Dear Editor,Etv2  was specifically expressed in the zebrafish vascular endothelial cells and was required for vascular development in zebrafish  are highly amendable for this endothelial induction. We first detected the potential of endothelial transdifferentiation induced by ETV2 overexpression in five human cell types. Cells used in this experiment were human skeletal muscle cells (HSkMCs), adipose-derived mesenchymal stem cells (ADMSCs), umbilical cord-derived mesenchymal stem cells (UCMSCs), human embryonic lung fibroblast cells HFL-1) and human skin fibroblast cells (HSFs). All these cells were infected with equal numbers of ETV2 lentivirus. Following ETV2 lentivirus infection, cell morphologies and expression of endothelial specific genes were analyzed. The infected cells all appeared to change morphologies into an endothelial-like shape. Among them, ETV2-HSkMCs showed the most uniform conversion, similar to the morphology of human umbilical vein endothelial cells (HUVECs)  and about 90% at 3 dpi .Skeletal muscle tissue is highly vascularized and accounts for approximately 40% of the body mass in human  skeletal muscle tissue and generated through detergent perfusion and enzymatic digestion, which removed all cellular material while maintained the three-dimensional networks of collagen and elastic fibers . Synthetic materials like PLLA (poly-(L-lactic acid)) and PLGA (polylactic-glycolic acid) provide physical support for cells with highly porous biodegradable structures that are easy to operate and suitable for large-scale production  could contribute to vascularization of engineered muscle tissue for transplantation, we adopted the procedures shown in Figs. in vitro was tested. Approximately 1.5 × 106 cells per scaffold, half were CDH5 positive cells and the rest were skeletal muscle cells, were injected into the scaffolds and allowed to grow under tissue culture condition. After 5 days of culture in decellularized scaffolds or 7 days of culture in PLLA/PLGA scaffolds, the status of engineered muscle tissue was analyzed by sectioning and histological staining. The results showed that CDH5 positive cells that organized into vessel-like structures were only detectable in the muscle tissue mixed with ETV2-iEC or HUVECs but not in the cell mass by HSkMCs alone Below is the link to the electronic supplementary material.


Uncoupling protein 1 (UCP1) is a mitochondral protein transporter that uncouples electron transport from ATP production. UCP1 is highly expressed in brown adipose tissue (BAT), including hibernomas, but its expression in other adipose tumours is uncertain. UCP1 has also been found in other tissues (e.g. smooth muscle) but whether it is expressed in non-adipose benign and malignant soft tissue tumours is unknown.Immunohistochemical staining of normal (axillary) BAT and subcutaneous/abdominal white adipose tissue (WAT) as well as a wide range of benign and malignant primary soft tissue tumours (n = 171) was performed using a rabbit polyclonal antibody to UCP1. BAT and hibernomas were also stained by immunohistochemistry with monoclonal and polyclonal antibodies to adipose/non-adipose tumour markers in order to characterise the immunophenotype of BAT cells.UCP1 was strongly expressed in the cytoplasm of brown fat cells in BAT and hibernomas, both of which also expressed aP2, S100, CD31, vimentin and calponin. UCP1 was not expressed in WAT or other adipose tumours with the exception a few tumour cells in pleomorphic liposarcoma. UCP1 was variably expressed by tumour cells in a few non-adipose sarcomas including leiomyosarcoma, rhabdomyosarcoma, alveolar soft part sarcoma, synovial sarcoma and clear cell sarcoma.UCP1 is strongly expressed in BAT but not WAT and is found in all hibernomas and a few pleomorphic liposarcomas but not in other adipose tumours. UCP1 expression in a few non-adipose soft tissue sarcomas may possibly reflect origin of tumour cells from a common mesenchymal stem cell precursor and/or developmental pathway. Benign and malignant tumours of adipose differentiation represent a large category of soft tissue tumours, many of which are frequently encountered in diagnostic pathology . These tUncoupling proteins (UCPs) comprise a class of mitochondrial proteins that act to dissipate the proton gradient across the mitochondrial membrane; protons are transported from the inner membrane space back to the matrix, the energy lost in dissipating the proton gradient being generated as heat –8. This UCP1 is highly expressed in hibernoma, a tumour containing abundant BAT cells –17. TherSamples of axillary adipose tissue that contained brown fat cells were employed as BAT-positive controls and subcutaneous (thigh) and mesenteric adipose tissue as WAT -positive controls. Bone marrow adipose tissue from cancellous bone of a lumbar vertebra and a femur were also examined. Other non-neoplastic tissues stained with UCP1 included sections of normal skin, appendix, bone, lymph node and tonsil. Paraffin-embedded tissue sections from formalin-fixed samples of WAT and BAT controls and 171 soft tissue tumours, retrieved from the files of the Nuffield Orthopaedic Centre, Oxford were analysed in this study. Negative controls for UCP1 staining consisted of BAT-positive control tissue sections stained without the primary antibody. The number and nature of the tumours examined in this study are shown in Table Four micrometre sections were cut and immunohistochemical staining was carried out using an Envision FLEX Minikit and the Dako Autostainer (Dako Agilent UK) as previously described , 19, 20.In axillary adipose tissue there was strong staining of the multiple small fat vacuoles in the cytoplasm of brown fat cells with the anti-UCP1 polyclonal antibody; this was noted even when a low concentration (1:1000–1:20000) of the antibody was employed  in a manner similar to that noted for axillary BAT  soft tissue tumours examined, including those of smooth muscle, nerve sheath and vascular differentiation. Both uterine and soft tissue leiomyomas were UCP1-negative. Benign fibrohistiocytic tumours and fibroblastic/myofibroblastic lesions such as myofibroma, nodular fascitiis and fibromatosis were also UCP1 negative. Most malignant (non-adipose) mesenchymal soft tissue neoplasms Table a–c. WeakTumours of adipose differentiation represent a large category of mesenchymal soft tissue tumours with lipoma and liposarcoma being respectively the most common benign and malignant soft tissue tumours diagnosed in adults. In this study we have shown that UCP1 is strongly expressed in BAT but not WAT and that, with the exception of all hibernomas and a minority of pleomorphic liposarcomas, UCP1 is not expressed in benign and maligant adipose tumours. UCP1 was not expressed in non-adipose tissues and was not seen in benign, non-adipose soft tissue tumours, but was found in some non-adipose soft tissue sarcomas, notably rhabdomyosarcoma and leiomyosarcoma.It has been shown that normal WAT cells can differentiate into BAT cells to form beige/brite cells , 10. SchIn contrast to BAT cells in hibernoma, mature WAT cells in simple benign lipoma and in the various lipoma subtypes were UCP1-negative. UCP1-mRNA expression in lipomas of multiple symmetric lipomatosis, bearing pathogenic mitochondrial mutations has been reported, but UCP1 expression in simple lipomas or lipoma subtypes has not been identified . LipoblaUCPs are mitochondrial membrane proteins that enable a rapid backflow of protons through the mitochondrial inner membrane, bypassing ATP synthase. This mitochondrial uncoupling is mediated by UCP1 and its homologues –8, 27; tAs in normal BAT, strong diffuse staining for UCP1 was seen in all hibernomas even when a low concentration (1:20000) of the anti-UCP1 antibody was employed. BAT cells in hibernoma have been shown to express vimentin, S100, aP2 and CD31 , 26, 34.UCP1 is strongly expressed in BAT but not WAT, and is found in all hibernomas. Absence of UCP1 expression in other adipose tumours (with the exception of a few pleomorphic liposarcomas) suggests that these tumours are derived from WAT. UCP1 expression in a few non-adipose soft tissue sarcomas may possibly reflect origin of tumour cells from a common mesenchymal stem cell precursor and/or developmental pathway.


Flow cytometry, with its high throughput nature, combined with the ability to measure an increasing number of cell parameters at once can surpass the throughput of prevalent genomic and metagenomic approaches in the study of microbiomes. Novel computational approaches to analyze flow cytometry data will result in greater insights and actionability as compared to traditional tools used in the analysis of microbiomes. This paper is a demonstration of the fruitfulness of machine learning in analyzing microbial flow cytometry data generated in anaerobic microbiome perturbation experiments.Autoencoders were found to be powerful in detecting anomalies in flow cytometry data from nanoparticles and carbon sources perturbed anaerobic microbiomes but was marginal in predicting perturbations due to antibiotics. A comparison between different algorithms based on predictive capabilities suggested that gradient boosting (GB) and deep learning, i.e. feed forward artificial neural network with three hidden layers (DL) were marginally better under tested conditions at predicting overall community structure while distributed random forests (DRF) worked better for predicting the most important putative microbial group(s) in the anaerobic digesters viz. methanogens, and it can be optimized with better parameter tuning. Predictive classification patterns with DL  were found to be comparable to previously demonstrated multivariate analysis. The potential applications of this approach have been demonstrated for monitoring the syntrophic resilience of the anaerobic microbiomes perturbed by synthetic nanoparticles as well as antibiotics.Machine learning can benefit the microbial flow cytometry research community by providing rapid screening and characterization tools to discover patterns in the dynamic response of microbiomes to several stimuli.The online version of this article (10.1186/s13036-018-0112-9) contains supplementary material, which is available to authorized users. The science of microbial ecology is on the verge of revolution  partly dPreviously, we demonstrated the utility of flow cytometry in classifying microbial consortia based on morphological and metabolic characteristics complementing existing genomic technologies in rapid characterization of microbiome dynamics . With adSimilar to our previous demonstration , there aHence, here we demonstrate the use of open-source machine learning tools to analyze flow cytometry data generated in the anaerobic microbiome perturbation experiments exploiting the three-dimensional flow cytometry signals namely cell size (FSC or forward scatter), cell granularity/morphology (SSC or side scatter) and autofluorescence (corresponding to the same excitation/emission wavelength as in AmCyan standard dye). We also demonstrated for the first time, the use of unsupervised autoencoders for microbial flow cytometry data. Applications of the machine learning analysis of microbial flow cytometry data in potentially improving the performance of biodigesters, and to characterize the syntrophic resilience  of the mFigure Table The limitations of the DL  in predicting methanogens Table  were alsSince DL is increasingly popular because of its facility in handling large amount of data , and sinIn the additional experimental set, when the cytometric fingerprints from individual carbon sources namely glucose (GLUC), cellulose (CELL), propionate (PROP), butyrate (BUTY), acetate (ACET) were trained with cytometric fingerprints from all carbon sources combined (WFED) and that from the starved community (STVD) samples. The expectation was that well-fed (WFED) community would display some degree of misclassification towards one of the five carbon sources. However, to our surprise, WFED community looked totally different than the rest and was more like STVD than individual carbon sources  beyond carbon sources, model implementation exercises were carried out with a separate set of perturbations namely a) nanoparticles and b) antibiotics. Interestingly even though the community looked quite different from one another, none showed any toxic impact on anaerobic digestion process observed in terms of biogas production, methane composition, Total Chemical Oxygen Demand (TCOD)/ Soluble Chemical Oxygen Demand (SCOD) reduction and Volatile Fatty Acids (VFA) accumulation (data not presented due to non-significance) with respect to both positive and negative controls for the nanoparticles and antibiotic (tetracycline) tested.The deep learning  analysis of flow cytometry data of nanoparticles perturbed community is shown in the Fig. Unsupervised autoencoders were also found to be marginally useful in predicting structural changes in the antibiotic-perturbed community. As shown in Fig. The machine learning approaches to the microbial flow cytometry dataset are still in infancy but the results presented here from the analysis of 1,500,000 microbes corresponding to each perturbation incidence  demonstrate that it has much greater potential compared to the tradition microbial ecology statistical analysis like MDS as demonstrated in our previous publication  or similEven though dimensionality reduction was not an objective, autoencoders were found to be powerful in learning a lower dimensional representation of the data. We do not suggest it replace dimensionality reduction analysis, nor do we intend it as a preprocessing step for DL models. Our work using autoencoders suggests that tools like the autoencoders can identify perturbations and hence may be valuable in stochastic optimization of the classical process design in the field of bioprocess engineering. While the current paradigm of process design is based on throughput rate, process yield and product purity , with thThe motivation behind the model comparison exercise was to see if one algorithm was better at predicting putative microbial group phenotypes than other. The efficiency in terms of speed and resources for model optimization, training and validation would be altogether different track of inquiry and it was not used for comparison in the proposed approach. AUC measured the probability that given two random points, one that is positive and other negative for the class of interest, the classifier scored the positive point higher than the negative one . ConsideUnder tested conditions, DRF worked better for putative methanogens, however the performance can be optimized with better parameter tuning. DRF generated a forest of classification (or regression) trees, rather than a single classification (or regression) tree. Each of these trees was a weak learner built on a subset of rows and columns . More trSmithllela, Syntrophobacter, and Pelotomaculum might have the ability to break down CELL faster than classical hydrolyzers like Clostridium or have greater doubling time [Smithllela, Syntrophobacter, and Pelotomaculum [Syntrophus and Syntrophomonas [The substrate composition determines the microbial community composition and organization , 53. Inding time . PROP spomaculum , 62 whilphomonas . There hphomonas , 65.The composition of newspaper is reported as cellulose (glucose polymer), wood fiber  . The newEven though the present results and the current associated literature  suggestsAutoencoders were found to be powerful in detecting anomalies in flow cytometry data from nanoparticle- and carbon source-perturbed anaerobic microbiomes but marginally so for antibiotic-perturbed communities. Anaerobic microbiomes displayed functional redundancy under nanotoxicity and antibiotic perturbations. Predictive classification patterns with supervised feed forward artificial neural network with three hidden layers were found to be robust. Model comparison exercise based on predictive capabilities concluded that under tested conditions, gradient boosting (GB) and deep learning (DL)  were marginally better at predicting overall community structure while distributed random forests (DRF) worked better at predicting methanogens, and it can be optimized with better parameter tuning. Flow cytometry data generated in various anaerobic microbiome perturbation experiments could be analyzed by various machine learning approaches for actionable insights.The source of anaerobic culture, controlled perturbations and flow cytometric analysis were same as described in previously . In summIndividual carbon sources with 2000 mg/L chemical oxygen demand (COD) of glucose (GLUC), cellulose (CELL), propionate (PROP), butyrate (BUTY), acetate (ACET) separately as well as five of them combined (WFED) were added to individual batch assays. The chemical properties of the sludge (SLUD) used in this research are summarized in Additional file For the clubbed flow frame analysis, the sample names were clubbed as per the conceptual division of putative microbial groups in anaerobic digestion process. The four groups were: (1) Putative hydrolyzes represented by “HYDRO” which were samples fed with cellulose (CELL) (2) Putative acidogens represented by “ACIDO” which were samples fed with glucose (GLUC) (3) Putative syntrophic acetogens represented by “ACETO” which were clubbed samples fed with propionate (PROP) and butyrate (BUTY) individually (4) Putative methanogens represented by “METHA” which were clubbed samples fed with acetate (ACET), sludge (SLUD) individually as well as those fed with no carbon source (BLAN and NONE). The reason behind clubbing ACET, SLUD, BLAN and NONE into METHA putative group was because all these samples were supposedly methanogenic samples with acetate utilizing methanogens were the predominant group in the waste water treatment plant anaerobic digesters .Daily Illini Newspaper obtained from the waste sorting facility. Newsprint samples were either ground to powder, shredded to strips in an office paper shredder or whole pieces hand cut to size for required weight in each experiment. The goal was to predict the newspaper cytometric fingerprints (abbreviated as NEWS) off the carbon sources dataset generated with the proposed flow cytometry methodology.The feedstock used for the lab tests for the proposed campus anaerobic digester was newsprint from day-old 2/g obtained from Sigma Aldrich . FENPs are iron nanopowder, 25 nm average particle size, 99.5% trace metals basis from Sigma Aldrich . CNTs are multi-walled carbon nanotube which are reported thin and short, < 5% metal oxide powder also from Sigma Aldrich .For the nanotoxicity experiments, the sludge samples (SLUD) were mixed with various nanoparticles in low (L) 1 mg/g-TS  and high (H) 10 mg/g-TS concentrations. Plain sludge with anaerobic inoculum from active anaerobic digesters but no nanoparticles added was set up as a positive control (PCH) and anaerobic inoculum from the active anaerobic digesters without sludge or nanoparticles was set up as a negative control (NCL). The alleged toxic impact of nanoparticles on anaerobic digestion process was quantified by monitoring the biogas production daily along with gas composition, TCOD/ SCOD reduction and VFA production on biweekly basis. Drawing the samples for flow cytometric analysis every week helped build the cytometric fingerprints of the perturbed community, which forms the basis for subsequent machine learning analysis. TINPs are titanium (IV) oxide, anatase nanopowder, < 25 nm particle size, 99.7% trace metals basis, specific surface area 45–55 mFor the antibiotics perturbation experiments, the sludge samples (SLUD) were mixed with tetracycline  obtained from Sigma Aldrich (CAS Number: 60–54-8). Tetracycline was reported to be profound inhibitors of anaerobic digestion, inhibiting methane production by up to 50% with a concentration of 40 mg/L [Samples of the anaerobic microbial communities were collected from each serum bottle via a syringe with a 18 gauge needle (every 24 h following a biogas measurement for carbon sources). Initial sample (NONE_0) was the same for all the assays, which was the fresh inoculum. 750 μL of sample from each serum bottle was strained prior to flow cytometry using BD Falcon 12 × 75 mm Tube with Cell Strainer Cap having a 35 μm nylon mesh . The strained samples were suspended in Phosphate-buffered saline (PBS)-1X. Analyses were performed immediately on a Bio-sciences LSR II Flow Cytometry Analyzer. The excitation laser was tuned for 405-nm. Autofluorescence was measured as light passing a 450/50 photomultiplying tubes (PMT) and band pass filter with no long pass dichroic mirror. Signals were amplified with a 4-decade log amplifier and collected at a rate of approximately 1000 events per second. The fixed gating template as shown in the Additional file For the machine learning based analysis, the fcs files obtained from a BD Bioscience LSR II Flow Cytometry Analyzer were imported. Each file comprised of 100,000 events representing 100,000 cells. The fcs file contained values for multiple parameters for each cell. But for our analysis we only chose “FSC-A”, “SSC-A” and “AmCyan-A” because in our previous publication , FSC or Classifying single cells  was out of purview of present study. Rather, we were interested in classifying distributions of events to understand microbial community dynamics specific to each perturbation. Our data was normally distributed in the measured flow cytometry values, hence we decided to combine 1000 events into a data point or a vector of 3000 values  because the Wald Type confidence interval  with 3% Since there were 100,000 events in each fcs file, we got 100 such vectors. As described, we conducted each experiments in triplicate ranged over five time points, we had 1,500,000  events for each perturbation incidence  thereby generating a rich dataset with 1500 vectors representing each label. This dataset was then split into 75% training (1125 vectors) and 25% test (375 vectors) sets for each label. We decided to create a larger training split compared to testing since it is customary practice in machine learning community . Additiohttp://www.h2o.ai/) and the data was uploaded in individual comma-separated values (CSV) files. The fcs files and associated code are available at: https://github.com/adhoble/CFML-Perturb. The fcs files were also submitted to the Flow Repository (https://flowrepository.org/). (Repository ID: FR-FCM-ZYK4).An h2o.ai server was set-up as per instructions   and x’ is the vector reconstructed by the autoencoder . Higher the MSE value, more anomalous a sample in relation to the pattern found in a whole dataset and the threshold MSE would vary with the particular perturbation experiment under consideration.An autoencoder learns a lower dimensional representation of the data by trying to learn an approximation of the identity function . It is aIn our implementation of autoencoders, the h2o.ai function ‘deeplearning’ was used with ‘autoencoder’ option turned on and one hidden layer of 2000 nodes.A feed forward artificial neural network model or a deep neural network model is an arrangement of layers of neurons  with weighted connections such that there are no loops . These nIn our implementation of deep learning , the default rectifier with dropout was selected for activation. The network contained input dropout ratio of 0.1, hidden dropout ratios of 0.2, 0.2, 0.1 and a default hidden layer sizes 2000,1000,500 were used. Number of epochs (i.e. number of times to iterate the dataset) were selected to 10. To add stability and improve generalization, either L1 or L2 regularization was used with the value of 1e-5 each.Random forests are type of ensemble machine learning methods commonly used for classification and regression. They extend decision trees by training many trees on random subsets of training data sampled with replacement . AdditioIn our implementation of distributed random forests, for each classification task, the h2o.ai function ‘randomForest’ was used with 200 trees, maximum depth of 20 levels and respective training and validation data for the task. The other parameters were set to their default values.Gradient boosting machine is an ensemble method based on the idea that multiple weak learners can perform better than a single strong learner . In the In our implementation of gradient boosting, for each classification task, the h2o.ai function ‘gbm’ was used with 200 trees and maximum depth of 5 levels as per recommended range of 4 to 8 levels  wherein k | X1,….,Xn), for each category Ck, where X1,….,Xn are the features used in training [Naïve Bayes is a supervised classification method that constructs conditional probability distributions, p  analysis, the variable importance option was turned and visualized in h2o.ai’s Flow platform. Variable importance is notoriously difficult to compute for deep learning . The implemented method by Gedeon  considerArea under the curve (AUC) analysis was performed for the clubbed data set on its test set split to test for robustness of the models. Since the models were multi class classification models, AUC values and receiver operating characteristic (ROC) curves were calculated by dividing the four-class prediction problem into four one-vs-all binary classification problems using the pROC package in R .Additional file 1: Experimental design, flow cytometry controls, hyperparameters and additional material. (DOCX 984 kb)


The central purpose of this data article is to empirically investigate the relationships among social media marketing (SMM) activities, brand loyalty and revisit intention in five-star hotels in Northern Cyprus. Few researchers have investigated SMM activities, while none has looked at how SMM activities can be used toward improving brand loyalty and revisit intention in the tourism service industry. Hence, data gathered for the purposes of this research add to our understanding of today׳s social media marketing as a new generation marketing tool. This data was generated via a structured questionnaire, a total of 389 customers were surveyed who used five (5) hotels Facebook profiles, the hotels were all five-star ranked and located in Kyrenia city (Northern Cyprus). The data were examined by Structural Equation Modelling (SEM). Several analysis techniques have been used, the result showed a significant influence of SMM activities on brand loyalty and revisit intention, also the mediation outcome of brand trust is partially supported. Thus, consequential recommendations have been put forward. Specifications tableValue of the data•This data article reports the role of social media marketing activities in enhancing brand loyalty and revisit intention in the hospitality industry by considering brand trust for hotel Facebook pages.•The dataset describes the knowledge gap by developing a dataset model to examine the growing position of SMM. It similarly offers a model for marketers interested in predicting brand loyalty and revisit intention.•The results acquired from the dataset showed a positive relationship between SMM activities and brand loyalty, revisit intention in the five-star hotel in Northern Cyprus.•The dataset can be developed in the future in new data article or new research article – it can be extended to include new comparative study to explore social media platforms difference , contexts , countries , demographic differences, international differences, culture differences .•For researchers interested in social media we present a dataset that is the first to examine SMM activities role in predicting brand loyalty and revisit intentions while accounting for the effect of brand trust.1The data produced here resulted from surveying SMM activities on brand loyalty and revisit intention while considering the mediating role of brand trust at a five-star hotel in Northern Cyprus through employing a 5-Likert scale. The social media marketing activities in our study context refer to a new framework that has already been developed by previous scholars 2, the five hotels in this data article selection was based those with the biggest bed capacity hotels in Kyrenia city The data sample was drawn from hotel customers of selected five (5) hotels in Kyrenia city from the list of 19 five stars hotels in northern Cyprus [3], the five hotels in this data article selection was based those with the biggest bed capacity hotels in Kyrenia city with minimum 500 beds. The number of valid responses was 389.The dataset presented a quantitative study based on experiment design. The data article examined the hospitality service industry focusing on five-star hotels in Kyrenia city in Northern Cyprus. The total population of five stars hotels customers derived is 789,903 tourists in 2017 [3]x2) measure, goodness-of-fit index (GFI), comparative fit index (CFI), Normed fit index (NFI), adjusted goodness of fit (AGFI) and the root mean square error of approximation (RMSEA). All model fit indices match with cut-off values depend on recommendations commonly used in literature 2 = 2.20 < 3, CFI = 0.92 > 0.90, NFI = 0.92 > 80, CFI = 0.95 > 80,AGFI = 0.92 > 80, RMSEA = 0.05 < 0.08, and PCLOSE = 0.10 > 0.05. Finally, The authors used Confirmatory factor analysis (CFA) and goodness of fit indices to examine the validity of the measurement model. Several model indices were tested namely: (The discriminant validity has been tested by adhering to tested recommendations Accessible in Final analysis step is produced in


Ocean gliders are a quiet and efficient mobile autonomous platform for passive acoustic monitoring and oceanographic measurements in remote marine environments. During July 20—August 6 2012, we used a Teledyne Webb Research Slocum G2 glider equipped with a hydrophone logging system to record ocean sound along a 458 km north to south traverse of the outer continental shelf break along the U.S. Pacific Northwest coast. Glider derived recordings yielded a unique perspective on the variation of ambient sound with depth, where natural wind generated surface processes were identified as a dominant acoustic contributor to spectral levels in the region. Near and far-field vessel radiated noise were also found to add significant energy to ambient conditions. Spatially distributed measurements of ambient sound levels recorded from the glider were consistent with long-term spectral estimates from fixed station, deep ocean hydrophone array measurements during the 1990–2000’s in the region. Ocean sound level measurements captured by a mobile glider are shown to be an effective and valuable asset for describing ocean surface wind conditions and characterizing spatial and temporal changes in the underwater acoustic environment over a broad regional scale. UsingThe use of ocean gliders as mobile platforms for bioacoustic recordings coupled with other in-situ environmental oceanographic sensors  has provided important new ecological insights for behavioral studies of low frequency baleen whales –8, high Here we demonstrate a unique application for glider-based, passive acoustic recordings to measure spatial and short-term temporal patterns in underwater ambient sound levels along a broad region of the North American continental shelf break. Acoustic characterizations and sound level measurements in the northeast Pacific from fixed hydrophone stations have provided for robust time series descriptions of noise levels and long-term comparisons spanning decades –17. The th-August 6th) from north to south along the 1000 m depth contour of the outer continental shelf break. Over the course of this nearly three-week period, the glider performed a continuous series of 402 sawtooth dive profiles (i.e. “yo’s”) to a median dive depth of 625 m while covering a horizontal distance of 458 km at an average horizontal speed of 32 cm/s. The glider was not operated to its maximum depth rating of 1000 m to avoid collision with bathymetric complexity along the shelf break.In 2012, the National Oceanic and Atmospheric Administration’s (NOAA) Pacific Marine Environmental Laboratory operated a 1000-m-rated, Teledyne Marine G2 Slocum ocean glider down the continental shelf break from Grays Harbor, Washington to Brookings, Oregon . The glire 1V/1μPa) fastened externally on the dorsal mid-section of the glider body using a semi-soft rubber mount. Previous work has shown that acoustic receiver performance from a hydrophone mounted on the dorsal mid-section of the Slocum glider is largely unaffected by the vehicle attitude and orientation [fc) at 4400 Hz and a stop band rejection ratio of -82 dB at 1.5 x fc. The conditioned signal was then digitized by a data logging system at 10 kHz sample rate and 16-bit resolution. Data is recorded continuously with each acoustic data file 12 MB in size and archived every 10 minutes from buffer storage onto a 32 GB compact flashcard. At the apogee of every third dive in a sequence, the glider would rise to the sea surface to transmit summary data of oceanographic measurements , turbidity, chlorophyll), navigation data  location, heading) and glider system diagnostics  back to shore via satellite telemetry. The acoustic system, with the exception of battery power, was installed independent of the glider scientific logging system and therefore does not share circuitry that could introduce noise from other instrument sampling. Due to the large size of the files, no acoustic data was transmitted in real time during glider surfacing.The glider was outfitted with a single, calibrated, omni-directional hydrophone  and long (~0.6 sec) durations. The longer duration rudder noise was observed most often at the top of dives within the first 100 m of the glider descent, resulting from sustained course corrections in the stronger near surface currents. Meanwhile, the shorter duration rudder noise was found more consistently throughout the dive profiles associated with minor course adjustments. To address rudder noise contamination, rudder-generated signals were identified and removed from the acoustic time series through an automated energy summation detection algorithm [2/Hz in particular frequency bands most influenced by the rudder sound.An electric servo-motor controls the glider’s rudder for directional adjustments, emitting distinct sounds at irregular intervals that are not logged by the glider’s navigation system. Rudder noise contamination in the acoustic record was characterized by a wide-band signal  with elelgorithm , and weiAnother problematic source of system noise contamination was generated by the buoyancy engine pumping oil to and from the external bladder reservoir and glider housing to initiate dives and ascents. The glider pump noise at the top and bottom of the dives consists of a long duration (10’s of seconds), high amplitude mechanical sound that is easily recognized [https://coastwatch.pfeg.noaa.gov/). ASCAT winds tracked the glider throughout its mission, providing more temporally coarse, but spatially consistent daily averages of local surface wind measurements on a 0.25° spatial scale.Wind surface measurements during the glider mission were collected from a NOAA surface buoy, National Data Buoy Center (NDBC) station 46089, 160 km WNW off the coast of Tillamook, Oregon . Buoy wihttps://marinecadastre.gov/ais/). Raw AIS fixes including latitude, longitude, and time for a variety of vessels  were compiled for the glider area during the mission time frame and then transformed into daily tracklines (https://marinecadastre.gov/ais/). A daily metric for localized vessel activity was calculated using the density of tracklines within a 50 km buffer of the daily median glider position. A 50 km buffer distance for local ship traffic was used to account for the coarse temporal and spatial resolution associated with a daily median values for the glider location and daily vessel tracks. This is based on a doubling of the average total distance traveled by the glider during a single day (27 km/day). Using a coarse transmission loss approximation TL = 15 log10(r), where TL is transmission loss and r is range from source to receiver, a 50 km daily buffer size accounts for 70 dB of transmission loss from vessel traffic sources to the glider at the most extreme ranges. This is a conservative estimate based on this simple approximation as spreading loss is likely much higher when considering propagation paths. Therefore, the loudest, low frequency ship noise radiated from large commercial vessels with source levels near 180 dB re 1 μPa [re 1 μPa even at the most extreme ranges. The daily value of the total number of combined vessel-trackline kilometers within the 50 km buffer of the glider position was used for comparisons with acoustic conditions to determine the influence of localized ship traffic on ambient sound levels. Buffer ranges up to 80 km were explored without significant changes in results due to propagation energy loss effects.Vessel traffic information surrounding the glider mission space was downloaded from NOAA and the Bureau of Ocean Energy Management’s (BOEM) MarineCadastre.gov automatic identification system (AIS) database  airgun explosions during this time (R/V Langseth had completed their survey and the glider was located off the Columbia River (46.1°N) where it continued its southward progression recording at levels within the dynamic range of the logging system for the duration of the mission.Acoustic data were converted to absolute pressure using a calibration correction for the hydrophone acquisition system response and sensitivity prior to analysis. Acoustic power spectral density (PSD) was calculated using a 4-second hanning window, no padding and no overlap resulting in PSD values that when combined with the average vehicle speed of 32 cm/s provide a spatial resolution on the order of 1.25 meter. Spectral levels from 10 Hz to the upper corner frequency roll-off  mission  show pershington . The glihis time . When thth percentile curves representing “typical” acoustic conditions within the distributions remained consistent between the mid and deep water depths indicating there was little change in the frequency distribution of acoustic energy along the glider depth profiles below 200 m. Within the shallow depth interval (< 200 m), energy levels are less well constrained with lower levels of probability distributed more broadly around the median acoustic energy value at each frequency. In the shallow SPD for f < 30 Hz, some noise levels are observed at much lower amplitudes than the mid and deep water recordings. This is likely a flow noise effect as the glider speed is reduced near the surface. Additionally, the glider never reached the depths of the low velocity sound channel where more efficient propagation from distant low frequency sources could strongly affect received levels as a function of depth. Instead, spectral distributions show a characteristic broad, low frequency rise at all depths centered between 40 Hz < f < 60 Hz that we attribute to persistent regional scale commercial shipping activity [A detailed example of the acoustic effects of nearby ship-radiated noise is shown in activity , 27. SPDNon-acoustic pressure fluctuations at the hydrophone sensor interface caused by turbulent eddy vortex shedding from the glider as it moves through the water results in flow-noise contamination in certain low frequencies of the acoustic recordings. The significant change to an upward slope in spectral levels below ~ 50 Hz  is attriSeasonal changes in surface wind speeds can greatly alter the underwater acoustic environment, substantially raising or lowering ocean sound levels within a region , 28, 29.NLf) at a particular frequency or within a band is a function of the empirically derived parameters Bf and nf and the logarithm of the wind speed v. Band averaged glider derived sound levels in the frequencies most influenced by surface winds (1–1.05 kHz) [BkHz)(1−1.05 = 54.4 and nkHz) including a majority of values falling within the 95% confidence intervals (f) of wind-dependent noise levels have typically been reported in the range of 0.5–1 for higher frequencies. The shallower spectral slope observed in this study (nf = 0.37) is largely a consequence of including contributions from non-wind dependent acoustic sources (e.g. vessel noise) in the model fit.where the noise level (.05 kHz) , 29 are ntervals  resultinf < 100 Hz) [2/Hz (f < 100 Hz) with superimposed changes in ship band energy levels resulting from noise radiated from local (< 50 km) vessel activity.As a result of the reduced effects of wind induced surface wave breaking on underwater low frequency ( 100 Hz)  acoustic 100 Hz) , 37 and  100 Hz)  showed tz) [2/Hz  only thez) [2/Hz . AdditioScatter plots of wind and ship band sound levels  show theLagenorhynchus obliquidens) whistles [Megaptera novaeangliae)  . These cangliae) . Vocal eangliae)  & 5a andth percentile) from over 12 years of data (1994–2007) at a deepwater, fixed hydrophone array off the Oregon coast [et al.’s 12 year median values for frequencies between 50–200 Hz  to further constrain this relationship. Furthermore, the transition from wind driven noise processes observed between 4–10 m/s to a second regime with wind speeds 10–20 m/s involving insulating bubble clouds and other high wind generated phenomenon near the surface need to be accounted for in order to properly describe the full range of influence on wind-dependent sound levels .Results from this study and another in the Mediterranean Sea , have shIn addition to sea surface wind speed estimates, we’ve also shown that glider derived acoustic measurements are effective for characterizing regional-scale variability of ocean sound levelsin the northeast Pacific. Ocean gliders provide capacity for filling knowledge gaps on acoustic conditions where information is limited. Repeated glider surveys within a region of concern for acoustic habitat degradation can provide important real-time measurements of changing noise levels resulting from anthropogenic activities. Considering alternate mobile platforms capable of noise level assessments over large, remote ocean areas, ocean gliders provide significant advantages. For instance, the Wave Glider is a highly efficient, far ranging unmanned surface vehicle (USV) with towed hydrophone system capabilities. Nevertheless, the surface limited vehicle tows hydrophones at fixed, shallow depth (~10m), where rS1 Appendix(DOCX)Click here for additional data file.S1 FigUpper panel shows the raw acoustic waveform with rudder generated noise in black compared with the filtered and smoothed time series in gray. The lower panel shows the power spectra before and after the algorithm is applied, illustrating the significant reduction in rudder-generated self-noise contamination.(TIF)Click here for additional data file.


As anticipated, immune phenotyping experiments demonstrate that CD19−/− mice develop a severe B cell deficiency in gut-associated lymphoid tissues that result in significant reductions to antibody concentrations in the gut lumen. Antibody deficiency was associated with defective anti-commensal IgA responses and the outgrowth of anaerobic bacteria in the gut. Expansion of anaerobic bacteria coincides with the development of a chronic inflammatory condition in the gut of CD19−/− mice that results in an intestinal malabsorption characterized by defects in lipid metabolism and transport. Administration of the antibiotic metronidazole to target anaerobic members of the microbiota rescues mice from disease indicating that intestinal malabsorption is a microbiota-dependent phenomenon. Finally, intestinal malabsorption in CD19−/− mice is a gluten-sensitive enteropathy as exposure to a gluten-free diet also significantly reduces disease severity in CD19−/− mice. Collectively, these results support an effect of antibody deficiency on steady-state gut physiology that compliment emerging data from human studies linking IgA deficiency with non-infectious complications associated with CVID. They also demonstrate that CD19−/− mice are a useful model for studying the role of B cell deficiency and gut dysbiosis on gluten-sensitive enteropathies; a rapidly emerging group of diseases in humans with an unknown etiology.Primary immunodeficiencies are heritable disorders of immune function. CD19 is a B cell co-receptor important for B cell development, and CD19 deficiency is a known genetic risk factor for a rare form of primary immunodeficiency known as “common variable immunodeficiency” (CVID); an antibody deficiency resulting in low levels of serum IgG and IgA. Enteropathies are commonly observed in CVID patients but the underlying reason for this is undefined. Here, we utilize CD19 Primary immunodeficiencies are heritable disorders of immune system function that most commonly manifest as antibody deficiencies . Over twIgA is by far the most abundantly secreted antibody in the gut and several structural features distinguish secretory IgA from other immunoglobulins as well as from its monomeric form found in serum. Secretory IgA is dimeric due to incorporation of the J-chain during secretion from plasma cells, is heavily glycosylated, and is found complexed with secretory component (SC); a cleavage product of the polymeric Ig receptor (PIgR) that facilitates transcytosis of IgA through gut epithelial cells , 9. SC iCVID patients are acutely susceptible to recurrent pneumococcal infection of the respiratory tract because of a lack of natural serum IgG and IgM antibodies reactive to capsular polysaccharides . However−/− mice present with very similar humoral defects to those observed in human CVID patients   transport and metabolism. Furthermore, antibiotic treatment and exposure to a gluten-free diet rescue CD19−/− mice from malabsorption indicating involvement of both the microbiota and dietary gluten exposure in pathogenesis.From a genetic standpoint, CVID is generally considered a polygenic disorder –17, howeand IgM) . CD81 anand IgM) . CD20 isand IgM) . Here, w−/− mice  were bred and maintained at the University of South Carolina Animal Care Facility. To initially generate this colony, WT C57BL/6 (Jax#000664) and CD19−/− (Jax#006785) mice were purchased from Jackson Laboratories. WT and CD19−/− mice were crossed to generate F1 heterozygotes and were subsequently crossed to re-derive homozygote breeding lines. All animals used in the experiments described here are derived from this colony. Animals were reared and maintained under identical SPF conditions in a single environmentally-controlled room exclusively used to house this mouse colony. Experimental data were derived from mixed groups of male and female mice between the ages of 8 and 16 weeks old. Animals are maintained under constant environmental conditions  and were given ad libitum access to autoclaved drinking water and an irradiated soy-free mouse chow that utilizes gluten as the dominant protein source . For antibiotic exposure experiments, animals were weaned at 3 weeks of age and given ad libitum access to autoclaved drinking water containing 0.5 mg/mL of metronidazole for 5 weeks. Antibiotic water was replaced weekly and stool samples were collected for weekly enumeration of fecal anaerobic CFUs. To test whether malabsorption is a gluten-sensitive enteropathy in CD19−/− mice, animals were exposed at 3 weeks of age until 8 weeks of age to a gluten-free mouse chow that incorporated casein (rather than gluten) as the dominant protein source . To avoid GFD diet contamination animals were maintained in autoclaved cages and housed and handled separately from animals on standard chow. All animal use strictly adhered to federal regulations and guidelines set forth by the University of South Carolina Institutional Animal Care and Use Committee (Protocol#101292).WT C57BL/6 and CD19Peritoneal lavage cell isolation: The peritoneum of euthanized mice were exposed with scissors and a 10 cc syringe was used to fill the peritoneal cavity of animals with 5 mL of sterile 1X HBSS buffer. The body cavity was then gently massaged to dislodge cells. Peritoneal lavage fluids were drawn back into the same syringe and placed in a 15 mL conical tube on ice for downstream processing. Peyer's patch (PP) cell isolation: PPs were collected from animals and a single cell suspension was made by passing cells through a 40 μm cell strainer. Cells were washed once in complete RPMI media  and collected for analysis. Lamina propria cell isolation: Lymphocytes from small intestine or colon lamina propria were isolated via enzymatic digestion of gut tissues as previously described  was conducted as described previously , , , , , . Specific information on the antibodies used in this study is provided in All flow cytometry was carried out on a FACS Aria II instrument (BD). Primary cells derived from PPs, peritoneal lavage, or LP isolations were washed once in ice cold 1X PBS and then re-suspended in 100 μL volumes of staining buffer (1XHBSS + 5 mL FBS + 5 mL 0.5 M EDTA) containing antibodies against cell surface markers to discriminate the following cell subsets:  was splayed open with scissors in a sterile petri dish and loose fecal matter was rinsed from tissues with sterile 1X PBS. Using sterile forceps  the entire mucosal surface was scraped and materials were placed in a bead-beater tube for downstream DNA isolation. DNA from fecal pellets and mucosal scrapings was isolated using the Powerfecal DNA isolation kit (with 3 min bead-beating step) (Qiagen). DNA concentration was measured using a Nanodrop spectrophotometer. The relative abundance of bacterial 16S rRNA gene copies was enumerated on a 7300 qPCR instrument (Applied Biosystems) using the PowerUp SYBR Green Master kit and universal bacterial primers. Total copies of 16S rRNA gene copies were standardized to the amount of input DNA per reaction. For gene expression experiments, ileal tissues  were collected and placed in 1.5 mL microfuge tubes containing 250 μL RNAlater solution (Ambion) and frozen at −80°C until downstream RNA isolation. RNA was extracted from tissues using an RNeasy kit (Qiagen). DNase I digestion (Sigma) was used to remove contaminating DNA from RNA preparations and was subsequently converted to cDNA using the qScript cDNA Synthesis Kit (Quanta). The iTaq Universal SYBR Green Master Mix (Bio-Rad) was used for qRT-PCR reactions and reactions were run on a CFX96 Real-time PCR System (Bio-Rad). Gene expression data was normalized to GAPDH or β-actin housekeeping genes. Primer sets used in this study are as follows: ;  via flow cytometry. The total cellularity of PPs in CD19−/− mice was significantly reduced in comparison to WT controls (+IgDlo−GL7+FAS+) development was significantly reduced in CD19−/− mice as was the abundance of GC-TFH cells (CD4+B220−CXCR5hiPD-1hi) ; B-1b (CD4−B220+CD5−Cd43+)] are also important for generating T-cell-independent anti-commensal IgA responses in the gut  in the effector sites of the gut . Interestingly, CD19−/− mice had significantly reduced abundance of IgA+ plasmablasts in the SiLP but not the CLP, indicating a possible site-specific defect in the recruitment of plasmablasts to the small intestine  . Thus, C the gut . As repo the gut , we conf WT mice , which w WT mice . We nextntestine .−/− mice bound a significantly lower fraction of commensal microbes with IgA than WT animals (3.4% in CD19−/− mice vs. 8.8% in WT mice)  did not reveal any differences between WT and CD19−/− mice in total bacterial abundance within these materials , Staphylococcus spp., Sutterella spp., an undescribed species of alphaproteobacteria, and Bilophila spp.  . In factaterials . Howeverenotypes . To vali metrics . In bothila spp. . In the iniphila . See Sup−/− mice. While we did not see any evidence of inflammation in the colons of CD19−/− mice   and CD8 (CD8α+CD45+) T cell subsets by flow cytometry. Both the percentage of CD4 and CD8 T cells were significantly increased in the gut of CD19−/− mice, while only the absolute abundance of CD8 T cells were significantly increased     (Supplem+ cells) . The maj+ cells) . We perf−/− mice . Finallyncreased . Collect−/− mice we depleted anaerobic bacteria via administration of metronidazole (a bacteriostatic antibiotic with selective activity against anaerobic bacteria) in cohorts of CD19−/− mice. Animals were reared on drinking water containing metronidazole for 5 weeks (from weaning age of 3 weeks to 8 weeks of age) and then malabsorption, fecal bile acid output, and gut permeability were compared to that of untreated CD19−/− mice. Metronidazole treatment significantly reduced the abundance of fecal anaerobic bacteria in CD19−/− mice  is one method for assessing gluten-sensitivity in humans. Given their antibody deficiency, we did not anticipate that CD19−/− mice would produce more total antibodies against gluten antigens. We instead hypothesized that they may have a higher “relative sensitivity” to gluten. To measure “relative sensitivity” we measured the concentrations of anti-gliadin antibodies (AGAs) in mice and correlated these values with total serum antibody titers measured in the same animals. A higher “relative sensitivity” would be indicated by a stronger correlation between AGAs and total serum antibody titers in one genotype over another. The AGA kit utilized measures serum IgG and IgA AGAs so we compared these values with the summed total of serum IgG and IgA titers. As expected, CD19−/− animals had lower total serum IgG and IgA antibodies  which we interpret to mean that CD19−/− mice are more responsive to dietary gluten antigens  despite their antibody deficiency (−/− mice may be due to dietary gluten exposure we modeled a strict gluten-free diet (GFD) by weaning 3-week-old CD19−/− mice onto a modified gluten-free mouse chow. This chow substitutes casein for gluten as the dominant dietary protein source. Five weeks after initial GFD-exposure we sacrificed mice and measured malabsorption histologically. Results from our experiments indicate that exposure to a GFD rescues the malabsorption phenotype in CD19−/− mice to a similar degree that metronidazole treatment does (6B). Exposure to GFD also reduced the number of mucosally-associated T cells observed in the ileum of CD19−/− mice by immunohistochemistry  a common feature to all gluten-sensitive enteropathies is increased activation of T cell responses in the gut. We demonstrate that CD19−/− mice (consistent with studies on the systemic compartment of these mice) have developmental abnormalities in the both the B2 and B1 B cell compartments relevant to anti-commensal antibody responses. Both B cell subsets are important for barrier integrity by contributing to the luminal IgA pool. Whether the absence of B1 vs. B2 cells from the gut are more important for controlling abberrant T cell activation and ensuing intestinal malabsorption is not currently known. We believe that much of the aberrant T cell response associated with B cell deficiency arises due to enhanced exposure of T cells in the gut to luminal antigens. However, B cells may also dampen immune responses through the secretion of IL10  and both splenic B cells and B cells from the peritoneal cavity have been shown to control inflammatory T cell responses in chemically-induced models of colitis though this pathway , which supports these authors conclusions that lipid malabsorption observed in CVID patients may result from a physiological tradeoff between the gut epitheliums need to regulate its' own immunity at the expense of its metabolic function under conditions of antibody deficiency. However, despite a genetic signature of malabsorption, these authors were unable to detect any of the classic histological features of malabsorption in the jejunum of their animals, whereas we do see histological features of malabsorption in the ileums of CD19−/− mice. This discrepancy could be due to differences in the microbial communities between animal facilities that drive a more severe malabsorption in our animals, or due to differences in where malabsorption presents histologically in a mouse compared to humans. More recently, another study by Shulzhenko et al. compared transcriptomic profiles from duodenal biopsies collected from CVID patients presenting with an enteropathy (E-CVID patients)  vs. CVID patients without enteropathy (noE-CVID patients). Consistent with results from our mouse model and the mouse models and CVID patients described by the Matzinger group, these authors again found that upregulation of immunity genes in E-CVID patients was inversely correlated with lipid metabolism genes, whereas this was not observed in noE-CVID patients . SIBO is characterized as a pathological overgrowth of anaerobic bacteria in the human small intestine . We would also like to acknowledge that the GFD used in this study was not completely nutritionally matched to the gluten-containing mouse chow so it is possible that other nutritional differences could play a role in the protective effect observed in our mice. However, given the fact that simply eliminating gluten from the diet leads to complete remission of gut inflammation in the vast majority of celiac patients (that are otherwise eating highly diversified diets) we would conclude that the effect is driven by the absence of dietary gluten. Moreover, even with the use of a completely nutritionally-matched diet, lipid malabsorption in CD19−/− mice profoundly alters the biochemical environment of the gut making it impossible to say that gluten antigens alone are the specific driver of inflammation . Experiments are underway in our lab to address these points. Despite these limitations, we believe that the CD19−/− mouse will be useful for future research on how gut dysbiosis driven by primary antibody deficiency influences gluten-sensitive enteropathies; a rapidly emerging group of related diseases in humans with a currently undefined etiology.Results from our experiments indicate that intestinal malabsorption in CD19the diet . There athe diet . All thrthe diet –68. An ithe diet . Howeverthe diet  and onlythe diet . Thus, ithe diet . Furtherthe diet , and selthe diet –77. Therpeptides . AdditioAll datasets generated for this manuscript are provided in the manuscript or supplementary materials. Raw 16S fastq files will be made available upon request.The animal study was reviewed and approved by University of South Carolina Institutional Animal Care and Use Committee.AM conducted all experiments shown and assisted in manuscript writing. MK conducted experiments and assisted in manuscript writing. IC performed all histological analyses of ileal samples. DC, KW-K, and MP performed bacterial CFU enumeration and qPCR of bacteria. RE and AM were involved in bacterial SCFA measurements and DEXA scanning of animals. GG and CO provided advice and assistance with mast cell work. GG assisted in flow cytometric analysis of peritoneal mast cells. CO and AA performed qPCR of Cpa3 gene. ZP, YC, and SH performed blinded colon inflammation scoring. AJ performed all mouse husbandry and ran fecal ELISAs to quantify antibody titers. JK conceived of all experiments, oversaw all aspects of the work detailed here, and wrote the first and final draft of this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.