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Surgery Surgery

Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning.

In Scientific reports ; h5-index 158.0

For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms.

Serra Bragança F M, Broomé S, Rhodin M, Björnsdóttir S, Gunnarsson V, Voskamp J P, Persson-Sjodin E, Back W, Lindgren G, Novoa-Bravo M, Roepstorff C, van der Zwaag B J, Van Weeren P R, Hernlund E


General General

A contemporary baseline record of the world's coral reefs.

In Scientific data

Addressing the global decline of coral reefs requires effective actions from managers, policymakers and society as a whole. Coral reef scientists are therefore challenged with the task of providing prompt and relevant inputs for science-based decision-making. Here, we provide a baseline dataset, covering 1300 km of tropical coral reef habitats globally, and comprised of over one million geo-referenced, high-resolution photo-quadrats analysed using artificial intelligence to automatically estimate the proportional cover of benthic components. The dataset contains information on five major reef regions, and spans 2012-2018, including surveys before and after the 2016 global bleaching event. The taxonomic resolution attained by image analysis, as well as the spatially explicit nature of the images, allow for multi-scale spatial analyses, temporal assessments (decline and recovery), and serve for supporting image recognition developments. This standardised dataset across broad geographies offers a significant contribution towards a sound baseline for advancing our understanding of coral reef ecology and thereby taking collective and informed actions to mitigate catastrophic losses in coral reefs worldwide.

Rodriguez-Ramirez Alberto, González-Rivero Manuel, Beijbom Oscar, Bailhache Christophe, Bongaerts Pim, Brown Kristen T, Bryant Dominic E P, Dalton Peter, Dove Sophie, Ganase Anjani, Kennedy Emma V, Kim Catherine J S, Lopez-Marcano Sebastian, Neal Benjamin P, Radice Veronica Z, Vercelloni Julie, Beyer Hawthorne L, Hoegh-Guldberg Ove


General General

Deep learning-assisted comparative analysis of animal trajectories with DeepHL.

In Nature communications ; h5-index 260.0

A comparative analysis of animal behavior (e.g., male vs. female groups) has been widely used to elucidate behavior specific to one group since pre-Darwinian times. However, big data generated by new sensing technologies, e.g., GPS, makes it difficult for them to contrast group differences manually. This study introduces DeepHL, a deep learning-assisted platform for the comparative analysis of animal movement data, i.e., trajectories. This software uses a deep neural network based on an attention mechanism to automatically detect segments in trajectories that are characteristic of one group. It then highlights these segments in visualized trajectories, enabling biologists to focus on these segments, and helps them reveal the underlying meaning of the highlighted segments to facilitate formulating new hypotheses. We tested the platform on a variety of trajectories of worms, insects, mice, bears, and seabirds across a scale from millimeters to hundreds of kilometers, revealing new movement features of these animals.

Maekawa Takuya, Ohara Kazuya, Zhang Yizhe, Fukutomi Matasaburo, Matsumoto Sakiko, Matsumura Kentarou, Shidara Hisashi, Yamazaki Shuhei J, Fujisawa Ryusuke, Ide Kaoru, Nagaya Naohisa, Yamazaki Koji, Koike Shinsuke, Miyatake Takahisa, Kimura Koutarou D, Ogawa Hiroto, Takahashi Susumu, Yoda Ken


General General

Immune Response and Microbiota Profiles during Coinfection with Plasmodium vivax and Soil-Transmitted Helminths.

In mBio

The role of the gut microbiota during coinfection with soil-transmitted helminths (STH) and Plasmodium spp. is poorly understood. We examined peripheral blood and fecal samples from 130 individuals who were either infected with Plasmodium vivax only, coinfected with P. vivax and STH, infected with STH alone, or not infected with either P. vivax or STH. In addition to a complete blood count (CBC) with differential, transcriptional profiling of peripheral blood samples was performed by transcriptome sequencing (RNA-Seq), fecal microbial communities were determined by 16S rRNA gene sequencing, and circulating cytokine levels were measured by bead-based immunoassays. Differences in blood cell counts, including an increased percentage of neutrophils, associated with a transcriptional signature of neutrophil activation, were driven primarily by P. vivax infection. P. vivax infection was also associated with increased levels of interleukin 6 (IL-6), IL-8, and IL-10; these cytokine levels were not affected by STH coinfection. Surprisingly, P. vivax infection was more strongly associated with differences in the microbiota than STH infection. Children infected with only P. vivax exhibited elevated Bacteroides and reduced Prevotella and Clostridiaceae levels, but these differences were not observed in individuals coinfected with STH. We also observed that P. vivax parasitemia was higher in the STH-infected population. When we used machine learning to identify the most important predictors of the P. vivax parasite burden (among P. vivax-infected individuals), bacterial taxa were the strongest predictors of parasitemia. In contrast, circulating transforming growth factor β (TGF-β) was the strongest predictor of the Trichuris trichiura egg burden. This study provides unexpected evidence that the gut microbiota may have a stronger link with P. vivax than with STH infection.IMPORTANCEPlasmodium (malaria) and helminth parasite coinfections are frequent, and both infections can be affected by the host gut microbiota. However, the relationship between coinfection and the gut microbiota is unclear. By performing comprehensive analyses on blood/stool samples from 130 individuals in Colombia, we found that the gut microbiota may have a stronger relationship with the number of P. vivax (malaria) parasites than with the number of helminth parasites infecting a host. Microbiota analysis identified more predictors of the P. vivax parasite burden, whereas analysis of blood samples identified predictors of the helminth parasite burden. These results were unexpected, because we expected each parasite to be associated with greater differences in its biological niche (blood for P. vivax and the intestine for helminths). Instead, we find that bacterial taxa were the strongest predictors of P. vivax parasitemia levels, while circulating TGF-β levels were the strongest predictor of helminth parasite burdens.

Easton Alice V, Raciny-Aleman Mayra, Liu Victor, Ruan Erica, Marier Christian, Heguy Adriana, Yasnot Maria Fernanda, Rodriguez Ana, Loke P’ng


Colombia, Plasmodium vivax\n, STH, Trichuris trichiura\n, malaria, microbiota, soil-transmitted helminths

Pathology Pathology

Transcriptional and proteomic insights into the host response in fatal COVID-19 cases.

In Proceedings of the National Academy of Sciences of the United States of America

Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, has resulted thus far in greater than 933,000 deaths worldwide; yet disease pathogenesis remains unclear. Clinical and immunological features of patients with COVID-19 have highlighted a potential role for changes in immune activity in regulating disease severity. However, little is known about the responses in human lung tissue, the primary site of infection. Here we show that pathways related to neutrophil activation and pulmonary fibrosis are among the major up-regulated transcriptional signatures in lung tissue obtained from patients who died of COVID-19 in Wuhan, China. Strikingly, the viral burden was low in all samples, which suggests that the patient deaths may be related to the host response rather than an active fulminant infection. Examination of the colonic transcriptome of these patients suggested that SARS-CoV-2 impacted host responses even at a site with no obvious pathogenesis. Further proteomics analysis validated our transcriptome findings and identified several key proteins, such as the SARS-CoV-2 entry-associated protease cathepsins B and L and the inflammatory response modulator S100A8/A9, that are highly expressed in fatal cases, revealing potential drug targets for COVID-19.

Wu Meng, Chen Yaobing, Xia Han, Wang Changli, Tan Chin Yee, Cai Xunhui, Liu Yufeng, Ji Fenghu, Xiong Peng, Liu Ran, Guan Yuanlin, Duan Yaqi, Kuang Dong, Xu Sanpeng, Cai Hanghang, Xia Qin, Yang Dehua, Wang Ming-Wei, Chiu Isaac M, Cheng Chao, Ahern Philip P, Liu Liang, Wang Guoping, Surana Neeraj K, Xia Tian, Kasper Dennis L


COVID-19, NETosis, SARS-CoV-2, fibrosis, neutrophil

oncology Oncology

Galectin-9-based immune risk score model helps to predict relapse in stage I-III small cell lung cancer.

In Journal for immunotherapy of cancer

BACKGROUND : For small cell lung cancer (SCLC) therapy, immunotherapy might have unique advantages to some extent. Galectin-9 (Gal-9) plays an important role in antitumor immunity, while little is known of its function in SCLC.

MATERIALS AND METHODS : By mean of immunohistochemistry (IHC), we tested the expression level of Gal-9 and other immune markers on both tumor cells and tumor-infiltrating lymphocytes (TILs) in 102 surgical-resected early stage SCLC clinical samples. On the basis of statistical analysis and machine learning results, the Gal-9-based immune risk score model was constructed and its predictive performance was evaluated. Then, we thoroughly explored the effects of Gal-9 and immune risk score on SCLC immune microenvironment and immune infiltration in different cohorts and platforms.

RESULTS : In the SCLC cohort for IHC, the expression level of Gal-9 on TILs was statistically correlated with the levels of program death-1 (p=0.001), program death-ligand 1 (PD-L1) (p<0.001), CD3 (p<0.001), CD4 (p<0.001), CD8 (p<0.001), and FOXP3 (p=0.047). High Gal-9 protein expression on TILs indicated better recurrence-free survival (30.4 months, 95% CI: 23.7-37.1 vs 39.4 months, 95% CI: 31.6-47.3, p=0.009). The immune risk score model which consisted of Gal-9 on TILs, CD4, and PD-L1 on TILs was established and validated so as to differentiate high-risk or low-risk patients with SCLC. The prognostic predictive performance of immune risk score model was better than single immune biomarker (area under the curve 0.671 vs 0.621-0.644). High Gal-9-related enrichment pathways in SCLC were enriched in immune system diseases and rheumatic disease. Furthermore, we found that patients with SCLC with low immune risk score presented higher fractions of activated memory CD4 T cells than patients with high immune risk score (p=0.048).

CONCLUSIONS : Gal-9 is markedly related to tumor-immune microenvironment and immune infiltration in SCLC. This study emphasized the predictive value and promising clinical applications of Gal-9 in stage I-III SCLC.

Chen Peixin, Zhang Liping, Zhang Wei, Sun Chenglong, Wu Chunyan, He Yayi, Zhou Caicun


T-lymphocytes, lung neoplasms, lymphocytes, programmed cell death 1 receptor, tumor microenvironment, tumor-infiltrating