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Verticillium dahliae is an important fungal pathogen affecting cotton yield and quality. Therefore, the mining of V. dahlia-resistance genes is urgently needed. Proteases and protease inhibitors play crucial roles in plant defense responses. However, the functions and regulatory mechanisms of the protease inhibitor PR6 gene family remain largely unknown. This study provides a comprehensive analysis of the PR6 gene family in the cotton genome. We performed genome-wide identification and functional characterization of the cotton GhPR6 gene family, which belongs to the potato protease inhibitor I family of inhibitors. Thirty-nine PR6s were identified in Gossypium arboreum, G. raimondii, G. barbadense, and G. hirsutum, and they were clustered into four groups. Based on the analysis of pathogen-induced and Ghlmm transcriptome data, GhPR6-5b was identified as the key gene for V. dahliae resistance. Virus-induced gene silencing experiments revealed that cotton was more sensitive to V. dahliae V991 after PR6-5b silencing. The present study established that GhWRKY75 plays an important role in resistance to Verticillium wilt in cotton by positively regulating GhPR6-5b expression by directly binding to the W-box TTGAC(T/C). Our findings established that GhWRKY75 is a potential candidate for improving cotton resistance to V. dahliae, and provide primary information for further investigations and the development of specific strategies to bolster the defense mechanisms of cotton against V. dahliae.

Leaf shape is a vital agronomic trait that affects plant and canopy architecture, yield, and other production attributes of upland cotton. Compared with normal leaves, lobed leaves have potential advantages in improving canopy structure and increasing cotton yield. A chromosomal introgression segment from Gossypium barbadense L. conferring sub-okra leaf shape to Gossypium hirsutum L. was identified on chromosome D01. To determine the effects of this transferred sub-okra leaf shape on the leaf anatomical characteristics, photosynthesis-related traits, and yield of short-season cotton, we performed a field experiment with three sets of near-isogenic lines carrying okra, sub-okra, and normal leaf shape in Lu54 (L54) and Shizao 2 (SZ2) backgrounds. Compared with normal leaves, sub-okra leaves exhibited reduced leaf thickness and smaller leaf mass per area; moreover, the deeper lobes of sub-okra leaves improved the plant canopy structure by decreasing leaf area index by 11.24%–22.84%. Similarly, the intercepted PAR rate of lines with sub-okra leaf shape was also reduced. The chlorophyll content of sub-okra leaves was lower than that of okra and normal leaf shapes; however, the net photosynthetic rate of sub-okra leaves was 8.17%–29.81% higher than that of other leaf shapes at most growth stages. Although the biomass of lines with sub-okra leaf shape was less than that of lines with normal leaves, the average first harvest yield and total yield of lines with the sub-okra leaf shape increased by 6.36% and 5.72%, respectively, compared with those with normal leaves. Thus, improvements in the canopy structure and photosynthetic and physiological characteristics contributed to optimizing the light environment, thereby increasing the yield of lines with sub-okra leaf shape. Our results suggest that the sub-okra leaf trait from G. barbadense L. may have practical applications for cultivating short-season varieties with high photosynthetic efficiency, and improving yield, which will be advantageous for short-season varieties.

Abstract To identify the mechanism underlying macrosteatosis (MaS)‐related graft failure (GF) in liver transplantation (LT) by multi‐omics network analysis. The transcriptome and metabolome were assayed in graft and recipient plasma in discovery (n = 68) and validation (n = 89) cohorts. Differentially expressed molecules were identified by MaS and GF status. Transcriptional regulatory networks were generated to explore the mechanism for MaS‐related inferior post‐transplant prognosis. The differentially expressed molecules associated with MaS and GF were enriched in ferroptosis and peroxisome‐related pathways. Core features of MaS‐related GF were presented on decreased transferrin and impaired anti‐oxidative capacity dependent upon dysregulation of transcription factors hepatocyte nuclear factor 4A (HNF4A) and hypoxia‐inducible factor 1A (HIF1A). Furthermore, miR‐362‐3p and miR‐299‐5p inhibited transferrin and HIF1A expression, respectively. Lower M2 macrophages but higher memory CD4 T cells were observed in MaS‐related GF cases. These results were validated in clinical specimens and cellular models. Systemic analysis of multi‐omics data depicted a panorama of biological pathways deregulated in MaS‐related GF. Transcriptional regulatory networks centered on transferrin and anti‐oxidant responses were associated with poor MaS graft quality, qualifying as potential targets to improve prognosis of patients after LT.

In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned aerial vehicles is proposed. Visible and multispectral images of sunflower seedlings were acquired using a UAV. The thresholding method was used to segment the excess green image of the visible image into vegetation and non-vegetation, to obtain the center point of the vegetation to generate a buffer, and to mask the visible image to achieve weed removal. The components of color models such as the hue–saturation value (HSV), green-relative color space (YCbCr), cyan-magenta-yellow-black (CMYK), and CIELAB color space (L*A*B) models were compared and analyzed. The A component of the L*A*B model was preferred for the optimization of K-means clustering to segment sunflower seedlings and mulch using the genetic algorithm, and the segmentation accuracy was improved by 4.6% compared with the K-means clustering algorithm. All told, 10 geometric features of sunflower seedlings were extracted using segmented images, and 10 vegetation indices and 48 texture features of sunflower seedlings were calculated based on multispectral images. The Pearson’s correlation coefficient method was used to filter the three types of features, and the geometric feature set, the vegetation index set, the texture feature set, and the preferred feature set were constructed. The construction of a sunflower plant number estimation model using the crested porcupine optimizer–support vector machine is proposed and compared with the sunflower plant number estimation models constructed based on decision tree regression, BP neural network, and support vector machine regression. The results show that the accuracy of the model based on the preferred feature set is higher than that of the other three feature sets, indicating that feature screening can improve the accuracy and stability of models; assessed using the CPO-SVR model, the accuracy of the preferred feature set was the highest, with an R² of 0.94, an RMSE of 5.16, and an MAE of 3.03. Compared to the SVR model, the value of the R2 is improved by 3.3%, the RMSE decreased by 18.3%, and the MAE decreased by 18.1%. The results of the study can be cost-effective, accurate, and reliable in terms of obtaining the seedling emergence rate of sunflower field breeding.

BackgroudAlthough recent studies have reported the regulation of the immune response in hepatocellular carcinoma (HCC) through DNA methylation, the comprehensive impact methylation modifications on tumor microenvironment characteristics and immunotherapy efficacy has not been fully elucidated.MethodsIn this research, we conducted a comprehensive assessment of the patterns of DNA methylation regulators and the profiles of the tumor microenvironment (TME) in HCC, focusing on 21 specific DNA methylation regulators. We subsequently developed a unique scoring system, a DNA methylation score (DMscore), to assess the individual DNA methylation modifications among the three distinct methylation patterns for differentially expressed genes (DEGs).ResultsThree distinct methylation modification patterns were identified with distinct TME infiltration characteristics. We demonstrated that the DMscore could predict patient subtype, TME infiltration, and patient prognosis. A low DMscore, characterized by an elevated tumor mutation burden (TMB), hepatitis B virus (HBV)/hepatitis C virus (HCV) infection, and immune activation, indicates an inflamed tumor microenvironment phenotype with a 5-year survival rate of 7.8%. Moreover, a low DMscore appeared to increase the efficacy of immunotherapy in the anti-CTLA-4/PD-1/PD-L1 cohort.ConclusionsIn brief, this research has enhanced our understanding of the correlation between modifications in DNA methylation patterns and the profile of the tumor microenvironment in individuals diagnosed with HCC. The DMscore may serve as an alternative biomarker for survival and efficacy of immunotherapy in patients with HCC.

This manuscript considers the state feedback stabilization problem for a class of stochastic high-order and low-order nonlinear systems with time-delay. Compared with the previous results, a distinctive feature to be studied is that the considered systems involve high-order, low-order, intricate stochastic diffusion terms and time-delay simultaneously. First, the homogeneous domination approach and suitable coordinate transformations are introduced to obtain the updating laws. Then, a state feedback controller is devised to make the closed-loop systems globally asymptotically stable in probability. Finally, a simulation example is shown to prove the proposed approach powerfully.

Sesame is one of the most important oilseed crops and attracts significant attention because of its huge nutritional capacity. However, the molecular mechanisms underlying oil accumulation in sesame remains poorly understood. In this study, lipidomic and transcriptomic analyses in different stages of sesame seed (Luzhi No.1, seed oil content 56%) development were performed to gain insight into the regulatory mechanisms that govern differences in lipid composition, content, biosynthesis, and transport. In total, 481 lipids, including fatty acids (FAs, 38 species), triacylglycerol (TAG, 127 species), ceramide (33 species), phosphatidic acid (20 species), and diacylglycerol (17 species), were detected in developing sesame seed using gas and liquid chromatography-mass spectrometry. Most FAs and other lipids accumulated 21–33 days after flowering. RNA-sequence profiling in developing seed highlighted the enhanced expression of genes involved in the biosynthesis and transport of FAs, TAGs, and membrane lipids, which was similar to that seen during lipid accumulation. Through the differential expression analysis of genes involved in lipid biosynthesis and metabolism during seed development, several candidate genes were found to affect the oil content and FA composition of sesame seed, including ACCase, FAD2, DGAT, G3PDH, PEPCase, WRI1 and WRI1-like genes. Our study reveals the patterns of lipid accumulation and biosynthesis-related gene expression and lays an important foundation for the further exploration of sesame seed lipid biosynthesis and accumulation.

Taking the main bearing-main journal of a six-cylinder diesel engine as the research object，based on the elastic- -fluid lubrication theory and the micro-bump contact theory and considering factors such as journal misalignment and main bearing surface roughness，the main bearing lubrication characteristics model under the flexible whole body is established，and the influence of the main bearing surface profile on the main bearing lubrication characteristics and main journal vibration characteristics is analyzed. The results show that，compared with the result that does not take into account the main bearing surface profile，when the main bearing surface profile is included，the minimum oil film thickness of the main bearing is increased by 75.53%，the maximum oil film pressure is reduced by 6.15%，the average friction loss is reduced by 2%，the horizontal flexural vibration amplitudes of the main journal is reduced by 6%，the vertical flexural vibration amplitudes is reduced by 5%，and both the axial vibration and torsional vibration amplitudes are reduced by 2%. With the increase of the main bearing surface profile height，the minimum oil film thickness of the main bearing increases，the maximum oil film pressure decreases，the average friction loss decreases，and both the maximum roughness contact force amplitude and the number of occurrences are reduced; the amplitudes of flexural vibration，axial vibration and torsional vibration of the main journal are all reduced.

Taking the main bearing of a diesel engine as the study object， the lubrication state analysis model of the main bearing is established by taking the roughness and elastic deformation of the main bearing surface into account， and the influence of the radial profile of the main bearing on the lubrication state is analyzed. The results show that， compared with the calculated results without considering the radial journal profile， when the influence of the radial journal profile is taken into account， the minimum oil film thickness of the main bearing increases by 25.95%， the maximum oil film pressure decreases by 17.69%， the mean friction power loss decreases by 6.14% and the inclination of the spindle neck is improved. With the increase of journal roughness， the minimum oil film thickness increases， the maximum oil film pressure almost invariably， and the mean friction power loss increases.

Recently, emerging evidence has indicated that aberrant enhancers, especially super-enhancers, play pivotal roles in the transcriptional reprogramming of multiple cancers, including hepatocellular carcinoma (HCC). In this study, we performed integrative analyses of ChIP-seq, RNA-seq, and whole-genome bisulfite sequencing (WGBS) data to identify intergenic differentially expressed enhancers (DEEs) and genic differentially methylated enhancers (DMEs), along with their associated differentially expressed genes (DEE/DME-DEGs), both of which were also identified in independent cohorts and further confirmed by HiC data. Functional enrichment and prognostic model construction were conducted to explore the functions and clinical significance of the identified enhancer aberrations. We identified a total of 2,051 aberrant enhancer-associated DEGs (AE-DEGs), which were highly concurrent in multiple HCC datasets. The enrichment results indicated the significant overrepresentations of crucial biological processes and pathways implicated in cancer among these AE-DEGs. A six AE-DEG-based prognostic signature, whose ability to predict the overall survival of HCC was superior to that of both clinical phenotypes and previously published similar prognostic signatures, was established and validated in TCGA-LIHC and ICGC-LIRI cohorts, respectively. In summary, our integrative analysis depicted a landscape of aberrant enhancers and associated transcriptional dysregulation in HCC and established an aberrant enhancer-derived prognostic signature with excellent predictive accuracy, which might be beneficial for the future development of epigenetic therapy for HCC.

Acid fracturing is an indispensable technology in the efficient development of carbonate oil and gas reservoirs. As for deep and ultra-deep marine carbonate oil and gas reservoirs, however, existing acid fracturing technologies can hardly create complex fracture networks because of their particular engineering and geological characteristics. In order to realize the efficient three-dimensional development of deep and ultra-deep marine carbonate oil and gas reservoirs, this paper revealed the acid fracturing difficulties of this kind of reservoirs based on the engineering and geological characteristics of deep marine carbonate oil and gas reservoirs in China. Then, by taking the efficient three-dimensional development of deep and ultra-deep marine carbonate oil and gas reservoirs as the target, the technical concept of three-dimensional acid fracturing (3D-AF) was put forward based on the techniques of volume acid fracturing and deep acid fracturing. In addition, its technical connotation was illustrated, and its key technologies were described in detail. Finally, its development direction in the next step was pointed out. And the following research results were obtained. First, the 3D-AF technology contains three basic connotations: (1) selecting the corresponding acid penetration technology according to reservoir types so as to realize sufficient reservoir stimulation on the plane; (2) creating complex acid-fracture volume with higher flow conductivity under high closed pressure; (3) deploying acid-fracture volume rationally along the long hole section so as to realize sufficient reservoir stimulation in the wellbore direction. Second, there are three key technologies of 3D-AF, i.e., the technology for predicting the effective acid-penetrate distance under the coupling action of multi-field, multi-scale and multi-fluid, the technology for optimizing the flow conductivity of complex acid-fracture volume, and the technology for precisely deploying acid in the long section of horizontal well or highly deviated well. Third, in order to promote the further development of 3D-AF technology, it is necessary to carry out researches on three technologies, i.e., the technology for predicting and reducing the fracturing pressure of ultra-deep reservoir, the technology for predicting the geometry of acid-fracture body in a strongly heterogeneous reservoir, and the staged reservoir production technology and new fluid technology with high temperature resistance, low reaction rate and low friction resistance coefficient for long hole sections (over 7000 m).

Background: Low-grade glioma (LGG) is considered a fatal disease for young adults, with overall survival widely ranging from 1 to 15 years depending on histopathologic and molecular subtypes. As a novel type of programmed cell death, ferroptosis was reported to be involved in tumorigenesis and development, which has been intensively studied in recent years.Methods: For the discovery cohort, data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to identify the differentially expressed and prognostic ferroptosis-related genes (FRGs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox were used to establish a prognostic signature with the above-selected FRGs. Then, the signature was developed and validated in TCGA and Chinese Glioma Genome Atlas (CGGA) databases. By combining clinicopathological features and the FRG signature, a nomogram was established to predict individuals’ one-, three-, and five-year survival probability, and its predictive performance was evaluated by Harrell’s concordance index (C-index) and calibration curves. Enrichment analysis was performed to explore the signaling pathways regulated by the signature.Results: A novel risk signature contains seven FRGs that were constructed and were used to divide patients into two groups. Kaplan–Meier (K−M) survival curve and receiver-operating characteristic (ROC) curve analyses confirmed the prognostic performance of the risk model, followed by external validation based on data from the CGGA. The nomogram based on the risk signature and clinical traits was validated to perform well for predicting the survival rate of LGG. Finally, functional analysis revealed that the immune statuses were different between the two risk groups, which might help explain the underlying mechanisms of ferroptosis in LGG.Conclusion: In conclusion, this study constructed a novel and robust seven-FRG signature and established a prognostic nomogram for LGG survival prediction.

Aiming at the phenomenon of timing gear whine noise caused by high power density diesel engine, considering the transmission error and other factors, a 12V150 diesel engine is taken as the research object and valve train timing gear transmission system model is established by Romax software. The tooth surface loading and peak value and fluctuation range of transmission error under the excitation of the transmission error are analyzed. The size and distribution of the unit length load of the tooth surface is used as the standard, the middle gear pair of the timing gear system is modified. It shows that the tooth surface load can be distributed to the middle part by gear modification, the load distribution area increase and the load reduce, transmission error reduce, two different modification schemes are compared and analyzed, the vibration acceleration is reduced by 38% and 56.9% respectively.

Extensive research has been paid for event detection in the past decades. However, the timeliness requirement of real-world applications cannot be satisfied by these approaches. Early event detector is thus proposed recently to deal with this issue. Early detection aims to recognize the target as early as possible, i.e., it can detect partial events and create a monotonous function to rank them. Although important and practical, few studies have been given for early detection due to its complexity. Max-margin Early Event Detector (MMED) is a well-known approach, which achieves satisfied performance in identifying partial events. However, the MMED works in an offline manner and may fail in this era of streaming sequence. In addition, the large memory consumption and high retraining time cost of the MMED are hard to be satisfied in general platform conditions. In this paper, we introduce an online learning technique with max-margin to early event detection. The proposed model could be adapted to the changing data distribution of the streaming sequences. No historical data need to be stored. Therefore, both the memory requirement and retraining time cost are decreased significantly. We evaluate the proposed approach on three benchmark datasets with various complexities. The extensive results demonstrate both the effectiveness and efficiency of the proposed framework.

In the present study, Mo was added to Cu–15Ni–8Sn alloy as the fourth element to solve the limitation of service performance of the alloy by composition design. The phase composition, microstructure transformation and mechanical properties of Cu–15Ni–8Sn–xMo (x = 0.3, 0.9, 1.5 wt.%) alloy were systematically studied by simulation calculation and experimental characterization. The results show that the addition of Mo can improve the as-cast structure of Cu–15Ni–8Sn alloy and reduce segregation and Cu–Mo phase precipitates on the surface with the increase in Mo contents. During solution treatment, Mo can partially dissolve into the matrix, which may be the key to improving the properties of the alloy. Furthermore, the discontinuous precipitation of Sn can be effectively inhibited by adding the appropriate amount of Mo to Cu–15Ni–8Sn alloy, and the hardness of alloy does not decrease greatly after a long-time aging treatment. When Mo content is 0.9 wt.%, the alloy reaches the peak hardness of 384 HV at 4 h of aging. These results provide new ideas for composition optimization of Cu–15Ni–8Sn alloy.

The microstructure evolution of Cu-Sn-P alloy subjected to hot deformation was researched through electron backscatter diffraction (EBSD) and transmission electron microscope (TEM) in the present study. The results indicated that after hot deformation, grains perpendicular to the force direction were elongated, and mostly became deformed grains, and then exhibited an obvious hardening effect. The Cu-Sn-P alloy could be strain hardened during hot deformation, but, with recrystallization, a softening effect occurred. Changes in dislocation density, textures, and grain sizes play different roles in flow stress behaviors of Cu-Sn-P alloy, and the dislocation density has a more evident effect at low temperature. However, with increase in temperature, recrystallization softening gradually dominates. Low-angle grain boundaries (LABs) account for the majority of hot deformed microstructures of Cu-Sn-P alloy. High dislocation densities in these zones make it easy to initiate the dislocation slipping systems. Deformation is realized through dislocation slipping and the slipping of edge dislocation pairs. The dislocation pile-up zones have high distortion energies, and, thus, elements of diffusion and recrystallization nucleation can occur easily. At different temperatures, the maximum polar density of textures gradually increases, and there are preferred orientations of grains. At 500 °C, stacking faults accumulate and promote the growth of twins. The twin growth direction is mainly determined by the migration of high-angle grain boundaries (HABs) and the clustering of high-stress zones.

BackgroundSolute carrier family 1 member 5 (SLC1A5) is a major glutamine transporter and plays a key role in tumor growth. The main objectives of this study were to visualize the prognostic landscape of SLC1A5 in multiple cancers and determine the relations between SLC1A5 expression and tumor immunity.MethodsSLC1A5 expression and its effect on tumor prognosis were analyzed using multiple online tools Oncomine, Gene Expression Profiling Interactive Analysis, PrognoScan, and Kaplan-Meier plotter with their own datasets as well as the data from The Cancer Genome Atlas. The correlations between SLC1A5 and tumor immune infiltrates were determined via TIMER.ResultsSLC1A5 expression was significantly higher in several types of cancers, including hepatocellular carcinoma (HCC), compared with corresponding normal tissues. High SLC1A5 expression correlated with poor overall survival and with disease-free survival related to alcohol consumption. Moreover, SLC1A5 expression correlated positively with the numbers of tumor-infiltrating B cells, CD4+ T and CD8+ T cells, macrophages, neutrophils, and dendritic cells in HCC and in lower-grade glioma (LGG). Also, SLC1A5 expression showed strong correlations with diverse immune marker sets in HCC and LGG, indicating its role in regulating tumor immunity.ConclusionsSLC1A5 represents a useful prognostic biomarker in multiple cancers, and its expression correlates highly with tumor immune-cell infiltration, especially in HCC and LGG.

Molecular dynamics simulation is one kinds of important methods to research the nanocrystalline materials which is difficult to be studied through experimental characterization. In order to study the effects of Sn content and strain rate on the mechanical properties of nanopolycrystalline Cu–Sn alloy, the tensile simulation of nanopolycrystalline Cu–Sn alloy was carried out by molecular dynamics in the present study. The results demonstrate that the addition of Sn reduces the ductility of Cu–Sn alloy. However, the elastic modulus and tensile strength of Cu–Sn alloy are improved with increasing the Sn content initially, but they will be reduced when the Sn content exceeds 4% and 8%, respectively. Then, strain rate ranges from 1 × 109 s−1 to 5 × 109 s−1 were applied to the Cu–7Sn alloy, the results show that the strain rate influence elastic modulus of nanopolycrystalline Cu–7Sn alloy weakly, but the tensile strength and ductility enhance obviously with increasing the strain rate. Finally, the microstructure evolution of nanopolycrystalline Cu–Sn alloy during the whole tensile process was studied. It is found that the dislocation density in the Cu–Sn alloy reduces with increasing the Sn content. However, high strain rate leads to stacking faults more easily to generate and high dislocation density in the Cu–7Sn alloy.

In the present study, a Cu–6Ni–6Sn–0.6Si alloy is fabricated through frequency induction melting, then subjected to solution treatment, rolling, and annealing. The phase composition, microstructure evolution, and transition mechanism of the Cu–6Ni–6Sn–0.6Si alloy are researched systematically through simulation calculation and experimental characterization. The ultimate as-annealed sample simultaneously performs with high strength and good ductility according to the uniaxial tensile test results at room temperature. There are amounts of precipitates generated, which are identified as belonging to the DO22 and L12 phases through the transmission electron microscope (TEM) analysis. The DO22 and L12 phase precipitates have a significant strengthening effect. Meanwhile, the generation of the common discontinuous precipitation of the γ phase, which is harmful to the mechanical properties of the copper–nickel–tin alloy, is inhibited mightily during the annealing process, possibly due to the existence of the Ni5Si2 primary phase. Therefore, the as-annealed sample of the Cu–6Ni–6Sn–0.6Si alloy possesses high tensile strength and elongation, which are 967 MPa and 12%, respectively.

Abstract Litchi (Litchi chinensis Sonn.) is an important fruit that is widely cultivated in tropical and subtropical areas. In this study, we used RNA-Seq and iTRAQ technologies to compare the transcriptomes and proteomes of pollinated (polLFs) and parthenocarpic (parLFs) litchi fruits during early development (1 day, 2 days, 4 days and 6 days). We identified 4,864 DEGs in polLFs and 3,672 in parLFs, of which 2,835 were shared and 1,051 were specifically identified in parLFs. Compared to po1LFs, 768 DEGs were identified in parLFs. iTRAQ analysis identified 551 DEPs in polLFs and 1,021 in parLFs, of which 305 were shared and 526 were exclusively identified in parLFs. We found 1,127 DEPs in parLFs compared to polLFs at different stages. Further analysis revealed some DEGs/DEPs associated with abscisic acid, auxin, ethylene, gibberellin, heat shock protein (HSP), histone, ribosomal protein, transcription factor and zinc finger protein (ZFP). WGCNA identified a large set of co-expressed genes/proteins in polLFs and parLFs. In addition, a cross-comparison of transcriptomic and proteomic data identified 357 consistent DEGs/DEPs in polLFs and parLFs. This is the first time that protein/gene changes have been studied in polLFs and parLFs, and the findings improve our understanding of litchi parthenocarpy.

Background: Several studies have revealed significant associations between single nucleotide polymorphisms (SNPs) in the cannabinoid receptor 1 (CNR1) gene and a broad spectrum of psychiatric disorders such as major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), and schizophrenia. Personality traits that are highly related to susceptibility to these conditions have been associated with the CNR1 variants in subjects of Caucasian origin. However, there are no reported studies regarding the effects of CNR1 polymorphisms on personality traits in the African-American (AA) population.Methods: We performed an imputation-based association analysis for 26 CNR1 variants with five dimensions of personality in 3,046 AAs.Results: SNPs rs806372 and rs2180619 showed a significant association with extraversion after Bonferroni correction for multiple testing (p < 0.0019). Further, several extraversion-associated SNPs were significantly associated with conscientiousness, agreeableness, and openness. SNP priority score analysis indicated that SNPs rs806368, rs806371, and rs2180619 play a role in the modulation of personality and psychiatric conditions.Conclusion:CNR1 is important in determining personality traits in the AA population.

For decades, Mackey-Glass chaotic time series prediction has attracted more and more attention. When the multilayer perceptron is used to predict the Mackey-Glass chaotic time series, what we should do is to minimize the loss function. As is well known, the convergence speed of the loss function is rapid in the beginning of the learning process, while the convergence speed is very slow when the parameter is near to the minimum point. In order to overcome these problems, we introduce the Levenberg-Marquardt algorithm (LMA). Firstly, a rough introduction is given to the multilayer perceptron, including the structure and the model approximation method. Secondly, we introduce the LMA and discuss how to implement the LMA. Lastly, an illustrative example is carried out to show the prediction efficiency of the LMA. Simulations show that the LMA can give more accurate prediction than the gradient descent method.

Verticillium wilt (VW) caused by Verticillium dahliae Kleb is one of the most destructive diseases of cotton. Development and use of a VW resistant variety is the most practical and effective way to manage this disease. Identification of highly resistant genes/QTL and the underlining genetic architecture is a prerequisite for developing a VW resistant variety. A major QTL qVW-c6-1 conferring resistance to the defoliating isolate V991 was identified on chromosome 6 in LHB22×JM11 F2∶3 population inoculated and grown in a greenhouse. This QTL was further validated in the LHB22×NNG F2∶3 population that was evaluated in an artificial disease nursery of V991 for two years and in its subsequent F4 population grown in a field severely infested by V991. The allele conferring resistance within the QTL qVW-c6-1 region originated from parent LHB22 and could explain 23.1-27.1% of phenotypic variation. Another resistance QTL qVW-c21-1 originated from the susceptible parent JM11 was mapped on chromosome 21, explaining 14.44% of phenotypic variation. The resistance QTL reported herein provides a useful tool for breeding a cotton variety with enhanced resistance to VW.