Monocytes Expose Factor XIII-A and Stabilize Thrombi against Fibrinolytic Degradation

Factor XIII (FXIII) is a transglutaminase that promotes thrombus stability by cross-linking fibrin. The cellular form, a homodimer of the A subunits, denoted FXIII-A, lacks a classical signal peptide for its release; however, we have shown that it is exposed on activated platelets. Here we addressed whether monocytes expose intracellular FXIII-A in response to stimuli. Using flow cytometry, we demonstrate that FXIII-A antigen and activity are up-regulated on human monocytes in response to stimulation by IL-4 and IL-10. Higher basal levels of the FXIII-A antigen were noted on the membrane of the monocytic cell line THP-1, but activity was significantly enhanced following stimulation with IL-4 and IL-10. In contrast, treatment with lipopolysaccharide did not upregulate exposure of FXIII-A in THP-1 cells. Quantification of the FXIII-A activity revealed a significant increase in THP-1 cells in total cell lysates following stimulation with IL-4 and IL-10. Following fractionation, the largest pool of FXIII-A was membrane associated. Monocytes were actively incorporated into the fibrin mesh of model thrombi. We found that stimulation of monocytes and THP-1 cells with IL-4 and IL-10 stabilized FXIII-depleted thrombi against fibrinolytic degradation, via a transglutaminase-dependent mechanism. Our data suggest that monocyte-derived FXIII-A externalized in response to stimuli participates in thrombus stabilization.     

Content and task-based view selection from multiple video streams

We present a content-aware multi-camera selection technique that uses object- and frame-level features. First objects are detected using a color-based change detector. Next trajectory information for each object is generated using multi-frame graph matching. Finally, multiple features including size and location are used to generate an object score. At frame-level, we consider total activity, event score, number of objects and cumulative object score. These features are used to generate score information using a multivariate Gaussian distribution. The algorithm. The best view is selected using a Dynamic Bayesian Network (DBN), which utilizes camera network information. DBN employs previous view information to select the current view thus increasing resilience to frequent switching. The performance of the proposed approach is demonstrated on three multi-camera setups with semi-overlapping fields of view: a basketball game, an indoor airport surveillance scenario and a synthetic outdoor pedestrian dataset. We compare the proposed view selection approach with a maximum score based camera selection criterion and demonstrate a significant decrease in camera flickering. The performance of the proposed approach is also validated through subjective testing.     

Speech Recognition-Based Automated Visual Acuity Testing with Adaptive Mel Filter Bank

One of the most commonly reported disabilities is vision loss, which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient. This procedure, however, usually requires an appointment with an ophthalmologist, which is both time-consuming and expensive process. Other issues that can arise include a lack of appropriate equipment and trained practitioners, especially in rural areas. Centered on a cognitively motivated attribute extraction and speech recognition approach, this paper proposes a novel idea that immediately determines the eyesight deficiency. The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for feature extraction. The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment. Comparative performance evaluation demonstrates the potential of our automated visual acuity test method to achieve comparable results to the clinical ground truth, established by the expert ophthalmologist’s tests. The overall accuracy achieved by the proposed model when compared with the expert ophthalmologist test is 91.875%. The proposed method potentially offers a second opinion to ophthalmologists, and serves as a cost-effective pre-screening test to predict eyesight loss at an early stage.     

A Cost-Effective Approach for NDN-Based Internet of Medical Things Deployment

Nowadays, healthcare has become an important area for the Internet of Things (IoT) to automate healthcare facilities to share and use patient data anytime and anywhere with Internet services. At present, the host-based Internet paradigm is used for sharing and accessing healthcare-related data. However, due to the location-dependent nature, it suffers from latency, mobility, and security. For this purpose, Named Data Networking (NDN) has been recommended as the future Internet paradigm to cover the shortcomings of the traditional host-based Internet paradigm. Unfortunately, the novel breed lacks a secure framework for healthcare. This article constructs an NDN-Based Internet of Medical Things (NDN-IoMT) framework using a lightweight certificateless (CLC) signature. We adopt the Hyperelliptic Curve Cryptosystem (HCC) to reduce cost, which provides strong security using a smaller key size compared to Elliptic Curve Cryptosystem (ECC). Furthermore, we validate the safety of the proposed scheme through AVISPA. For cost-efficiency, we compare the designed scheme with relevant certificateless signature schemes. The final result shows that our proposed scheme uses minimal network resources. Lastly, we deploy the given framework on NDN-IoMT.     

Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network

The precise detection and segmentation of pectoral muscle areas in mediolateral oblique (MLO) views is an essential step in the development of a computer-aided diagnosis system to access breast malignant lesions or parenchyma. The goal of this article is to develop a robust and fully automatic algorithm for pectoral muscle segmentation from mammography images. This paper presents an image enhancement approach that improves the quality of mammogram scans and a convolutional neural network-based fully convolutional network architecture enhanced with residual connections for automatic segmentation of the pectoral muscle from the MLO views of a digital mammogram. For this purpose, the model is tested and trained on three different mammogram datasets named MIAS, INBREAST, and DDSM. The ground truth labels of the pectoral muscle were identified under the supervision of experienced radiologists. For training and testing, 10-fold cross-validation was used. The proposed model was compared with baseline U-Net-based architecture. Finally, we used a postprocessing step to find the actual boundary of the pectoral muscle. Our presented architecture generated a mean Intersection over Union (IoU) of 97%, dice similarity coefficient (DSC) of 96% and 98% accuracy on testing data. The proposed architecture for pectoral muscle segmentation from the MLO views of mammogram images with high accuracy and dice score can be quickly merged with the breast tumor segmentation problem.     

Trace-Level,Multi-Gas Detection for Food Quality Assessment Based on Decorated Silicon Transistor Arrays

Multiplexed gas detection at room temperature is critical for practical applications, such as for tracking the complex chemical environments associated with food decomposition and spoilage. An integrated array of multiple silicon-based, chemical-sensitive field effect transistors (CSFETs) is presented to realize selective, sensitive, and simultaneous measurement of gases typically associated with food spoilage. CSFETs decorated with sensing materials based on ruthenium, silver, and silicon oxide are used to obtain stable room-temperature responses to ammonia (NH₃), hydrogen sulfide (H₂S), and humidity, respectively. For example, one multi-CSFET sensor signal changes from its baseline by 13.34 in response to 1 ppm of NH₃, 724.45 under 1 ppm H₂S, and 23.46 under 80% relative humidity, with sensitive detection down to 10 ppb of NH₃ and H₂S. To demonstrate this sensor for practical applications, the CSFET sensor array is combined with a custom-printed circuit board into a compact, fully integrated, and portable system to conduct real-time monitoring of gases generated by decomposing food. By using existing silicon-based manufacturing methodologies, this room-temperature gas sensing array can be fabricated reproducibly and at low cost, making it an attractive platform for ambient gas measurement needed in food safety applications.     

Cold deformation of dezincification resistant yellow brass for plumbing applications

In the present work, the room temperature deformation behavior of dezincification-resistant (DZR) brass was performed by varying strain rates (1?×?10^?4?s^?1, 0.55?×?10^?3?s^?1, 1?×?10^?3?s^?1, 0.55?×?10^?2?s^?1, 1?×?10^?2?s^?1) and percent cold works (15 to 65% with step of 10%). These parameters are important to plumbing parts of its forming. Room temperature deformation workability map was developed that provides the selection of safe deformation parameters without cracking. The as-received and deformed DZR brass samples were carefully characterized by various microscopes. The results revealed that more dislocations lines and twinning were observed through transmission electron microscope images as the strain rate (SR) increases which led to early failure of the sample before reaching the set height reduction. It was determined that more amount of strain hardening with designed height reduction was achieved at lower SR whereas less amount of strain hardening was achieved at higher SR due to strain mismatching phenomenon and various deformation mechanisms.     

Frequency limited Gramians-based structure preserving model order reduction for discrete time second-order systems

A new technique for frequency limited model order reduction of discrete time second-order systems is presented. Discrete time frequency limited Gramians (DFLGs) and corresponding discrete algebraic Lyapunov equations are developed. An efficient technique for the computation of DFLGs and their Cholesky factors is presented. Computed DFLGs are partitioned to obtain position and velocity Gramians. These Gramians are balanced with different combinations to obtain various balanced transformations that yield Hankel singular values (HSVs) for order reduction. Frequency limited discrete time balanced truncation framework is proposed and truncation based on magnitudes of HSVs is applied to obtain the reduced order model. Moreover, stability conditions for reduced order models are stated. Results of the proposed technique are compared with infinite Gramians balancing scheme in order to certify the usefulness of the presented technique for frequency limited applications.     

Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging system

In recent years, researchers have been developing algorithms for the automatic mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. We are detecting semantic heterogeneities that occur due to conflicts among the set of Generalized Concept Inclusions, Property Subsumption Criteria, and Constraint Satisfaction Mechanism in local heterogeneous ontologies, which become obstacles for the generation of semantically consistent global merged ontology. We present several algorithms to detect such semantic inconsistencies based on subsumption analysis of concepts and properties in local ontologies from the list of initial mappings. We provide ontological patterns for resolving these inconsistencies automatically. This results global merged ontology free from ??circulatory error in class/property hierarchy??, ??common class between disjoint classes/properties??, ??redundancy of subclass/subproperty of relations?? and other types of ??semantic inconsistency?? errors. Experiments on the real ontologies show that our algorithms save time and cost of traversing local ontologies, improve system??s performance by producing only consistent accurate mappings, and reduce the users?? dependability for ensuring the satisfiability of merged ontology.