EMG Analysis of Trunk Musculature following a Nine Hole Round of Golf: The Fatigue Factor

The purpose of this study is to determine the fatigue component in trunk musculature following a simulated 9 hole round of golf by analyzing the EMG output of the erector spinae, gluteus maximus and abdominal obliques during the golf swing. Four males, ages 22-26 performed 5 EMG monitored golf swings with a driver prior to and following a simulated 9 hole round of golf. The data was subjected to analysis by the Fast Fourier Transformation to determine median frequency. The results show that a significant shift in median frequency occurred, signifying muscle fatigue, in 2 of the 4 subjects when all muscles were analyzed collectively. When individual muscles were analyzed each muscle experienced a significant shift in median frequency except the left abdominal oblique. The swing times for each subject were also analyzed and compared. The 2 subjects who fatigued demonstrated faster swing times suggesting a possible relationship between speed of the golf swing with resulting increased muscle force output, and increased muscle fatigue. This study provides initial support to the theory of muscle fatigue as a possible contributor to faulty swing mechanics associated with golfing and low back pain. The results will attempt to provide information on establishing training and conditioning programs targeting the muscles shown to fatigue. These programs can be developed to increase muscle endurance and decrease the likelihood of faulty swing mechanics and injury

A Novel Approach to Multimedia Ontology Engineering for Automated Reasoning over Audiovisual LOD Datasets

Multimedia reasoning, which is suitable for, among others, multimedia content analysis and high-level video scene interpretation, relies on the formal and comprehensive conceptualization of the represented knowledge domain. However, most multimedia ontologies are not exhaustive in terms of role definitions, and do not incorporate complex role inclusions and role interdependencies. In fact, most multimedia ontologies do not have a role box at all, and implement only a basic subset of the available logical constructors. Consequently, their application in multimedia reasoning is limited. To address the above issues, VidOnt, the very first multimedia ontology with SROIQ(D) expressivity and a DL-safe ruleset has been introduced for next-generation multimedia reasoning. In contrast to the common practice, the formal grounding has been set in one of the most expressive description logics, and the ontology validated with industry-leading reasoners, namely HermiT and FaCT++. This paper also presents best practices for developing multimedia ontologies, based on my ontology engineering approach

Mining livestock genome datasets for an unconventional characterization of animal DNA viromes

Whole genome sequencing (WGS) datasets, usually generated for the investigation of the individual animal genome, can be used for additional mining of the fraction of sequencing reads that remains unmapped to the respective reference genome. A significant proportion of these reads contains viral DNA derived from viruses that infected the sequenced animals. In this study, we mined more than 480 billion sequencing reads derived from 1471 WGS datasets produced from cattle, pigs, chickens and rabbits. We identified 367 different viruses among which 14, 11, 12 and 1 might specifically infect the cattle, pig, chicken and rabbit, respectively. Some of them are ubiquitous, avirulent, highly or potentially damaging for both livestock and humans. Retrieved viral DNA information provided a first unconventional and opportunistic landscape of the livestock viromes that could be useful to understand the distribution of some viruses with potential deleterious impacts on the animal food production systems

How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting

Accurate prediction of future human positions is an essential task for modern video-surveillance systems. Current state-of-the-art models usually rely on a "history" of past tracked locations (e.g., 3 to 5 seconds) to predict a plausible sequence of future locations (e.g., up to the next 5 seconds). We feel that this common schema neglects critical traits of realistic applications: as the collection of input trajectories involves machine perception (i.e., detection and tracking), incorrect detection and fragmentation errors may accumulate in crowded scenes, leading to tracking drifts. On this account, the model would be fed with corrupted and noisy input data, thus fatally affecting its prediction performance.In this regard, we focus on delivering accurate predictions when only few input observations are used, thus potentially lowering the risks associated with automatic perception. To this end, we conceive a novel distillation strategy that allows a knowledge transfer from a teacher network to a student one, the latter fed with fewer observations (just two ones). We show that a properly defined teacher supervision allows a student network to perform comparably to state-of-the-art approaches that demand more observations. Besides, extensive experiments on common trajectory forecasting datasets highlight that our student network better generalizes to unseen scenarios

Population genomic structures and signatures of selection define the genetic uniqueness of several fancy and meat rabbit breeds

Following the recent domestication process of the European rabbit (Oryctolagus cuniculus), many different breeds and lines, distinguished primarily by exterior traits such as coat colour, fur structure and body size and shape, have been constituted. In this study, we genotyped, with a high-density single-nucleotide polymorphism panel, a total of 645 rabbits from 10 fancy breeds (Belgian Hare, Champagne d'Argent, Checkered Giant, Coloured Dwarf, Dwarf Lop, Ermine, Giant Grey, Giant White, Rex and Rhinelander) and three meat breeds (Italian White, Italian Spotted and Italian Silver). ADMIXTURE analysis indicated that breeds with similar phenotypic traits (e.g. coat colour and body size) shared common ancestries. Signatures of selection using two haplotype-based approaches (iHS and XP-EHH), combined with the results obtained with other methods previously reported that we applied to the same breeds, we identified a total of 5079 independent genomic regions with some signatures of selection, covering about 1777 Mb of the rabbit genome. These regions consistently encompassed many genes involved in pigmentation processes (ASIP, EDNRA, EDNRB, KIT, KITLG, MITF, OCA2, TYR and TYRP1), coat structure (LIPH) and body size, including two major genes (LCORL and HMGA2) among many others. This study revealed novel genomic regions under signatures of selection and further demonstrated that population structures and signatures of selection, left into the genome of these rabbit breeds, may contribute to understanding the genetic events that led to their constitution and the complex genetic mechanisms determining the broad phenotypic variability present in these untapped rabbit genetic resources

Survey on Vision-based Path Prediction

Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths. Many prediction approaches that include understanding the environment and the internal state have been proposed. In this survey, we systematically summarize methods of path prediction that take video as input and and extract features from the video. Moreover, we introduce datasets used to evaluate path prediction methods quantitatively.Comment: DAPI 201

CAR-Net: Clairvoyant Attentive Recurrent Network

We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task. Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories. To study the impact of space on agents' trajectories, we build a new dataset made of top-view images of hundreds of scenes (Formula One racing tracks) where agents' behaviors are heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize to unseen scenes.Comment: The 2nd and 3rd authors contributed equall

Single-marker and haplotype-based genome-wide association studies for the number of teats in two heavy pig breeds

The number of teats is a reproductive-related trait of great economic relevance as it affects the mothering ability of the sows and thus the number of properly weaned piglets. Moreover, genetic improvement of this trait is fundamental to parallelly help the selection for increased litter size. We present the results of single-marker and haplotypes-based genome-wide association studies for the number of teats in two large cohorts of heavy pig breeds (Italian Large White and Italian Landrace) including 3990 animals genotyped with the 70K GGP Porcine BeadChip and other 1927 animals genotyped with the Illumina PorcineSNP60 BeadChip. In the Italian Large White population, genome scans identified three genome regions (SSC7, SSC10, and SSC12) that confirmed the involvement of the VRTN gene (as we previously reported) and highlighted additional loci known to affect teat counts, including the FRMD4A and HOXB1 gene regions. A different picture emerged in the Italian Landrace population, with a total of 12 genome regions in eight chromosomes (SSC3, SSC6, SSC8, SSC11, SSC13, SSC14, SSC15, and SSC16) mainly detected via the haplotype-based genome scan. The most relevant QTL was close to the ARL4C gene on SSC15. Markers in the VRTN gene region were not significant in the Italian Landrace breed. The use of both single-marker and haplotype-based genome-wide association analyses can be helpful to exploit and dissect the genome of the pigs of different populations. Overall, the obtained results supported the polygenic nature of the investigated trait and better elucidated its genetic architecture in Italian heavy pigs