Towards an interdisciplinary formalization of soundscapes.

Soundscapes have been studied by researchers from various disciplines, each with different perspectives, approaches, and terminologies. Consequently, the research field determines the actual concept of a specific soundscape with the associated components and also affects the definition itself. This complicates interdisciplinary communication and comparison of results, especially when research areas are involved which are not directly focused on soundscapes. For this reason, we present a formalization that aims to be independent of the concepts from the various disciplines, with the goal of being able to capture the heterogeneous data structure in one layered model. Our model consists of time-dependent sound sources and geodata that influence the acoustic composition of a soundscape represented by our sensor function. Using a case study, we present the application of our formalization by classifying land use types. For this we analyze soundscapes in the form of recordings from different devices at 23 different locations using three-dimensional convolutional neural networks and frequency correlation matrices. In our results, we present that soundscapes can be grouped into classes, but the given land use categories do not have to correspond to them.

Reading Direct-Part Marking Data Matrix Code in the Context of Polymer-Based Additive Manufacturing.

A novel approach to detect and decode direct-part-marked, low-contrast data matrix codes on polymer-based selective laser sintering manufactured parts, which is able to work on lightweight devices, is presented. Direct-part marking is a concept for labeling parts directly, which can be carried out during the additive manufacturing's design process. Because of low contrast in polymer-based selective laser sintering manufactured parts, it is a challenging task to detect and read codes on unicolored parts. To achieve this, at first, codes are located using a deep-learning-based approach. Afterwards, the calculated regions of interest are passed into an image encoding network in order to compute readable standard data matrix codes. To enhance the training process, rendered images, improved with a generative adversarial network, are used. This process fulfills the traceability task in assembly line production and is suitable for running on mobile devices such as smartphones or cheap sensors placed in the assembly line. The results show that codes can be localized with 97.38% mean average precision, and a readability of 89.36% is achieved.

Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms.

A mobile system that can detect viruses in real time is urgently needed, due to the combination of virus emergence and evolution with increasing global travel and transport. A biosensor called PAMONO (for Plasmon Assisted Microscopy of Nano-sized Objects) represents a viable technology for mobile real-time detection of viruses and virus-like particles. It could be used for fast and reliable diagnoses in hospitals, airports, the open air, or other settings. For analysis of the images provided by the sensor, state-of-the-art methods based on convolutional neural networks (CNNs) can achieve high accuracy. However, such computationally intensive methods may not be suitable on most mobile systems. In this work, we propose nanoparticle classification approaches based on frequency domain analysis, which are less resource-intensive. We observe that on average the classification takes 29 µ s per image for the Fourier features and 17 µ s for the Haar wavelet features. Although the CNN-based method scores 1-2.5 percentage points higher in classification accuracy, it takes 3370 µ s per image on the same platform. With these results, we identify and explore the trade-off between resource efficiency and classification performance for nanoparticle classification of images provided by the PAMONO sensor.

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller.

Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given.

Application of the PAMONO-Sensor for Quantification of Microvesicles and Determination of Nano-Particle Size Distribution.

The PAMONO-sensor (plasmon assisted microscopy of nano-objects) demonstrated an ability to detect and quantify individual viruses and virus-like particles. However, another group of biological vesicles-microvesicles (100-1000 nm)-also attracts growing interest as biomarkers of different pathologies and needs development of novel techniques for characterization. This work shows the applicability of a PAMONO-sensor for selective detection of microvesicles in aquatic samples. The sensor permits comparison of relative concentrations of microvesicles between samples. We also study a possibility of repeated use of a sensor chip after elution of the microvesicle capturing layer. Moreover, we improve the detection features of the PAMONO-sensor. The detection process utilizes novel machine learning techniques on the sensor image data to estimate particle size distributions of nano-particles in polydisperse samples. Altogether, our findings expand analytical features and the application field of the PAMONO-sensor. They can also serve for a maturation of diagnostic tools based on the PAMONO-sensor platform.

Application of surface plasmon resonance imaging technique for the detection of single spherical biological submicrometer particles.

Recent proof-of-principle studies demonstrated the suitability of the surface plasmon resonance imaging (SPRi) technique for the detection of individual submicrometer and nanoparticles in solutions. In the current study, we used the SPRi technique for visualization of the binding of round-shaped viruses (inactivated influenza A virus) and virus-like particles (human immunodeficiency virus (HIV)-based virus-like particles) to the functionalized sensor surface. We show the applicability of the SPRi technique for the detection of individual virus-like particles in buffers without serum as well as in buffers containing different concentrations of serum. Furthermore, we prove the specificity of visualized binding events using two different pseudotypes of HIV virus-like particles. We also demonstrate the applicability of the SPRi technique for the determination of relative particle concentrations in solutions. Moreover, we suggest a technical approach, which allows enhancing the magnitude of binding signals. Our studies indicate that the SPRi technique represents an efficient research tool for quantification and characterization of biological submicrometer objects such as viruses or virus-like particles, for example.

Macrophages and bisphosphonate-related osteonecrosis of the jaw (BRONJ): evidence of local immunosuppression of macrophages in contrast to other infectious jaw diseases.

OBJECTIVES: Bisphosphonates (BIP) are well established in bone diseases. A serious side effect is the bisphosphonate-related osteonecrosis of the jaw (BRONJ). Among different aetiology factors, local suppression of immune functions is gaining interest. The aim of this study was to analyze the function of macrophages in BRONJ in contrast to patients with osteoradionecrosis (ORN) and secondary chronic osteomyelitis (OM) of the jaws. Samples were also taken from patients with bisphosphonate medication (BP) without signs of infection, radiation therapy (RA), and osteoporosis (OP) as controls. MATERIAL AND METHODS: One hundred five patients with surgery to the jaw were included in this study: 33 patients with BRONJ, 17 with ORN, 11 with secondary chronic OM, 8 with RA, 25 with BP medication and 11 with OP. Samples were histologically analysed and monocytes/macrophages stained using CD14 and CD68. The number of positively marked cells was counted per view (pv), and the CD68/CD14 ratio was calculated. Statistically, the Naïve-Bayes and decision-tree classifier were used. RESULTS: The number of CD14 positive cells was 10.3 cells/pv in the BRONJ-group in as compared to 5 in the ORN- and 3.8 in the OM-group respectively. The number of CD68 positive cells was 11.4/pv (BRONJ-group) as compared to 14/pv (ORN-group) and 12.7/pv (OM-group). With 0.89, the BRONJ-group showed a statistically different CD68/CD14 ratio than ORN-group with 3.39 and OM-group with 3.03. CONCLUSIONS: Our results indicate a different expression of CD14 and CD68 markers of monocytes/macrophages in BRONJ as compared to other jaw infections. This could be a sign of macrophage immunosuppression by BPs. In contrast, patients receiving BP medication without BRONJ showed no differences to other controls. CLINICAL RELEVANCE: This is the first study that clinically indicates a compromised macrophage function at BRONJ sites in contrast to ORN or secondary OM sites. The BRONJ itself could be forwarded by this effect.

MicroRNA target prediction: theory and practice.

The present study is one of the few that includes tissue samples in the evaluation of target prediction algorithms designed to detect microRNA (miRNA) sequences that might interact with particular messenger RNA (mRNA) sequences. Twelve different target prediction tools were used to find miRNA sequences that might interact with CCL20 gene expression. Different algorithms predicted controversial miRNA sequences for CCL20 regulation due to a different weighting of parameters. Hsa-miR-21 and hsa-miR-145 suggested by four or more programs were chosen for further investigation. Possible real interaction of these miRNA sequences with CCL20 gene expression was monitored using luciferase assays and expression analyses of tissue samples of colorectal adenocarcinoma by either qRT-PCR or ELISA. Folding status of seed-binding sites in complete mRNA and 3'UTR of CCL20 was predicted. Prediction of miRNA expression was attempted based on CCL20 expression data. Eight of the target prediction tools forecasted a role for hsa-miR-21 and four mentioned hsa-miR-145 in CCL20 gene regulation. Laboratory experimentation showed that CCL20 may serve as a target of hsa-miR-21 but not hsa-miR-145. Expression of the molecules resulted in no clear assertion. Folding of seed-binding sites was predicted to be relatively constant for the complete mRNA and 3'UTR. Predicting miRNA expression based on target gene expression was impossible. This might be attributable to the fact that effects of miRNA activity may oscillate between gene product repression and activation. Additional systematic studies are needed to address this issue.

Evaluation of the leap motion controller as a new contact-free pointing device.

This paper presents a Fitts' law-based analysis of the user's performance in selection tasks with the Leap Motion Controller compared with a standard mouse device. The Leap Motion Controller (LMC) is a new contact-free input system for gesture-based human-computer interaction with declared sub-millimeter accuracy. Up to this point, there has hardly been any systematic evaluation of this new system available. With an error rate of 7.8% for the LMC and 2.8% for the mouse device, movement times twice as large as for a mouse device and high overall effort ratings, the Leap Motion Controller's performance as an input device for everyday generic computer pointing tasks is rather limited, at least with regard to the selection recognition provided by the LMC.

Analysis of the accuracy and robustness of the leap motion controller.

The Leap Motion Controller is a new device for hand gesture controlled user interfaces with declared sub-millimeter accuracy. However, up to this point its capabilities in real environments have not been analyzed. Therefore, this paper presents a first study of a Leap Motion Controller. The main focus of attention is on the evaluation of the accuracy and repeatability. For an appropriate evaluation, a novel experimental setup was developed making use of an industrial robot with a reference pen allowing a position accuracy of 0.2 mm. Thereby, a deviation between a desired 3D position and the average measured positions below 0.2 mm has been obtained for static setups and of 1.2 mm for dynamic setups. Using the conclusion of this analysis can improve the development of applications for the Leap Motion controller in the field of Human-Computer Interaction.

Agent-based simulation of pedestrian dynamics for exposure time estimation in epidemic risk assessment.

Purpose: With the coronavirus disease 2019 (COVID-19) pandemic spreading across the world, protective measures for containing the virus are essential, especially as long as no vaccine or effective treatment is available. One important measure is the so-called physical distancing or social distancing. Methods: In this paper, we propose an agent-based numerical simulation of pedestrian dynamics in order to assess the behavior of pedestrians in public places in the context of contact transmission of infectious diseases like COVID-19, and to gather insights about exposure times and the overall effectiveness of distancing measures. Results: To abide by the minimum distance of 1.5 m stipulated by the German government at an infection rate of 2%, our simulation results suggest that a density of one person per 16m2 or below is sufficient. Conclusions: The results of this study give insight into how physical distancing as a protective measure can be carried out more efficiently to help reduce the spread of COVID-19.

The Employment of the Surface Plasmon Resonance (SPR) Microscopy Sensor for the Detection of Individual Extracellular Vesicles and Non-Biological Nanoparticles.

A wide-field surface plasmon resonance (SPR) microscopy sensor employs the surface plasmon resonance phenomenon to detect individual biological and non-biological nanoparticles. This sensor enables the detection, sizing, and quantification of biological nanoparticles (bioNPs), such as extracellular vesicles (EVs), viruses, and virus-like particles. The selectivity of bioNP detection does not require biological particle labeling, and it is achieved via the functionalization of the gold sensor surface by target-bioNP-specific antibodies. In the current work, we demonstrate the ability of SPR microscopy sensors to detect, simultaneously, silica NPs that differ by four times in size. Employed silica particles are close in their refractive index to bioNPs. The literature reports the ability of SPR microscopy sensors to detect the binding of lymphocytes (around 10 µm objects) to the sensor surface. Taken together, our findings and the results reported in the literature indicate the power of SPR microscopy sensors to detect bioNPs that differ by at least two orders in size. Modifications of the optical sensor scheme, such as mounting a concave lens, help to achieve homogeneous illumination of a gold sensor chip surface. In the current work, we also characterize the improved magnification factor of the modified SPR instrument. We evaluate the effectiveness of the modified and the primary version of the SPR microscopy sensors in detecting EVs isolated via different approaches. In addition, we demonstrate the possibility of employing translation and rotation stepper motors for precise adjustments of the positions of sensor optical elements-prism and objective-in the primary version of the SPR microscopy sensor instrument, and we present an algorithm to establish effective sensor-actuator coupling.

New details on the clefted uvular muscle: analyzing its role at histological scale by model-based deformation analyses.

OBJECTIVE: As an initial step to a complex reconstruction model for virtual surgery, the present study was carried out to provide data on the prenatal cleft lip and palate uvular muscle in eight specimens. METHOD: Serial sections of viscerocrania of 18 aborted embryos and fetuses were studied microscopically and segmented manually. Registration, three-dimensional reconstruction, and finite element analyses were conducted. RESULTS: Incompletely clefted uvulae showed anterior fusion and dorsal fission of the bilateral uvular muscle bodies. A complete cleft lip and palate specimen evidenced single bilateral uvular muscle bodies lying medially and orally below the cleft shelf, its central longitudinal fibers running beneath the oral-median mucosa. In incompletely clefted uvulae, 10% to 50% of circular peripheral fibers crossed the midline within the central third of the anterioposterior muscle, behind the levator loop. Of the fibers, 30% to 60% crossed to the ipsilateral palatopharyngeus muscle. Fibers inserted into the uvular basal membrane in a 60% nasal and 40% oral distribution at the middle third of the macroscopically clefted uvula. The macroscopic uvula itself consisted of loose connective tissue and salivary glands. Deformation analysis did disclose local stress, suggesting the uvular muscle contributes to velopharyngeal closure in normal anatomy and extends the cleft edges in cleft palate. CONCLUSION: Cleft lip and palate reconstruction should reasonably use the uvular muscle to augment the velar midline bulk. Uvular muscle deformation calculation was successful, permitting functional insight on the basis of microanatomical specimens, so far a bigger complete velar model can be ventured.

Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario.

With the COVID-19 pandemic, the role of infectious disease spreading in public places has been brought into focus more than ever. Places that are of particular interest regarding the spread of infectious diseases are international airport terminals, not only for the protection of staff and ground crew members but also to help minimize the risk of the spread of infectious entities such as COVID-19 around the globe. Computational modelling and simulation can help in understanding and predicting the spreading of infectious diseases in any such scenario. In this paper, we propose a model, which combines a simulation of high geometric detail regarding virus spreading with an account of the temporal progress of infection dynamics. We, thus, introduce an agent-based social force model for tracking the spread of infectious diseases by modelling aerosol traces and concentration of virus load in the air. We complement this agent-based model to have consistency over a period of several days. We then apply this model to investigate simulations in a realistic airport setting with multiple virus variants of varying contagiousness. According to our experiments, a virus variant has to be at least twelve times more contagious than the respective control to result in a level of infection of more than 30%. Combinations of agent-based models with temporal components can be valuable tools in an attempt to assess the risk of infection attributable to a particular virus and its variants.

Histology and function: analyzing the uvular muscle.

OBJECTIVE: Virtual surgery and virtual patients necessitate quantitative data on the area of interest. The study was conducted to exactly describe the embryonic and fetal uvular muscle (MU), relevant for clinical as well as virtual surgery and virtual patient generation. METHOD: Serially sectioned viscerocrania of 10 aborted embryos and fetuses underwent three-dimensional reconstruction to obtain detailed anatomic data and perform finite element analyses. RESULTS: The MU was paired in 80% of cases, while 20% allowed no clear-cut distinction. The MU merged with the levator muscle beneath the palatal aponeurosis without a hard palate insertion. Superior longitudinal central fibers ran below the nasal mucosa, and few circular peripheral fibers crossed in the central third to the contralateral side. This was seen in 30% of the paired muscles and in all cases when no differentiation was possible; about 40% to 80% MU fibers crossed to the ipsilateral and contralateral palatopharyngeus muscle behind the levator loop. MU fibers inserted 60% nasal and 40% oral to the basal membrane at the middle third of the macroscopic uvula, made of loose connective tissue and salivary glands. The results of the finite element simulation of the uvula showed no distinct patterns or distributions of local stress. CONCLUSIONS: Detailed anatomical study supported the concept of mediocranial MU repositioning during corrective surgery, although the impact is minor to the levator muscle's action. Future mathematical models describing effects of such a maneuver should integrate surrounding structures.

A Data-Centric Augmentation Approach for Disturbed Sensor Image Segmentation.

In the context of sensor-based data analysis, the compensation of image artifacts is a challenge. When the structures of interest are not clearly visible in an image, algorithms that can cope with artifacts are crucial for obtaining the desired information. Thereby, the high variation of artifacts, the combination of different types of artifacts, and their similarity to signals of interest are specific issues that have to be considered in the analysis. Despite the high generalization capability of deep learning-based approaches, their recent success was driven by the availability of large amounts of labeled data. Therefore, the provision of comprehensive labeled image data with different characteristics of image artifacts is of importance. At the same time, applying deep neural networks to problems with low availability of labeled data remains a challenge. This work presents a data-centric augmentation approach based on generative adversarial networks that augments the existing labeled data with synthetic artifacts generated from data not present in the training set. In our experiments, this augmentation leads to a more robust generalization in segmentation. Our method does not need additional labeling and does not lead to additional memory or time consumption during inference. Further, we find it to be more effective than comparable augmentations based on procedurally generated artifacts and the direct use of real artifacts. Building upon the improved segmentation results, we observe that our approach leads to improvements of 22% in the F1-score for an evaluated detection problem. Having achieved these results with an example sensor, we expect increased robustness against artifacts in future applications.

Evaluation of two 3D virtual computer reconstructions for comparison of cleft lip and palate to normal fetal microanatomy.

Cleft lip and palate reconstructive surgery requires thorough knowledge of normal and pathological labial, palatal, and velopharyngeal anatomy. This study compared two software algorithms and their 3D virtual anatomical reconstruction because exact 3D micromorphological reconstruction may improve learning, reveal spatial relationships, and provide data for mathematical modeling. Transverse and frontal serial sections of the midface of 18 fetal specimens (11th to 32nd gestational week) were used for two manual segmentation approaches. The first manual segmentation approach used bitmap images and either Windows-based or Mac-based SURFdriver commercial software that allowed manual contour matching, surface generation with average slice thickness, 3D triangulation, and real-time interactive virtual 3D reconstruction viewing. The second manual segmentation approach used tagged image format and platform-independent prototypical SeViSe software developed by one of the authors (F.W.). Distended or compressed structures were dynamically transformed. Registration was automatic but allowed manual correction, such as individual section thickness, surface generation, and interactive virtual 3D real-time viewing. SURFdriver permitted intuitive segmentation, easy manual offset correction, and the reconstruction showed complex spatial relationships in real time. However, frequent software crashes and erroneous landmarks appearing "out of the blue," requiring manual correction, were tedious. Individual section thickness, defined smoothing, and unlimited structure number could not be integrated. The reconstruction remained underdimensioned and not sufficiently accurate for this study's reconstruction problem. SeViSe permitted unlimited structure number, late addition of extra sections, and quantified smoothing and individual slice thickness; however, SeViSe required more elaborate work-up compared to SURFdriver, yet detailed and exact 3D reconstructions were created.

Simulating soft data to make soft data applicable to simulation.

BACKGROUND: Biomedical processes are often influenced by measures considered "non-crisp", "soft" or "subjective". Despite the growing awareness of the importance of such measures, they are rarely considered in biomedical simulation. This study introduces an input generator for soft data (input generator SD) that makes soft data applicable to simulation. MATERIALS AND METHODS: Machine learning approaches and standard regression techniques were applied to simulate odour intensity ratings. RESULTS: The performance of all the applied methods was satisfactory and the results can be used to modify systems biological mathematical models. CONCLUSION: Soft data should no longer be discounted in systems biological simulations. Exemplarily, it can be demonstrated that the input generator SD produces results that are similar to those that the simulated system can generate. Machine learning and/or appropriate conventional mathematical approaches may be applied to simulate noncrisp processes that can be used to modify mathematical models of any granularity.

Text mining, a race against time? An attempt to quantify possible variations in text corpora of medical publications throughout the years.

BACKGROUND: The continuous growth of medical sciences literature indicates the need for automated text analysis. Scientific writing which is neither unitary, transcending social situation nor defined by a timeless idea is subject to constant change as it develops in response to evolving knowledge, aims at different goals, and embodies different assumptions about nature and communication. The objective of this study was to evaluate whether publication dates should be considered when performing text mining. METHODS: A search of PUBMED for combined references to chemokine identifiers and particular cancer related terms was conducted to detect changes over the past 36 years. Text analyses were performed using freeware available from the World Wide Web. TOEFL Scores of territories hosting institutional affiliations as well as various readability indices were investigated. Further assessment was conducted using Principal Component Analysis. Laboratory examination was performed to evaluate the quality of attempts to extract content from the examined linguistic features. RESULTS: The PUBMED search yielded a total of 14,420 abstracts (3,190,219 words). The range of findings in laboratory experimentation were coherent with the variability of the results described in the analyzed body of literature. Increased concurrence of chemokine identifiers together with cancer related terms was found at the abstract and sentence level, whereas complexity of sentences remained fairly stable. CONCLUSIONS: The findings of the present study indicate that concurrent references to chemokines and cancer increased over time whereas text complexity remained stable.