Summary Background The aim of this interim analysis of a large, international phase III study was to assess the efficacy of an AS04 adjuvanted L1 virus-like-particle prophylactic candidate vaccine ...against infection with human papillomavirus (HPV) types 16 and 18 in young women. Methods 18 644 women aged 15–25 years were randomly assigned to receive either HPV16/18 vaccine (n=9319) or hepatitis A vaccine (n=9325) at 0, 1, and 6 months. Of these women, 88 were excluded because of high-grade cytology and 31 for missing cytology results. Thus, 9258 women received the HPV16/18 vaccine and 9267 received the control vaccine in the total vaccinated cohort for efficacy, which included women who had prevalent oncogenic HPV infections, often with several HPV types, as well as low-grade cytological abnormalities at study entry and who received at least one vaccine dose. We assessed cervical cytology and subsequent biopsy for 14 oncogenic HPV types by PCR. The primary endpoint—vaccine efficacy against cervical intraepithelial neoplasia (CIN) 2+ associated with HPV16 or HPV18—was assessed in women who were seronegative and DNA negative for the corresponding vaccine type at baseline (month 0) and allowed inclusion of lesions with several oncogenic HPV types. This interim event-defined analysis was triggered when at least 23 cases of CIN2+ with HPV16 or HPV18 DNA in the lesion were detected in the total vaccinated cohort for efficacy. Analyses were done on a modified intention-to-treat basis. This trial is registered with the US National Institutes of Health clinical trial registry, number NCT00122681. Findings Mean length of follow-up for women in the primary analysis for efficacy at the time of the interim analysis was 14·8 (SD 4·9) months. Two cases of CIN2+ associated with HPV16 or HPV18 DNA were seen in the HPV16/18 vaccine group; 21 were recorded in the control group. Of the 23 cases, 14 (two in the HPV16/18 vaccine group, 12 in the control group) contained several oncogenic HPV types. Vaccine efficacy against CIN2+ containing HPV16/18 DNA was 90·4% (97·9% CI 53·4–99·3; p<0·0001). No clinically meaningful differences were noted in safety outcomes between the study groups. Interpretation The adjuvanted HPV16/18 vaccine showed prophylactic efficacy against CIN2+ associated with HPV16 or HPV18 and thus could be used for cervical cancer prevention.
Understanding pollinator networks requires species level data on pollinators. New photographic approaches to identification provide avenues to data collection that reduce impacts on declining ...bumblebee species, but limited research has addressed their accuracy. Using blind identification of 1418 photographed bees, of which 561 had paired specimens, we assessed identification and agreement across 20 bumblebee species netted in Montana, North Dakota, and South Dakota by people with minimal training. An expert identified 92.4% of bees from photographs, whereas 98.2% of bees were identified from specimens. Photograph identifiability decreased for bees that were wet or matted; bees without clear pictures of the abdomen, side of thorax, or top of thorax; bees photographed with a tablet, and for species with more color morphs. Across paired specimens, the identification matched for 95.1% of bees. When combined with a second opinion of specimens without matching identifications, data suggested a similar misidentification rate (2.7% for photographs and 2.5% specimens). We suggest approaches to maximize accuracy, including development of rulesets for collection of a subset of specimens based on difficulty of identification and to address cryptic variation, and focused training on identification that highlights detection of species of concern and species frequently confused in a study area.
More training instances could lead to better classification accuracy. However, accuracy could also degrade if more training instances mean further noises and outliers. Additional training instances ...arguably need additional computational resources in future data mining operations. Instance selection algorithms identify subsets of training instances that could desirably increase accuracy or at least do not decrease accuracy significantly. There exist many instance selection algorithms, but no single algorithm, in general, dominates the others. Moreover, existing instance selection algorithms do not allow direct controlling of the instance selection rate. In this paper, we present a simple and generic cluster-oriented instance selection algorithm for classification problems. Our proposed algorithm runs an unsupervised K Means Clustering algorithm on the training instances and with a given selection rate, selects instances from the centers and the borders of the clusters. On 24 benchmark classification problems, when very similar percentages of instances are selected by various instance selection algorithms, K Nearest Neighbours classifiers achieve more than 2%–3% better accuracy when using instances selected by our proposed method than when using those selected by other state-of-the-art generic instance selection algorithms.
•Studying mixed blocking flowshop scheduling problems with sequence-dependent setup times (PFSP-BS).•A new acceleration method to compute makespan for each solution in the insertion neighbourhood.•A ...constraint-guided local search algorithm to solve PFSP-BS instances.•Comprehensive numerical and statistical tests to evaluate the proposed methods.
Permutation flowshop scheduling problem (PFSP) is a classical NP-Hard combinatorial optimisation problem. Existing PFSP variants capture different realistic scenarios, but significant modelling gaps still remain with respect to many real-world industrial applications. Inspired by the cider industry, in this paper, we propose a new PFSP variant that generalises over simultaneous use of several types of blocking constraints and various settings of sequence-dependent setup times. We also present a computational model for makespan minimisation of the new variant and show that solving this variant remains NP-Hard. For this PFSP variant, we then present an acceleration method to compute makespan efficiently and thus evaluate the neighbourhoods generated by insertion operators. We develop a new constructive heuristic taking both blocking constraints and setup times into account. We also develop a new local search algorithm that uses a constraint guided intensification method and a random-path guided diversification method. Our comprehensive experimental results on a set of benchmark instances demonstrate that our proposed algorithms significantly outperform several state-of-the-art adapted algorithms.
•Studying customer order scheduling problem (COSP) with total completion time as objective.•A new constructive heuristic with 8 different initial priority lists.•A perturbative search algorithm for ...solving COSP.•Comprehensive numerical and statistical tests to evaluate the proposed methods.
Customer Order Scheduling Problem (COSP) with minimisation of the total completion time as the objective is NP-Hard. COSP has many applications that include the pharmaceutical and the paper industries. However, most existing COSP algorithms struggle to find very good solutions in large-sized problems. One key reason behind is that those algorithms are based on generic templates and as such lack problem specific structural knowledge. In this paper, we capture such knowledge in the form of heuristics and then embed those heuristics within constructive and perturbative search algorithms. In the proposed deterministic constructive search algorithm, we use processing times in various ways to obtain initial dispatching sequences that are later used in prioritising customer orders during search. We also augment the construction process with solution exploration. In the proposed stochastic perturbative search, we intensify its diversification phase by prioritising rescheduling of customer orders that are affected badly. Our tailoring of the search in this case is to make informed decisions when the search has lost its direction. On the contrary to that, in the intensification phase, we then take diversifying measures and use multiple neighbourhood operators randomly so that the search does not get stuck very quickly. Our experimental results show that the proposed algorithms outperform existing state-of-the-art COSP algorithms.
Background Carrying the cyclin-dependent kinase inhibitor 2A (CDKN2A) germline mutations is associated with a high risk for melanoma. Penetrance of CDKN2A mutations is modified by pigmentation ...characteristics, nevus phenotypes, and some variants of the melanocortin-1 receptor gene (MC1R), which is known to have a role in the pigmentation process. However, investigation of the associations of both MC1R variants and host phenotypes with melanoma risk has been limited. Methods We included 815 CDKN2A mutation carriers (473 affected, and 342 unaffected, with melanoma) from 186 families from 15 centers in Europe, North America, and Australia who participated in the Melanoma Genetics Consortium. In this family-based study, we assessed the associations of the four most frequent MC1R variants (V60L, V92M, R151C, and R160W) and the number of variants (1, ≥2 variants), alone or jointly with the host phenotypes (hair color, propensity to sunburn, and number of nevi), with melanoma risk in CDKN2A mutation carriers. These associations were estimated and tested using generalized estimating equations. All statistical tests were two-sided. Results Carrying any one of the four most frequent MC1R variants (V60L, V92M, R151C, R160W) in CDKN2A mutation carriers was associated with a statistically significantly increased risk for melanoma across all continents (1.24 × 10−6 ≤ P ≤ .0007). A consistent pattern of increase in melanoma risk was also associated with increase in number of MC1R variants. The risk of melanoma associated with at least two MC1R variants was 2.6-fold higher than the risk associated with only one variant (odds ratio = 5.83 95% confidence interval = 3.60 to 9.46 vs 2.25 95% confidence interval = 1.44 to 3.52; Ptrend = 1.86 × 10−8). The joint analysis of MC1R variants and host phenotypes showed statistically significant associations of melanoma risk, together with MC1R variants (.0001 ≤ P ≤ .04), hair color (.006 ≤ P ≤ .06), and number of nevi (6.9 × 10−6 ≤ P ≤ .02). Conclusion Results show that MC1R variants, hair color, and number of nevi were jointly associated with melanoma risk in CDKN2A mutation carriers. This joint association may have important consequences for risk assessments in familial settings.
•Studying mixed blocking flowshop scheduling problem (MBPFSP).•A new Acceleration method for insertion neighbourhood operator.•A constraint-guided local search algorithm for solving ...MBPFSP.•Comprehensive numerical and statistical tests to evaluate the proposed methods.
Mixed Blocking Permutation Flowshop Scheduling Problem (MBPFSP) with the objective of makespan minimisation is NP-Hard. It has important industrial applications that include the cider production industry. MBPFSP has made some progress in recent years. However, within practical time limits, existing incomplete algorithms still either find low quality solutions or struggle with large problems. One key reason behind this is the typical way of using generic heuristics or metaheuristics that usually lack problem specific structural knowledge. In MBPFSP, a machine could be blocked with the currently finished job until the subsequent machine is available to process the same job. These blocking constraints affect the makespan. So MBPFSP search should naturally take explicit steps to take the blocking constraints into account. Unfortunately, existing research on MBPFSP just uses only the makespan to compare generated solutions, but the search otherwise is not aware of the blocking constraints. Moreover, existing such methods use either an exhaustive or a random neighbourhood generation strategy. In this work, we aim to advance MBPFSP search by better exploiting the problem specific structural knowledge. We use the constraint and the objective functions to obtain such problem specific knowledge and we exploit such knowledge both in a constructive search method and in a local search method. In this paper, we also present an acceleration method to efficiently evaluate insertion-based neighbourhoods of MBPFSP. Our experimental results on three standard testbeds demonstrate that our proposed algorithms significantly improve over existing best-performing algorithms.
•An effective heuristic and search algorithm for aircraft sequencing problem.•Advance the algorithms by exploiting the problem specific structural knowledge.•Outperforms state-of-the-art algorithms ...on well-known benchmark problem sets.
Aircraft sequencing problem (ASP) is an NP-Hard problem. It involves allocation of aircraft to runways for landing and takeoff, minimising total tardiness. ASP has made significant progress in recent years. However, within practical time limits, existing incomplete algorithms still either find low quality solutions or struggle with large problems. One key reason behind this is the typical way of using generic heuristics or metaheuristics that usually lack problem specific structural knowledge. As a result, existing such methods use either an exhaustive or a random neighbourhood generation strategy. So their search guidance comes only from the evaluation function that is used mainly after the neighbourhood generation. In this work, we aim to advance ASP search by better exploiting the problem specific structural knowledge. We use the constraint and the objective functions to obtain such problem specific knowledge and we exploit such knowledge both in a constructive search method and in a local search method. Our motivation comes from the constraint optimisation paradigm in artificial intelligence, where instead of random decisions, constraint-guided more informed optimisation decisions are of particular interest. We run our experiments on a range of standard benchmark problem instances that include instances from real airports and instances crafted using real airport parameters, and contain scenarios involving multiple runways and both landing and takeoff operations. We show that our proposed algorithms significantly outperform existing state-of-the-art aircraft sequencing algorithms.
Protein structure prediction (PSP) is a vital challenge in bioinformatics, structural biology and drug discovery. Protein secondary structure (SS) prediction is critical since three-dimensional (3D) ...structures are primarily made up of secondary structures. With the advancement of deep learning approaches, SS classification accuracy has been significantly improved. Many existing methods use an ensemble of complex neural networks to improve SS prediction. Because of the high dimensionality of the hyperparameter space, deep neural networks with complex architectures are typically challenging to train effectively. Also, predicting secondary structures in the boundary regions between different types of SS is challenging. This study presents Multi-S3P, which employs bidirectional Long-Short-Term-Memory (BILSTM) and Convolutional Neural Networks (CNN) with a self-attention mechanism to improve the secondary structure prediction using an effective training strategy to capture the unique characteristics of each type of secondary structure and combine them more effectively. The ensemble of CNN and BILSTM can learn both contextual information and long-range interactions between the residues. In addition, using a self-attention mechanism allows the model to focus on the most important features for improving performance. We used the SPOT-1D dataset for the training and validation of our model using a set of four input features derived from amino acid sequences. Further, the model was tested on four popular independent test datasets and compared with various state-of-the-art predictors. The presented results show that Multi-S3P outperformed the other methods in terms of Q3, Q8 accuracy and other performance metrics, achieving the highest Q3 accuracy of 87.57% and a Q8 accuracy of 77.56% on the TEST2016 test set. More importantly, Multi-S3P demonstrates high performance in SS boundary regions. Our experiment also demonstrates that the combination of different input features and a multi-network-based training strategy significantly improved the performance.
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