Identification of metal ion-binding sites in RNA structures using deep learning method.

Metal ion is an indispensable factor for the proper folding, structural stability and functioning of RNA molecules. However, it is very difficult for experimental methods to detect them in RNAs. With the increase of experimentally resolved RNA structures, it becomes possible to identify the metal ion-binding sites in RNA structures through in-silico methods. Here, we propose an approach called Metal3DRNA to identify the binding sites of the most common metal ions (Mg2+, Na+ and K+) in RNA structures by using a three-dimensional convolutional neural network model. The negative samples, screened out based on the analysis for binding surroundings of metal ions, are more like positive ones than the randomly selected ones, which are beneficial to a powerful predictor construction. The microenvironments of the spatial distributions of C, O, N and P atoms around a sample are extracted as features. Metal3DRNA shows a promising prediction power, generally surpassing the state-of-the-art methods FEATURE and MetalionRNA. Finally, utilizing the visualization method, we inspect the contributions of nucleotide atoms to the classification in several cases, which provides a visualization that helps to comprehend the model. The method will be helpful for RNA structure prediction and dynamics simulation study. Availability and implementation: The source code is available at https://github.com/ChunhuaLiLab/Metal3DRNA.

Computational methods, databases and tools for synthetic lethality prediction.

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

The clinical efficacy of Medial Sustain Nail(MSN) and Proximal femoral nail anti-rotation(PFNA) for fixation of medial comminuted trochanteric fractures: a prospective randomized controlled trial.

PURPOS: To evaluate the clinical efficacy of the Medial Sustain Nail (MSN) for medial comminuted trochanteric fractures fixation in comparison to Proximal Femoral Nail Antirotation (PFNA) through a clinical study. METHODS: A non-inferiority randomized controlled trial was conducted at a single centre between July 2019 and July 2020. Fifty patients diagnosed comminuted trochanteric fractures were randomly assigned to either the MSN group (n = 25) or the PFNA group (n = 25). A total of forty-three patients were included in the final study analysis. The primary outcome measure was Short Form 36 health surgery physical component summary (SF-36 PCS) score. Secondary outcomes included the Oxford Hip Scores (OHS), weight bearing, complication relate to implant and so on. This study was not blined to surgeons, but to patients and data analysts. RESULTS: The MSN demonstrated significantly better functional outcomes as measured by SF-36 PCS and OHS at six months postoperative compared to PFNA (p < 0.05). Union of fractures in the MSN group reached 90.9% at three months after surgery, whereas the PFNA group achieved a union rate of 57.1% (p < 0.05). Furthermore, weight-bearing time of MSN group was earlier than PFNA group (p < 0.05). Additionally, complications related to implant usage were more prevalent in the PFNA group (33.3%) compared to the MSN group (4.5%) (p < 0.05). CONCLUSION: MSN exhibited superior quality of life outcomes compared to PFNA at six months postoperative. This indicates that MSN effectively reconstructs medial femoral support in patients with comminuted trochanteric fractures, which facilitates early weight-bearing and accelerates the recovery process. TRIAL REGISTRATION: Trial registration number: NCT01437176, Date of the trial registration:2011-9-1, Date of commencement of the study:2011-9, Date of enrolment/recruitment of the study subjects:2019-7.

Analysis of PAT1 subfamily members in the GRAS family of upland cotton and functional characterization of GhSCL13-2A in Verticillium dahliae resistance.

KEY MESSAGE: GhSCL13-2A, a member of the PAT1 subfamily in the GRAS family, positively regulates cotton resistance to Verticillium dahliae by mediating the jasmonic acid and salicylic acid signaling pathways and accumulation of reactive oxygen species. Verticillium wilt (VW) is a devastating disease of upland cotton (Gossypium hirsutum) that is primarily caused by the soil-borne fungus Verticillium dahliae. Scarecrow-like (SCL) proteins are known to be involved in plant abiotic and biotic stress responses, but their roles in cotton defense responses are still unclear. In this study, a total of 25 GhPAT1 subfamily members in the GRAS family were identified in upland cotton. Gene organization and protein domain analysis showed that GhPAT1 members were highly conserved. GhPAT1 genes were widely expressed in various tissues and at multiple developmental stages, and they were responsive to jasmonic acid (JA), salicylic acid (SA), and ethylene (ET) signals. Furthermore, GhSCL13-2A was induced by V. dahliae infection. V. dahliae resistance was enhanced in Arabidopsis thaliana by ectopic overexpression of GhSCL13-2A, whereas cotton GhSCL13-2A knockdowns showed increased susceptibility. Levels of reactive oxygen species (ROS) and JA were also increased and SA content was decreased in GhSCL13-2A knockdowns. At the gene expression level, PR genes and SA signaling marker genes were down-regulated and JA signaling marker genes were upregulated in GhSCL13-2A knockdowns. GhSCL13-2A was shown to be localized to the cell membrane and the nucleus. Yeast two-hybrid and luciferase complementation assays indicated that GhSCL13-2A interacted with GhERF5. In Arabidopsis, V. dahliae resistance was enhanced by GhERF5 overexpression; in cotton, resistance was reduced in GhERF5 knockdowns. This study revealed a positive role of GhSCL13-2A in V. dahliae resistance, establishing it as a strong candidate gene for future breeding of V. dahliae-resistant cotton cultivars.

aPRBind: protein-RNA interface prediction by combining sequence and I-TASSER model-based structural features learned with convolutional neural networks.

MOTIVATION: Protein-RNA interactions play a critical role in various biological processes. The accurate prediction of RNA-binding residues in proteins has been one of the most challenging and intriguing problems in the field of computational biology. The existing methods still have a relatively low accuracy especially for the sequence-based ab-initio methods. RESULTS: In this work, we propose an approach aPRBind, a convolutional neural network-based ab-initio method for RNA-binding residue prediction. aPRBind is trained with sequence features and structural ones (particularly including residue dynamics information and residue-nucleotide propensity developed by us) that are extracted from the predicted structures by I-TASSER. The analysis of feature contributions indicates the sequence features are most important, followed by dynamics information, and the sequence and structural features are complementary in binding site prediction. The performance comparison of our method with other peer ones on benchmark dataset shows that aPRBind outperforms some state-of-the-art ab-initio methods. Additionally, aPRBind can give a better prediction for the modeled structures with TM-score≥0.5, and meanwhile since the structural features are not very sensitive to the refined 3D structures, aPRBind has only a marginal dependence on the accuracy of the structure model, which allows aPRBind to be applied to the RNA-binding site prediction for the modeled or unbound structures. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/ChunhuaLiLab/aPRbind. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prognostic prediction of systemic immune-inflammation status for patients with colorectal cancer: a novel pyroptosis-related model.

Pyroptosis and related gasdermin family proteins play an important role in the tumorigenesis of colorectal cancer (CRC). However, the prognostic roles of pyroptosis-related genes (PRGs) and their relation to infiltrates of immune cells in the pathogenesis of CRC remain unclear. Using this study, we set up a prognostic gene pattern on the basis of 13 PRGs (AIM2, CASP1, CASP5, CASP6, CASP8, CASP9, ELANE, GPX4, GSDMD, NLRP7, NOD2, PJVK, and PRKACA) for CRC patients. A comprehensive bioinformatics analysis based on these genes was then performed. With the good AUC prediction value of the ROC curves, the group with high hazard first had a poorer survival prognosis than the group with low hazard. Second, we found that PRGs were significantly related to inflammation-associated genes and immune-associated genes in CRC. Then, we identified a correlation of PRGs with immune infiltrations in CRC. For instance, the abundances of resting NK cells resting and neutrophils were higher in the low hazard group than in the high hazard group. Overall, this work indicated that PRGs contributed to generate heterogeneity of the tumor microenvironment (TME) in CRC. This prognostic PRG model may provide a starting point for the early diagnosis and medication use of CRC.

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction.

BACKGROUND: Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA-protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA-protein interactions. RESULTS: In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA-protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA-protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA-protein networks of Mus musculus successfully. CONCLUSIONS: In general, our proposed method EDLMFC improved the accuracy of ncRNA-protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC .

Equally Weighted Multiscale Elastic Network Model and Its Comparison with Traditional and Parameter-Free Models.

Dynamical properties of proteins play an essential role in their function exertion. The elastic network model (ENM) is an effective and efficient tool in characterizing the intrinsic dynamical properties encoded in biomacromolecule structures. The Gaussian network model (GNM) and anisotropic network model (ANM) are the two often-used ENM models. Here, we introduce an equally weighted multiscale ENM (equally weighted mENM) based on the original mENM (denoted as mENM), in which fitting weights of Kirchhoff/Hessian matrixes in mENM are removed since they neglect the details of pairwise interactions. Then, we perform its comparison with the mENM, traditional ENM, and parameter-free ENM (pfENM) in reproducing dynamical properties for the six representative proteins whose molecular dynamics (MD) trajectories are available in http://mmb.pcb.ub.es/MoDEL/. In the results, for B-factor prediction, mENM performs best, while the equally weighted mENM performs also well, better than the traditional ENM and pfENM models. As to the dynamical cross-correlation map calculation, mENM performs worst, while the results produced from the equally weighted mENM and pfENM models are close to those from MD trajectories with the latter a little better than the former. Furthermore, encouragingly, the equally weighted mANM displays the best performance in capturing the functional motional modes, followed by pfANM and traditional ANM models, while the mANM fails in all the cases. This work is helpful for strengthening the understanding of the elastic network model and provides a valuable guide for researchers to utilize the model to explore protein dynamics.

Rate limiting factors for DNA transduction induced by weak electromagnetic field.

DNA transduction across aqueous solutions has been reported previously. In this study, we examined a few key factors affecting DNA transduction rate in an extremely low frequency electromagnetic field. These include: the chemical composition of the aqueous solutions, the type of experimental vessel, the dilution step, and the origin of the DNA fragments. The results indicate that partially introducing essential ingredients for DNA amplification (i.e. dNTPs and PCR buffer) to the aqueous solution enhanced the transduction rate greatly, and transduction vessels made of hydrophilic quartz yielded more favorable results than vessels made of hydrophobic plastic. In addition, performing a serial dilution to the transduction solution more than doubled the transduction rate compared to that without the dilution step. For the DNA fragments used in this study, there was one with a pathogenic origin and two with non-pathogenic origins. However, all three fragments achieved DNA transduction regardless of the difference in their origins. The experimental setup for eliminating the false positives caused by both biological and potentially physical contamination is also described.

Molecular evolution of the plastid genome during diversification of the cotton genus.

Cotton (Gossypium spp.) is commonly grouped into eight diploid genomic groups, designated A-G and K, and one tetraploid genomic group, namely AD. To gain insight into the phylogeny of Gossypium and molecular evolution of the chloroplast genome duringdiversification, chloroplast genomes (cpDNA) from 6 D-genome and 2 G-genome species of Gossypium (G. armourianum D2-1, G. harknessii D2-2, G. davidsonii D3-d, G. klotzschianum D3-k, G. aridum D4, G. trilobum D8, and G. australe G2, G. nelsonii G3) were newly reported here. In combination with the 26 previously released cpDNA sequences, we performed comparative phylogenetic analyses of 34 Gossypium chloroplast genomes that collectively represent most of the diversity in the genus. Gossypium chloroplasts span a small range in size that is mostly attributable to indels that occur in the large single copy (LSC) region of the genome. Phylogenetic analysis using a concatenation of all genes provides robust support for six major Gossypium clades, largely supporting earlier inferences but also revealing new information on intrageneric relationships. Using Theobroma cacao as an outgroup, diversification of the genus was dated, yielding results that are in accord with previous estimates of divergence times, but also offering new perspectives on the basal, early radiation of all major clades within the genus as well as gaps in the record indicative of extinctions. Like most higher-plant chloroplast genomes, all cotton species exhibit a conserved quadripartite structure, i.e., two large inverted repeats (IR) containing most of the ribosomal RNA genes, and two unique regions, LSC (large single sequence) and SSC (small single sequence). Within Gossypium, the IR-single copy region junctions are both variable and homoplasious among species. Two genes, accD and psaJ, exhibited greater rates of synonymous and non-synonymous substitutions than did other genes. Most genes exhibited Ka/Ks ratios suggestive of neutral evolution, with 8 exceptions distributed among one to several species. This research provides an overview of the molecular evolution of a single, large non-recombining molecular during the diversification of this important genus.

Overexpression of MicroRNA-221 is associated with poor prognosis in non-small cell lung cancer patients.

The abnormal expression of microRNA-221 was detected in several cancers and some studies had indicated that microRNA-221 was associated with cancer prognosis. This study was aimed to evaluate the prognostic significance of microRNA-221 in non-small cell lung cancer (NSCLC). Quantitative real-time polymerase chain reaction (qRT-PCR) was used for detecting the relative expression levels of microRNA-221 in the pathological tissues and corresponding normal tissues of 104 NSCLC patients. The relationship between the expression levels and the clinical features was estimated by Chi-square method and the overall survival of patients at different expression levels was demonstrated by Kaplan-Meier method. Cox regression analysis was used to evaluate the prognostic significance of microRNA-221. The relative expression levels of microRNA-221 in the pathological tissues were remarkably higher than that in the corresponding normal tissues (1.71 vs 1.07, P = 0.000). The expression level was associated with lymph node metastasis (P = 0.001). The results of Kaplan-Meier method indicated that patients with high expression level of microRNA-221 had shorter overall survival time than those with low expression level (36.8 vs 45.2 months, P = 0.001). Moreover, Cox regression analysis suggested that microRNA-221 was a useful independent biomarker for NSCLC prognosis (HR = 1.873, 95 % CI = 1.267-2.768, P = 0.002). The aberrant expression of microRNA-221 is associated with NSCLC progression and it might be a potential biomarker for NSCLC prognosis.

Epistasis together with partial dominance, over-dominance and QTL by environment interactions contribute to yield heterosis in upland cotton.

KEY MESSAGE: QTL mapping based on backcross and RIL populations suggests that epistasis together with partial dominance, over-dominance and their environmental interactions of QTLs play an important role in yield heterosis in upland cotton. A backcross population (BC) was constructed to explore the genetic basis of heterosis in upland cotton (Gossypium hirsutum L.). For yield and yield components, recombinant inbred line (RIL) and BC populations were evaluated simultaneously at three different locations. A total of 35 and 30 quantitative trait loci (QTLs) were detected based on the RILs and BC data, respectively. Six (16.7 %) additive QTLs, 19 (52.8 %) partial dominant QTLs and 11 (30.6 %) over-dominant QTLs were detected by single-locus analysis using composite interval mapping in BC population. QTLs detected for mid-parent heterosis (MPH) were mostly related to those detected in the BC population. No significant correlation was found between marker heterozygosity and performance. It indicated that heterozygosity was not always favorable for performance. Two-locus analysis revealed 46, 25 and 12 QTLs with main effects (M-QTLs), and 55, 63 and 33 QTLs involved in digenic interactions (E-QTLs) were detected for yield and yield components in RIL, BC and MPH, respectively. A large number of M-QTLs and E-QTLs showed QTL by environment interactions (QEs) in three environments. These results suggest that epistasis together with partial dominance, over-dominance and QEs all contribute to yield heterosis in upland cotton.

The Effect of Polymorphisms in SPP1 on Risk of Fracture: A Case-Control Study.

BACKGROUND The purpose of the study was to investigate the correlation between rs4754 and rs6840362 polymorphisms of secreted phosphoprotein 1 (SPP1) gene and fracture risk. MATERIAL AND METHODS rs4754 and rs6840362 were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in 130 patients with fracture and 107 healthy controls matched with the former by age and sex. Hardy-Weinberg equilibrium (HWE) was assessed in the control group based on the genotype distributions of SSP1 poylmorphisms. The differences in genotype, allele, and haplotype frequencies between cases and controls were detected by the chi-square test, and the relative risk of fracture is expressed by odds ratio (OR) and 95% confidence interval (CI). The linkage disequilibrium (LD) and haplotype analyses were conducted with HaploView software. RESULTS The TT genotype in rs4754 had significant difference in patients with fracture and controls (10.77% and 4.59%, P=0.04) and the results showed that people carrying TT genotype of rs4754 were more susceptible to fractures than CC genotype carriers (OR=3.00, 95%CI=1.02-8.89). The T allele also had 1.54 times higher risk of fractures (OR=1.54, 95%CI=1.04-2.30), but this was not true for the rs6840362 polymorphism. LD between the 2 polymorphisms and haplotype C-T (rs6840362-rs4754) increased the susceptibility to fracture (OR=2.01, 95%CI=1.23-3.28). CONCLUSIONS SPP1 rs4754 polymorphism may be related to risk of fracture, but not rs6840362.

[Application of fluorescence spectra and parallel factor analysis in the classification of edible vegetable oils].

The fluorescence spectra of 22 samples of 8 kinds of edible vegetable oils (soybean oil, maize oil, olive oil, rice oil, peanut oil, walnut oil, sunflower oil and sesame oil) were measured with FS920 fluorescence spectrometer and the fluorescence matrixs (EEMs) were analyzed with parallel factor (PARAFAC) analysis model. To synthesize the capabilities of material characterization and component identification, fluorescence spectra combined with PARAFAC fulfill the classification of vegetable oils. The map feature (peak position, peak value and peak number) was obtained by analyzing three dimensional spectra and con tour maps in the range of emission wavelength from 260 to 750 nm, and excitation wavelengths from 250 to 550 nm. The fluorescent substances (unsaturated fatty acids, vitamin E and its derivatives, chlorophyll and carotenoid) corresponding to spectrum peaks were determined. The factor-number was selected and the components (vitamin E and its derivatives, linoleic acid and linolenic acid, fatty acid oxidation products, vegetable oil oxidation products) corresponding to each factor were ascertained. The four-factor excitation and emission profiles and projection score plots of PARAFAC model were plotted. Different vegetable oils can be characterized and distinguished with the map features of fluorescence spectra and sample projection plots of PARAFAC model. The results demonstrate the capability of the combination of fluorescence spectra technology and four-factor PARAFAC model for differentiating and characterizing vegetable oils.

The Lnc-ENST00000602558/IGF1 axis as a predictor of response to treatment with tripterygium glycosides in rheumatoid arthritis patients.

AIMS: Growing clinical evidence suggests that not all patients with rheumatoid arthritis (RA) benefit to the same extent by treatment with tripterygium glycoside (TG), which highlights the need to identify RA-related genes that can be used to predict drug responses. In addition, single genes as markers of RA are not sufficiently accurate for use as predictors. Therefore, there is a need to identify paired expression genes that can serve as biomarkers for predicting the therapeutic effects of TG tablets in RA. METHODS: A total of 17 pairs of co-expressed genes were identified as candidates for predicting an RA patient's response to TG therapy, and genes involved in the Lnc-ENST00000602558/GF1 axis were selected for that purpose. A partial-least-squares (PLS)-based model was constructed based on the expression levels of Lnc-ENST00000602558/IGF1 in peripheral blood. The model showed high efficiency for predicting an RA patient's response to TG tablets. RESULTS: Our data confirmed that genes co-expressed in the Lnc-ENST00000602558/IGF1 axis mediate the efficacy of TG in RA treatment, reduce tumor necrosis factor-α induced IGF1 expression, and decrease the inflammatory response of MH7a cells. CONCLUSION: We found that genes expressed in the Lnc-ENST00000602558/IGF1 axis may be useful for identifying RA patients who will not respond to TG treatment. Our findings provide a rationale for the individualized treatment of RA in clinical settings.

Three-dimensionally ordered macroporous iron oxide for removal of H2S at medium temperatures.

A series of iron oxide sorbents with novel structures of three-dimensionally ordered macropores (3DOM), ranging in size from 60 to 550 nm, were fabricated and creatively used as sorbents for the removal of H2S at medium temperatures of 300-350 °C. Evaluation tests using thermogravimetric analysis (TGA) and a fixed-bed reactor showed that, in comparison to the iron oxide sorbent prepared by a conventional mixing method, the fabricated iron oxide sorbent with a 3DOM structure exhibited much higher reactivity and efficiency, as well as high sorbent utilization with low regeneration temperature. The excellent performance of 3DOM iron oxide as a sulfur sorbent is attributed to its special texture, i.e., the open and interconnected macroporous, large surface area, and nanoparticles of iron oxide, which are revealed by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and nitrogen adsorption techniques. The investigation results of the pore effect on the performance of the sorbent show that sorbents with pores size around 150 nm in diameter revealed the best performance. The reason is that pores of this size are large enough to allow gas to pass through even if the channel is partially blocked during the reaction process while remaining a large surface area that can provide more active sites for the reaction.

Experimental Research on Improving Activity of Calcinated Coal Gangue via Increasing Calcium Content.

In this investigation, non-spontaneous combustion coal gangue was activated by two methods: (1) low-temperature calcination and (2) calcium addition. Differences in the activity of the activated coal gangue were studied at various calcination temperatures and amounts of calcium addition. Meanwhile, the cementation activity of the activated coal gangue was evaluated according to the activity effect analysis. Furthermore, the influences of the activated coal gangue on the cementation activity of cement were investigated. The results indicated that the activities of the activated coal gangue increased at a temperature between 500 °C and 700 °C. The calcium addition method can also increase the activity of coal gangue, with the effect being better when the gangue is mixed with slag. The addition of calcium and the calcination of coal gangue can promote the production of active minerals such as metakaolin, which is the main reason for the increased cementation activity.

GhERF.B4-15D: A Member of ERF Subfamily B4 Group Positively Regulates the Resistance against Verticillium dahliae in Upland Cotton.

Verticillium wilt is a fungal disease in upland cotton and exerts a significant effect on growth and potential productivity. This disease is mainly caused by V. dahliae Kleb. Ethylene response factor (ERF) is one of the superfamilies of transcription factors that is involved in the development and environmental adaption of crops. A total of 30 ERF.B4 group members were detected in upland cotton and divided into 6 subgroups. Gene structures, conserved motifs, and domain analysis revealed that members in each subgroup are highly conserved. Further, the 30 GhERF.B4 group members were distributed on 18 chromosomes, and 36 gene synteny relationships were found among them. GhERF.B4 genes were ubiquitously expressed in various tissues and developmental stages of cotton. Amongst them, GhERF.B4-15D was predominantly expressed in roots, and its expression was induced by V. dahliae infection. In addition, GhERF.B4-15D responded to methyl jasmonate (MeJA), methyl salicylate (MeSA), and ethylene (ET) phytohormones. It was also found that the V. dahliae resistance was enhanced due to overexpression of GhERF.B4-15D in Arabidopsis thaliana. On the contrary, interference of GhERF.B4-15D by virus-induced gene silencing (VIGS) technology decreased the V. dahliae resistance level in upland cotton. The subcellular localization experiment showed that GhERF.B4-15D was located in the nucleus. Yeast two-hybrid (Y2H) and luciferase complementation (LUC) approaches demonstrated that GhERF.B4-15D interacted with GhDREB1B. Additionally, the V. dahliae resistance was significantly decreased in GhDREB1B knockdowns. Our results showed that GhERF.B4-15D plays a role during V. dahliae infection in cotton.

Intramedullary nailing versus plating fixation for the treatment of midshaft clavicular fractures: A meta-analysis of randomized controlled trials.

PURPOSE: This study was to test the hypothesis that intramedullary (IM) nailing fixation of midshaft clavicle fractures could result in better clinical outcomes and lower complications rates than plating fixation. METHODS: PubMed, Embase, and the Cochrane Library database were used to search all English language published randomized controlled trials (RCTs) of midshaft clavicle fractures using plating versus IM nailing. The characteristics of the study participants were collected. Outcomes of postoperative shoulder functional measurements, operative data and complications rates were meta-analyzed. RESULTS: Eight hundred and ninety-five patients in ten RCTs and three quasi-RCTs were involved in the meta-analysis. The results of meta-analysis of these studies showed that the functional outcome evaluated by the Constant Shoulder and Disabilities of the Arm, Shoulder and Hand (DASH) scores after accepting IM nailing was significantly better than that of plating fixation at one year post-operatively (P < 0.01), with the heterogeneity of 43% and 91%, respectively. Sensitivity analyses of the pooled results of Constant and DASH scores displayed that the functional advantage of IM nailing fixation comes from the subgroup of locked IM nailing. Further, regarding the operative statistics, operative time, blood loss and wound length were significantly less in the IM nailing group than the plating group (P < 0.001). The rates of infection, major complications and complications-related revision surgery were significantly higher in the plating group than the IM nailing group; however, there were no significantly statistical differences in other complications, e.g., nonunion, refracture after hardware removal, implant failure, symptomatic hardware, etc. (P > 0.05). CONCLUSION: The observations in this review suggested that IM nailing, especially locked IM nailing, could provide better shoulder functional outcome at one-year follow-up. Moreover, IM nailing fixation could effectively reduce operative time, blood loss, rates of infection, major complications, and revision surgery than plating. Further high-quality clinical trials with large samples and consistent designs are still needed to verify the long-term functional advantage of locked and unlocked IM nailing for midshaft clavicle fractures. LEVEL OF EVIDENCE: Level II.

Interactions between Verticillium dahliae and cotton: pathogenic mechanism and cotton resistance mechanism to Verticillium wilt.

Cotton is widely grown in many countries around the world due to the huge economic value of the total natural fiber. Verticillium wilt, caused by the soil-borne pathogen Verticillium dahliae, is the most devastating disease that led to extensive yield losses and fiber quality reduction in cotton crops. Developing resistant cotton varieties through genetic engineering is an effective, economical, and durable strategy to control Verticillium wilt. However, there are few resistance gene resources in the currently planted cotton varieties, which has brought great challenges and difficulties for breeding through genetic engineering. Further revealing the molecular mechanism between V. dahliae and cotton interaction is crucial to discovering genes related to disease resistance. In this review, we elaborated on the pathogenic mechanism of V. dahliae and the resistance mechanism of cotton to Verticillium wilt. V. dahliae has evolved complex mechanisms to achieve pathogenicity in cotton, mainly including five aspects: (1) germination and growth of microsclerotia; (2) infection and successful colonization; (3) adaptation to the nutrient-deficient environment and competition of nutrients; (4) suppression and manipulation of cotton immune responses; (5) rapid reproduction and secretion of toxins. Cotton has evolved multiple physiological and biochemical responses to cope with V. dahliae infection, including modification of tissue structures, accumulation of antifungal substances, homeostasis of reactive oxygen species (ROS), induction of Ca2+ signaling, the mitogen-activated protein kinase (MAPK) cascades, hormone signaling, and PAMPs/effectors-triggered immune response (PTI/ETI). This review will provide an important reference for the breeding of new cotton germplasm resistant to Verticillium wilt through genetic engineering.