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Background Cystic fibrosis (CF) is a genetic disease caused by mutations in the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR), which results in impaired airway mucociliary clearance, inflammation, infection, and respiratory insufficiency. The development of new therapeutics for CF are limited by the lack of reliable preclinical models that recapitulate the structural, immunological, and bioelectrical features of human CF lungs. Methods We leveraged organ-on-a-chip technology to develop a microfluidic device lined by primary human CF bronchial epithelial cells grown under an air-liquid interface and interfaced with pulmonary microvascular endothelial cells (CF Airway Chip) exposed to fluid flow. The responses of CF and healthy Airway Chips were analyzed in the presence or absence of polymorphonuclear leukocytes (PMNs) and the bacterial pathogen, Pseudomonas aeruginosa. Results The CF Airway Chip faithfully recapitulated many features of the human CF airways, including enhanced mucus accumulation, increased cilia density, and a higher ciliary beating frequency compared to chips lined by healthy bronchial epithelial cells. The CF chips also secreted higher levels of IL-8, which was accompanied by enhanced PMN adhesion to the endothelium and transmigration into the airway compartment. In addition, CF Airway Chips provided a more favorable environment for Pseudomonas aeruginosa growth, which resulted in enhanced secretion of inflammatory cytokines and recruitment of PMNs to the airway. Conclusions The human CF Airway Chip may provide a valuable preclinical tool for pathophysiology studies as well as for drug testing and personalized medicine.

In this work we explore confidence elicitation methods for crowdsourcing "soft" labels, e.g., probability estimates, to reduce the annotation costs for domains with ambiguous data. Machine learning research has shown that such "soft" labels are more informative and can reduce the data requirements when training supervised machine learning models. By reducing the number of required labels, we can reduce the costs of slow annotation processes such as audio annotation. In our experiments we evaluated three confidence elicitation methods: 1) "No Confidence" elicitation, 2) "Simple Confidence" elicitation, and 3) "Betting" mechanism for confidence elicitation, at both individual (i.e., per participant) and aggregate (i.e., crowd) levels. In addition, we evaluated the interaction between confidence elicitation methods, annotation types (binary, probability, and z-score derived probability), and "soft" versus "hard" (i.e., binarized) aggregate labels. Our results show that both confidence elicitation mechanisms result in higher annotation quality than the "No Confidence" mechanism for binary annotations at both participant and recording levels. In addition, when aggregating labels at the recording level, results indicate that we can achieve comparable results to those with 10-participant aggregate annotations using fewer annotators if we aggregate "soft" labels instead of "hard" labels. These results suggest that for binary audio annotation using a confidence elicitation mechanism and aggregating continuous labels we can obtain higher annotation quality, more informative labels, with quality differences more pronounced with fewer participants. Finally, we propose a way of integrating these confidence elicitation methods into a two-stage, multi-label annotation pipeline.

Most bacterial vaccines work for a subset of bacterial strains or require the modification of the antigen or isolation of the pathogen before vaccine development. Here we report injectable biomaterial vaccines that trigger potent humoral and T-cell responses to bacterial antigens by recruiting, reprogramming and releasing dendritic cells. The vaccines are assembled from regulatorily approved products and consist of a scaffold with absorbed granulocyte-macrophage colony-stimulating factor and CpG-rich oligonucleotides incorporating superparamagnetic microbeads coated with the broad-spectrum opsonin Fc-mannose-binding lectin for the magnetic capture of pathogen-associated molecular patterns from inactivated bacterial-cell-wall lysates. The vaccines protect mice against skin infection with methicillin-resistant Staphylococcus aureus, mice and pigs against septic shock from a lethal Escherichia coli challenge and, when loaded with pathogen-associated molecular patterns isolated from infected animals, uninfected animals against a challenge with different E. coli serotypes. The strong immunogenicity and low incidence of adverse events, a modular manufacturing process, and the use of components compatible with current good manufacturing practice could make this vaccine technology suitable for responding to bacterial pandemics and biothreats.

The Sounds of New York City (SONYC) project (2016–2022) was a project to monitor and mitigate urban noise pollution using a smart acoustic sensor network, citizen scientists, and collaboration with city agencies. During its lifetime, the project deployed 75 fixed-location sensors and collected over 150 × 106 10-s audio recordings. A key element of the project was the development of machine listening models to detect the sources of noise pollution rather than just the overall noise level. In this talk, we first discuss our initial approach to data collection and machine listening including sensor development and deployment, citizen-science data annotation, self-supervised audio representation learning, and downstream sound-event detection model training. We then discuss analysis results using the outputs of these models, followed by the challenges and limitations of our initial approach. Finally, we discuss our solutions to overcome those challenges, such as citizen-deployed sensors, source-specific loudness estimation, and few-shot sound-event detection.

Noise is one of the primary quality-of-life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low-cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytical challenges. One way to address these challenges is through machine listening techniques, which are used to extract features in attempts to classify the source of noise and understand temporal patterns of a city's noise situation. However, the overwhelming number of noise sources in the urban environment and the scarcity of labeled data makes it nearly impossible to create classification models with large enough vocabularies that capture the true dynamism of urban soundscapes In this paper, we first identify a set of requirements in the yet unexplored domain of urban soundscape exploration. To satisfy the requirements and tackle the identified challenges, we propose Urban Rhapsody, a framework that combines state-of-the-art audio representation, machine learning, and visual analytics to allow users to interactively create classification models, understand noise patterns of a city, and quickly retrieve and label audio excerpts in order to create a large high-precision annotated database of urban sound recordings. We demonstrate the tool's utility through case studies performed by domain experts using data generated over the five-year deployment of a one-of-a-kind sensor network in New York City., Comment: Accepted at EuroVis 2022. Source code available at: https://github.com/VIDA-NYU/Urban-Rhapsody

Current therapeutic strategies against bacterial infections focus on reduction of pathogen load through antibiotics; however, stimulation of host tolerance to infection might offer an alternative approach. Here we used computational transcriptomics and a Xenopus embryo infection model to rapidly discover infection response pathways, identify potential tolerance inducer drugs, and validate their ability to induce broad tolerance. Xenopus embryos exhibit natural tolerance to A. baumanii, K. pneumoniae, S. aureus, and S. pneumoniae bacteria, whereas A. hydrophila and P. aeruginosa produce infection that leads to death. Transcriptional profiling led to definition of a 20-gene signature that allows for discrimination between tolerant and susceptible states, as well as identification of active and passive tolerance responses based on the degree of engagement of gene transcription modulation. Upregulation of metal ion transport and hypoxia pathways reminiscent of responses observed in primate and mouse infection models were identified as tolerance mediators, and drug screening in the susceptible A. hydrophila infection model confirmed that a metal chelator (deferoxamine) and HIF-1α agonist (1,4-DPCA) increase embryo survival despite high pathogen load. These data demonstrate the value of combining the Xenopus embryo infection model with multi-omics analyses for mechanistic discovery and drug repurposing to induce host tolerance to bacterial infection.

BackgroundCystic fibrosis (CF) is a genetic disease caused by mutations in the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR), which results in impaired airway mucociliary clearance, inflammation, infection, and respiratory insufficiency. The development of new therapeutics for CF are limited by the lack of reliable preclinical models that recapitulate the structural, immunological, and bioelectrical features of human CF lungs.MethodsWe leveraged organ-on-a-chip technology to develop a microfluidic device lined by primary human CF bronchial epithelial cells grown under an air-liquid interface and interfaced with pulmonary microvascular endothelial cells (CF Airway Chip) exposed to fluid flow. The responses of CF and healthy Airway Chips were analyzed in the presence or absence of polymorphonuclear leukocytes (PMNs) and the bacterial pathogen, Pseudomonas aeruginosa.ResultsThe CF Airway Chip faithfully recapitulated many features of the human CF airways, including enhanced mucus production, increased cilia density and a higher ciliary beating frequency compared to chips lined by healthy bronchial epithelial cells. The CF chips also secreted higher levels of IL-8, which was accompanied by enhanced PMN adhesion to the endothelium and transmigration into the airway compartment. In addition, CF Airway Chips provided a more favorable environment for Pseudomonas aeruginosa growth, which resulted in enhanced secretion of inflammatory cytokines and recruitment of PMNs to the airway.ConclusionsThe human CF Airway Chip may provide a valuable preclinical tool for pathophysiology studies as well as for drug testing and personalized medicine.

Embedding models that encode semantic information into low-dimensional vector representations are useful in various machine learning tasks with limited training data. However, these models are typically too large to support inference in small edge devices, which motivates training of smaller yet comparably predictive student embedding models through knowledge distillation (KD). While knowledge distillation traditionally uses the teacher’s original training dataset to train the student, we hypothesize that using a dataset similar to the student’s target domain allows for better compression and training efficiency for the said domain, at the cost of reduced generality across other (non-pertinent) domains. Hence, we introduce Specialized Embedding Approximation (SEA) to train a student featurizer to approximate the teacher’s embedding manifold for a given target domain. We demonstrate the feasibility of SEA in the context of acoustic event classification for urban noise monitoring and show that leveraging a dataset related to this target domain not only improves the baseline performance of the original embedding model but also yields competitive students with >1 order of magnitude lesser storage and activation memory. We further investigate the impact of using random and informed sampling techniques for dimensionality reduction in SEA.

Supervised learning for audio classification typically imposes a fixed class vocabulary, which can be limiting for real-world applications where the target class vocabulary is not known a priori or changes dynamically. In this work, we introduce a few-shot continual learning framework for audio classification, where we can continuously expand a trained base classifier to recognize novel classes based on only few labeled data at inference time. This enables fast and interactive model updates by end-users with minimal human effort. To do so, we leverage the dynamic few-shot learning technique and adapt it to a challenging multi-label audio classification scenario. We incorporate a recent state-of-the-art audio feature extraction model as a backbone and perform a comparative analysis of our approach on two popular audio datasets (ESC-50 and AudioSet). We conduct an in-depth evaluation to illustrate the complexities of the problem and show that, while there is still room for improvement, our method outperforms three baselines on novel class detection while maintaining its performance on base classes.

In the early years of music information retrieval (MIR), research problems were often centered around conceptually simple tasks, and methods were evaluated on small, idealized data sets. A canonical example of this is genre recognition-i.e., Which one of n genres describes this song?-which was often evaluated on the GTZAN data set (1,000 musical excerpts balanced across ten genres) [1]. As task definitions were simple, so too were signal analysis pipelines, which often derived from methods for speech processing and recognition and typically consisted of simple methods for feature extraction, statistical modeling, and evaluation. When describing a research system, the expected level of detail was superficial: it was sufficient to state, e.g., the number of mel-frequency cepstral coefficients used, the statistical model (e.g., a Gaussian mixture model), the choice of data set, and the evaluation criteria, without stating the underlying software dependencies or implementation details. Because of an increased abundance of methods, the proliferation of software toolkits, the explosion of machine learning, and a focus shift toward more realistic problem settings, modern research systems are substantially more complex than their predecessors. Modern MIR researchers must pay careful attention to detail when processing metadata, implementing evaluation criteria, and disseminating results.

In the context of automatic speech recognition and acoustic event detection, an adaptive procedure named per-channel energy normalization (PCEN) has recently shown to outperform the pointwise logarithm of mel-frequency spectrogram (logmelspec) as an acoustic frontend. This letter investigates the adequacy of PCEN for spectrogram-based pattern recognition in far-field noisy recordings, both from theoretical and practical standpoints. First, we apply PCEN on various datasets of natural acoustic environments and find empirically that it Gaussianizes distributions of magnitudes while decorrelating frequency bands. Second, we describe the asymptotic regimes of each component in PCEN: temporal integration, gain control, and dynamic range compression. Third, we give practical advice for adapting PCEN parameters to the temporal properties of the noise to be mitigated, the signal to be enhanced, and the choice of time-frequency representation. As it converts a large class of real-world soundscapes into additive white Gaussian noise, PCEN is a computationally efficient frontend for robust detection and classification of acoustic events in heterogeneous environments.

The global COVID-19 pandemic motivates accelerated research to develop safe and efficacious vaccines. To address this need, we leveraged a biomaterial vaccine technology that consists of mesoporous silica rods (MSRs) that provide a sustained release of granulocyte-macrophage colony-stimulating factor (GM-CSF) and adjuvants to concentrate and mature antigen-presenting cells at the vaccine site. Here we explored the humoral responses resulting from the use of monophosphoryl lipid A (MPLA) as the adjuvant and SARS-CoV-2 spike proteins S1, S2, the nucleocapsid (N) protein, and receptor binding domain (RBD) as the target antigens. The dose of antigen and impact of pre-manufacturing of vaccines as versus loading antigen just-in-time was explored in these studies. Single shot MSR vaccines induced rapid and robust antibody titers to the presented antigens, even without the use of a boost, and sera from vaccinated animals demonstrated neutralizing activity against a SARS-CoV-2 pseudovirus. Overall, these results suggest the MSR vaccine system may provide potent protective immunity when utilized to present SARS-CoV-2 antigens.

Broad spectrum vaccines could provide a solution to the emergence of antibiotic resistant microbes, pandemics and engineered biothreat agents. Here, we describe a modular vaccine (composite infection vaccine technology (ciVAX)) which can be rapidly assembled and in which 4 of the 5 components are already approved for human use. ciVAX consists of an injectable biomaterial scaffold with factors to recruit and activate dendritic cells (DC) in vivo and microbeads conjugated with the broad-spectrum opsonin Fc-Mannose-binding Lectin (FcMBL) that is pre-bound to polysaccharide-rich cell wall antigens, such as the pathogen-associated molecular patterns (PAMPs) fractions, captured from whole inactivated bacteria. Vaccination of mice and rabbits with ciVAX generates potent humoral and T cell responses to PAMPs isolated from native antibiotic-resistant E. coli and S. aureus, and ciVAX protects mice and pigs against lethal E coli challenge in sepsis and septic shock models. In addition to the efficacy of ciVAX against homologous challenge, PAMPS isolated from an infected animal protects other animals against infection by heterologous challenge using different E. coli serotypes – demonstrating the potential for use of ciVAX in controlling pandemics. The advantage of the ciVAX technology is the strong immunogenicity with limited reactogenicity, the use of inactivated pathogens, and the modular manufacture using cGMP approved products which can be stockpiled ready for the next pandemic.One Sentence SummaryBiomaterial vaccine induces strong immunogenicity, weak reactogenicity, and protects from E. coli sepsis in rodents and pigs, and MRSA skin abscess.

Data-driven approaches to automatic drum transcription (ADT) are often limited to a predefined, small vocabulary of percussion instrument classes. Such models cannot recognize out-of-vocabulary classes nor are they able to adapt to finer-grained vocabularies. In this work, we address open vocabulary ADT by introducing few-shot learning to the task. We train a Prototypical Network on a synthetic dataset and evaluate the model on multiple real-world ADT datasets with polyphonic accompaniment. We show that, given just a handful of selected examples at inference time, we can match and in some cases outperform a state-of-the-art supervised ADT approach under a fixed vocabulary setting. At the same time, we show that our model can successfully generalize to finer-grained or extended vocabularies unseen during training, a scenario where supervised approaches cannot operate at all. We provide a detailed analysis of our experimental results, including a breakdown of performance by sound class and by polyphony., Comment: ISMIR 2020 camera-ready

Audio annotation is key to developing machine-listening systems; yet, effective ways to accurately and rapidly obtain crowdsourced audio annotations is understudied. In this work, we seek to quantify the reliability/redundancy trade-off in crowdsourced soundscape annotation, investigate how visualizations affect accuracy and efficiency, and characterize how performance varies as a function of audio characteristics. Using a controlled experiment, we varied sound visualizations and the complexity of soundscapes presented to human annotators. Results show that more complex audio scenes result in lower annotator agreement, and spectrogram visualizations are superior in producing higher quality annotations at lower cost of time and human labor. We also found recall is more affected than precision by soundscape complexity, and mistakes can be often attributed to certain sound event characteristics. These findings have implications not only for how we should design annotation tasks and interfaces for audio data, but also how we train and evaluate machine-listening systems.

The purpose of this research was to assess variability in pharmacokinetic profiles (PK variability) in preclinical species and identify the risk factors associated with the properties of a drug molecule that contribute to the variability. Exposure data in mouse, rat, dog, and monkey for a total of 16,592 research compounds studied between 1999 and 2013 were included in the analysis. Both in vivo study parameters and in silico/experimental physicochemical properties of the molecules were analyzed. Areas under the plasma concentration vs time curves (AUC) were used to assess PK variability. PK variability was calculated as the ratio of the highest AUC within a defined set of AUC values (AUCmax) over the lowest AUC within that set (AUCmin). Both intra- and inter-animal variability were analyzed, with intra-animal exposures found to be more variable than inter-animal exposures. While several routes of administration were initially studied, the analysis was focused on the oral route, which corresponds to the l...

Self-supervised representation learning with deep neural networks is a powerful tool for machine learning tasks with limited labeled data but extensive unlabeled data. To learn representations, self-supervised models are typically trained on a pretext task to predict structure in the data (e.g. audio-visual correspondence, short-term temporal sequence, word sequence) that is indicative of higher-level concepts relevant to a target, downstream task. Sensor networks are promising yet unexplored sources of data for self-supervised learning—they collect large amounts of unlabeled yet timestamped data over extended periods of time and typically exhibit long-term temporal structure (e.g., over hours, months, years) not observable at the short time scales previously explored in self-supervised learning (e.g., seconds). This structure can be present even in single-modal data and therefore could be exploited for self-supervision in many types of sensor networks. In this work, we present a model for learning audio representations by predicting the long-term, cyclic temporal structure in audio data collected from an urban acoustic sensor network. We then demonstrate the utility of the learned audio representation in an urban sound event detection task with limited labeled data.

Monitoring health and noise pollution in urban environments often entails deploying acoustic sensor networks to passively collect data in public spaces. Although spaces are technically public, people in the environment may not fully realize the degree to which they may be recorded by the sensor network, which may be perceived as a violation of expected privacy. Therefore, we propose a method to anonymize and blur the voices of people recorded in public spaces–a novel, yet increasingly important task as acoustic sensing becomes ubiquitous in sensor-equipped smart cities. This method is analogous to Google’s face blurring in its Street View photographs, which arose from similar concerns in the visual domain. The proposed blurring method aims to anonymize voices by removing both the linguistic content and personal identity from voices, while preserving the rest of the acoustic scene.The method consists of a three-step process. First, voices are separated from non-voice content by a deep U-Net source separation model. Second, we evaluate two approaches to obscure the identity and intelligibility of the extracted voices: a low pass filter to remove most of the formants in the voices, and an inversion of Mel-Frequency Cepstral Coefficients (MFCC). Finally, the blurred vocal content is mixed with the separated non-vocal signal to reconstruct the acoustic scene. Using background recordings from a real urban acoustic sensor network in New York City, we present a complete evaluation of our method, with automatic speech recognition, speaker identification, sound event detection, and human perceptual evaluation.

Crowdsourcing and active learning have been combined in the past with the goal of reducing annotation costs. However, two issues arise with using AL and crowdsourcing: quality of the labels and user engagement. In this work, we propose a novel machine -> crowd

Annotating rich audio data is an essential aspect of training and evaluating machine listening systems. We approach this task in the context of temporally-complex urban soundscapes, which require multiple labels to identify overlapping sound sources. Typically this work is crowdsourced, and previous studies have shown that workers can quickly label audio with binary annotation for single classes. However, this approach can be difficult to scale when multiple passes with different focus classes are required to annotate data with multiple labels. In citizen science, where tasks are often image-based, annotation efforts typically label multiple classes simultaneously in a single pass. This paper describes our data collection on the Zooniverse citizen science platform, comparing the efficiencies of different audio annotation strategies. We compared multiple-pass binary annotation, single-pass multi-label annotation, and a hybrid approach: hierarchical multi-pass multi-label annotation. We discuss our findings, which support using multi-label annotation, with reference to volunteer citizen scientists' motivations.

There are many sound classification problems that have target classes which are rare or unique to the context of the problem. For these problems, existing data sets are not sufficient and we must create new problem-specific datasets to train classification models. However, annotating a new dataset for every new problem is costly. Active learning could potentially reduce this annotation cost, but it has been understudied in the context of audio annotation. In this work, we investigate active learning to reduce the annotation cost of a sound classification dataset unique to a particular problem. We evaluate three certainty-based active learning query strategies and propose a new strategy: alternating confidence sampling. Using this strategy, we demonstrate reduced annotation costs when actively training models with both experts and non-experts, and we perform a qualitative analysis on 20k unlabeled recordings to show our approach results in a model that generalizes well to unseen data.

Urban noise sensing in deeply embedded devices at the edge of the Internet of Things (IoT) is challenging not only because of the lack of sufficiently labeled training data but also because device resources are quite limited. Look, Listen, and Learn (L3), a recently proposed state-of-the-art transfer learning technique, mitigates the first challenge by training self-supervised deep audio embeddings through binary Audio-Visual Correspondence (AVC), and the resulting embeddings can be used to train a variety of downstream audio classification tasks. However, with close to 4.7 million parameters, the multi-layer L3-Net CNN is still prohibitively expensive to be run on small edge devices, such as "motes" that use a single microcontroller and limited memory to achieve long-lived self-powered operation. In this paper, we comprehensively explore the feasibility of compressing the L3-Net for mote-scale inference. We use pruning, ablation, and knowledge distillation techniques to show that the originally proposed L3-Net architecture is substantially overparameterized, not only for AVC but for the target task of sound classification as evaluated on two popular downstream datasets. Our findings demonstrate the value of fine-tuning and knowledge distillation in regaining the performance lost through aggressive compression strategies. Finally, we present EdgeL3, the first L3-Net reference model compressed by 1-2 orders of magnitude for real-time urban noise monitoring on resource-constrained edge devices, that can fit in just 0.4 MB of memory through half-precision floating point representation.

We describe the discovery of MK-6169, a potent and pan-genotype hepatitis C virus NS5A inhibitor with optimized activity against common resistance-associated substitutions. SAR studies around the combination of changes to both the valine and aminal carbon region of elbasvir led to the discovery of a series of compounds with substantially improved potency against common resistance-associated substitutions in the major genotypes, as well as good pharmacokinetics in both rat and dog. Through further optimization of key leads from this effort, MK-6169 (21) was discovered as a preclinical candidate for further development.

Automated objective methods of audio source separation evaluation are fast, cheap, and require little effort by the investigator. However, their output often correlates poorly with human quality assessments and typically require ground-truth (perfectly separated) signals to evaluate algorithm performance. Subjective multi-stimulus human ratings (e.g. MUSHRA) of audio quality are the gold standard for many tasks, but they are slow and require a great deal of effort to recruit participants and run listening tests. Recent work has shown that a crowdsourced multi-stimulus listening test can have results comparable to lab-based multi-stimulus tests. While these results are encouraging, MUSHRA multi-stimulus tests are limited to evaluating 12 or fewer stimuli, and they require ground-truth stimuli for reference. In this work, we evaluate a web-based pairwise-comparison listening approach that promises to speed and facilitate conducting listening tests, while also addressing some of the shortcomings of multi-stimulus tests. Using audio source separation quality as our evaluation task, we compare our web-based pairwise-comparison listening test to both web-based and lab-based multi-stimulus tests. We find that pairwise-comparison listening tests perform comparably to multi-stimulus tests, but without many of their shortcomings.

Audio annotation is an important step in developing machine-listening systems. It is also a time consuming process, which has motivated investigators to crowdsource audio annotations. However, there are many factors that affect annotations, many of which have not been adequately investigated. In previous work, we investigated the effects of visualization aids and sound scene complexity on the quality of crowdsourced sound-event annotations. In this paper, we extend that work by investigating the effect of sound-event loudness on both sound-event source annotations and sound-event proximity annotations. We find that the sound class, loudness, and annotator bias affect how listeners annotate proximity. We also find that loudness affects recall more than precision and that the strengths of these effects are strongly influenced by the sound class. These findings are not only important for designing effective audio annotation processes, but also for effectively training and evaluating machine-listening systems.

Sound event detection (SED) in environmental recordings is a key topic of research in machine listening, with applications in noise monitoring for smart cities, self-driving cars, surveillance, bioa-coustic monitoring, and indexing of large multimedia collections. Developing new solutions for SED often relies on the availability of strongly labeled audio recordings, where the annotation includes the onset, offset and source of every event. Generating such precise annotations manually is very time consuming, and as a result existing datasets for SED with strong labels are scarce and limited in size. To address this issue, we present Scaper, an open-source library for soundscape synthesis and augmentation. Given a collection of iso-lated sound events, Scaper acts as a high-level sequencer that can generate multiple soundscapes from a single, probabilistically defined, “specification”. To increase the variability of the output, Scaper supports the application of audio transformations such as pitch shifting and time stretching individually to every event. To illustrate the potential of the library, we generate a dataset of 10,000 sound-scapes and use it to compare the performance of two state-of-the-art algorithms, including a breakdown by soundscape characteristics. We also describe how Scaper was used to generate audio stimuli for an audio labeling crowdsourcing experiment, and conclude with a discussion of Scaper's limitations and potential applications.

We describe the research that led to the discovery of compound 40 (ruzasvir, MK-8408), a pan-genotypic HCV nonstructural protein 5A (NS5A) inhibitor with a “flat” GT1 mutant profile. This NS5A inhibitor contains a unique tetracyclic indole core while maintaining the imidazole–proline–valine Moc motifs of our previous NS5A inhibitors. Compound 40 is currently in early clinical trials and is under evaluation as part of an all-oral DAA regimen for the treatment of chronic HCV infection.

Background:Fc-mannose-binding lectin (FcMBL), an engineered version of the blood opsonin MBL that contains the carbohydrate recognition domain (CRD) and flexible neck regions of MBL fused to the Fc portion of human IgG1, has been shown to bind various microbes and pathogen-associated molecular patterns (PAMPs). FcMBL has also been used to create an enzyme-linked lectin sorbent assay (ELLecSA) for use as a rapid (Methods:Here we extended this work by using the ELLecSA to test FcMBL’s ability to bind to more than 190 different isolates from over 95 different pathogen species.Results:FcMBL bound to 85% of the isolates and 97 of the 112 (87%) different pathogen species tested, including bacteria, fungi, viral antigens and parasites. FcMBL also bound to PAMPs including, lipopolysaccharide endotoxin (LPS) and lipoteichoic acid (LTA) from Gram-negative and Gram-positive bacteria, as well as lipoarabinomannan (LAM) and phosphatidylinositol mannoside 6 (PIM6) fromMycobacterium tuberculosis.Conclusions:The efficiency of pathogen detection and variation between binding of different strains of the same species could be improved by treating the bacteria with antibiotics, or mechanical disruption using a bead mill, prior to FcMBL capture to reveal previously concealed binding sites within the bacterial cell wall. As FcMBL can bind to pathogens and PAMPs in urine as well as blood, its broad-binding capability could be leveraged to develop a variety of clinically relevant technologies, including infectious disease diagnostics, therapeutics, and vaccines.

Older adults and people with vision impairments are increasingly using phones to receive audio-based information and want to publish content online but must use complex audio recording/editing tools that often rely on inaccessible graphical interfaces. This poster describes the design of an accessible audio-based interface for post-processing audio content created by visually impaired seniors. We conducted a diary study with five older adults with vision impairments to understand how to design a system that would allow them to edit content they record using an audio-only interface. Our findings can help inform the development of accessible audio-editing interfaces for people with vision impairments more broadly.

Automated objective methods of audio evaluation are fast, cheap, and require little effort by the investigator. However, objective evaluation methods do not exist for the output of all audio processing algorithms, often have output that correlates poorly with human quality assessments, and require ground truth data in their calculation. Subjective human ratings of audio quality are the gold standard for many tasks, but are expensive, slow, and require a great deal of effort to recruit subjects and run listening tests. Moving listening tests from the lab to the micro-task labor market of Amazon Mechanical Turk speeds data collection and reduces investigator effort. However, it also reduces the amount of control investigators have over the testing environment, adding new variability and potential biases to the data. In this work, we compare multiple stimulus listening tests performed in a lab environment to multiple stimulus listening tests performed in web environment on a population drawn from Mechanical Turk.

We have been focused on identifying a structurally different next generation inhibitor to MK-5172 (our Ns3/4a protease inhibitor currently under regulatory review), which would achieve superior pangenotypic activity with acceptable safety and pharmacokinetic profile. These efforts have led to the discovery of a novel class of HCV NS3/4a protease inhibitors containing a unique spirocyclic-proline structural motif. The design strategy involved a molecular-modeling based approach, and the optimization efforts on the series to obtain pan-genotypic coverage with good exposures on oral dosing. One of the key elements in this effort was the spirocyclization of the P2 quinoline group, which rigidified and constrained the binding conformation to provide a novel core. A second focus of the team was also to improve the activity against genotype 3a and the key mutant variants of genotype 1b. The rational application of structural chemistry with molecular modeling guided the design and optimization of the structure-activity relationships have resulted in the identification of the clinical candidate MK-8831 with excellent pan-genotypic activity and safety profile.

Fat mass and tissue distribution change dramatically throughout life. Fat depot sizes reach a peak by middle or early old age, followed by a substantial decline, together with fat tissue dysfunction and redistribution in advanced old age. These changes are associated with health complications, including type 2 diabetes, atherosclerosis, dyslipidemia, thermal dysregulation, and skin ulcers, particularly in advanced old age. Fat tissue growth occurs through increases in size and number of fat cells. Fat cells turn over throughout the lifespan, with new fat cells developing from preadipocytes, which are of mesenchymal origin. The pool of preadipocytes comprises 15 to 50% of the cells in fat tissue. Since fat tissue turns over throughout life, characteristics of these cells very likely have a significant impact on fat tissue growth, plasticity, function, and distribution. The aims of this review are to highlight recent findings regarding changes in preadipocyte cell dynamics and function with aging, and to consider how inherent characteristics of these cells potentially contribute to age- and depot-dependent changes in fat tissue development and function.

A natural way of communicating an audio concept is to imitate it with one's voice. This creates an approximation of the imagined sound (e.g. a particular owl's hoot), much like how a visual sketch approximates a visual concept (e.g a drawing of the owl). If a machine could understand vocal imitations, users could communicate with software in this natural way, enabling new interactions (e.g. programming a music synthesizer by imitating the desired sound with one's voice). In this work, we collect thousands of crowd-sourced vocal imitations of a large set of diverse sounds, along with data on the crowd's ability to correctly label these vocal imitations. The resulting data set will help the research community understand which audio concepts can be effectively communicated with this approach. We have released the data set so the community can study the related issues and build systems that leverage vocal imitation as an interaction modality.

Here we describe development of an extracorporeal hemoadsorption device for sepsis therapy that employs commercially available polysulfone or polyethersulfone hollow fiber filters similar to those used clinically for hemodialysis, covalently coated with a genetically engineered form of the human opsonin Mannose Binding Lectin linked to an Fc domain (FcMBL) that can cleanse a broad range of pathogens and endotoxin from flowing blood without having to first determine their identity. When tested with human whole blood in vitro, the FcMBL hemoadsorption filter (FcMBL-HF) produced efficient (90-99%) removal of Gram negative (Escherichia coli) and positive (Staphylococcus aureus) bacteria, fungi (Candida albicans) and lipopolysaccharide (LPS)-endotoxin. When tested in rats, extracorporeal therapy with the FcMBL-HF device reduced circulating pathogen and endotoxin levels by more than 99%, and prevented pathogen engraftment and inflammatory cell recruitment in the spleen, lung, liver and kidney when compared to controls. Studies in rats revealed that treatment with bacteriocidal antibiotics resulted in a major increase in the release of microbial fragments or 'pathogen-associated molecular patterns' (PAMPs) in vivo, and that these PAMPs were efficiently removed from blood within 2 h using the FcMBL-HF; in contrast, they remained at high levels in animals treated with antibiotics alone. Importantly, cleansing of PAMPs from the blood of antibiotic-treated animals with the FcMBL-hemoadsorbent device resulted in reduced organ pathogen and endotoxin loads, suppressed inflammatory responses, and resulted in more stable vital signs compared to treatment with antibiotics alone. As PAMPs trigger the cytokine cascades that lead to development of systemic inflammatory response syndrome and contribute to septic shock and death, co-administration of FcMBL-hemoadsorption with antibiotics could offer a more effective approach to sepsis therapy.

While programming an audio synthesizer can be difficult, if a user has a general idea of the sound they are trying to program, they may be able to imitate it with their voice. In this technical demonstration, we demonstrate SynthAssist, a system that allows the user to program an audio synthesizer using vocal imitation and interactive feedback. This system treats synthesizer programming as an audio information retrieval task. To account for the limitations of the human voice, it compares vocal imitations to synthesizer sounds by using both absolute and relative temporal shapes of relevant audio features, and it refines the query and feature weights using relevance feedback.

There is an urgent need to develop vaccines against bacteria due to the rise of Multi-drug resistant (MDR & XDR) organisms. To date, it has been difficult to produce protective vaccines against bacterial pathogens; there is a danger of outgrowth of fast growing attenuated bacterial strains while polysaccharide cell walls are poorly immunogenic and subunit vaccines are ineffective, generating TI (T-independent) immune responses, characterized by low affinity, short lived, non-class switched IgM antibodies. We are developing technologies for generating vaccines in mice against multiple pathogen species, including bacterial; MDR E. coli, MRSA, MTb LAM, Viral; HIV gp120, parasitic; P. falciparum and fungal; C. albicans. We capture pathogens using FcMBL Opsonin technology (which binds more than 90 different pathogen species), and present the killed pathogens in an immune modulating biomaterial system. This lyophilized product provides long-lasting protection with a single dose through a novel self-boosting mechanism of action. Our vaccines can protect mice against MDR strains of E. coli which are lethal within 12 hours. We have raised antibodies with titers which are sustained beyond 90 days (to date) with a single vaccination (the biomaterial system has demonstrated titers beyond 1 year in other indications). Using this opsonin and immune modulating technology, E. coli can be captured from the blood and tissues of one animal and used to vaccinate other animals.

A typical audio mixer interface consists of faders and knobs that control the amplitude level as well as processing (e.g. equalization, compression and reverberation) parameters of individual tracks. This interface, while widely used and effective for optimizing a mix, may not be the best interface to facilitate exploration of different mixing options. In this work, we rethink the mixer interface, describing an alternative interface for exploring the space of possible mixes of four audio tracks. In a user study with 24 participants, we compared the effectiveness of this interface to the traditional paradigm for exploring alternative mixes. In the study, users responded that the proposed alternative interface facilitated exploration and that they considered the process of rating mixes to be beneficial.

Here we describe a blood-cleansing device for sepsis therapy inspired by the spleen, which can continuously remove pathogens and toxins from blood without first identifying the infectious agent. Blood flowing from an infected individual is mixed with magnetic nanobeads coated with an engineered human opsonin--mannose-binding lectin (MBL)--that captures a broad range of pathogens and toxins without activating complement factors or coagulation. Magnets pull the opsonin-bound pathogens and toxins from the blood; the cleansed blood is then returned back to the individual. The biospleen efficiently removes multiple Gram-negative and Gram-positive bacteria, fungi and endotoxins from whole human blood flowing through a single biospleen unit at up to 1.25 liters per h in vitro. In rats infected with Staphylococcus aureus or Escherichia coli, the biospleen cleared90% of bacteria from blood, reduced pathogen and immune cell infiltration in multiple organs and decreased inflammatory cytokine levels. In a model of endotoxemic shock, the biospleen increased survival rates after a 5-h treatment.

Most algorithms for estimating audio similarity either completely disregard time or they treat each moment in time equally. However, many studies over the years have noted several factors that affect how much attention we give to certain sounds or parts of sounds (e.g. loudness, the attack, novelty). These findings suggest that some time segments of audio may be more salient than others when making similarity judgments. We believe that if we could estimate this information, we could improve audio similarity measures. This paper presents the results of a human subject study designed to test the hypothesis that sounds segments with high timbral change are more salient than segments with low timbral change. We then investigate whether we can use this information to improve two audio similarity measures: a "bag-of-frames" approach and a dynamic time warping approach.

Here we describe how Staphylococcus aureus bacteria can be rapidly isolated from clinical samples of articular fluid and synovial tissue using magnetic beads coated with the engineered chimeric human opsonin protein, Fc-mannose-binding lectin (FcMBL). The FcMBL-beads were used to capture and magnetically remove bacteria from purified cultures of 12 S. aureus strains, and from 8 articular fluid samples and 4 synovial tissue samples collected from patients with osteoarthritis or periprosthetic infections previously documented by positive S. aureus cultures. While the capture efficiency was high (85%) with purified S. aureus strains grown in vitro, direct FcMBL-bead capture from the clinical samples was initially disappointing (< 5% efficiency). Further analysis revealed that inhibition of FcMBL binding was due to coating of the bacteria by immunoglobulins and immune cells that masked FcMBL binding sites, and to the high viscosity of these complex biological samples. Importantly, capture of pathogens using the FcMBL-beads was increased to 76% efficiency by pretreating clinical specimens with hypotonic washes, hyaluronidase and a protease cocktail. Using this approach, S. aureus bacteria could be isolated from infected osteoarthritic tissues within 2 hours after sample collection. This FcMBL-enabled magnetic method for rapid capture and concentration of pathogens from clinical samples could be integrated upstream of current processes used in clinical microbiology laboratories to identify pathogens and perform antibiotic sensitivity testing when bacterial culture is not possible or before colonies can be detected.

We previously studied the transcriptional mechanisms involved in expression of the murine nerve growth factor (NGF) gene. To investigate the regulation of transcription of the human NGF gene, the promoter region was cloned. The nucleotide sequences of the human and mouse genes are greater than 90% similar near their promoters. The cloned human promoter was transcriptionally active in mouse L929 fibroblasts. 5' Deletion analyses indicated that the -85 to -45 region stimulates basal transcription 6-fold. This segment is greater than 80% identical in human and mouse genes except for an AP-1 consensus sequence found only in the human gene. A second AP-1 consensus sequence at +34, previously shown to function as a regulatory element in the mouse gene, is identical in both genes. Gel shift analyses of L929 cell extracts revealed binding of protein to oligonucleotide probes spanning each of the two AP-1 consensus sequences of the human gene. The gel shift patterns differed, suggesting interaction of different proteins with the two probes. Our results demonstrate that the human NGF gene promoter is transcriptionally active in mouse fibroblasts, and implicate an upstream region in basal transcription.

Fat depot sizes peak in middle age but decrease by advanced old age. This phenomenon is associated with ectopic fat deposition, decreased adipocyte size, impaired differentiation of preadipocytes into fat cells, decreased adipogenic transcription factor expression, and increased fat tissue inflammatory cytokine generation. To define the mechanisms contributing to impaired adipogenesis with aging, we examined the release of TNFalpha, which inhibits adipogenesis, and the expression of CCAAT/enhancer-binding protein (C/EBP) homologous protein (CHOP), which blocks activity of adipogenic C/EBP family members, in preadipocytes cultured from young, middle-aged, and old rats. Medium conditioned by fat tissue, as well as preadipocytes, from old rats impeded lipid accumulation by preadipocytes from young animals. More TNFalpha was released by preadipocytes from old than young rats. Differences in TNFalpha-converting enzyme, TNFalpha degradation, or the presence of macrophages in cultures were not responsible. TNFalpha induced rat preadipocyte CHOP expression. CHOP was higher in undifferentiated preadipocytes from old than younger animals. Overexpression of CHOP in young rat preadipocytes inhibited lipid accumulation. TNFalpha short interference RNA reduced CHOP and partially restored lipid accumulation in old rat preadipocytes. CHOP normally increases during late differentiation, potentially modulating the process. This late increase in CHOP was not affected substantially by aging: CHOP was similar in differentiating preadipocytes and fat tissue from old and young animals. Hypoglycemia, which normally causes an adaptive increase in CHOP, was less effective in inducing CHOP in preadipocytes from old than younger animals. Thus increased TNFalpha release by undifferentiated preadipocytes with elevated basal CHOP contributes to impaired adipogenesis with aging.

This report presents the design and construct ion of Rage in Conjunction with the Machine, a simple but novel pairing of musical interface and sound sculpture. The , , authors discuss the design and creation of this instrument , focusing on the unique aspects of it, including the use of physical systems, large gestural input, scale, and the electronic coupling of a physical input to a physical output.

Anatomically separate fat depots differ in size, function, and contribution to pathological states, such as the metabolic syndrome. We isolated preadipocytes from different human fat depots to determine whether the basis for this variation is partly attributable to differences in inherent properties of fat cell progenitors. We found that genome-wide expression profiles of primary preadipocytes cultured in parallel from abdominal subcutaneous, mesenteric, and omental fat depots were distinct. Interestingly, visceral fat was not homogeneous. Preadipocytes from one of the two main visceral depots, mesenteric fat, had an expression profile closer to that of subcutaneous than omental preadipocytes, the other main visceral depot. Expression of genes that regulate early development, including homeotic genes, differed extensively among undifferentiated preadipocytes isolated from different fat depots. These profiles were confirmed by real-time PCR analysis of preadipocytes from additional lean and obese male and female subjects. We made preadipocyte strains from single abdominal subcutaneous and omental preadipocytes by expressing telomerase. Depot-specific developmental gene expression profiles persisted for 40 population doublings in these strains. Thus, human fat cell progenitors from different regions are effectively distinct, consistent with different fat depots being separate mini-organs.

Functional alpha1- and alpha2-adrenoreceptor subtype pharmacology was characterized in an in vitro human nasal mucosa contractile bioassay.Nasal mucosa was obtained from 49 donor patients and mucosal strips were placed in chambers filled with Krebs-Ringer solution and attached to isometric force transducers.Nonselective a-adrenoreceptor agonists epinephrine, norepinephrine, and oxymetazoline produced concentration-dependent contractions of isolated human nasal mucosa (pD2 = 5.2, 4.9, and 6.5, respectively). The alpha2-adrenoreceptor agonist BHT-920 (10 microM)-induced contractions were blocked by yohimbine (0.01-1 microM) and prazosin (0.01-1 microM) inhibited the contractile response to the alpha1-adrenoreceptor agonist phenylephrine (10 microM). Histological analysis showed that phenylephrine and BHT-920 differentially contracted the arteries and veins of human nasal mucosa, respectively.Our results indicate that functional alpha1- and alpha2-adrenoceptors are present and functional in human nasal mucosa. The alpha2-adrenoceptors display a predominant role in contracting the veins and the alpha1-adrenoceptors appear to preferentially constrict the human nasal arteries.