Efficient 5 '-3 ' DNA end resection by HerA and NurA is essential for cell viability in the crenarchaeon <i>Sulfolobus islandicus</i>

BACKGROUND: ATPase/Helicases and nucleases play important roles in homologous recombination repair (HRR). Many of the mechanistic details relating to these enzymes and their function in this fundamental and complicated DNA repair process remain poorly understood in archaea. Here we employed Sulfolobus islandicus, a hyperthermophilic archaeon, as a model to investigate the in vivo functions of the ATPase/helicase HerA, the nuclease NurA, and their associated proteins Mre11 and Rad50. RESULTS: We revealed that each of the four genes in the same operon, mre11, rad50, herA, and nurA, are essential for cell viability by a mutant propagation assay. A genetic complementation assay with mutant proteins was combined with biochemical characterization demonstrating that the ATPase activity of HerA, the interaction between HerA and NurA, and the efficient 5′-3′ DNA end resection activity of the HerA-NurA complex are essential for cell viability. NurA and two other putative HRR proteins: a PIN (PilT N-terminal)-domain containing ATPase and the Holliday junction resolvase Hjc, were co-purified with a chromosomally encoded N-His-HerA in vivo. The interactions of HerA with the ATPase and Hjc were further confirmed by in vitro pull down. CONCLUSION: Efficient 5′-3′ DNA end resection activity of the HerA-NurA complex contributes to necessity of HerA and NurA in Sulfolobus, which is crucial to yield a 3′-overhang in HRR. HerA may have additional binding partners in cells besides NurA. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12867-015-0030-z) contains supplementary material, which is available to authorized users

The oyster genome reveals stress adaptation and complexity of shell formation

The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa. © 2012 Macmillan Publishers Limited. All rights reserved

Inductive Query by Examples (IQBE): A Machine Learning Approach

Artificial Intelligence Lab, Department of MIS, University of ArizonaThis paper presents an incremental, inductive learning approach to query-by examples for information retrieval (IR) and database management systems (DBMS). After briefly reviewing conventional information retrieval techniques and the prevailing database query paradigms, we introduce the ID5R algorithm, previously developed by Utgoff, for ``intelligent'' and system-supported query processing. We describe in detail how we adapted the ID5R algorithm for IR/DBMS applications and we present two examples, one for IR applications and the other for DBMS applications, to demonstrate the feasibility of the approach. Using a larger test collection of about 1000 document records from the COMPEN CD-ROM computing literature database and using recall as a performance measure, our experiment showed that the incremental ID5R performed significantly better than a batch inductive learning algorithm (called ID3) which we developed earlier. Both algorithms, however, were robust and efficient in helping users develop abstract queries from examples. We believe this research has shed light on the feasibility and the novel characteristics of a new query paradigm, namely, inductive query-by examples (IQBE). Directions of our current research are summarized at the end of the paper

Research on knowledge concept extraction method based on few-shot learning and chain-of-thought prompting

Knowledge concept extraction has important application value in the fields of education, medical care, and finance. Knowledge concept extraction is a sub-task of named entity recognition. However, due to the lack of data sets and the particularity of knowledge concept entity types, directly applying general named entity recognition methods to knowledge concept extraction tasks often has poor results. In view of the above challenges, a method based on few-shot learning and chain-of-thought prompting for knowledge concept extraction was proposed, utilizing open-source large language models. Firstly, text representations focusing on entity semantics were trained through contrastive learning, and the relevance of the retrieved few-shot examples was enhanced using the K-nearest neighbors algorithm. Secondly, a method utilizing chain-of-thought prompting was adopted to present the samples, with the aim of improving the reasoning ability of large language models in knowledge concept extraction. Experimental results on multiple datasets demonstrate that the few-shot learning and chain-of-thought prompting for knowledge concept extraction method, onthe whole, has shown results superior over existing methods

A Machine Learning Approach to Inductive Query by Examples: An Experiment Using Relevance Feedback, ID3, Genetic Algorithms, and Simulated Annealing

Artificial Intelligence Lab, Department of MIS, University of ArizonaInformation retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to â â intelligentâ â information retrieval and indexing. More recently, information science researchers have turned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to familiarize readers with the techniques, we present three promising methods: The symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval. An experiment using a 8000-record COMPEN database was performed to examine the performances of these inductive query-by-example techniques in comparison with the performance of the conventional relevance feedback method. The machine learning techniques were shown to be able to help identify new documents which are similar to documents initially suggested by users, and documents which contain similar concepts to each other. Genetic algorithms, in particular, were found to out-perform relevance feedback in both document recall and precision. We believe these inductive machine learning techniques hold promise for the ability to analyze usersâ preferred documents (or records), identify usersâ underlying information needs, and also suggest alternatives for search for database management systems and Internet applications

Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing

Artificial Intelligence Lab, Department of MIS, University of ArizonaFor our research, we investigated a different problem-solving scenario called game playing, which is unstructured, complex, and seldom-studied. We considered several real-life game-playing scenarios and decided on greyhound racing. The large amount of historical information involved in the search poses a challenge for both human experts and machine-learning algorithms. The questions then become: Can machine-learning techniques reduce the uncertainty in a complex game-playing scenario? Can these methods outperform human experts in prediction? Our research sought to answer these questions