real home environments, equipped with appropriate sensory devices, to identify outliers in the activities of daily living of the user. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers in activities distinguishing between the normal and abnormal behavioral patterns.

Learning to detect vocal hyperfunction from ambulatory neck-surface acceleration features: initial results for vocal fold nodules.

Voice disorders are medical conditions that often result from vocal abuse/misuse which is referred to generically as vocal hyperfunction. Standard voice assessment approaches cannot accurately determine the actual nature, prevalence, and pathological impact of hyperfunctional vocal behaviors because such behaviors can vary greatly across the course of an individual's typical day and may not be clearly demonstrated during a brief clinical encounter. Thus, it would be clinically valuable to develop noninvasive ambulatory measures that can reliably differentiate vocal hyperfunction from normal patterns of vocal behavior. As an initial step toward this goal we used an accelerometer taped to the neck surface to provide a continuous, noninvasive acceleration signal designed to capture some aspects of vocal behavior related to vocal cord nodules, a common manifestation of vocal hyperfunction. We gathered data from 12 female adult patients diagnosed with vocal fold nodules and 12 control speakers matched for age and occupation. We derived features from weeklong neck-surface acceleration recordings by using distributions of sound pressure level and fundamental frequency over 5-min windows of the acceleration signal and normalized these features so that intersubject comparisons were meaningful. We then used supervised machine learning to show that the two groups exhibit distinct vocal behaviors that can be detected using the acceleration signal. We were able to correctly classify 22 of the 24 subjects, suggesting that in the future measures of the acceleration signal could be used to detect patients with the types of aberrant vocal behaviors that are associated with hyperfunctional voice disorders.

Cryptochrome could help blackcaps find way

Setting off on their 9500 km migration south from their summer home in Latvia, blackcaps set a bearing toward their wintering grounds in Kenya that guides them on their way. Augusto Foà and collaborators from the University of Ferrara, Italy, explain that the tiny aviators are probably able to detect the earth's magnetic field, and use it to set their bearing as they head south. Some migratory species sense magnetic fields using a chemical interaction between cryptochrome proteins in the eye and blue light, which they use as a compass during migration. Could migrating birds increase cryptochrome levels in their eyes in preparation for migration in order to guide them on their way (p. 918)?Leonida Fusani and Cristiano Bertolucci travelled to the birds' nesting site near the Pape Ornithological Station, Latvia – where the birds prepare for migration – in August 2011 and intercepted the voyagers just before they embarked on their ambitious journey. Having transported the animals back to the University of Ferrara, the team then waited for the birds to begin showing the tell-tale signs that they were ready to set off – they become more active by night, are more sedentary during the day and have gained weight. The team then allowed some of the birds to continue hopping around at night to simulate their migration. However, in another group they switched off the urge to migrate by recreating the return to normal behaviour that birds experience when they stop to refuel after two days of migration. The team did this by not feeding the birds for two days – to simulate the flight – and then resumed feeding the birds on the third day to induce the return to normal behaviour during the refuelling stop. The team then collected samples of the birds' eyes to look for evidence of cryptochrome production and compared them with samples from birds that were hopping around ready to embark on migration, birds that had completed their virtual migration for the year and were back to their normal daily routine, and birds that had just gone through a night of virtual migrationThey found that the birds that were experiencing the simulated migration at night had high levels of the mRNA that produces the crytochrome proteins. However, the birds that had returned to their normal behaviour patterns had much lower levels of the cryptochrome RNAs. So, blackcaps that are on the verge of migration and those that are experiencing migration have access to one of the key components for detecting magnetic fields that might guide them on their way. And although this is not conclusive evidence that the birds set their bearing using a magnetic compass, it is another piece of compelling evidence that they probably do.

Mobile malware detection through analysis of deviations in application network behavior

In this paper we present a new behavior-based anomaly detection system for detecting meaningful deviations in a mobile application's network behavior. The main goal of the proposed system is to protect mobile device users and cellular infrastructure companies from malicious applications by: (1) identification of malicious attacks or masquerading applications installed on a mobile device, and (2) identification of republished popular applications injected with a malicious code (i.e., repackaging). More specifically, we attempt to detect a new type of mobile malware with self-updating capabilities that were recently found on the official Google Android marketplace. Malware of this type cannot be detected using the standard signatures approach or by applying regular static or dynamic analysis methods. The detection is performed based on the application's network traffic patterns only. For each application, a model representing its specific traffic pattern is learned locally (i.e., on the device). Semi-supervised machine-learning methods are used for learning the normal behavioral patterns and for detecting deviations from the application's expected behavior. These methods were implemented and evaluated on Android devices. The evaluation experiments demonstrate that: (1) various applications have specific network traffic patterns and certain application categories can be distinguished by their network patterns; (2) different levels of deviation from normal behavior can be detected accurately; (3) in the case of self-updating malware, original (benign) and infected versions of an application have different and distinguishable network traffic patterns that in most cases, can be detected within a few minutes after the malware is executed while presenting very low false alarms rate; and (4) local learning is feasible and has a low performance overhead on mobile devices.

DETECTING ABNORMAL BEHAVIOR IN SOCIAL NETWORK WEBSITES BY USING A PROCESS MINING TECHNIQUE

Detecting abnormal user activity in social network websites could prevent from cyber-crime occurrence. The previous research focused on data mining while this research is based on user behavior process. In this study, the first step is defining a normal user behavioral pattern and the second step is detecting abnormal behavior. These two steps are applied on a case study that includes real and syntactic data sets to obtain more tangible results. The chosen technique used to define the pattern is process mining, which is an affordable, complete and noise-free event log. The proposed model discovers a normal behavior by genetic process mining technique and abnormal activities are detected by the fitness function, which is based on Petri Net rules. Although applying genetic mining is time consuming process, it can overcome the risks of noisy data and produces a comprehensive normal model in Petri net representation form.

On the use of play theory in analyses of online public commentary

The concept of play mediates between deliberation as a mode of reason and resistance as a mode of culture, thus opening a way to think about hostile comment (e.g., ‘flaming’) on online news forums as normal patterns of behaviour, instead of a departure from the received view of how citizens ought to consider matters of public interest. The play concept corresponds with current thinking around the notion of cultural citizenship. To illustrate the relevance of play theory in the analysis of online political discourse, this article uses an example from recent posts concerning the Protection of Information Bill (POIB) in the online site of the South African Mail & Guardian newspaper. The cogency of play theory to the concept of citizenship is argued through a discussion of how citizenship has been understood from the 1930s to the late 1980s and early 1990s, when the improved capacities of information and communications technologies (ICTs) made online deliberation a normal site for citizenship to be exercised.

Adolescence: Challenges and responses

Adolescence: Challenges and responses

An in vivo mouse model of intraosseous spinal cancer causing evolving paraplegia

The spine is the commonest site of skeletal metastatic disease and uncontrolled growth of cancer in the spine will inevitably cause pain and neurologic compromise. Improved understanding of the pathobiology behind this devastating condition is urgently needed. For this reason, the aim of this study was to establish a clinically relevant, animal model of spinal cancer. A percutaneous orthotopic injection of human breast (MDA-MB-231) or human prostate (PC-3) cancer cells was administered into the upper lumbar spine of nude mice (n = 6). Animals were monitored twice daily for general welfare, gait asymmetry or disturbance, and hindlimb weakness. After sacrifice, plain radiographs, micro-CT imaging and histological analysis of the spines were performed on each mouse. All mice recovered fully from the inoculation procedure and displayed normal gait and behaviour patterns for at least 3 weeks post-inoculation. Subsequently, between 3 and 5 weeks post-inoculation, each mouse developed evolving paralysis in their hindlimbs over 48-72 h. All followed the same pattern of decline following onset of neurological dysfunction; from gait asymmetry and unilateral hindlimb weakness, to complete unilateral hindlimb paralysis and finally to complete bilateral hindlimb paralysis. Plain radiographs, micro-CT scanning and histological analysis confirmed local tumour growth and destruction of the spine in all six mice. An in vivo mouse model of human intraosseous spinal cancer has been established forming cancers that grow within the spine and cause epidural spinal cord compression, resulting in a reproducible, evolving neurological deficit and paralysis that closely resembles the human condition.

An Intrusion Detection Model Based on Mining Maximal Frequent Itemsets over Data Streams

Ming association rules have been proved as an important method to detect intrusions. To improve response speed and detecting precision in the current intrusion detection system, this papers proposes an intrusion detection system model of MMFIID-DS. Firstly, to improve response speed of the system by greatly reducing search space, various pruning strategies are proposed to mine the maximal frequent itemsets on trained normal data set, abnormal data set and current data streams to establish normal and abnormal behavior pattern as well as user behavior pattern of the system. Besides, to improve detection precision of the system, misuse detection and anomaly detection techniques are combined. Both theoretical and experimental results indicate that the MMFIID-DS intrusion detection system is fairly sound in performance.

The Promise of Neuro-Recovery After Stroke: Introduction

The study of brain repair and behavioral recovery after stroke continues to accelerate with points of convergence between findings in humans and results in animal models of stroke. Approaches to motor recovery after stroke can be usefully broken into 3 categories: reduction in impairment with a return to more normal patterns of motor behavior; compensatory responses with the other limb or even muscles within the same limb; and use of neurally controlled prosthetics that substitute for the affected limb. The future choice of any of these approaches to neurorehabilitation is likely to depend on both the severity of the initial impairment and the time since …

Home-Based Services for High-Risk Youth: Assessment, Wraparound Planning, and Service Delivery

Home-Based Services for High-Risk Youth: Assessment, Wraparound Planning, and Service Delivery

Personalized Behavior Pattern Recognition and Unusual Event Detection for Mobile Users

Mobile phones have become widely used for obtaining help in emergencies, such as accidents, crimes, or health emergencies. The smartphone is an essential device that can record emergency situations, which can be used for clues or evidence, or as an alert system in such situations. In this paper, we focus on mobile-based identification of potentially unusual, or abnormal events, occurring in a mobile user's daily behavior patterns. For purposes of this research, we have classified events as for a mobile user when an event is an infrequently occurring one from the user's normal behavior patterns --all of which are collected and recorded on a user's mobile phone. We build a general unusual event classification model to be automated on the smartphone for use by any mobile phone users. To classify both normal and unusual events, we analyzed the activity, location, and audio sensor data collected from 20 mobile phone users to identify these users' personalized normal daily behavior patterns and any unusual events occurring in their daily activity. We used binary fusion classification algorithms on the subjects' recorded experimental data and ultimately identified the most accurately performing fusion algorithm for unusual event detection.

Personalized Behavior Pattern Recognition and Unusual Event Detection for Mobile Users

Mobile phones have become widely used for obtaining help in emergencies, such as accidents, crimes, or health emergencies. The smartphone is an essential device that can record emergency situations, which can be used for clues or evidence, or as an alert system in such situations. In this paper, we focus on mobile-based identification of potentially unusual, or abnormal events, occurring in a mobile user's daily behavior patterns. For purposes of this research, we have classified events as “unusual” for a mobile user when an event is an infrequently occurring one from the user's normal behavior patterns–all of which are collected and recorded on a user's mobile phone. We build a general unusual event classification model to be automated on the smartphone for use by any mobile phone users. To classify both normal and unusual events, we analyzed the activity, location, and audio sensor data collected from 20 mobile phone users to identify these users' personalized normal daily behavior patterns and any unusual events occurring in their daily activity. We used binary fusion classification algorithms on the subjects' recorded experimental data and ultimately identified the most accurately performing fusion algorithm for unusual event detection.

Assessing Acute Effects of Trapping, Handling, and Tagging on the Behavior of Wildlife Using GPS Telemetry: A Case Study of the Common Brushtail Possum

Trapping, handling, and deployment of tracking devices (tagging) are essential aspects of many research and conservation studies of wildlife. However, often these activities place nonhuman animals under considerable physical or psychological distress, which disrupts normal patterns of behavior and may ultimately result in deleterious effects on animal welfare and the validity of research results. Thus, knowledge of how trapping, handling, and tagging alter the behavior of research animals is essential if measures to ameliorate stress-related effects are to be developed and implemented. This article describes how time-stamped location data obtained by global-positioning-system telemetry can be used to retrospectively characterize acute behavioral responses to trapping, handling, and tagging in free-ranging animals used for research. Methods are demonstrated in a case study of the common brushtail possum, a semiarboreal phalangerid marsupial native to Australia. The study discusses possible physiological causes of observed effects and offers general suggestions regarding simple means to reduce trapping-handling-and-tagging-related stress in field studies of vertebrates.

Aphrodisiac activity of ethanolic extract of Pedalium murex Linn fruit

ObjectiveThe study represents an interesting case report for a very good aphrodisiac activity observed during an oral glucose tolerance test performed while evaluating the antidiabetic potential of Pedalium murex Linn. fruit. MethodsEthanolic extract of Pedalium murex at a dose of 125, 250 and 500 mg/kg p.o. was given to the rats followed by administration of 2 g/kg p.o. of glucose 30 min after the administration of extract. ResultsPregnancy was observed in the treated groups after 20-25 days of treatment in females which resulted in birth of pubs ranging upto ten in some females (more significant in case of 500 mg/kg p.o.). The observation also showed a significant increase in weights of pubs along with a normal behavior pattern. The increased pregnancy rate in the drug treated groups may be due to the healthy viable sperm and enhancement of sexual desire of the rats. ConclusionFrom the results it may be concluded that the fruits of the plant may be used as a good aphrodisiac agent to promote fertility rate.

Social transmission of physiological and behavioural responses to castration in suckling Merino lambs

In social species like sheep, social context can modify both physiological and behavioural responses to stressors and normal behavioural patterns. Presence of conspecifics can ameliorate responses to noxious stimuli, an effect termed social buffering, whereas the presence of a distressed conspecific can invoke a distress response in an observer animal not receiving a noxious organic insult, an effect termed social contagion. Furthermore, synchrony of normal behaviours can occur within a group. Here a range of social contexts were created by grouping suckling lambs undergoing knife, ring or sham castration treatments either homogeneously or heterogeneously by castration treatment in pens within an animal house. The impact of social grouping on cortisol, rectal temperature, pain avoidance behaviours, postural behaviours and synchrony of behaviour in the first 12 h following castration treatment was examined. Irrespective of castration treatment, lambs grouped homogeneously by castration treatment had higher cortisol concentrations across the four time points measured than lambs grouped heterogeneously. They also spent 1.9% more of the time in restless behaviour in the first 1 h following castration, and in the 12 h following castration spent 6.7% more of their time lying ventrally, 4.9% less time standing normally, 1.6% more time walking normally, and 4.9% less time in total standing postures. Interactions between social grouping and castration treatment were not significant for physiological variables, pain related behaviours or postures with the exception of lateral lying which was decreased in knife castrated lambs grouped heterogeneously with other castration treatments from 1.9% (homogeneous grouping) to 0.4% (heterogeneous group). Synchronisation of behaviour was seen for walking in lambs grouped heterogeneously by castration treatment and for feeding at the trough in lambs grouped homogenously by ring and sham castration treatments. Lying and standing respectively, were negatively synchronised (occurred less frequently than predicted by chance alone) in ring and knife castrated lambs grouped homogeneously. The results indicate that mixing lambs that received the three castration treatments within a pen modified the activity profile of the lambs but did not substantially modify the behavioural or physiological changes that are stigmatic of responses to the specific castration treatments. The social contexts contrasted in the study did not result in marked contagion or social buffering between lambs of the measured responses and did not confound comparison of castration treatments. None-the-less, the comparability of responses observed in castration treatment groups penned homogenously or heterogeneously might not necessarily occur in other classes of sheep or in other social contexts. The absence of social transmission of behavioural and physiological responses indicates that grouping non-castrated lambs with castrated lambs did not compromise the welfare of the non-castrated lambs when their mothers were present.

An ensemble design of intrusion detection system for handling uncertainty using Neutrosophic Logic Classifier

In the real world it is a routine that one must deal with uncertainty when security is concerned. Intrusion detection systems offer a new challenge in handling uncertainty due to imprecise knowledge in classifying the normal or abnormal behaviour patterns. In this paper we have introduced an emerging approach for intrusion detection system using Neutrosophic Logic Classifier which is an extension/combination of the fuzzy logic, intuitionistic logic, paraconsistent logic, and the three-valued logics that use an indeterminate value. It is capable of handling fuzzy, vague, incomplete and inconsistent information under one framework. Using this new approach there is an increase in detection rate and the significant decrease in false alarm rate. The proposed method tripartitions the dataset into normal, abnormal and indeterministic based on the degree of membership of truthness, degree of membership of indeterminacy and degree of membership of falsity. The proposed method was tested up on KDD Cup 99 dataset. The Neutrosophic Logic Classifier generates the Neutrosophic rules to determine the intrusion in progress. Improvised genetic algorithm is adopted in order to detect the potential rules for performing better classification. This paper exhibits the efficiency of handling uncertainty in Intrusion detection precisely using Neutrosophic Logic Classifier based Intrusion detection System.

An ensemble design of intrusion detection system for handling uncertainty using Neutrosophic Logic Classifier

In the real world it is a routine that one must deal with uncertainty when security is concerned. Intrusion detection systems offer a new challenge in handling uncertainty due to imprecise knowledge in classifying the normal or abnormal behaviour patterns. In this paper we have introduced an emerging approach for intrusion detection system using Neutrosophic Logic Classifier which is an extension/combination of the fuzzy logic, intuitionistic logic, paraconsistent logic, and the three-valued logics that use an indeterminate value. It is capable of handling fuzzy, vague, incomplete and inconsistent information under one framework. Using this new approach there is an increase in detection rate and the significant decrease in false alarm rate. The proposed method tripartitions the dataset into normal, abnormal and indeterministic based on the degree of membership of truthness, degree of membership of indeterminacy and degree of membership of falsity. The proposed method was tested up on KDD Cup 99 dataset. The Neutrosophic Logic Classifier generates the Neutrosophic rules to determine the intrusion in progress. Improvised genetic algorithm is adopted in order to detect the potential rules for performing better classification. This paper exhibits the efficiency of handling uncertainty in Intrusion detection precisely using Neutrosophic Logic Classifier based Intrusion detection System.