Miracle-dz/UniFAR-FTUs huggingface dataset:
Modern matrons and infection control practices: aspirations and realities

Modern matrons were introduced in 2001 by the Department of Health to lead clinical teams in the prevention of healthcare associated infection. The facilitative role of modern matron requires both managerial and entrepreneurial skills and senior nurses are expected to lead by example, inspire, motivate and empower others, and thus conform to the `transformational leadership' style that foregrounds the importance of interpersonal and influencing skills. In this paper we identify problems that challenge this model of the modern matron and link them to possible problems in infection control. The study describes cases of difficulty in fulfilling leadership requirements because of organisational barriers to empowerment despite arguments to the contrary. Unless a significant budgetary responsibility is made part of the modern matron's role, personal skills (communication, problem solving) alone may not be sufficient to sustain it and may not lead to achieving control over infection, which was the initial trigger for instituting this role.
Short Communication: A simple workflow for robust low-cost UAV-derived change detection without ground control points

Abstract. High-quality 3-D point clouds generated from repeat camera-equipped unmanned aerial vehicle (UAV) surveys are increasingly being used to investigate landscape changes and geomorphic processes. Point cloud quality can be expressed as accuracy in a comparative (i.e., from survey to survey) and absolute (between survey and an external reference system) sense. Here we present a simple workflow for calculating pairs or sets of point clouds with a high comparative accuracy, without the need for ground control points or a differential GNSS (dGNSS)-equipped UAV. The method is based on the automated detection of common tie points in stable portions of the survey area. We demonstrate the efficacy of the new approach using a consumer-grade UAV in two contrasting landscapes: the coastal cliffs on the island of Rügen, Germany, and the tectonically active Daan River gorge in Taiwan. Compared to a standard approach using ground control points, our workflow results in a nearly identical distribution of measured changes. Compared to a standard approach without ground control, our workflow reduces the level of change detection from several meters to 10–15 cm. This approach enables robust change detection using UAVs in settings where ground control is not feasible.
Fast Nonsmooth Regularized Risk Minimization with Continuation

In regularized risk minimization, the associated optimization problem becomes particularly difficult when both the loss and regularizer are nonsmooth. Existing approaches either have slow or unclear convergence properties, are restricted to limited problem subclasses, or require careful setting of a smoothing parameter. In this paper, we propose a continuation algorithm that is applicable to a large class of nonsmooth regularized risk minimization problems, can be flexibly used with a number of existing solvers for the underlying smoothed subproblem, and with convergence results on the whole algorithm rather than just one of its subproblems. In particular, when accelerated solvers are used, the proposed algorithm achieves the fastest known rates of $O(1/T^2)$ on strongly convex problems, and $O(1/T)$ on general convex problems. Experiments on nonsmooth classification and regression tasks demonstrate that the proposed algorithm outperforms the state-of-the-art.
A Study of Qing-Fei-Tang

強い咳と痰を伴った急性気管支炎の患者に対する清肺湯の投与により, 症状の改善が認められた。 清肺湯の投与前には, 患者の末梢血白血球の活性酸素化学発光は異常亢進を示した。 この活性酸素化学発光の異常亢進は, 5週間の清肺湯の内服により正常化した。 さらに in vitro の実験系においても清肺湯は, その添加によりオプソニン化ザイモザン刺激時のヒト白血球における活性酸素化学発光, および抗原刺激時の感作モルモット肺組織からのSRS-A遊離を用量依存的に抑制した。 これらの結果は, 清肺湯が一部ではSRS-ロイコトリエンのようなケミカルメディエーターの生成をコントロールすることにより気管支炎の症状の改善に寄与しうるものと考えられる。
A deep learning approach for dual-energy CT imaging using a single-energy CT data

In a standard computed tomography (CT) image, pixels having the same Hounsfield Units (HU) can correspond to different materials and it is therefore challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but DECT scanners are not widely available as singleenergy CT (SECT) scanners. Here we develop a deep learning approach to perform DECT imaging by using standard SECT data. The end point of the deep learning approach is a model capable of providing the high-energy CT image for a given input low-energy CT image. We retrospectively studied 22 patients who received contrast-enhanced abdomen DECT scan. The difference between the predicted and original high-energy CT images are 3.47 HU, 2.95 HU, 2.38 HU, and 2.40 HU for spine, aorta, liver and stomach, respectively. The difference between virtual non-contrast (VNC) images obtained from original DECT and deep learning DECT are 4.10 HU, 3.75 HU, 2.33 HU and 2.92 HU for spine, aorta, liver and stomach, respectively. The aorta iodine quantification difference between iodine maps obtained from original DECT and deep learning DECT images is 0.9%. This study demonstrates that highly accurate DECT imaging with single low-energy data is achievable by using a deep learning approach. The proposed method can significantly simplify the DECT system design, reducing the scanning dose and imaging cost.
Rotavirus and norovirus acute gastroenteritis presenting to a paediatric emergency service

Resumo Introducao: Rotavirus (RV) e norovirus (NoV) sao descritos como causa importante de gastrenterite aguda (GEA) na cri- anca, os ultimos particularmente em surtos. Os dados nacio- nais sobre NoV sao escassos. Este estudo pretende determinar o papel destes virus na etiologia da GEA num Servico de Urgencia (SU) pediatrico. Abstract Background: Rotavirus (RV) and norovirus (NoV) are important causes of acute gastroenteritis (AG) in children, the latter particularly in outbreaks. Data on NoV infection in Portugal are very scarce. This study aimed to determine the role of these viruses in the aetiology of AG in children seen in a paediatric Emergency Service (PES). Methods: Prospective study including all cases of AG, observed from January to May 2009 in a PES of a tertiary hospital. Demographic and clinical data were registered. Stools were tested for NoV, sapovirus, RV, adenovirus, Campylobacter jejuni, Shigella, Yersinia and Salmonella spp. Results: A total of 1071 children under 13 years of age with AG were observed of whom 371 (35%) had a stool sample available. One or more pathogens were identified in 179 cases (48%), mainly RV (89/371, 24%) and NoV (49/371, 13%). The main NoV genotypes were GII.4 and GII.7. Coinfection occurred in 8% of the cases. NoV infection mainly affected children under two years of age and fever and vomiting were present in half of them. The majority were sporadic infections. Discussion: NoV was the second most frequent pathogen causing AG in a PES in the winter-spring season, following RV. These two viruses were responsible for 34% of AG in the period. Addition of an effective NoV vaccine, combined with effective implementation of existing RV vaccines, has the potential to make a very substantial impact on paediatric AG in the winter-spring in Portugal.
Learning principal curves inside divide-and-combine framework

This paper proposes a divide-and-combine learning framework for finding principal curves. The framework is based on the definition of K principal curves. It consists of two phases: divide phase and combine phase. Divide phase finds appropriate division for the distribution under the constraints of two necessary conditions. Then the combine phase combines the representations of the division components into a principal curve according to the neighbor relationship between these components. Through the operations of these two phases, the framework reaches the object of K principal curves and solves the main problems in other methods in learning K principal curves. In practice, a variety of algorithms can be generated by specializing the learning framework for different problem domains. Clustering principal curve algorithm is one of these algorithms specialized from the framework by some simplest definitions and algorithms. Experimental results illustrate the outstanding performance of the clustering principal curve algorithm compared to other existing approaches.
HWIM : a computer model of language comprehension and production

A computer natural-language system called HWIM (Hear What I,Mean), is described in this report. Noting that the system 4. accepts either typed or spoken inputs and produces both typedand ,. spoken,responses, the report proposes HWIM as an example of a Telatilielytomplete language system ,illustrating how'the many components of languasae.processing interact. The report focuses an HWIM's d4coui.se processing capabilities and on the integration -of dilVerse types of knowledge for the' purpose ,Of comprehending and producing natural language. 01)
Phase II trial of the Hsp90 inhibitor tanespimycin (Tan) + trastuzumab (T) in patients (pts) with HER2-positive metastatic breast cancer (MBC)

1027 Background: Tan (17-AAG) inhibits Hsp90 activity, resulting in degradation of client proteins including the HER2 receptor. A phase 1 trial demonstrated clinical activity of T + Tan in pts with HER2+ MBC (1 PR + 4 MRs in 15 pts with median of 2 prior T-based regimens; Modi et al, JCO 2007). Tan was initially a Cremophor-based product; a suspension became available and was substituted in this phase 2 trial. Methods: Eligible pts have HER2+ MBC and either PD within 3m following adjuvant T or PD following 1-line of T-based therapy for MBC. Pts receive standard weekly doses of T followed by Tan (450 mg/m2). Early pts received a Cremophor-based Tan product with antihistamine/steroid premeds. Later pts received the suspension Tan product without premeds. Primary endpoint: ORR by RECIST, disease status assessed every 8w. Results: As of 19Dec07, 29 pts enrolled (mean age 54 yrs, range 31–71; median KPS 90). 21 pts received Cremophor product; 8 pts received suspension product (4 pts crossed-over to the suspens.
The Reproducibility and Accuracy of Biometry Parameter Measurement from IOL Master(R)

Purpose: To evaluate the reproducibility and accuracy of axial length and anterior chamber depthmeasurements from IOL Master . Methods: The axial length and anterior chamber depth measurements in 30 eyes with normal crystalline lens, 30 eyes with cataract and 30 pseudophakic eyes were measured by two practitioners using IOL Master followed by A-scan and Orbscan II. The reproducibility of IOL Master was analyzed by comparing the results from the first and second practitioners. To evaluate the accuracy of IOL Master , the axial length was compared to A-scan and the anterior chamber depth was compared to A-scan and Orbscan II. Results: The difference between the axial length and anterior chamber depth measurements from the two practitioners using IOL Master were not statistically significant (p>0.05). The axial length of measurement from IOL Master was 0.16 mm, 0.18 mm, and 0.96 mm longer than that from A-scan, in the normal, cataract and pseudophakic groups, respectively. In addition, the anterior chamber depth measurement from IOL Master was significantly deeper than that from A-scan and OrbscanII(p . However, some patients who had media opacity were not measured using IOL Master . It is a noncontact method, which provides an alternative to A-scan.
A STATISTICAL POTENTIAL FOR PROTEIN STRUCTURE PREDICTION

We use position specific scoring matrix columns as an abstraction of amino acid types to derive statistical potentials for protein structure prediction. The new potentials are compared to that derived using residue specific amino acid identity. For the test, we carried out folding simulations of 91 residue A chain of protein 2gpi, and found that the abstract amino acid type improved the quality of the one-body type statistical potential, but not for the two-body type statistical potential which describes long range interactions.
The Dual Nature of Iron in Relation to Cancer: A Review

Background: Although iron is essential for all cells, it could be potentially hazardous. This is mostly due to iron participation in oxidation-reduction reactions. In terms of cancer risk, the role of both iron deficiency and overload can be studied in two directions, protective or therapeutic effects, and carcinogenic effects. Materials and Methods:: PubMed, Science direct, Ovid, Proquest, Medline, and Google scholar databases were used to obtain articles identified by using the key words “Iron”, “Iron deficiency”, “Iron overload”, “Cancer”, and “Neoplasm” without any time limit. The relevance of studies was identified by reviewing the titles and the abstracts. 185 articles published in English, including experimental, observational, molecular and cellular studies were reviewed. Results and Conclusion: Considering the dual nature of iron, maintaining an optimal iron level in the body is important. This can be achieved when regulatory mechanisms of body iron level work properly and the amount of iron input is within the physiological range. Current evidence generally supports this possibility that higher animal-derived (heme) iron may increase the risk of some cancers. The release of iron or nitrosyl heme from iron complexes during some processing of meat may increase production of cancer promoter lipoperoxides. On the other hand, use of iron chelation or iron mediated oxidative assault for the treatment or prevention of cancer appears to be promising; but further research is required. Considering this background, this article reviews the literature and evidence, focusing on the role of iron overload and deficiency in developing and treatment of various cancers.
A Comparison of Phrase Based and Word based Language Model for Punjabi

A language model provides connection to the decoding process to determine a precise word from several available options in the information base or phrase table. The language model can be generated using n-gram approach. Various language models and smoothing procedures are there to determine this model, like unigram, bigram, trigram, interpolation, backoff language model etc. We have done some experiments with different language models where we have used phrases in place of words as the smallest unit. Experiments have shown that phrase based language model yield more accurate results as compared to simple word based mode. We have also done some experiments with machine translation system where we have used phrase based language model rather than word based model and system yield great improvement.
A Robotic Program that Acquires Concepts and Begins Introspection

This paper firstly describes concept acquisition by a robot, and then explains a robotic program that acquires concepts. Concept acquisition by a robot can be described as follows. Suppose one has given the robot instances of a concept one after another. Then the one gives the robot a beginning part of an instance new to the robot, and sees how the robot behaves. If the robot finishes the rest of the instance, one can say that the robot has acquired the concept. The paper explains our robotic program that shows the behavior just described. The program has a preprogrammed program and components. The preprogrammed program generates programs by assembling components in such a way that the assemblies match inputs to the robot and robotic actions guided by a person. The preprogrammed program generalizes assemblies of components and applies generalized assemblies to a new instance. As a result of the application, the robot, given a beginning part of the instance, finishes the rest of the instance.
Maternal mortality and caesarean delivery: A five year review

Aim: To find out maternal mortality related to caesarean delivery Methods: A review of maternal mortality in Government Medical College Hospital Chandigarh, India was conducted for the last five years. [2003 to 2007]. Results: Maternal mortality ratio was found to be 496.4 per 100,000 live births and of which 23.59% was causally or incidentally related to caesarean delivery. The caesarean delivery rates for the hospital was 29.08% in 2003 and a rising trend to 36.60% in 2007 was also noted. Conclusion: Caesarean remains a good option when rationally indicated; however the maternal and fetal conditions that indicate the operative delivery may be inherently related to mortality and morbidity. Key words: Maternal mortality; maternal mortality ratio; caesarean delivery DOI: 10.3126/njog.v4i1. 3331 Nepal Journal of Obstetrics and Gynaecology June-July 2009; 4(1): 42-45
Application Sessions: Middleware for Application Development Targeting Pervasive Environments

Meeting the demand for dynamic pervasive computing applications requires programming abstractions and development tools tailored to this unique environment. This paper introduces a middleware implementation that is specifically designed for pervasive computing applications and allows developers to express their applications’ requirements in terms of application sessions. These sessions are initially defined by constraints and rules provided by an application, and subsequently managed by the application sessions middleware to present remote resources in a consistent and flexible manner. In this paper, we present the model that underlies application sessions, and we detail the user-defined constraints necessary to create them. Through the use of examples and a description of our prototype implementation, we show how the middleware solution provides a robust, ecient, consistent, and stable interface to application programmers.
An expandable approach for design and personalization of digital, just-in-time adaptive interventions

Abstract Objective We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people’s individual needs, momentary contexts, and psychosocial variables. Materials and Methods We propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions. Results We evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns. Conclusion While the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.
Balancing Accuracy, Efficiency, and Flexibility in Radiation Calculations for Dynamical Models

This paper describes the initial implementation of a new toolbox that seeks to balance accuracy, efficiency, and flexibility in radiation calculations for dynamical models. The toolbox consists of two related code bases: Radiative Transfer for Energetics (RTE), which computes fluxes given a radiative transfer problem defined in terms of optical properties, boundary conditions, and source functions; and RRTM for General circulation model applications—Parallel (RRTMGP), which combines data and algorithms to map a physical description of the gaseous atmosphere into such a radiative transfer problem. The toolbox is an implementation of well‐established ideas, including the use of a k‐distribution to represent the spectral variation of absorption by gases and the use of two‐stream, plane‐parallel methods for solving the radiative transfer equation. The focus is instead on accuracy, by basing the k‐distribution on state‐of‐the‐art spectroscopy and on the sometimes‐conflicting goals of flexibility and efficiency. Flexibility is facilitated by making extensive use of computational objects encompassing code and data, the latter provisioned at runtime and potentially tailored to specific problems. The computational objects provide robust access to a set of high‐efficiency computational kernels that can be adapted to new computational environments. Accuracy is obtained by careful choice of algorithms and through tuning and validation of the k‐distribution against benchmark calculations. Flexibility with respect to the host model implies user responsibility for maps between clouds and aerosols and the radiative transfer problem, although comprehensive examples are provided for clouds.
Lipid emulsion therapy: non-nutritive uses of lipid emulsions in anaesthesia and intensive care

Lipid emulsions were introduced into clinical practice more than five decades ago as a calorically dense, non-glucose-based energy source for parenteral nutrition. Recently, intravenous lipid emulsions have been used as rescue for systemic local anaesthetic toxicity. However, the non-nutritive, therapeutic roles of lipid emulsions have recently expanded. This review considers these newer uses of lipid emulsions as drug administration vehicles, for treatment of lipophilic drug toxicity, and as modifiers of ischaemia-reperfusion injury in the anaesthetic and critical care environments. The potential adverse effects of lipid emulsion administration are also succinctly addressed.
BOUNDARY DETECTION USING STEERABLE FILTERS

There is disclosed an apparatus for manually locking a pilot valve in an "in service" position independent of an actuating, or pilot fluid pressure. The apparatus may be either manually disengaged or will be automatically disengaged whenever the ambient temperature exceeds a predetermined level. The device includes a valve manipulating knob attached to the exterior end of a valving element for manually shifting the element from a normally closed position to the in service position. The knob includes an "L" shaped locking member which conforms to the contour of the knob, and is adapted to pivot towards the valve body and lock the valving element out in the "in service" position. This pivotal member includes an indicating surface visible from the front when the valving element is locked in an "in service" position. The locking member is comprised of a fusible material selected to yield at a predetermined temperature so as to return the valve to an operating mode responsive to the pilot fluid pressure.