A Constraint-directed Local Search Approach to Nurse Rostering Problems

In this paper, we investigate the hybridization of constraint programming and local search techniques within a large neighbourhood search scheme for solving highly constrained nurse rostering problems. As identified by the research, a crucial part of the large neighbourhood search is the selection of the fragment (neighbourhood, i.e. the set of variables), to be relaxed and re-optimized iteratively. The success of the large neighbourhood search depends on the adequacy of this identified neighbourhood with regard to the problematic part of the solution assignment and the choice of the neighbourhood size. We investigate three strategies to choose the fragment of different sizes within the large neighbourhood search scheme. The first two strategies are tailored concerning the problem properties. The third strategy is more general, using the information of the cost from the soft constraint violations and their propagation as the indicator to choose the variables added into the fragment. The three strategies are analyzed and compared upon a benchmark nurse rostering problem. Promising results demonstrate the possibility of future work in the hybrid approach

Geometric combinatorics and computational molecular biology: branching polytopes for RNA sequences

Questions in computational molecular biology generate various discrete optimization problems, such as DNA sequence alignment and RNA secondary structure prediction. However, the optimal solutions are fundamentally dependent on the parameters used in the objective functions. The goal of a parametric analysis is to elucidate such dependencies, especially as they pertain to the accuracy and robustness of the optimal solutions. Techniques from geometric combinatorics, including polytopes and their normal fans, have been used previously to give parametric analyses of simple models for DNA sequence alignment and RNA branching configurations. Here, we present a new computational framework, and proof-of-principle results, which give the first complete parametric analysis of the branching portion of the nearest neighbor thermodynamic model for secondary structure prediction for real RNA sequences.Comment: 17 pages, 8 figure

Tracking ALMA System Temperature with Water Vapor Data at High Frequency

The ALMA observatory is now putting more focus on high-frequency observations (frequencies from 275-950 GHz). However, high-frequency observations often suffer from rapid variations in atmospheric opacity that directly affect the system temperature $T_{sys}$. Current observations perform discrete atmospheric calibrations (Atm-cals) every few minutes, with typically 10-20 occurring per hour for high frequency observation and each taking 30-40 seconds. In order to obtain more accurate flux measurements and reduce the number of atmospheric calibrations (Atm-cals), a new method to monitor $T_{sys}$ continuously is proposed using existing data in the measurement set. In this work, we demonstrate the viability of using water vapor radiometer (WVR) data to track the $T_{sys}$ continuously. We find a tight linear correlation between $T_{sys}$ measured using the traditional method and $T_{sys}$ extrapolated based on WVR data with scatter of 0.5-3%. Although the exact form of the linear relation varies among different data sets and spectral windows, we can use a small number of discrete $T_{sys}$ measurements to fit the linear relation and use this heuristic relationship to derive $T_{sys}$ every 10 seconds. Furthermore, we successfully reproduce the observed correlation using atmospheric transmission at microwave (ATM) modeling and demonstrate the viability of a more general method to directly derive the $T_{sys}$ from the modeling. We apply the semi-continuous $T_{sys}$ from heuristic fitting on a few data sets from Band 7 to Band 10 and compare the flux measured using these methods. We find the discrete and continuous $T_{sys}$ methods give us consistent flux measurements with differences up to 5%. Furthermore, this method has significantly reduced the flux uncertainty due to $T_{sys}$ variability for one dataset, which has large precipitable water vapor (PWV) fluctuation, from 10% to 0.7%.Comment: 24 pages, 18 figures, accepted to PAS

Negative feedback control of jasmonate signaling by an alternative splice variant of JAZ10

The plant hormone jasmonate (JA) activates gene expression by promoting ubiquitin-dependent degradation of JAZ transcriptional repressor proteins. A key feature of all JAZ proteins is the highly conserved Jas motif, which mediates both JAZ degradation and JAZ binding to the transcription factor MYC2. Rapid expression of JAZ genes in response to JA is thought to attenuate JA responses, but little is known about the mechanisms by which newly synthesized JAZ proteins exert repression in the presence of the hormone. Here, we show that desensitization to JA is mediated by an alternative splice variant (JAZ10.4) of JAZ10 that lacks the Jas motif. Unbiased protein-protein interaction screens identified three related bHLH transcription factors (MYC2, MYC3, and MYC4) and the co-repressor NINJA as JAZ10.4-binding partners. We show that the N-terminal region of JAZ10.4 contains a cryptic MYC2-binding site that resembles the Jas motif, and that the ZIM motif of JAZ10.4 functions as a transferable repressor domain whose activity is associated with recruitment of NINJA. Functional studies showed that expression of JAZ10.4 from the native JAZ10 promoter complemented the JA-hypersensitive phenotype of a jaz10 mutant. Moreover, treatment of these complemented lines with JA resulted in rapid accumulation of JAZ10.4 protein. Our results provide an explanation for how the unique domain architecture of JAZ10.4 links transcription factors to a co-repressor complex, and suggest how JA-induced transcription and alternative splicing of JAZ10 pre-mRNA creates a regulatory circuit to attenuate JA responses.Fil: Moreno, Javier Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentina. Michigan State University; Estados UnidosFil: Shyu, Christine. Michigan State University; Estados UnidosFil: Campos, Marcelo L.. Michigan State University; Estados UnidosFil: Patel, Lalita C.. Michigan State University; Estados UnidosFil: Chung, Hoo Sun. Michigan State University; Estados UnidosFil: Yao, Jian. Michigan State University; Estados UnidosFil: He, Sheng Hang. Michigan State University; Estados UnidosFil: Howe, Gregg A.. Michigan State University; Estados Unido

Mental health problems: Are they or are they not a risk factor for dropout from drug treatment? A systematic review of the evidence

Background: A sizeable number of recent studies investigating whether clients with substance misuse and mental health problems (dual diagnosis clients) are at heightened risk of dropout from drug treatment have been published. It is timely that their findings are brought together in a comprehensive review of the current evidence. Aims: The aim of the review is to examine whether dually diagnosed clients are less likely to be retained in drug treatment than clients without mental health problems, and, if so, whether this varies for clients diagnosed with different types of mental health problems. Methods: The review considers peer-reviewed research published after 1 January 1990, which was located using the literature databases Medline and PsycInfo. Predefined search terms were used. Further papers were identified from the bibliographies of relevant publications. Findings: 58 studies (84% from the USA) met the inclusion criteria for the review. The findings suggest that for most clients, having a past history of mental health problems does not influence the likelihood of being retained in drug treatment. The body of evidence regarding concurrent mental health problems is contradictory. On the whole, the majority of studies suggest that neither presence nor severity of depressive, anxiety, or other Axis-I disorders is related to retention, but these findings are not entirely unequivocal, as a few studies report strong positive or negative associations between depression and anxiety disorders and retention. Few researchers looked separately at psychotic spectrum disorders hence no conclusions could be drawn. The presence of most personality disorders also did not appear to affect treatment tenure, with the exception of antisocial personality disorder, for which the evidence points towards a greater risk of dropout. Conclusions: The balance of evidence suggests that, overall, dual diagnosis clients with Axis-I disorders who seek treatment in drug treatment services are retained as well as clients without dual diagnosis. Subgroups of clients who appear more vulnerable to premature dropout include those with antisocial personality disorder. Methodological shortcomings of the reviewed studies and resulting implications for this review and future research are discussed

Natural variation in linalool metabolites: One genetic locus, many functions?

The ubiquitous volatile linalool is metabolized in plants to nonvolatile derivatives. We studied Nicotiana attenuata plants which naturally vary in (S)‐(+)‐linalool contents, and lines engineered to produce either (R)‐(‐)‐ or (S)‐(+)‐linalool. Only (S)‐(+)‐linalool production was associated with slower growth of a generalist herbivore, and a large fraction was present as nonvolatile derivatives. We found that variation in volatile linalool and its nonvolatile glycosides mapped to the same genetic locus which harbored the biosynthetic gene, NaLIS, but that free linalool varied more in environmental responses. This study reveals how (S)‐(+)‐linalool and conjugates differ in their regulation and possible functions in resistance

Ants (Hymenoptera: Formicidae) increase predation of Belenois solilucis (Lepidoptera: Pieridae) eggs in organic agriculture production systems: a multiple-site field study at Rashad, Sudan

Organic farming is becoming more popular as there is a greater demand for pesticide-free food. Pest control in organic agricultural production requires a set of skills, including the identification of effective predators and land-use practices. Predation by selected Coleopteran, Dipteran, and Hemipteran insects and Araneae is well established, whereas the predatory role of ants (Hymenoptera: Formicidae) has received little attention in the Rashad district, Sudan. This study was carried out to assess the predation rates of Belenois solilucis eggs and the impact of the land use type around the properties on these rates. An experimente involving predation tests on Belenois solilucis eggs and fauna sampling were conducted in 18 areas of organic agriculture in the Rashad district. The study showed that ants can reduce the eggs population by 26.8% per day. At the same time, other predator taxa, primarily Coleoptera, from Coccinellidae and Staphylinidae families, removed only 13% of the eggs. Ant species with the most significant recruiting power were Axinidris acholli, Tapinoma carininotum, and Technomyrmex moerens. Ant genera such as Linepithema, Dorymyrmex, and Camponotus ants were also frequently observed. The proportion of the planted area within a 500-meter radius, in addition to the interaction of other landscape categories, had a minor influence on predation, but only when the predators were not ants. The landscape does not affect predation by predators in general, including ants, or on ant predation in particular

On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation

Understanding which function classes are easy and which are hard for a given algorithm is a fundamental question for the analysis and design of bio-inspired search heuristics. A natural starting point is to consider the easiest and hardest functions for an algorithm. For the (1+1) EA using standard bit mutation (SBM) it is well known that OneMax is an easiest function with unique optimum while Trap is a hardest. In this paper we extend the analysis of easiest function classes to the contiguous somatic hypermutation (CHM) operator used in artificial immune systems. We define a function MinBlocks and prove that it is an easiest function for the (1+1) EA using CHM, presenting both a runtime and a fixed budget analysis. Since MinBlocks is, up to a factor of 2, a hardest function for standard bit mutations, we consider the effects of combining both operators into a hybrid algorithm. We rigorously prove that by combining the advantages of k operators, several hybrid algorithmic schemes have optimal asymptotic performance on the easiest functions for each individual operator. In particular, the hybrid algorithms using CHM and SBM have optimal asymptotic performance on both OneMax and MinBlocks. We then investigate easiest functions for hybrid schemes and show that an easiest function for an hybrid algorithm is not just a trivial weighted combination of the respective easiest functions for each operator.publishersversionPeer reviewe