Combinatorial testing (CT) can efficiently detect failures caused by interactions of parameters of software under test. The CT study has undergone a transition from traditional CT to constrained CT, which is crucial for real-world systems testing. Under this scenario, constrained covering array generation (CCAG), a vital combinatorial optimisation issue targeted with constructing a test suite of minimal size while properly addressing constraints, remains challenging in CT. To the authors' best knowledge, this paper presents a synergic method first based on quantum particle swarm optimisation (QPSO) for the CCAG problems. Three auxiliary strategies, including contractionexpansion coefficient adaptive change strategy, differential evolution strategy, and discretisation strategy, are proposed to improve the performance of QPSO. Meanwhile, the improved QPSO method combines with the three different constraint handling strategies and an enhanced one-test-at-a-time strategy as a synergic QPSO method named QPIO to solve the CCAG problem. In the experiment, we investigate the impacts of parameter settings on the performance of the QPIO. Extensive experimental results show that the QPIO algorithm is a competitive method compared to the representative methods for CCAG. Besides, the QPIO method enriches the application of the QPSO algorithm in the context of CT.

In the present study, cashmere goat fetal fibroblasts were transfected with pCDsR-KI, a hair-follicle-cell specific expression vector for insulin-like growth factor 1 (IGF1) that contains two markers for selection (red fluorescent protein gene and neomycin resistant gene). The transgenic fibroblasts cell lines were obtained after G418 selection. Prior to the somatic cell nuclear transfer (SCNT), the maturation rate of caprine cumulus oocytes complexes (COCs) was optimized to an in vitro maturation time of 18 h. Parthenogenetic ooctyes were used as a model to investigate the effect of two activation methods, one with calcium ionophore IA23187 plus 6-DMAP and the other with ethanol plus 6-DMAP. The cleavage rates after 48 h were respectively 88.7% and 86.4%, with no significant difference (P>0.05). There was no significant difference between the cleavage rate and the blastocyst rate in two different media (SO-Faa and CR1aa; 86.3% vs 83.9%, P>0.05 and 23.1% vs 17.2%, P>0.05). The fusion rate of a 190 V/mm group (62.4%) was significantly higher than 130 V/mm (32.8%) and 200 V/mm (42.9%), groups (P>0.05). After transgenic somatic cell nuclear transfer (TSCNT) manipulation, 203 reconstructed embryos were obtained in which the cleavage rate after in vitro development (IVD) for 48 h was 79.3% (161/203). The blastocyst rate after IVD for 7 to 9 d was 15.3% (31/203). There were 17 embryos out of 31 strongly expressing red fluorescence. Two of the red fluorescent blastocysts were randomly selected to identify transgene by polymerase chain reaction. Both were positive. These results showed that: (i) RFP and Neo ( r ) genes were correctly expressed indicating that transgenic somatic cell lines and positive transgenic embryos were obtained; (ii) one more selection at the blastocyst stage was necessary although the donor cells were transgenic positive, because only partially transgenic embryos expressing red fluorescence were obtained; and (iii) through TSCNT manipulation and optimization, transgenic cashmere goat embryos expressing red fluorescence and containing an IGF1 expression cassette were obtained, which was sufficient for production of transgenic cashmere goats.

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Covering array generation (CAG) is the key research problem in combinatorial testing and is an NP-complete problem. With the increasing complexity of software under test and the need for higher interaction covering strength t, the techniques for constructing highstrength covering arrays are expected. This paper presents a hybrid heuristic memetic algorithm named QSSMA for high-strength CAG problem. The sub-optimal solution acceptance rate is introduced to generate multiple test cases after each iteration to improve the efficiency of constructing high-covering strength test suites. The QSSMA method could successfully build high-strength test suites for some instances where t up to 15 within one day cutoff time and report five new best test suite size records. Extensive experiments demonstrate that QSSMA is a competitive method compared to state-of-theart methods. K E Y W O R D S optimisation, particle swarm optimisation, software engineering | INTRODUCTIONSoftware testing is an important area of software engineering and has great significance in ensuring software quality. Due to the large amount of test data required by modern software, if a complete test is performed, the size of the test data will increase sharply as increasing parameters or parameter values, leading to a lower testing efficiency. Thus, effective failure detection at a low cost is a critical issue for test case generation. Combinatorial testing (CT), a popular testing method, can detect failures triggered by various interactions of factors [1].There are two key issues in the field of combinatorial testing: (1) how to construct a test suite as small as possible; (2) how to find the combination of parameters that trigger the fault in a particular test case that triggers a software under test (SUT) failure. Researchers are more concerned with problem one, known as covering array generation (CAG) [2]. The main idea of CT is to construct a test suite represented by a covering array (CA) that contains all combinations of parameter's value of the SUT at least once and hopes the array is as small as possible. The generation of the smallest size test suite has been proven to be an NP-complete problem [3]. Numerous methods have been proposed, including mathematical, greedy, and artificial intelligence-based search methods. However, all the proposed mathematical methods apply only to restrictive parameter sets. Besides, greedy algorithms often result in oversized test suites. Due to this limitation, scholars focus more on AI-based (heuristic) search methods [4].Many efficient heuristic search algorithms have been successfully applied to CA generation, including geneticThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Liaoning cashmere goats are the most precious genetic resources in China. The function of LAMTOR3 [late endosomal/lysosomal adaptor, mitogen-activated protein kinase (MAPK), and mammalian target of rapamycin activator 3/MAPK scaffold protein 1] gene is expressed in the skin of Liaoning cashmere goats. In situ hybridization (ISH) found that LAMTOR3 is expressed in the inner root sheath (IRS) of hair follicles. During the anagen or catagen phase, the expression of LAMTOR3 is higher in secondary hair follicles than in primary hair follicles. Expression of LAMTOR3 in skin cells treated with melatonin or insulin-like growth factor-1 (IGF-1) is lower than in untreated cells. In addition, the simultaneous treatment of fibroblast growth factor 5 and melatonin decrease the expression of LAMTOR3 in skin cells. The simultaneous treatment with melatonin and 10 g/L IGF-1 or 10 g/L IGF-1 increases the expression of LAMTOR3 gene in skin cells. If Noggin expression is decreased, then LAMTOR3 expression is increased. This hypothesis suggested that LAMTOR3 influences the character of cashmere fiber, and it may regulate the development of hair follicle and cashmere growth by inducing the MAPK signaling pathway.