Stalks and roots are the main battlefield for the coevolution between maize and Fusarium verticillioides.

Fusarium species are the dominant cause of maize ear rot, but they also inflict serious damage to the roots and stalks. Theoretically, the organ where the host interacts with the pathogen most frequently should exhibit the highest degree of symptom-genotype correlation. Because that symptom-genotype correlation is an indicator reflecting the degree of coevolution between pathogen and its hosts. We wonder which organ is the main battlefield for the antagonism between maize and Fusarium. For this purpose, 43 isolates of Fusarium were isolated from infected maize ears. Fusarium verticillioides and F. graminearum are the two dominant pathogens, accounting for 44% and 30%, respectively. Furthermore, 14 elite maize inbreds were exposed to 43 Fusarium isolates and the symptoms of ear rot, stalk rot and root rot were investigated. In general, symptoms caused by F. graminearum were significantly more severe than those caused by other Fusarium species. Surprisingly, the genotype of F. verticillioides showed a strong correlation with stalk and root rot, but not with ear rot. Accordingly, our study may provide the first evidence that the stalk and root of maize, rather than the ear, is the main battlefield for the coevolution between maize and F. verticillioides.

Eosinophils and drugs for eosinophilia are associated with the risk of colorectal cancer: a Mendelian randomization study.

Eosinophils have the potential to exhibit both anti-tumor properties and tumor-promoting effects. However, the impact of eosinophil levels in the bloodstream on tumorigenesis risk remains inadequately explored. Furthermore, investigations regarding the association between drugs regulating eosinophils and cancer risk are currently absent. In this study, we conducted a Mendelian randomization (MR) analysis utilizing eosinophil count and eosinophil percentage as exposures. In both cohorts, a significant association was observed between eosinophil count and the risk of colorectal cancer and skin malignancies. However, upon conducting a sensitivity analysis, heterogeneity was detected specifically in relation to skin malignancies. Subsequent reverse Mendelian randomization analysis did not indicate any evidence of reverse causality. Furthermore, the multivariate Mendelian randomization analysis results suggested that eosinophils act as a mediating factor in reducing the risk of colorectal cancer and skin malignancies in individuals with asthma. And the use of drugs that modulate eosinophilia may increase the risk of colorectal cancer. It is evident that the statistical evidence supporting a negative correlation between eosinophils count and the susceptibility to colorectal cancer is particularly robust. And, it is plausible to suggest that pharmaceutical interventions aimed at modulating eosinophilia may potentially heighten the risk of colorectal cancer. Hence, it is imperative to exercise caution and remain mindful of the potential risk of colorectal cancer when employing these medications.

Parallel convolutional contrastive learning method for enzyme function prediction.

The function labeling of enzymes has a wide range of application value in the medical field, industrial biology and other fields. Scientists define enzyme categories by enzyme commission (EC) numbers. At present, although there are some tools for enzyme function prediction, their effects have not reached the application level. To improve the precision of enzyme function prediction, we propose a parallel convolutional contrastive learning (PCCL) method to predict enzyme functions. First, we use the advanced protein language model ESM-2 to preprocess the protein sequences. Second, PCCL combines convolutional neural networks (CNNs) and contrastive learning to improve the prediction precision of multifunctional enzymes. Contrastive learning can make the model better deal with the problem of class imbalance. Finally, the deep learning framework is mainly composed of three parallel CNNs for fully extracting sample features. we compare PCCL with state-of-art enzyme function prediction methods based on three evaluation metrics. The performance of our model improves on both two test sets. Especially on the smaller test set, PCCL improves the AUC by 2.57%. The source code can be downloaded from https://github.com/biomg/PCCL.

Drought-induced circular RNAs in maize roots: Separating signal from noise.

Circular RNAs (circRNAs) play an important role in diverse biological processes; however, their origin and functions, especially in plants, remain largely unclear. Here, we used 2 maize (Zea mays) inbred lines, as well as 14 of their derivative recombination inbred lines with different drought sensitivity, to systematically characterize 8,790 circRNAs in maize roots under well-watered (WW) and water-stress (WS) conditions. We found that a diverse set of circRNAs expressed at significantly higher levels under WS. Enhanced expression of circRNAs was associated with longer flanking introns and an enrichment of long interspersed nuclear element retrotransposable elements. The epigenetic marks found at the back-splicing junctions of circRNA-producing genes were markedly different from canonical splicing, characterized by increased levels of H3K36me3/H3K4me1, as well as decreased levels of H3K9Ac/H3K27Ac. We found that genes expressing circRNAs are subject to relaxed selection. The significant enrichment of trait-associated sites along their genic regions suggested that genes giving rise to circRNAs were associated with plant survival rate under drought stress, implying that circRNAs play roles in plant drought responses. Furthermore, we found that overexpression of circMED16, one of the drought-responsive circRNAs, enhances drought tolerance in Arabidopsis (Arabidopsis thaliana). Our results provide a framework for understanding the intricate interplay of epigenetic modifications and how they contribute to the fine-tuning of circRNA expression under drought stress.

TSVM: Transfer Support Vector Machine for Predicting MPRA Validated Regulatory Variants.

Genome-wide association studies have shown that common genetic variants associated with complex diseases are mostly located in non-coding regions, which may not be causal. In addition, the limited number of validated non-coding functional variants makes it difficult to develop an effective supervised learning model. Therefore, improving the accuracy of predicting non-coding causal variants has become critical. This study aims to build a transfer learning-based machine learning method for predicting regulatory variants to overcome the problem of limited sample size. This paper presents a supervised learning method transfer support vector machine (TSVM) for massively parallel reporter assays (MPRA) validated regulatory variants prediction. First, uses a convolutional neural network to extract features with transfer learning. Second, the extracted features are selected by random forest method. Third, the selected features are used to train support vector machine for classification. We performed scale sensitivity experiments on the MPRA dataset and validated the effectiveness of transfer learning. The model achieves the Mcc of 0.326 and the AUC of 0.720, which are higher than the state-of-the-art method.

Profiling the selected hotspots for ear traits in two maize-teosinte populations.

KEY MESSAGE: Eight selected hotspots related to ear traits were identified from two maize-teosinte populations. Throughout the history of maize cultivation, ear-related traits have been selected. However, little is known about the specific genes involved in shaping these traits from their origins in the wild progenitor, teosinte, to the characteristics observed in modern maize. In this study, five ear traits (kernel row number [KRN], ear length [EL], kernel number per row [KNR], cob diameter [CD], and ear diameter [ED]) were investigated, and eight quantitative trait loci (QTL) hotspots were identified in two maize-teosinte populations. Notably, our findings revealed a significant enrichment of genes showing a selection signature and expressed in the ear in qbdCD1.1, qbdCD5.1, qbpCD2.1, qbdED1.1, qbpEL1.1, qbpEL5.1, qbdKNR1.1, and qbdKNR10.1, suggesting that these eight QTL are selected hotspots involved in shaping the maize ear. By combining the results of the QTL analysis with data from previous genome-wide association study (GWAS) involving two natural panels, we identified eight candidate selected genes related to KRN, KNR, CD, and ED. Among these, considering their expression pattern and sequence variation, Zm00001d025111, encoding a WD40/YVTN protein, was proposed as a positive regulator of KNR. This study presents a framework for understanding the genomic distribution of selected loci crucial in determining ear-related traits.

ZmARF1 positively regulates low phosphorus stress tolerance via modulating lateral root development in maize.

Phosphorus (P) deficiency is one of the most critical factors for plant growth and productivity, including its inhibition of lateral root initiation. Auxin response factors (ARFs) play crucial roles in root development via auxin signaling mediated by genetic pathways. In this study, we found that the transcription factor ZmARF1 was associated with low inorganic phosphate (Pi) stress-related traits in maize. This superior root morphology and greater phosphate stress tolerance could be ascribed to the overexpression of ZmARF1. The knock out mutant zmarf1 had shorter primary roots, fewer root tip number, and lower root volume and surface area. Transcriptomic data indicate that ZmLBD1, a direct downstream target gene, is involved in lateral root development, which enhances phosphate starvation tolerance. A transcriptional activation assay revealed that ZmARF1 specifically binds to the GC-box motif in the promoter of ZmLBD1 and activates its expression. Moreover, ZmARF1 positively regulates the expression of ZmPHR1, ZmPHT1;2, and ZmPHO2, which are key transporters of Pi in maize. We propose that ZmARF1 promotes the transcription of ZmLBD1 to modulate lateral root development and Pi-starvation induced (PSI) genes to regulate phosphate mobilization and homeostasis under phosphorus starvation. In addition, ZmERF2 specifically binds to the ABRE motif of the promoter of ZmARF1 and represses its expression. Collectively, the findings of this study revealed that ZmARF1 is a pivotal factor that modulates root development and confers low-Pi stress tolerance through the transcriptional regulation of the biological function of ZmLBD1 and the expression of key Pi transport proteins.

The trend of hypertension-related chronic kidney disease from 1990 to 2019 and its predictions over 25 years: an analysis of the Global Burden of Disease Study 2019.

BACKGROUND: High blood pressure is a key pathogenetic factor that contributes to the deterioration of kidney function. However, the incidence trend of hypertension-related chronic kidney disease (CKD) has rarely been studied; therefore, we aimed to analyze the global, regional, and national patterns, temporal trends as well as burden of hypertension-related CKD. METHODS: We extracted data on hypertension-related CKD from the Global Burden of Disease (GBD) study database, including the incidence, prevalence, disability-adjusted life years (DALYs), and mortality numbers and rates (per 100,000 population) and further described according to year, location, sex, age, and socio-demographic index (SDI). The estimated annual percentage changes (EAPCs) were calculated to assess the variation in incidence, DALYs, and mortality. We used an age-period-cohort (APC) model framework to analyze the underlying trends in prevalence by age, period, and birth cohort. Nordpred APC analysis was performed to predict the future morbidity and mortality of hypertension-related CKD. RESULTS: In 2019, a total of over 1.57 million new hypertension-related CKD cases were reported worldwide, a 161.97% increase from 1990. Compared to 1990, the age-standardized incidence rates (ASIR) increased in all 21 regions in 2019. In all countries and territories except Iceland, the EAPC in ASIR and the lower boundary of its 95% confidence interval (CI) were higher than 0. ASIR, age-standardized prevalence rates (ASPR), age-standardized DALYs rates (ASDR), and age-standardized mortality rates (ASMR) were not identical among countries with different SDI regions in 2019; additionally, ASIR and ASMR were significantly different among sexes in all SDI regions in 2019. The predicted incidence and mortality counts globally continue to increase to 2044, and there is an upward trend in ASIR for both men and women. CONCLUSIONS: Between 1990 and 2019, the ASIR of hypertension-related CKD demonstrated an ascending trend, and according to our projections, it would remain on the rise for the next 25 years. With remarkable global population growth, aging, and an increasing number of patients with hypertension, the burden of disease caused by hypertension-related CKD continues to increase.

Two all-biomass cellulose/amino acid spherical nanoadsorbents based on a tri-aldehyde spherical nanocellulose II amino acid premodification platform for the efficient removal of Cr(VI) and Cu(II).

Adsorbents consisting of spherical nanoparticles exhibit superior adsorption performance and hence, have immense potential for various applications. In this study, a tri-aldehyde spherical nanoadsorbent premodification platform (CTNAP), which can be grafted with various amino acids, was synthesized from corn stalk. Subsequently, two all-biomass spherical nanoadsorbents, namely, cellulose/l-lysine (CTNAP-Lys) and cellulose/L-cysteine (CTNAP-Cys), were prepared. The morphologies as well as chemical and crystal structures of the two adsorbents were studied in detail. Notably, the synthesized adsorbents exhibited two important characteristics, namely, a spherical nanoparticle morphology and cellulose II crystal structure, which significantly enhanced their adsorption performance. The mechanism of the adsorption of Cr(VI) onto CTNAP-Lys and that of Cu(II) onto CTNAP-Cys were studied in detail, and the adsorption capacities were determined to be as high as 361.69 (Cr(VI)) and 252.38 mg/g (Cu(II)). Using the proposed strategy, it should be possible to prepare other all-biomass cellulose/amino acid spherical nanomaterials with high functional group density for adsorption, medical, catalytic, analytical chemistry, corrosion, and photochromic applications.

Cross-Modal Plasticity during Self-Motion Perception.

To maintain stable and coherent perception in an ever-changing environment, the brain needs to continuously and dynamically calibrate information from multiple sensory sources, using sensory and non-sensory information in a flexible manner. Here, we review how the vestibular and visual signals are recalibrated during self-motion perception. We illustrate two different types of recalibration: one long-term cross-modal (visual-vestibular) recalibration concerning how multisensory cues recalibrate over time in response to a constant cue discrepancy, and one rapid-term cross-modal (visual-vestibular) recalibration concerning how recent prior stimuli and choices differentially affect subsequent self-motion decisions. In addition, we highlight the neural substrates of long-term visual-vestibular recalibration, with profound differences observed in neuronal recalibration across multisensory cortical areas. We suggest that multisensory recalibration is a complex process in the brain, is modulated by many factors, and requires the coordination of many distinct cortical areas. We hope this review will shed some light on research into the neural circuits of visual-vestibular recalibration and help develop a more generalized theory for cross-modal plasticity.

Identification and Functional Analysis of Drought-Responsive Long Noncoding RNAs in Maize Roots.

Long noncoding RNAs (lncRNAs) are transcripts with lengths of more than 200 nt and limited protein-coding potential. They were found to play important roles in plant stress responses. In this study, the maize drought-tolerant inbred line AC7643 and drought-sensitive inbred line AC7729/TZSRW, as well as their recombinant inbred lines (RILs) were selected to identify drought-responsive lncRNAs in roots. Compared with non-responsive lncRNAs, drought-responsive lncRNAs had different sequence characteristics in length of genes and number of exons. The ratio of down-regulated lncRNAs induced by drought was significantly higher than that of coding genes; and lncRNAs were more widespread expressed in recombination sites in the RILs. Additionally, by integration of the modifications of DNA 5-methylcytidine (5mC), histones, and RNA N6-methyladenosine (m6A), it was found that the enrichment of histone modifications associated with transcriptional activation in the genes generated lncRNAs was lower that coding genes. The lncRNAs-mRNAs co-expression network, containing 15,340 coding genes and 953 lncRNAs, was constructed to investigate the molecular functions of lncRNAs. There are 13 modules found to be associated with survival rate under drought. We found nine SNPs located in lncRNAs among the modules associated with plant survival under drought. In conclusion, we revealed the characteristics of lncRNAs responding to drought in maize roots based on multiomics studies. These findings enrich our understanding of lncRNAs under drought and shed light on the complex regulatory networks that are orchestrated by the noncoding RNAs in response to drought stress.

Amino acids and risk of colon adenocarcinoma: a Mendelian randomization study.

BACKGROUND: The existence of amino acid metabolic reprogramming in tumor cells is well established. However, the potential correlation between blood amino acids and the risk of colon adenocarcinoma remains largely unexplored. METHODS: We utilized Mendelian randomization (MR) analysis to examine the association between 20 amino acids in the blood and the risk of colon adenocarcinoma. Additionally, reverse MR analysis was employed to identify the presence of reverse causality. A two-step MR analysis was conducted to ascertain the potential mediating effect. Lastly, the alanine detection data from colon adenocarcinoma patients in our hospital were utilized to investigate the differences in alanine levels among healthy individuals and patients with colon cancer, as well as among patients with different stages and locations of colon cancer. Furthermore, a Kaplan-Meier curve was employed to examine the correlation between alanine and overall survival, followed by the implementation of COX univariate analysis. RESULTS: The results of our study indicate that there is an inverse correlation between alanine and the risk of colon adenocarcinoma. Additionally, we found no significant evidence to support a causal relationship between colon adenocarcinoma and alanine. Furthermore, our analysis revealed that alanine aminotransferase (ALT) and blood glucose do not act as mediators in this causal pathway. Moreover, individuals diagnosed with colon adenocarcinoma exhibited a significant decrease in alanine levels, particularly in cases of stage IV colon adenocarcinoma with distant metastasis. Additionally, elevated alanine levels were associated with improved overall survival rates among colon adenocarcinoma patients. CONCLUSIONS: The results of this study indicate that alanine exhibits protective characteristics against the onset of colon adenocarcinoma and may play a role in promoting a more favorable disease prognosis. Consequently, dietary interventions aimed at increasing alanine intake may serve as a potential strategy for the prevention and treatment of colon adenocarcinoma.

WVDL: Weighted Voting Deep Learning Model for Predicting RNA-Protein Binding Sites.

RNA-binding proteins are important for the process of cell life activities. High-throughput technique experimental method to discover RNA-protein binding sites is time-consuming and expensive. Deep learning is an effective theory for predicting RNA-protein binding sites. Using weighted voting method to integrate multiple basic classifier models can improve model performance. Thus, in our study, we propose a weighted voting deep learning model (WVDL), which uses weighted voting method to combine convolutional neural network (CNN), long short term memory network (LSTM) and residual network (ResNet). First, the final forecast result of WVDL outperforms the basic classifier models and other ensemble strategies. Second, WVDL can extract more effective features by using weighted voting to find the best weighted combination. And, the CNN model also can draw the predicted motif pictures. Third, WVDL gets a competitive experiment result on public RBP-24 datasets comparing with other state-of-the-art methods. The source code of our proposed WVDL can be found in https://github.com/biomg/WVDL.

CRMSNet: A deep learning model that uses convolution and residual multi-head self-attention block to predict RBPs for RNA sequence.

RNA-binding proteins (RBPs) play significant roles in many biological life activities, many algorithms and tools are proposed to predict RBPs for researching biological mechanisms of RNA-protein binding sites. Deep learning algorithms based on traditional machine learning get better result for predicting RBPs. Recently, deep learning method fused with attention mechanism has attracted huge attention in many fields and gets competitive result. Thus, attention mechanism module may also improve model performance for predicting RNA-protein binding sites. In this study, we propose convolutional residual multi-head self-attention network (CRMSNet) that combines convolutional neural network (CNN), ResNet, and multi-head self-attention blocks to find RBPs for RNA sequence. First, CRMSNet incorporates convolutional neural networks, recurrent neural networks, and multi-head self-attention block. Second, CRMSNet can draw binding motif pictures from the convolutional layer parameters. Third, attention mechanism module combines the local and global RNA sequence information for capturing long sequence feature. CRMSNet gets competitive AUC (area under the receiver operating characteristic [ROC] curve) result in a large-scale dataset RBP-24. And CRMSNet experiment result is also compared with other state-of-the-art methods. The source code of our proposed CRMSNet method can be found in https://github.com/biomg/CRMSNet.

TrichomeYOLO: A Neural Network for Automatic Maize Trichome Counting.

Plant trichomes are epidermal structures with a wide variety of functions in plant development and stress responses. Although the functional importance of trichomes has been realized, the tedious and time-consuming manual phenotyping process greatly limits the research progress of trichome gene cloning. Currently, there are no fully automated methods for identifying maize trichomes. We introduce TrichomeYOLO, an automated trichome counting and measuring method that uses a deep convolutional neural network, to identify the density and length of maize trichomes from scanning electron microscopy images. Our network achieved 92.1% identification accuracy on scanning electron microscopy micrographs of maize leaves, which is much better performed than the other 5 currently mainstream object detection models, Faster R-CNN, YOLOv3, YOLOv5, DETR, and Cascade R-CNN. We applied TrichomeYOLO to investigate trichome variations in a natural population of maize and achieved robust trichome identification. Our method and the pretrained model are open access in Github (https://github.com/yaober/trichomecounter). We believe TrichomeYOLO will help make efficient trichome identification and help facilitate researches on maize trichomes.

The value of convolutional neural networks-based deep learning model in differential diagnosis of space-occupying brain diseases.

Objectives: It is still a challenge to differentiate space-occupying brain lesions such as tumefactive demyelinating lesions (TDLs), tumefactive primary angiitis of the central nervous system (TPACNS), primary central nervous system lymphoma (PCNSL), and brain gliomas. Convolutional neural networks (CNNs) have been used to analyze complex medical data and have proven transformative for image-based applications. It can quickly acquire diseases' radiographic features and correct doctors' diagnostic bias to improve diagnostic efficiency and accuracy. The study aimed to assess the value of CNN-based deep learning model in the differential diagnosis of space-occupying brain diseases on MRI. Methods: We retrospectively analyzed clinical and MRI data from 480 patients with TDLs (n = 116), TPACNS (n = 64), PCNSL (n = 150), and brain gliomas (n = 150). The patients were randomly assigned to training (n = 240), testing (n = 73), calibration (n = 96), and validation (n = 71) groups. And a CNN-implemented deep learning model guided by clinical experts was developed to identify the contrast-enhanced T1-weighted sequence lesions of these four diseases. We utilized accuracy, sensitivity, specificity, and area under the curve (AUC) to evaluate the performance of the CNN model. The model's performance was then compared to the neuroradiologists' diagnosis. Results: The CNN model had a total accuracy of 87% which was higher than senior neuroradiologists (74%), and the AUC of TDLs, PCNSL, TPACNS and gliomas were 0.92, 0.92, 0.89 and 0.88, respectively. Conclusion: The CNN model can accurately identify specific radiographic features of TDLs, TPACNS, PCNSL, and gliomas. It has the potential to be an effective auxiliary diagnostic tool in the clinic, assisting inexperienced clinicians in reducing diagnostic bias and improving diagnostic efficiency.

Enhancement of Bonding and Mechanical Performance of Epoxy Asphalt Bond Coats with Graphene Nanoplatelets.

Improving bonding and mechanical strengths is important for the application of bond coats used in the construction of steel deck bridges. Graphene nanoplatelets (GNPs) are attractive nanofillers for polymer modification because of their low cost, ultra-high aspect ratio, and extraordinary thermal and mechanical performance. In this paper, GNPs were used to reinforce the epoxy asphalt bond coat (EABC). The morphology, viscosity-time behavior, contact angle, dynamic mechanical properties, and mechanical and bonding strengths of GNP-reinforced EABCs were investigated using laser confocal microscopy, a Brookfield rotational viscometer, a contact angle meter, dynamic mechanical analysis, a universal test machine, and single-lap shear and pull-off adhesion tests. GNP dispersed non-uniformly in the asphalt phase of EABC. The viscosity of the neat EABC was lowered with the inclusion of GNPs and thus the allowable construction time was extended. The existence of GNPs enhances the hydrophobicity of the neat EABC. When adding more than 0.2% GNP, the storage modulus, crosslinking density and glass transition temperatures of both asphalt and epoxy of the neat EABC increased. The mechanical and bonding properties of the neat EABC were greatly enhanced with the incorporation of GNPs. Furthermore, the mechanical and bonding strengths of the modified EABCs increased with the GNP content. GNP-reinforced EABCs can be utilized in the pavement of long-span steel bridges with long durability.

MCNN: Multiple Convolutional Neural Networks for RNA-Protein Binding Sites Prediction.

Computational prediction of the RBP bound sites using features learned from existing annotation knowledge is an effective method because high-throughput experiments are complex, expensive and time-consuming. Many methods have been proposed to predict RNA-protein binding sites. However, the partial information of RNA sequence is not fully used. In this study, we propose multiple convolutional neural networks (MCNN) method, which predicts RNA-protein binding sites by integrating multiple convolutional neural networks constructed by RNA sequence information extracted from windows with different lengths. First, MCNN trains multiple CNNs base on RNA sequences extracted by different window lengths. Second, MCNN can extract more binding patterns of RBPs by combining these trained multiple CNNs previously. Third, MCNN only uses RNA base sequence information for RNA-protein binding sites prediction, which extracts sequence binding features and predicts the result with same architecture. This avoids the information loss of feature extraction step. Our proposed MCNN demonstrates a competitive performance comparing with other methods on a large-scale dataset derived from CLIP-seq, which is an effective method for RNA-protein binding sites prediction. The source code of our proposed MCNN method can be found in https://github.com/biomg/MCNN.

Central nervous system tumefactive demyelinating lesions: Risk factors of relapse and follow-up observations.

Objective: To track the clinical outcomes in patients who initially presented with tumefactive demyelinating lesions (TDLs), we summarized the clinical characteristics of various etiologies, and identified possible relapse risk factors for TDLs. Methods: Between 2001 and 2021, 116 patients initially presented with TDLs in our hospital were retrospectively evaluated. Patients were followed for relapse and clinical outcomes, and grouped according to various etiologies. Demographic information, clinical data, imaging data, and laboratory results of patients were obtained and analyzed. The risk factors of relapse were analyzed by the Log-Rank test and the Cox proportional hazard model in multivariate analysis. Result: During a median follow-up period of 72 months, 33 patients were diagnosed with multiple sclerosis (MS), 6 patients with Balo, 6 patients with neuromyelitis optica spectrum disorders (NMOSD), 10 patients with myelin oligodendrocyte glycoprotein antibody-associated demyelination (MOGAD), 1 patient with acute disseminated encephalomyelitis (ADEM), and the remaining 60 patients still have no clear etiology. These individuals with an unknown etiology were categorized independently and placed to the other etiology group. In the other etiology group, 13 patients had recurrent demyelinating phases, while 47 patients did not suffer any more clinical events. Approximately 46.6% of TDLs had relapses which were associated with multiple functional system involvement, first-phase Expanded Disability Status Scale score, lesions morphology, number of lesions, and lesions location (P<0.05). And diffuse infiltrative lesions (P=0.003, HR=6.045, 95%CI:1.860-19.652), multiple lesions (P=0.001, HR=3.262, 95%CI:1.654-6.435) and infratentorial involvement (P=0.006, HR=2.289, 95%CI:1.064-3.853) may be independent risk factors for recurrence. Relapse free survival was assessed to be 36 months. Conclusions: In clinical practice, around 46.6% of TDLs relapsed, with the MS group showing the highest recurrence rate, and lesions location, diffuse infiltrative lesions, and multiple lesions might be independent risk factors for relapse. Nevertheless, despite extensive diagnostic work and long-term follow-up, the etiology of TDLs in some patients was still unclear. And these patients tend to have monophase course and a low rate of relapse.

Cbln1 regulates axon growth and guidance in multiple neural regions.

The accurate construction of neural circuits requires the precise control of axon growth and guidance, which is regulated by multiple growth and guidance cues during early nervous system development. It is generally thought that the growth and guidance cues that control the major steps of axon development have been defined. Here, we describe cerebellin-1 (Cbln1) as a novel cue that controls diverse aspects of axon growth and guidance throughout the central nervous system (CNS) by experiments using mouse and chick embryos. Cbln1 has previously been shown to function in late neural development to influence synapse organization. Here, we find that Cbln1 has an essential role in early neural development. Cbln1 is expressed on the axons and growth cones of developing commissural neurons and functions in an autocrine manner to promote axon growth. Cbln1 is also expressed in intermediate target tissues and functions as an attractive guidance cue. We find that these functions of Cbln1 are mediated by neurexin-2 (Nrxn2), which functions as the Cbln1 receptor for axon growth and guidance. In addition to the developing spinal cord, we further show that Cbln1 functions in diverse parts of the CNS with major roles in cerebellar parallel fiber growth and retinal ganglion cell axon guidance. Despite the prevailing role of Cbln1 as a synaptic organizer, our study discovers a new and unexpected function for Cbln1 as a general axon growth and guidance cue throughout the nervous system.