This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencing (snRNA-seq), and scPagwas (pathway-based polygenic regression framework) methods to explore the genetic links between UDIPs and GBM. Two-sample Mendelian randomization analyses were conducted to identify causal relationships between UDIPs and GBM. Colocalization analysis was performed to validate genetic associations, while scPagwas analysis was used to evaluate the relevance of key UDIPs to GBM at the cellular level. Among 512 UDIPs tested, 23 were found to have significant causal associations with GBM. Notably, UDIPs such as T1-33 (OR = 1.007, 95% CI = 1.001 to 1.012, P = .022), T1-34 (OR = 1.012, 95% CI = 1.001-1.023, P = .028), and T1-96 (OR = 1.009, 95% CI = 1.001-1.019, P = .046) were found to have a genetic association with GBM. Furthermore, T1-34 and T1-96 were significantly associated with GBM recurrence, with P-values < .0001 and P < .001, respectively. In addition, scPagwas analysis revealed that T1-33, T1-34, and T1-96 are distinctively linked to different GBM subtypes, with T1-33 showing strong associations with the neural progenitor-like subtype (NPC2), T1-34 with mesenchymal (MES2) and neural progenitor (NPC1) cells, and T1-96 with the NPC2 subtype. T1-33, T1-34, and T1-96 hold significant potential for predicting tumor recurrence and aiding in the development of personalized GBM treatment strategies.

Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection. Considering that GBM cancer stem cells (CSCs) play a non-negligible role in tumorigenesis and chemoradiotherapy resistance, we proposed a novel stemness-based classification of GBM and screened out certain population more responsive to immunotherapy. The one-class logistic regression algorithm was used to calculate the stemness index (mRNAsi) of 518 GBM patients from The Cancer Genome Atlas (TCGA) database based on transcriptomics of GBM and pluripotent stem cells. Based on their stemness signature, GBM patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS but poorer progression-free survival than Stemness Subtype II. Genomic variations revealed patients in Stemness Subtype I had higher somatic mutation loads and copy number alteration burdens. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. Tumor Immune Dysfunction and Exclusion and subclass mapping analysis further demonstrated patients in Stemness Subtype I were more likely to respond to immunotherapy, especially anti-PD1 treatment. The pRRophetic algorithm also indicated patients in Stemness Subtype I were more resistant to temozolomide therapy. Finally, multiple machine learning algorithms were used to develop a 7-gene Stemness Subtype Predictor, which were further validated in two external independent GBM cohorts. This novel stemness-based classification could provide a promising prognostic predictor for GBM and may guide physicians in selecting potential responders for preferential use of immunotherapy.

SUMMARY: The CRISPR/Cas System has been shown to be an efficient and accurate genome-editing technique. There exist a number of tools to design the guide RNA sequences and predict potential off-target sites. However, most of the existing computational tools on gRNA design are restricted to small deletions. To address this issue, we present pgRNAFinder, with an easy-to-use web interface, which enables researchers to design single or distance-free paired-gRNA sequences. The web interface of pgRNAFinder contains both gRNA search and scoring system. After users input query sequences, it searches gRNA by 3' protospacer-adjacent motif (PAM), and possible off-targets, and scores the conservation of the deleted sequences rapidly. Filters can be applied to identify high-quality CRISPR sites. PgRNAFinder offers gRNA design functionality for 8 vertebrate genomes. Furthermore, to keep pgRNAFinder open, extensible to any organism, we provide the source package for local use. AVAILABILITY AND IMPLEMENTATION: The pgRNAFinder is freely available at http://songyanglab.sysu.edu.cn/wangwebs/pgRNAFinder/, and the source code and user manual can be obtained from https://github.com/xiexiaowei/pgRNAFinder. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Business-intelligence queries often involve SQL functions and algebraic expressions. There can be clear semantic relationships between a column’s values and the values of a function over that column. A common property is monotonicity: as the column’s values ascend, so do the function’s values. This we call an order dependency (OD). Queries can be evaluated more efficiently when the query optimizer uses order dependencies. They can be run even faster when the optimizer can also reason over known ODs to infer new ones. Order dependencies can be declared as integrity constraints, and they can be detected automatically for many types of SQL functions and algebraic expressions. We present optimization techniques using ODs for queries that involve join, order by, group by, partition by, and distinct. Essentially, ODs can further exploit interesting orders to eliminate or simplify potentially expensive sorts in the query plan. We evaluate these techniques over our implementation in IBM R ° DB2 R ° V10 using the TPC-DS R ° benchmark schema and some IBM customer inspired queries. Our experimental results demonstrate a significant performance gain. We additionally devise an algorithm for testing logical implication for ODs which is polynomial over the size of the set of given ODs. We show that the inference algorithm which we have implemented in DB2 is sound and complete over sets of ODs over natural domains. This enables the optimizer to infer useful ODs from known ODs.

Data warehouses are repositories of electronically stored data which are designed to support reporting and analysis. The analysis of historical data often involves aggregation over time. Thus, time is critical in the design of a data warehouse. We describe novel techniques for storing date information and optimization of queries that reference the date dimension. We show how to embed intelligence into the date key and how to exploit monotonic dependencies. We present the value of these techniques for the improvement of performance when combined with partitioning and indexes. We evaluate these techniques on our prototype implemented in IBM® DB2® V9.7 over the current draft version of the TPC-DS benchmark.

There is much effort to develop comprehensive support for the storage and querying of XML data in database management systems. The major developers have extended their systems to handle XML data natively. These have the advantage over stand-alone XML database systems that relational and XML data can be queried mutually. Indeed, recent SQL standards specify means to query relational and XML data together (called SQL/XML). These systems also now support XQuery, in addition to SQL. It is thus possible to mix the processing of relational and XML data via either query language. While there has been significant progress in efficient native storage systems for XML, there remain numerous challenges to handle efficiently queries over XML. There are efforts to adapt the strong optimization techniques used for relational ("SQL") queries for XML (and mixed) queries as well. One such technique, the materialized view, has been well studied, and well adopted, over the last decade as an effective technique for optimizing relational queries. Our work extends the use of materialized views for SQL/XML, and could be applied to XQuery. Within IBM DB2 9 (Viper), we implement query rewrite rules that enable the use of materialized views in the evaluation of queries over XML. % (We enable views over queries that employ XMLTable.) To accomplish this, it was necessary to extend the existing query matching and compensation framework in DB2 with new functionality. We consider what types of query rewrites based on XMLTable are possible, and which are feasible. We present a linear-time algorithm to determine the locality (self-containment) of XPath expressions within a schema-unaware environment, which we have implemented. We demonstrate the efficacy of our techniques via an experimental evaluation over a representative suite of SQL/XML queries and materialized views, executed over our DB2 prototype.

We propose three inter-domain routing schemes for ad hoc networks, namely the implicit foreign degree based protocol (IFD), the explicit locally optimal protocol (ELO) and the explicit limited scope protocol (ELS). We studied the performance of these different schemes. Our simulation studies reveal that the explicit schemes can achieve high packet delivery ratio and good average end-to-end delay at a lower overhead cost compared to IFD. We also compared our approaches with LANMAR, an existing scalable routing protocol for ad hoc networks. The results show that our approach outperforms LANMAR in terms of the packet delivery ratio and control overhead, while maintaining comparable end-to-end delay at low and medium mobility rate (less than 8 m/s).

Service advertisement and discovery are important components for computing in mobile ad hoc network (MANET). In this paper, a lightweight protocol of service advertisement and discovery has been implemented, which is based on a MANET multicast protocol ODMRP (On-Demand Multicast Routing Protocol). In this protocol, service advertisement and discovery information is piggybacked in ODMRP routing control packets. Simulation results of the implementation prove that the implementation workload and resource consumption of the protocol are lightweight.

Database queries often take the form of correlated SQL queries. Correlation refers to the use of values from the outer query block to compute the inner subquery. This is a convenient paradigm for SQL programmers and closely mimics a function invocation paradigm in a typical computer programming language. Queries with correlated subqueries are also often created by SQL generators that translate queries from application domain-specific languages into SQL. Another significant class of queries that use this correlated subquery form is that involving temporal databases using SQL. Performance of these queries is an important consideration particularly in large databases. Several proposals to improve the performance of SQL queries containing correlated subqueries can be found in database literature. One of the main ideas in many of these proposals is to suitably decorrelate the subquery internally to avoid a tuple-at-a-time invocation of the subquery. Magic decorrelation is one method that has been successfully used. Another proposal is to cache the portion of the subquery that is invariant with the changing values of the outer query block. What we propose here is a new technique to handle some typical correlated queries. We go a step further than to simply decorrelate the subquery. By making use of extended window aggregation capabilities, we eliminate redundant access to common tables referenced in the outer query block and the subquery. This technique can be exploited even for non-correlated subqueries. It is possible to get a huge boost in performance for queries that can exploit this technique, which we call WinMagic. This technique was implemented in IBM® DB2® Universal Database" Version 7 and Version 8. In addition to improving DB2 customer queries that contain aggregation subqueries, it has provided significant improvements in a number of TPCH benchmarks that IBM has published since late in 2001.