nils-herrmann/clean_scirepeval huggingface dataset:
Transcriptomic and behavioural characterisation of a mouse model of burn pain identify the cholecystokinin 2 receptor as an analgesic target
Venue: Molecular pain

Burn injury is a cause of significant mortality and morbidity worldwide and is frequently associated with severe and long lasting pain that remains difficult to manage throughout recovery. We characterised a mouse model of burn induced pain using pharmacological and transcriptomic approaches. Mechanical allodynia elicited by burn injury was partially reversed by meloxicam (5 mg/kg) gabapentin (100 mg/kg) and oxycodone (3 and 10 mg/kg) while thermal allodynia and gait abnormalities were only significantly improved by amitriptyline (3 mg/kg) and oxycodone (10 mg/kg) The need for relatively high opioid doses to elicit analgesia suggested a degree of opioid resistance, similar to that shown clinically in burn patients. We thus assessed the gene expression changes in dorsal root ganglion neurons and pathophysiological mechanisms underpinning burn injury induced pain using a transcriptomic approach. Burn injury was associated with significantly increased expression of genes associated with axon guidance, neuropeptide signalling, behavioural defence response and extracellular signalling, confirming a mixed neuropathic and inflammatory aetiology. Notably, among the pain related genes that were upregulated post injury was the cholecystokinin 2 receptor (Cckbr) a G protein coupled receptor known as a pain target involved in reducing opioid effectiveness. Indeed, the clinically used cholecystokinin receptor antagonist proglumide (30 mg/kg) was effective at reversing mechanical allodynia, with additional analgesia evident in combination with low dose oxycodone (1 mg/kg) including significant reversal of thermal allodynia. These findings highlight the complex pathophysiological mechanisms underpinning burn injury induced pain and suggest that cholecystokinin 2 receptor antagonists may be useful clinically as adjuvants to decrease opioid requirements and improve analgesic management.
Internet of things network management system architecture for smart healthcare
Venue: 2016 Sixth International Conference on Digital Information and Communication Technology and its Applications (DICTAP)

The fast and wide advancement of the Internet of Things (IoT) boosted by the remarkable development of sensor networks, RFID, software platforms and the Internet as a global container of IoT, have opened the appetite of several vertical industries to include IoT in their strategic vision for the coming years. Academic institutions have also established projects to vigorously investigate the potential of IoT and develop new protocols in all layers to suit IoT applications and/or adapt the current IP protocols to suit it. Some organizations have already implemented IoT and others have plans to implement it according to Gartner Inc. study. Healthcare industry is no exception, have already adopted IoT, and are planning to use it extensively in the following years for the benefits of patients, elderly people and caregivers. In this research paper, we develop a system architecture to manage IoT for the smart healthcare. For IoT in healthcare to succeed, it should be reliable, effective, well performing, secure and profitable for caregivers. This will guarantee continued operation and service delivery for patients and elderly to avoid any health risks.
Hardware oriented Approximation of Convolutional Neural Networks
Venue: ArXiv

High computational complexity hinders the widespread usage of Convolutional Neural Networks (CNNs) especially in mobile devices. Hardware accelerators are arguably the most promising approach for reducing both execution time and power consumption. One of the most important steps in accelerator development is hardware oriented model approximation. In this paper we present Ristretto, a model approximation framework that analyzes a given CNN with respect to numerical resolution used in representing weights and outputs of convolutional and fully connected layers. Ristretto can condense models by using fixed point arithmetic and representation instead of floating point. Moreover, Ristretto fine tunes the resulting fixed point network. Given a maximum error tolerance of 1% Ristretto can successfully condense CaffeNet and SqueezeNet to 8 bit. The code for Ristretto is available.
Ristretto: Hardware Oriented Approximation of Convolutional Neural Networks
Venue: ArXiv

Convolutional neural networks (CNN) have achieved major breakthroughs in recent years. Their performance in computer vision have matched and in some areas even surpassed human capabilities. Deep neural networks can capture complex non linear features; however this ability comes at the cost of high computational and memory requirements. State of art networks require billions of arithmetic operations and millions of parameters. To enable embedded devices such as smartphones, Google glasses and monitoring cameras with the astonishing power of deep learning, dedicated hardware accelerators can be used to decrease both execution time and power consumption. In applications where fast connection to the cloud is not guaranteed or where privacy is important, computation needs to be done locally. Many hardware accelerators for deep neural networks have been proposed recently. A first important step of accelerator design is hardware oriented approximation of deep networks, which enables energy efficient inference. We present Ristretto, a fast and automated framework for CNN approximation. Ristretto simulates the hardware arithmetic of a custom hardware accelerator. The framework reduces the bit width of network parameters and outputs of resource intense layers, which reduces the chip area for multiplication units significantly. Alternatively, Ristretto can remove the need for multipliers altogether, resulting in an adder only arithmetic. The tool fine tunes trimmed networks to achieve high classification accuracy. Since training of deep neural networks can be time consuming, Ristretto uses highly optimized routines which run on the GPU. This enables fast compression of any given network. Given a maximum tolerance of 1% Ristretto can successfully condense CaffeNet and SqueezeNet to 8 bit. The code for Ristretto is available.
DSCNN: Hardware oriented optimization for Stochastic Computing based Deep Convolutional Neural Networks
Venue: 2016 IEEE 34th International Conference on Computer Design (ICCD)

Deep Convolutional Neural Networks (DCNN) a branch of Deep Neural Networks which use the deep graph with multiple processing layers, enables the convolutional model to finely abstract the high level features behind an image. Large scale applications using DCNN mainly operate in high performance server clusters, GPUs or FPGA clusters; it is restricted to extend the applications onto mobile/wearable devices and Internet of Things (IoT) entities due to high power/energy consumption. Stochastic Computing is a promising method to overcome this shortcoming used in specific hardware based systems. Many complex arithmetic operations can be implemented with very simple hardware logic in the SC framework, which alleviates the extensive computation complexity. The exploration of network wise optimization and the revision of network structure with respect to stochastic computing based hardware design have not been discussed in previous work. In this paper, we investigate Deep Stochastic Convolutional Neural Network (DSCNN) for DCNN using stochastic computing. The essential calculation components using SC are designed and evaluated. We propose a joint optimization method to collaborate components guaranteeing a high calculation accuracy in each stage of the network. The structure of original DSCNN is revised to accommodate SC hardware design's simplicity. Experimental Results show that as opposed to software inspired feature extraction block in DSCNN, an optimized hardware oriented feature extraction block achieves as higher as 59.27% calculation precision. And the optimized DSCNN can achieve only 3.48% network test error rate compared to 27.83% for baseline DSCNN using software inspired feature extraction block.
Craft vs. industrial: Habits, attitudes and motivations towards beer consumption in Mexico
Venue: Appetite

Food choices tend to be stable over time; they do not change fast, since consumers tend to act like creatures of habits. However, food habits can evolve, like currently the craft beer category. A change of habits involves a change of perception towards a product. Therefore, what is changing in the perception of beer? Two studies were conducted to address this question. First study was preliminary and aimed at exploring beer consumption habits in Mexico and a better understanding of craft beer representation among beer users. A questionnaire was administrated to 207 consumers in Mexico City during a beer festival. Results showed that respondents could be classified in: industrial beer (41.1% occasional industrial (24.1% and craft beer (34.8% consumers. Craft cluster included mostly 25 35 years old men with high income level. Among the craft beers cited by respondents from this cluster some are industrial, suggesting that the concept of craft beer might not be well defined, or defined in ideological terms. The second and main study was conducted using consumer ethnographies to understand the motivations and benefits of craft beer consumption. Opposite to industrial, craft beer emerges as an experience based and symbolic product rather than a utilitarian one. The main motivation for drinking craft beer seems to be the quest of authenticity. Respondents' motivations to drink craft beer are generated by three important factors: desire for more knowledge, new taste experiences, and move away from the mainstream beer consumption. Craft consumers do not drink the product for its functional attributes, they consume it for what it means and as a consequence they build an identity, perceived as more authentic and unique, in comparison to the mainstream industrial beer consumption in Mexico.
A systematical evaluation of polynomial based equivalent circuit model for charge redistribution dominated self discharge process in supercapacitors

Accurate modeling of the charge redistribution dominated self discharge process plays a significant role in power management systems for supercapacitors. Although equivalent circuit models (ECMs) are widely used to describe the nonlinear behaviors of the self discharge process, they are usually separately established at different initial voltages, which might result in poor prediction performances at other unseen initial voltages. In this study, a three branch model with a leakage resistance is used to describe the nonlinear dynamic behavior of the supercapacitor self discharge dominated by charge redistribution and the circuit parameters in ECMs are explicitly modeled as a function of the initial voltage. Polynomial functions with different orders are systematically evaluated by means of fitting and prediction accuracy. The impacts of initial voltage and temperature on the charge redistribution dominated self discharge process are experimentally investigated with a 3000 F commercial supercapacitor. The modeling results show that a 5th order polynomial function is sufficient high enough to characterize the nonlinear effect of initial voltage on the charge redistribution dominated self discharge in terms of prediction accuracy. Moreover, the prediction accuracy of polynomial function based ECMs are significantly better than that of interpolation based ECMs, which further validates the effectiveness of the proposed model.
Emergence of plasmid mediated colistin resistance mechanism MCR 1 in animals and human beings in China: a microbiological and molecular biological study.
Venue: The Lancet. Infectious diseases

Until now, polymyxin resistance has involved chromosomal mutations but has never been reported via horizontal gene transfer. During a routine surveillance project on antimicrobial resistance in commensal Escherichia coli from food animals in China, a major increase of colistin resistance was observed. When an E coli strain, SHP45, possessing colistin resistance that could be transferred to another strain, was isolated from a pig, we conducted further analysis of possible plasmid mediated polymyxin resistance. Herein, we report the emergence of the first plasmid mediated polymyxin resistance mechanism, MCR 1, in Enterobacteriaceae.  The mcr 1 gene in E coli strain SHP45 was identified by whole plasmid sequencing and subcloning. MCR 1 mechanistic studies were done with sequence comparisons, homology modelling, and electrospray ionisation mass spectrometry. The prevalence of mcr 1 was investigated in E coli and Klebsiella pneumoniae strains collected from five provinces between April, 2011, and November, 2014. The ability of MCR 1 to confer polymyxin resistance in vivo was examined in a murine thigh model.  Polymyxin resistance was shown to be singularly due to the plasmid mediated mcr 1 gene. The plasmid carrying mcr 1 was mobilised to an E coli recipient at a frequency of 10( 1) to 10( 3) cells per recipient cell by conjugation, and maintained in K pneumoniae and Pseudomonas aeruginosa. In an in vivo model, production of MCR 1 negated the efficacy of colistin. MCR 1 is a member of the phosphoethanolamine transferase enzyme family, with expression in E coli resulting in the addition of phosphoethanolamine to lipid A. We observed mcr 1 carriage in E coli isolates collected from 78 (15% of 523 samples of raw meat and 166 (21% of 804 animals during 2011 14, and 16 (1% of 1322 samples from inpatients with infection.  The emergence of MCR 1 heralds the breach of the last group of antibiotics, polymyxins, by plasmid mediated resistance. Although currently confined to China, MCR 1 is likely to emulate other global resistance mechanisms such as NDM 1. Our findings emphasise the urgent need for coordinated global action in the fight against pan drug resistant Gram negative bacteria.
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Venue: IEEE Transactions on Medical Imaging

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine tune a CNN that has been pre trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre trained deep CNNs with sufficient fine tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre trained CNNs fine tuned in a layer wise manner. Our experiments consistently demonstrated that 1) the use of a pre trained CNN with adequate fine tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer wise fine tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
Venue: IEEE Trans. Medical Imaging

The papers in this special section focus on the technology and applications supported by deep learning. Deep learning is a growing trend in general data analysis and has been termed one of the 10 breakthrough technologies of 2013. Deep learning is an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data. To date, it is emerging as the leading machine learning tool in the general imaging and computer vision domains. In particular, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. Deep CNNs automatically learn mid level and high level abstractions obtained from raw data (e.g. images) Recent results indicate that the generic descriptors extracted from CNNs are extremely effective in object recognition and localization in natural images. Medical image analysis groups across the world are quickly entering the field and applying CNNs and other deep learning methodologies to a wide variety of applications.
Confined linear carbon chains as a route to bulk carbyne.
Venue: Nature materials

Strong chemical activity and extreme instability in ambient conditions characterize carbyne, an infinite sp(1) hybridized carbon chain. As a result, much less has been explored about carbyne as compared to other carbon allotropes such as fullerenes, nanotubes and graphene. Although end capping groups can be used to stabilize carbon chains, length limitations are still a barrier for production, and even more so for application. We report a method for the bulk production of long acetylenic linear carbon chains protected by thin double walled carbon nanotubes. The synthesis of very long arrangements is confirmed by a combination of transmission electron microscopy, X ray diffraction and (near field) resonance Raman spectroscopy. Our results establish a route for the bulk production of exceptionally long and stable chains composed of more than 6,000 carbon atoms, representing an elegant forerunner towards the final goal of carbyne's bulk production.
The Effects of Physical Exercises on Ocular Physiology: A Review
Venue: Journal of glaucoma

Sport has been known to be one of the most important factors in preventing cardiovascular disorders; some studies suggest its role in preventing neurodegenerative diseases. This review discusses the results of various studies regarding the effects of physical exercises on intraocular pressure (IOP) myopia, certain physical parameters of the eye, ocular blood flow, and retinal electrical function. Although dynamic exercises are known to reduce IOP from 1.2 to 5.9 mm Hg only for a short period of time, uncertainty persists about whether isometric exercises or activities such as yoga can increase IOP up to 16.7 mm Hg. There has been an established connection between the time being spent outdoor and reduction in the odds of myopia 2% less odds for every hour spent outdoor. Physical activity and the level of physical fitness have an impact on the changes of b wave electroretinogram and P100. Physical exercises increase perfusion pressure up to 190% baseline and also increase choroidal blood flow up to 140% thus providing more blood to retina. Sport induced change on visual field is a subject of controversy. Majority of patients show a better ocular physiological function due to sports and sports are thus considered essential for preventing common ocular diseases. Further research should focus more on long term effects of sport induced changes in ocular physiology and newly discovered techniques may be utilized for such purposes.
Visa and Immigration Trends: A Comparative Examination of International Student Mobility in Canada, Australia, the United Kingdom, and the United States

This comparative study examines visa and immigration regulations for international students and focuses on macro level policy trends during the past five to ten years in four English speaking countries: Canada, Australia, the United Kingdom, and the United States. The study highlights the trends and motivations to recruit international graduate students as valuable skilled labor and potential designer immigrants in order to compete in the global economic market. Based on the analysis of policy documents and secondary data, it was found that there is a weak connection between higher education institutions and national security or customs and border protection agencies and the role that they play on international education. Also, the ease and expense of obtaining a student visa greatly influences international student enrollment trends.
Lithospheric structure beneath NW Iran using regional and teleseismic travel time tomography

We compute a 2 D tomogram using the P wave arrival time readings from a temporary seismic experiment to study the seismic structure of the crust and upper mantle in NW Iran. The study area includes the western margins of the South Caspian Basin (SCB) and the Sahand and Sabalan post collisional volcanoes in NW Iran. We invert 2780 regional and teleseismic relative P wave arrival times recorded by 23 stations along the seismic profile extending from the western shoreline of the Caspian Sea to Lake Urumieh. Our tomographic results show a higher velocity region beneath the SCB. The observed higher velocities strongly correlate with the observed positive gravity anomalies over the southwestern margins of the Caspian Sea, suggesting an oceanic like nature for the SCB lithosphere. The tomographic results also show several lower velocity anomalies in the crust. The Sabalan volcano is underlain by a low velocity zone in the lower crust, which is most likely thermal in nature. In the Sahand region, the lower velocities are considerably shallower in depth and might be controlled by shallow sedimentary structures, as well as an anomalously warm upper crust. The shallow low velocity regions are connected with deeper low velocity zones 60 100 km deep in the upper mantle. This pattern points to a possible mantle source of post collisional volcanism in NW Iran, i.e. the melting of a subducted slab.
Progression and Issues in the Mesoamerican Geospatial Revolution
Venue: Advances in Archaeological Practice

The use of airborne mapping lidar (Light Detection and Ranging) a.k.a airborne laser scanning (ALS) has had a major impact on archaeological research being carried out in Mesoamerica. Since being introduced in 2009, mapping lidar has revolutionized the spatial parameters of Mesoamerican, and especially Maya, archaeology by permitting the recovery of a complete landscape and settlement pattern for further analysis. However, like any new technology, there are learning curves to be overcome, resulting in a feedback relationship between the on the ground archaeologists, the virtually grounded computer analysts, and the instrument designers. Archaeologists have been able to identify problems and issues with data production and visualization for the determination of archaeological remains caused by vegetation, special terrain conditions, and modern disturbance. The identification of these concerns helps the technician to develop new techniques, especially when working in conjunction with the field researcher. As seen through the papers in this volume, this symbiotic relationship promises to yield both new breakthroughs in landscape and settlement analysis for Mesoamerican archaeology and enhanced analytic and visualization techniques for lidar with the potential for applicability in other contexts. In many regards, the development of lidar has parallels to the development of radiocarbon dating as a revolutionary technology.