Notes

Design, synthesis, stereochemical dedication, molecular docking examine, throughout silico pre-ADMET idea as well as anti-proliferative activities of indole-pyrimidine types as Mcl-1 inhibitors.
Hence, LAB can inhibit the growth of mycotoxin-producing fungi, thereby preventing the production of mycotoxins. Fermentation is also an efficient technique for improving nutrient bioavailability and other functional properties of cereal-based products. This review seeks to provide evidence of the potential of LAB from African fermented cereal-based products as potential biological agents against mycotoxin-producing fungi.
The purpose of the current study was to analyze the influence of radiological "disappearing liver metastasis" (DLM) on the efficacy and prognosis of patients with colorectal liver metastases (CRLM) undergoing conversion therapy.

Patients with CRLM by the multidisciplinary team (MDT) of the First Affiliated Hospital of Chongqing Medical University were retrospectively enrolled from January 2014 to January 2021. The relationship between the occurrence and recurrence of DLM and different clinical factors was analyzed.

Thirty-five of the 113 patients (31.0%) with initially unresectable CRLM developed DLM, and of the 361 lesions, 177 disappeared (49.0%). Within 6 months, 6-12 months, and 12-24 months groups, the recurrence rate was 3.4%, 16.8%, and 34.8%, but there is no recurrence in after 24 months group. There was a statistical difference between chemotherapy alone and chemotherapy combined with the targeted therapy group on the occurrence of DLM (58.3% vs. 37.1%,
< 0.001). There were significant differences between <5 mm group and >10 mm group on occurrence of DLM（76.7% vs. 30.4%,
< 0.001) and between 5-10 mm group and >10 mm group also (70.0% vs. 30.4%,
< 0.001). Through univariate and multivariate analyses, it was concluded that age (
= 0.026, 95%CI = 3.690) and treatment regimens (
= 0.033, 95%CI = 2.703) had a significant influence on the progression-free survival (PFS) time of DLM.

Younger patients, who use chemotherapy alone to achieve a therapeutic effect, might have better survival benefits when the lesions do not progress within 2 years after the appearance of DLMs.
Younger patients, who use chemotherapy alone to achieve a therapeutic effect, might have better survival benefits when the lesions do not progress within 2 years after the appearance of DLMs.Breast cancer is an important factor affecting human health. This issue has various diagnosis process which were evolved such as mammography, fine needle aspirate, and surgical biopsy. These techniques use pathological breast cancer images for diagnosis. Breast cancer surgery allows the forensic doctor to histologist to access the microscopic level of breast tissues. The conventional method uses an optimized radial basis neural network using a cuckoo search algorithm. Existing radial basis neural network techniques utilized feature extraction and reduction parts separately. It is proposed that it overcomes the CNN approach for all the feature extraction and classification process to reduce time complexity. In this proposed method, a convolutional neural network is proposed based on an artificial fish school algorithm. The breast cancer image dataset is taken from cancer imaging archives. In the preprocessing step of classification, the breast cancer image is filtered with the support of a wiener filter for classification. The convolutional neural network has set the intense data of an image and is used to remove the features. After executing the extraction procedure, the reduction process is performed to speed up the train and test data processing. Here, the artificial fish school optimization algorithm is utilized to give the direct training data to the deep convolutional neural network. find more The extraction, reduction, and classification of features are utilized in the single deep convolutional neural network process. In this process, the optimization technique helps to decrease the error rate and increases the performance efficiency by finding the number of epochs and training images to the Deep CNN. In this system, the normal, benign, and malignant tissues are predicted. By comparing the existing RBF technique with the cuckoo search algorithm, the presented model attains the outcome in the way of sensitivity, accuracy, specificity, F1 score, and recall.In view of the impact of COVID-19 on the mental health of college students, this paper proposes a study on the relationship between psychological status and epidemic situation of university students based on BP neural network, so as to provide theoretical basis for universities to take targeted mental health education. This paper investigates the effects of COVID-19 on the psychological emotions of college students. According to the behavior and psychological characteristics of college students, the relevant investigation results are obtained through event monitoring, early warning, and usual performance, and a relationship model between college students' psychological status and epidemic situation based on BP neural network is constructed. This paper studies several factors through the relationship model and uses the principal component analysis method to analyze the impact of various factors on college students' psychology. According to the model prediction and result analysis, it concluded that the influence of COVID-19 should focus on improving the professional quality, physical quality, humanistic quality, and moral quality of university students, so as to improve the stability of colleges and universities in the event of public health emergencies. The model constructed in this paper can provide reference for carrying out mental health education and formulating effective intervention programs.With the advent of the era of big data, in the face of massive data mining, cognitive computing will play a pivotal role in future data processing. This study aims to study how to analyze the knowledge transfer and management of virtual enterprises based on structured cognitive computing. This study explains the basic concepts of structured cognitive computing and virtual enterprises and discusses knowledge transfer. In the experiment of this study, it can be seen from Table 1that in 2015, the growth rate of virtual enterprises reached 13%, the growth rate of virtual enterprises reached 36% in 2019, and it increased by 23% from 2015 to 2019. It can be seen that the development of virtual enterprises is getting faster and faster. From the data in Table 2 in the experiment of this study, we can see that with the increase in virtual enterprises in recent years, it has also brought development to the economy of various regions in China. The average employment rate in each region has increased by 24% at the lowest and reached 30% at the highest. It shows that the virtual enterprise has a great effect on the development of the region. Virtual enterprises can not only promote the growth of regional employment rate but also promote the flow of funds between different regions. Knowledge transfer is an important part of the sustainable development of virtual enterprises. How to carry out effective knowledge transfer and management has become a problem that contemporary virtual enterprises need to think about. There are three main factors that affect the knowledge transfer of virtual enterprises knowledge transfer party, knowledge receiver, and virtual enterprise's own factors.Computer vision is one of the hottest research directions in artificial intelligence at present, and its research goal is to give computers the ability to perceive and cognize their surroundings from a single image. Image recognition is an important research direction in the field of computer vision, which has important research significance and application value in industrial applications such as video surveillance, biometric identification, unmanned vehicles, human-computer interaction, and medical image recognition. In this article, we propose an end-to-end, pixel-to-pixel IoT-oriented fuzzy support tensor product adaptive image classification method. Considering the problem that traditional support tensor product classification methods are difficult to directly produce pixel-to-pixel classification results, the research is based on the idea of inverse convolution network design, which directly outputs dense pixel-by-pixel classification results for images to be classified of arbitrary size to achieve truehm is improved under a small number of labeled training samples.Technology affects almost every aspect of life and is constantly changing. Digital communication technology has made it easier and faster to connect people all over the world. Digital technology is used in varies fields, including business, industries, companies, and educational institutions. There are various benefits of technology; it is also associated with a number of risks and dangerous threats known as cybercrimes. Cybercrime is a criminal activity that targets digital technology, like a computer, a computer network, or a mobile device. Cybersecurity is the way we reduce the risk of becoming a victim of cybercrime. Cybersecurity is the process of defending against cyberattacks. By using these concepts, we investigated the interval-valued complex T-spherical fuzzy relations (IVCT-spherical-FRs) introduced in this paper. We studied the relationships between different types of cybersecurity and the sources of cyberattacks. Furthermore, the Hasse diagram for the interval-valued complex T-spherical partial order set and relation is developed. The concepts of Hasse diagram are being used to examine various cybersecurity techniques and practices. The most effective method is identified using the features of Hasse diagrams. Finally, comparison tests are used to demonstrate the benefits of the proposed methods.The emergence of powerful deep learning architectures has resulted in breakthrough innovations in several fields such as healthcare, precision farming, banking, education, and much more. Despite the advantages, there are limitations in deploying deep learning models in resource-constrained devices due to their huge memory size. This research work reports an innovative hybrid compression pipeline for compressing neural networks exploiting the untapped potential of z-score in weight pruning, followed by quantization using DBSCAN clustering and Huffman encoding. The proposed model has been experimented with state-of-the-art LeNet Deep Neural Network architectures using the standard MNIST and CIFAR datasets. Experimental results prove the compression performance of DeepCompNet by 26x without compromising the accuracy. The synergistic blend of the compression algorithms in the proposed model will ensure effortless deployment of neural networks leveraging DL applications in memory-constrained devices.At present, there are many chess styles in piano education, but there is a lack of comprehensive, scientific, and guiding teaching mode. It highlights many educational problems and cannot meet the development requirements of piano education at this stage. However, the piano scoring system can partially replace teachers' guidance to piano players. This paper extracts the signal characteristics of playing music, establishes the piano performance scoring model using Big Data and BP neural network technology, and selects famous works to test the effect of the scoring system. The results show that the model can test whether the piano works fairly. It can effectively evaluate the player's performance level and accurately score each piece of music. This not only provides a reference for the player to improve the music level but also provides a new idea for the research results and the application of new technology in music teaching. This paper puts forward reasonable solutions to the problems existing in piano education at the present stage, which is helpful to cultivate high-quality piano talents.
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