Leonardbond5986
Based on the risk score and clinical features, we constructed a nomogram.
Epigenetic modification-related genes have been identified as important prognostic markers that may assist in osteosarcoma therapy therapeutic decision-making.
Epigenetic modification-related genes have been identified as important prognostic markers that may assist in osteosarcoma therapy therapeutic decision-making.
Patients having hip fracture surgery are at high risk for postoperative delirium. Red blood cell (RBC) transfusion may increase postoperative delirium risk by causing neuroinflammation. We hypothesized that RBC transfusion would be associated with postoperative delirium in patients having hip fracture surgery.
An observational cohort study was performed using the United States National Surgical Quality Improvement Program (NSQIP) participant use files for hip fracture from 2016 to 2018. Propensity score analysis and inverse probability of treatment weighting (IPTW) were used to reduce bias from confounding. An IPTW adjusted odds ratio for developing postoperative delirium was calculated for patients who received RBC transfusion during surgery or in the 72 hours after.
There were 20,838 patients who had eligible current procedural terminology (CPT) codes for primary hip fracture surgery and complete study data. After employing strict exclusions to balance covariates and reduce bias, 3,715 patients remained in the IPTW cohort. Of these, 626 patients (16.9%) received RBC transfusion and 665 patients (17.9%) developed postoperative delirium. IPTW adjustment led to good covariate balance between patients who received RBC transfusion and those who did not. Patients who received RBC transfusion had significantly higher odds of postoperative delirium, IPTW adjusted odds ratio = 1.21, 95% CI = 1.03 to 1.43, and
= 0.02. Discharge location also differed significantly between patients who received RBC transfusion and those who did not (
< 0.001) with in-hospital mortality or referral to hospice occurring in 1.6% of patients who received RBC transfusion and 1.3% of patients who were not transfused.
RBC transfusion is associated with increased odds of postoperative delirium after hip fracture surgery and may be associated with worse clinical outcome.
RBC transfusion is associated with increased odds of postoperative delirium after hip fracture surgery and may be associated with worse clinical outcome.
To study the changes in gait characteristics of stroke patients with foot drop after the combination treatment of foot drop stimulator and moving treadmill training and thus provide a basis for the improvement in a foot drop gait after stroke.
Sixty patients with hemiplegia and foot drop caused by stroke were randomly divided into two groups of 30 the test group and the control group. Both groups received basic rehabilitation training. On this basis, the test group received the combination treatment of foot drop stimulator and moving treadmill training. The control group received foot drop stimulator training. Both groups received consecutive treatment for 3 weeks, five times a week, and every single time lasted for 30 minutes. Before and after the treatment, a gait watch three-dimensional gait analysis system was used to measure and record the maximum angles of flexion of the affected side's hip, knee, and ankle; the pace; the step length asymmetry; the iEMG of the tibialis anterior muscle; the functionaadmill can significantly improve stroke patients' foot gait and promote the normalization of hip flexion, knee flexion, and ankle flexion. It can increase the pace, significantly reduce the step length asymmetry, reduce the muscle tone of the gastrocnemius, and improve walking function.In digital media art, expressive force is an important art form of media. This paper studies digital images that have the same effect when applied to media art. The research object is media art images, and the application effect of the proposed algorithm is related to the media art images. The development of digital image technology has brought revolutionary changes to traditional media art expression techniques. In this paper, a partial-pixel interpolation technique based on convolutional neural network is proposed. Supervised training of convolutional neural networks requires predetermining the input and target output of the network, namely, integer image and fractional image in this paper. To solve the problem that the subpixel sample cannot be obtained, this paper first analyzes the imaging principle of digital image and proposes a subpixel sample generation algorithm based on Gaussian low-pass filter and polyphase sampling. From the perspective of rate distortion optimization, the purpose of pixel motion compensation is to improve the accuracy of interframe prediction. Therefore, this paper defines pixel motion compensation as an interframe regression problem, that is, the mapping process of the reference image integral pixel sample to the current image sample to be encoded. In this paper, a generalized partial-pixel interpolation model is proposed for bidirectional prediction. The partial-pixel interpolation of bidirectional prediction is regarded as a binary regression model; that is, the integral pixel reference block in two directions is mapped to the current block to be coded. It further studies how to apply the trained digital images to media art design more flexibly and efficiently.In this paper, we present a novel classifier based on fuzzy logic and wavelet transformation in the form of a neural network. This classifier includes a layer to predict the numerical feature corresponded to labels or classes. The presented classifier is implemented in brain tumor diagnosis. For feature extraction, a fractal model with four Gaussian functions is used. The classification is performed on 2000 MRI images. Regarding the results, the accuracy of the DT, KNN, LDA, NB, MLP, and SVM is 93.5%, 87.6%, 61.5%, 57.5%, 68.5%, and 43.6%, respectively. Based on the results, the presented FWNNet illustrates the highest accuracy of 100% with the fractal feature extraction method and brain tumor diagnosis based on MRI images. Based on the results, the best classifier for diagnosis of the brain tumor is FWNNet architecture. However, the second and third high-performance classifiers are the DT and KNN, respectively. Moreover, the presented FWNNet method is implemented for the segmentation of brain tumors. In this paper, we present a novel supervised segmentation method based on the FWNNet layer. check details In the training process, input images with a sweeping filter should be reshaped to vectors that correspond to reshaped ground truth images. In the training process, we performed a PSO algorithm to optimize the gradient descent algorithm. For this purpose, 80 MRI images are used to segment the brain tumor. Based on the results of the ROC curve, it can be estimated that the presented layer can segment the brain tumor with a high true-positive rate.Electric shovels are widely used in the mining industry to dig ore, and the teeth in shovels' bucket can be lost due to the tremendous pressure exerted by ore materials during operation. When the teeth fall off and enter the crusher with other ore materials, serious damages to crusher gears and other equipment happen, which causes millions of economic loss, because it is made of high-manganese steel. Thus, it is urgent to develop an efficient and automatic algorithm for detecting broken teeth. However, existing methods for detecting broken teeth have little effect and most research studies depended on sensor skills, which will be disturbed by closed cavity in shovel and not stable in practice. In this paper, we present an intelligent computer vision system for monitoring teeth condition and detecting missing teeth. Since the pixel-level algorithm is carried out, the amount of calculation should be reduced to improve the superiority of the algorithm. To release computational pressure of subsequent work, salient detection based on deep learning is proposed for extracting the key frame images from video flow taken by the camera installed on the shovel including the teeth we intend to analyze. Additionally, in order to more efficiently monitor teeth condition and detect missing teeth, semantic segmentation based on deep learning is processed to get the relative position of the teeth in the image. Once semantic segmentation is done, floating images containing the shape of teeth are obtained. Then, to detect missing teeth effectively, image registration is proposed. Finally, the result of image registration shows whether teeth are missing or not, and the system will immediately alert staff to check the shovel when teeth fall off. Through sufficient experiments, statistical result had demonstrated superiority of our presented model that serves more promising prospect in mining industry.Computer-aided composition is an attempt to use a formalized process to minimize human (or composer) involvement in the creation of music using a computer. Exploring the problem of computer-aided composition can enable us to understand and simulate the thinking mode of composers in the special process of music creation, which is an important application of artificial intelligence in the field of art. Feature extraction on the MIDI files has been introduced in this paper. Based on the genetic algorithm in this paper, a platform of the sampling coding method to optimize the character representation has solved the traditional algorithmic music composition study. Music directly from the pitch and duration can be derived from the characteristics, respectively, in the form of a one-hot encoding independently said. Failure to the rhythm of the characterization of the pitch and duration are problems that lead to the inability of compositional networks to learn musical styles better. Rhythm is the combination of pitch and time values according to certain rules. The rhythm of music affects the overall style of music. By associating the pitch and time value coding, the rhythm style of music can be preserved better so that the composition network can learn the style characteristics of music more easily.The prediction of gross domestic product (GDP) is a research hotspot, and its importance is self-evident. Its complex internal change mechanism also increases the difficulty of analyzing GDP data. The genetic algorithm (GA) is applied to the parameter design of the radial basis function neural network (RBFNN) based on genetic algorithm optimization (RBFNN-GA). An economic zone GDP image prediction model is proposed, which realizes the optimal design of the center vector, the base width vector of the RBFNN node function, and the weight between the hidden layer and output layer. Based on the GDP data over the years, this paper uses the RBFNN-GA prediction model to analyze and predict the GDP image and compares the image prediction results. The results show that the genetic algorithm is used to optimize RBFNN, which gives full play to the advantages of the two algorithms. The relative error of the RBFNN-GA prediction model is only 3.52%. Compared with the prediction results, the prediction accuracy is significantly higher than the ARIMA time series model and GM (1,1) model.
