Interventions to control nosocomial infections : study designs and statistical issues

There is a wide range of potential study designs for intervention studies to decrease nosocomial infections in hospitals. The analysis is complex due to competing events, clustering, multiple timescales and time-dependent period and intervention variables. This review considers the popular pre-post quasi-experimental design and compares it with randomized designs. Randomization can be done in several ways: randomization of the cluster [intensive care unit (ICU) or hospital] in a parallel design; randomization of the sequence in a cross-over design; and randomization of the time of intervention in a stepped-wedge design. We introduce each design in the context of nosocomial infections and discuss the designs with respect to the following key points: bias, control for nonintervention factors, and generalizability. Statistical issues are discussed. A pre-post-intervention design is often the only choice that will be informative for a retrospective analysis of an outbreak setting. It can be seen as a pilot study with further, more rigorous designs needed to establish causality. To yield internally valid results, randomization is needed. Generally, the first choice in terms of the internal validity should be a parallel cluster randomized trial. However, generalizability might be stronger in a stepped-wedge design because a wider range of ICU clinicians may be convinced to participate, especially if there are pilot studies with promising results. For analysis, the use of extended competing risk models is recommended.

Stress at the synapse : signal transduction mechanisms of adrenal steroids at neuronal membranes

As the key neuron-to-neuron interface, the synapse is involved in learning and memory, including traumatic memories during times of stress. However, the signal transduction mechanisms by which stress mediates its lasting effects on synapse transmission and on memory are not fully understood. A key component of the stress response is the increased secretion of adrenal steroids. Adrenal steroids (e.g., cortisol) bind to genomic mineralocorticoid and glucocorticoid receptors (gMRs and gGRs) in the cytosol. In addition, they may act through membrane receptors (mMRs and mGRs), and signal transduction through these receptors may allow for rapid modulation of synaptic transmission as well as modulation of membrane ion currents. mMRs increase synaptic and neuronal excitability; mechanisms include the facilitation of glutamate release through extracellular signal-regulated kinase signal transduction. In contrast, mGRs decrease synaptic and neuronal excitability by reducing calcium currents through N-methyl-D-aspartate receptors and voltage-gated calcium channels by way of protein kinase A- and G protein-dependent mechanisms. This body of functional data complements anatomical evidence localizing GRs to the postsynaptic membrane. Finally, accumulating data also suggest the possibility that mMRs and mGRs may show an inverted U-shaped dose response, whereby glutamatergic synaptic transmission is increased by low doses of corticosterone acting at mMRs and decreased by higher doses acting at mGRs. Thus, synaptic transmission is regulated by mMRs and mGRs, and part of the stress signaling response is a direct and bidirectional modulation of the synapse itself by adrenal steroids.

Learning through modelling in the primary years

Many nations are highlighting the need for a renaissance in the mathematical sciences as essential to the well-being of all citizens (e.g., Australian Academy of Science, 2006; 2010; The National Academies, 2009). Indeed, the first recommendation of The National Academies’ Rising Above the Storm (2007) was to vastly improve K–12 science and mathematics education. The subsequent report, Rising Above the Gathering Storm Two Years Later (2009), highlighted again the need to target mathematics and science from the earliest years of schooling: “It takes years or decades to build the capability to have a society that depends on science and technology . . . You need to generate the scientists and engineers, starting in elementary and middle school” (p. 9). Such pleas reflect the rapidly changing nature of problem solving and reasoning needed in today’s world, beyond the classroom. As The National Academies (2009) reported, “Today the problems are more complex than they were in the 1950s, and more global. They’ll require a new educated workforce, one that is more open, collaborative, and cross-disciplinary” (p. 19). The implications for the problem solving experiences we implement in schools are far-reaching. In this chapter, I consider problem solving and modelling in the primary school, beginning with the need to rethink the experiences we provide in the early years. I argue for a greater awareness of the learning potential of young children and the need to provide stimulating learning environments. I then focus on data modelling as a powerful means of advancing children’s statistical reasoning abilities, which they increasingly need as they navigate their data-drenched world.

Collaborative learning model of infrastructure construction: A capability perspective

Purpose Performance heterogeneity between collaborative infrastructure projects is typically examined by considering procurement systems and their governance mechanisms at static points in time. The literature neglects to consider the impact of dynamic learning capability, which is thought to reconfigure governance mechanisms over time in response to evolving market conditions. This conceptual paper proposes a new model to show how continuous joint learning of participant organisations improves project performance. Design/methodology/approach There are two stages of conceptual development. In the first stage, the management literature is analysed to explain the Standard Model of dynamic learning capability that emphasises three learning phases for organisations. This Standard Model is extended to derive a novel Circular Model of dynamic learning capability that shows a new feedback loop between performance and learning. In the second stage, the construction management literature is consulted, adding project lifecycle, stakeholder diversity and three organisational levels to the analysis, to arrive at the Collaborative Model of dynamic learning capability. Findings The Collaborative Model should enable construction organisations to successfully adapt and perform under changing market conditions. The complexity of learning cycles results in capabilities that are imperfectly imitable between organisations, explaining performance heterogeneity on projects. Originality/value The Collaborative Model provides a theoretically substantiated description of project performance, driven by the evolution of procurement systems and governance mechanisms. The Model’s empirical value will be tested in future research.

The paradox of creative uncertainty in learning and teaching

In an ever-changing and globalised world there is a need for higher education to adapt and evolve its models of learning and teaching. The old industrial model has lost traction, and new patterns of creative engagement are required. These new models potentially increase relevancy and better equip students for the future. Although creativity is recognised as an attribute that can contribute much to the development of these pedagogies, and creativity is valued by universities as a graduate capability, some educators understandably struggle to translate this vision into practice. This paper reports on selected survey findings from a mixed methods research project which aimed to shed light on how creativity can be designed for in higher education learning and teaching settings. A social constructivist epistemology underpinned the research and data was gathered using survey and case study methods. Descriptive statistical methods and informed grounded theory were employed for the analysis reported here. The findings confirm that creativity is valued for its contribution to the development of students’ academic work, employment opportunities and life in general; however, tensions arise between individual educator’s creative pedagogical goals and the provision of institutional support for implementation of those objectives. Designing for creativity becomes, paradoxically, a matter of navigating and limiting complexity and uncertainty, while simultaneously designing for those same states or qualities.

An Adaptive Scheme for Learning the Probability Threshold in Pattern Recognition

The statistical minimum risk pattern recognition problem, when the classification costs are random variables of unknown statistics, is considered. Using medical diagnosis as a possible application, the problem of learning the optimal decision scheme is studied for a two-class twoaction case, as a first step. This reduces to the problem of learning the optimum threshold (for taking appropriate action) on the a posteriori probability of one class. A recursive procedure for updating an estimate of the threshold is proposed. The estimation procedure does not require the knowledge of actual class labels of the sample patterns in the design set. The adaptive scheme of using the present threshold estimate for taking action on the next sample is shown to converge, in probability, to the optimum. The results of a computer simulation study of three learning schemes demonstrate the theoretically predictable salient features of the adaptive scheme.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

Stress and Resilience in Combat-Related PTSD: Integration of Psychological Theory and Biological Mechanisms

Post-traumatic stress disorder (PTSD) is a debilitating psychiatric disorder that has a major impact on the ability to function effectively in daily life. PTSD may develop as a response to exposure to an event or events perceived as potentially harmful or life-threatening. It has high prevalence rates in the community, especially among vulnerable groups such as military personnel or those in emergency services. Despite extensive research in this field, the underlying mechanisms of the disorder remain largely unknown. The identification of risk factors for PTSD has posed a particular challenge as there can be delays in onset of the disorder, and most people who are exposed to traumatic events will not meet diagnostic criteria for PTSD. With the advent of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM V), the classification for PTSD has changed from an anxiety disorder into the category of stress- and trauma-related disorders. This has the potential to refocus PTSD research on the nature of stress and the stress response relationship. This review focuses on some of the important findings from psychological and biological research based on early models of stress and resilience. Improving our understanding of PTSD by investigating both genetic and psychological risk and coping factors that influence stress response, as well as their interaction, may provide a basis for more effective and earlier intervention.

Learning Optimal Discriminant Functions through a Cooperative Game of Automata

The problem of learning correct decision rules to minimize the probability of misclassification is a long-standing problem of supervised learning in pattern recognition. The problem of learning such optimal discriminant functions is considered for the class of problems where the statistical properties of the pattern classes are completely unknown. The problem is posed as a game with common payoff played by a team of mutually cooperating learning automata. This essentially results in a probabilistic search through the space of classifiers. The approach is inherently capable of learning discriminant functions that are nonlinear in their parameters also. A learning algorithm is presented for the team and convergence is established. It is proved that the team can obtain the optimal classifier to an arbitrary approximation. Simulation results with a few examples are presented where the team learns the optimal classifier.

Learning with symmetric label noise: The importance of being unhinged

Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio [2008] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. This ostensibly shows that convex losses are not SLN-robust. In this paper, we propose a convex, classification-calibrated loss and prove that it is SLN-robust. The loss avoids the Long and Servedio [2008] result by virtue of being negatively unbounded. The loss is a modification of the hinge loss, where one does not clamp at zero; hence, we call it the unhinged loss. We show that the optimal unhinged solution is equivalent to that of a strongly regularised SVM, and is the limiting solution for any convex potential; this implies that strong l2 regularisation makes most standard learners SLN-robust. Experiments confirm the unhinged loss’ SLN-robustness.

Susceptibility genes and neurodevelopmental mechanisms in dyslexia

Developmental dyslexia is a specific reading disability, which is characterised by unexpected difficulty in reading, spelling and writing despite adequate intelligence, education and social environment. It is the most common childhood learning disorder affecting 5-10 % of the population and thus constitutes the largest portion of all learning disorders. It is a persistent developmental failure although it can be improved by compensation. According to the most common theory, the deficit is in phonological processing, which is needed in reading when the words have to be divided into phonemes, or distinct sound elements. This occurs in the lowest level of the hierarchy of the language system and disturbs processes in higher levels, such as understanding the meaning of words. Dyslexia is a complex genetic disorder and previous studies have found nine locations in the genome that associate with it. Altogether four susceptibility genes have been found and this study describes the discovery of the first two of them, DYX1C1 and ROBO1. The first clues were obtained from two Finnish dyslexic families that have chromosomal translocations which disrupt these genes. Genetic analyses supported their role in dyslexia: DYX1C1 associates with dyslexia in the Finnish population and ROBO1 was linked to dyslexia in a large Finnish pedigree. In addition a genome-wide scan in Finnish dyslexic families was performed. This supported the previously detected dyslexia locus on chromosome 2 and revealed a new locus on chromosome 7. Dyslexia is a neurological disorder and the neurobiological function of the susceptibility genes DYX1C1 and ROBO1 are consistent with this. ROBO1 is an axon guidance receptor gene, which is involved in axon guidance across the midline in Drosophila and axonal pathfinding between the two hemispheres via the corpus callosum, as well as neuronal migration in the brain of mice. The translocation and decreased ROBO1 expression in dyslexic individuals indicate that two functional copies of ROBO1 gene are required in reading. DYX1C1 was a new gene without a previously known function. Inhibition of Dyx1c1 expression showed that it is needed in normal brain development in rats. Without Dyx1c1 protein, the neurons in the developing brain will not migrate to their final position in the cortex. These two dyslexia susceptibility genes DYX1C1 and ROBO1 revealed two distinct neurodevelopmental mechanisms of dyslexia, axonal pathfinding and neuronal migration. This study describes the discovery of the genes and our research to clarify their role in developmental dyslexia.

Relaxation Labeling with Learning Automata

Relaxation labeling processes are a class of mechanisms that solve the problem of assigning labels to objects in a manner that is consistent with respect to some domain-specific constraints. We reformulate this using the model of a team of learning automata interacting with an environment or a high-level critic that gives noisy responses as to the consistency of a tentative labeling selected by the automata. This results in an iterative linear algorithm that is itself probabilistic. Using an explicit definition of consistency we give a complete analysis of this probabilistic relaxation process using weak convergence results for stochastic algorithms. Our model can accommodate a range of uncertainties in the compatibility functions. We prove a local convergence result and show that the point of convergence depends both on the initial labeling and the constraints. The algorithm is implementable in a highly parallel fashion.

POP-inhibiittorin KYP-2047 vaikutukset angiogeneesiin in vitro

Angiogeneesi on tärkeä ilmiö elimistön fysiologiassa, mutta myös lukuisissa patologisissa tiloissa. Angiogeneesi on monivaiheinen prosessi, joka sisältää angiogeneesiä indusoivia ja sitä inhiboivia tekijöitä tasapainossa keskenään. Useat tutkimukset puoltavat sitä, että tymosiini ȕ4 (Tȕ4) ja tetrapeptidi Ac-SDKP (N-asetyyliseryyli- aspartyyli-lysyyli-proliini) indusoivat angiogeneesiä in vitro ja in vivo. Tutkimukset viittaavat myös siihen, että prolyylioligopeptidaasi (POP) hydrolysoi peptidifragmentin Ac- SDKP Tȕ4:n (43 ah) proliinin jälkeen. POP on laajalti esiintyvä seriiniproteaasi, joka pystyy pilkkomaan vain alle 30 aminohapon oligopeptidejä. Tȕ4:n tulee siksi pilkkoutua ensin jonkin, vielä tuntemattoman peptidaasin johdosta. POP:ia on löydetty eniten aivoista, minkä vuoksi sitä on tutkittu varsinkin muistin ja oppimisen häiriötiloissa sekä neurodegeneratiivisten sairausten yhteydessä. POP:in todellinen fysiologinen merkitys on kuitenkin vielä selvittämättä. Tämän pro gradun kirjallisuusosiossa selvitetään angiogeneesiin liittyvien tekijöiden yhteyksiä sekä kuvataan angiogeenisten Tȕ4:n, Ac-SDKP:n ja POP:in ominaisuuksia, esiintymistä ja toimintaa. Kokeellisen osion tarkoituksena oli osoittaa, osallistuvatko POP ja Tȕ4 tetrapeptidin Ac-SDKP muodostumiseen ja kapillaarimuodostumiseen ja edelleen, voidaanko POPaktiivisuutta, tetrapeptidi- ja kapillaarimuodostumista estää spesifisellä POP-inhibiittorilla, KYP-2047:llä. Kokeellinen osa oli kaksiosainen. Ensimmäisessä osassa tutkittiin POPaktiivisuutta ja suoritettiin Ac-SDKP –pitoisuusmittauksia ajanjaksolla 0-180 min Wistarkannan rotista tehdyillä homogenaateilla. Tutkimusryhminä olivat 0,1 ja 0,5 μM KYP-2047 (+2 μM Tȕ4), 1:20 (0,625 μM) humaaniperäinen rekombinantti-POP (+ 2 μM Tȕ4), 2 μM Tȕ4 (pos. kontrolli) ja raakahomogenaatti (neg. kontrolli). Toisessa osassa tutkittiin kapillaarimuodostumista ajanjaksolla 0-180 min humaaniperäisillä napanuoralaskimon primaariendoteelisoluilla MatrigelTM Matrix -päällystetyllä 48- kuoppalevyllä, jolle oli siirrostettu 50 000 solua/kuoppa. Naudan seerumilla ja antibiooteilla käsitellyt tutkimusryhmät olivat 5 ja 10 μM KYP-2047 (+4 μM Tȕ4), 1:20 (0,625 μM) humaaniperäinen rekombinantti-POP (+4 μM Tȕ4), 4 μM Tȕ4 (pos. kontrolli) ja DMEM (neg. kontrolli). Kuoppia inkuboitiin ja kapillaarimuodostuminen kuvattiin valomikroskoopilla digitaalikameralla. Kutakin tutkimusryhmää pipetoitiin kolmeen rinnakkaiseen kuoppaan ja kokeet toistettiin neljästi. Sulkeutuneiden kapillaarien lukumäärä laskettiin manuaalisesti ja tuloksista tehtiin tilastollinen analyysi. 7ȕ4:n ja POP:in havaittiin molempien osallistuvan tetrapeptidin AC-SDKP muodostumiseen munuaishomogenaateissa. Primaariendoteelisolut muodostivat selkeitä kapillaareja Matrigelilla, erityisesti POP- ja Tȕ4–ryhmissä. KYP-2047 inhiboi tehokkaasti POP:ia kaikissa kokeissa osoittautuen hyväksi antiangiogeeniseksi yhdisteeksi. Angiogeneesin mekanismien ja POP:in, Tȕ4:n ja Ac-SDKP:n yhteyksien selvittäminen vaatii luonnollisesti vielä lisätutkimuksia.

Data fusion and matching by maximizing statistical dependencies

The core aim of machine learning is to make a computer program learn from the experience. Learning from data is usually defined as a task of learning regularities or patterns in data in order to extract useful information, or to learn the underlying concept. An important sub-field of machine learning is called multi-view learning where the task is to learn from multiple data sets or views describing the same underlying concept. A typical example of such scenario would be to study a biological concept using several biological measurements like gene expression, protein expression and metabolic profiles, or to classify web pages based on their content and the contents of their hyperlinks. In this thesis, novel problem formulations and methods for multi-view learning are presented. The contributions include a linear data fusion approach during exploratory data analysis, a new measure to evaluate different kinds of representations for textual data, and an extension of multi-view learning for novel scenarios where the correspondence of samples in the different views or data sets is not known in advance. In order to infer the one-to-one correspondence of samples between two views, a novel concept of multi-view matching is proposed. The matching algorithm is completely data-driven and is demonstrated in several applications such as matching of metabolites between humans and mice, and matching of sentences between documents in two languages.