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The randomized cycle Three review researching trastuzumab emtansine using trastuzumab, pertuzumab and docetaxel in elderly patients together with innovative phase HER2-positive breast cancers: Asia Clinical Oncollgy Team Study (JCOG1607, HERB Green tea examine).
A new hybrid system is proposed to capture CO2 as well as generate electricity with the low CO2 inlet condition of confined space. Within the system, a novel photocatalytic porous framework coated by g-C3N4/TiO2 is prepared to avoid the inhibition of microalgae growth caused by the direct addition of photocatalyst. Under 0.8% v/v CO2 inlet condition, chemical oxygen demand (COD) yields from the photocatalytic framework immersed in the phosphate buffer and the algae suspension are 1.63 mg L-1 h-1 and 1.90 mg L-1 h-1, respectively. CO2 sequestration rate of a 60L cylindrical photobioreactor increases from 12% to 22%. The combination modes between photobioreactor and photocatalytic framework can be selected flexibly depends on the demands of application. This hybrid system not only benefits to enhance the CO2 sequestration rate of photobioreactor but also has the potential to be served as the power source in a confined space.
The triply periodic minimal surface (TPMS) method effectively mimics the porous scaffold for tissue engineering with continuous topology, pore interconnections, and high surface area to volume ratio. However, the process to generate a three-dimensional (3D) mesh of porous structure from the mimicked organs is complicated for biologists and sometimes requires various software. this website Herein, we present the standalone program called "Scaffolder" for generating the porous topology from the user-input 3D model to the open-source community.

The 3D mesh of a porous scaffold was used by the proposed method and dual-marching cubes algorithm. Afterward, the mesh was sliced into the contours to examine pore sizes by Feret diameter and Gilbert-Johnson-Keerthi distance. The relationships between the program parameters (i.e., grid size, angular frequency, and iso-level) and scaffold properties (i.e., pore size, porosity, and surface area ratio) were investigated.

The developed program can generate and evaluate a porous scaffold. The median (IQR) absolute errors in grid size of 200, 300, 400, and 500 divisions were 1.92 (0.35-3.80), 1.00 (0.18-2.22), 0.53 (0-1.37), and 0.24 (0-0.74), respectively. Spearman's correlation showed the impact of angular frequency and iso-level on the pore size, porosity, and surface area of the generated scaffold (p<0.05).

This study enables researchers to rapidly design the 3D mesh of porous scaffold design, evaluate scaffold properties, and customize the implicit function for various applications, especially in tissue engineering and computational structural analysis.
This study enables researchers to rapidly design the 3D mesh of porous scaffold design, evaluate scaffold properties, and customize the implicit function for various applications, especially in tissue engineering and computational structural analysis.The aim of this study is to present a new methodology to explore a field of research and exercise this technique to find good mathematical models to solve the problem of territorial alignment applied to health services. For this purpose we show a methodology that combines three methods of analysis social network analysis, longitudinal analysis, and mapping change analysis. In this paper, we applied the mapping change method, originally used in large networks, to small and medium ones, and used the Tabu search scheme instead of simulated annealing. Finally, to highlight the significant changes over time of keywords networks, an alluvial diagram is used to show the significance clusterings through the subperiods studied. The work reports on the most relevant authors on the subject and the most widely used mathematical models applied to solve the problem.
After two months of implementing a partial lockdown, the Indonesian government had announced the "New Normal" policy to prevent a further economic crash in the country. This policy received many critics, as Indonesia still experiencing a fluctuated number of infected cases. Understanding public perception through effective risk communication can assist the government in relaying an appropriate message to improve people's compliance and to avoid further disease spread.

This study observed how risk communication using social media platforms like Twitter could be adopted to measure public attention on COVID-19 related issues "New Normal".

From May 21 to June 18, 2020, we archived all tweets related to COVID-19 containing keywords "#NewNormal", and "New Normal" using Drone Emprit Academy (DEA) engine. DEA search API collected all requested tweets and described the cumulative tweets for trend analysis, word segmentation, and word frequency. We further analyzed the public perception using sentiment analysis ahe process of quick decision-making and policy evaluation during uncertain times in response to the COVID-19 pandemic.
Our findings offer an opportunity for the government to use Twitter in the process of quick decision-making and policy evaluation during uncertain times in response to the COVID-19 pandemic.
Lower back pain in humans has become a major risk. Classical approaches follow a non-invasive imaging technique for the assessment of spinal intervertebral disc (IVDs) abnormalities, where identification and segmentation of discs are done separately, making it a time-consuming phenomenon. This necessitates designing a robust automated and simultaneous IVDs identification and segmentation of multi-modality MRI images.

We introduced a novel deep neural network architecture coined as 'RIMNet', a Region-to-Image Matching Network model, capable of performing an automated and simultaneous IVDs identification and segmentation of MRI images. The multi-modal input data is being fed to the network with a dropout strategy, by randomly disabling modalities in mini-batches. The performance accuracy as a function of the testing dataset was determined. The execution of the deep neural network model was evaluated by computing the IVDs Identification Accuracy, Dice coefficient, MDOC, Average Symmetric Surface Distance, Jaccard Coefficient, Hausdorff Distance and F1 Score.

Proposed model has attained 94% identification accuracy, dice coefficient value of 91.7±1% in segmentation and MDOC 90.2±1%. Our model also achieved 0.87±0.02 for Jaccard Coefficient, 0.54±0.04 for ASD and 0.62±0.02mm Hausdorff Distance. The results have been validated and compared with other methodologies on dataset of MICCAI IVD 2018 challenge.

Our proposed deep-learning methodology is capable of performing simultaneous identification and segmentation on IVDs MRI images of the human spine with high accuracy.
Our proposed deep-learning methodology is capable of performing simultaneous identification and segmentation on IVDs MRI images of the human spine with high accuracy.
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