Chronic non-healing wounds have become a major worldwide healthcare burden. The impact of biofilms on chronic wound infection is well established. Despite increasing understanding of the underlying ...mechanism of biofilm formation in chronic wounds, current strategies for biofilm diagnosis in chronic wounds are still far from ideal. In this review, we briefly summarize the mechanism of biofilm formation and focus on current diagnostic approaches of chronic wound biofilms based on morphology, microbiology, and molecular assays. Innovative biotechnological approaches, such as wound blotting and transcriptomic analysis, may further shed light on this unmet clinical need. The continuous development of these sophisticated diagnostic approaches can markedly contribute to the future implementation of point-of-care biofilm detection in chronic wound care.
The impact of biofilms on delayed wound healing has drawn increasing attention. Their importance led to the establishment of biofilm-based wound care where chronic wounds are treated using multipronged strategies to remove biofilms over wound beds to facilitate the recovery of epithelial integrity.
Current clinical and preclinical diagnostic techniques fail to accurately identify pathogens and the precise location of biofilms over wound surfaces, rendering timely medical or surgical intervention to eradicate biofilms elusive.
Wound blotting is a novel biotechnology that predicts wound outcomes and localizes biofilms on wound surfaces by determining the distribution pattern of tumor necrosis factor-alpha (TNF-α) and biofilm mucopolysaccharides. The rapid and objective analysis offered by this technique may assist clinicians in treating chronic wound biofilms.
Although the taxonomic composition of the human microbiome varies tremendously across individuals, its gene composition or functional capacity is highly conserved - implying an ecological property ...known as functional redundancy. Such functional redundancy has been hypothesized to underlie the stability and resilience of the human microbiome, but this hypothesis has never been quantitatively tested. The origin of functional redundancy is still elusive. Here, we investigate the basis for functional redundancy in the human microbiome by analyzing its genomic content network - a bipartite graph that links microbes to the genes in their genomes. We find that this network exhibits several topological features that favor high functional redundancy. Furthermore, we develop a simple genome evolution model to generate genomic content network, finding that moderate selection pressure and high horizontal gene transfer rate are necessary to generate genomic content networks with key topological features that favor high functional redundancy. Finally, we analyze data from two published studies of fecal microbiota transplantation (FMT), finding that high functional redundancy of the recipient's pre-FMT microbiota raises barriers to donor microbiota engraftment. This work elucidates the potential ecological and evolutionary processes that create and maintain functional redundancy in the human microbiome and contribute to its resilience.
College education and teaching management are experiencing a transition from traditional modes to informational modes. This paper constructs a management mode for university teaching informatization ...based on RFID technology and designs the workflow and functions for RFID. For the positioning of student RFID tags on campus, a time-gap ALOHA algorithm is proposed to reduce the collision rate by dividing the processing time of student tags into several time gaps. After obtaining the student tags, an improved K-weighted center-of-mass localization algorithm is proposed for multi-point localization, which makes the weight of multiple known nodes in the process of operation more reasonably distributed through the setting of the weight size. In the simulation test of campus student localization, when the number of beacon nodes is 40, the average error of the K-weighted prime is 6.5%, 4.25%, and 2.1% lower than that of the prime algorithm, weighted prime, and weighted differential prime, respectively.
Elastic Scaling for Data Stream Processing Gedik, Bugra; Schneider, Scott; Hirzel, Martin ...
IEEE transactions on parallel and distributed systems,
06/2014, Letnik:
25, Številka:
6
Journal Article
Recenzirano
Odprti dostop
This article addresses the profitability problem associated with auto-parallelization of general-purpose distributed data stream processing applications. Auto-parallelization involves locating ...regions in the application's data flow graph that can be replicated at run-time to apply data partitioning, in order to achieve scale. In order to make auto-parallelization effective in practice, the profitability question needs to be answered: How many parallel channels provide the best throughput? The answer to this question changes depending on the workload dynamics and resource availability at run-time. In this article, we propose an elastic auto-parallelization solution that can dynamically adjust the number of channels used to achieve high throughput without unnecessarily wasting resources. Most importantly, our solution can handle partitioned stateful operators via run-time state migration, which is fully transparent to the application developers. We provide an implementation and evaluation of the system on an industrial-strength data stream processing platform to validate our solution.
In this study we execute a rational screen to identify Chinese medical herbs that are commonly used in treating viral respiratory infections and also contain compounds that might directly inhibit ...2019 novel coronavirus (2019-nCoV), an ongoing novel coronavirus that causes pneumonia.
There were two main steps in the screening process. In the first step we conducted a literature search for natural compounds that had been biologically confirmed as against sever acute respiratory syndrome coronavirus or Middle East respiratory syndrome coronavirus. Resulting compounds were cross-checked for listing in the Traditional Chinese Medicine Systems Pharmacology Database. Compounds meeting both requirements were subjected to absorption, distribution, metabolism and excretion (ADME) evaluation to verify that oral administration would be effective. Next, a docking analysis was used to test whether the compound had the potential for direct 2019-nCoV protein interaction. In the second step we searched Chinese herbal databases to identify plants containing the selected compounds. Plants containing 2 or more of the compounds identified in our screen were then checked against the catalogue for classic herbal usage. Finally, network pharmacology analysis was used to predict the general in vivo effects of each selected herb.
Of the natural compounds screened, 13 that exist in traditional Chinese medicines were also found to have potential anti-2019-nCoV activity. Further, 125 Chinese herbs were found to contain 2 or more of these 13 compounds. Of these 125 herbs, 26 are classically catalogued as treating viral respiratory infections. Network pharmacology analysis predicted that the general in vivo roles of these 26 herbal plants were related to regulating viral infection, immune/inflammation reactions and hypoxia response.
Chinese herbal treatments classically used for treating viral respiratory infection might contain direct anti-2019-nCoV compounds.
Diabetic retinopathy (DR) produces bleeding, exudation, and new blood vessel formation conditions. DR can damage the retinal blood vessels and cause vision loss or even blindness. If DR is detected ...early, ophthalmologists can use lasers to create tiny burns around the retinal tears to inhibit bleeding and prevent the formation of new blood vessels, in order to prevent deterioration of the disease. The rapid improvement of deep learning has made image recognition an effective technology; it can avoid misjudgments caused by different doctors' evaluations and help doctors to predict the condition quickly. The aim of this paper is to adopt visualization and preprocessing in the ResNet-50 model to improve module calibration, to enable the model to predict DR accurately.
This study compared the performance of the proposed method with other common CNNs models (Xception, AlexNet, VggNet-s, VggNet-16 and ResNet-50). In examining said models, the results alluded to an over-fitting phenomenon, and the outcome of the work demonstrates that the performance of the revised ResNet-50 (Train accuracy: 0.8395 and Test accuracy: 0.7432) is better than other common CNNs (that is, the revised structure of ResNet-50 could avoid the overfitting problem, decease the loss value, and reduce the fluctuation problem).
This study proposed two approaches to designing the DR grading system: a standard operation procedure (SOP) for preprocessing the fundus image, and a revised structure of ResNet-50, including an adaptive learning rating to adjust the weight of layers, regularization and change the structure of ResNet-50, which was selected for its suitable features. It is worth noting that the purpose of this study was not to design the most accurate DR screening network, but to demonstrate the effect of the SOP of DR and the visualization of the revised ResNet-50 model. The results provided an insight to revise the structure of CNNs using the visualization tool.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•A latent class cluster analysis (LCA) model was used in the study.•Homogenous latent class clusters for pedestrian crashes were identified.•Crash injury severity models were developed for the ...identified clusters.•LCA approach enhances the prediction power of the severity model.
One of the major challenges in traffic safety analyses is the heterogeneous nature of safety data, due to the sundry factors involved in it. This heterogeneity often leads to difficulties in interpreting results and conclusions due to unrevealed relationships. Understanding the underlying relationship between injury severities and influential factors is critical for the selection of appropriate safety countermeasures. A method commonly employed to address systematic heterogeneity is to focus on any subgroup of data based on the research purpose. However, this need not ensure homogeneity in the data. In this paper, latent class cluster analysis is applied to identify homogenous subgroups for a specific crash type-pedestrian crashes. The manuscript employs data from police reported pedestrian (2009–2012) crashes in Switzerland. The analyses demonstrate that dividing pedestrian severity data into seven clusters helps in reducing the systematic heterogeneity of the data and to understand the hidden relationships between crash severity levels and socio-demographic, environmental, vehicle, temporal, traffic factors, and main reason for the crash. The pedestrian crash injury severity models were developed for the whole data and individual clusters, and were compared using receiver operating characteristics curve, for which results favored clustering. Overall, the study suggests that latent class clustered regression approach is suitable for reducing heterogeneity and revealing important hidden relationships in traffic safety analyses.
•Identify sites with promise by crash type.•A crash frequency model considers both multivariate and spatial correlation.•The correlation among all crash types is strong.
Many studies have proposed ...the use of a systemic approach to identify sites with promise (SWiPs). Proponents of the systemic approach to road safety management suggest that it is more effective in reducing crash frequency than the traditional hot spot approach. The systemic approach aims to identify SWiPs by crash type(s) and, therefore, effectively connects crashes to their corresponding countermeasures. Nevertheless, a major challenge to implementing this approach is the low precision of crash frequency models, which results from the systemic approach considering subsets (crash types) of total crashes leading to higher variability in modeling outcomes. This study responds to the need for more precise statistical output and proposes a multivariate spatial model for simultaneously modeling crash frequencies for different crash types. The multivariate spatial model not only induces a multivariate correlation structure between crash types at the same site, but also spatial correlation among adjacent sites to enhance model precision. This study utilized crash, traffic, and roadway inventory data on rural two-lane highways in Pennsylvania to construct and test the multivariate spatial model. Four models with and without the multivariate and spatial correlations were tested and compared. The results show that the model that considers both multivariate and spatial correlation has the best fit. Moreover, it was found that the multivariate correlation plays a stronger role than the spatial correlation when modeling crash frequencies in terms of different crash types.
•Travel lane configuration and lane width (TLC-LW) affect driving/riding behaviors.•Two sites with opposite TLC-LW adjustments before and after were investigated.•Increase in lane width resulted in ...increases in the number of risky events.
The design of travel lane configuration and lane width is crucial to traffic safety, especially in an urban mixed traffic environment where Powered-Two-Wheelers (PTWs) are prevalent and share the same roads with larger vehicles such as cars, buses, and trucks. However, there have been limited studies on the effects of the design of travel lane configuration and lane width on safety in such a mixed traffic environment. It’s true the above-mentioned research question can be evaluated simply in terms of the number of crashes. However, doing so not only requires a few years of crash and traffic data, but limited insight can be gained in terms of how driver and rider behaviours are affected, and this has implications for further improvement in road safety. This study examines the changes in driving/riding behaviours and surrogate events before and after the adjustments of travel lane configuration and lane width by proposing a micro perspective approach as a complement to conventional studies. A before-and-after site-based investigation was conducted at two study sites which had opposite adjustments for travel lane configuration and lane widths: at one site the number of lanes was reduced, thereby widening the lane width in the outer lane on one road section, and at the second site the number of lanes was increased, thereby narrowing lane width in the outer lane on the other road section. The results showed that an increase in lane width resulted in a considerable increase in the number of speeding events as well as unsafe driving/riding behaviours and surrogate events related to lane splitting, lane sharing, and overtaking.
Transport of fluid through a pipe is essential for the operation of macroscale machines and microfluidic devices. Conventional fluids only flow in response to external pressure. We demonstrate that ...an active isotropic fluid, composed of microtubules and molecular motors, autonomously flows through meter-long three-dimensional channels. We establish control over the magnitude, velocity profile, and direction of the self-organized flows and correlate these to the structure of the extensile microtubule bundles. The inherently three-dimensional transition from bulk-turbulent to confined-coherent flows occurs concomitantly with a transition in the bundle orientational order near the surface and is controlled by a scale-invariant criterion related to the channel profile. The nonequilibrium transition of confined isotropic active fluids can be used to engineer self-organized soft machines.
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