PSEN1 Compound Heterozygous Mutations Associated with Cerebral Amyloid Angiopathy and Cognitive Decline Phenotype

Cerebral amyloid angiopathy (CAA) is a cerebrovascular disorder caused by the deposition of amyloid beta-peptide (Aβ) aggregates. Aβ aggregates lead to vessel rupture and intracerebral hemorrhages, detected by magnetic resonance imaging (MRI). Presenile CAA is usually genetically determined by mutations in the amyloid precursor protein (APP) gene. However, mutations after codon 200 in the presenilin 1 (PSEN1) gene have been reported to facilitate CAA onset. Here, we analyzed the genetic bases in a patient of 55 years old affected by CAA and cognitive decline. DNA was isolated and genetic analysis was performed by Next-Generation Sequencing (NGS). RNA was extracted and retro-transcribed to perform segregation analysis by TOPO-TA cloning. WB analysis was carried out to check the impact of the mutations on protein. Two compound heterozygous mutations in PSEN1 exon 10, such as a novel stop-gain mutation (c.1070C > G) and a pathogenic splice variant (c.1129A > T), were found by NGS. Both mutations altered the presenilin 1 protein, truncating its C-terminal portion. This is the first case of CAA and cognitive decline caused by two compound mutations in PSEN1. With this report, we suggest extending the genetic analysis to PSEN1 when cerebral microbleeds are observed by MRI investigation in a patient affected by presenile cognitive decline.     

Engineering Single Nanopores on Gold Nanoplates by Tuning Crystal Screw Dislocation

Compared with the large variety of solid gold nanostructures, synthetic approaches for their hollow counterparts are limited, largely confined to chemical and irradiation‐based etching of preformed nanostructures. In particular, the preparation of through nanopore structures is extremely challenging. Here, a unique strategy for direct synthesis of gold nanopores in solution without the need for sacrificial templates or postsynthesis processing is reported. By controlling the degree of crystal screw dislocation, a single through pore with diameter ranging from sub‐nanometer to tens of nanometers, in the center of large gold nanoplates, can be engineered with precision. Ionic current rectification behaviors are observed using the gold nanopore, potentially enabling new capabilities in biosensing, sequencing, and imaging.     

Heuristic approaches for mixed-model sequencing problem with stochastic processing times

Despite many pioneering efforts and works over the past decades, stochastic events have not been studied extensively in mixed-model assembly lines thus far. For a mixed-model sequencing problem with stochastic processing times, this paper aims to minimise expected total work overload. It also focuses on the most critical workstation of the line. In practice, this assumption is useful when the whole or a big portion of the assembly line is considered as a single station. In order to tackle the problem, a dynamic programming (DP) algorithm as well as two greedy heuristics from the literature is employed. However, it is realised that the DP cannot guarantee the optimal sequence neither for stochastic nor deterministic problems. It is because the calculation of work overload is involved in a recursive procedure that affects the states’ value functions. Therefore, by the use of network representation, the problem is modelled as a shortest path problem and a new heuristic, inspired by Dijkstra’s algorithm is developed to deal with it. Numerical results show that the proposed method outperforms other algorithms strongly. Finally, some discussion is provided about why one should consider stochastic parameters and why the proposed heuristic performs well in this regard.     

Solution algorithms for the number of tardy jobs minimisation scheduling with a time-dependent learning effect

This paper deals with a single-machine scheduling problem with a time-dependent learning effect. The goal is to determine the job sequence that minimise the number of tardy jobs. Two dominance properties, two heuristic algorithms and a lower bound to speed up the search process of the branch-and-bound algorithm are proposed. Computational experiments show that the branch-and-bound algorithm can solve instances up to 18 jobs in a reasonable amount of time, and the proposed heuristic algorithm MFLA performs effectively and efficiently     

Minimisation of non-machining times in operating automatic tool changers of machine tools under dynamic operating conditions

In many optimisation studies, it is assumed that problem related data does not change once the generated solution plan or schedule is currently in use. However, majority of real-life manufacturing problems are time-varying in their nature due to unpredictable events such as changes in lot sizes, fluctuating capacities of manufacturing constraints, changes in costs or profits. A problem, which contains at least one of these feature is referred as dynamic optimisation problem (DOP) in the related literature. The present study introduces a practical industrial application of a DOP, emerging particularly in flexible manufacturing systems (FMSs), where numerically controlled machine tools with automatic tool changers are employed. It is already known in FMSs that minimisation of non-machining times is vital for an efficient use of scarce resources. Therefore, fast response to possible changes in production is crucial in order to attain flexibility. In this context, first, a benchmarking environment is created by making use of already published problems and by introducing dynamic events. Next, effective strategies, including simulated annealing (SA) algorithm along with SA with multiple starts are developed for the introduced problem. Numerical results show that the developed SA with multiple starts is a promising approach for the introduced problem.     

Selective disassembly sequencing with random operation times in parallel disassembly environment

Selective disassembly sequencing is the problem of determining the sequence of disassembly operations to extract one or more target components of a product. This study considers the problem with random operation times in the parallel disassembly environment in which one or more components can be removed at the same time by a single disassembly operation. After representing all possible disassembly sequences using the extended process graph, a stochastic integer programming model is developed for the objective of minimising the sum of disassembly and penalty costs, where the disassembly costs consist of sequence-dependent set-up and operation costs and the penalty cost is the expectation of the costs incurred when the total disassembly time exceeds a threshold value. A sample average approximation-based solution algorithm is proposed that incorporates an optimal algorithm to solve the sample average approximating problem under a given set of scenarios for disassembly operation times. The algorithm is illustrated with a hand-light case and a large-sized random instance, and the results are reported.     

Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line

This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently.     

六倍体小麦基因组注释流程构建与优化

野生小麦是异源六倍体,基因组规模较大（约14 GB）,且包含大量重复序列.为了培育具有优良性状的新品种,首先要定位控制目标性状的基因,因此建立一个完整准确的基因组注释软件流程至关重要.传统的基因组注释方法基于数据库比对,具有三个明显的缺点：一是比对速度慢;二是难以发现新基因;三是软件选择没有统一标准.本文提出了一种新的生物信息学注释流程,结合了基因数据库比对、转录组高通量测序数据分析、全长转录组单分子测序数据分析等多种技术手段,实现了六倍体小麦科农9204基因组完整准确的注释,为揭示小麦生长发育规律和培育新品种提供了重要参考和软件技术支撑.