Speech data analysis for semantic indexing of video of simulated medical crises.

The Simulation for Pediatric Assessment, Resuscitation, and Communication (SPARC) group within the Department of Pediatrics at the University of Louisville, was established to enhance the care of children by using simulation based educational methodologies to improve patient safety and strengthen clinician-patient interactions. After each simulation session, the physician must manually review and annotate the recordings and then debrief the trainees. The physician responsible for the simulation has recorded 100s of videos, and is seeking solutions that can automate the process. This dissertation introduces our developed system for efficient segmentation and semantic indexing of videos of medical simulations using machine learning methods. It provides the physician with automated tools to review important sections of the simulation by identifying who spoke, when and what was his/her emotion. Only audio information is extracted and analyzed because the quality of the image recording is low and the visual environment is static for most parts. Our proposed system includes four main components: preprocessing, speaker segmentation, speaker identification, and emotion recognition. The preprocessing consists of first extracting the audio component from the video recording. Then, extracting various low-level audio features to detect and remove silence segments. We investigate and compare two different approaches for this task. The first one is threshold-based and the second one is classification-based. The second main component of the proposed system consists of detecting speaker changing points for the purpose of segmenting the audio stream. We propose two fusion methods for this task. The speaker identification and emotion recognition components of our system are designed to provide users the capability to browse the video and retrieve shots that identify ”who spoke, when, and the speaker’s emotion” for further analysis. For this component, we propose two feature representation methods that map audio segments of arbitary length to a feature vector with fixed dimensions. The first one is based on soft bag-of-word (BoW) feature representations. In particular, we define three types of BoW that are based on crisp, fuzzy, and possibilistic voting. The second feature representation is a generalization of the BoW and is based on Fisher Vector (FV). FV uses the Fisher Kernel principle and combines the benefits of generative and discriminative approaches. The proposed feature representations are used within two learning frameworks. The first one is supervised learning and assumes that a large collection of labeled training data is available. Within this framework, we use standard classifiers including K-nearest neighbor (K-NN), support vector machine (SVM), and Naive Bayes. The second framework is based on semi-supervised learning where only a limited amount of labeled training samples are available. We use an approach that is based on label propagation. Our proposed algorithms were evaluated using 15 medical simulation sessions. The results were analyzed and compared to those obtained using state-of-the-art algorithms. We show that our proposed speech segmentation fusion algorithms and feature mappings outperform existing methods. We also integrated all proposed algorithms and developed a GUI prototype system for subjective evaluation. This prototype processes medical simulation video and provides the user with a visual summary of the different speech segments. It also allows the user to browse videos and retrieve scenes that provide answers to semantic queries such as: who spoke and when; who interrupted who? and what was the emotion of the speaker? The GUI prototype can also provide summary statistics of each simulation video. Examples include: for how long did each person spoke? What is the longest uninterrupted speech segment? Is there an unusual large number of pauses within the speech segment of a given speaker

Recent trends in functional characteristics and degradation methods of alginate

The total area of the Earth's oceans is 360 million square kilometers, accounting for approximately 71% of the Earth's surface area. It is a huge treasure trove of resources, containing abundant mineral resources, oil and gas resources, microbial resources, etc. The production of marine biomass is enormous, and as a third-generation renewable energy source, it has more sustainable development potential than terrestrial biomass. The main source of marine biomass is marine algae, so the development and excavation of marine algae resources is imperative. At present, alginate has become the second largest sustainable development resource in terms of production, second only to cellulose, and has enormous application value. The biological enzyme method for degrading alginate utilizes alginate lyase to β The elimination mechanism breaks the glycosidic bond, which has more degradation advantages than physical and chemical methods

Nonlinear Analysis of Actuation Performance of Shape Memory Alloy Composite Film Based on Silicon Substrate

The mechanical model of the shape memory alloy (SMA) composite film with silicon (Si) substrate was established by the method of mechanics of composite materials. The coupled action between the SMA film and Si substrate under thermal loads was analyzed by combining static equilibrium equations, geometric equations, and physical equations. The material nonlinearity of SMA and the geometric nonlinearity of bending deformation were both considered. By simulating and analyzing the actuation performance of the SMA composite film during one cooling-heating thermal cycle, it is found that the final cooling temperature, boundary condition, and the thickness of SMA film have significant effects on the actuation performance of the SMA composite film. Besides, the maximum deflection of the SMA composite film is affected obviously by the geometric nonlinearity of bending deformation when the thickness of SMA film is very large

Causal associations between gut microbiota and Cholestatic liver diseases: a Mendelian randomization study

BackgroundThe etiological factors of Cholestatic Liver Diseases especially primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) are not fully illustrated. It has been reported in previous observational studies that gut microbiota are associated with cholestatic liver diseases. However, there is uncertainty regarding the causality of this association. By using Mendelian randomization, this study aimed to examine the causal impact of gut microbiota on cholestatic liver diseases.MethodsFrom large-scale genome-wide association studies, genetic instruments for each gut microbiota taxa as well as primary biliary cholangitis and primary sclerosing cholangitis were developed. Subsequently, we conducted a two-sample Mendelian randomization analysis, supplemented by multiple post hoc sensitivity analyses. Additionally, we performed reverse MR analyses to investigate the possibility of the reverse causal association.ResultThis two-sample MR study indicated that the order Bacillales, family Peptostreptococcaceae, family Ruminococcaceae, genus Anaerotruncu was associated with a decreased risk of developing PBC, and that order Selenomonadales, family Bifidobacteriaceae may be factors that increase the risk of PBC. On the other hand, we also identified order Selenomonadales, family Rhodospirillaceae, and genus RuminococcaceaeUCG013 were positively associated with PSC. The order Actinomycetales, family Actinomycetaceae, genus Actinomyces, genus Alloprevotella, genus Barnesiella, and genus Peptococcus were found negative associations with the risk of PSC. The reverse MR analysis demonstrated no statistically significant relationship between PBC, PSC and these specific gut microbial taxa.ConclusionOur findings offered novel evidence that the abundance of particular bacteria contributes to the risk of PBC and PSC, which may contribute to more effective approaches to PBC and PSC therapy and prevention

Antimicrobial peptide temporin derivatives inhibit biofilm formation and virulence factor expression of Streptococcus mutans

IntroductionTemporin-GHa obtained from the frog Hylarana guentheri showed bactericidal efficacy against Streptococcus mutans. To enhance its antibacterial activity, the derived peptides GHaR and GHa11R were designed, and their antibacterial performance, antibiofilm efficacy and potential in the inhibition of dental caries were evaluated.MethodsBacterial survival assay, fluorescent staining assay and transmission electron microscopy observation were applied to explore how the peptides inhibited and killed S. mutans. The antibiofilm efficacy was assayed by examining exopolysaccharide (EPS) and lactic acid production, bacterial adhesion and cell surface hydrophobicity. The gene expression level of virulence factors of S. mutans was detected by qRT-PCR. Finally, the impact of the peptides on the caries induced ability of S. mutans was measured using a rat caries model.ResultsIt has been shown that the peptides inhibited biofilm rapid accumulation by weakening the initial adhesion of S. mutans and reducing the production of EPS. Meanwhile, they also decreased bacterial acidogenicity and aciduricity, and ultimately prevented caries development in vivo.ConclusionGHaR and GHa11R might be promising candidates for controlling S. mutans infections

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND: The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS: We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS: The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION: Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms

Proteomics and network pharmacology of Ganshu Nuodan capsules in the prevention of alcoholic liver disease

IntroductionGanshu Nuodan is a liver-protecting dietary supplement composed of Ganoderma lucidum (G. lucidum) spore powder, Pueraria montana (Lour.) Merr. (P. montana), Salvia miltiorrhiza Bunge (S. miltiorrhiza) and Astragalus membranaceus (Fisch.) Bunge. (A. membranaceus). However, its pharmacodynamic material basis and mechanism of action remain unknown.MethodsA mouse model of acute alcohol liver disease (ALD) induced by intragastric administration of 50% alcohol was used to evaluate the hepatoprotective effect of Ganshu Nuodan. The chemical constituents of Ganshu Nuodan were comprehensively identified by UPLC-QTOF/MS, and then its pharmacodynamic material basis and potential mechanism of action were explored by proteomics and network pharmacology.ResultsGanshu Nuodan could ameliorate acute ALD, which is mainly manifested in the significant reduction of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in serum and malondialdehyde (MDA) content in liver and the remarkably increase of glutathione (GSH) content and superoxide dismutase (SOD) activity in liver. Totally 76 chemical constituents were identified from Ganshu Nuodan by UPLC-QTOF/MS, including 21 quinones, 18 flavonoids, 11 organic acids, 7 terpenoids, 5 ketones, 4 sterols, 3 coumarins and 7 others. Three key signaling pathways were identified via proteomics studies, namely Arachidonic acid metabolism, Retinol metabolism, and HIF-1 signaling pathway respectively. Combined with network pharmacology and molecular docking, six key targets were subsequently obtained, including Ephx2, Lta4h, Map2k1, Stat3, Mtor and Dgat1. Finally, these six key targets and their related components were verified by molecular docking, which could explain the material basis of the hepatoprotective effect of Ganshu Nuodan.ConclusionGanshu Nuodan can protect acute alcohol-induced liver injury in mice by inhibiting oxidative stress, lipid accumulation and apoptosis. Our study provides a scientific basis for the hepatoprotective effect of Ganshu Nuodan in acute ALD mice and supports its traditional application