Diffuse large B-cell lymphoma: sub-classification by massive parallel quantitative RT-PCR.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity with remarkably variable clinical outcome. Gene expression profiling (GEP) classifies DLBCL into activated B-cell like (ABC), germinal center B-cell like (GCB), and Type-III subtypes, with ABC-DLBCL characterized by a poor prognosis and constitutive NF-κB activation. A major challenge for the application of this cell of origin (COO) classification in routine clinical practice is to establish a robust clinical assay amenable to routine formalin-fixed paraffin-embedded (FFPE) diagnostic biopsies. In this study, we investigated the possibility of COO-classification using FFPE tissue RNA samples by massive parallel quantitative reverse transcription PCR (qRT-PCR). We established a protocol for parallel qRT-PCR using FFPE RNA samples with the Fluidigm BioMark HD system, and quantified the expression of the COO classifier genes and the NF-κB targeted-genes that characterize ABC-DLBCL in 143 cases of DLBCL. We also trained and validated a series of basic machine-learning classifiers and their derived meta classifiers, and identified SimpleLogistic as the top classifier that gave excellent performance across various GEP data sets derived from fresh-frozen or FFPE tissues by different microarray platforms. Finally, we applied SimpleLogistic to our data set generated by qRT-PCR, and the ABC and GCB-DLBCL assigned showed the respective characteristics in their clinical outcome and NF-κB target gene expression. The methodology established in this study provides a robust approach for DLBCL sub-classification using routine FFPE diagnostic biopsies in a routine clinical setting.The research in Du lab was supported by research grants (LLR10006 & LLR13006) from Leukaemia & Lymphoma Research, U.K. XX was supported by a visiting fellowship from the China Scholarship Council, Ministry of Education, P.R. China.This is the accepted manuscript. The final version is available from NPG at http://www.nature.com/labinvest/journal/v95/n1/full/labinvest2014136a.html

Collective Entity Alignment via Adaptive Features

Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration. When generating EA results, current solutions treat entities independently and fail to take into account the interdependence between entities. To fill this gap, we propose a collective EA framework. We first employ three representative features, i.e., structural, semantic and string signals, which are adapted to capture different aspects of the similarity between entities in heterogeneous KGs. In order to make collective EA decisions, we formulate EA as the classical stable matching problem, which is further effectively solved by deferred acceptance algorithm. Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks against state-of-the-art solutions, and the empirical results verify its effectiveness and superiority.Comment: ICDE2

The Exploration and Practice of the Construction of Civic Politics in Computer Science Courses under the Perspective of “Three-Whole Education”

Taking the computer application technology professional group of Guangdong Innovation Technology College as an example, we study the problems of computer professional group in the construction of curriculum thinking politics, firstly, we explore the basic requirements of curriculum thinking politics from the perspective of “ Three-Whole Education “, then we explore the value of curriculum thinking politics, and then we study the top design and bottom implementation of talent training of professional group from many aspects. Then, we will explore the value of curriculum thinking and politics, and then carry out research from various aspects such as top-level design and bottom-level implementation of professional group talent training. We will build the construction pattern of “Three-Whole Education”, and combine with the construction of professional groups to build the construction pattern of the curriculum of professional groups in terms of talent training objectives, thinking and political objectives, thinking and political content, etc., so as to promote the construction of the curriculum of the provincial-level professional group of computer application technology in our university and realize the educational goal of cultivating people with moral character

Oral microbiome-driven virulence factors: A novel approach to pancreatic cancer diagnosis

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy, often associated with a poor prognosis for patients. One of the major challenges in managing PDAC is the difficulty in early diagnosis, owing to the limited and invasive nature of current diagnostic methods. Recent studies have identified the oral microbiome as a potential source of non-invasive biomarkers for diseases, including PDAC. In this study, we focused on leveraging the differential expression of virulence factors (VFs) encoded by the oral microbiome to create a diagnostic tool for PDAC. We observed a higher alpha diversity in VF categories among PDAC patients compared to healthy controls. We then identified a panel of VF categories that were significantly upregulated in PDAC patients, these being associated with bacterial adherence, exoenzyme production, and nutritional/metabolic processes. Moreover, Streptococcus-derived VFs were notably enriched in PDAC patients. We developed a diagnostic model using random forest analysis based on the levels of these VFs. The model's diagnostic accuracy was evaluated using receiver operating characteristic (ROC) curve analysis, with an area under the curve (AUC) of 0.88, indicating high accuracy in differentiating PDAC patients from healthy controls. Our findings suggest that VFs encoded by the oral microbiome hold potential as diagnostic tools for PDAC, offering a non-invasive approach that could significantly enhance early detection and prognosis, ultimately leading to improved patient outcomes

Constrained Path Search with Submodular Function Maximization

In this paper, we study the problem of constrained path search with submodular function maximization (CPS-SM). We aim to find the path with the best submodular function score under a given constraint (e.g., a length limit), where the submodular function score is computed over the set of nodes in this path. This problem can be used in many applications. For example, tourists may want to search the most diversified path (e.g., a path passing by the most diverse facilities such as parks and museums) given that the traveling time is less than 6 hours. We show that the CPS-SM problem is NP-hard. We first propose a concept called “submodular α -dominance” by utilizing the submodular function properties, and we develop an algorithm with a guaranteed error bound based on this concept. By relaxing the submodular α -dominance conditions, we design another more efficient algorithm that has the same error bound. We also utilize the way of bi-directional path search to further improve the efficiency of the algorithms. We finally propose a heuristic algorithm that is efficient yet effective in practice. The experiments conducted on several real datasets show that our proposed algorithms can achieve high accuracy and are faster than one state-of-the-art method by orders of magnitude

Somatic Mutation Screening Using Archival Formalin-Fixed, Paraffin-Embedded Tissues by Fluidigm Multiplex PCR and Illumina Sequencing.

High-throughput somatic mutation screening using FFPE tissues is a major challenge because of a lack of established methods and validated variant calling algorithms. We aimed to develop a targeted sequencing protocol by Fluidigm multiplex PCR and Illumina sequencing and to establish a companion variant calling algorithm. The experimental protocol and variant calling algorithm were first developed and optimized against a series of somatic mutations (147 substitutions, 12 indels ranging from 1 to 33 bp) in seven genes, previously detected by Sanger sequencing of DNA from 163 FFPE lymphoma biopsy specimens. The optimized experimental protocol and variant calling algorithm were further ascertained in two separate experiments by including the seven genes as a part of larger gene panels (22 or 13 genes) using FFPE and high-molecular-weight lymphoma DNAs, respectively. We found that most false-positive variants were due to DNA degradation, deamination, and Taq polymerase errors, but they were nonreproducible and could be efficiently eliminated by duplicate experiments. A small fraction of false-positive variants appeared in duplicate, but they were at low alternative allele frequencies and could be separated from mutations when appropriate threshold value was used. In conclusion, we established a robust practical approach for high-throughput mutation screening using archival FFPE tissues.The research was supported by grants [LLR10006, LLR13006] from Leukaemia & Lymphoma Research, U.K. and Kay Kendal Leukaemia Fund. SM is a PhD student supported by Medical Research Council, Department of Pathology, University of Cambridge, and Addenbrooke’s Charitable Trust. LEI is a PhD student supported by the Pathological Society of UK & Ireland. XX was supported by a visiting fellowship from the China Scholarship Council, Ministry of Education, P.R. China. NG was supported by a Kay Kendal Leukaemia Fund [KKL649] and an Addenbrooke’s Charitable Trust fellowship.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.jmoldx.2015.04.008 This article is permanently embargoed in this repository. However, the full text is freely available in PubMed Central 6 months via http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4597275

A Reference Proteomic Database of Lactobacillus plantarum CMCC-P0002

Lactobacillus plantarum is a widespread probiotic bacteria found in many fermented food products. In this study, the whole-cell proteins and secretory proteins of L. plantarum were separated by two-dimensional electrophoresis method. A total of 434 proteins were identified by tandem mass spectrometry, including a plasmid-encoded hypothetical protein pLP9000_05. The information of first 20 highest abundance proteins was listed for the further genetic manipulation of L. plantarum, such as construction of high-level expressions system. Furthermore, the first interaction map of L. plantarum was established by Blue-Native/SDS-PAGE technique. A heterodimeric complex composed of maltose phosphorylase Map3 and Map2, and two homodimeric complexes composed of Map3 and Map2 respectively, were identified at the same time, indicating the important roles of these proteins. These findings provided valuable information for the further proteomic researches of L. plantarum