nils-herrmann/clean_scirepeval huggingface dataset:
Assessing effective physician patient communication skills: "Are you listening to me, doc?"
Venue: Korean journal of medical education

The education of a physician includes much more than acquiring competence in medical knowledge and technical expertise. Physicians also need to have excellent communication skills in order to communicate with patients. Communication skills form the foundation for a more positive patient provider relationship, leading to greater patient satisfaction and better patient compliance. In the patient's eyes, the ability to communicate well forms a major component of a provider's clinical competence. The ability to communicate effectively with patients can contribute significantly to improved patient outcomes. Because of their importance in the practice of medicine, teaching interviewing and communication skills are a part of the curriculum for medical schools. At the Mayo Medical School, a tool was developed to assist in the assessment of medical students when communicating with patients as they elicit a medical history. This rubric has been very useful for both teaching and assessment, and it may be applicable to other healthcare settings as well. Patients commonly complain that physicians do not listen to them. "Being a good doctor requires not only knowledge and technical skills, but also communication"  Communication skills are not just restricted to talking, but also to listening and nonverbal communication [2] Assessment of the provider's ability to apply communication skills continues to be a concern of medical educators. Developing a positive relationship has historically contributed a great deal to a physician's effectiveness and satisfaction within the profession  Such relationships are crucial for successful medical treatment [4] Good communication skills are a key to establishing these desired relationships, and the inability to display good communication skills has been shown to negatively affect many areas in patient care [5] On the other hand, good communication skills have been shown to improve patient treatment compliance, thus leading to improved patient outcomes  The benefits of good communication skills have been documented in the literature and recognized by physicians as early as Osler [7] who encouraged physicians to "care more particularly for the individual patient than for the special features of the disease." Ludmerer  noted, "For over a century, the goal of medical education has been to produce thinking physicians, scientifically competent, who are sensitive to the emotional as well as the medical condition of the patient." Unfortunately, there is evidence that suggests good communication skills are not universally practiced by physicians
Myeloid derived suppressor cells and tumor escape from immune surveillance
Venue: Seminars in Immunopathology

Tumor progression is known to be supported by chronic inflammatory conditions developed in the tumor microenvironment. It is characterized by the long term secretion of various inflammatory soluble factors (including cytokines, chemokines, growth factors, reactive oxygen and nitrogen species, prostaglandins, etc. and strong leukocyte infiltration. Among leukocytes infiltrating tumors, myeloid derived suppressor cells (MDSCs) represent one of the most important players mediating immunosuppression and supporting tumor escape. These cells can strongly inhibit antitumor immune reactions mediated by T cells and NK cells. Moreover, MDSCs are generated, recruited to the tumor site, and activated not only under the influence of soluble inflammatory mediators but also due to extracellular vesicles (EVs) secreted by tumor cells. EVs play a key role in the formation of MDSCs via the conversion of normal myeloid cells and altering the normal myelopoiesis. In addition, EVs help create a suitable microenvironment for the metastatic process.
The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies
Venue: Human Genomics

BackgroundPrecision medicine in oncology relies on rapid associations between patient specific variations and targeted therapeutic efficacy. Due to the advancement of genomic analysis, a vast literature characterizing cancer associated molecular aberrations and relative therapeutic relevance has been published. However, data are not uniformly reported or readily available, and accessing relevant information in a clinically acceptable time frame is a daunting proposition, hampering connections between patients and appropriate therapeutic options. One important therapeutic avenue for oncology patients is through clinical trials. Accordingly, a global view into the availability of targeted clinical trials would provide insight into strengths and weaknesses and potentially enable research focus. However, data regarding the landscape of clinical trials in oncology is not readily available, and as a result, a comprehensive understanding of clinical trial availability is difficult.ResultsTo support clinical decision making, we have developed a data loader and mapper that connects sequence information from oncology patients to data stored in an in house database, the JAX Clinical Knowledgebase (JAX CKB) which can be queried readily to access comprehensive data for clinical reporting via customized reporting queries. JAX CKB functions as a repository to house expertly curated clinically relevant data surrounding our 358 gene panel, the JAX Cancer Treatment Profile (JAX CTP) and supports annotation of functional significance of molecular variants. Through queries of data housed in JAX CKB, we have analyzed the landscape of clinical trials relevant to our 358 gene targeted sequencing panel to evaluate strengths and weaknesses in current molecular targeting in oncology. Through this analysis, we have identified patient indications, molecular aberrations, and targeted therapy classes that have strong or weak representation in clinical trials.ConclusionsHere, we describe the development and disseminate system methods for associating patient genomic sequence data with clinically relevant information, facilitating interpretation and providing a mechanism for informing therapeutic decision making. Additionally, through customized queries, we have the capability to rapidly analyze the landscape of targeted therapies in clinical trials, enabling a unique view into current therapeutic availability in oncology.
Physical and Mathematical Modeling of Heat Transfer in Intumescent Thermal Protective Coatings Under Radiative Heating

Radiative heating of a metal plate protected by an intumescent coating layer has been studied experimentally and theoretically. Special aspects of physical modeling of intumescent coating heating on a test bench for radiative heating are considered. Heat resistance testing of metal structures is justified. A conjugate mathematical model of radiative conductive heat transfer in an intumesced material layer is proposed. The experimental and calculation data on the metal plate temperature beneath the coating are shown to agree well.
Relaxation of mask design for single shot phase imaging with a coded aperture.
Venue: Applied optics

We present a method of relaxing the conditions of mask design in single shot phase imaging with a coded aperture (SPICA) for extending the applications of SPICA. SPICA, based on compressive sensing, enables the acquisition of wide, high resolution optical complex fields in a single exposure without the need for reference light. In our previous work on SPICA, a coded aperture (CA) was implemented with only amplitude modulation, resulting in a low transmission factor and low light efficiency because of the need for an independent phase retrieval process in the reconstruction. We attempt to alleviate these limitations by adapting a reconstruction algorithm to directly associate the phase retrieval process with a sparsity based reconstruction. With this approach, it is possible to realize SPICA with an amplitude modulation based CA having a high transmission factor, a phase modulation based CA, and a complex amplitude (amplitude and phase) modulation based CA. We verified the effectiveness of these relaxed CA designs numerically and experimentally.
Surveillance of Infectious Diseases by the Sentinel Laboratory Network in Belgium: 30 Years of Continuous Improvement
Venue: PloS one

In 1983 the sentinel laboratory network was established because of the need to describe the epidemiological evolution of infectious diseases. During the study period of 30 years (1983 2013) microbiology laboratories reported on weekly basis the laboratory diagnosed cases for a selection of infectious diseases. This resulted in a large longitudinal laboratory based database allowing to provide trends over time and distribution by person and place. During this period, adaptations to data collection were made due to changes in diagnostic methods and public health priorities, introduction and application of digital revolution, and multiple reorganizations of the laboratories. Since the surveillance network is dynamic, it necessitates a continuous evaluation to ensure that, over time, it continues to be representative of the general epidemiological trends in the country. Secondly the aim is to examine the robustness and stability of this surveillance system. Here we demonstrated that the flexibility of the data collection methodology by the sentinel laboratory network is unique and that adaptations do not affect the capacity of the system to follow trends. Therefore, the surveillance by this network is representative of the current epidemiological situation in Belgium. To our knowledge, no such surveillance network with such a long term follow up and demonstrated stability for multiple infectious diseases in the general population was earlier described. Furthermore, expected trends due to the implementation of vaccination or other events were accurately detected. The collected data obtained from this network allows interesting comparisons with other national and international information sources.
A Methodology for Exploring, Documenting, and Improving Humanitarian Service Learning in the University

Through the use of service learning in higher education, universities hope to both provide real benefit to the partnering community and allow students to develop a greater understanding of course curriculum, their discipline, and their personal positioning within society. Through these educational activities, service learning seeks to engage students in critical thinking processes while simultaneously achieving a greater sense of civic and social responsibility through targeted participation in meaningful community service activities. However, in practice, service learning can take a variety of forms predicated on technical, cultural, societal, and political constraints. Thus, while some work shows positive effects on students' attitudes, social behaviour, and academic performance, less research has demonstrated long term community impact. Nor has much research shown that participation in service learning has a long term impact on students' ethical perspectives and frameworks, and whether those ethical frames carry on to their professional careers. Moreover, as institutions partner with such humanitarian service groups as Engineers Without Borders USA, we know considerably less about the institutional cultures and climates that are developed through such partnerships and how sustainable they are, given those inherent technical, political and cultural limitations. As a first step towards these goals, this paper proposes a methodology for investigating the impacts of service learning activities on both the students and communities involved.
Lean and green in the transport and logistics sector a case study of simultaneous deployment

The transport and logistics sector is of vital importance for the stimulation of trade and hence the economic development of nations. However, over the last few years, this sector has taken central stage in the green agenda due to the negative environmental effects derived from its operations. Several disciplines including operations research and sub areas of supply chain management such as green supply chains, green logistics and reverse logistics have tried to address this problem. However, despite the work undertaken through these disciplines, theoretical or empirical research into the sequential or simultaneous deployment of the lean and green paradigms, particularly, in the road transport and logistics sector is limited. This paper presents a case study where both paradigms have been combined to improve the transport operations of a world leader logistics organisation in the metropolitan area of Monterrey, Mexico. To do this, a systematic methodology and a novel tool called Sustainable Transportation Value Stream Map (STVSM) were proposed. The results obtained from the case study indicate that the concurrent deployment of the green and lean paradigms through such methodology and the STVSM tool is an effective approach to improve both operational efficiency and environmental performance of road transport operations. The paper can be used as a guiding reference for transport and logistics organisations to undertake improvement projects similar to the one presented in this paper. Additionally, this research also intends to stimulate scholarly research into the application of lean and green paradigms in the transport and logistics sector to expand the limited research pursued in this area.
Patient Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: Protocol for a Randomized Study Funded by the US Department of Veterans Affairs Health Services Research and Development Program
Venue: JMIR research protocols

Cognitive behavioral therapy (CBT) is one of the most effective treatments for chronic low back pain. However, only half of Department of Veterans Affairs (VA) patients have access to trained CBT therapists, and program expansion is costly. CBT typically consists of 10 weekly hour long sessions. However, some patients improve after the first few sessions while others need more extensive contact.  We are applying principles from "reinforcement learning" (a field of artificial intelligence or AI) to develop an evidence based, personalized CBT pain management service that automatically adapts to each patient's unique and changing needs (AI CBT) AI CBT uses feedback from patients about their progress in pain related functioning measured daily via pedometer step counts to automatically personalize the intensity and type of patient support. The specific aims of the study are to (1) demonstrate that AI CBT has pain related outcomes equivalent to standard telephone CBT, (2) document that AI CBT achieves these outcomes with more efficient use of clinician resources, and (3) demonstrate the intervention's impact on proximal outcomes associated with treatment response, including program engagement, pain management skill acquisition, and patients' likelihood of dropout.  In total, 320 patients with chronic low back pain will be recruited from 2 VA healthcare systems and randomized to a standard 10 sessions of telephone CBT versus AI CBT. All patients will begin with weekly hour long telephone counseling, but for patients in the AI CBT group, those who demonstrate a significant treatment response will be stepped down through less resource intensive alternatives including: (1) 15 minute contacts with a therapist, and (2) CBT clinician feedback provided via interactive voice response calls (IVR) The AI engine will learn what works best in terms of patients' personally tailored treatment plans based on daily feedback via IVR about their pedometer measured step counts, CBT skill practice, and physical functioning. Outcomes will be measured at 3 and 6 months post recruitment and will include pain related interference, treatment satisfaction, and treatment dropout. Our primary hypothesis is that AI CBT will result in pain related functional outcomes that are at least as good as the standard approach, and that by scaling back the intensity of contact that is not associated with additional gains in pain control, the AI CBT approach will be significantly less costly in terms of therapy time.  The trial is currently in the start up phase. Patient enrollment will begin in the fall of 2016 and results of the trial will be available in the winter of 2019.  This study will evaluate an intervention that increases patients' access to effective CBT pain management services while allowing health systems to maximize program expansion given constrained resources.
Validation of a CFD model for the simulation of heat transfer in a tubes in tank PCM storage unit

A computational fluid dynamics (CFD) model was developed for the simulation of a phase change thermal energy storage process in a 100 l cylindrical tank, horizontally placed. The model is validated with experimental data obtained for the same configuration. The cold storage unit was charged using water as the heat transfer medium, flowing inside a horizontal tube bundle, and the selected phase change material (PCM) was microencapsulated slurry in 45% w/w concentration. The mathematical model is based on the three dimensional transient Navier Stokes equations with nonlinear temperature dependent thermo physical properties of the PCM during the phase change range. These properties were experimentally determined using analytical methods. The governing equations were solved using the ANSYS/FLUENT commercial software package. The mathematical model is validated with experimental data for three different flow rates of the heat transfer fluid during the charging process. Bulk temperature, heat transfer rate and amount of energy stored were used as performance indicators. It was found that the PCM bulk temperatures were predicted within 5% of the experimental data. The results have also shown that the total accumulated energy was within 10% of the observed value, and thus it can be concluded that the model predicts the heat transfer inside the storage system with good accuracy.
Integration of thermal energy storage unit in a metal hydride hydrogen storage tank

In metal hydride hydrogen storage tank, a thermal energy storage unit can be efficiently integrated as it is economical by replacing the use of an external heat source. Hence, a Metal Hydride (MH) bed integrating a Phase Change Material (PCM) as latent heat storage system is appropriately selected and investigated in this study. Heat and mass transfer analysis is made to understand the influence of the bed geometry on the relationship between hydrogen absorption (or desorption) efficiency and heat storage processes. A mathematical model is developed and is used to simulate a few 3D novel configurations of MH PCM system (with spherical shells, hexagonal tubes and cylindrical tubes as phase change heat exchangers) For each of these cases, Mg2Ni alloy is used as a hydride bed and the sodium nitrate (NaNO3) is selected as the heat storage medium (PCM) Also, the effect of aluminum foam for enhancing the heat transfer is analyzed. Moreover, a sensitivity analysis is made on various operating parameters and material properties. Besides showing a thermal coupling between the MH bed and the PCM medium, the computational results indicate a considerable improvement of the thermal efficiency of metal hydride hydrogen storage tank, especially with cylindrical phase change heat exchanger which indicated 58% improvement in filling time over the basic configuration.
What is an Appropriate Temporal Sampling Rate to Record Floating Car Data with a GPS?
Venue: ISPRS Int. J. Geo Inf.

Floating car data (FCD) recorded with the Global Positioning System (GPS) are an important data source for traffic research. However, FCD are subject to error, which can relate either to the accuracy of the recordings (measurement error) or to the temporal rate at which the data are sampled (interpolation error) Both errors affect movement parameters derived from the FCD, such as speed or direction, and consequently influence conclusions drawn about the movement. In this paper we combined recent findings about the autocorrelation of GPS measurement error and well established findings from random walk theory to analyse a set of real world FCD. First, we showed that the measurement error in the FCD was affected by positive autocorrelation. We explained why this is a quality measure of the data. Second, we evaluated four metrics to assess the influence of interpolation error. We found that interpolation error strongly affects the correct interpretation of the car's dynamics (speed, direction) whereas its impact on the path (travelled distance, spatial location) was moderate. Based on these results we gave recommendations for recording of FCD using the GPS. Our recommendations only concern time based sampling, change based, location based or event based sampling are not discussed. The sampling approach minimizes the effects of error on movement parameters while avoiding the collection of redundant information. This is crucial for obtaining reliable results from FCD.
A Neurocognitive Perspective on the Development of Social Decision Making

In this chapter, we review evidence for the hypothesis that developmental changes in cognitive control and perspective taking are crucial in understanding age related changes in social behavior. Studies that have examined the developmental roots of prosocial behavior using experimental economic games show that other oriented concern and a preference for fairness emerge early in development. Continued development of intentionality understanding and strategic behavior in bargaining situations suggest that perspective taking and cognitive control undergo extended development and continue to contribute to changes in social behavior well into adolescence. Functional neuroimaging studies have shown that these behavioral changes are accompanied by an increased recruitment of brain regions implicated in cognitive control (e.g. dorsolateral prefrontal cortex) and perspective taking (e.g. temporoparietal junction) Together these studies show that developmental changes in cognitive control and perspective taking and their underlying neural circuitry are associated with progressively more strategic thinking and an increased incorporation of other's perspectives into social decision making across development.
LS GKM: a new gkm SVM for large scale datasets
Venue: Bioinform.

UNLABELLED gkm SVM is a sequence based method for predicting and detecting the regulatory vocabulary encoded in functional DNA elements, and is a commonly used tool for studying gene regulatory mechanisms. Here we introduce new software, LS GKM, which removes several limitations of our previous releases, enabling training on much larger scale (LS) datasets. LS GKM also provides additional advanced gapped k mer based kernel functions. With these improvements, LS GKM achieves considerably higher accuracy than the original gkm SVM. AVAILABILITY AND IMPLEMENTATION C/C source codes and related scripts are freely available from http:/github.com/Dongwon Lee/lsgkm/ and supported on Linux and Mac OS X. CONTACT dwlee@jhu.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.