20E-mediated regulation of BmKr-h1 by BmKRP promotes oocyte maturation.

BACKGROUND: Krüppel homolog 1 (Kr-h1) is a critical transcription factor for juvenile hormone (JH) signaling, known to play a key role in regulating metamorphosis and adult reproduction in insects. Kr-h1 can also be induced by molting hormone 20-hydroxyecdysone (20E), however, the underlying mechanism of 20E-induced Kr-h1 expression remains unclear. In the present study, we investigated the molecular mechanism of Kr-h1 induction by 20E in the reproductive system of a model lepidopteran insect, Bombyx mori. RESULTS: Developmental and tissue-specific expression analysis revealed that BmKr-h1 was highly expressed in ovaries during the late pupal and adult stages and the expression was induced by 20E. RNA interference (RNAi)-mediated depletion of BmKr-h1 in female pupae severely repressed the transcription of vitellogenin receptor (VgR), resulting in the reduction in vitellogenin (Vg) deposition in oocytes. BmKr-h1 specifically bound the Kr-h1 binding site (KBS) between - 2818 and - 2805 nt upstream of BmVgR and enhanced the transcription of BmVgR. A 20E cis-regulatory element (CRE) was identified in the promoter of BmKr-h1 and functionally verified using luciferase reporter assay, EMSA and DNA-ChIP. Using pull-down assays, we identified a novel transcription factor B. mori Kr-h1 regulatory protein (BmKRP) that specifically bound the BmKr-h1 CRE and activated its transcription. CRISPR/Cas9-mediated knockout of BmKRP in female pupae suppressed the transcription of BmKr-h1 and BmVgR, resulting in arrested oogenesis. CONCLUSION: We identified BmKRP as a new transcription factor mediating 20E regulation of B. mori oogenesis. Our data suggests that induction of BmKRP by 20E regulates BmKr-h1 expression, which in turn induces BmVgR expression to facilitate Vg uptake and oogenesis.

Experimental Research on a Hybrid Algorithm for Localisation and Reconstruction of the Impact Force Applied to a Rectangular Steel Plate Structure.

Impact force is the most common form of load which acts on engineering structures and presents a great hidden risk to the healthy operation of machinery. Therefore, the identification or monitoring of impact forces is a significant issue in structural health monitoring. The conventional optimisation scheme based on inversion techniques requires a significant amount of time to identify random impact forces (impact force localisation and time history reconstruction) and is not suitable for engineering applications. Recently, a pattern recognition method combined with the similarity metric, PRMCSM, has been proposed, which exhibits rapidity in practical engineering applications. This study proposes a novel scheme for identifying unknown random impact forces which hybridises two existing methods and combines the advantages of both. The experimental results indicate that the localisation accuracy of the proposed algorithm (100%) is higher than that of PRMCSM (92%), and the calculation time of the hybrid algorithm (179 s) for 25 validation cases is approximately one nineteenth of the traditional optimisation strategy (3446 s).

An adaptive discretized RNN algorithm for posture collaboration motion control of constrained dual-arm robots.

Although there are many studies on repetitive motion control of robots, few schemes and algorithms involve posture collaboration motion control of constrained dual-arm robots in three-dimensional scenes, which can meet more complex work requirements. Therefore, this study establishes the minimum displacement repetitive motion control scheme for the left and right robotic arms separately. On the basis of this, the design mentality of the proposed dual-arm posture collaboration motion control (DAPCMC) scheme, which is combined with a new joint-limit conversion strategy, is described, and the scheme is transformed into a time-variant equation system (TVES) problem form subsequently. To address the TVES problem, a novel adaptive Taylor-type discretized recurrent neural network (ATT-DRNN) algorithm is devised, which fundamentally solves the problem of calculation accuracy which cannot be balanced well with the fast convergence speed. Then, stringent theoretical analysis confirms the dependability of the ATT-DRNN algorithm in terms of calculation precision and convergence rate. Finally, the effectiveness of the DAPCMC scheme and the excellent convergence competence of the ATT-DRNN algorithm is verified by a numerical simulation analysis and two control cases of dual-arm robots.

Energy efficiency identification and surface roughness prediction using cutting force signal for computer numerical controlled machine systems.

The energy efficiency identification of machining process plays an indispensable part in achieving energy-efficient manufacturing and improving energy utilization as well as productivity and surface quality. However, there is a great difficulty to track energy efficiency in real-time based on one kind of traditional power signal. Because energy consumption is affected by many factors such as machine tool current performance, tool wear conditions and cutting parameters selection. This paper puts forward an energy efficiency recognition method as well as surface roughness prediction model based on the cutting force signals. The CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) algorithm is employed to decompose the cutting force signal into multiple IMF (intrinsic mode function) components; and characterization of energy efficiency of machining process is recognized through proportion of components based on PCA-Fast ICA algorithm. Then, a surface roughness prediction model is proposed using support vector regression (SVR) based on specific cutting energy consumption (SCEC). The orthogonal test is designed considering spindle speed, feed rate, depth of cutting and width of cutting in 3 levels to obtain the influence degree of cutting parameters on cutting force, specific energy consumption, and the surface roughness. The energy efficiency of 27 group experiments is classified into high, medium and low levels according to energy efficiency value. Finally, using the data of orthogonal test, energy efficiency state was identified. The result show that time-frequency of cutting force signals for high, medium and low energy efficiency could be extracted, and the average absolute error of surface roughness predict is 0.058. That illustrated that the proposed method could meet the industry requirement for energy efficiency monitoring and surface roughness prediction to achieve sustainable manufacturing.

Discrete-Time Advanced Zeroing Neurodynamic Algorithm Applied to Future Equality-Constrained Nonlinear Optimization With Various Noises.

This research first proposes the general expression of Zhang et al. discretization (ZeaD) formulas to provide an effective general framework for finding various ZeaD formulas by the idea of high-order derivative simultaneous elimination. Then, to solve the problem of future equality-constrained nonlinear optimization (ECNO) with various noises, a specific ZeaD formula originating from the general ZeaD formula is further studied for the discretization of a noise-perturbed continuous-time advanced zeroing neurodynamic model. Subsequently, the resulting noise-perturbed discrete-time advanced zeroing neurodynamic (NP-DTAZN) algorithm is proposed for the real-time solution to the future ECNO problem with various noises suppressed simultaneously. Moreover, theoretical and numerical results are presented to show the convergence and precision of the proposed NP-DTAZN algorithm in the perturbation of various noises. Finally, comparative numerical and physical experiments based on a Kinova JACO2 robot manipulator are conducted to further substantiate the efficacy, superiority, and practicability of the proposed NP-DTAZN algorithm for solving the future ECNO problem with various noises.

Analyses of microRNAs and transcriptomes in the midgut of Spodoptera litura feeding on Brassica juncea.

Spodoptera litura is a destructive agricultural pest in tropical and subtropical areas. Understanding the molecular mechanisms of S. litura adaptation to its preferred host plants may help identify target genes useful for pest control. We used high-throughput sequencing to characterize the expression patterns of messenger RNAs (mRNAs) and microRNAs (miRNAs) in the midgut of S. litura fed on Brassica juncea for 6 h and 48 h. A total of 108 known and 134 novel miRNAs were identified, 29 miRNAs and 237 mRNAs were differentially expressed at 6 h of B. juncea feeding, 26 miRNAs and 433 mRNAs were differentially expressed at 48 h. For the mRNAs, the up-regulated genes were mostly enriched in detoxification enzymes (cytochrome P450, esterase, glutathione S-transferase, uridine diphosphate-glucuronosyl transferase), while the down-regulated genes were mostly enriched in proteinases and immune-related genes. Furthermore, most detoxification enzymes begin to up-regulate at 6 h, while most digestion and immune-related genes begin to up- or down-regulate at 48 h. Eighteen and 37 differently expressed transcription factors were identified at 6 h and 48 h, which may regulate the functional genes. We acquired 136 and 41 miRNA versus mRNA pairs at 6 h and 48 h, respectively. Some down-regulated and up-regulated miRNAs were predicted to target detoxification enzymes and proteinases, respectively. Real-time quantitative polymerase chain reaction of nine randomly selected miRNAs and 28 genes confirmed the results of RNA-seq. This analyses of miRNA and mRNA transcriptomes provides useful information about the molecular mechanisms of S. litura response to B. juncea.

General 7-Instant DCZNN Model Solving Future Different-Level System of Nonlinear Inequality and Linear Equation.

In this article, a novel and challenging problem called future different-level system of nonlinear inequality and linear equation (FDLSNILE) is proposed and investigated. To solve FDLSNILE, the corresponding continuous different-level system of nonlinear inequality and linear equation (CDLSNILE) is first analyzed, and then, a continuous combined zeroing neural network (CCZNN) model for solving CDLSNILE is proposed. To obtain a discrete combined zeroing neural network (DCZNN) model for solving FDLSNILE, a high-precision general 7-instant Zhang et al. discretization (ZeaD) formula for the first-order time derivative approximation is proposed. Furthermore, by applying the general 7-instant ZeaD formula to discretize the CCZNN model, a general 7-instant DCZNN (7IDCZNN) model is thus proposed for solving FDLSNILE. For comparison, by using three conventional ZeaD formulas, three conventional DCZNN models are also developed. Meanwhile, theoretical analyses and results guarantee the efficacy and superiority of the general 7IDCZNN model compared with the other three conventional DCZNN models for solving FDLSNILE. Finally, several comparative numerical experiments, including the motion control of a 5-link redundant manipulator, are provided to substantiate the efficacy and superiority of the general 7-instant ZeaD formula and the corresponding 7IDCZNN model.

BR-C Z4 and FoxJ interact to regulate expression of a chitin synthase gene CHSA-2b in the pupal wing discs of the silkworm, Bombyx mori.

Elaborate regulation of tissue- and stage-specific expression of genes is prerequisite for insect development. The hormone 20-hydroxyecdysone (20E) initiates metamorphosis by regulating the expression of a series of genes. However, how 20E orderly regulates the pupa-specific expression of genes remains unclear. In this study, we report a regulatory mechanism for the pupa-specific expression of chitin synthase A 2b (CHSA-2b) in Bombyx mori. We found that Broad-Complex Z4 (BR-C Z4) was up-regulated by 20E just before pupation, while transcription factor FoxJ and CHSA-2b were up-regulated during the pupal stage. There is a Fox cis-regulatory element in the CHSA-2b promoter region, and FoxJ protein bound to this element, enhancing the CHSA-2b transcription during the pupal stage. In addition to CHSA-2b, FoxJ also up-regulated the expression of 16 out of 19 pupa-specific genes tested. However, at the prepupal stage, 20E-induced BR-C Z4 inhibited the FoxJ transcription, indirectly inhibiting the CHSA-2b transcription. These data suggest that at the pre-pupation stage, 20E-induced BR-C Z4 inhibited the expression of pupa-stage genes like CHSA-2b by inhibiting the expression of FoxJ; by the pupal stage, the expression of BR-C Z4 decreased, releasing its inhibition on FoxJ, which then up-regulated the expression of the pupa-specific genes. This study explains the elaborate regulation of the pupa-specific gene expression during metamorphosis in B. mori.

Two New Discrete-Time Neurodynamic Algorithms Applied to Online Future Matrix Inversion With Nonsingular or Sometimes-Singular Coefficient.

In this paper, a high-precision general discretization formula using six time instants is first proposed to approximate the first-order derivative. Then, such a formula is studied to discretize two continuous-time neurodynamic models, both of which are derived by applying the neurodynamic approaches based on neural networks (i.e., zeroing neurodynamics and gradient neurodynamics). Originating from the general six-instant discretization (6ID) formula, a specific 6ID formula is further presented. Subsequently, two new discrete-time neurodynamic algorithms, i.e., 6ID-type discrete-time zeroing neurodynamic (DTZN) algorithm and 6ID-type discrete-time gradient neurodynamic (DTGN) algorithm, are proposed and investigated for online future matrix inversion (OFMI). In addition to analyzing the usual nonsingular situation of the coefficient, this paper investigates the sometimes-singular situation of the coefficient for OFMI. Finally, two illustrative numerical examples, including an application to the inverse-kinematic control of a PUMA560 robot manipulator, are provided to show respective characteristics and advantages of the proposed 6ID-type DTZN and DTGN algorithms for OFMI in different situations, where the coefficient matrix to be inverted is always-nonsingular or sometimes-singular during time evolution.

New Discrete-Time ZNN Models for Least-Squares Solution of Dynamic Linear Equation System With Time-Varying Rank-Deficient Coefficient.

In this brief, a new one-step-ahead numerical differentiation rule called six-instant -cube finite difference (6I CFD) formula is proposed for the first-order derivative approximation with higher precision than existing finite difference formulas (i.e., Euler and Taylor types). Subsequently, by exploiting the proposed 6I CFD formula to discretize the continuous-time Zhang neural network model, two new-type discrete-time ZNN (DTZNN) models, namely, new-type DTZNNK and DTZNNU models, are designed and generalized to compute the least-squares solution of dynamic linear equation system with time-varying rank-deficient coefficient in real time, which is quite different from the existing ZNN-related studies on solving continuous-time and discrete-time (dynamic or static) linear equation systems in the context of full-rank coefficients. Specifically, the corresponding dynamic normal equation system, of which the solution exactly corresponds to the least-squares solution of dynamic linear equation system, is elegantly introduced to solve such a rank-deficient least-squares problem efficiently and accurately. Theoretical analyses show that the maximal steady-state residual errors of the two new-type DTZNN models have an pattern, where denotes the sampling gap. Comparative numerical experimental results further substantiate the superior computational performance of the new-type DTZNN models to solve the rank-deficient least-squares problem of dynamic linear equation systems.