Purpose
Collateral evaluation is typically done using visual inspection of cerebral images and thus suffers from intra- and inter-rater variability. Large open databases of ischemic stroke patients ...are rare, limiting the use of deep learning methods in treatment decision-making.
Methods
We adapted a pre-trained EfficientNet B0 network through transfer learning to improve collateral evaluation using slice-based and subject-level classification. Our method uses stacking and overlapping of 2D slices from a patient’s 4D computed tomography angiography (CTA) and a majority voting scheme to determine a patient’s final collateral grade based on all classified 2D MIPs. Class imbalance is handled in the evaluation process by using the focal loss with class weight to penalize the majority class.
Results
We evaluated our method using a nine-fold cross-validation performed with 83 subjects. Mean sensitivity of 0.71, specificity of 0.84, and a weighted F1 score of 0.71 in multi-class (good, intermediate, and poor) classification were obtained. Considering treatment effect, a dichotomized decision is also made for collateral scoring of a subject based on two classes (good/intermediate and poor) which achieves a sensitivity of 0.89 and specificity of 0.96 with a weighted F1 score of 0.95.
Conclusion
An automatic and robust collateral assessment method that mitigates the issues with the small imbalanced dataset was developed. Computer-aided evaluation of collaterals can help decision-making of ischemic stroke treatment strategy in clinical settings.
Purpose
Neuronavigation systems making use of augmented reality (AR) have been the focus of much research in the last couple of decades. In recent years, there has been considerable interest in using ...mobile devices for AR in the operating room (OR). We propose a complete system that performs real-time AR video augmentation on a mobile device in the context of image-guided neurosurgery.
Methods
MARIN (mobile augmented reality interactive neuronavigation system) improves upon the state of the art in terms of performance, allowing real-time augmentation, and interactivity by allowing users to interact with the displayed data. The system was tested in a user study with 17 subjects for qualitative and quantitative evaluation in the context of target localization and brought into the OR for preliminary feasibility tests, where qualitative feedback from surgeons was obtained.
Results
The results of the user study showed that MARIN performs significantly better in terms of both time (
p
<
0.0004
) and accuracy (
p
<
0.04
) for the task of target localization in comparison with a traditional image-guided neurosurgery (IGNS) navigation system. Further, MARIN AR visualization was found to be more intuitive and allowed users to estimate target depth more easily.
Conclusion
MARIN improves upon previously proposed mobile AR neuronavigation systems with its real-time performance, higher accuracy, full integration in the normal workflow and greater interactivity and customizability of the displayed information. The improvement in efficiency and usability over previous systems will facilitate bringing AR into the OR.
The ST3GAL4 gene encodes the enzyme Galβ1-4GlcNAc α2,3 sialyltransferase (ST3Gal IV). This enzyme participates in the synthesis of the sialyl Lewis x antigen. In different cancer types altered ...expression of this antigen has been reported. The transcriptional regulation of this gene is very complex, different mRNA variants (V1-V10) have been reported and are originated by the activity of different promoters and alternative splicing. Only the promoter that gives rise to the V3 variant has not been previously reported. The objective of this work was to identify and characterize the V3 promoter of the ST3GAL4 gene. For this, the putative V3 promoter of the ST3GAL4 gene was delimited by in silico analysis. The complete promoter and smaller versions were cloned in a reporter plasmid. The constructs were transfected in the HaCaT cells and the promoter activity was evaluated by luciferase reporter assays. The cloned region showed promoter activity, and the basal activity was not dependent on TATA boxes. However, the GC boxes, an initiator element (Inr) and downstream promoter element (DPE) could contribute to basal activity. The promoter contains several binding sites for the nuclear factor of activated T-cells (NFAT) that could participate in inducible activity during the immune response. The minimal promoter corresponds to a fragment of approximately 300 bp, located in the position -347 b to -40 b. The characterization of the V3 promoter of the ST3GAL4 gene completes the study of the four promoters of this gene, this contributes to the understanding of its complex transcription regulation.
In image-guided neurosurgery, a registration between the patient and their pre-operative images and the tracking of surgical tools enables GPS-like guidance to the surgeon. However, factors such as ...brainshift, image distortion, and registration error cause the patient-to-image alignment accuracy to degrade throughout the surgical procedure no longer providing accurate guidance. The authors present a gesture-based method for manual registration correction to extend the usage of augmented reality (AR) neuronavigation systems. The authors’ method, which makes use of the touchscreen capabilities of a tablet on which the AR navigation view is presented, enables surgeons to compensate for the effects of brainshift, misregistration, or tracking errors. They tested their system in a laboratory user study with ten subjects and found that they were able to achieve a median registration RMS error of 3.51 mm on landmarks around the craniotomy of interest. This is comparable to the level of accuracy attainable with previously proposed methods and currently available commercial systems while being simpler and quicker to use. The method could enable surgeons to quickly and easily compensate for most of the observed shift. Further advantages of their method include its ease of use, its small impact on the surgical workflow and its small-time requirement.
The use of online social networks empowers its users to efficiently disseminate information across traditional social networks. Typically, the weight and value of messages are relative to its ...readers’ culture and interests. Nevertheless, in some instances, messages take the form of viral phenomenon, which circulates around the world in very short periods of time. Therefore, despite the actual content of the message spread over the network, the determination of the effectiveness of message dissemination across the social network becomes an attractive opportunity for scientific study. Since a meticulous analysis of a complete online social network would require the acquisition of security permissions from its providers, a vast quantity of computer resources and a substantial amount of time for collection of online content in the network, the aim of this thesis is to address these challenges by building a system capable to simulate an online social network. More precisely, we focus our investigation on the Twitter social network, which is one of the most prominent micro-blogging social network providers nowadays. The main contributions of this work include the following: (i) the provision of a tool, for investigations in the area of social networks, to bypass the common challenges observed during data gathering, (ii) the design of a extensible system that minimizes the cost of implementation of newly resampling techniques, and (iii) the invention of a workbench that allows empirical analysis of properties of an online social network (in the context of this study, the dissemination of messages and the dynamics of influence on the Twitter social network, are the key properties under investigation). Production data from the Twitter network is used to present evidence to suffice the evaluation of different measurements of influence. The collection of live data in this investigation was performed for a period of three days (respecting the 15-minute window between GET requests- imposed by the Twitter API). The data collected served to build the grounds for modeling the social networks described in this thesis.
The ST3GAL4 gene encodes the enzyme Galβ1-4GlcNAc α2,3 sialyltransferase (ST3Gal IV). This enzyme participates in the synthesis of the sialyl Lewis x antigen. In different cancer types altered ...expression of this antigen has been reported. The transcriptional regulation of this gene is very complex, different mRNA variants (V1-V10) have been reported and are originated by the activity of different promoters and alternative splicing. Only the promoter that gives rise to the V3 variant has not been previously reported. The objective of this work was to identify and characterize the V3 promoter of the ST3GAL4 gene. For this, the putative V3 promoter of the ST3GAL4 gene was delimited by in silico analysis. The complete promoter and smaller versions were cloned in a reporter plasmid. The constructs were transfected in the HaCaT cells and the promoter activity was evaluated by luciferase reporter assays. The cloned region showed promoter activity, and the basal activity was not dependent on TATA boxes. However, the GC boxes, an initiator element (Inr) and downstream promoter element (DPE) could contribute to basal activity. The promoter contains several binding sites for the nuclear factor of activated T-cells (NFAT) that could participate in inducible activity during the immune response. The minimal promoter corresponds to a fragment of approximately 300 bp, located in the position -347 b to -40 b. The characterization of the V3 promoter of the ST3GAL4 gene completes the study of the four promoters of this gene, this contributes to the understanding of its complex transcription regulation.
Federated Learning using the Federated Averaging algorithm has shown great advantages for large-scale applications that rely on collaborative learning, especially when the training data is either ...unbalanced or inaccessible due to privacy constraints. We hypothesize that Federated Averaging underestimates the full extent of heterogeneity of data when the aggregation is performed. We propose Precision-weighted Federated Learning a novel algorithm that takes into account the variance of the stochastic gradients when computing the weighted average of the parameters of models trained in a Federated Learning setting. With Precision-weighted Federated Learning, we provide an alternate averaging scheme that leverages the heterogeneity of the data when it has a large diversity of features in its composition. Our method was evaluated using standard image classification datasets with two different data partitioning strategies (IID/non-IID) to measure the performance and speed of our method in resource-constrained environments, such as mobile and IoT devices. We obtained a good balance between computational efficiency and convergence rates with Precision-weighted Federated Learning. Our performance evaluations show 9% better predictions with MNIST, 18% with Fashion-MNIST, and 5% with CIFAR-10 in the non-IID setting. Further reliability evaluations ratify the stability in our method by reaching a 99% reliability index with IID partitions and 96% with non-IID partitions. In addition, we obtained a 20x speedup on Fashion-MNIST with only 10 clients and up to 37x with 100 clients participating in the aggregation concurrently per communication round. The results indicate that Precision-weighted Federated Learning is an effective and faster alternative approach for aggregating private data, especially in domains where data is highly heterogeneous.
The contamination and dispersion of antibiotics into the environment through effluents from sewage treatment plants have produced serious consequences like the generation of resistant bacteria, ...generating a health emergency, so the need arises to develop processes to eliminate antibiotics in effluents. One of the methodologies used is advanced oxidation where a complete degradation of antibiotics can be reached, among the advanced oxidation processes, photocatalysis stands out as an efficient degradation pathway of organic pollutants from a photocatalyst. In the present investigation SiO2–TiO2 fibers were obtained by sol-gel and electrospinning techniques which had a cylindrical morphology and a smooth surface free of defects with a diameter 266 ± 62 nm. In increasing photocatalytic properties, the SiO2–TiO2 fibers were doped with silver by electrodeposition resulting in the deposition of particles with a branched dendritic morphology with a size of approximately 1 μm. The characterization of SiO2–TiO2–Ag fibers by infrared in the fibers eluted bands characteristic of the Si–O–Si and Ti–O–Ti bonds, and by means of the X-ray diffraction were identified crystalline phases corresponding to the anatase and rutile of the Titania, as well as face-centered cubic silver. Oxytetracycline was used as a contaminant to investigate the photocatalytic performance of SiO2–TiO2–Ag material, where from an initial concentration of 30 ppm in the presence of SiO2–TiO2 fibers its degradation percentage was 65%, unlike 90% in 7 h using silver-doped fibers. The increased degradation of oxytetracycline is attributed to the success in decreasing the banned band of SiO2–TiO2 fibers from 3.1eV to 2.5eV to silver doping.
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•Currently, the preparation and synthesis of efficient and appropriate materials for the elimination of dangerous contaminants such as antibiotics in wastewater is very necessary.•The use of catalytic nanofibers or nanocomposites with photocatalytic properties have great potential.•The objective of this contribution was to prepare silica-titania-silver nanofibers using a simple approach involving Sol-Gel and electrospinning methods.•Homogeneous Silica-Titania fibers with an average diameter of 331 nm were obtained by sol-gel and electrospinning processes.•The increase of photocatalytic properties SiO2–TiO2 fibers were studied in present of silver by electrodeposition, the deposition results in particles with a branched dendritic morphology with a size of approximately 1 μm.•Fibers with electrodeposition treatment of silver showed greater photocatalytic activity by degrading oxytetracycline by 90%.
Oxytetracycline (OTC) is listed as an emerging contaminant due to the adverse effects that it has on human health and the environment. Being a broad spectrum antibiotic, OTC is widely used and is ...found in large concentrations in wastewater. Advanced wastewater treatment is an effective method for the removal of this pollutant; however, these tertiary treatments are expensive and generally not applied in nondeveloped countries. In this work, the removal of OTC by adsorption using sustainable materials synthesized from hydroxyapatite (HA) and aluminosilicates by chemical precipitation method was performed. Four different adsorbent materials were obtained: mixing hydroxyapatite and kaolin (HA-K), hydroxyapatite with natural clay (HA-NC), hydroxyapatite with synthetic zeolite (HA-SZ), and hydroxyapatite with natural aluminosilicates (HA-NA). The adsorbent materials were characterized by FT-IR, pH
pzc
, cation exchange capacity (CEC), SEM/EDX, TEM (particle size), and XRD. Kinetic tests and adsorption isotherms of OTC were carried out by varying conditions of the aqueous media as pH value, temperature, and the presence of ionic strength. Different kinetic and isothermal models were applied to the obtained data. All the synthesized materials showed an acceptable sorption capacity for OTC removal. The elimination of this antibiotic was greater than 50% in the four synthesized materials. Experimental data showed a better fit to the Elovich kinetic model, indicative of a heterogeneous chemical sorption process. Kinetic and thermodynamic parameters showed that the synthetized materials from HA and aluminosilicates are potential and alternative adsorbent materials for OTC removal from water.
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•The objective of this contribution was to prepare silica-titania-copper nanofibers using a simple approach involving Sol-Gel and electrospinning methods.•Homogeneous Silica-Titania ...fibers with an average diameter of 331 nm were obtained by sol–gel and electrospinning processes.•Functionalized fibers with Cu presented structures distributed throughout the material in forms of clusters of needles, stars, bows and spheres with sizes of 0.5 to 2 µm and form is depend on time of electrodeposition. Preparation of silica-titania-copper nanofibers by Sol-Gel and electrospinning methods for SERS
The development of copper nanomaterials with dendritic shapes or hedgehog morphologies is of interest due to their optical, electrical, and catalytic properties; and their applications in catalysis and surface enhanced Raman spectroscopy (SERS). Placing the particles on a ceramic matrix gives them support and the properties of both materials is benefit. In this research, a matrix of TiO2SiO2 ceramic fibers doped with copper microstructures was developed, to take advantage of the synergistic effect of both materials, and their ability to amplify signals in Raman spectroscopy was evaluated. Sol-gel and electrospinning techniques were used to obtain the ceramic matrix, and electrodeposition to obtain the copper structures. Characterization by infrared spectroscopy and X-ray diffraction allowed the identification of copper species such as CuO, Cu2O, and Cu(OH)2. In addition, the characterization with scanning electron microscopy allowed to identify morphologies on the fibers of spheres, dendrites, and needles, belonging to copper species. The materials presented SERS amplification factors of 20.23 and 9.51 for TiO2SiO2 fibers and TiO2SiO2Cu doped by 0.75 min respectively, for the 1373 cm−1 band of a 1 × 10−4 M violet crystal solution.
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