Using Electronic Technology to Improve Clinical Care -- Results from a Before-after Cluster Trial to Evaluate Assessment and Classification of Sick Children According to Integrated Management of Childhood Illness (IMCI) Protocol in Tanzania.

Poor adherence to the Integrated Management of Childhood Illness (IMCI) protocol reduces the potential impact on under-five morbidity and mortality. Electronic technology could improve adherence; however there are few studies demonstrating the benefits of such technology in a resource-poor settings. This study estimates the impact of electronic technology on adherence to the IMCI protocols as compared to the current paper-based protocols in Tanzania. In four districts in Tanzania, 18 clinics were randomly selected for inclusion. At each site, observers documented critical parts of the clinical assessment of children aged 2 months to 5 years. The first set of observations occurred during examination of children using paper-based IMCI (pIMCI) and the next set of observations occurred during examination using the electronic IMCI (eIMCI). Children were re-examined by an IMCI expert and the diagnoses were compared. A total of 1221 children (671 paper, 550 electronic) were observed. For all ten critical IMCI items included in both systems, adherence to the protocol was greater for eIMCI than for pIMCI. The proportion assessed under pIMCI ranged from 61% to 98% compared to 92% to 100% under eIMCI (p < 0.05 for each of the ten assessment items). Use of electronic systems improved the completeness of assessment of children with acute illness in Tanzania. With the before-after nature of the design, potential for temporal confounding is the primary limitation. However, the data collection for both phases occurred over a short period (one month) and so temporal confounding was expected to be minimal. The results suggest that the use of electronic IMCI protocols can improve the completeness and consistency of clinical assessments and future studies will examine the long-term health and health systems impact of eIMCI

Can people guess what happened to others from their reactions?

Are we able to infer what happened to a person from a brief sample of his/her behaviour? It has been proposed that mentalising skills can be used to retrodict as well as predict behaviour, that is, to determine what mental states of a target have already occurred. The current study aimed to develop a paradigm to explore these processes, which takes into account the intricacies of real-life situations in which reasoning about mental states, as embodied in behaviour, may be utilised. A novel task was devised which involved observing subtle and naturalistic reactions of others in order to determine the event that had previously taken place. Thirty-five participants viewed videos of real individuals reacting to the researcher behaving in one of four possible ways, and were asked to judge which of the four ‘scenarios’ they thought the individual was responding to. Their eye movements were recorded to establish the visual strategies used. Participants were able to deduce successfully from a small sample of behaviour which scenario had previously occurred. Surprisingly, looking at the eye region was associated with poorer identification of the scenarios, and eye movement strategy varied depending on the event experienced by the person in the video. This suggests people flexibly deploy their attention using a retrodictive mindreading process to infer events

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset

Pathological or physiological erosion—is there a relationship to age?

This conventional literature review discusses whether pathological tooth wear is age dependant. It briefly reviews the components of tooth wear and the prevalence of tooth wear in children, adolescents and adults. The emphasis on terminology relating to tooth wear varies. In some countries, the role of erosion is considered the most important, whereas others consider the process to be a combination of erosion, attrition and abrasion often with one being more dominant. The importance of tooth wear or erosion indices in the assessment and the evidence for progression within subject and within lesions is described. The data from the few studies reporting pathological levels of wear reported in children and adults are discussed, in particular its relationship with age. There is little evidence to support the concept that pathological levels of erosion or wear are age dependant. There is, however, some evidence to suggest that normal levels of erosion or wear are age dependant

Entanglement-free Heisenberg-limited phase estimation

Measurement underpins all quantitative science. A key example is the measurement of optical phase, used in length metrology and many other applications. Advances in precision measurement have consistently led to important scientific discoveries. At the fundamental level, measurement precision is limited by the number N of quantum resources (such as photons) that are used. Standard measurement schemes, using each resource independently, lead to a phase uncertainty that scales as 1/sqrt(N) - known as the standard quantum limit. However, it has long been conjectured that it should be possible to achieve a precision limited only by the Heisenberg uncertainty principle, dramatically improving the scaling to 1/N. It is commonly thought that achieving this improvement requires the use of exotic quantum entangled states, such as the NOON state. These states are extremely difficult to generate. Measurement schemes with counted photons or ions have been performed with N <= 6, but few have surpassed the standard quantum limit and none have shown Heisenberg-limited scaling. Here we demonstrate experimentally a Heisenberg-limited phase estimation procedure. We replace entangled input states with multiple applications of the phase shift on unentangled single-photon states. We generalize Kitaev's phase estimation algorithm using adaptive measurement theory to achieve a standard deviation scaling at the Heisenberg limit. For the largest number of resources used (N = 378), we estimate an unknown phase with a variance more than 10 dB below the standard quantum limit; achieving this variance would require more than 4,000 resources using standard interferometry. Our results represent a drastic reduction in the complexity of achieving quantum-enhanced measurement precision.Comment: Published in Nature. This is the final versio

Cigarette Smoking and Effects on Hormone Function in Premenopausal Women

Cigarette smoke contains compounds that are suspected to cause reproductive damage and possibly affect hormone activity; therefore, we examined hormone metabolite patterns in relation to validated smoking status. We previously conducted a prospective study of women of reproductive age (n = 403) recruited from a large health maintenance organization, who collected urine daily during an average of three to four menstrual cycles. Data on covariates and daily smoking habits were obtained from a baseline interview and daily diary, and smoking status was validated by cotinine assay. Urinary metabolite levels of estrogen and progesterone were measured daily throughout the cycles. For the present study, we measured urinary levels of the pituitary hormone follicle-stimulating hormone (FSH) in a subset of about 300 menstrual cycles, selected by smoking status, with the time of transition between two cycles being of primary interest. Compared with nonsmokers, moderate to heavy smokers (≥ 10 cigarettes/day) had baseline levels (e.g., early follicular phase) of both steroid metabolites that were 25–35% higher, and heavy smokers (≥ 20 cigarettes/day) had lower luteal-phase progesterone metabolite levels. The mean daily urinary FSH levels around the cycle transition were increased at least 30–35% with moderate smoking, even after adjustment. These patterns suggest that chemicals in tobacco smoke alter endocrine function, perhaps at the level of the ovary, which in turn effects release of the pituitary hormones. This endocrine disruption likely contributes to the reported associations of smoking with adverse reproductive outcomes, including menstrual dysfunction, infertility, and earlier menopause

Engaging Undergraduates in Science Research: Not Just About Faculty Willingness.

Despite the many benefits of involving undergraduates in research and the growing number of undergraduate research programs, few scholars have investigated the factors that affect faculty members' decisions to involve undergraduates in their research projects. We investigated the individual factors and institutional contexts that predict faculty members' likelihood of engaging undergraduates in their research project(s). Using data from the Higher Education Research Institute's 2007-2008 Faculty Survey, we employ hierarchical generalized linear modeling to analyze data from 4,832 science, technology, engineering, and mathematics (STEM) faculty across 194 institutions to examine how organizational citizenship behavior theory and social exchange theory relate to mentoring students in research. Key findings show that faculty who work in the life sciences and those who receive government funding for their research are more likely to involve undergraduates in their research project(s). In addition, faculty at liberal arts or historically Black colleges are significantly more likely to involve undergraduate students in research. Implications for advancing undergraduate research opportunities are discussed

Volunteering in the care of people with severe mental illness: a systematic review

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Writhe in the Stretch-Twist-Fold Dynamo

This is an Author's Original Manuscript of an article whose final and definitive form, the Version of Record, has been published in Geophysical and Astrophysical Fluid Dynamics (2008) Copyright © 2008 Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/03091920802531791This article looks at the influence of writhe in the stretch-twist-fold dynamo. We consider a thin flux tube distorted by simple stretch, twist, and fold motions and calculate the helicity and energy spectra. The writhe number assists in the calculations, as it tells us how much the internal twist changes as the tube is distorted. In addition it provides a valuable diagnostic for the degree of distortion. Non mirror-symmetric dynamos typically generate magnetic helicity of one sign on large-scales and the opposite sign on small scales. The calculations presented here confirm the hypothesis that the large-scale helicity corresponds to writhe and the small scale corresponds to twist. In addition, the writhe helicity spectrum exhibits an interesting oscillatory behavior. The technique of calculating Fourier spectra for the writhe helicity may be useful in other areas of research, for example, the study of highly coiled molecules

Normal levels of p27Xic1 are necessary for somite segmentation and determining pronephric organ size

The Xenopus laevis cyclin dependent kinase inhibitor p27Xic1 has been shown to be involved in exit from the cell cycle and differentiation of cells into a quiescent state in the nervous system, muscle tissue, heart and retina. We show that p27Xic1 is expressed in the developing kidney in the nephrostomal regions. Using over-expression and morpholino oligonucleotide (MO) knock-down approaches we show normal levels of p27Xic1 regulate pronephros organ size by regulating cell cycle exit. Knock-down of p27Xic1 expression using a MO prevented myogenesis, as previously reported; an effect that subsequently inhibits pronephrogenesis. Furthermore, we show that normal levels of p27Xic1 are required for somite segmentation also through its cell cycle control function. Finally, we provide evidence to suggest correct paraxial mesoderm segmentation is not necessary for pronephric induction in the intermediate mesoderm. These results indicate novel developmental roles for p27Xic1, and reveal its differentiation function is not universally utilised in all developing tissues