nils-herrmann/clean_scirepeval huggingface dataset:
A Highly Sensitive and Quantitative Test Platform for Detection of NSCLC EGFR Mutations in Urine and Plasma
Venue: Journal of thoracic oncology official publication of the International Association for the Study of Lung Cancer
Year: 2016

In approximately 60% of patients with NSCLC who are receiving EGFR tyrosine kinase inhibitors, resistance develops through the acquisition of EGFR T790M mutation. We aimed to demonstrate that a highly sensitive and quantitative next generation sequencing analysis of EGFR mutations from urine and plasma specimens is feasible.  Short footprint mutation enrichment next generation sequencing assays were used to interrogate EGFR activating mutations and the T790M resistance mutation in urine or plasma specimens from patients enrolled in TIGER X (NCT01526928) a phase 1/2 clinical study of rociletinib in previously treated patients with EGFR mutant positive advanced NSCLC.  Of 63 patients, 60 had evaluable tissue specimens. When the tissue result was used as a reference, the sensitivity of EGFR mutation detection in urine was 72% (34 of 47 specimens) for T790M, 75% (12 of 16) for L858R, and 67% (28 of 42) for exon 19 deletions. With specimens that met a recommended volume of 90 to 100 mL, the sensitivity was 93% (13 of 14 specimens) for T790M, 80% (four of five) for L858R, and 83% (10 of 12) for exon 19 deletions. A comparable sensitivity of EGFR mutation detection was observed in plasma: 93% (38 of 41 specimens) for T790M, 100% (17 of 17) for L858R, and 87% (34 of 39) for exon 19 deletions. Together, urine and plasma testing identified 12 additional T790M positive cases that were either undetectable or inadequate by tissue test. In nine patients monitored while receiving treatment with rociletinib, a rapid decrease in urine T790M levels was observed by day 21.  DNA derived from NSCLC tumors can be detected with high sensitivity in urine and plasma, enabling diagnostic detection and monitoring of therapeutic response from these noninvasive "liquid biopsy" samples.
Wireless sensor network for structural health monitoring using System on Chip with Android smartphone
Venue: 2013 IEEE SENSORS
Year: 2013

Critical structures such as aircrafts, bridges, dams and buildings require periodic inspections to ensure safe operation. Reliable inspection of structures can be achieved by combining ultrasound non destructive testing techniques with other sensors (for example, temperature sensor and accelerometers) In this study, we show that adapting wireless embedded systems to the task of structural health monitoring improves inspection productivity, increases mobility, and allows the aggregation of critical data to enhance inspection accuracy. To achieve this objective, we developed a customized system based on Reconfigurable Ultrasonic System on chip Hardware (RUSH) platform. RUSH collects and analyzes ultrasonic data to detect structural flaws such as cracks, voids, or fatigue. The collected data is then transferred through a Bluetooth transceiver to an Android smartphone referred to as Mobile Sensor Data Collector (MSDC) where the data is instantly displayed and forwarded to a central server for expert review over the Internet.
Extreme Learning Machine as a New Framework in Predicting Material Properties Methodology and Comparison
Year: 2008

Material properties are very important in most mechanical engineering computations. Numerous approaches have been proposed to estimate these material properties such as State of Equations, Statistical Regression, and Neural Networks modeling schemes. Unfortunately, accuracy of some of these earlier approaches is often limited. Recently, extreme learning machine has been proposed as a new computational intelligence paradigm in building decision based on both hidden and unhidden information. This paper proposes the extreme learning machine in identify and predict the material properties, such as, the strength of material (gray cast iron) A comparative study on system performance is conducted between extreme learning machine and the most common existing data mining and machine learning modeling techniques, namely, multiple regression, backpropagation neural networks, and radial basis function. Results show that extreme learning machines needs up to four orders of magnitude less training time compared to neural network. The forecasting/classification accuracy of extreme learning machine is also higher than those of existing modeling schemes. For given network architecture, the new computational intelligence scheme does not have any control parameters, such as, stopping criteria, learning rate, learning epoches, etc) to be manually tuned and can be implemented easily.
Creosote bush lignans for human disease treatment and prevention: Perspectives on combination therapy
Venue: Journal of Traditional and Complementary Medicine
Year: 2015

The medicinal properties of the most successful plant in the deserts of the western hemisphere, the creosote bush (Larrea tridentata) are evidenced by the long traditional usage of the plants by the Native Americans Indian tribes in Southwestern North America and the Amerindians from South America. The plant is rich in simple bisphenyl lignans and tricyclic lignans known as cyclolignans. These compounds are responsible for many of the pharmacological activities of extracts of the plants. Some of these activities, namely antiherpes, antioxidant, antifungal, and anti inflammatory, were known a century ago. Only recently have further studies revealed other crucial activities of the same plant molecules as powerful agents against human immunodeficiency virus, human papillomavirus, cancer, neurodegenerative diseases, and symptoms of aging. Molecular mechanisms underlying the antiviral and anticancer activities have been elucidated and involve the inhibition of SP1 dependent gene transcription. This review summarizes the recent findings on creosote bush lignans. We introduce the concept of a cocktail of safe well characterized natural products from the creosote bush that would represent a bridge between oriental herbal medicines and Western drug based therapies.
Angular momentum vector drift model and its parameter identification for high speed rotational sphere
Year: 2020

Rotational sphere with high angular speed is able to maintain a fixed angular momentum vector in inertial space when it is freely supported without disturbing torque. This property allows the sphere being widely used as a star in bottle for two degree angular reference. Angular momentum vector's drift model is the key to guarantee the high precision. In this paper, we deduced the drift pattern based on general 20 coefficients model for the specific orientation of the sphere in servo platform with consideration of case rotation dwell auto compensating device. Our model comprehensively considered sphere's drift error, apparent motion of the earth, platform's alignment error, case assembly error and misalignment angle between case rotating axis and sphere's angular momentum axis. We further deduced the kinematic equation of the platform and constructed Kalman filter to identify the sphere's drift coefficients. Simulation results show that all the drift coefficients can be identified and the residual is less than 1.3'/day. 14 days' real experimental result shows that residual is less than 10'/day and all the coefficients converged within 0.0001deg/h. Both simulation and experiment on the estimation precision confirm that our model is accurate and efficient.
Life cycle oriented low carbon operation models of machinery manufacturing industry
Year: 2015

It is urgent for machinery manufacturing industry to implement low carbon manufacturing to solve the problems of high energy consumption and high carbon emissions. Combining the theory of product life cycle, a system framework of low carbon operation models in machinery manufacturing industry is established, which consists of an objective layer, a strategy layer, a process layer and a supporting layer. In addition, four typical low carbon operation models of machinery manufacturing industry are proposed from the perspectives of low carbon product design, source control, process control and EOL (end of life) product disposal respectively. Finally, the proposed models are illustrated in a case of implementing low carbon manufacturing by a machine tool manufacturer.
Sensor based activity recognition of solitary elderly via stigmergy and two layer framework
Venue: Eng. Appl. Artif. Intell.
Year: 2020

With the acceleration of aging process of population structure, the single resident lifestyle is increasing on account of the high cost of care services and the privacy invasion concern. It is essential to monitor the activities of solitary elderly to find the emergency and lifestyle deviation, as independent life cannot be maintained due to physical or mental problems. The unobtrusive systems are the most preferred choice for the real life long term monitoring, while the camera and wearable devices based systems are not suitable due to the privacy and uncomfortableness, respectively. We propose a novel sensor based activity recognition model based on the two layer multi granularity framework and the emergent paradigm with marker based stigmergy. The stigmergy based marking subsystem builds features by aggregating the context aware information and generating the two dimensional activity pheromone trail. The two layer framework consists of coarse grained and fine grained classification subsystems. The coarse grained subsystem identifies whether the input completed activity segmented by the traditional method is easily confused, and utilizes our generalized segmentation method to increase the inter cluster distance. The fine grained subsystem employs machine learning or deep learning classifiers to realize the activity recognition task. The proposed model is a data driven model based on the information self organization. It does not need sophisticated domain knowledge, and can fully mine the hidden feature structure containing semantically related information and spatio temporal characteristics. The experimental results demonstrate the effectiveness of the proposed method.
The interplay between teachers' approaches to teaching, students' approaches to learning and learning outcomes: a qualitative multi case study
Year: 2018

The interaction between approaches to teaching and students' learning has been the focus of research for more than 20 years. Previous studies concerning approaches to teaching in higher education have identified two broad categories: content and learning focused approaches. Some studies indicate that teachers do not always adopt a consonant teaching or learning focused approach, but might employ elements of both, which results in a dissonant approach. Research on how dissonant approaches to teaching and students' quality of learning are related is scant. This study explored relationships between how teachers' approaches to teaching and undergraduate students' self reported approaches to learning and learning outcomes. The data for this study consisted of 33 semi structured interviews with students from three courses. Interviews were analysed with qualitative content analysis. The results demonstrated that, when the teacher used a consonant learning focused approach to teaching, students' learning outcomes and approaches to learning were of a slightly higher quality than when the teacher employed a dissonant approach to teaching. However, a dissonant approach to teaching did not always result in a lower quality of approaches to learning and learning outcomes.
Mapping green infrastructure based on ecosystem services and ecological networks: A Pan European case study
Year: 2015

Identifying, promoting and preserving a strategically planned green infrastructure (GI) network can provide ecological, economic and social benefits. It has also become a priority for the planning and decision making process in sectors such as conservation, (land) resource efficiency, agriculture, forestry or urban development. In this paper we propose a methodology that can be used to identify and map GI elements at landscape level based on the notions of ecological connectivity, multi functionality of ecosystems and maximisation of benefits both for humans and for natural conservation. Our approach implies, first, the quantification and mapping of the natural capacity to deliver ecosystem services and, secondly, the identification of core habitats and wildlife corridors for biota. All this information is integrated and finally classified in a two level GI network. The methodology is replicable and flexible (it can be tailored to the objectives and priorities of the practitioners) and it can be used at different spatial scales for research, planning or policy implementation. The method is applied in a continental scale analysis covering the EU 27 territory, taking into account the delivery of eight regulating and maintenance ecosystem services and the requirements of large mammals' populations. The best performing areas for ecosystem services and/or natural habitat provision cover 23% of Europe and are classified as the core GI network. Another 16% of the study area with relatively good ecological performance is classified as the subsidiary GI network. There are large differences in the coverage of the GI network among countries ranging from 73% of the territory in Estonia to 6% in Cyprus. A potential application of these results is the implementation of the EU Biodiversity Strategy, assuming that the core GI network might be crucial to maintain biodiversity and natural capital and, thus, should be conserved; while the subsidiary network could be restored to increase both the ecological and social resilience. This kind of GI analysis could be also included in the negotiations of the European Regional Development Funds or the Rural Development Programmes.
Epidemiology and risk factors of asthma chronic obstructive pulmonary disease overlap in low and middle income countries
Venue: The Journal of allergy and clinical immunology
Year: 2019

Asthma chronic obstructive pulmonary disease (COPD) overlap (ACO) represents the confluence of bronchial airway hyperreactivity and chronic airflow limitation and has been described as leading to worse lung function and quality of life than found with either singular disease process.  We aimed to describe the prevalence and risk factors for ACO among adults across 6 low and middle income countries (LMICs)  We compiled cross sectional data for 11,923 participants aged 35 to 92 years from 4 population based studies in 12 settings. We defined COPD as postbronchodilator FEV1/forced vital capacity ratio below the lower limit of normal, asthma as wheeze or medication use in 12 months or self reported physician diagnosis, and ACO as having both.  The prevalence of ACO was 3.8% (0% in rural Puno, Peru, to 7.8% in Matlab, Bangladesh) The odds of having ACO were higher with household exposure to biomass fuel smoke (odds ratio [OR] 1.48; 95% CI, 0.98 2.23) smoking tobacco (OR, 1.28 per 10 pack years; 95% CI, 1.22 1.34) and having primary or less education (OR, 1.35; 95% CI, 1.07 1.70) as compared to nonobstructed nonasthma individuals. ACO was associated with severe obstruction (FEV1 <50; 31.6% of ACO vs 10.9% of COPD alone) and severe spirometric deficits compared with participants with asthma 1.61 z scores FEV1; 95% CI, 1.48 to 1.75) or COPD alone 0.94 z scores; 95% CI, 0.78 to 1.10) Conclusions ACO may be as prevalent and more severe in LMICs than has been reported in high income settings. Exposure to biomass fuel smoke may be an overlooked risk factor, and we favor diagnostic criteria for ACO that include environmental exposures common to LMICs.
Multi robot formation control in obstacle environments
Year: 2019

Multi robot system has wide applications in both civil and military fields, due to its high efficiency for task completion and robustness with respect to robot failures. Formation keeping, which is one of the fundamental tasks for multi robot control, has been extensively investigated in the past decade, resulting in a large variety of methodologies. However, most of the existing approaches are subject to certain limitations when implemented for addressing real formation control problems, mainly due to practical constraints introduced, such as sensor limitations, obstacle and inter robot collision avoidance in unknown environments, connectivity maintenance, unavailability of global pose measurements and so forth. This thesis deals with formation control problem for real robot applications by taking practical constraints into account. First of all, the virtual structure based formation tracking problem is studied, where obstacle and inter robot collision avoidance as well as connectivity maintenance are explicitly considered. To address this problem, a multi region control scheme is proposed, where three regions, including safe region, dangerous region and transition region, are defined. In different regions, priority is given to different control objectives. In safe region where trajectory tracking holds the priority, the proposed control scheme guarantees the bounded reference tracking for each robot. In dangerous region where avoidance control is the main objective, a new bounded potential function is designed to formulate multiple constraints. By introducing a series of transition functions, smooth switching between trajectory tracking and avoidance Nanyang Technological University Singapore
Delay aware fountain codes for video streaming with optimal sampling strategy
Venue: Journal of Communications and Networks
Year: 2019

The explosive demand for online video from smart mobile devices poses unprecedented challenges to delivering high quality of experience (QoE) over wireless networks. Streaming highdefinition video with low delay is difficult mainly due to the stochastic nature of wireless channels and the fluctuating videos bit rate. To address this, we propose a novel delay aware fountain coding (DAF) technique that integrates channel coding and video coding. In this paper, we reveal that the fluctuation of video bit rate can also be exploited to further improve fountain codes for wireless video streaming. Specifically, we develop two coding techniques: the time based sliding window and the optimal window wise sampling strategy. By adaptively selecting the window length and optimally adjusting the sampling pattern according to the ongoing video bit rate, the proposed schemes deliver significantly higher video quality than existing schemes, with low delay and constant data rate. To validate our design, we implement the protocols of DAF, DAF L (a low complexity version) and the existing delay aware video streaming schemes by streaming H.264/AVC and high efficiency video coding (HEVC) standard videos over an 802.11b network on CORE emulation platform. The results show that the decoding ratio of our scheme is 15% to 100% higher than the state of the art techniques.
Riesgos laborales del personal de Enfermeria del Hospital Homero Castanier Crespo Azogues, 2016
Year: 2017

La salud y el trabajo son dos aspectos fundamentales en nuestra vida estan unidos y se influyen mutuamente, el trabajo permite el crecimiento del hombre, asi como tambien puede ocasionar enfermedad y muerte bajo ciertas circunstancias. La exposicion laboral para las enfermeras/os es diversa e incluye: agentes biologicos, quimicos, incompatibilidades ergonomicas, condiciones ambientales y fisicas. La naturaleza del trabajo demanda de una alta manipulacion de materiales corto punzantes, asi como de liquidos organicos potencialmente infecciosos, capaces de transmitir enfermedades tal es el caso de la hepatitis B, C y el VIH.(1) Objetivo: identificar los riesgos laborales del personal de enfermeria del hospital "Homero Castanier Crespo" de la ciudad de Azogues, 2016. Metodologia: se realizo un estudio cuantitativo, descriptivo de corte transversal en un universo finito constituido por 201 personas, Enfermeras y Auxiliares de Enfermeria, durante el periodo febrero abril del 2016. Para la recoleccion de la informacion se aplico un formulario estructurado y validado por las investigadoras. Posteriormente para el analisis de los resultados se utilizo el programa SPSS version 20.0. Resultados: se encontro que el 95 de enfermeras/os son mujeres, el 48.3% son licenciadas, el 39.2 del personal auxiliar de enfermeria ha cursado secundaria, 9.1 de las enfermeras tienen cuarto nivel, 3. 4 son auxiliares de enfermeria, segun los riesgos que mas estan expuestos el 22.2 han sufrido pinchazos con material contaminado, el 1.1% caidas y el 6.8% traumatismos segun el cumplimento de las medidas de proteccion de los riesgos el 73.3% considera que si se cumple el 26.7% considera que no se cumple las medidas de proteccion de los riesgos.  a pesar de que el personal de enfermeria utiliza las prendas y medidas de proteccion, sufren accidentes durante su jornada laboral
Survivable Traffic Grooming Routing and Spectrum Assignment in Flex Grid Optical Network under Dynamic Multicast Traffic
Venue: TENCON 2019 2019 IEEE Region 10 Conference (TENCON)
Year: 2019

Flex grid optical networks provide flexible grid architecture and elastic assignment of spectral resources, unlike fixed grid optical networks. The two prime objectives in this domain are to efficiently use spectral resources and to provide suitable protection against link failures which would cause a huge loss of data. In this study, we propose a heuristic approach to solve the traffic grooming problem integrated with survivability problem under dynamic traffic scenario. The traffic demands are multicast so they are routed through a light tree. The proposed approach performs better than state of the art approach concerning blocking probability and bandwidth blocking probability.
Geographically Resolved Rhythms in Twitter Use Reveal Social Pressures on Daily Activity Patterns
Venue: Current Biology
Year: 2018

Daily rhythms in human physiology and behavior are driven by the interplay of circadian rhythms, environmental cycles, and social schedules. Much research has focused on the mechanism and function of circadian rhythms in constant conditions or in idealized light dark environments. There have been comparatively few studies into how social pressures, such as work and school schedules, affect human activity rhythms day to day and season to season. To address this issue, we analyzed activity on Twitter in >1,500 US counties throughout the 2012 2013 calendar years in 15 min intervals using geographically tagged tweets representing 0.1% of the total population each day. We find that sustained periods of low Twitter activity are correlated with sufficient sleep as measured by conventional surveys. We show that this nighttime lull in Twitter activity is shifted to later times on weekends relative to weekdays, a phenomenon we term "Twitter social jet lag." The magnitude of this social jet lag varies seasonally and geographically with the West Coast experiencing less Twitter social jet lag compared to the Central and Eastern US and is correlated with average commuting schedules and disease risk factors such as obesity. Most counties experience the largest amount of Twitter social jet lag in February and the lowest in June or July. We present evidence that these shifts in weekday activity coincide with relaxed social pressures due to local K 12 school holidays and that the direct seasonal effect of altered day length is comparatively weaker.