Genetic analysis in the Collaborative Cross breeding population.

Genetic reference populations in model organisms are critical resources for systems genetic analysis of disease related phenotypes. The breeding history of these inbred panels may influence detectable allelic and phenotypic diversity. The existing panel of common inbred strains reflects historical selection biases, and existing recombinant inbred panels have low allelic diversity. All such populations may be subject to consequences of inbreeding depression. The Collaborative Cross (CC) is a mouse reference population with high allelic diversity that is being constructed using a randomized breeding design that systematically outcrosses eight founder strains, followed by inbreeding to obtain new recombinant inbred strains. Five of the eight founders are common laboratory strains, and three are wild-derived. Since its inception, the partially inbred CC has been characterized for physiological, morphological, and behavioral traits. The construction of this population provided a unique opportunity to observe phenotypic variation as new allelic combinations arose through intercrossing and inbreeding to create new stable genetic combinations. Processes including inbreeding depression and its impact on allelic and phenotypic diversity were assessed. Phenotypic variation in the CC breeding population exceeds that of existing mouse genetic reference populations due to both high founder genetic diversity and novel epistatic combinations. However, some focal evidence of allele purging was detected including a suggestive QTL for litter size in a location of changing allele frequency. Despite these inescapable pressures, high diversity and precision for genetic mapping remain. These results demonstrate the potential of the CC population once completed and highlight implications for development of related populations.

Using accelerometers to identify a high risk of catastrophic musculoskeletal injury in three racing Thoroughbreds.

OBJECTIVE: To describe the process whereby the screening of racing Thoroughbreds with accelerometer-based inertial measurement unit (IMU) sensors followed by clinical evaluation and advanced imaging identified potentially catastrophic musculoskeletal injuries in 3 horses. ANIMALS: 3 Thoroughbred racehorses. CLINICAL PRESENTATION: All cases demonstrated an abnormal stride pattern either during racing (cases 1 and 2) or while breezing (case 3) and were identified as being at very high risk of catastrophic musculoskeletal injury by an algorithm derived from IMU sensor files from > 20,000 horses' race starts. Veterinary examination and 18F-sodium fluoride (18F-NaF) positron emission tomography were performed within 10 days of the respective race or breeze in each of the cases. RESULTS: The intensity and location of the 18F-NaF uptake in the condyles of the third metacarpal bone in cases 1 and 2 identified them as at potential increased risk of condylar fracture. The pattern and intensity of the 18F-NaF uptake in case 3 indicated that the third carpal bone was likely responsible for the horse's lameness, with an impending slab fracture subsequently identified on radiographs. Following periods of convalescence, cases 1 and 2 returned to racing and were identified by the sensor system as no longer being at high risk of catastrophic musculoskeletal injury. Case 3 returned to training but has yet to return to racing. CLINICAL RELEVANCE: When worn by Thoroughbreds while racing or breezing, these IMU sensors can identify horses at high risk of catastrophic musculoskeletal injury, allowing for veterinary intervention and the potential avoidance of such injuries.

Optimal irregular microphone distributions with enhanced beamforming performance in immersive environments.

Complex relationships between array gain patterns and microphone distributions limit the application of optimization algorithms on irregular arrays. This paper proposes a Genetic Algorithm (GA) for microphone array optimization in immersive (near-field) environments. Geometric descriptors for irregular arrays are proposed for use as objective functions to reduce optimization time by circumventing the need for direct array gain computations. In addition, probabilistic descriptions of acoustic scenes are introduced for incorporating prior knowledge of the source distribution. To verify the effectiveness of the proposed optimization, signal-to-noise ratios are compared for GA-optimized arrays, regular arrays, and arrays optimized through direct exhaustive simulations. Results show enhancements for GA-optimized arrays over arbitrary randomly generated arrays and regular arrays, especially at low microphone densities where placement becomes critical. Design parameters for the GA are identified for improving optimization robustness for different applications. The rapid convergence and acceptable processing times observed during the experiments establish the feasibility of this approach for optimizing array geometries in immersive environments where rapid deployment is required with limited knowledge of the acoustic scene, such as in mobile platforms and audio surveillance applications.

A Wearable Fiber-Free Optical Sensor for Continuous Monitoring of Cerebral Blood Flow in Freely Behaving Mice.

OBJECTIVE: Wearable technologies for functional brain monitoring in freely behaving subjects can advance our understanding of cognitive processing and adaptive behavior. Existing technologies are lacking in this capability or need procedures that are invasive and/or otherwise impede brain assessments during social behavioral conditions, exercise, and sleep. METHODS: In response a complete system was developed to combine relative cerebral blood flow (rCBF) measurement, O2 and CO2 supplies, and behavior recording for use on conscious, freely behaving mice. An innovative diffuse speckle contrast flowmetry (DSCF) device and associated hardware were miniaturized and optimized for rCBF measurements in small subject applications. The use of this wearable, fiber-free, near-infrared DSCF head-stage/probe allowed no craniotomy, minimally invasive probe implantation, and minimal restraint of the awake animal. RESULTS AND CONCLUSIONS: Significant correlations were found between measurements with the new DSCF design and an optical standard. The system successfully detected rCBF responses to CO2-induced hypercapnia in both anesthetized and freely behaving mice. SIGNIFICANCE: Collecting rCBF and activity information together during natural behaviors provides realistic physiological results and opens the path to exploring their correlations with pathophysiological conditions.

Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection.

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900-1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars.

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review.

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers' expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects' attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods' application in the detection and classification of insect infestation in fruits and vegetables.

Genetic variation regulates opioid-induced respiratory depression in mice.

In the U.S., opioid prescription for treatment of pain nearly quadrupled from 1999 to 2014. The diversion and misuse of prescription opioids along with increased use of drugs like heroin and fentanyl, has led to an epidemic in addiction and overdose deaths. The most common cause of opioid overdose and death is opioid-induced respiratory depression (OIRD), a life-threatening depression in respiratory rate thought to be caused by stimulation of opioid receptors in the inspiratory-generating regions of the brain. Studies in mice have revealed that variation in opiate lethality is associated with strain differences, suggesting that sensitivity to OIRD is genetically determined. We first tested the hypothesis that genetic variation in inbred strains of mice influences the innate variability in opioid-induced responses in respiratory depression, recovery time and survival time. Using the founders of the advanced, high-diversity mouse population, the Diversity Outbred (DO), we found substantial sex and genetic effects on respiratory sensitivity and opiate lethality. We used DO mice treated with morphine to map quantitative trait loci for respiratory depression, recovery time and survival time. Trait mapping and integrative functional genomic analysis in GeneWeaver has allowed us to implicate Galnt11, an N-acetylgalactosaminyltransferase, as a gene that regulates OIRD.

A Microbe Associated with Sleep Revealed by a Novel Systems Genetic Analysis of the Microbiome in Collaborative Cross Mice.

The microbiome influences health and disease through complex networks of host genetics, genomics, microbes, and environment. Identifying the mechanisms of these interactions has remained challenging. Systems genetics in laboratory mice (Mus musculus) enables data-driven discovery of biological network components and mechanisms of host-microbial interactions underlying disease phenotypes. To examine the interplay among the whole host genome, transcriptome, and microbiome, we mapped QTL and correlated the abundance of cecal messenger RNA, luminal microflora, physiology, and behavior in a highly diverse Collaborative Cross breeding population. One such relationship, regulated by a variant on chromosome 7, was the association of Odoribacter (Bacteroidales) abundance and sleep phenotypes. In a test of this association in the BKS.Cg-Dock7m +/+ Leprdb/J mouse model of obesity and diabetes, known to have abnormal sleep and colonization by Odoribacter, treatment with antibiotics altered sleep in a genotype-dependent fashion. The many other relationships extracted from this study can be used to interrogate other diseases, microbes, and mechanisms.

Effects of acute lying and sleep deprivation on the behavior of lactating dairy cows.

The objective was to determine the effects of sleep or lying deprivation on the behavior of dairy cows. Data were collected from 8 multi- and 4 primiparous cows (DIM = 199 ± 44 (mean ± SD); days pregnant = 77 ± 30). Using a crossover design, each cow experienced: 1) sleep deprivation implemented by noise or physical contact when their posture suggested sleep, and 2) lying deprivation imposed by a grid placed on the pen floor. One day before treatment (baseline), and treatment day (treatment) were followed by a 12-d washout period (with the first 7 d used to evaluate recovery). Study days were organized from 2100 to 2059. During habituation (d -3 and -2 before treatment), baseline (d -1), and trt (d 0), housing was individual boxstalls (mattress with no bedding). After treatment, cows returned to sand-bedded freestalls for a 7-d recovery period (d 1 to 7) where data on lying behaviors were collected. Following the recovery period, an additional 5-d period was provided to allow the cows a 12-d period between exposures to treatments. Daily lying time, number lying bouts, bout duration, and number of steps were recorded by dataloggers attached to the hind leg of cows throughout the study period. Data were analyzed using a mixed model including fixed effects of treatment (sleep deprivation vs. sleep and lying deprivation), day, and their interaction with significant main effects separated using a PDIFF statement (P ≤ 0.05). Interactions between treatment and day were detected for daily lying time and the number of bouts. Lying time was lower for both treatments during the treatment period compared to baseline. Lying time increased during the recovery period for both lying and sleep deprived cows. However, it took 4 d for the lying deprived cows to fully recover their lying time after treatment, whereas it took the sleep deprived cows 2 d for their lying time to return to baseline levels. Results suggest that both sleep and lying deprivation can have impact cow behavior. Management factors that limit freestall access likely reduce lying time and sleep, causing negative welfare implications for dairy cows.

Assessment of a non-invasive high-throughput classifier for behaviours associated with sleep and wake in mice.

This work presents a non-invasive high-throughput system for automatically detecting characteristic behaviours in mice over extended periods of time, useful for phenotyping experiments. The system classifies time intervals on the order of 2 to 4 seconds as corresponding to motions consistent with either active wake or inactivity associated with sleep. A single Polyvinylidine Difluoride (PVDF) sensor on the cage floor generates signals from motion resulting in pressure. This paper develops a linear classifier based on robust features extracted from normalized power spectra and autocorrelation functions, as well as novel features from the collapsed average (autocorrelation of complex spectrum), which characterize transient and periodic properties of the signal envelope. Performance is analyzed through an experiment comparing results from direct human observation and classification of the different behaviours with an automatic classifier used in conjunction with this system. Experimental results from over 28.5 hours of data from 4 mice indicate a 94% classification rate relative to the human observations. Examples of sequential classifications (2 second increments) over transition regions between sleep and wake behaviour are also presented to demonstrate robust performance to signal variation and explain performance limitations.

Detection of breast lesion regions in ultrasound images using wavelets and order statistics.

Accurate detection and segmentation of suspicious regions within the complex and irregular tissues of the breast, as depicted with ultrasonic B scans, typically require human analysis and decision making. Tissue characterization methods for classifying suspicious regions often depend on identifying and then accurately segmenting these regions. Motivated by an ultimate goal to automate this critical identification and segmentation step for tissue characterization problems, this work examines ultrasonic signal characteristics between various regions of breast tissue broadly classified as normal tissue and breast lesions. This paper introduces a nonparametric model based on order statistics (OS) estimated from multiresolution (MR) decompositions of energy-normalized subregions. Experimental results demonstrate the classification performance of the OS-based features extracted from the tumor and normal tissue regions in multiple scans from 84 patients, which resulted in a total of 204 tumor regions (from 43 malignant and 161 benign) and 816 normal tissue regions. Performance results indicate that OS-based features achieved an area under the receiver-operator characteristic curve of 91% in the discrimination between breast lesions and surrounding normal tissues.

Effects of Vocal Fold Nodules on Glottal Cycle Measurements Derived from High-Speed Videoendoscopy in Children.

The goal of this study is to quantify the effects of vocal fold nodules on vibratory motion in children using high-speed videoendoscopy. Differences in vibratory motion were evaluated in 20 children with vocal fold nodules (5-11 years) and 20 age and gender matched typically developing children (5-11 years) during sustained phonation at typical pitch and loudness. Normalized kinematic features of vocal fold displacements from the mid-membranous vocal fold point were extracted from the steady-state high-speed video. A total of 12 kinematic features representing spatial and temporal characteristics of vibratory motion were calculated. Average values and standard deviations (cycle-to-cycle variability) of the following kinematic features were computed: normalized peak displacement, normalized average opening velocity, normalized average closing velocity, normalized peak closing velocity, speed quotient, and open quotient. Group differences between children with and without vocal fold nodules were statistically investigated. While a moderate effect size was observed for the spatial feature of speed quotient, and the temporal feature of normalized average closing velocity in children with nodules compared to vocally normal children, none of the features were statistically significant between the groups after Bonferroni correction. The kinematic analysis of the mid-membranous vocal fold displacement revealed that children with nodules primarily differ from typically developing children in closing phase kinematics of the glottal cycle, whereas the opening phase kinematics are similar. Higher speed quotients and similar opening phase velocities suggest greater relative forces are acting on vocal fold in the closing phase. These findings suggest that future large-scale studies should focus on spatial and temporal features related to the closing phase of the glottal cycle for differentiating the kinematics of children with and without vocal fold nodules.

Noninvasive dissection of mouse sleep using a piezoelectric motion sensor.

BACKGROUND: Changes in autonomic control cause regular breathing during NREM sleep to fluctuate during REM. Piezoelectric cage-floor sensors have been used to successfully discriminate sleep and wake states in mice based on signal features related to respiration and other movements. This study presents a classifier for noninvasively classifying REM and NREM using a piezoelectric sensor. NEW METHOD: Vigilance state was scored manually in 4-s epochs for 24-h EEG/EMG recordings in 20 mice. An unsupervised classifier clustered piezoelectric signal features quantifying movement and respiration into three states: one active; and two inactive with regular and irregular breathing, respectively. These states were hypothesized to correspond to Wake, NREM, and REM, respectively. States predicted by the classifier were compared against manual EEG/EMG scores to test this hypothesis. RESULTS: Using only piezoelectric signal features, an unsupervised classifier distinguished Wake with high (89% sensitivity, 96% specificity) and REM with moderate (73% sensitivity, 75% specificity) accuracy, but NREM with poor sensitivity (51%) and high specificity (96%). The classifier sometimes confused light NREM sleep - characterized by irregular breathing and moderate delta EEG power - with REM. A supervised classifier improved sensitivities to 90, 81, and 67% and all specificities to over 90% for Wake, NREM, and REM, respectively. COMPARISON WITH EXISTING METHODS: Unlike most actigraphic techniques, which only differentiate sleep from wake, the proposed piezoelectric method further dissects sleep based on breathing regularity into states strongly correlated with REM and NREM. CONCLUSIONS: This approach could facilitate large-sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.

Tunable somatosensory stimulation for selective sleep restriction studies in rodents.

Many methods for sleep restriction in rodents have emerged, but most are intrusive, lack fine control, and induce stress. Therefore, a versatile, non-intrusive means of sleep restriction that can alter sleep in a controlled manner could be of great value in sleep research. In previous work, we proposed a novel system for closed-loop somatosensory stimulation based on mechanical vibration and applied it to the task of restricting Rapid Eye Movement (REM) sleep in mice [1]. While this system was effective, it was a crude prototype and did not allow precise control over the amplitude and frequency of stimulation applied to the animal. This paper details the progression of this system from a binary, "all-or-none" version to one that allows dynamic control over perturbation to accomplish graded, state-dependent sleep restriction. Its preliminary use is described in two applications: deep sleep restriction in rats, and REM sleep restriction in mice.

Kinematic measurements of the vocal-fold displacement waveform in typical children and adult populations: quantification of high-speed endoscopic videos.

PURPOSE: This article presents a quantitative method for assessing instantaneous and average lateral vocal-fold motion from high-speed digital imaging, with a focus on developmental changes in vocal-fold kinematics during childhood. METHOD: Vocal-fold vibrations were analyzed for 28 children (aged 5-11 years) and 28 adults (aged 21-45 years) without voice disorders. The following kinematic features were analyzed from the vocal-fold displacement waveforms: relative velocity-based features (normalized average and peak opening and closing velocities), relative acceleration-based features (normalized peak opening and closing accelerations), speed quotient, and normalized peak displacement. RESULTS: Children exhibited significantly larger normalized peak displacements, normalized average and peak opening velocities, normalized average and peak closing velocities, peak opening and closing accelerations, and speed quotient compared to adult women. Values of normalized average closing velocity and speed quotient were higher in children compared to adult men. CONCLUSIONS: When compared to adult men, developing children typically have higher estimates of kinematic features related to normalized displacement and its derivatives. In most cases, the kinematic features of children are closer to those of adult men than adult women. Even though boys experience greater changes in glottal length and pitch as they mature, results indicate that girls experience greater changes in kinematic features compared to boys.

ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis.

Breast cancer diagnosis through ultrasound tissue characterization was studied using receiver operating characteristic (ROC) analysis of combinations of acoustic features, patient age, and radiological findings. A feature fusion method was devised that operates even if only partial diagnostic data are available. The ROC methodology uses ordinal dominance theory and bootstrap resampling to evaluate A(z) and confidence intervals in simple as well as paired data analyses. The combined diagnostic feature had an A(z) of 0.96 with a confidence interval of at a significance level of 0.05. The combined features show statistically significant improvement over prebiopsy radiological findings. These results indicate that ultrasound tissue characterization, in combination with patient record and clinical findings, may greatly reduce the need to perform biopsies of benign breast lesions.

Malignant and benign breast tissue classification performance using a scatterer structure preclassifier.

Benign and malignant breast tissue classification is examined for generalized-spectrum parameters computed from RF ultrasound data when a preclassification of subregions based on general scattering properties is performed. Results using a clinical database of 84 patients show statistically significant improvements (over 10% in receiver operation characteristic (ROC) areas) when only coherent scatterer subregions are used as compared to using all subregions within the region of interest.

Validation of a closed-loop sensory stimulation technique for selective sleep restriction in mice.

Experimental manipulation of sleep in rodents is an important tool for analyzing the mechanisms of sleep and related disorders in humans. Sleep restriction systems have relied in the past on manual sensory stimulation and recently on more sophisticated automated means of delivering the same. The ability to monitor and track behavior through the electroencephalogram (EEG) and other modalities provides the opportunity to implement more selective sleep restriction that is targeted at particular stages of sleep with flexible control over their amount, duration, and timing. In this paper we characterize the performance of a novel tactile stimulation system operating in closed-loop to interrupt rapid eye movement (REM) sleep in mice when it is detected in real time from the EEG. Acute experiments in four wild-type mice over six hours showed that a reduction of over 50% of REM sleep was feasible without affecting non-REM (NREM) sleep. The animals remained responsive to the stimulus over the six hour duration of the experiment.

Evaluation of a piezoelectric system as an alternative to electroencephalogram/ electromyogram recordings in mouse sleep studies.

STUDY OBJECTIVES: Traditionally, sleep studies in mammals are performed using electroencephalogram/electromyogram (EEG/EMG) recordings to determine sleep-wake state. In laboratory animals, this requires surgery and recovery time and causes discomfort to the animal. In this study, we evaluated the performance of an alternative, noninvasive approach utilizing piezoelectric films to determine sleep and wakefulness in mice by simultaneous EEG/EMG recordings. The piezoelectric films detect the animal's movements with high sensitivity and the regularity of the piezo output signal, related to the regular breathing movements characteristic of sleep, serves to automatically determine sleep. Although the system is commercially available (Signal Solutions LLC, Lexington, KY), this is the first statistical validation of various aspects of sleep. DESIGN: EEG/EMG and piezo signals were recorded simultaneously during 48 h. SETTING: Mouse sleep laboratory. PARTICIPANTS: Nine male and nine female CFW outbred mice. INTERVENTIONS: EEG/EMG surgery. MEASUREMENTS AND RESULTS: The results showed a high correspondence between EEG/EMG-determined and piezo-determined total sleep time and the distribution of sleep over a 48-h baseline recording with 18 mice. Moreover, the piezo system was capable of assessing sleep quality (i.e., sleep consolidation) and interesting observations at transitions to and from rapid eye movement sleep were made that could be exploited in the future to also distinguish the two sleep states. CONCLUSIONS: The piezo system proved to be a reliable alternative to electroencephalogram/electromyogram recording in the mouse and will be useful for first-pass, large-scale sleep screens for genetic or pharmacological studies. CITATION: Mang GM, Nicod J, Emmenegger Y, Donohue KD, O'Hara BF, Franken P. Evaluation of a piezoelectric system as an alternative to electroencephalogram/electromyogram recordings in mouse sleep studies.

Diffuse brain injury induces acute post-traumatic sleep.

OBJECTIVE: Clinical observations report excessive sleepiness immediately following traumatic brain injury (TBI); however, there is a lack of experimental evidence to support or refute the benefit of sleep following a brain injury. The aim of this study is to investigate acute post-traumatic sleep. METHODS: Sham, mild or moderate diffuse TBI was induced by midline fluid percussion injury (mFPI) in male C57BL/6J mice at 9:00 or 21:00 to evaluate injury-induced sleep behavior at sleep and wake onset, respectively. Sleep profiles were measured post-injury using a non-invasive, piezoelectric cage system. In separate cohorts of mice, inflammatory cytokines in the neocortex were quantified by immunoassay, and microglial activation was visualized by immunohistochemistry. RESULTS: Immediately after diffuse TBI, quantitative measures of sleep were characterized by a significant increase in sleep (>50%) for the first 6 hours post-injury, resulting from increases in sleep bout length, compared to sham. Acute post-traumatic sleep increased significantly independent of injury severity and time of injury (9:00 vs 21:00). The pro-inflammatory cytokine IL-1ß increased in brain-injured mice compared to sham over the first 9 hours post-injury. Iba-1 positive microglia were evident in brain-injured cortex at 6 hours post-injury. CONCLUSION: Post-traumatic sleep occurs for up to 6 hours after diffuse brain injury in the mouse regardless of injury severity or time of day. The temporal profile of secondary injury cascades may be driving the significant increase in post-traumatic sleep and contribute to the natural course of recovery through cellular repair.