Recurrent idiopathic neuroretinitis: natural history and effect of treatment.

BACKGROUND: Previous reports have emphasized the self-limited nature of idiopathic neuroretinitis. There is less information about a subgroup of patients who suffer recurrent episodes with worse visual outcome. We sought to better characterize the clinical features of recurrent idiopathic neuroretinitis including the effects of immunosuppressive treatment. METHODS: Retrospective chart review of neuroretinitis patients from a single institution from 1983 to 2008. Inclusion criteria included two or more episodes of acute visual loss with disc oedema and macular exudates in a star pattern. Cases due to a specific infectious or inflammatory aetiology were excluded. RESULTS: Forty-one patients were included with an average follow up of 67 months. Median age at the time of the first episode was 28 years (range 10-54 years). Attacks were bilateral sequential in 34 patients (83%). We documented a total of 147 episodes in 75 eyes with an average of 3.6 attacks per patient. The average interval between attacks was 3 years. Visual field loss had a nerve fibre bundle pattern in most cases. Only 36% of eyes retained 6/12 or better visual acuity and greater than two-thirds of their visual field. Long-term immunosuppressive treatment in 13 patients decreased the attack rate by 72%. CONCLUSIONS: Recurrent idiopathic neuroretinitis typically affects young adults, with no gender preference. Recovery is limited and visual loss is cumulative with repeated attacks, often resulting in severe permanent visual loss. Immunosuppressive treatment appears to lessen the attack frequency.

Supramolecular chemistry for nanomedicine

Mimicking Nature, supramolecular chemistry represents the chemistry beyond the molecule, in view that intermolecularinteractions constitute the driving force for the preparation of molecular and supramolecular assemblies, using the chemicalinformation contained in molecular building blocks. Upon molecular recognition between discrete units, chemical processessuch as self-assembly and self-organisation start operating, and are the leading processes to build up supramolecular aggregates and materials. When those materials have dimensions on thenanometric scale, a recently emerging scientific discipline is defined,Nanoscience. Nanomaterials are promising tools for many applications, and their use in biomedical and clinical applicationsdefines the so-called Nanomedicine. In this review we present a few selected examples of nanomaterials designed for therapeutical purposes, emphasizing the importance of the preparation methodology in terms of their therapeutical use.

A simulation model of self-organising evolvability in software systems

The evolvability of a software artifact is its capacity for producing heritable or reusable variants; the inverse quality is the artifact's inertia or resistance to evolutionary change. Evolvability in software systems may arise from engineering and/or self-organising processes. We describe our 'Conditional Growth' simulation model of software evolution and show how, it can be used to investigate evolvability from a self-organisation perspective. The model is derived from the Bak-Sneppen family of 'self-organised criticality' simulations. It shows good qualitative agreement with Lehman's 'laws of software evolution' and reproduces phenomena that have been observed empirically. The model suggests interesting predictions about the dynamics of evolvability and implies that much of the observed variability in software evolution can be accounted for by comparatively simple self-organising processes.

Autobiographical memory distributions for negative self-images: memories are organised around negative as well as positive aspects of identity

The relationship between developmental experiences, and an individual’s emerging beliefs about themselves and the world, is central to many forms of psychotherapy. People suffering from a variety of mental health problems have been shown to use negative memories when defining the self, however little is known about how these negative memories might be organised and relate to negative self-images. In two online studies with middle-aged (N = 18; Study 1) and young (N = 56; Study 2) adults, we found that participants’ negative self-images (e.g., I am a failure) were associated with sets of autobiographical memories that formed clustered distributions around times of self-formation, in much the same pattern as for positive self-images (e.g., I am talented). This novel result shows that highly organised sets of salient memories may be responsible for perpetuating negative beliefs about the self. Implications for therapy are discussed.

A 2D coordination polymer with brick-wall network topology based on the [Cu(NCS)(2)(pn)] monomer

The new complex [Cu(NCS)(2)(pn)] (1) (pn = 1,3-propanediamine) has been synthesized and characterized by elemental analysis, infrared and electronic spectroscopy. Single crystal X-ray diffraction studies revealed that complex 1 is made up of neutral [Cu(NCS)(2)(pn)] units which are connected by mu-1,3,3-thiocyanato groups to yield a 2D metal-organic framework with a brick-wall network topology. Intermolecular hydrogen bonds of the type NH...SCN and NH...NCS are also responsible for the stabilization of the crystal structure. (c) 2007 Elsevier B.V. All rights reserved.

Biologia, morfologia e bioquímica de veneno da formiga lava-pés Solenopsis saevissima Smith (Insecta, Hymenoptera, Formicidae)

Pós-graduação em Ciências Biológicas (Zoologia) - IBRC

A semi-supervised classification technique based on interacting forces

Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed technique achieves good classification results on artificial data sets and is comparable to well-known semi-supervised techniques using benchmark data sets.

Multi-level models and infrastructures for simulating biological system development

The hierarchical organisation of biological systems plays a crucial role in the pattern formation of gene expression resulting from the morphogenetic processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. Being able to reproduce the systems dynamics at different levels of such a hierarchy might be very useful for studying such a complex phenomenon of self-organisation. The idea is to model the phenomenon in terms of a large and dynamic network of compartments, where the interplay between inter-compartment and intra-compartment events determines the emergent behaviour resulting in the formation of spatial patterns. According to these premises the thesis proposes a review of the different approaches already developed in modelling developmental biology problems, as well as the main models and infrastructures available in literature for modelling biological systems, analysing their capabilities in tackling multi-compartment / multi-level models. The thesis then introduces a practical framework, MS-BioNET, for modelling and simulating these scenarios exploiting the potential of multi-level dynamics. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespie’s direct method. The thesis finally proposes the adoption of the agent-based model as an approach capable of capture multi-level dynamics. To overcome the problem of parameter tuning in the model, the simulators are supplied with a module for parameter optimisation. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. The problem is tackled with a metaheuristic algorithm. As an example of application of the MS-BioNET framework and of the agent-based model, a model of the first stages of Drosophila Melanogaster development is realised. The model goal is to generate the early spatial pattern of gap gene expression. The correctness of the models is shown comparing the simulation results with real data of gene expression with spatial and temporal resolution, acquired in free on-line sources.

Test anxiety, self-control, and knowledge retrieval in secondary school students

Es wurde kürzlich gezeigt, dass die Stärke des Zusammenhangs zwischen Angst und Leistung von der momentan vorhandenen Selbstkontrollkraft abhängt. Wir untersuchten an einer Stichprobe aus Wirtschaftsschülern (N = 136), ob dieser Befund auf den Abruf von Wissen generalisierbar ist. Die Leistungsängstlichkeit der Teilnehmenden wurde erfasst und deren Selbstkontrollkraft experimentell manipuliert, woraufhin sie einen standardisierten Wortschatztest bearbeiteten. Während das Wissen nicht von der Leistungsängstlichkeit oder der Selbstkontrollkraft abhing, sagte die Interaktion aus beiden Variablen das gezeigte Wissen vorher. Übereinstimmend mit früheren Studien fiel die Leistung von Schülern mit niedriger Selbstkontrollkraft umso geringer aus, je leistungsängstlicher sie waren. Bei Schülern mit hoher Selbstkontrollkraft hingen die Leistungsängstlichkeit und die Wortschatzleistung hingegen nicht zusammen. Wir interpretieren dieses Muster dergestalt, dass Leistungsängstlichkeit den Wissensabruf nur dann behindert, wenn Selbstkontrolle nicht zur Kompensierung angstbezogener Aufmerksamkeitsdefizite herangezogen werden kann. Die Befunde implizieren, dass gründliche Vorbereitung keine guten Testnoten garantiert.

Age differences in instability, contingency, and level of self-esteem across the life span

We investigated age differences in instability, contingency, and level of self-esteem from age 13 to 72 years, using data from 1386 individuals who participated in a diary study over 25 days. Instability and contingency of self-esteem decreased from adolescence to old age, whereas level of self-esteem increased. Big Five personality traits predicted the level, but not the slope, of the trajectories of self-esteem characteristics. Age differences in self-esteem characteristics did not merely reflect age differences in instability and level of positive and negative affect. Finally, self-esteem characteristics showed a stable pattern of interrelations across the life span. Overall, the findings suggest that people’s self-esteem tends to become better adjusted—i.e., more stable, less contingent, and higher—across the life course.

Resilient Adaptation to Climate Change in African Agriculture

Africa’s agriculture faces varying climate change impacts which mainly worsen production conditions and adversely affect its economies. Adaptations thus need to build the resilience of farming systems. Using “resilient adaptation” as a concept, this study analyses how adaptations at farm and policy/institutional-levels contribute to the resilience of Sub-Saharan African agriculture. The developed tool, “the Resilience Check”, provides socio-economic data which complements existing adaptation tools. The underlying development gaps such as insecure property rights, poverty, low self-organisation, inadequate climate data and infrastructure limit resilient adaptations. If farmers could implement recommended practices, existing measures and improved crops can address most impacts expected in the medium-term. However, resource use efficiency remains critical for all farm management types. Development-oriented adaptation measures are needed to provide the robust foundations for building resilience. Reaching the very poor remains a challenge and the externally driven nature of many interventions raises concern about their sustainability. The study recommends practical measures such as decentralising various services and integrating the action plans of the multilateral environmental agreements into one national action plan.

Biochemical characterization and DNA binding properties of the MocR family member GabR, a PLP-dependent transcription factor

GabR è un fattore di trascrizione chimerico appartenente alla famiglia dei MocR/GabR, costituito da un dominio N-terminale elica-giro-elica di legame al DNA e un dominio effettore e/o di oligomerizzazione al C-terminale. I due domini sono connessi da un linker flessibile di 29 aminoacidi. Il dominio C-terminale è strutturalmente omologo agli enzimi aminotransferasici fold-type I, i quali, utilizzando il piridossal-5’-fosfato (PLP) come cofattore, sono direttamente coinvolti nel metabolismo degli aminoacidi. L’interazione contemporanea di PLP e acido γ-aminobutirrico (GABA) a GabR fa sì che questa promuova la trascrizione di due geni, gabT e gabD, implicati nel metabolismo del GABA. GabR cristallizza come un omodimero con una configurazione testa-coda. Il legame con la regione promotrice gabTD avviene attraverso il riconoscimento specifico di due sequenze dirette e ripetute (ATACCA), separate da uno spacer di 34 bp. In questo studio sono state indagate le proprietà biochimiche, strutturali e di legame al DNA della proteina GabR di Bacillus subtilis. L’analisi spettroscopica dimostra che GabR interagisce con il PLP formando l’aldimina interna, mentre in presenza di GABA si ottiene l’aldimina esterna. L’interazione fra il promotore gabTD e le forme holo e apo di GabR è stata monitorata mediante Microscopia a Forza atomica (AFM). In queste due condizioni di legame è stata stimata una Kd di circa 40 ηM. La presenza di GABA invece, determinava un incremento di circa due volte della Kd, variazioni strutturali nei complessi GabR-DNA e una riduzione del compattamento del DNA alla proteina, indipendentemente dalla sequenza del promotore in esame. Al fine di valutare il ruolo delle caratteristiche topologiche del promotore, sono state inserite cinque e dieci bp all’interno della regione spacer che separa le due sequenze ripetute dirette riconosciute da GabR. I significativi cambiamenti topologici riscontrati nel frammento aggiunto di cinque bp si riflettono anche sulla forte riduzione dell’affinità di legame verso la proteina. Al contrario, l’inserzione di 10 bp provoca solamente l’allontanamento delle sequenze ripetute dirette. L’assenza quindi di cambiamenti significativi nella topologia di questo promotore fa sì che l’affinità di legame per GabR rimanga pressoché inalterata rispetto al promotore non mutato. L’analisi del potenziale elettrostatico superficiale di GabR mostra la presenza di una fascia carica positivamente che si estende lungo un’intera faccia della proteina. Per verificare l’importanza di questa caratteristica di GabR nel meccanismo di interazione al DNA, sono stati preparati ed indagati i mutanti R129Q e K362-366Q, in cui la carica positiva superficiale risultava indebolita. L’affinità di legame dei mutanti di GabR per il DNA era inferiore rispetto alla proteina non mutata, in particolar modo nel mutante K362-366Q. Le evidenze acquisite suggeriscono che la curvatura intrinseca del promotore ed il corretto orientamento delle sequenze sulla doppia elica, più della distanza che le separa, siano critici per sostenere l’interazione con GabR. Oltre a questo, la superficie positiva di GabR è richiesta per accomodare la curvatura del DNA sul corpo della proteina. Alla luce di questo, l’interazione GabR-gabTD è un esempio di come il riconoscimento specifico di sequenze, la topologia del DNA e le caratteristiche strutturali della proteina siano contemporaneamente necessarie per sostenere un’interazione proteina-DNA stabile.

The nature of goodness /

"References" at end of chapters.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

The influence of memory in a threshold model for distributed task assignment

A nature inspired decentralised multi-agent algorithm is proposed to solve a problem of distributed task selection in which cities produce and store batches of different mail types. Agents must collect and process the mail batches, without a priori knowledge of the available mail at the cities or inter-agent communication. In order to process a different mail type than the previous one, agents must undergo a change-over during which it remains inactive. We propose a threshold based algorithm in order to maximise the overall efficiency (the average amount of mail collected). We show that memory, i.e. the possibility for agents to develop preferences for certain cities, not only leads to emergent cooperation between agents, but also to a significant increase in efficiency (above the theoretical upper limit for any memoryless algorithm), and we systematically investigate the influence of the various model parameters. Finally, we demonstrate the flexibility of the algorithm to changes in circumstances, and its excellent scalability.