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Summary: Tassel branch number (TBN) is a key agronomic trait for adapting to high‐density planting and grain yield in maize. However, the molecular regulatory mechanisms underlying tassel branching are still largely unknown.Here, we used molecular and genetic studies together to show that ZmELF3.1 plays a critical role in regulating TBN in maize.Previous studies showed that ZmELF3.1 forms the evening complex through interacting with ZmELF4 and ZmLUX to regulate flowering in maize and that RA2 and TSH4 (ZmSBP2) suppresses and promotes TBN in maize, respectively. In this study, we show that loss‐of‐function mutants of ZmELF3.1 exhibit a significant increase of TBN. We also show that RA2 directly binds to the promoter of TSH4 and represses its expression, thus leading to reduced TBN. We further demonstrate that ZmELF3.1 directly interacts with both RA2 and ZmELF4.2 to form tri‐protein complexes that further enhance the binding of RA2 to the promoter of TSH4, leading to suppressed TSH4 expression and consequently decreased TBN.Our combined results establish a novel functional link between the ELF3‐ELF4‐RA2 complex and miR156‐SPL regulatory module in regulating tassel branching and provide a valuable target for genetic improvement of tassel branching in maize. [ABSTRACT FROM AUTHOR]

Maize (Zea mays) originated in southern Mexico and has spread over a wide latitudinal range. Maize expansion from tropical to temperate regions has necessitated a reduction of its photoperiod sensitivity. In this study, we cloned a quantitative trait locus (QTL) regulating flowering time in maize and show that the maize ortholog of Arabidopsis thaliana EARLY FLOWERING3, ZmELF3.1, is the causal locus. We demonstrate that ZmELF3.1 and ZmELF3.2 proteins can physically interact with ZmELF4.1/4.2 and ZmLUX1/2, to form evening complex(es; ECs) in the maize circadian clock. Loss-of-function mutants for ZmELF3.1/3.2 and ZmLUX1/2 exhibited delayed flowering under long-day and short-day conditions. We show that EC directly represses the expression of several flowering suppressor genes, such as the CONSTANS, CONSTANS-LIKE, TOC1 (CCT) genes ZmCCT9 and ZmCCT10, ZmCONSTANS-LIKE 3, and the PSEUDORESPONSE REGULATOR (PRR) genes ZmPRR37a and ZmPRR73, thus alleviating their inhibition, allowing florigen gene expression and promoting flowering. Further, we identify two closely linked retrotransposons located in the ZmELF3.1 promoter that regulate the expression levels of ZmELF3.1 and may have been positively selected during postdomestication spread of maize from tropical to temperate regions during the pre-Columbian era. These findings provide insights into circadian clock-mediated regulation of photoperiodic flowering in maize and new targets of genetic improvement for breeding. ZmELF3.1/3.2 bridge ZmELF4.1/4.2 and ZmLUX1/2 to form evening complexes and promote flowering and adaptation of maize to temperate regions. [ABSTRACT FROM AUTHOR]

The current study examined the characteristics of targets of bullying using a sample of students from two distinct school types (key vs. non-key), wherein key middle schools are defined as having better teaching resources and higher performing students than non-key middle schools. Open-ended, self-report surveys were collected from 322 Chinese students in sixth to 11th grades. Two cycle coding methods analyses generated 3,566 original codes in which 21 main categories and 77 subcategories were extracted. Five major themes emerged: multiple deviant personalities; prominent puniness and imperfect body characteristics accompanied by polarization of appearance; polarization school engagement and academic performance; psychological and behavioral problems; and problematic family and social contexts. Results of chi-square analysis indicated characteristics of targets of bullying were significantly different between key and non-key school in 12 main categories. Implications for ecological systems theory and person-group dissimilarity theory as well as bullying prevention approaches in schools are discussed. [ABSTRACT FROM AUTHOR]

The impact of motors breakdown and failures on mobile robot motor bearing is an important concern for robot industries. For this reason, predictive motor lifetime and bearing fault classification techniques are being investigated extensively as a method of decreasing motor downtime and enhancing mobile robot reliability. With increasing attention on neural network technologies, many researchers have carried out lots of the relevant experiments and analyses, very plentiful and important conclusions are obtained. In this article, a classification method based on discrete wavelet transform (DWT) and long short-term memory network (LSTM) a proposed to find and classify fault type of mobile robot permanent magnet synchronous motor (PMSM). First, a set of mobile robot motor vibration signal were collected by the sensors. Second, the obtained vibration signal is decomposed into six frequency bands by the DWT. Haar function is selected as the mother function in the processing. The energy of every frequency band was calculated as a classification feature. Thirdly, four classification features with high classification rate are obtained. The feature vector is used as input of the neural network, and the fault type is identified by LSTM classifier with deviation unit. From the results of the experiments provided in the paper, the method can detect the fault type accurately and it is feasible and effective under different motor speed. [ABSTRACT FROM AUTHOR]

Maize (Zea mays ssp. mays) is an agronomically important crop and also a classical genetic model for studying the regulation of plant architecture formation, which is a critical determinant of grain yield. Since the 1930s, increasing planting density has been a major contributing factor to the >7-fold increase in maize grain yield per unit land area in the USA, which is accompanied by breeding and utilization of cultivars characterized by high-density-tolerant plant architecture, including decreased ear height, lodging resistance, more upright leaves, reduced tassel branch number, and reduced anthesis–silking interval (ASI). Recent studies demonstrated that phytochrome-mediated red/far-red light signaling pathway and the miR156/SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE (SPL) regulatory module co-ordinately regulate the shade avoidance response and diverse aspects of plant architecture in responding to shading in Arabidopsis. The maize genome contains 30 ZmSPL genes, and 18 of them are predicted as direct targets of zma-miR156s. Accumulating evidence indicates that ZmSPL genes play important roles in regulating maize flowering time, plant/ear height, tilling, leaf angle, tassel and ear architecture, and grain size and shape. Finally, we discuss ways to exploit maize SPL genes and downstream targets for improving maize plant architecture tailored for high-density planting. [ABSTRACT FROM AUTHOR]

Psychopathy is the most notorious trait in the Dark Triad, and it is strongly linked to many kinds of aggressive behaviors. However, not every individual who is characterized by psychopathy engages in aggression, which suggests that certain factors may attenuate the intensity of the relations between psychopathy and aggression. The purpose of the current study was to explore the protective roles of the consideration of future consequences (CFC) (high CFC-Future and low CFC- Immediate) in attenuating aggression related to psychopathy using proactive aggression (Study 1) and cyber-aggression (Study 2) behavior indexes. College students (Study 1; N = 1,058) and adults (Study 2; N = 350) voluntarily participated in this study. The results demonstrated that the relationship between psychopathy and aggressive behaviors was moderated by CFC-Future and CFC-Immediate. Individuals with high psychopathy scores who also had high CFC-Future scores or low CFC-Immediate scores exhibited less proactive aggression (Study 1) and left fewer aggressive online comments on news websites (Study 2). The results of the present study suggested that CFC serves as a buffer against aggression related to psychopathy and may extend the knowledge of the relationship between psychopathy and aggression. [ABSTRACT FROM AUTHOR]

In this study, we address the problem of noisy image super-resolution. Noisy low resolution (LR) image is always obtained in applications, while most of the existing algorithms assume that the LR image is noise-free. As to this situation, we present an algorithm for noisy image super-resolution which can achieve simultaneously image super-resolution and denoising. And in the training stage of our method, LR example images are noise-free. For different input LR images, even if the noise variance varies, the dictionary pair does not need to be retrained. For the input LR image patch, the corresponding high resolution (HR) image patch is reconstructed through weighted average of similar HR example patches. To reduce computational cost, we use the atoms of learned sparse dictionary as the examples instead of original example patches. We proposed a distance penalty model for calculating the weight, which can complete a second selection on similar atoms at the same time. Moreover, LR example patches removed mean pixel value are also used to learn dictionary rather than just their gradient features. Based on this, we can reconstruct initial estimated HR image and denoised LR image. Combined with iterative back projection, the two reconstructed images are applied to obtain final estimated HR image. We validate our algorithm on natural images and compared with the previously reported algorithms. Experimental results show that our proposed method performs better noise robustness. [ABSTRACT FROM AUTHOR]

Background and Aims MicroRNAs (miRNAs) play an important role in the responses and adaptation of plants to many stresses including low nitrogen (LN). Characterizing relevant miRNAs will improve our understanding of nitrogen (N) use efficiency and LN tolerance and thus contribute to sustainable maize production. The objective of this study was to identify novel and known miRNAs and their targets involved in the response and adaptation of maize (Zea mays) to LN stress. Methods MiRNAs and their targets were identified by combined analysis of deep sequencing of small RNA and degradome libraries. The identity of target genes was confirmed by gene-specific RNA ligase-mediated rapid amplification of 5′ cDNA ends (RLM-RACE) and by quantitative expression analysis. Key Results Over 150 million raw reads of small RNA and degradome sequence data were generated. A total of 46 unique mature miRNA sequences belonging to 23 maize miRNA families were sequenced. Eighty-five potentially new miRNAs were identified, with corresponding miRNA* also identified for 65 of them. Twenty-five new miRNAs showed >2-fold relative change in response to LN. In addition to known miR169 species, two novel putative miR169 species were identified. Deep sequencing of miRNAs and the degradome, and RLM-RACE and quantitative polymerase chain reaction (PCR) analyses of their targets showed that miRC10- and miRC68-mediated target cleavage may play a major role among miR169 families in the adaptation to LN by maize seedlings. Conclusions Small RNA and degradome sequencing combined with quantitative reverse transcription–PCR and RLM-RACE verification enabled the efficient identification of miRNAs and their target genes. The generated data sets and the two novel miR169 species that were identified will contribute to our understanding of the physiological basis of adaptation to LN stress in maize plants. [ABSTRACT FROM PUBLISHER]

A new optimizing method of mathematical analytical modeling of induced current for electronic anti-fouling technology is used in the present study to solve the problem of large calculated amount and complex deducing process caused by traditional segmented function method under different excitation signal, and an exact 2D finite element method is used to calculate induced current in a real electronic anti-fouling system, and the finite element calculation results can fit well with the results calculated by the optimizing analytical model of induced current under the excitation signal with different waveform. Furthermore, the average power density of induced current and the efficiency of energy transformation for EAF system are calculated to estimate the system performance under different excitation signal. The calculation result shows that: 1 the positive - negative square wave excitation signal can make the largest average power density of induced current and the sine wave excitation signal makes the smallest; 2 the sawtooth wave excitation signal can make the highest efficiency of energy transformation and the positive - negative square wave excitation signal makes the lowest. [ABSTRACT FROM AUTHOR]

The inter-annual variability of the tropical Pacific Subsurface Ocean Temperature Anomaly (SOTA) and the associated anomalous atmospheric circulation over the Asian North Pacific during the El Niño-Southern Oscillation (ENSO) were investigated using National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) atmospheric reanalysis data and simple ocean data simulation (SODA). The relationship between the ENSO and the climate of China was revealed. The main results indicated the following: 1) there are two ENSO modes acting on the subsurface tropical Pacific. The first mode is related to the mature phase of ENSO, which mainly appears during winter. The second mode is associated with a transition stage of the ENSO developing or decaying, which mainly occurs during summer; 2) during the mature phase of El Niño, the meridionality of the atmosphere in the mid-high latitude increases, the Aleutian low and high pressure ridge over Lake Baikal strengthens, northerly winds prevail in northern China, and precipitation in northern China decreases significantly. The ridge of the Ural High strengthens during the decaying phase of El Niño, as atmospheric circulation is sustained during winter, and the northerly wind anomaly appears in northern China during summer. Due to the ascending branch of the Walker circulation over the western Pacific, the western Pacific Subtropical High becomes weaker, and south-southeasterly winds prevail over southern China. As a result, less rainfall occurs over northern China and more rainfall over the Changjiang River basin and the southwestern and eastern region of Inner Mongolia. The flood disaster that occurred south of Changjiang River can be attributed to this. The La Niña event causes an opposite, but weaker effect; 3) the ENSO cycle can influence climate anomalies within China via zonal and meridional heat transport. This is known as the 'atmospheric-bridge', where the energy anomaly within the tropical Pacific transfers to the mid-high latitude in the northern Pacific through Hadley cells and Rossby waves, and to the western Pacific-eastern Indian Ocean through Walker circulation. This research also discusses the special air-sea boundary processes during the ENSO events in the tropical Pacific, and indicates that the influence of the subsurface water of the tropical Pacific on the atmospheric circulation may be realized through the sea surface temperature anomalies of the mixed water, which contact the atmosphere and transfer the anomalous heat and moisture to the atmosphere directly. Moreover, the reason for the heavy flood within the Changjiang River during the summer of 1998 is reviewed in this paper. [ABSTRACT FROM AUTHOR]

A mathematical analytical modeling of induced current for electronic anti-fouling technology is deduced in the present study, which can directly measure induced electric field generated by excitation coil. Based on the analytical expression of induced current, average power density is presented to measure the force of electronic anti-fouling system acting on the aqueous solution, and the functional relationship between average power density and electronic anti-fouling system parameters is established, which can be made as the basis of the system parameters optimizing. By discussing the functional relationship it can be found: 1 when excitation signal frequency is higher than a certain frequency value, the increase of excitation signal frequency can't help increasing average power density effectively; 2 the coil radius is shorter or coil turns are less then the average power becomes larger. Furthermore, for proving the correctness of the proposed analytical model of induced current, an exact 2D finite element method is used to calculate induced current in a real electronic anti-fouling system, and the finite element calculation results can fit well with the results calculated by the proposed analytical model of induced current. [ABSTRACT FROM AUTHOR]

Abstract: The hard support vector regression (HSVR) usually has a risk of suffering from overfitting due to the presence of noise. The main reason is that it does not utilize the regularization technique to set an upper bound on the Lagrange multipliers so they can be magnified infinitely. Hence, we propose a greedy stagewise based algorithm to approximately train HSVR. At each iteration, the sample which has the maximal predicted discrepancy is selected and its weight is updated only once so as to avoid being excessively magnified. Actually, this early stopping rule can implicitly control the capacity of the regression machine, which is equivalent to a regularization technique. In addition, compared with the well-known software LIBSVM2.82, our algorithm to a certain extent has advantages in both the training time and the number of support vectors. Finally, experimental results on the synthetic and real-world benchmark data sets also corroborate the efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

After combining the classical ν-SVR with the rough theory, we propose a rough ν-SVR. Double εs are utilized to construct the rough margin for rough ν-SVR instead of single ε for the classical ν-SVR, and this rough margin consisting of positive region, boundary region, and negative region yields the feasible set of the rough ν-SVR larger than that of the classical ν-SVR, which makes the objective function of the rough ν-SVR not more than that of the classical ν-SVR. This may lead to the improvement of the performance. Meantime, experimental results on benchmark data sets confirm the validation and feasibility of our proposed rough ν-SVR. [Copyright &y& Elsevier]

Abstract: Combining reduced technique with iterative strategy, we propose a recursive reduced least squares support vector regression. The proposed algorithm chooses the data which make more contribution to target function as support vectors, and it considers all the constraints generated by the whole training set. Thus it acquires less support vectors, the number of which can be arbitrarily predefined, to construct the model with the similar generalization performance. In comparison with other methods, our algorithm also gains excellent parsimoniousness. Numerical experiments on benchmark data sets confirm the validity and feasibility of the presented algorithm. In addition, this algorithm can be extended to classification. [Copyright &y& Elsevier]

Abstract: The classical support vector regressions (SVRs) are constructed based on convex loss functions. Since non-convex loss functions to a certain extent own superiority to convex ones in generalization performance and robustness, we propose a non-convex loss function for SVR, and then the concave-convex procedure is utilized to transform the non-convex optimization to convex one. In the following, a Newton-type optimization algorithm is developed to solve the proposed robust SVR in the primal, which can not only retain the sparseness of SVR but also oppress outliers in the training examples. The effectiveness, namely better generalization, is validated through experiments on synthetic and real-world benchmark data sets. [Copyright &y& Elsevier]

A critical shortcoming of determining co-occurrence probability texture features using Haralick''s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. In this paper, the design, implementation, and testing of a more efficient algorithm to perform this task are presented. This algorithm, known as the grey level co-occurrence integrated algorithm (GLCIA), is a dramatic improvement on earlier implementations. This algorithm is created by integrating the preferred aspects of two algorithms: the grey level co-occurrence hybrid structure (GLCHS) and the grey level co-occurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated two-dimensional data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient one-dimensional data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. Using a variety of test parameters, experiments indicate that the GLCIA requires a fraction (27–54%) of the computational time compared to using the GLCHS alone. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04% to 16%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large digital images. [Copyright &y& Elsevier]

Calculation of co-occurrence probabilities is a popular method for determining texture features within remotely sensed digital imagery. Typically, the co-occurrence features are calculated by using a grey level co-occurrence matrix (GLCM) to store the co-occurring probabilities. Statistics are applied to the probabilities in the GLCM to generate the texture features. This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities when applying the statistics. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. An improvement on the GLCLL is to utilize a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL.The GLCHS method is implemented using the C language in a Unix environment. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4% (σ=3.08%) of the computational time required by the GLCLL. Significant computational gains are made using the GLCHS method. [Copyright &y& Elsevier]

• CIRR significantly positively predicted SBV and depression. • CIRR could directly decrease resilience and increase SBV and depression. • CIRR could indirectly decrease resilience by SBV and increase depression. • CIRR linear models for SBV and depression were constructed. • Two mediation models of resilience in the relationships among CIRR, SBV and depression were constructed. Studies have documented that school bullying victimization (SBV) triggered by interpersonal relationship risks (IRRs) can increase depression. However, the predictive patterns of cumulative IRRs (CIRR; classmate, parent-child, teacher-student, and friend relationships risks) on SBV and depression and its psychological mechanism remain unclear from a positive psychological perspective. This study explored CIRR models for SBV and depression and tested two mediation models of resilience in relationships among CIRR, SBV and depression. Self-report questionnaires were answered by 742 valid participants between 11 and 19 years of age. Findings showed that CIRR significantly predicted SBV and depression, with a proportionate increase in SBV and depression as the CIRR increased. Furthermore, the results confirmed the two mediation hypotheses. CIRR could directly decrease one's resilience and, therefore, increase one's SBV and level of depression. CIRR could also indirectly decrease one's resilience by SBV and further increase one's level of depression. The results construct CIRR linear model for SBV and depression and mediation models of resilience among CIRR, SBV and depression. Our results indicate that, to decrease SBV and depression based on multiple IRRs exposures, comprehensive intervening in four IRRs from a psychopathological perspective or cultivating resilience in adolescents from a positive psychological perspective is required. [ABSTRACT FROM AUTHOR]

Previous studies reported that peripheral inflammation was associated with cognitive performance and brain structure in schizophrenia. However, the moderating effect of inflammation has not been extensively studied. This study investigated whether inflammation markers moderated the association between negative symptoms and neurocognition in schizophrenia. This cross-sectional study included 137 drug-naïve schizophrenia patients (DNS) and 67 healthy controls (HC). We performed the Measurements and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) for cognitive assessment and the Positive and Negative Syndrome Scale (PANSS) for psychiatric symptoms. Plasma concentrations of interferon-gamma (IFN-γ), neutrophil gelatinase-associated lipocalin (NGAL), and nuclear factor kappa B (NF-κB) were measured. The MCCB neurocognition score, social cognition score, and total score; the plasma concentrations of NGAL, IFN-γ, and NF-κB were significantly decreased in DNS than in HC (all P's < 0.001). PANSS negative subscale (PNS), PANSS reduced expressive subdomain (RES) negatively correlated with neurocognition score (P = 0.007; P = 0.011, respectively). Plasma concentrations of IFN-γ and NGAL positively correlated with neurocognition score (P = 0.043; P = 0.008, relatively). The interactions of PNS × NGAL; PNS × IFN-γ; RES × IFN-γ accounted for significant neurocognition variance (P = 0.025; P = 0.029, P = 0.007, respectively). Simple slope analysis showed that all the above moderating effects only occurred in patients with near normal IFN-γ and NGAL levels. Plasma concentrations of IFN-γ and NGAL moderated the relationship between negative symptoms (especially RES) and neurocognition in schizophrenia. Treatment targeting inflammation may contribute to neurocognition improvement in schizophrenia. [ABSTRACT FROM AUTHOR]

Keywords: maize; dense planting; lodging resistance; D1; ZmSPL12 EN maize dense planting lodging resistance D1 ZmSPL12 622 624 3 04/11/22 20220401 NES 220401 Introduction of the semi-dwarf genes ( I sd1 i in rice, I RhtB1b i and I RhtD1b i in wheat) has drastically increased lodging resistance and grain yields in these two important crops since the 1960s, resulting in the so called "Green Revolution" (Hedden, 2003). Together, our results demonstrate that I ZmSPL12 i could mimic the function of "Green Revolution" genes to confer reduced plant height and increased lodging resistance, thus facilitating high-density planting and increased yield in maize. [Extracted from the article]

Summary: Maize is a major staple crop widely used for food, feedstocks and industrial products. Shade‐avoidance syndrome (SAS), which is triggered when plants sense competition of light from neighbouring vegetation, is detrimental for maize yield production under high‐density planting conditions. Previous studies have shown that the red and far‐red photoreceptor phytochromes are responsible for perceiving the shading signals and triggering SAS in Arabidopsis; however, their roles in maize are less clear. In this study, we examined the expression patterns of ZmPHYC1 and ZmPHYC2 and found that ZmPHYC1, but not ZmPHYC2, is highly expressed in leaves and is regulated by the circadian clock. Both ZmPHYC1 and ZmPHYC2 proteins are localized to both the nucleus and cytoplasm under light conditions and both of them can interact with themselves or with ZmPHYBs. Heterologous expression of ZmPHYCs can complement the Arabidopsis phyC‐2 mutant under constant red light conditions and confer an attenuated SAS in Arabidopsis in response to shading. Double knockout mutants of ZmPHYC1 and ZmPHYC2 created using the CRISPR/Cas9 technology display a moderate early‐flowering phenotype under long‐day conditions, whereas ZmPHYC2 overexpression plants exhibit a moderately reduced plant height and ear height. Together, these results provided new insight into the function of ZmPHYCs and guidance for breeding high‐density tolerant maize cultivars. [ABSTRACT FROM AUTHOR]

In response to competition for light from their neighbors, shade-intolerant plants flower precociously to ensure reproductive success and survival. However, the molecular mechanisms underlying this key developmental switch are not well understood. Here, we show that a pair of Arabidopsis transcription factors essential for phytochrome A signaling, FAR-RED ELONGATED HYPOCOTYL3 (FHY3) and FAR-RED IMPAIRED RESPONSE1 (FAR1), regulate flowering time by integrating environmental light signals with the miR156-SPL module-mediated aging pathway. We found that FHY3 and FAR1 directly interact with three flowering-promoting SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE (SPL) transcription factors, SPL3, SPL4, and SPL5, and inhibit their binding to the promoters of several key flowering regulatory genes, including FRUITFUL (FUL), LEAFY (LFY), APETALA1 (AP1), and MIR172C , thus downregulating their transcript levels and delaying flowering. Under simulated shade conditions, levels of SPL3/4/5 proteins increase, whereas levels of FHY3 and FAR1 proteins decline, thus releasing SPL3/4/5 from FHY3/FAR1 inhibition to allow activation of FUL , LFY , AP1 , and MIR172C and, consequently, early flowering. Taken together, these results unravel a novel mechanism whereby plants regulate flowering time by integrating environmental cues (such as light conditions) and an internal developmental program (the miR156-SPL module-mediated aging pathway). In response to shading, plants accelerate flowering. This study reveals that Arabidopsis FHY3 and FAR1 act to prevent precocious flowering by directly interacting with and interfering with the DNA-binding activity of three flowering-promoting transcription factors, SPL3/4/5, and consequently downregulating the expression levels of FRUITFUL , LEAFY , APETALA1 , and MIR172C , thus delaying flowering. [ABSTRACT FROM AUTHOR]

This study aimed to explore the relationship between alterations in plasma metabolites and treatment responses amongst antipsychotic-naïve female patients with schizophrenia. A total of 38 antipsychotic-naïve female schizophrenia patients (ANS) and 19 healthy female controls (HC) were recruited. Plasma samples were obtained from all participants, and targeted metabolomics were measured with FIA-MS/MS and LC-MS/MS. The positive and negative syndrome scale (PANSS) was used to assess the severity of psychotic symptoms before and after eight weeks of treatment. Receiver operator characteristics (ROC) curves were used to predict diagnostic and therapeutic responses. A total of 186 metabolites passed quality control procedures and were used in statistical analysis to identify potential biomarkers. Before treatment, the ANS patients had lower levels of γ -Aminobutyric Acid (GABA) and higher levels of Cholesteryl esters (CE) (20:3), Cholic Acid (CA) and Glycocholic Acid (GCA) compared to the HCs. These four differential metabonomic markers were synthesised into a combinatorial biomarker panel. This panel significantly distinguished ANS from HC. Moreover, this biomarker panel was able to effectively predict therapeutic responses. Our results suggest that plasma CE (20:3), CA, GCA, and GABA levels may be useful for diagnosing and predicting antipsychotic efficacy amongst female schizophrenia patients. [ABSTRACT FROM AUTHOR]

Emotion recognition based on EEG signals is a critical component in Human-Machine collaborative environments and psychiatric health diagnoses. However, EEG patterns have been found to vary across subjects due to user fatigue, different electrode placements, and varying impedances, etc. This problem renders the performance of EEG-based emotion recognition highly specific to subjects, requiring time-consuming individual calibration sessions to adapt an emotion recognition system to new subjects. Recently, domain adaptation (DA) strategies have achieved a great deal success in dealing with inter-subject adaptation. However, most of them can only adapt one subject to another subject, which limits their applicability in real-world scenarios. To alleviate this issue, a novel unsupervised DA strategy called Multi-Subject Subspace Alignment (MSSA) is proposed in this paper, which takes advantage of subspace alignment solution and multi-subject information in a unified framework to build personalized models without user-specific labeled data. Experiments on a public EEG dataset known as SEED verify the effectiveness and superiority of MSSA over other state of the art methods for dealing with multi-subject scenarios. [ABSTRACT FROM AUTHOR]

Totems are symbols or representations of human's affiliations with, and/or categorizations of, animals, plants and inanimate objects. Totemism is related to fundamental human belief systems based on totems. Investigating totems and totemism psychologically is a unique way to explore human minds. We have critically examined Wundt, Freud and many other scholars and scientists who made distinguished contributions to scientific research on totems and totemism almost in the past two centuries –i.e., totemic psychology, which is the study of our mind's categorization and affiliation in the human and natural world today. Understanding and appreciating their totemic psychology can help psychologists today enhance their understanding in other fields—e.g., ecological and environmental psychology, biological psychology, cognitive psychology, personality, social and ethnic psychology, clinical and counseling psychology, cultural psychology, and religious or spiritual psychology. Unfortunately, recent data from a content analysis via PsycInfo and a cross-cultural survey study (N =273) showed that well-trained psychologists around the world and psychology students in the United States and in China are unfamiliar with Wundt and Freud's totemic contributions to psychology today. The implications, benefits, and lessons of totems and today's totemic psychology are discussed here. [ABSTRACT FROM AUTHOR]

Summary: Flowering time is a key agronomic trait determining environmental adaptation and yield potential of crops. The regulatory mechanisms of flowering in maize still remain rudimentary.In this study, we combine expressional, genetic, and molecular studies to identify two homologous SQUAMOSA PROMOTER BINDING PROTEIN‐LIKE (SPL) transcription factors ZmSPL13 and ZmSPL29 as positive regulators of juvenile‐to‐adult vegetative transition and floral transition in maize.We show that both ZmSPL13 and ZmSPL29 are preferentially expressed in leaf phloem, vegetative and reproductive meristem. We show that vegetative phase change and flowering time are moderately delayed in the Zmspl13 and Zmspl29 single knockout mutants and more significantly delayed in the Zmspl13/29 double mutants. Consistently, the ZmSPL29 overexpression plants display precocious vegetative phase transition and floral transition, thus early flowering. We demonstrate that ZmSPL13 and ZmSPL29 directly upregulate the expression of ZmMIR172C and ZCN8 in the leaf, and of ZMM3 and ZMM4 in the shoot apical meristem, to induce juvenile‐to‐adult vegetative transition and floral transition.These findings establish a consecutive signaling cascade of the maize aging pathway by linking the miR156‐SPL and the miR172‐Gl15 regulatory modules and provide new targets for genetic improvement of flowering time in maize cultivars. [ABSTRACT FROM AUTHOR]

Combined the mechanism of gob-side entry retaining by roof cutting pressure relief and the geological conditions of Jiaozishan coal mine, the influence of gob-side entry retaining from the depth of blasting bore in the process of roof cutting pressure relief was studied by the method of FLAC3D numerical simulation. When the depth of blasting bore was 4m, it was most significant that the surrounding rock stress of roadway transferred to the roadway deep; meanwhile, the surrounding rock deformation was also the minimum. And the result was applied to an industrial practice in the intake airflow roadway of the 9706 working face. When the working face was mined, the surrounding rock deformation was basically identical between the coalside entry in front of the working face and the gob-side entry behind the working face, which met the requirements of retaining roadway and production in the next working face. It successfully reduced the difficulty of protecting roadway and the loss of coal pillar, and achieved remarkable economic and social benefits. [ABSTRACT FROM AUTHOR]

Increasing planting density has been adopted as an effective means to increase maize (Zea mays) yield. Competition for light from neighbors can trigger plant shade avoidance syndrome, which includes accelerated flowering. However, the regulatory networks of maize inflorescence development in response to high-density planting remain poorly understood. In this study, we showed that shade-mimicking treatments cause precocious development of the tassels and ears. Comparative transcriptome profiling analyses revealed the enrichment of phytohormone-related genes and transcriptional regulators among the genes co-regulated by developmental progression and simulated shade. Network analysis showed that three homologous Squamosa promoter binding protein (SBP)-like (SPL) transcription factors, Unbranched2 (UB2), Unbranched3 (UB3), and Tasselsheath4 (TSH4), individually exhibited connectivity to over 2,400 genes across the V3-to-V9 stages of tassel development. In addition, we showed that the ub2 ub3 double mutant and tsh4 single mutant were almost insensitive to simulated shade treatments. Moreover, we demonstrated that UB2/UB3/TSH4 could directly regulate the expression of Barren inflorescence2 (BIF2) and Zea mays teosinte branched1/cycloidea/proliferating cell factor30 (ZmTCP30). Furthermore, we functionally verified a role of ZmTCP30 in regulating tassel branching and ear development. Our results reveal a UB2/UB3/TSH4-anchored transcriptional regulatory network of maize inflorescence development and provide valuable targets for breeding shade-tolerant maize cultivars. Three homologous SPL transcription factors, Unbranched2 (UB2), Unbranched3 (UB3), and Tasselsheath4 (TSH4), act as a central hub in regulating maize inflorescence development in response to shade. [ABSTRACT FROM AUTHOR]

This study introduces an innovative fuzzy finite element model (fFEM) based on inherent strain theory, optimized with artificial intelligence, to predict welding deformations in critical components of Cargo Transfer Vessel (CTV). By integrating a genetic algorithm with the commercial finite element software ABAQUS, optimal inherent strain coefficients are identified. The fFEM significantly reduces computation time, achieving approximately 60% efficiency compared to traditional thermo-elastic-plastic models. The model demonstrates high accuracy, with an average prediction error of less than 10%. The efficacy of this method is validated through comparative analyses against traditional models and experimental results, highlighting its precision and effectiveness in predicting welding deformations in large marine structures. This approach provides a robust framework for addressing welding deformations, ensuring structural integrity and precision in marine engineering applications. • The fuzzy finite element model (fFEM) efficiently predicts welding deformation in groove and fillet weld joints. • The coefficient matrix of fFEM is optimized based on genetic algorithm in conjunction with ABAQUS and Python. • The welding deformation of the sub assembly and CTV flange is effectively predicted by optimized fFEM. [ABSTRACT FROM AUTHOR]

The upregulation of immune and inflammatory response may play a role in the negative symptoms of schizophrenia. Berberine is an effective drug with anti-inflammatory property, and may be beneficial for the treatment of negative symptoms. The aim of this study is to test this hypothesis through a randomized, double-blind, placebo-controlled, clinical trial. Eligible patients with schizophrenia were randomized to receive placebo or berberine (900 mg/day) for 8 weeks as adjunctive treatment to single atypical antipsychotic drug. The Positive and Negative Syndrome Scale (PANSS) was used to evaluate clinical symptoms at three time points (baseline, 4th and 8th week). Blood samples were collected at the above three time points to determine the concentrations of inflammatory markers including interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and C-reactive protein (CRP). 59 patients with intention-to-treat were analyzed, 32 in the berberine group and 27 in the placebo group. From the baseline to the 8th week, berberine treatment significantly improved the negative symptom subscale of PANSS (F = 18.981; p < 0.001). From the baseline to the 8th week, the plasma CRP concentration decreased in the berberine group, while increased in the placebo group (F = 5.373; p = 0.024). Furthermore, in the berberine group, the change of CRP concentration was significantly positively correlated with the change of PANSS negative symptom subscale within 8 weeks (r = 0.56; p = 0.002). There was no significant difference in adverse events between the two groups (p's > 0.05). Our study suggests that berberine treatment is well tolerated in patients with schizophrenia. Berberine may improve negative symptoms through anti-inflammatory effect. Trial registration: Clinicaltrials.gov identifier: NCT 03548155. [ABSTRACT FROM AUTHOR]

The tracking and recognition of facial activities from images or videos have attracted great attention in computer vision field. Facial activities are characterized by three levels. First, in the bottom level, facial feature points around each facial component, i.e., eyebrow, mouth, etc., capture the detailed face shape information. Second, in the middle level, facial action units, defined in the facial action coding system, represent the contraction of a specific set of facial muscles, i.e., lid tightener, eyebrow raiser, etc. Finally, in the top level, six prototypical facial expressions represent the global facial muscle movement and are commonly used to describe the human emotion states. In contrast to the mainstream approaches, which usually only focus on one or two levels of facial activities, and track (or recognize) them separately, this paper introduces a unified probabilistic framework based on the dynamic Bayesian network to simultaneously and coherently represent the facial evolvement in different levels, their interactions and their observations. Advanced machine learning methods are introduced to learn the model based on both training data and subjective prior knowledge. Given the model and the measurements of facial motions, all three levels of facial activities are simultaneously recognized through a probabilistic inference. Extensive experiments are performed to illustrate the feasibility and effectiveness of the proposed model on all three level facial activities. [ABSTRACT FROM PUBLISHER]

Stability orderings of 150 stable complexes formed by metal ions (Na+, K+, Ca2+, Mg2+, and Zn2+) and 13 stable thymine tautomers in both solvent and gas phases are obtained, and the optimal binding site for a metal ion in a specific thymine tautomer is identified. Results indicate that the complex with the canonical thymine tautomer (T1) is more stable than those with the rare ones, and the monodentate complex M-T1 o4(o2) are their ground-state form in the solvent phase. The ground-state thymine complexes bound by Ca2+, Mg2+, or Zn2+ become bidentate M-T3o4l o2,n3, which is derived from a rare thymine tautomer T3o4l, whereas those bound by Na+ and K+ are still monodentate complexes M-T1 o4(o2), however, in the gas phase. The differences in stability are discussed in detail from the binding strength of metal ions, relative energy of the corresponding thymine tautomers, and solution effect. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In order to simulate the hydrogen bonding and proton transfer (PT) in protein-DNA/RNA interactions, a series of simplified models were employed and investigated in the gas phase. These models included various neutral, anionic and cationic glycine-uracil dimers, and their methylated derivatives generated by the mono- or dimethylation of glycine and/or uracil moieties of the dimer. The results reveal that the only process that can occur in the neutral complexes is a double-PT process leading to proton exchange between the two moieties (i.e., point mutation). The first methyl substitute can reduce the activation energy of the PT process and thus promote the isomerization of the two moieties; further methylation can reduce the isomerization in only some of the cases. In the anionic complexes, only the one-way PT (i.e., amino acid → nucleic acid base) process is energetically favorable, and this PT process is an interesting barrier-free one (BFPT), with the attached electron locating itself at the base moiety. Methylation will disfavor BFPT, but it cannot alter the nature of BFPT. In the cationic complexes, three different PT processes can occur. These processes can transform mutually by adjusting either or both of the methylated sites and methyl number, indicating that the methylation can regulate the dynamics of these PT processes. [ABSTRACT FROM AUTHOR]

Summary: The epidermal hair and stomata are two types of specialized structures on the surface of plant leaves. On mature maize leaves, stomatal complexes and three types of hairs are distributed in a stereotyped pattern on the adaxial epidermis. However, the spatiotemporal relationship between epidermal hair and stomata development and the regulatory mechanisms governing their formation in maize remain largely unknown.Here, we report that three homologous ZmSPL transcription factors, ZmSPL10, ZmSPL14 and ZmSPL26, act in concert to promote epidermal hair fate on maize leaf. Cytological analyses revealed that Zmspl10/14/26 triple mutants are completely glabrous, but possess ectopic stomatal files. Strikingly, the precursor cells for prickle and bicellular hairs are transdifferentiated into ectopic stomatal complexes in the Zmspl10/14/26 mutants.Molecular analyses demonstrated that ZmSPL10/14/26 bind directly to the promoter of a WUSCHEL‐related homeobox gene, ZmWOX3A, and upregulate its expression in the hair precursor cells. Moreover, several auxin‐related genes are downregulated in the Zmspl10/14/26 triple mutants.Our results suggest that ZmSPL10/14/26 play a key role in promoting epidermal hair fate on maize leaves, possibly through regulating ZmWOX3A and auxin‐related gene expression, and that the fates of epidermal hairs and stomata are switchable. [ABSTRACT FROM AUTHOR]

Abstract Crop breeding aims to generate pure inbred lines with multiple desired traits. Doubled haploid (DH) and genome editing using CRISPR/Cas9 are two powerful game-changing technologies in crop breeding. However, both of them still fall short for rapid generation of pure elite lines with integrated favorable traits. Here, we report the development of a Haploid-Inducer Mediated Genome Editing (IMGE) approach, which utilizes a maize haploid inducer line carrying a CRISPR/Cas9 cassette targeting for a desired agronomic trait to pollinate an elite maize inbred line and to generate genome-edited haploids in the elite maize background. Homozygous pure DH lines with the desired trait improvement could be generated within two generations, thus bypassing the lengthy procedure of repeated crossing and backcrossing used in conventional breeding for integrating a desirable trait into elite commercial backgrounds. We report here the development of a novel approach, termed Haploid-Inducer Mediated Genome Editing (IMGE), that combines haploid induction and genome editing technologies to create genome-edited haploids and doubled haploids, so so to accelerate crop breeding. [ABSTRACT FROM AUTHOR]

Facial action unit (AU) recognition has been applied in a wild range of fields, and has attracted great attention in the past two decades. Most existing works on AU recognition assumed that the complete label assignment for each training image is available, which is often not the case in practice. Labeling AU is expensive and time consuming process. Moreover, due to the AU ambiguity and subjective difference, some AUs are difficult to label reliably and confidently. Many AU recognition works try to train the classifier for each AU independently, which is of high computation cost and ignores the dependency among different AUs. In this work, we formulate AU recognition under incomplete data as a multi-label learning with missing labels (MLML) problem. Most existing MLML methods usually employ the same features for all classes. However, we find this setting is unreasonable in AU recognition, as the occurrence of different AUs produce changes of skin surface displacement or face appearance in different face regions. If using the shared features for all AUs, much noise will be involved due to the occurrence of other AUs. Consequently, the changes of the specific AUs cannot be clearly highlighted, leading to the performance degradation. Instead, we propose to extract the most discriminative features for each AU individually, which are learned by the supervised learning method. The learned features are further embedded into the instance-level label smoothness term of our model, which also includes the label consistency and the class-level label smoothness. Both a global solution using st-cut and an approximated solution using conjugate gradient (CG) descent are provided. Experiments on both posed and spontaneous facial expression databases demonstrate the superiority of the proposed method in comparison with several state-of-the-art works. [ABSTRACT FROM AUTHOR]

Automatic facial expression analysis has received great attention in different applications over the last two decades. Facial Action Coding System (FACS), which describes all possible facial expressions based on a set of facial muscle movements called Action Unit (AU), has been used extensively to model and analyze facial expressions. FACS describes methods for coding the intensity of AUs, and AU intensity measurement is important in some studies in behavioral science and developmental psychology. However, in majority of the existing studies in the area of facial expression recognition, the focus has been on basic expression recognition or facial action unit detection. There are very few investigations on measuring the intensity of spontaneous facial actions. In addition, the few studies on AU intensity recognition usually try to measure the intensity of facial actions statically and individually, ignoring the dependencies among multilevel AU intensities as well as the temporal information. However, these spatiotemporal interactions among facial actions are crucial for understanding and analyzing spontaneous facial expressions, since these coherent, coordinated, and synchronized interactions are that produce a meaningful facial display. In this paper, we propose a framework based on Dynamic Bayesian Network (DBN) to systematically model the dynamic and semantic relationships among multilevel AU intensities. Given the extracted image observations, the AU intensity recognition is accomplished through probabilistic inference by systematically integrating the image observations with the proposed DBN model. Experiments on Denver Intensity of Spontaneous Facial Action (DISFA) database demonstrate the superiority of our method over single image-driven methods in AU intensity measurement. [ABSTRACT FROM AUTHOR]

Li-ion battery has attracted more and more attention as it is a promising storage device which has long service life, higher energy, and power density. However, battery ageing always occurs during operation and leads to performance degradation and system fault which not only causes inconvenience, but also risks serious consequences such as thermal runaway or even explosion. This paper reviews recent research and development of ageing mechanisms of Li-ion batteries to understand the origins and symptoms of Li-ion battery faults. Common ageing factors are covered with their effects and consequences. Through ageing tests, relationship between performance and ageing factors, as well as cross-dependence among factors can be quantified. Summary of recent research about fault diagnosis technology for Li-ion batteries is concluded with their cons and pros. The suggestions on novel fault diagnosis approach and remaining challenges are provided at the end of this paper. [ABSTRACT FROM AUTHOR]

• Methotrexate (MTX) side effects can cause intestinal mucositis (IM). • Forsythiaside A (FTA) alleviated MTX-induced IM. • FTA might suppress the activation of NLRP3 signaling pathway. Most chemotherapeutic drugs can kill the tumor cells, but also cause a vast damage to body, such as intestinal mucositis (IM). The present study was design to find out the effect of Forsythiaside A (FTA) on chemotherapeutic-induced IM in rats. Briefly, for 3 consecutive days, male Sprague–Dawley rats were treated with 7 mg / kg methotrexate (MTX) to establish IM and simultaneously administered with 40 or 80 mg / kg FTA for 7 days. Our results showed that the final body weight and daily food intake were increased, and the disease activity index was reduced in the MTX group after FTA treatment. The MTX group showed the pathological alterations like the inflammatory cells infiltration, the mucosal layer destruction, glands expansion, intestinal villi structure disorder and goblet cells reduction, while we found that 80 mg / kg FTA treatment displayed evident reversal effects. ELISA further suggested that TNF-α, IL-1β and IL-18 levels in serum in MTX-induced rats were reduced after 80 mg / kg FTA treatment. Moreover, FTA decreased the number of leukocytes, neutrophils and lymphocytes in peripheral blood. Western blot and immunofluorescence results indicated that the expression levels of NLRP3, cleaved caspase 1, cleaved IL-1β and CD68 positive rate were down-regulated in MTX-induced rats after 80 mg / kg FTA intervention. The findings of the current study suggested that FTA effectively inhibited MTX-induced IM in rats by attenuating the activation of the NLRP3 signaling pathways. [ABSTRACT FROM AUTHOR]

• Berberine may reduce dyslipidemia and improve insulin resistance in schizophrenia patients. • Baseline BMI and serum prolactin may predict the effect of berberine on improving insulin resistance Previous studies have shown that berberine can improve metabolic disturbances in non-psychiatric patients, but no clinical research has been conducted in schizophrenia. This study was a randomized, double-blind, placebo-controlled clinical trial. Eligible patients diagnosed with schizophrenia were randomized to receive placebo or berberine (900mg/day) as an adjunctive treatment for eight weeks. Peripheral glycolipid metabolism parameters were measured at baseline, week 4, and week 8. Sixty-five patients were included, and forty-nine patients completed the 8-week trial. Berberine led to significant declines in total cholesterol, low-density lipoprotein cholesterol, fasting serum insulin, and insulin resistance(all p <0.05) compared with placebo. Baseline body mass index and serum prolactin concentration could predict the effect of berberine on insulin resistance. Berberine adjunctive treatment may reduce the risk of glycolipid metabolic disturbances in patients with schizophrenia. [ABSTRACT FROM AUTHOR]