Notes

Evaluation between the Vision Plus device and gold standard methods for computing hemoglobin amounts and loaded mobile or portable amounts throughout cattle.
The emergence of crowdfunding has given many capital demanders a new fund-raising channel, but the overall project success rate is very low. Many scholars have begun to discover key suscessful factors of crowdfunding projects. Previous studies have used questionnaires survey to identify important project features. Nirogacestat In addition to requiring a lot of manpower and time, there may also be sampling bias. Moreover, related studies also reported that the project description will affect the success of the crowdfunding project, but there is no research to tell fundraisers which success factors should be included in the content of the project description. Besides, in recent years, game crowdfunding projects have been attracted lots of attention in terms of total fundraising amount and number of projects. Moreover, in traditional feature selection and text mining approaches, the selected terms are un-organized and hard to be explained. Therefore, this study will focus on real video and mobile game project descriptions to replace conventional questionnaires. To solve these issues, we present a lexicon-based feature selection method. We attempt to define "content features" and build lexicons to determine the attributes' values. Three feature selection methods including decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE) will be employed to find organized candidate key successful factors. Then, support vector machines (SVM) will be used to evaluate the performances of candidate feature subsets. Finally, this study has identified the key successful factors for video and mobile games, respectively. Based on the experimental results, we can give fundraisers some useful suggestions to improve the success rate of crowdfunding projects.In this research, A Deep Convolutional Neural Network was proposed to detect Pneumonia infection in the lung using Chest X-ray images. The proposed Deep CNN models were trained with a Pneumonia Chest X-ray Dataset containing 12,000 images of infected and not infected chest X-ray images. The dataset was preprocessed and developed from the Chest X-ray8 dataset. The Content-based image retrieval technique was used to annotate the images in the dataset using Metadata and further contents. The data augmentation techniques were used to increase the number of images in each of class. The basic manipulation techniques and Deep Convolutional Generative Adversarial Network (DCGAN) were used to create the augmented images. The VGG19 network was used to develop the proposed Deep CNN model. The classification accuracy of the proposed Deep CNN model was 99.34 percent in the unseen chest X-ray images. The performance of the proposed deep CNN was compared with state-of-the-art transfer learning techniques such as AlexNet, VGG16Net and InceptionNet. The comparison results show that the classification performance of the proposed Deep CNN model was greater than the other techniques.The asymmetric amination of secondary racemic allylic alcohols bears several challenges like the reactivity of the bi-functional substrate/product as well as of the α,β-unsaturated ketone intermediate in an oxidation-reductive amination sequence. Heading for a biocatalytic amination cascade with a minimal number of enzymes, an oxidation step was implemented relying on a single PQQ-dependent dehydrogenase with low enantioselectivity. This enzyme allowed the oxidation of both enantiomers at the expense of iron(III) as oxidant. The stereoselective amination of the α,β-unsaturated ketone intermediate was achieved with transaminases using 1-phenylethylamine as formal reducing agent as well as nitrogen source. Choosing an appropriate transaminase, either the (R)- or (S)-enantiomer was obtained in optically pure form (>98 % ee). The enantio-convergent amination of the racemic allylic alcohols to one single allylic amine enantiomer was achieved in one pot in a sequential cascade.Through a rapid screening of Cp*Ir complexes based on a turn-on type fluorescence readout, a [Cp*Ir(dipyrido[3,2-a  2',3'-c]phenazine)Cl]+ complex was found to catalyze the blue-light promoted dehydrogenation of N-heterocycles under physiological conditions. In the dehydrogenation of tetrahydroisoquinolines, the catalyst preferentially yielded the monodehydrogenated product, accompanying H2O2 generation. We surmise that this mechanism may be reminiscent of flavin-dependent oxidases.The aim of the study is, therefore, to analyze the formation of the UHIs in eight different cities in arid and semi-arid regions. The analysis is based on land cover (LC) classification (urban, green, and bare areas). The study found that bare areas had the highest mean LST values compared to the urban and green areas. The results show that the difference in temperatures between the bare areas and the urban areas ranges between 1 and 2 °C, between the bare areas and green areas ranges between 1 and 7 °C, and between the urban areas and green areas ranges between 1 and 5 °C. Furthermore, the LST values varied for each of the LULC categories, and hence some areas in the three categories had lower or higher LST values than in other categories. Hence, one category may not always have the highest LST value compared to other categories. The outcomes of this study may, therefore, have critical implications for urban planners who seek to mitigate UHI effects in arid and semi-arid urban areas.The diazabicyclooctane (DBO) scaffold is the backbone of non-β-lactam-based second generation β-lactamase inhibitors. As part of our efforts, we have synthesized a series of DBO derivatives A1-23 containing amidine substituents at the C2 position of the bicyclic ring. These compounds, alone and in combination with meropenem, were tested against ten bacterial strains for their antibacterial activity in vitro. All compounds did not show antibacterial activity when tested alone (MIC >64 mg/L), however, they exhibited a moderate inhibition activity in the presence of meropenem by lowering its MIC values. The compound A12 proved most potent among the other counterparts against all bacterial species with MIC from less then 0.125 mg/L to 2 mg/L, and is comparable to avibactam against both E. coli strains with a MIC value of less then 0.125 mg/L.
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