Purpose. The study aim was 2-fold: to quantify and compare the weekly external training load that preceded matches; to compare in-match activities depending on the opponent level (top, middle, bottom) in a top-level team from the first professional Asian national league. Methods. The load for 6 matches played against top-, 11 against middle-, and 11 against bottom-level teams was monitored. With a 15-Hz Global Positioning System, total duration, total distance, high-speed (18-23 km), DecZ2 (-2 to -4 m • s -2 ), DecZ3 (< -4 m • s -2 ), player load, and metabolic power were collected in 12 players. Results. DecZ3 showed higher values against top-level compared with middle-(effect size [ES] = 0.91) and bottom-level opponents (ES = 1.50). The training was significantly longer against middle-level compared with top-and bottom-level opponents (all, p 0.001). Total distance was bigger against middle-level compared with top-(p = 0.011, ES = -0.92) and bottom-level opponents (p = 0.027, ES = 1.50). AccZ2 presented higher values when middle-level came close compared with bottom-level opponents (p = 0.05, ES = 0.79). Conclusions. Opponent's level influences the load experienced by soccer players during matches. Total distance, highspeed running distance, AccZ1, and AccZ2 exhibited higher training values when a win or a draw approached. Decelerations in all zones were highest in matches against top-level teams.

The global outbreak and rapid spread of SARS-CoV-2 has created an urgent need for large scale testing of populations. There is a demand for high throughput testing protocols that can be used for efficient and rapid testing of clinical specimens. We evaluated a pooled-PCR protocol for testing nasopharyngeal swabs using known positive/negative and untested clinical samples that were assigned to pools of 5 or 10. Nasopharyngeal swabs were accurately identified as positive or negative for SARS-CoV-2 in pools of 5 (100% sensitivity; 100% specificity). Even though specificity remained unaffected (100%), the detection sensitivity was reduced (66.67%) when 10 samples were pooled together. Pooling of up to 5 samples can be employed in laboratories for the diagnosis of COVID-19 for efficient utilization of resources, rapid screening of a greater number of people, and faster reporting of test results.

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Advances in next-generation metagenome sequencing have the potential to revolutionize the point-of-care diagnosis of novel pathogen infections, which could help prevent potential widespread transmission of diseases. Given the high volume of metagenome sequences, there is a need for scalable frameworks to analyze and segment metagenome sequences from clinical samples, which can be highly imbalanced. There is an increased need for learning robust representations from metagenome reads since pathogens within a family can have highly similar genome structures (some more than 90%) and hence enable the segmentation and identification of novel pathogen sequences with limited labeled data. In this work, we propose Metagenome2Vec -a contextualized representation that captures the global structural properties inherent in metagenome data and local contextualized properties through self-supervised representation learning. We show that the learned representations can help detect six (6) related pathogens from clinical samples with less than 100 labeled sequences. Extensive experiments on simulated and clinical metagenome data show that the proposed representation encodes compositional properties that can generalize beyond annotations to segment novel pathogens in an unsupervised setting.