Low temperature reduces potato wound formation by inhibiting phenylpropanoid metabolism and fatty acid biosynthesis

IntroductionPotato tubers have the healing capacity to prevent surface water transpiration and pathogen invasion after mechanical damage. Previous research has shown the inability to form healing periderm in potatoes under low temperatures, but the potential mechanism is still unclear.MethodsTo explore the effects and mechanisms of low-temperature potato healing, wounded potatoes were stored at low temperature (4°C) and room temperature (22°C), respectively.ResultsIn this study, compared with 22°C healing, low temperature reduced the content of hydrogen peroxide, and the down-regulation of StAMY23 inhibited the conversion of starch to sugar, alleviated the degradation of starch, and reduced the content of soluble sugars and sucrose. Meanwhile, inhibition of phenylalanine metabolism by suppression of StPAL1 and St4CL expression reduced lignin accumulation. Low temperature also down-regulated the expression of StKCS6, StFAOH, StGPAT5, and StPrx, causing the lower deposition amount of suberin in wounds of potato tubers.DiscussionThe above results suggested that low temperature led to less wound tissue deposition at the wound surfaces via suppressing phenylpropanoid metabolism and fatty acid biosynthesis in potato tubers

Distributed human computation framework for linked data co-reference resolution

Distributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud

Lymph node yield less than 12 is not a poor predictor of survival in locally advanced rectal cancer after laparoscopic TME following neoadjuvant chemoradiotherapy

PurposePrevious studies have confirmed that neoadjuvant chemoradiotherapy (nCRT) may reduce the number of lymph nodes retrieved in rectal cancer. However, it is still controversial whether it is necessary to harvest at least 12 lymph nodes for locally advanced rectal cancer (LARC) patients who underwent nCRT regardless of open or laparoscopic surgery. This study was designed to evaluate the relationship between lymph node yield (LNY) and survival in LARC patients who underwent laparoscopic TME following nCRT.MethodsPatients with LARC who underwent nCRT followed by laparoscopic TME were retrospectively analyzed. The relationship between LNY and survival of patients was evaluated, and the related factors affecting LNY were explored. To further eliminate the influence of imbalance of clinicopathological features on prognosis between groups, propensity score matching was conducted.ResultsA total of 257 consecutive patients were included in our study. The median number of LNY was 10 (7 to 13) in the total cohort. There were 98 (38.1%) patients with 12 or more lymph nodes harvested (LNY ≥12 group), and 159 (61.9%) patients with fewer than 12 lymph nodes retrieved (LNY <12 group). There was nearly no significant difference between the two groups in clinicopathologic characteristics and surgical outcomes except that the age of LNY <12 group was older (P<0.001), and LNY <12 group tended to have more TRG 0 cases (P<0.060). However, after matching, when 87 pairs of patients obtained, the clinicopathological features were almost balanced between the two groups. After a median follow-up of 65 (54 to 75) months, the 5-year OS was 83.9% for the LNY ≥12 group and 83.6% for the LNY <12 group (P=0.893), the 5-year DFS was 78.8% and 73.4%, respectively (P=0.621). Multivariate analysis showed that only patient age, TRG score and ypN stage were independent factors affecting the number of LNY (all P<0.05). However, no association was found between LNY and laparoscopic surgery-related factors.ConclusionsFor LARC patients who underwent nCRT followed by laparoscopic TME, the number of LNY less than 12 has not been proved to be an adverse predictor for long-term survival. There was no correlation between LNY and laparoscopic surgery-related factors

Clinicopathological characteristics and treatment outcome of resectable gastric cancer patients with small para-aortic lymph node

BackgroundResectable gastric cancer (GC) patients with small para-aortic lymph node (smaller than 10mm in diameter, sPAN) were seldom reported, and existing guidelines did not provide definite treatment recommendation for them.MethodsA total of 667 consecutive resectable GC patients were enrolled. 98 patients were in the sPAN group, and 569 patients without enlarged para-aortic lymph node were in the nPAN group. Standard D2 lymphadenectomy was performed. Neoadjuvant and adjuvant chemotherapy were administrated according to the cTNM and pTNM stage, respectively. Clinicopathological features and prognosis were compared between these two groups.ResultsThe median size of sPAN was 6 (range, 2−9) mm and the distribution was prevalent in No. 16b1. cN stage (p=0.001) was significantly related to the presence of sPAN. sPAN was both independent risk factor for OS (p=0.031) and RFS (p=0.046) of all patients. The prognosis of patients with sPAN was significantly worse than that of patients with nPAN (OS: p=0.008; RFS: p=0.007). Preoperative CEA and CA19-9 were independent risk factors for prognosis of patients with sPAN. Furthermore, patients in the sPAN group with normal CEA and CA19-9 exhibited acceptable prognosis (5-year OS: 67%; RFS: 64%), while those with elevated CEA or CA19-9 suffered significantly poorer prognosis (5-year OS: 17%; RFS: 17%) than patients in the nPAN group (5-year OS: 64%; RFS 62%) (both p < 0.05).ConclusionsStandard D2 lymphadenectomy should be considered a valid approach for GC patients with sPAN associate to normal preoperative CEA and CA19-9 levels. Patients with sPAN associated to elevated CEA or CA19-9 levels could benefit from a multimodal approach: neoadjuvant chemotherapy; radical surgery with D2 plus lymph nodal dissection extended to No. 16 station

Analysis of University Researcher Collaboration Networks using co-authorship

Social network analysis gives evidence for the connections between groups of individuals. It is these connections that channel flow of information and the sharing of knowledge. As universities move towards more interdisciplinary modes of research and funding, an effective network that links its entire cohort of active researchers is vital. This project conducted a co-authorship network analysis and a path length analysis on a small institutional database. The major advantage of our analysis over other similar work is that we used author's background details in supporting our analysis and generated co-authorship graphs with authors' names and groups. The network metrics have been compared and contrasted to similar work conducted with large-scale cross-institutional databases in several domains. We found the most of metrics are not affected by the network size and showed that the ECS community is a small-world network with similar knowledge sharing to those communities formed by an entire discipline

Understanding institutional collaboration networks: effects of collaboration on research impact and productivity

There is substantial competition among academic institutions. They compete for students, researchers, reputation, and funding. For success, they need not only to excel in teaching, but also their research profile is considered an important factor. Institutions accordingly take actions to improve their research profiles. They encourage researchers to publish frequently and regularly (publish or perish) on the assumption that this generates both more and better research. Collaboration has also been encouraged by institutions and even required by some funding calls.This thesis examines the empirical evidence on the interrelations among institutional research productivity, impact and collaborativity. It studies article publication data across ACM and Web of Science covering five disciplines { Computer Science, Pharmacology, Materials Science, Psychology and Law. Institutions that publish less seek to publish collaboratively with other institutions. Collaboration boosts productivity for all the disciplines investigated excepted Law; however, the amount of productivity increase resulting from the institutions' attempt to collaborate more is small. The world's most productive institutions publish at least 50% of their papers on their own. Institutions doing more collaborative work are not found to correlate strongly with their impact either. The correlation between collaborativity and individual paper impact or institutional impact is small once productivity has been partialled out. In Computer Science, Pharmacology and Materials Science, no correlation is found. The decisive factor appears to be productivity. Partialling out productivity results in the largest reductions in the remaining correlations. It may be that only better equipped and well-funded institutions can publish without having to rely on external collaborators. These institutions have been publishing most of their output non-collaboratively, and are also of high quality and highly reputable, which may have equipped and funded them in the first place

Understanding institutional collaboration networks computer science vs. psychology

Institutions assume that if they are more productive (i.e., publish more papers), they will produce more high quality research. They also assume that if they collaborate more, they will be more productive. We test these causal assumptions using nearly 30 years of worldwide publication and citation data in Computer Science and Psychology. Four quality metrics, three collaboration metrics and one productivity metric were used. Spearman’s Rank Order non-parametric correlation shows that these three groups of variables are highly inter-correlated. Regression analysis was used to partial out the effect of the third variable and reveal the independent correlation between each pair of the variables. In Computer Science, the more productive institutions publish higher quality research as measured by citation counts (including citation counts recursively weighted by the citation counts of the citing institution); the effect is the same, but not as strong, in Psychology. Higher average paper quality in both Computer Science and Psychology are more likely to be a result of greater institutional collaboration than of higher institutional productivity. The proportion of the institutional collaboration is closely linked to institutional quality and productivity. The more proportionally collaborated institutions in fact are less qualitative as well as less productive<br/

OR2014 Dev Challenge presentation - Team FML

<p>Slides used for Team FML's presentation/pitch at the OR2014 developer challenge</p

Epigenetic Regulation of IL-17-Induced Chemokines in Lung Epithelial Cells

Epithelial cells are known to have barrier functions in multiple organs and regulate innate immune responses. Airway epithelial cells respond to IL-17 by altering their transcriptional profiles and producing antimicrobial proteins and neutrophil chemoattractants. Although IL-17 has been shown to promote inflammation through stabilizing mRNA of CXCR2 ligands, how IL-17 exerts its downstream effects on its target cells through epigenetic mechanisms is largely unknown. Using primary human bronchial epithelial cells and immortalized epithelial cell line from both human and mouse, we demonstrated that IL-17-induced CXCR2 ligand production is dependent on histone acetylation specifically through repressing HDAC5. Furthermore, the chemokine production induced by IL-17 is strictly dependent on the bromodomain and extraterminal domain (BET) family as BET inhibition abolished the IL-17A-induced proinflammatory chemokine production, indicating a pivotal role of the recognition of acetylated histones. In combination with single-cell RNA-seq analysis, we revealed that the cell lines we employed represent specific lineages and their IL-17 responses were regulated differently by the DNA methylation mechanisms. Taken together, our data strongly support that IL-17 sustains epithelial CXCR2 ligand production through epigenetic regulation and the therapeutic potential of interrupting histone modification as well as the recognition of modified histones could be evaluated in neutrophilic lung diseases