Knockdown of TNFAIP1 mitigates sevoflurane-induced cognitive dysfunction by activating CREB/Nrf2 pathway

Purpose: To investigate the role of tumor necrosis factor-induced protein 1 (TNFAIP1) and cAMPresponsive element binding protein (CREB)/nuclear factor-erythroid factor 2-related factor 2 (Nrf2) pathway in sevoflurane (SEV)-induced cognitive dysfunction. Methods: A SEV-induced cognitive dysfunction rat model was developed. Bcl-2, Bax, heme oxygenase-1, Nrf2, p-CREB, and CREB protein levels in rat hippocampal tissue were assessed by western blot. Learning and long-term memory were evaluated using Morris water maze test. Glutathione peroxidase, malondialdehyde, and superoxide dismutase levels in hippocampal tissue were measured by enzyme-linked immunosorbent assay (ELISA). The 2,7-dichlorodihydro-fluorescein diacetate fluorescent assay was used to measure reactive oxygen species, while TUNEL staining was used to assess neuronal cell apoptosis. Results: Knockdown of TNFAIP1 attenuated SEV-induced learning and long-term memory dysfunction (p < 0.005), oxidative stress (p < 0.005), apoptosis (p < 0.005), and inhibition of the CREB/Nrf2 signaling pathway. Conclusion: This study demonstrates that knockdown of TNFAIP1 alleviates SEV-induced cognitive dysfunction by reversing inhibition of the CREB/Nrf2 signaling pathway. Keywords: TNFAIP1; Postoperative cognitive dysfunction; Sevoflurane; cAMP-responsive element binding protein (CREB); Nuclear factor-erythroid factor 2-related factor 2 (Nrf2

Plant Sedimentary Ancient DNA From Far East Russia Covering the Last 28,000 Years Reveals Different Assembly Rules in Cold and Warm Climates

Woody plants are expanding into the Arctic in response to the warming climate. The impact on arctic plant communities is not well understood due to the limited knowledge about plant assembly rules. Records of past plant diversity over long time series are rare. Here, we applied sedimentary ancient DNA metabarcoding targeting the P6 loop of the chloroplast trnL gene to a sediment record from Lake Ilirney (central Chukotka, Far Eastern Russia) covering the last 28 thousand years. Our results show that forb-rich steppe-tundra and dwarf-shrub tundra dominated during the cold climate before 14 ka, while deciduous erect-shrub tundra was abundant during the warm period since 14 ka. Larix invasion during the late Holocene substantially lagged behind the likely warmest period between 10 and 6 ka, where the vegetation biomass could be highest. We reveal highest richness during 28–23 ka and a second richness peak during 13–9 ka, with both periods being accompanied by low relative abundance of shrubs. During the cold period before 14 ka, rich plant assemblages were phylogenetically clustered, suggesting low genetic divergence in the assemblages despite the great number of species. This probably originates from environmental filtering along with niche differentiation due to limited resources under harsh environmental conditions. In contrast, during the warmer period after 14 ka, rich plant assemblages were phylogenetically overdispersed. This results from a high number of species which were found to harbor high genetic divergence, likely originating from an erratic recruitment process in the course of warming. Some of our evidence may be of relevance for inferring future arctic plant assembly rules and diversity changes. By analogy to the past, we expect a lagged response of tree invasion. Plant richness might overshoot in the short term; in the long-term, however, the ongoing expansion of deciduous shrubs will eventually result in a phylogenetically more diverse community

Production Across the Nordics

In the uncertain and volatile market that companies are currently facing worldwide, researchers and engineers\ua0become a key link to\ua0strengthen the industry and universities\ua0in order to\ua0understand, communicate, and tackle\ua0current challenges. In the PhD course, International Production, the goal is to investigate what makes Sweden and Iceland booming industrial hubs\ua0driven by technology. Through the\ua0visits to different types of industries, such as fintech, medical, or automotive industry,\ua0we as researchers have gained a better understanding of the challenges they are currently facing. This report is a summary of our findings and observations.\ua0\ua0The participants have focused on the\ua0six challenge areas highlighted within the Produktion2030 graduate school and summarize their findings as:\ua0\ua0•Resource-efficient production:\ua0Data as a resource is becoming increasingly important for the majority of companies in the Nordics and the application of traditional resource management tools on data is a suggested area for future research. \ua0•Flexible production:To strengthen organizations by\ua0enabling\ua0production systems to be flexible to address\ua0market variations is a key\ua0challenge to consider in the manufacturing industry•Virtual production development:Digitalization level is distinct in each Nodic country with the reason that each country has its own digitalization transformation policy and different measures on digitalization level.\ua0•Humans in the production system:Humans are central in the production systems of the visited companies. Use of automation technology and AI to support humans in their work may become more common in the future.•Circular production systems and maintenance:Circular production systems require a complex approach through the whole value chain. Industry in the Nordics has started the adoption of a circularity approach.\ua0•Integrated product and production development:\ua0Integration of product and production development is a key business factor for the Nordic countries, and geographical proximity between the two departments can have a beneficial effect. \ua0We hope that this report provides more\ua0details\ua0regarding\ua0the success and current challenges of the Swedish and Icelandic enterprises

Transcriptome analysis showed that tomato-rootstock enhanced salt tolerance of grafted seedlings was accompanied by multiple metabolic processes and gene differences

IntroductionGrafting is a commonly used cultural practice to counteract salt stress and is especially important for vegetable production. However, it is not clear which metabolic processes and genes are involved in the response of tomato rootstocks to salt stress.MethodsTo elucidate the regulatory mechanism through which grafting enhances salt tolerance, we first evaluated the salt damage index, electrolyte permeability and Na+ accumulation in tomato (Solanum lycopersicum L.) leaves of grafted seedlings (GSs) and nongrafted seedlings (NGSs) subjected to 175 mmol·L− 1 NaCl for 0-96 h, covering the front, middle and rear ranges.ResultsCompared with the NGS, the GSs were more salt tolerant, and the Na+ content in the leaves decreased significantly. Through transcriptome sequencing data analysis of 36 samples, we found that GSs exhibited more stable gene expression patterns, with a lower number of DEGs. WRKY and PosF21 transcription factors were significantly upregulated in the GSs compared to the NGSs. Moreover, the GSs presented more amino acids, a higher photosynthetic index and a higher content of growth-promoting hormones. The main differences between GSs and NGSs were in the expression levels of genes involved in the BR signaling pathway, with significant upregulation of XTHs. The above results show that the metabolic pathways of “photosynthetic antenna protein”, “amino acid biosynthesis” and “plant hormone signal transduction” participate in the salt tolerance response of grafted seedlings at different stages of salt stress, maintaining the stability of the photosynthetic system and increasing the contents of amino acids and growth-promoting hormones (especially BRs). In this process, the transcription factors WRKYs, PosF21 and XTHs might play an important role at the molecular level.DiscussionThe results of this study demonstrates that grafting on salt tolerant rootstocks can bring different metabolic processes and transcription levels changes to scion leaves, thereby the scion leaves show stronger salt tolerance. This information provides new insight into the mechanism underlying tolerance to salt stress regulation and provides useful molecular biological basis for improving plant salt resistance

Factors associated with distant metastasis in pediatric thyroid cancer: evaluation of the SEER database

Objectives: Controversies regarding factors associated with distant metastasis in pediatric thyroid cancer remain among the scientific community. The aim of this study was to investigate factors influencing distant metastasis in pediatric thyroid cancer. Methods: We reviewed 1376 patients (aged 2 to 18 years) with thyroid cancer treated between 2003 and 2014. Data collected and analyzed included sex, race, age at diagnosis, year of diagnosis, pathological type, number of tumor foci, tumor extension, T-stage, N-stage, surgical procedure and radiation. Univariate and multivariate analyses were conducted to evaluate factors influencing distant metastasis of pediatric thyroid cancer. Results: In the univariate analysis, factors influencing distant metastasis of thyroid cancer were age at diagnosis (P 0.05). Furthermore, according to chi-squared test, younger pediatric thyroid cancer patients with higher T- and N-stages are more likely to have distant metastasis. Conclusion: Age at diagnosis, T-stage and N-stage influence distant metastasis of thyroid cancer patients aged 2 to 18 years; accordingly, more radical treatments may need to be used for patients with those risk elements

Multimodal Human-Robot Collaboration in Assembly

Human-robot collaboration (HRC) envisioned for factories of the future would require close physical collaboration between humans and robots in safe and shared working environments with enhanced efficiency and flexibility. The PhD study aims for multimodal human-robot collaboration in assembly. For this purpose, various modalities controlled by high-level human commands are adopted to facilitate multimodal robot control in assembly and to support efficient HRC. Voice commands, as a commonly used communication channel, are firstly considered and adopted to control robots. Also, hand gestures work as nonverbal commands that often accompany voice instructions, and are used for robot control, specifically for gripper control in robotic assembly. Algorithms are developed to train and identify the commands so that the voice and hand gesture instructions are associated with valid robot control commands at the controller level. A sensorless haptics modality is developed to allow human operators to haptically control robots without using any external sensors. Within such context, an accurate dynamic model of the robot (within both the pre-sliding and sliding regimes) and an adaptive admittance observer are combined for reliable haptic robot control. In parallel,  brainwaves work as an emerging communication modality and are used for adaptive robot control during seamless assembly, especially in noisy environments with unreliable voice recognition or when an operator is occupied with other tasks and unable to make gestures. Deep learning is explored to develop a robust brainwave classification system for high-accuracy robot control, and the brainwaves act as macro commands to trigger pre-defined function blocks that in turn provide micro control for robots in collaborative assembly. Brainwaves offer multimodal support to HRC assembly, as an alternative to haptics, auditory and gesture commands. Next, a multimodal data-driven control approach to HRC assembly assisted by event-driven function blocks is explored to facilitate collaborative assembly and adaptive robot control. The proposed approaches and system design are analysed and validated through experiments of a partial car engine assembly. Finally, conclusions and future directions are given.Samarbete mellan människa och robot (HRC) i framtidens fabriker kräver en nära fysisk samverkan mellan människor och robotar i säkra och delade arbetsmiljöer, för ökad effektivitet och flexibilitet. Doktorandstudien syftar till multimodalt samarbete mellan människa och robot vid montering. För detta ändamål används olika modaliteter som styrs av mänskliga kommandon på hög nivå för att stödja effektiv HRC och underlätta robotstyrning vid montering. Röstkommandon, som är en vanlig kommunikationskanal, används i första hand för att styra roboten. Handgester för icke-verbala kommandon åtföljer ofta röstinstruktioner och används för robotstyrning, speciellt för gripkontroll vid robotmontering. Algoritmer har utvecklats för att träna och identifiera kommandona så att röst- och handgestinstruktionerna associeras med giltiga robotkontrollkommandon på styrenhetsnivå. En sensorlös haptikmodalitet har utvecklats för att tillåta mänskliga operatörer att haptiskt styra robotar utan att använda några externa sensorer. I ett sådant sammanhang kombineras en exakt dynamisk modell av roboten (inom både glid- och förglidningsregimer) och en adaptiv inträdesobservatör för tillförlitlig haptisk robotkontroll. Parallellt är hjärnvågor en framväxande kommunikationsmodalitet som används för adaptiv robotstyrning under sömlös montering, särskilt i bullriga miljöer med opålitlig röstigenkänning eller när en operatör är upptagen med andra uppgifter och inte kan göra gester. Maskininlärning, Deep learning, utforskas för att utveckla ett robust hjärnvågsklassificeringssystem för robotstyrning med hög noggrannhet, och hjärnvågorna fungerar som makrokommandon för att aktivera fördefinierade funktionsblock som i sin tur ger mikrokontroll för robotar i kollaborativ montering. Hjärnvågorna ger ett multimodalt stöd till HRC-montering, som ett alternativ till haptik, hörsel- och gestkommandon. Därefter utforskas en multimodal datadriven kontrollmetod för HRC-montering med hjälp av händelsestyrda funktionsblock för att underlätta samverkande montering och adaptiv robotstyrning. De föreslagna tillvägagångssätten och systemdesignen analyseras och valideras genom experiment på ett delmontage av en bilmotor. Slutligen presenteras slutsatser och framtida riktningar

Multimodal Human-Robot Collaboration in Assembly

Human-robot collaboration (HRC) envisioned for factories of the future would require close physical collaboration between humans and robots in safe and shared working environments with enhanced efficiency and flexibility. The PhD study aims for multimodal human-robot collaboration in assembly. For this purpose, various modalities controlled by high-level human commands are adopted to facilitate multimodal robot control in assembly and to support efficient HRC. Voice commands, as a commonly used communication channel, are firstly considered and adopted to control robots. Also, hand gestures work as nonverbal commands that often accompany voice instructions, and are used for robot control, specifically for gripper control in robotic assembly. Algorithms are developed to train and identify the commands so that the voice and hand gesture instructions are associated with valid robot control commands at the controller level. A sensorless haptics modality is developed to allow human operators to haptically control robots without using any external sensors. Within such context, an accurate dynamic model of the robot (within both the pre-sliding and sliding regimes) and an adaptive admittance observer are combined for reliable haptic robot control. In parallel,  brainwaves work as an emerging communication modality and are used for adaptive robot control during seamless assembly, especially in noisy environments with unreliable voice recognition or when an operator is occupied with other tasks and unable to make gestures. Deep learning is explored to develop a robust brainwave classification system for high-accuracy robot control, and the brainwaves act as macro commands to trigger pre-defined function blocks that in turn provide micro control for robots in collaborative assembly. Brainwaves offer multimodal support to HRC assembly, as an alternative to haptics, auditory and gesture commands. Next, a multimodal data-driven control approach to HRC assembly assisted by event-driven function blocks is explored to facilitate collaborative assembly and adaptive robot control. The proposed approaches and system design are analysed and validated through experiments of a partial car engine assembly. Finally, conclusions and future directions are given.Samarbete mellan människa och robot (HRC) i framtidens fabriker kräver en nära fysisk samverkan mellan människor och robotar i säkra och delade arbetsmiljöer, för ökad effektivitet och flexibilitet. Doktorandstudien syftar till multimodalt samarbete mellan människa och robot vid montering. För detta ändamål används olika modaliteter som styrs av mänskliga kommandon på hög nivå för att stödja effektiv HRC och underlätta robotstyrning vid montering. Röstkommandon, som är en vanlig kommunikationskanal, används i första hand för att styra roboten. Handgester för icke-verbala kommandon åtföljer ofta röstinstruktioner och används för robotstyrning, speciellt för gripkontroll vid robotmontering. Algoritmer har utvecklats för att träna och identifiera kommandona så att röst- och handgestinstruktionerna associeras med giltiga robotkontrollkommandon på styrenhetsnivå. En sensorlös haptikmodalitet har utvecklats för att tillåta mänskliga operatörer att haptiskt styra robotar utan att använda några externa sensorer. I ett sådant sammanhang kombineras en exakt dynamisk modell av roboten (inom både glid- och förglidningsregimer) och en adaptiv inträdesobservatör för tillförlitlig haptisk robotkontroll. Parallellt är hjärnvågor en framväxande kommunikationsmodalitet som används för adaptiv robotstyrning under sömlös montering, särskilt i bullriga miljöer med opålitlig röstigenkänning eller när en operatör är upptagen med andra uppgifter och inte kan göra gester. Maskininlärning, Deep learning, utforskas för att utveckla ett robust hjärnvågsklassificeringssystem för robotstyrning med hög noggrannhet, och hjärnvågorna fungerar som makrokommandon för att aktivera fördefinierade funktionsblock som i sin tur ger mikrokontroll för robotar i kollaborativ montering. Hjärnvågorna ger ett multimodalt stöd till HRC-montering, som ett alternativ till haptik, hörsel- och gestkommandon. Därefter utforskas en multimodal datadriven kontrollmetod för HRC-montering med hjälp av händelsestyrda funktionsblock för att underlätta samverkande montering och adaptiv robotstyrning. De föreslagna tillvägagångssätten och systemdesignen analyseras och valideras genom experiment på ett delmontage av en bilmotor. Slutligen presenteras slutsatser och framtida riktningar

Clustering and Genetic Algorithm Based Hybrid Flowshop Scheduling with Multiple Operations

This research is motivated by a flowshop scheduling problem of our collaborative manufacturing company for aeronautic products. The heat-treatment stage (HTS) and precision forging stage (PFS) of the case are selected as a two-stage hybrid flowshop system. In HTS, there are four parallel machines and each machine can process a batch of jobs simultaneously. In PFS, there are two machines. Each machine can install any module of the four modules for processing the workpeices with different sizes. The problem is characterized by many constraints, such as batching operation, blocking environment, and setup time and working time limitations of modules, and so forth. In order to deal with the above special characteristics, the clustering and genetic algorithm is used to calculate the good solution for the two-stage hybrid flowshop problem. The clustering is used to group the jobs according to the processing ranges of the different modules of PFS. The genetic algorithm is used to schedule the optimal sequence of the grouped jobs for the HTS and PFS. Finally, a case study is used to demonstrate the efficiency and effectiveness of the designed genetic algorithm