Cognitive impairment in diffuse axonal injury patients with favorable outcome

Background and purposeTraumatic brain injury (TBI), especially the severe TBI are often followed by persistent cognitive sequalae, including decision-making difficulties, reduced neural processing speed and memory deficits. Diffuse axonal injury (DAI) is classified as one of the severe types of TBI. Part of DAI patients are marginalized from social life due to cognitive impairment, even if they are rated as favorable outcome. The purpose of this study was to elucidate the specific type and severity of cognitive impairment in DAI patients with favorable outcome.MethodsThe neurocognition of 46 DAI patients with favorable outcome was evaluated by the Chinese version of the Montreal Cognitive Assessment Basic (MoCA-BC), and the differences in the domains of cognitive impairment caused by different grades of DAI were analyzed after data conversion of scores of nine cognitive domains of MoCA-BC by Pearson correlation analysis.ResultsAmong the 46 DAI patients with favorable outcome, eight had normal cognitive function (MoCA-BC ≥ 26), and 38 had cognitive impairment (MoCA-BC < 26). The MoCA-BC scores were positively correlated with pupillary light reflex (r = 0.361, p = 0.014), admission Glasgow Coma Scale (GCS) (r = 0.402, p = 0.006), and years of education (r = 0.581, p < 0.001). Return of consciousness (r = −0.753, p < 0.001), Marshall CT (r = −0.328, p = 0.026), age (r = −0.654, p < 0.001), and DAI grade (r = −0.403, p = 0.006) were found to be negatively correlated with the MoCA-BC scores. In patients with DAI grade 1, the actually deducted scores (Ads) of memory (r = 0.838, p < 0.001), abstraction (r = 0.843, p < 0.001), and calculation (r = 0.782, p < 0.001) were most related to the Ads of MoCA-BC. The Ads of nine cognitive domains and MoCA-BC were all proved to be correlated, among patients with DAI grade 2. However, In the DAI grade 3 patients, the highest correlation with the Ads of MoCA-BC were the Ads of memory (r = 0.904, p < 0.001), calculation (r = 0.799, p = 0.006), orientation (r = 0.801, p = 0.005), and executive function (r = 0.869, p = 0.001).ConclusionDAI patients with favorable outcome may still be plagued by cognitive impairment, and different grades of DAI cause different domains of cognitive impairment

Deep-Sequencing Analysis of the Mouse Transcriptome Response to Infection with Brucella melitensis Strains of Differing Virulence

Brucella melitensis is an important zoonotic pathogen that causes brucellosis, a disease that affects sheep, cattle and occasionally humans. B. melitensis strain M5-90, a live attenuated vaccine cultured from B. melitensis strain M28, has been used as an effective tool in the control of brucellosis in goats and sheep in China. However, the molecular changes leading to attenuated virulence and pathogenicity in B. melitensis remain poorly understood. In this study we employed the Illumina Genome Analyzer platform to perform genome-wide digital gene expression (DGE) analysis of mouse peritoneal macrophage responses to B. melitensis infection. Many parallel changes in gene expression profiles were observed in M28- and M5-90-infected macrophages, suggesting that they employ similar survival strategies, notably the induction of anti-inflammatory and antiapoptotic factors. Moreover, 1019 differentially expressed macrophage transcripts were identified 4 h after infection with the different B. melitensis strains, and these differential transcripts notably identified genes involved in the lysosome and mitogen-activated protein kinase (MAPK) pathways. Further analysis employed gene ontology (GO) analysis: high-enrichment GOs identified endocytosis, inflammatory, apoptosis, and transport pathways. Path-Net and Signal-Net analysis highlighted the MAPK pathway as the key regulatory pathway. Moreover, the key differentially expressed genes of the significant pathways were apoptosis-related. These findings demonstrate previously unrecognized changes in gene transcription that are associated with B. melitensis infection of macrophages, and the central signaling pathways identified here merit further investigation. Our data provide new insights into the molecular attenuation mechanism of strain M5-90 and will facilitate the generation of new attenuated vaccine strains with enhanced efficacy

Identification of candidate antimicrobial peptides derived from abalone hemocyanin

Haemocyanins present in invertebrate hemolymph are multifunctional proteins, responsible for oxygen transport and contributing to innate immunity through phenoloxidase-like activity. In arthropods, haemocyanin has been identified as a source of broad-spectrum antimicrobial peptides during infection. Conversely, no haemocyanin-derived antimicrobial peptides have been reported for molluscs. The present study describes a putative antimicrobial region, termed haliotisin, located within the linking sequence between the α-helical domain and β-sheet domain of abalone (Haliotis tuberculata) haemocyanin functional unit E. A series of synthetic peptides based on overlapping fragments of the haliotisin region were tested for their bactericidal potential. Incubating Gram-positive and Gram-negative bacteria in the presence of certain haliotisin peptides, notably peptides 3-4-5 (DTFDYKKFGYRYDSLELEGRSISRIDELIQQRQEKDRTFAGFLLKGFGTSAS) led to reductions in microbial growth. Furthermore, transmission electron micrographs of haliotisin-treated bacteria revealed damages to the microbial cell wall. Data discussed here provides the first evidence to suggest that molluscan haemocyanin may act as a source of anti-infective peptides

Estimation of grain yield in wheat using source–sink datasets derived from RGB and thermal infrared imaging

Abstract Timely and efficient monitoring of crop aboveground biomass (AGB) and grain yield (GY) forecasting before harvesting are critical for improving crop yields and ensuring food security in precision agriculture. The purpose of this study is to explore the potential of fusing source–sink‐level color, texture, and temperature values extracted from RGB images and thermal images based on proximal sensing technology to improve grain yield prediction. High‐quality images of wheat from flowering to maturity under different treatments of nitrogen application were collected using proximal sensing technology over a 2‐year trial. Numerous variables based on source and sink organs were extracted from the acquired subsample images, including 30 color features, 10 texture features, and two temperature values. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) were used to screen variables. Support vector regression (SVR) and random forest (RF) were applied to establish AGB estimation models, and the GY prediction models were built by RF. The source dataset and sink dataset performed differently on AGB and GY estimation, but the combined source–sink dataset performed best for estimating both AGB and GY. Based on the source–sink dataset, the LASSO‐RF model was the best combination for predicting AGB and GY, with the coefficient of determination (R2) of 0.85 and 0.86, root mean square error (RMSE) of 1179.09 and 609.61 kg ha−1, and the ratio of performance to deviation (RPD) of 2.10 and 2.45, respectively. This study demonstrates that the multivariate eigenvalues of both source and sink organs have the potential to predict wheat yield and that the combination of machine learning models and variable selection methods can significantly affect the accuracy of yield prediction models and achieve effective monitoring of crop growth at late reproductive stages

Diagnostic model constructed by five EMT-related genes for renal fibrosis and reflecting the condition of immune-related cells

BackgroundRenal fibrosis is a physiological and pathological characteristic of chronic kidney disease (CKD) to end-stage renal disease. Since renal biopsy is the gold standard for evaluating renal fibrosis, there is an urgent need for additional non-invasive diagnostic biomarkers.MethodsWe used R package “limma” to screen out differently expressed genes (DEGs) based on Epithelial-mesenchymal transformation (EMT), and carried out the protein interaction network and GO, KEGG enrichment analysis of DEGs. Secondly, the least absolute shrinkage and selection operator (LASSO), random forest tree (RF), and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to identify candidate diagnostic genes. ROC curves were plotted to evaluate the clinical diagnostic value of these genes. In addition, mRNA expression levels of candidate diagnostic genes were analyzed in control samples and renal fibrosis samples. CIBERSORT algorithm was used to evaluate immune cells level. Additionally, gene set enrichment analysis (GSEA) and drug sensitivity were conducted.ResultsAfter obtaining a total of 24 DEGs, we discovered that they were mostly involved in several immunological and inflammatory pathways, including NF-KappaB signaling, AGE-RAGE signaling, and TNF signaling. Five genes (COL4A2, CXCL1, TIMP1, VCAM1, and VEGFA) were subsequently identified as biomarkers for renal fibrosis through machine learning, and their expression levels were confirmed by validation cohort data sets and in vitro RT-qPCR experiment. The AUC values of these five genes demonstrated significant clinical diagnostic value in both the training and validation sets. After that, CIBERSORT analysis showed that these biomarkers were strongly associated with immune cell content in renal fibrosis patients. GSEA also identifies the potential roles of these diagnostic genes. Additionally, diagnostic candidate genes were found to be closely related to drug sensitivity. Finally, a nomogram for diagnosing renal fibrosis was developed.ConclusionCOL4A2, CXCL1, TIMP1, VCAM1, and VEGFA are promising diagnostic biomarkers of tissue and serum for renal fibrosis

Investigation of Viscoelastic Properties of Polymer-Modified Asphalt at Low Temperature Based on Gray Relational Analysis

Funding Information: This research was funded by the Scientific and Technological Development Project of Guangxi Communications Investment Group Corporation Ltd., grant number 2021-001. Publisher Copyright: © 2023 by the authors.As the investigation indexes of low-temperature viscoelastic properties of polymer-modified asphalt (PMA) are unclear at present, in this paper, the creep stiffness (S), creep rate (m), low-temperature continuous classification temperature (TC), ΔTC, m/S, relaxation time ((Formula presented.)), and dissipation energy ratio ((Formula presented.)) were taken as a comparison sequence. The maximum flexural tensile strain (εB) of porous asphalt mixture (PAM) in a low-temperature bending test was selected as a reference sequence. Gray relational analysis was used to investigate the PMA’s low-temperature viscoelastic properties based on a bending beam rheometer (BBR). The results show certain contradictions in investigating the low-temperature properties of PMA when only considering the low-temperature deformation capacity or the stress relaxation capacity. The modulus and relaxation capacity should be considered when selecting the investigation indexes of the low-temperature viscoelastic properties of PMA. When rheological method is used to evaluate the low-temperature of polymer modified asphalt, TC and m/S are preferred. When only S or m is contradictory, m should be preferred. ΔTC can determine whether the low-temperature performance of PMA is dominated by S or m. The result can better guide the construction of asphalt pavement in areas with low temperatures. Asphalt can be selected quickly and accurately to avoid the waste of resources.Peer reviewe

Research on High- and Low-Temperature Rheological Properties of High-Viscosity Modified Asphalt Binder

This study evaluates the critical high- and low-temperature rheological properties of a high-viscosity modified asphalt (HVMA) binder by analyzing one neat and three high-viscosity modified binders (B-type, Y-type, and H-type) using temperature sweep tests and multi-stress creep recovery tests (MSCR) through the dynamic shear rheometer (DSR), and low-temperature creep stiffness properties by the bending beam rheometer (BBR). Technical indexes such as the softening point temperature, dynamic viscosity, rutting factor, unrecoverable creep compliance, and the creep recovery rate are measured and calculated for high-temperature properties, while the m/S value, dissipation energy ratio, relaxation time, elongation, creep stiffness, and creep speed are used as technical indexes for low-temperature properties. The results show that the incorporation of high-viscosity modifiers reduces the unrecoverable creep compliance and increases the creep recovery rate of the asphalt binder. Non-recoverable creep compliance is found to be a reliable indicator for high-temperature performance, while at low temperatures, the relaxation time decreases, the dissipation energy increases, and the stress relaxation ability improves. The dissipation energy ratio and m/S value are suggested to evaluate the low-temperature performance of HVMA binders using the Burgers model based on the BBR bending creep stiffness test. Therefore, this study recommends using the unrecoverable creep compliance via MSCR to evaluate high-temperature properties and dissipation energy ratio and m/S value for low-temperature properties in the evaluation of HVMA binders

Unmanned Aerial Vehicle-Scale Weed Segmentation Method Based on Image Analysis Technology for Enhanced Accuracy of Maize Seedling Counting

The number of maize seedlings is a key determinant of maize yield. Thus, timely, accurate estimation of seedlings helps optimize and adjust field management measures. Differentiating “multiple seedlings in a single hole” of maize accurately using deep learning and object detection methods presents challenges that hinder effectiveness. Multivariate regression techniques prove more suitable in such cases, yet the presence of weeds considerably affects regression estimation accuracy. Therefore, this paper proposes a maize and weed identification method that combines shape features with threshold skeleton clustering to mitigate the impact of weeds on maize counting. The threshold skeleton method (TS) ensured that the accuracy and precision values of eliminating weeds exceeded 97% and that the missed inspection rate and misunderstanding rate did not exceed 6%, which is a significant improvement compared with traditional methods. Multi-image characteristics of the maize coverage, maize seedling edge pixel percentage, maize skeleton characteristic pixel percentage, and connecting domain features gradually returned to maize seedlings. After applying the TS method to remove weeds, the estimated R2 is 0.83, RMSE is 1.43, MAE is 1.05, and the overall counting accuracy is 99.2%. The weed segmentation method proposed in this paper can adapt to various seedling conditions. Under different emergence conditions, the estimated R2 of seedling count reaches a maximum of 0.88, with an RMSE below 1.29. The proposed approach in this study shows improved weed recognition accuracy on drone images compared to conventional image processing methods. It exhibits strong adaptability and stability, enhancing maize counting accuracy even in the presence of weeds

Effects of Salt Stress on Grain Yield and Quality Parameters in Rice Cultivars with Differing Salt Tolerance

Rice yield and grain quality are highly sensitive to salinity stress. Salt-tolerant/susceptible rice cultivars respond to salinity differently. To explore the variation in grain yield and quality to moderate/severe salinity stress, five rice cultivars differing in degrees of salt tolerance, including three salt-tolerant rice cultivars (Lianjian 5, Lianjian 6, and Lianjian 7) and two salt-susceptible rice cultivars (Wuyunjing 30 and Lianjing 7) were examined. Grain yield was significantly decreased under salinity stress, while the extent of yield loss was lesser in salt-tolerant rice cultivars due to the relatively higher grain filling ratio and grain weight. The milling quality continued to increase with increasing levels. There were genotypic differences in the responses of appearance quality to mild salinity. The appearance quality was first increased and then decreased with increasing levels of salinity stress in salt-tolerant rice but continued to decrease in salt-susceptible rice. Under severe salinity stress, the protein accumulation was increased and the starch content was decreased; the content of short branched-chain of amylopectin was decreased; the crystallinity and stability of the starch were increased, and the gelatinization temperature was increased. These changes resulted in the deterioration of cooking and eating quality of rice under severe salinity-stressed environments. However, salt-tolerant and salt-susceptible rice cultivars responded differently to moderate salinity stress in cooking and eating quality and in the physicochemical properties of the starch. For salt-tolerant rice cultivars, the chain length of amylopectin was decreased, the degrees of order of the starch structure were decreased, and pasting properties and thermal properties were increased significantly, whereas for salt-susceptible rice cultivars, cooking and eating quality was deteriorated under moderate salinity stress. In conclusion, the selection of salt-tolerant rice cultivars can effectively maintain the rice production at a relatively high level while simultaneously enhancing grain quality in moderate salinity-stressed environments. Our results demonstrate specific salinity responses among the rice genotypes and the planting of salt-tolerant rice under moderate soil salinity is a solution to ensure rice production in China