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Recently, Acritical Intelligent (AI) methodologies such as Long and Short-term Memory (LSTM) have been widely considered promising tools for engine performance calibration, especially for engine emission performance prediction and optimization, and Transformer is also gradually applied to sequence prediction. To carry out high-precision engine control and calibration, predicting long time step emission sequences is required. However, LSTM has the problem of gradient disappearance on too long input and output sequences, and Transformer cannot reflect the dynamic features of historic emission information which derives from cycle-by-cycle engine combustion events, which leads to low accuracy and weak algorithm adaptability due to the inherent limitations of the encoder-decoder structure. In this paper, considering the highly nonlinear relation between the multi-dimensional engine operating parameters the engine emission data outputs, an Embedding-Graph-Neural-Network (EGNN) model was developed combined with self-attention mechanism for the adaptive graph generation part of the GNN to capture the relationship between the sequences, improve the ability of predicting long time step sequences, and reduce the number of parameters to simplify network structure. Then, a sensor embedding method was adopted to make the model adapt to the data characteristics of different sensors, so as to reduce the impact of experimental hardware on prediction accuracy. The experimental results show that under the condition of long-time step forecasting, the prediction error of our model decreased by 31.04% on average compared with five other baseline models, which demonstrates the EGNN model can potentially be used in future engine calibration procedures. [ABSTRACT FROM AUTHOR]

P. gingivalis is a prominent periodontal pathogen that has potent effects on host cells. In this study we challenged gingival epithelial cells with P. gingivalis with the aim of assessing how mRNA levels of key target genes were modulated by P. gingivalis via the transcription factors FOXO1 and FOXO3. Primary mono- and multi-layer cultures of gingival epithelial cells were challenged and barrier function was examined by fluorescent dextran and apoptosis was measured by cytoplasmic histone associated DNA. Gene expression levels were measured by real-time PCR with and without FOXO1 and FOXO3 siRNA compared to scrambled siRNA. P. gingivalis induced a loss of barrier function and stimulated gingival epithelial cell apoptosis in multilayer cultures that was in part gingipain dependent. P. gingivalis stimulated an increase in FOXO1 and FOXO3 mRNA, enhanced mRNA levels of genes associated with differentiated keratinocyte function (keratin-1, -10, -14, and involucrin), increased mRNA levels of apoptotic genes (BID and TRADD), reduced mRNA levels of genes that regulate inflammation (TLR-2 and -4) and reduced those associated with barrier function (integrin beta-1, -3 and -6). The ability of P. gingivalis to modulate these genes was predominantly FOXO1 and FOXO3 dependent. The results indicate that P. gingivalis has pronounced effects on gingival keratinocytes and modulates mRNA levels of genes that affect host response, differentiation, apoptosis and barrier function. Moreover, this modulation is dependent upon the transcription factors FOXO1 or FOXO3. In addition, a new function for FOXO1 was identified, that of suppressing TLR-2 and TLR-4 and maintaining integrin beta -1, beta -3 and beta -6 basal mRNA levels in keratinocytes. [ABSTRACT FROM AUTHOR]

A direct cobalt-catalyzed oxidative coupling between C(sp 2 )-H in unactivated benzamides and C(sp 3 )-H in simple alkanes, ethers and toluene derivatives was explored. This protocol achieves direct C-C formation without using alkyl or aryl halide surrogates and exhibits high practicality with ample substrate scope. The method provides a new way to construct linear and five- or six-membered ring moieties in bioactive molecules., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Healing is delayed in diabetic wounds. We previously demonstrated that lineage-specific Foxo1 deletion in keratinocytes interfered with normal wound healing and keratinocyte migration. Surprisingly, the same deletion of Foxo1 in diabetic wounds had the opposite effect, significantly improving the healing response. In normal glucose media, forkhead box O1 (FOXO1) enhanced keratinocyte migration through up-regulating TGFβ1. In high glucose, FOXO1 nuclear localization was induced but FOXO1 did not bind to the TGFβ1 promoter or stimulate TGFβ1 transcription. Instead, in high glucose, FOXO1 enhanced expression of serpin peptidase inhibitor, clade B (ovalbumin), member 2 (SERPINB2), and chemokine (C-C motif) ligand 20 (CCL20). The impact of high glucose on keratinocyte migration was rescued by silencing FOXO1, by reducing SERPINB2 or CCL20, or by insulin treatment. In addition, an advanced glycation end product and tumor necrosis factor had a similar regulatory effect on FOXO1 and its downstream targets and inhibited keratinocyte migration in a FOXO1-dependent manner. Thus, FOXO1 expression can positively or negatively modulate keratinocyte migration and wound healing by its differential effect on downstream targets modulated by factors present in diabetic healing., (© 2015 Zhang et al.)