Search algorithms for regression test case prioritization

Regression testing is an expensive, but important, process. Unfortunately, there may be insufficient resources to allow for the re-execution of all test cases during regression testing. In this situation, test case prioritisation techniques aim to improve the effectiveness of regression testing, by ordering the test cases so that the most beneficial are executed first. Previous work on regression test case prioritisation has focused on Greedy Algorithms. However, it is known that these algorithms may produce sub-optimal results, because they may construct results that denote only local minima within the search space. By contrast, meta-heuristic and evolutionary search algorithms aim to avoid such problems. This paper presents results from an empirical study of the application of several greedy, meta-heuristic and evolutionary search algorithms to six programs, ranging from 374 to 11,148 lines of code for 3 choices of fitness metric. The paper addresses the problems of choice of fitness metric, characterisation of landscape modality and determination of the most suitable search technique to apply. The empirical results replicate previous results concerning Greedy Algorithms. They shed light on the nature of the regression testing search space, indicating that it is multi-modal. The results also show that Genetic Algorithms perform well, although Greedy approaches are surprisingly effective, given the multi-modal nature of the landscape

Amorphous slicing of extended finite state machines

Slicing is useful for many Software Engineering applications and has been widely studied for three decades, but there has been comparatively little work on slicing Extended Finite State Machines (EFSMs). This paper introduces a set of dependency based EFSM slicing algorithms and an accompanying tool. We demonstrate that our algorithms are suitable for dependence based slicing. We use our tool to conduct experiments on ten EFSMs, including benchmarks and industrial EFSMs. Ours is the first empirical study of dependence based program slicing for EFSMs. Compared to the only previously published dependence based algorithm, our average slice is smaller 40% of the time and larger only 10% of the time, with an average slice size of 35% for termination insensitive slicing

Memory Aware Synapses: Learning what (not) to forget

Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased. In artificial learning systems, lifelong learning so far has focused mainly on accumulating knowledge over tasks and overcoming catastrophic forgetting. In this paper, we argue that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively. Inspired by neuroplasticity, we propose a novel approach for lifelong learning, coined Memory Aware Synapses (MAS). It computes the importance of the parameters of a neural network in an unsupervised and online manner. Given a new sample which is fed to the network, MAS accumulates an importance measure for each parameter of the network, based on how sensitive the predicted output function is to a change in this parameter. When learning a new task, changes to important parameters can then be penalized, effectively preventing important knowledge related to previous tasks from being overwritten. Further, we show an interesting connection between a local version of our method and Hebb's rule,which is a model for the learning process in the brain. We test our method on a sequence of object recognition tasks and on the challenging problem of learning an embedding for predicting $$ triplets. We show state-of-the-art performance and, for the first time, the ability to adapt the importance of the parameters based on unlabeled data towards what the network needs (not) to forget, which may vary depending on test conditions.Comment: ECCV 201

Exploring surface cleaning strategies in hospital to prevent contact transmission of methicillin-resistant Staphylococcus aureus

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Targeted Assembly of Short Sequence Reads

As next-generation sequence (NGS) production continues to increase, analysis is becoming a significant bottleneck. However, in situations where information is required only for specific sequence variants, it is not necessary to assemble or align whole genome data sets in their entirety. Rather, NGS data sets can be mined for the presence of sequence variants of interest by localized assembly, which is a faster, easier, and more accurate approach. We present TASR, a streamlined assembler that interrogates very large NGS data sets for the presence of specific variants, by only considering reads within the sequence space of input target sequences provided by the user. The NGS data set is searched for reads with an exact match to all possible short words within the target sequence, and these reads are then assembled strin-gently to generate a consensus of the target and flanking sequence. Typically, variants of a particular locus are provided as different target sequences, and the presence of the variant in the data set being interrogated is revealed by a successful assembly outcome. However, TASR can also be used to find unknown sequences that flank a given target. We demonstrate that TASR has utility in finding or confirming ge-nomic mutations, polymorphism, fusion and integration events. Targeted assembly is a powerful method for interrogating large data sets for the presence of sequence variants of interest. TASR is a fast, flexible and easy to use tool for targeted assembly

PHYSIOCHEMICAL, PROXIMATE, AND SENSORY PROPERTIES OF UNFERMENTED AND FERMENTED SOY-CARROT BEVERAGES SWEETENED WITH SUGAR, DATE, AND HONEY

Objective: Physiochemical, proximate, and sensory properties of unfermented and fermented soy-carrot beverage sweetened with sugar, date, and honey were evaluated. Phytochemical content of soymilk, carrot juice, and their blend was also analyzed. Methods: Three sets of soy-carrot beverages were produced by homogenizing soy milk and carrot juice in a ratio of 2:1 and sweetened to 12% Brix. Each set was sweetened with sugar, date, and honey, respectively. A fourth set was unsweetened and served as control. After pasteurization, one part was fermented with pure culture of Lactobacillus acidophilus at 42°C for 24 h. Results: Fermentation significantly (p≤0.05) decreased pH (≥5.40–≤3.90), increased titratable acidity (≤0.55–≥0.90% lactic acid), and viscosity (≤0.65–≥0.87 Pa.S) of the soy-carrot beverages. Moisture, protein, fat, ash, carbohydrate, and energy content of unfermented beverages were 82.95– 93.95%, 2.15–2.87%, 0.42–1.21%, 0.10–0.20%, 3.21–12.55%, and 25.46–73.53 Kcal/g, respectively, while fermented beverages had 90.00–93.00%, 2.06–2.20%, 0.88–1.08%, 0.11–10.20%, 4.85–8.75%, and 36.76–52.20 Kcal/g, respectively. Total carotenoid, phenol, and DPPH radical scavenging activity varied, respectively, from 2.40–7.90, 14.81–26.59 mg tannic acid/ml, and 4.02–27.83% and were significantly (p≤0.05) highest in soy-carrot blend with carrot as major contributor. Degree of likeness of the sensory attributes for the sweetened and unfermented beverages was significantly (p≤0.05) higher than the fermented. Conclusion: Date and honey (12% Brix) can be used as sucrose alternatives in producing acceptable nutritious beverage from soymilk and carrot juice

Cetuximab ameliorates suppressive phenotypes of myeloid antigen presenting cells in head and neck cancer patients

Background: Myeloid-derived suppressor cells (MDSC) and M2 monocytes/macrophages are two types of suppressive myeloid antigen presenting cells that have been shown to promote tumor progression and correlate with poor prognosis in cancer patients. Tumor antigen specific monoclonal antibodies (mAb) have emerged as important agents for cancer therapy. In addition to the direct inhibition of tumor growth, the Fc portions of the therapeutic mAbs, such as the IgG1 portion of the anti-epidermal growth factor receptor (EGFR) mAb cetuximab, might interact with the Fc-gamma receptors (FcγR) on myeloid cells and modulate their suppressive activity. Methods: Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) on the UPCI 08-013 NCT01218048 trial were treated with single-agent cetuximab before surgery. Blood were collected pre- and post-cetuximab treatment to analyze frequency of monocytic MDSC (CD11b+CD14+HLA-DRlo/-), granulocytic MDSC (LIN-CD11b+CD15+) and CD11b+CD14+HLA-DRhi monocytes by flow cytometry. Besides, CD11b+CD14+HLA-DRhi monocytes were sorted for qPCR analysis of IL-10 and IL-12B transcripts. MDSC were generated in vitro with or without coated hIgG1 and tested for suppressive activity in mixed leukocyte reaction (MLR). Naïve monocytes from HNSCC patients co-cultured with tumor cell lines in the presence of cetuximab or hIgG1 were analyzed for M1/2 surface markers and cytokines. Results: We observed significantly increased monocytic MDSC in non-responders and decreased granulocytic MDSC in responders after cetuximab treatment. In addition, circulating CD11b+CD14+HLA-DRhi monocytes of cetuximab responders displayed attenuated M2 polarization, with decreased CD163+ expression and IL-10 transcripts after cetuximab treatment. This beneficial effect appeared to be FcγR dependent, since CD16 ligation reproduced the reversal of suppressive activity of MDSC in vitro. CD14+ naïve monocytes from the co-cultures of tumor cells, cetuximab and HNSCC patient PBMC or purified monocytes were skewed to an M1-like phenotype, with increased expression of HLA-DR, CD86 and production of IL-12 p70. Likewise, reduced M2 features (expression of CD163 and production of IL-10) were found after crosslinking CD16 on the surface of monocytes to cetuximab-coated tumor cells. Conclusion: Our studies demonstrate a novel function of cetuximab in ameliorating suppressive phenotypes of FcγR bearing myeloid cells in cancer patients, which is associated with better clinical outcome of cetuximab-treated patients. Clinical trial registry: # NCT01218048. Registered 7 October 2010

Factors that influence quality and yield of circulating-free DNA: A systematic review of the methodology literature.

BACKGROUND: Circulating-free DNA (cfDNA) is under investigation as a liquid biopsy of cancer for early detection, monitoring disease progression and therapeutic response. This systematic review of the primary cfDNA literature aims to identify and evaluate factors that influence recovery of cfDNA, and to outline evidence-based recommendations for standardization of methods. METHODS: A search of the Ovid and Cochrane databases was undertaken in May 2018 to obtain relevant literature on cfDNA isolation and quantification. Retrieved titles and abstracts were reviewed by two authors. The factors evaluated include choice of specimen type (plasma or serum); time-to-processing of whole blood; blood specimen tube; centrifugation protocol (speed, time, temperature and number of spins); and methods of cfDNA isolation and quantification. FINDINGS: Of 4,172 articles identified through the database search, 52 proceeded to full-text review and 37 met the criteria for inclusion. A quantitative analysis was not possible, due to significant heterogeneity in methodological approaches between studies. Therefore, included data was tabulated and a textual qualitative synthesis approach was taken. INTERPRETATION: This is the first systematic review of methodological factors that influence recovery and quantification of cfDNA, enabling recommendations to be made that will support standardization of methodological approaches towards development of blood-based cancer tests

Adding New Tasks to a Single Network with Weight Transformations using Binary Masks

Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues. Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy. We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks. We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task. Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge