Miracle-dz/UniFAR-FTUs huggingface dataset:
The Case of Insertional Adductor Tendinopathy of an International-Level 3,000-m Steeplechase Runner

Background: Groin pain is a frequent condition among athletes. One of the causes of groin pain is tendinopathy, a frequently diagnosed medical condition, which can also occur in the adductor muscles. Despite the high prevalence of this medical condition among athletes, it is infrequent to observe tendinopathic groin pain in steeplechase runners. The aim of this case study is to describe the case of an international-level 3,000-m steeplechase runner with groin pain, who was subsequently diagnosed with adductor insertional tendinopathy. Case Presentation: We present the case of an Italian 3,000-m steeplechase and long distance runner, Ala Zoghlami (180 cm, 57 kg), with groin pain, diagnosed as insertional adductor tendinopathy. The runner, after manifesting the painful symptomatology, underwent medical screening (ultrasound and MRI). The radiological investigations highlighted adductor tendinopathy. After refraining from training, the runner underwent medical and physical therapy which, in the first phase, did not improve the painful symptomatology. Further evaluation, after 6 months from the initial training cessation, highlighted a case of malocclusion. Such was treated from a dentistry perspective with the creation of a personalized dental bite. Results: A multidisciplinary approach which included medical and physical therapy, osteopathy, and dentistry, in adjunct with refraining from training, was able to reduce the symptomatology and allowed a correct return to run (after 9 months from the first painful manifestation) of the steeplechase runner. To date, Ala Zoghlami has fully recovered and was able to win the 3,000-m steeplechase race during the 2021 national Italian competition.
Incremental Values of T1 Mapping in the Prediction of Sudden Cardiac Death Risk in Hypertrophic Cardiomyopathy: A Comparison With Two Guidelines

Background: MRI native T1 mapping and extracellular volume fraction (ECV) are quantitative values that could reflect various myocardial tissue characterization. The role of these parameters in predicting the risk of sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM) is still poorly understood. Aim: This study aims to investigate the ability of native T1 mapping and ECV values to predict major adverse cardiovascular events (MACE) in HCM, and its incremental values over the 2014 European Society of Cardiology (ESC) and enhanced American College of Cardiology/American Heart Association (ACC/AHA) guidelines. Methods: Between July 2016 and October 2020, HCM patients and healthy individuals with sex and age matched who underwent cardiac MRI were prospectively enrolled. The native T1 and ECV parameters were measured. The SCD risk was evaluated by the 2014 ESC guidelines and enhanced ACC/AHA guidelines. MACE included cardiac death, transplantation, heart failure admission, and implantable cardioverter-defibrillator implantation. Results: A total of 203 HCM patients (54.2 ± 14.9 years) and 101 healthy individuals (53.2 ± 14.7 years) were evaluated. During a median follow-up of 15 months, 25 patients (12.3%) had MACE. In multivariate Cox regression analysis, global native T1 mapping (hazard ratio (HR): 1.446; 95% confidence interval (CI): 1.195–1.749; P < 0.001) and non-sustained ventricular tachycardia (NSVT) (HR: 4.949; 95% CI, 2.033–12.047; P < 0.001) were independently associated with MACE. Ten of 86 patients (11.6%) with low SCD risk assessed by the two guidelines had MACE. In this subgroup of patients, multivariate Cox regression analysis showed that global native T1 mapping was independently associated with MACE (HR: 1.532; 95% CI: 1.221–1.922; P < 0.001). In 85 patients with conflicting results assessed by the two guidelines, end-stage systolic dysfunction was independently associated with MACE (HR: 7.942, 95% CI: 1.322–47.707, P = 0.023). In 32 patients with high SCD risk assessed by the two guidelines, NSVT was independently associated with MACE (HR: 9.779, 95% CI: 1.953–48.964, P = 0.006). Conclusion: The global native T1 mapping could provide incremental values and serve as potential supplements to the current guidelines in the prediction of MACE.
A Metagenomic Insight Into the Hindgut Microbiota and Their Metabolites for Dairy Goats Fed Different Rumen Degradable Starch

High starch diets have been proven to increase the risk of hindgut acidosis in high-yielding dairy animals. As an effective measurement of dietary carbohydrate for ruminants, studies on rumen degradable starch (RDS) and the effects on the gut microbiota diversity of carbohydrate-active enzymes (CAZymes), and Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology functional categories are helpful to understand the mechanisms between gut microbiota and carbohydrate metabolism in dairy goats. A total of 18 lactating goats (45.8 ± 1.54 kg) were randomly divided equally into three dietary treatments with low dietary RDS concentrations of 20.52% (LRDS), medium RDS of 22.15% (MRDS), and high RDS of 24.88% (HRDS) on a DM basis for 5 weeks. Compared with the LRDS and MRDS groups, HRDS increased acetate molar proportion in the cecum. For the HRDS group, the abundance of family Ruminococcaceae and genus Ruminococcaceae UCG-010 were significantly increased in the cecum. For the LRDS group, the butyrate molar proportion and the abundance of butyrate producer family Bacteroidale_S24-7, family Lachnospiraceae, and genus Bacteroidale_S24-7_group were significantly increased in the cecum. Based on the BugBase phenotypic prediction, the microbial oxidative stress tolerant and decreased potentially pathogenic in the LRDS group were increased in the cecum compared with the HRDS group. A metagenomic study on cecal bacteria revealed that dietary RDS level could affect carbohydrate metabolism by increasing the glycoside hydrolase 95 (GH95) family and cellulase enzyme (EC 3.2.1.4) in the HRDS group; increasing the GH13_20 family and isoamylase enzyme (EC 3.2.1.68) in the LRDS group. PROBIO probiotics database showed the relative gene abundance of cecal probiotics significantly decreased in the HRDS group. Furthermore, goats fed the HRDS diet had a lower protein expression of Muc2, and greater expression RNA of interleukin-1β and secretory immunoglobulin A in cecal mucosa than did goats fed the LRDS diet. Combined with the information from previous results from rumen, dietary RDS level altered the degradation position of carbohydrates in the gastrointestinal (GI) tract and increased the relative abundance of gene encoded enzymes degrading cellulose in the HRDS group in the cecum of dairy goats. This study revealed that the HRDS diet could bring disturbances to the microbial communities network containing taxa of the Lachnospiraceae and Ruminococcaceae and damage the mucus layer and inflammation in the cecum of dairy goats.
Occupational Disorders, Daily Workload, and Fitness Levels Among Fitness and Swimming Instructors

Few data have been published on occupational disorders among sports instructors, especially regarding those who are expected to continuously practice while teaching. As the number of sports instructors increases, new specific information about their possible injuries, daily workload, and fitness levels is needed. The aim of this study was to assess occupational disorders, cardiorespiratory fitness, and daily workload of fitness (FI) and swimming instructors (SI). An online survey addressing occupational disorders was conducted among 435 instructors (256 FI and 179 SI). In one subgroup (57 FI and 42 SI), cardiorespiratory fitness levels were evaluated using maximal oxygen consumption (V∙O2max) as an indicator. Daily workload was assessed by monitoring the heart rate and perception of exertion (using the Borg scale). Of the two groups, FI exhibited a higher 2-year prevalence of musculoskeletal injuries and SI experienced more upper respiratory tract infections. V∙O2max ranged from 47.0 to 51.9 ml·kg−1·min−1 and was similar for both FI and SI. Regarding the daily workload, female SI had significantly higher mean heart rate and mean heart rate to maximal heart rate ratio compared to female FI, but no significant differences between male FI and SI were found. No significant differences were observed between the perceived exertion of FI and SI. Preventive strategies for the reduction of occupational disorders in FI and SI are needed.
Why Plants Harbor Complex Endophytic Fungal Communities: Insights From Perennial Bunchgrass Stipagrostis sabulicola in the Namib Sand Sea

All perennial plants harbor diverse endophytic fungal communities, but why they tolerate these complex asymptomatic symbioses is unknown. Using a multi-pronged approach, we conclusively found that a dryland grass supports endophyte communities comprised predominantly of latent saprophytes that can enhance localized nutrient recycling after senescence. A perennial bunchgrass, Stipagrostis sabulicola, which persists along a gradient of extreme abiotic stress in the hyper-arid Namib Sand Sea, was the focal point of our study. Living tillers yielded 20 fungal endophyte taxa, 80% of which decomposed host litter during a 28-day laboratory decomposition assay. During a 6-month field experiment, tillers with endophytes decomposed twice as fast as sterilized tillers, consistent with the laboratory assay. Furthermore, profiling the community active during decomposition using next-generation sequencing revealed that 59–70% of the S. sabulicola endophyte community is comprised of latent saprophytes, and these dual-niche fungi still constitute a large proportion (58–62%) of the litter community more than a year after senescence. This study provides multiple lines of evidence that the fungal communities that initiate decomposition of standing litter develop in living plants, thus providing a plausible explanation for why plants harbor complex endophyte communities. Using frequent overnight non-rainfall moisture events (fog, dew, high humidity), these latent saprophytes can initiate decomposition of standing litter immediately after tiller senescence, thus maximizing the likelihood that plant-bound nutrients are recycled in situ and contribute to the nutrient island effect that is prevalent in drylands.
Localized Oxygen Exchange Platform for Intravital Video Microscopy Investigations of Microvascular Oxygen Regulation

Intravital microscopy has proven to be a powerful tool for studying microvascular physiology. In this study, we propose a gas exchange system compatible with intravital microscopy that can be used to impose gas perturbations to small localized regions in skeletal muscles or other tissues that can be imaged using conventional inverted microscopes. We demonstrated the effectiveness of this system by locally manipulating oxygen concentrations in rat extensor digitorum longus muscle and measuring the resulting vascular responses. A computational model of oxygen transport was used to partially validate the localization of oxygen changes in the tissue, and oxygen saturation of red blood cells flowing through capillaries were measured as a surrogate for local tissue oxygenation. Overall, we have demonstrated that this approach can be used to study dynamic and spatial responses to local oxygen challenges to the microenvironment of skeletal muscle.
Mean Platelet Volume/Platelet Count Ratio and Risk of Progression in Glioblastoma

Objective The mean platelet volume/platelet count (MPV/PC) ratio is an emerging biomarker in selected types of cancer. The objective of this study is to analyze the association of MPV/PC ratio with progression and survival in glioblastoma (GB) patients, with consideration of patient demographics, tumor morphology, extent of resection, molecular pathology, and oncological therapy. Methods One hundred ninety-one patients with newly diagnosed GB were analyzed retrospectively. MPV/PC ratio groups (≤ or >0.0575) were dichotomized into low-MPV/PC ratio (≤0.0575) and high-MPV/PC ratio (>0.0575) groups according to the most significant split in the log-rank test. Results A two-sided Fisher’s exact test showed no significant differences in the confounders between the low- and high-MPV/PC ratio groups. The median progression-free survival (PFS) was 9.0 months (95% CI=8.0–10.0) in the low-MPV/PC ratio group (n=164) and 6.0 months (95% CI=3.0–8.9) in the high-MPV/PC group (n=28) (p=0.013). Multivariate Cox regression analysis including the O-6-methylguanine-DNA methyltransferase (MGMT) status, age (≤/>65 years), baseline Karnofsky Performance Status (KPS), and MPV/PC ratio showed high-MPV/PC ratio as a predictor of progression (p =0.04, HR=1.61, 95% CI=1.01–2.57). In the subgroup of IDH1 wild-type GBs, high MPV/PC ratio was still a significant predictor for shortened PFS (p=0.042, HR=1.60, 95% CI=1.02–2.52). MPV/PC ratio showed no significant effect in the overall survival (OS) analysis. Median OS was 15.0 months in the high-MPV/PC ratio group and 21.0 months in the low-MPV/PC ratio group (p=0.22). Conclusion MPV/PC ratio may independently predict the progression-free survival rates of patients with glioblastoma multiforme.
Genetic Diversity and Population Structure of Metaphire vulgaris Based on the Mitochondrial COI Gene and Microsatellites

The earthworm species Metaphire vulgaris (a member of the Clitellata class) is widely distributed across China, and has important ecological functions and medicinal value. However, investigations into its genetic diversity and differentiation are scarce. Consequently, we evaluated the genetic diversity of five populations of M. vulgaris (GM, HD, NYYZ, QDDY, and QDY) in Yancheng, China via the mitochondrial COI gene and the novel microsatellites developed there. A total of nine haplotypes were obtained by sequencing the mitochondrial COI gene, among which NYYZ and QDDY populations had the greatest number of haplotypes (nh = 5). Further, the nucleotide diversity ranged from 0.00437 to 0.1243. The neighbor-joining trees and the TCS network of haplotypes indicated that earthworm populations within close geographical range were not genetically isolated at these small scale distances. Results of the identification of microsatellite molecular markers revealed that the allele number in 12 microsatellite loci ranged from 4 to 13. The observed heterozygosity ranged from 0.151 to 0.644, whereas the expected heterozygosity ranged from 0.213 to 0.847. The polymorphism data content of most sites was >0.5, which indicated that the designed sites had high polymorphism. Structural analysis results indicated that GM, HD, and NYYZ had similar genetic structures across the five populations. The Nei’s genetic distance between HD and NYYZ populations was the smallest (Ds = 0.0624), whereas that between HD and QDY populations was the largest (Ds = 0.2364). The UPGMA tree showed that HD were initially grouped with NYYZ, followed by GM, and then with QDDY. Furthermore, cross-species amplification tests were conducted for Metaphire guillelmi, which indicated that the presented markers were usable for this species. This study comprised a preliminary study on the genetic diversity of M. vulgaris, which provides basic data for future investigations into this species.
Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review

Hashtag recommendation suggests hashtags to users while they write microblogs in social media platforms. Although researchers have investigated various methods and factors that affect the performance of hashtag recommendations in Twitter and Sina Weibo, a systematic review of these methods is lacking. The objectives of this study are to present a comprehensive overview of research on hashtag recommendation for tweets and present insights from previous research papers. In this paper, we search for articles related to our research between 2010 and 2020 from CiteSeer, IEEE Xplore, Springer and ACM digital libraries. From the 61 articles included in this study, we notice that most of the research papers were focused on the textual content of tweets instead of other data. Furthermore, collaborative filtering methods are seldom used solely in hashtag recommendation. Taking this perspective, we present a taxonomy of hashtag recommendation based on the research methodologies that have been used. We provide a critical review of each of the classes in the taxonomy. We also discuss the challenges remaining in the field and outline future research directions in this area of study.
Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving

Rule-based autonomous driving systems may suffer from increased complexity with large-scale intercoupled rules, so many researchers are exploring learning-based approaches. Reinforcement learning (RL) has been applied in designing autonomous driving systems because of its outstanding performance on a wide variety of sequential control problems. However, poor initial performance is a major challenge to the practical implementation of an RL-based autonomous driving system. RL training requires extensive training data before the model achieves reasonable performance, making an RL-based model inapplicable in a real-world setting, particularly when data are expensive. We propose an asynchronous supervised learning (ASL) method for the RL-based end-to-end autonomous driving model to address the problem of poor initial performance before training this RL-based model in real-world settings. Specifically, prior knowledge is introduced in the ASL pre-training stage by asynchronously executing multiple supervised learning processes in parallel, on multiple driving demonstration data sets. After pre-training, the model is deployed on a real vehicle to be further trained by RL to adapt to the real environment and continuously break the performance limit. The presented pre-training method is evaluated on the race car simulator, TORCS (The Open Racing Car Simulator), to verify that it can be sufficiently reliable in improving the initial performance and convergence speed of an end-to-end autonomous driving model in the RL training stage. In addition, a real-vehicle verification system is built to verify the feasibility of the proposed pre-training method in a real-vehicle deployment. Simulations results show that using some demonstrations during a supervised pre-training stage allows significant improvements in initial performance and convergence speed in the RL training stage.
MERL: Multimodal Event Representation Learning in Heterogeneous Embedding Spaces

Previous work has shown the effectiveness of using event representations for tasks such as script event prediction and stock market prediction. It is however still challenging to learn the subtle semantic differences between events based solely on textual descriptions of events often represented as (subject, predicate, object) triples. As an alternative, images offer a more intuitive way of understanding event semantics. We observe that event described in text and in images show different abstraction levels and therefore should be projected onto heterogeneous embedding spaces, as opposed to what have been done in previous approaches which project signals from different modalities onto a homogeneous space. In this paper, we propose a Multimodal Event Representation Learning framework (MERL) to learn event representations based on both text and image modalities simultaneously. Event textual triples are projected as Gaussian density embeddings by a dual-path Gaussian triple encoder, while event images are projected as point embeddings by a visual event component-aware image encoder. Moreover, a novel score function motivated by statistical hypothesis testing is introduced to coordinate two embedding spaces. Experiments are conducted on various multimodal event-related tasks and results show that MERL outperforms a number of unimodal and multimodal baselines, demonstrating the effectiveness of the proposed framework.
Exploiting Machine Learning for Improving In-Memory Execution of Data-Intensive Workflows on Parallel Machines

Workflows are largely used to orchestrate complex sets of operations required to handle and process huge amounts of data. Parallel processing is often vital to reduce execution time when complex data-intensive workflows must be run efficiently, and at the same time, in-memory processing can bring important benefits to accelerate execution. However, optimization techniques are necessary to fully exploit in-memory processing, avoiding performance drops due to memory saturation events. This paper proposed a novel solution, called the Intelligent In-memory Workflow Manager (IIWM), for optimizing the in-memory execution of data-intensive workflows on parallel machines. IIWM is based on two complementary strategies: (1) a machine learning strategy for predicting the memory occupancy and execution time of workflow tasks; (2) a scheduling strategy that allocates tasks to a computing node, taking into account the (predicted) memory occupancy and execution time of each task and the memory available on that node. The effectiveness of the machine learning-based predictor and the scheduling strategy were demonstrated experimentally using as a testbed, Spark, a high-performance Big Data processing framework that exploits in-memory computing to speed up the execution of large-scale applications. In particular, two synthetic workflows were prepared for testing the robustness of the IIWM in scenarios characterized by a high level of parallelism and a limited amount of memory reserved for execution. Furthermore, a real data analysis workflow was used as a case study, for better assessing the benefits of the proposed approach. Thanks to high accuracy in predicting resources used at runtime, the IIWM was able to avoid disk writes caused by memory saturation, outperforming a traditional strategy in which only dependencies among tasks are taken into account. Specifically, the IIWM achieved up to a 31% and a 40% reduction of makespan and a performance improvement up to 1.45× and 1.66× on the synthetic workflows and the real case study, respectively.
Opponent Hand Estimation in the Game of Gin Rummy

In this article, we describe various approaches to opponent hand estimation in the card game Gin Rummy. We use an application of Bayes' rule, as well as both simple and convolutional neural networks, to recognize patterns in simulated game play and predict the opponent's hand. We also present a new minimal-sized construction for using arrays to pre-populate hand representation images. Finally, we define various metrics for evaluating estimations, and evaluate the strengths of our different estimations at different stages of the game.
A New Robust Subspace Recovery Algorithm (Student Abstract)

A common task in data analysis is to compute an approximate embedding of the data in a low dimensional subspace. This is used, for example, for dimensionality reduction. Robust Subspace Recovery computes the embedding by ignoring a fraction of the data considered as outliers. Its performance can be evaluated by how accurate the inliers (non-outliers) are represented. We propose a new algorithm that outperforms the current state of the art when the data is dominated by outliers. The main idea is to rank each point by evaluating the change in the global PCA error when that point is considered as an outlier. We show that this lookahead procedure can be implemented efficiently by centered rank-one modifications.