Evemphyron sinense, a new genus and species infesting legume seedpods in China (Coleoptera, Attelabidae, Rhynchitinae)

A new genus Evemphyron Alonso-Zarazaga, Lv & Wang, gen. n., belonging to Attelabidae Rhynchitinae, is described. Its single species, Evemphyron sinense Alonso-Zarazaga, Lv & Wang, sp. n., was reared from larvae found inside seed pods of the legume Callerya dielsiana (Fabaceae, Millettieae) in Sichuan Province (China). The species is figured and placed in the Deporaini because of the presence of minute labial palpi, the strongly crescentic apex of the postmentum, and the apodemes of male IX sternite and female VIII sternite curved sinistro-anterially near their cephalic end. It shows 3-segmented labial palpi and male sex patches on the procoxae, characters that suggest a basal position in the tribe.Peer Reviewe

Yarrowia lipolytica: A versatile microbial workhorse for expanding nature’s biosynthetic capacity

Yarrowia lipolytica is an oleaginous yeast that have been substantially engineered for production of oleochemcials and drop-in transportation fuels. It has been considered as a ‘generally recognized as safe’ (GRAS) organism for the production of organic acids in the food and nutraceutical industry. The high precursor acetyl-CoA and malonyl-CoA flux along with the versatile carbon-utilization capability makes this yeast as a superior host to upgrade low-value carbons into high-value pharmaceuticals and plant natural products (PNPs). Bacteria system in general is less efficient to express the complex gene cluster of plant natural product pathway. Unlike bacteria, yeast has developed spatially separated organelles to partition specialized metabolic functions into distinct cellular compartments. In this talk, we will present strategies to harness the endogenous acetyl-CoA/malonyl-CoA/HMG-CoA metabolism toward engineering efficient yeast cell factories to produce complex oleochemicals, terpenes, polyketides and aromatic commodity chemicals. We identified pathway limitations and assessed genetic engineering strategies to elevate the level of acetyl-CoA, malonyl-CoA, HMG-CoA and NADPH. This work will provide a testbed for engineering Y. lipolytica and expanding nature’s biosynthetic capacity to produce complex fuels and chemicals from renewable feedstocks

Overcoming Cabbage Crossing Incompatibility by the Development and Application of Self-Compatibility-QTL- Specific Markers and Genome-Wide Background Analysis

Cabbage hybrids, which clearly present heterosis vigor, are widely used in agricultural production. We compared two S5 haplotype (Class II) cabbage inbred-lines 87–534 and 94–182: the former is highly SC while the latter is highly SI; sequence analysis of SI-related genes including SCR, SRK, ARC1, THL1, and MLPK indicates the some SNPs in ARC1 and SRK of 87–534; semi-quantitative analysis indicated that the SI-related genes were transcribed normally from DNA to mRNA. To unravel the genetic basis of SC, we performed whole-genome mapping of the quantitative trait loci (QTLs) governing self-compatibility using an F2 population derived from 87–534 × 96–100. Eight QTLs were detected, and high contribution rates (CRs) were observed for three QTLs: qSC7.2 (54.8%), qSC9.1 (14.1%) and qSC5.1 (11.2%). 06–88 (CB201 × 96–100) yielded an excellent hybrid. However, F1 seeds cannot be produced at the anthesis stage because the parents share the same S-haplotype (S57, class I). To overcome crossing incompatibility, we performed rapid introgression of the self-compatibility trait from 87–534 to 96–100 using two self-compatibility-QTL-specific markers, BoID0709 and BoID0992, as well as 36 genome-wide markers that were evenly distributed along nine chromosomes for background analysis in recurrent back-crossing (BC). The transfer process showed that the proportion of recurrent parent genome (PRPG) in BC4F1 was greater than 94%, and the ratio of individual SC plants in BC4F1 reached 100%. The newly created line, which was designated SC96–100 and exhibited both agronomic traits that were similar to those of 96–100 and a compatibility index (CI) greater than 5.0, was successfully used in the production of the commercial hybrid 06–88. The study herein provides new insight into the genetic basis of self-compatibility in cabbage and facilitates cabbage breeding using SC lines in the male-sterile (MS) system

Inverter-Based Subthreshold Amplifier Techniques and Their Application in 0.3-V ΔΣ -Modulators

Subthreshold amplifiers suffer from the limited voltage headroom which leaves little space for conventional analog techniques to enhance performance and efficiency. This paper presents an evolution process of implementing conventional structures with inverters, allowing ultralow-voltage operation with increased flexibility in adopting traditional circuit techniques. Based on the proposed inverter-based elementary structure and CMFB, both the Miller-compensated (MC) operational transconductance amplifier (OTA) and the feedforward-compensated (FFC) OTA achieve significantly improved performance as compared to previous works. The proposed amplifier techniques are verified in ΔΣ modulator (DSM) design, with MC-OTA for a DT-DSM and FFC-OTA for a CT-DSM, both fabricated in a 0.13- μm CMOS. The 0.3-V DT-DSM achieves 74.1-dB SNDR, 83.4-dB SFDR and 20-kHz bandwidth with 79.3- μW power, resulting in a Schreier figure of merit (FoM) of 158 dB. The 0.3-V CT-DSM achieves 68.5-dB SNDR, 82.6-dB SFDR, and 50-kHz bandwidth with 26.3- μW power, leading to a Schreier FoM of 161 dB. Both DSMs exhibit highly competitive performance among sub-0.5-V designs, validating the proposed subthreshold amplifier techniques

Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

The Internet of Things (IoT) technologies such as interconnection and edge computing help emotion recognition to be applied in healthcare, smart education, etc. However, the acquisition and transmission processes may have some situations, such as lost signals and serious interference noise caused by motion, which affect the quality of the received data and limit the performance of IoT emotion detection. We collectively refer to these as invalid data. A multi-step deep (MSD) system is proposed to reliably detect multimodal emotion by the collected records containing invalid data. Semantic compatibility and continuity are utilized to filter out the invalid data. The feature from invalid modal data is replaced through the imputation method to compensate for the impact of invalid data on emotion detection. In this way, the proposed system can automatically process invalid data and improve the recognition performance. Furthermore, considering the spatiotemporal information, the features of video and physiological signals are extracted by specific deep neural networks in the MSD system. The simulation experiments are conducted on a public multimodal database, and the performance of the MSD system measured by the unweighted average recall is better than that of the traditional system. The promising results observed in the experiments verify the potential influence of the proposed system in practical IoT applications

Micro-expression recognition by two-stream difference network

Facial micro-expression is a superposition of micro-expression features and identity information of a subject. For the problem of identity information interference in micro-expression recognition, this study proposes a new method for facial micro-expression recognition by de-identity information, called two-stream difference network (TSDN). First, a two-stream encoder-decoder network is trained by a convolutional neural network, where the input of the micro-expression stream is a micro-expression image, and the identity stream is a facial identity image. The micro-expression image is the apex image, and the identity image is the onset image in the micro-expression sequence. The identity information and micro-expression features are recorded in the intermediate layer of the micro-expression stream, while the intermediate layer of the identity stream contains only the identity information of a subject. Then, the identity information is removed by the difference network, but micro-expression features are stored in the intermediate layer of the micro-expression stream. Given the sequence of the micro-expressions, the TSDN model of de-identity information learns the difference that stores in the expression stream. Two public spontaneous facial micro-expression data sets (SMIC and CASME II) are employed in our experiments. The experiment results show that our model can achieve a superior performance in micro-expression recognition.</p

Hierarchical support vector machine for facial micro-expression recognition

The sample category distribution of spontaneous facial micro-expression datasets is unbalanced, due to the experimental environment, collection equipment, and individualization of subjects, which brings great challenges to micro-expression recognition. Therefore, this paper introduces a micro-expression recognition model based on the Hierarchical Support Vector Machine (H-SVM) to reduce the interference of sample category distribution imbalance. First, we calculated the position of the apex frame in the micro-expression image sequence. To keep micro-expression frames balanced, we sparsely sample the images sequence according to the apex frame. Then, the Low-level Descriptors of the region of interest of the micro-expression image sequence and the High-level Descriptors of apex frame are extracted. Finally, the H-SVM model is used to classify the fusion features of different levels. The experimental results on SMIC, CAMSE2, SAMM, and their composite datasets show that our method can achieve superior performance in micro-expression recognition.</p