El color en los códices prehispánicos del México Central: identificación material, cualidad plástica y valor estético

Este artículo investiga algunos de los valores plásticos y estéticos que presidieron la selección y la preparación de las materias colorantes empleadas para iluminar los códices creados por los nahuas del México Central durante el Posclásico Tardío. Estos códices son interesantes porque análisis arqueométricos y exámenes codicológicos recientes han permitido conocer la materialidad de su capa pictórica, así como las características formales y el comportamiento de los colores en estas obras. Uno de los aportes trascendentales de estos estudios ha sido averiguar que la paleta cromática que sirvió para pintar los códices del México Central era principalmente de origen orgánico, lo que contrasta con la naturaleza de los pigmentos detectados en restos de pintura mural y en esculturas creadas por los nahuas que son sobre todo minerales. El objetivo de este artículo es reflexionar sobre las razones de esas diferencias y demostrar que el uso de los colorantes orgánicos en los códices respondía a un fin plástico específico que concordaba con el canon estético imperante en la sociedad náhuatl.

Targeting treatment resistant breast cancer stem cells with FKBPL and its peptide derivative, AD-01

FKBPL and its peptide derivative, AD-01, have already demonstrated well-established inhibitory effects on breast cancer growth and CD44 dependent anti-angiogenic activity1, 2, 3. Since breast cancer stem cells (BCSCs) are CD44 positive, we wanted to explore if AD-01 could specifically target BCSCs. FKBPL stable overexpression or AD-01 treatment were highly effective at reducing the BCSC population measured by inhibiting mammosphere forming efficiency (MFE) in cell lines and primary breast cancer samples from both solid breast tumours and pleural effusions. Flow cytometry, to assess the ESA+/CD44+/CD24- subpopulation, validated these results. The ability of AD-01 to inhibit the self-renewal capacity of BCSCs was confirmed across three generations of mammospheres, where mammospheres were completely eradicated by the third generation (p<0.001). Clonogenic assays suggested that AD-01 mediated BCSC differentiation, with a significant decrease in the number of holoclones and an associated increase in meroclones/paraclones. In support of this, the stem cell markers, Nanog and Oct4 were significantly reduced following AD-01 treatment, whilst transfection of FKBPL-targeted siRNAs led to an increase in these markers and in mammosphere forming potential, highlighting the endogenous role of FKBPL in stem cell signalling. The clinical relevance of this was confirmed using a publically available microarray data set (GSE7390), where, high FKBPL and low Nanog expression were independently associated with improved overall survival in breast cancer patients (log rank test p=0.03; hazard ratio=3.01). When AD-01 was combined with other agents, we observed synergistic activity with the Notch inhibitor, DAPT and AD-01 was also able to abrogate a chemo- and radiotherapy induced enrichment in BCSCs. Importantly, using ‘gold standard’ in vivo limiting dilution assays we demonstrated a delay in tumour initiation and reoccurrence in AD-01 treated xenografts. In summary, AD-01 appears to have dual anti-angiogenic and anti-BCSC activity which will be advantageous as this agent enters clinical trial.

A DST-based Approach for Construction Project Risk Analysis

Despite its huge potential in risk analysis, the Dempster–Shafer Theory of Evidence (DST) has not received enough attention in construction management. This paper presents a DST-based approach for structuring personal experience and professional judgment when assessing construction project risk. DST was innovatively used to tackle the problem of lacking sufficient information through enabling analysts to provide incomplete assessments. Risk cost is used as a common scale for measuring risk impact on the various project objectives, and the Evidential Reasoning algorithm is suggested as a novel alternative for aggregating individual assessments. A spreadsheet-based decision support system (DSS) was devised to facilitate the proposed approach. Four case studies were conducted to examine the approach's viability. Senior managers in four British construction companies tried the DSS and gave very promising feedback. The paper concludes that the proposed methodology may contribute to bridging the gap between theory and practice of construction risk assessment.

2.2 eV luminescence in GaN

The yellow Luminescence in GaN centered at 2.2 eV has been studied in various epitaxial layers grown by MOVPE on sapphire and by the sandwich sublimation method on 6H-SiC substrates. The photoluminescence and optically detected magnetic resonance results can be consistently explained by a recombination model involving shallow donors and deep donors.

Designing Creative Destruction

This research aims to make a contribution in the context of design thinking at a global cultural scale and specifically how design methods are a feature of the homogenising and heterogenising forces of globalisation via creative destruction. Since Schumpeter’s description of economic innovation destroying the old and creating the new, a number of other interpretations of creative destruction have developed including those driving cultural evolution. However a design model showing the impact of different types of design method on cultural evolution can develop an understanding on a more systemic level from the medium to longer term impact of new designs that homogenise or increase the differences between various cultures. This research explores the theoretical terrain between creative destruction, design thinking and cybernetics in the context of exchanging cultural influences for collaborative creativity and concludes with an experiment that proposes a feedback loop between ubiquitising and differentiating design methods mediating cultural variety in creative ecosystems.

Vocational Rehabilitation in Early Psychosis: Cluster Randomised Trial

Background Individual placement and support (IPS) is effective in helping patients return to work but is poorly implemented because of clinical ambivalence and fears of relapse. Aims To assess whether a motivational intervention (motivational interviewing) directed at clinical staff to address ambivalence about employment improved patients’ occupational outcomes. Method Two of four early intervention teams that already provided IPS were randomised to receive motivational interviewing training for clinicians, focused on attitudinal barriers to employment. The trial was registered with the International Standard Randomised Controlled Trial Register (ISRCTN71943786). Results Of 300 eligible participants, 159 consented to the research. Occupational outcomes were obtained for 134 patients (85%) at 12-month follow-up. More patients in the intervention teams than in the IPS-only teams achieved employment by 12 months (29/68 v. 12/66). A random effects logistic regression accounting for clustering by care coordinator, and adjusted for participants’ gender, ethnicity, educational and employment history and clinical status scores, confirmed superiority of the intervention (odds ratio = 4.3, 95% CI 1.5-16.6). Conclusions Employment outcomes were enhanced by addressing clinicians’ ambivalence about their patients returning to work.

Designing Creative Destruction

This research aims to make a contribution in the context of design thinking at a global cultural scale and specifically how design methods are a feature of the homogenising and heterogenising forces of globalisation via creative destruction. Since Schumpeter’s description of economic innovation destroying the old and creating the new, a number of other interpretations of creative destruction have developed including those driving cultural evolution. However a design model showing the impact of different types of design method on cultural evolution can develop an understanding on a more systemic level from the medium to longer term impact of new designs that homogenise or increase the differences between various cultures. This research explores the theoretical terrain between creative destruction, design thinking and cybernetics in the context of exchanging cultural influences for collaborative creativity and concludes with an experiment that proposes a feedback loop between ubiquitising and differentiating design methods mediating cultural variety in creative ecosystems.

MEG-measured visually induced gamma-band oscillations in chronic schizophrenia: Evidence for impaired generation of rhythmic activity in ventral stream regions

Background: Gamma-band oscillations are prominently impaired in schizophrenia, but the nature of the deficit and relationship to perceptual processes is unclear. Methods: 16 patients with chronic schizophrenia (ScZ) and 16 age-matched healthy controls completed a visual paradigm while magnetoencephalographic (MEG) data was recorded. Participants had to detect randomly occurring stimulus acceleration while viewing a concentric moving grating. MEG data were analyzed for spectral power (1-100 Hz) at sensorand source-level to examine the brain regions involved in aberrant rhythmic activity, and for contribution of differences in baseline activity towards the generation of low- and highfrequency power. Results: Our data show reduced gamma-band power at sensor level in schizophrenia patients during stimulus processing while alpha-band and baseline spectrum were intact. Differences in oscillatory activity correlated with reduced behavioral detection rates in the schizophrenia group and higher scores on the “Cognitive Factor” of the Positive and Negative Syndrome Scale. Source reconstruction revealed that extra-striate (fusiform/lingual gyrus), but not striate (cuneus), visual cortices contributed towards the reduced activity observed at sensorlevel in ScZ patients. Importantly, differences in stimulus-related activity were not due to differences in baseline activity. Conclusions: Our findings highlight that MEG-measured high-frequency oscillations during visual processing can be robustly identified in ScZ. Our data further suggest impairments that involve dysfunctions in ventral stream processing and a failure to increase gamma-band activity in a task-context. Implications of these findings are discussed in the context of current theories of cortical-subcortical circuit dysfunctions and perceptual processing in ScZ.

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

Multiple-Retrieval Case-Based Reasoning for Course Timetabling Problems

The structured representation of cases by attribute graphs in a Case-Based Reasoning (CBR) system for course timetabling has been the subject of previous research by the authors. In that system, the case base is organised as a decision tree and the retrieval process chooses those cases which are sub attribute graph isomorphic to the new case. The drawback of that approach is that it is not suitable for solving large problems. This paper presents a multiple-retrieval approach that partitions a large problem into small solvable sub-problems by recursively inputting the unsolved part of the graph into the decision tree for retrieval. The adaptation combines the retrieved partial solutions of all the partitioned sub-problems and employs a graph heuristic method to construct the whole solution for the new case. We present a methodology which is not dependant upon problem specific information and which, as such, represents an approach which underpins the goal of building more general timetabling systems. We also explore the question of whether this multiple-retrieval CBR could be an effective initialisation method for local search methods such as Hill Climbing, Tabu Search and Simulated Annealing. Significant results are obtained from a wide range of experiments. An evaluation of the CBR system is presented and the impact of the approach on timetabling research is discussed. We see that the approach does indeed represent an effective initialisation method for these approaches.

Multiple-retrieval case-based reasoning for course timetabling problems

The structured representation of cases by attribute graphs in a Case-Based Reasoning (CBR) system for course timetabling has been the subject of previous research by the authors. In that system, the case base is organised as a decision tree and the retrieval process chooses those cases which are sub attribute graph isomorphic to the new case. The drawback of that approach is that it is not suitable for solving large problems. This paper presents a multiple-retrieval approach that partitions a large problem into small solvable sub-problems by recursively inputting the unsolved part of the graph into the decision tree for retrieval. The adaptation combines the retrieved partial solutions of all the partitioned sub-problems and employs a graph heuristic method to construct the whole solution for the new case. We present a methodology which is not dependant upon problem specific information and which, as such, represents an approach which underpins the goal of building more general timetabling systems. We also explore the question of whether this multiple-retrieval CBR could be an effective initialisation method for local search methods such as Hill Climbing, Tabu Search and Simulated Annealing. Significant results are obtained from a wide range of experiments. An evaluation of the CBR system is presented and the impact of the approach on timetabling research is discussed. We see that the approach does indeed represent an effective initialisation method for these approaches.

Simulation Optimization of the Crossdock Door Assignmnet Problem

The purpose of this report is to present the Crossdock Door Assignment Problem, which involves assigning destinations to outbound dock doors of Crossdock centres such that travel distance by material handling equipment is minimized. We propose a two fold solution; simulation and optimization of the simulation model - simulation optimization. The novel aspect of our solution approach is that we intend to use simulation to derive a more realistic objective function and use Memetic algorithms to find an optimal solution. The main advantage of using Memetic algorithms is that it combines a local search with Genetic Algorithms. The Crossdock Door Assignment Problem is a new domain application to Memetic Algorithms and it is yet unknown how it will perform.

An Indirect Genetic Algorithm for Set Covering Problems

This paper presents a new type of genetic algorithm for the set covering problem. It differs from previous evolutionary approaches first because it is an indirect algorithm, i.e. the actual solutions are found by an external decoder function. The genetic algorithm itself provides this decoder with permutations of the solution variables and other parameters. Second, it will be shown that results can be further improved by adding another indirect optimisation layer. The decoder will not directly seek out low cost solutions but instead aims for good exploitable solutions. These are then post optimised by another hill-climbing algorithm. Although seemingly more complicated, we will show that this three-stage approach has advantages in terms of solution quality, speed and adaptability to new types of problems over more direct approaches. Extensive computational results are presented and compared to the latest evolutionary and other heuristic approaches to the same data instances.