Molecular cloning, expression profiling of a carboxylesterase gene and its potential role in methyl farnesoate degradation in Eriocheir sinensis (Brachyura, Varunidae)

In crustaceans, methyl farnesoate (MF) is an important sesquiterpenoid to regulate many physiological processes, especially reproduction and ovarian maturation. In this study, a 1919 bp cDNA of carboxylesterases (Es-CXE6) with some conserved motifs of the CXE multifunctional enzyme family was cloned from Eriocheir sinensis. Tissue and stage-specific expression results suggested that Es-CXE6 expression in hepatopancreas was highest and associated with the haemolymph MF titer. In vitro and in vivo experiments showed that Es-CXE6 expression was significantly upregulated by MF treatment in the hepatopancreas but not in the ovary. Furthermore, an eyestalk ablation experiment showed that Es-CXE6 expression was significantly upregulated on days 1 and 3 post eyestalk ablation in the hepatopancreas. Together, these results indicate that Es-CXE6 may degrade MF in the hepatopancreas in E. sinensis. Our results offer a potential approach to maintain the MF titer at appropriate levels, which has potential applications in crab aquaculture.

Neural Potts Model

We propose the Neural Potts Model objective as an amortized optimization problem. The objective enables training a single model with shared parameters to explicitly model energy landscapes across multiple protein families. Given a protein sequence as input, the model is trained to predict a pairwise coupling matrix for a Potts model energy function describing the local evolutionary landscape of the sequence. Couplings can be predicted for novel sequences. A controlled ablation experiment assessing unsupervised contact prediction on sets of related protein families finds a gain from amortization for low-depth multiple sequence alignments; the result is then confirmed on a database with broad coverage of protein sequences.

Improve SegNet with feature pyramid for road scene parsing

Road scene parsing is a common task in semantic segmentation. Its images have characteristics of containing complex scene context and differing greatly among targets of the same category from different scales. To address these problems, we propose a semantic segmentation model combined with edge detection. We extend the segmentation network with an encoder-decoder structure by adding an edge feature pyramid module, namely Edge Feature Pyramid Network (EFPNet, for short). This module uses edge detection operators to get boundary information and then combines the multiscale features to improve the ability to recognize small targets. EFPNet can make up the shortcomings of convolutional neural network features, and it helps to produce smooth segmentation. After extracting features of the encoder and decoder, EFPNet uses Euclidean distance to compare the similarity between the presentation of the encoder and the decoder, which can increase the decoder’s ability to restore from the encoder. We evaluated the proposed method on Cityscapes datasets. The experiment on Cityscapes datasets demonstrates that the accuracies are improved by 7.5% and 6.2% over the popular SegNet and ENet. And the ablation experiment validates the effectiveness of our method.

Deep time-delay Markov network for prediction and modeling the stress and emotions state transition

To recognize stress and emotion, most of the existing methods only observe and analyze speech patterns from present-time features. However, an emotion (especially for stress) can change because it was triggered by an event while speaking. To address this issue, we propose a novel method for predicting stress and emotions by analyzing prior emotional states. We named this method the deep time-delay Markov network (DTMN). Structurally, the proposed DTMN contains a hidden Markov model (HMM) and a time-delay neural network (TDNN). We evaluated the effectiveness of the proposed DTMN by comparing it with several state transition methods in predicting an emotional state from time-series (sequences) speech data of the SUSAS dataset. The experimental results show that the proposed DTMN can accurately predict present emotional states by outperforming the baseline systems in terms of the prediction error rate (PER). We then modeled the emotional state transition using a finite Markov chain based on the prediction result. We also conducted an ablation experiment to observe the effect of different HMM values and TDNN parameters on the prediction result and the computational training time of the proposed DTMN.

The Study on Tuning Photoluminescence of Colloidal Graphene Quantum Dots Synthesized through Laser Ablation

We report photoluminescence study of Colloidal Graphene Quantum Dots (GQDs) that synthesized from ablation of rGO solution. The rGO solution was ablated using 800 nm Ti-Sapphire femtosecond laser by varying the synthesize parameters such as laser power and ablation time. We observed that changing laser power 1 Watt to become 1.7 Watt and time ablation 20 minutes to become 60 minuteswill alterate the Photoluminescence (PL) curve peak of GQDs. In case of ablation power variation, PL data shows that the PL curve peak excited by 280 nm laser changed from 369.09 nm to 371.02 nm, and when it excited by 290 nm the PL curve peak slightly changed from 388.17 nm to 393.8 nm. The alteration of Photoluminescence peak is also observed in the variation of time ablation experiment, The PL curve peaks from GQDs time ablation variation samples excited by 280 nm were slightly changed from 376.81 nm to 373.59 nm, and when it excited by 290 nm laser, the PL curve peak is 391.55 nm then changed to 392.11. The change of PL peak on laser power or time ablation variation shows that both parameters will alter either the size, shape, or the edge-type of GQDs.

Study of linear ablative rate of D6AC steel wing used on supersonic missile

The D6AC steel wing used on supersonic missile is the object in this study. Its service environment was generated and simulated. The ablation experiment of D6AC steel missile wing was carried out under different parameters of flow field. The ablation process of D6AC steel wing was studied and analyzed under the supersonic aerodynamic heating environment. The results show that the ablation process of D6AC steel missile wing could be divided into three stages: aerodynamic heating, oxidation reaction, and shear stripping. The influence factors of the D6AC, steel wing ablation include the total temperature, Mach number, oxygen content, and water content. The higher the total temperature is, the more early the initial ablation time of wing is. The linear ablative rate of the D6AC steel wing is the result of the interaction of the Mach number, oxygen content, and water content. The higher the Mach number is, the larger the oxygen content is, and the larger the linear ablative rate of missile wing is. The influence of water content on the linear ablative rate of D6AC steel wing is the opposite.

Physical association between a novel plasma-membrane structure and centrosome orients cell division

In the last mitotic division of the epidermal lineage in the ascidian embryo, the cells divide stereotypically along the anterior-posterior axis. During interphase, we found that a unique membrane structure invaginates from the posterior to the centre of the cell, in a microtubule-dependent manner. The invagination projects toward centrioles on the apical side of the nucleus and associates with one of them. Further, a cilium forms on the posterior side of the cell and its basal body remains associated with the invagination. A laser ablation experiment suggests that the invagination is under tensile force and promotes the posterior positioning of the centrosome. Finally, we showed that the orientation of the invaginations is coupled with the polarized dynamics of centrosome movements and the orientation of cell division. Based on these findings, we propose a model whereby this novel membrane structure orchestrates centrosome positioning and thus the orientation of cell division axis.