Forecast of pain degree of lumbar disc herniation based on back propagation neural network.

To further explore the pathogenic mechanism of lumbar disc herniation (LDH) pain, this study screens important imaging features that are significantly correlated with the pain score of LDH. The features with significant correlation imaging were included into a back propagation (BP) neural network model for training, including Pfirrmann classification, Michigan State University (MSU) regional localization (MSU protrusion size classification and MSU protrusion location classification), sagittal diameter index, sagittal diameter/transverse diameter index, transverse diameter index, and AN angle (angle between nerve root and protrusion). The BP neural network training model results showed that the specificity was 95 ± 2%, sensitivity was 91 ± 2%, and accuracy was 91 ± 2% of the model. The results show that the degree of intraspinal occupation of the intervertebral disc herniation and the degree of intervertebral disc degeneration are related to LDH pain. The innovation of this study is that the BP neural network model constructed in this study shows good performance in the accuracy experiment and receiver operating characteristic experiment, which completes the prediction task of lumbar Magnetic Resonance Imaging features for the pain degree of LDH for the first time, and provides a basis for subsequent clinical diagnosis.

Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning.

Background: Deep learning (DL) methods can noninvasively predict glioma subtypes; however, there is no set paradigm for the selection of network structures and input data, including the image combination method, image processing strategy, type of numeric data, and others. Purpose: To compare different combinations of DL frameworks (ResNet, ConvNext, and vision transformer (VIT)), image preprocessing strategies, magnetic resonance imaging (MRI) sequences, and numerical data for increasing the accuracy of DL models for differentiating glioma subtypes prior to surgery. Methods: Our dataset consisted of 211 patients with newly diagnosed gliomas who underwent preoperative MRI with standard and diffusion-weighted imaging methods. Different data combinations were used as input for the three different DL classifiers. Results: The accuracy of the image preprocessing strategies, including skull stripping, segment addition, and individual treatment of slices, was 5%, 10%, and 12.5% higher, respectively, than that of the other strategies. The accuracy increased by 7.5% and 10% following the addition of ADC and numeric data, respectively. ResNet34 exhibited the best performance, which was 5% and 17.5% higher than that of ConvNext tiny and VIT-base, respectively. Data Conclusions: The findings demonstrated that the addition of quantitatively numeric data, ADC images, and effective image preprocessing strategies improved model accuracy for datasets of similar size. The performance of ResNet was superior for small or medium datasets.

Alterations of Thalamic Nuclei Volumes and the Intrinsic Thalamic Structural Network in Patients with Multiple Sclerosis-Related Fatigue.

Fatigue is a debilitating and prevalent symptom of multiple sclerosis (MS). The thalamus is atrophied at an earlier stage of MS and although the role of the thalamus in the pathophysiology of MS-related fatigue has been reported, there have been few studies on intra-thalamic changes. We investigated the alterations of thalamic nuclei volumes and the intrinsic thalamic network in people with MS presenting fatigue (F-MS). The network metrics comprised the clustering coefficient (Cp), characteristic path length (Lp), small-world index (σ), local efficiency (Eloc), global efficiency (Eglob), and nodal metrics. Volumetric analysis revealed that the right anteroventral, right central lateral, right lateral geniculate, right pulvinar anterior, left pulvinar medial, and left pulvinar inferior nuclei were atrophied only in the F-MS group. Furthermore, the F-MS group had significantly increased Lp compared to people with MS not presenting fatigue (NF-MS) (2.9674 vs. 2.4411, PAUC = 0.038). The F-MS group had significantly decreased nodal efficiency and betweenness centrality of the right mediodorsal medial magnocellular nucleus than the NF-MS group (false discovery rate corrected p < 0.05). The F-MS patients exhibited more atrophied thalamic nuclei, poorer network global functional integration, and disrupted right mediodorsal medial magnocellular nuclei interconnectivity with other nuclei. These findings might aid the elucidation of the underlying pathogenesis of MS-related fatigue.

The Prediction of Sinter Drums Strength Using Hybrid Machine Learning Algorithms.

The prediction model with the sinter drum strength as the evaluation index was established based on the index data and historical sintering data generated during the sintering process. The regression prediction model in the algorithm of machine learning was applied to the prediction of the strength of the sinter drum. After verifying the feasibility of drum strength prediction, different data preprocessing methods were used to preprocess the data. Ten regression prediction algorithms such as linear regression, ridge regression, regression tree, support vector regression, and nearest neighbor regression were used for predicting the sinter drum strength to obtain preliminary prediction results. By comparing the prediction results, the most suitable combinations of data preprocessing algorithms and prediction algorithms for sinter drum strength prediction is obtained. The prediction results show that, for the drum strength of the sinter, using the function data standardization algorithm for data preprocessing has the best effect. Then, using gradient boosting regression, random forest regression, and extra tree regression prediction algorithms resulted in higher prediction accuracy. On this basis, the regression prediction model algorithm parameters are optimized and improved. The parameters of the regression prediction algorithm that are most suitable for the prediction of sinter drum strength are obtained.

A Model Study on Raw Material Chemical Composition to Predict Sinter Quality Based on GA-RNN.

The quality control process for sintered ore is cumbersome and time- and money-consuming. When the assay results come out and the ratios are found to be faulty, the ratios cannot be changed in time, which will produce sintered ore of substandard quality, resulting in a waste of resources and environmental pollution. For the problem of lagging sinter detection results, Long Short-Term Memory and Genetic Algorithm-Recurrent Neural Networks prediction algorithms were used for comparative analysis, and the article used GA-RNN quality prediction model for prediction. Through correlation analysis, the chemical composition of the sintered raw material was determined as the input parameter and the physical and metallurgical properties of the sintered ore were determined as the output parameters, thus successfully establishing a GA-RNN-based sinter quality prediction model. Based on 150 sets of original data, 105 sets of data were selected as the training sample set and 45 sets of data were selected as the test sample set. The results obtained were compared to the real value with an average prediction error of 1.24% for the drum index, 0.92% for the low-temperature reduction chalking index (RDI), 0.95% for the reduction index (RI), 0.40% for the load softening temperature T10%, and 0.43% for the load softening temperature T40%, with all within the running time thresholds. The study of this model enables the prediction of the quality of sintered ore prior to sintering, thus improving the yield of sintered ore, increasing corporate efficiency, saving energy, and reducing environmental pollution.

Characterization of selenium-containing polysaccharide from Spirulina platensis and its protective role against Cd-induced toxicity.

Herein, selenium-containing polysaccharide from Spirulina platensis (Se-SPP) was prepared and its structural characteristics and protective role against Cd-induced toxicity in vivo and in vitro were investigated. Se-SPP was alkali-extracted from selenium-containing Spirulina platensis which was cultured in Zarrouk medium supplemented with Na2SeO3. The contents of carbohydrate, protein, uronic acid, sulfate and elements (including Se, C, H, O, N, and S) as well as the monosaccharide composition, molecular weight, surface morphology and FT-IR spectra of Se-SPP was compared to that of selenium-free polysaccharide (SPP). The results revealed that SPP and Se-SPP were both high-molecular-weight heteropolysaccharide with similar molecular weight and monosaccharide composition but significantly different selenium content, indicating that the covalently-bonding of a small amount of selenium did not destroy the original structure of polysaccharide. Furthermore, CdCl2 was utilized to build Cd-intoxicated cells model in vitro and rats model in vivo respectively. Then, the protective effect of Se-SPP against cadmium-induced toxicity was assessed. The results demonstrated that Se-SPP treatment provided significant protection against Cd-induced toxicity, which was superior compared to that of SPP or Na2SeO3 alone. The enhancement of protective role may be affected by the covalently-bonding of selenium to polysaccharide.

An improved method for Daugman's iris localization algorithm.

Computer-based automatic recognition of persons for security reasons is highly desirable. Iris patterns provide an opportunity for separation of individuals to an extent that would avoid false positives and negatives. The current standard for this science is Daugman's iris localization algorithm. Part of the time required for analysis and comparison with other images relates to eyelid and eyelash positioning and length. We sought to remove the upper and lower eyelids and eyelashes to determine if separation of individuals could still be attained. Our experiments suggest separation can be achieved as effectively and more quickly by removing distracting and variable features while retaining enough stable factors in the iris to enable accurate identification.

Local coexpression domains in the genome of rice show no microsynteny with Arabidopsis domains.

Chromosomal coexpression domains are found in a number of different genomes under various developmental conditions. The size of these domains and the number of genes they contain vary. Here, we define local coexpression domains as adjacent genes where all possible pair-wise correlations of expression data are higher than 0.7. In rice, such local coexpression domains range from predominantly two genes, up to 4, and make up approximately 5% of the genomic neighboring genes, when examining different expression platforms from the public domain. The genes in local coexpression domains do not fall in the same ontology category significantly more than neighboring genes that are not coexpressed. Duplication, orientation or the distance between the genes does not solely explain coexpression. The regulation of coexpression is therefore thought to be regulated at the level of chromatin structure. The characteristics of the local coexpression domains in rice are strikingly similar to such domains in the Arabidopsis genome. Yet, no microsynteny between local coexpression domains in Arabidopsis and rice could be identified. Although the rice genome is not yet as extensively annotated as the Arabidopsis genome, the lack of conservation of local coexpression domains may indicate that such domains have not played a major role in the evolution of genome structure or in genome conservation.

In plants, highly expressed genes are the least compact.

In both the monocot rice and the dicot Arabidopsis, highly expressed genes have more and longer introns and a larger primary transcript than genes expressed at a low level: higher expressed genes tend to be less compact than lower expressed genes. In animal genomes, it is the other way round. Although the length differences in plant genes are much smaller than in animals, these findings indicate that plant genes are in this respect different from animal genes. Explanations for the relationship between gene configuration and gene expression in animals might be (or might have been) less important in plants. We speculate that selection, if any, on genome configuration has taken a different turn after the divergence of plants and animals.

In silico identification of putative promoter motifs of White Spot Syndrome Virus.

BACKGROUND: White Spot Syndrome Virus, a member of the virus family Nimaviridae, is a large dsDNA virus infecting shrimp and other crustacean species. Although limited information is available on the mode of transcription, previous data suggest that WSSV gene expression occurs in a coordinated and cascaded fashion. To search in silico for conserved promoter motifs (i) the abundance of all 4 through 8 nucleotide motifs in the upstream sequences of WSSV genes relative to the complete genome was determined, and (ii) a MEME search was performed in the upstream sequences of either early or late WSSV genes, as assigned by microarray analysis. Both methods were validated by alignments of empirically determined 5' ends of various WSSV mRNAs. RESULTS: The collective information shows that the upstream region of early WSSV genes, containing a TATA box and an initiator, is similar to Drosophila RNA polymerase II core promoter sequences, suggesting utilization of the cellular transcription machinery for generating early transcripts. The alignment of the 5' ends of known well-established late genes, including all major structural protein genes, identified a degenerate motif (ATNAC) which could be involved in WSSV late transcription. For these genes, only one contained a functional TATA box. However, almost half of the WSSV late genes, as previously assigned by microarray analysis, did contain a TATA box in their upstream region. CONCLUSION: The data may suggest the presence of two separate classes of late WSSV genes, one exploiting the cellular RNA polymerase II system for mRNA synthesis and the other generating messengers by a new virus-induced transcription mechanism.

Local coexpression domains of two to four genes in the genome of Arabidopsis.

Expression of genes in eukaryotic genomes is known to cluster, but cluster size is generally loosely defined and highly variable. We have here taken a very strict definition of cluster as sets of physically adjacent genes that are highly coexpressed and form so-called local coexpression domains. The Arabidopsis (Arabidopsis thaliana) genome was analyzed for the presence of such local coexpression domains to elucidate its functional characteristics. We used expression data sets that cover different experimental conditions, organs, tissues, and cells from the Massively Parallel Signature Sequencing repository and microarray data (Affymetrix) from a detailed root analysis. With these expression data, we identified 689 and 1,481 local coexpression domains, respectively, consisting of two to four genes with a pairwise Pearson's correlation coefficient larger than 0.7. This number is approximately 1- to 5-fold higher than the numbers expected by chance. A small (5%-10%) yet significant fraction of genes in the Arabidopsis genome is therefore organized into local coexpression domains. These local coexpression domains were distributed over the genome. Genes in such local domains were for the major part not categorized in the same functional category (GOslim). Neither tandemly duplicated genes nor shared promoter sequence nor gene distance explained the occurrence of coexpression of genes in such chromosomal domains. This indicates that other parameters in genes or gene positions are important to establish coexpression in local domains of Arabidopsis chromosomes.