Rootstock and seasonal variations affect anthocyanin accumulation and quality traits of ‘Kyoho’ grape berries in subtropical double cropping system

The double cropping system has been commercially adopted in subtropical viticulture regions. However, very limited information about rootstock and seasonal effects on berry quality traits are available for this unique production system. Developing ‘Kyoho’ berries from own-rooted vines and from vines on 5C and 1202C rootstocks were periodically sampled from veraison until harvest in two consecutive cropping cycles to document the potential seasonal influence on rootstock effects. Anthocyanin concentration in berry skin, total soluble solids content (TSS), and titratable acidity (TA) were analyzed. In both cropping cycles, own-rooted vines produced berries with the highest anthocyanin concentration while vines on 1202C produced berries with the lowest anthocyanin concentration among the three scion/rootstocks. Anthocyanin concentrations were not differentiated by the differential climate pattern between the summer and the winter cropping cycles. Berries of own-rooted ‘Kyoho’ and ‘Kyoho’/5C vines accumulated satisfactory and equal amount of TSS in both cropping cycles. 1202C rootstocks did not affect berry TSS in the summer cropping cycle but reduced TSS in the winter cropping cycle. Significant rootstock and seasonal effects on berry TA were detected. Own-rooted vines produced berries with the lowest TA while vines on 1202C produced berries with the highest TA among the three scion/rootstock combinations. TA of berries from the winter cropping cycle was significantly higher than that from the summer cropping cycle especially in ‘Kyoho’/1202C. Relationships between anthocyanins and TSS of developing berries after veraison properly fitted into a sigmoidal function regardless of rootstocks and cropping cycles. However, the duration of the initial lag phase, the onset and the trend of both quality triats in the increasing phase, and the presence and degree of the final lag phase in the relationship were all modulated by rootstocks and by seasonal variations.

On the Formation of Peer-to-Peer Networks: Self-Organized Sharing and Groups

In this paper, we investigate the formation of peer-to-peer (P2P) networks with rational participating agents (active peers). In the absence of a central planner, peers choose their own utility-maximizing strategies for coalition and peer formation. P2P networks evolve dynamically through the activities of interactions among individual nodes and group units. We propose a framework for multilevel formation dynamics, including an individual level (content sharing decision and group selection) and a group level (membership admission). The respective utilities of the individual node and the collective player are formulated as functions of operational performance metrics such as expected content availability, search delay, transmission delay, and download delay. We study the impacts of various system parameters on the emergence of self-organized P2P network configuration features such as free-riding level and group size. Furthermore, we investigate the stability and efficiency of P2P networks and propose internal transfer mechanisms that force stable networks to become efficient

IPO7 promotes pancreatic cancer progression via regulating ERBB pathway

Background: Importin 7 (IPO7) belongs to the Importin β family and is implicated in the progression of diverse human malignancies. This work is performed to probe the role of IPO7 in pancreatic cancer development and its potential downstream mechanisms. Methods: IPO7 expression in PC and paracancerous tissues were measured using Immunohistochemistry (IHC) staining and qRT-PCR. Western blotting was utilized to detect the expression level of IPO7 in PC cells and immortalize the pancreatic ductal epithelial cell line. After constructing the IPO7 overexpression and knockdown models, the effect of IPO7 on the proliferation of PC cells was analyzed by the CCK-8 and EdU assay. The migration and invasion of PC cells were examined by wound healing assay and Transwell experiment. The apoptosis rate of PC cells was analyzed by flow cytometry and TUNEL assay. The Gene Set Enrichment Analysis (GSEA) was used to determine the enrichment pathways of IPO7. The effect of IPO7 on the ERBB2 expression was determined using Western blotting. A xenograft mouse model was applied to investigate the carcinogenic effect of IPO7 in vivo. Results: IPO7 expression was remarkably elevated in the cancer tissues of PC patients. IPO7 overexpression remarkably enhanced PC cell proliferation, migration and invasion and suppressed apoptosis, while knockdown of IPO7 exerted the opposite effect. Mechanistically, IPO7 facilitated the malignant phenotype of PC cells by up-regulating ERBB2 expression. In addition, knockdown of IPO7 inhibited tumor growth and lung metastasis in vivo. Conclusion: IPO7 can act as an oncogenic factor and accelerate PC progression by modulating the ERBB pathway

Segatron: Segment-Aware Transformer for Language Modeling and Understanding

Transformers are powerful for sequence modeling. Nearly all state-of-the-art language models and pre-trained language models are based on the Transformer architecture. However, it distinguishes sequential tokens only with the token position index. We hypothesize that better contextual representations can be generated from the Transformer with richer positional information. To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token. We first introduce the segment-aware mechanism to Transformer-XL, which is a popular Transformer-based language model with memory extension and relative position encoding. We find that our method can further improve the Transformer-XL base model and large model, achieving 17.1 perplexity on the WikiText-103 dataset. We further investigate the pre-training masked language modeling task with Segatron. Experimental results show that BERT pre-trained with Segatron (SegaBERT) can outperform BERT with vanilla Transformer on various NLP tasks, and outperforms RoBERTa on zero-shot sentence representation learning.Comment: Accepted by AAAI 202

Leveraging Reaction-aware Substructures for Retrosynthesis Analysis

Retrosynthesis analysis is a critical task in organic chemistry central to many important industries. Previously, various machine learning approaches have achieved promising results on this task by representing output molecules as strings and autoregressively decoded token-by-token with generative models. Text generation or machine translation models in natural language processing were frequently utilized approaches. The token-by-token decoding approach is not intuitive from a chemistry perspective because some substructures are relatively stable and remain unchanged during reactions. In this paper, we propose a substructure-level decoding model, where the substructures are reaction-aware and can be automatically extracted with a fully data-driven approach. Our approach achieved improvement over previously reported models, and we find that the performance can be further boosted if the accuracy of substructure extraction is improved. The substructures extracted by our approach can provide users with better insights for decision-making compared to existing methods. We hope this work will generate interest in this fast growing and highly interdisciplinary area on retrosynthesis prediction and other related topics.Comment: Work in progres

Echocardiographic parameters versus CHA2DS2-VASc score in prediction of overall cardiac events, heart failure, and stroke in non-valvular atrial fibrillation

     Background: Apart from stroke, atrial fibrillation (AF) is associated with higher mortality and heart failure (HF), in which risk stratification scheme is lacking. Therefore this investigation examined the prognostic value of echocardiographic predictors against CHA2DS2-VASc score in permanent non- -valvular AF (NVAF). Methods: In 252 asymptomatic or mildly symptomatic consecutive patients with NVAF, comprehensive echocardiography was performed. Left atrial deformation parameters were also obtained by two-dimen­sional speckle tracking echocardiography. End-points pertaining to HF deterioration, ischemic stroke and cardiac death were recorded. Results: There were 74 cardiovascular events, including 44 deterioration of HF, 22 ischemic strokes and 8 cardiovascular deaths during an average follow-up period of 20.8 ± 13.5 months (interquartile range, 8–31 months). For prediction of overall prognosis and HF, left ventricular mass index, peak early filling velocity (E), and E to tissue Doppler mitral annular early diastolic velocity ratio (E/e’) outper­formed CHA2DS2-VASc score in multivariate analysis, area under curve, and stepwise nested regression models. Left ventricular hypertrophy and E/e’ > 8 showed worse overall and heart-failure free survival in Kaplan-Meier curves. For prediction of ischemic stroke, the addition of E or E/e’ to CHA2DS2-VASc score provides extra prognostic value. Conclusions: Echocardiographic parameters offer incremental value over CHA2DS2-VASc score for prediction of future cardiac events in NVAF. (Cardiol J 2018; 25, 1: 60–71

Motion Control of Two Mobile Robots under Allowable Collisions

This letter investigates the motion control problem of two mobile robots under allowable collisions. Here, the allowable collisions mean that the collisions do not damage the mobile robots. The occurrence of the collisions is discussed and the effects of the collisions on the mobile robots are analyzed to develop a hybrid model of each mobile robot under allowable collisions. Based on the effects of the collisions, we show the necessity of redesigning the motion control strategy for mobile robots. Furthermore, impulsive control techniques are applied to redesign the motion control strategy to guarantee the task accomplishment for each mobile robot. Finally, an example is used to illustrate the redesigned motion control strategy.Comment: 8 pages, 5 figure