nils-herrmann/clean_scirepeval huggingface dataset:
Glacial origin of the Cryogenian Nantuo Formation in eastern Shennongjia area (South China) Implications for macroalgal survival
Year: 2020

The Cryogenian Period was characterized by global glaciations, imposing extreme environmental stresses on early multicellular organisms. Globally distributed diamictite deposits are the major evidence for these glaciation events, but the glacial origin of at least some of these diamictite deposits has been disputed, complicating interpretations of the environmental conditions influencing the evolution of life in the Cryogenian ~720 635 Ma) In this study, we conducted a systematic sedimentological investigation of the Marinoan age ~650 635 Ma) Nantuo Formation in six sections representing a ~20 km long, east west transect in the eastern Shennongjia area of South China. The Nantuo Formation, which ranges in thickness from ~2 m in the east to ~300 m in the west, comprises diamictite, conglomerate, lonestone bearing sandstone, green siltstone, and black shale facies. The eastern sections (e.g. Longxi and Wushanhu) consist mainly of diamictite facies exhibiting micro scaled rotation structures, microshear, grain stacking, grain to grain contacts and water escape structures, supporting their deposition in a subglacial setting. In contrast, the western sections (Shijiahe, Liziping and Songluo) consist of massive and graded diamictite and conglomerate facies, consistent with an origin as glacially induced submarine debris flows. Furthermore, the lonestone bearing sandstone facies in these sections contain abundant dropstones and mud pellets within finely laminated turbidites, indicating an ice rafted origin with locally reworking by turbidity currents. The green siltstone and black shale facies lack glacigenic features and may represent late deglacial deposits. Based on thickness and facies variations among the study sections, we infer that the Nantuo Formation records glacial sedimentation on a complex paleotopographic surface that was incised into the underlying bedrock, with subglacial deposits to the east transitioning into glaciomarine deposits to the west of study area. The distal Songluo section likely records three cycles of glacial advance and retreat in the form of a facies alternation between diamictite and black shale, consistent with a dynamically fluctuating ice margin. In summary, our sedimentological investigation supports a glacial origin for the Nantuo Formation in the eastern Shennongjia area. The occurrence of the benthic macroalgal dominated Songluo Biota in the black shale facies at Songluo suggests that strong glacial retreats provided niches for macroalgal survival in the Cryogenian glacial ocean.
NEMO Neuro Evolution with Multiobjective Optimization of Deep Neural Network for Speed and Accuracy
Year: 2017

This paper proposes automatic machine learning approach (AutoML) of deep neural networks (DNNs) using multi objective evolutionary algorithms (MOEAs) for the accuracy and run time speed simultaneously. Traditional methods for optimizing DNNs have used Bayesian reasoning or reinforcement learning to improve the performance. Recently, evolutionary approaches for high accuracy has been adopted using a lot of GPUs. However, real world applications require rapid inference to deploy on embedded devices while maintaining high accuracy. To consider both accuracy and speed at the same time, we propose Neuro Evolution with Multiobjective Optimization (NEMO) algorithm. Experimental results show that proposed NEMO finds faster and more accurate DNNs than hand crafted DNN architectures. The proposed technique is verified for the image classification problems such as MNIST, CIFAR 10 and human status recognition.
Follow up outcomes after the frozen elephant trunk technique in chronic type B dissection.
Venue: European journal of cardio thoracic surgery official journal of the European Association for Cardio thoracic Surgery
Year: 2020

Our goal was to present our experience with a hybrid approach to the frozen elephant trunk (FET) technique for the treatment of patients with chronic type B aortic dissection.  Between January 2013 and July 2019, 86 patients underwent the FET procedure at our centre. In 20 patients, the indication was chronic type B aortic dissection with a concomitant proximal aortic lesion. We evaluated the sites of proximal and distal entries, luminal communication and originating visceral branches in the computed tomography scan data. Primary end points were hospital deaths, complications and follow up survival. Secondary end points included reintervention, thrombosis of the false lumen and aortic remodelling.  There were no deaths, neurological complications or paraplegia during hospitalization; however, a few patients (10% had temporary acute renal failure or required secondary aortic reintervention during the follow up period. We performed thoracic endovascular aortic repair with stable aortic remodelling during follow up. The follow up survival rate was 92.3% and 87.5% of cases did not require aortic reintervention.  The FET technique is an effective method for treating chronic Stanford type B aortic dissection in patients at high risk of retrograde type A aortic dissection, concomitant disease of the proximal aorta and unsuitable anatomy for thoracic endovascular aortic repair, which allows for single stage radical correction. Compared with thoracic endovascular aortic repair, the FET technique excludes the risk of type Ia endoleak, retrograde type A aortic dissection and possible stent graft migration. This technique provides comparable midterm follow up outcomes and freedom from reintervention.
Detection and prevention of assembly defects, by machine learning algorithms, in semiconductor industry for automotive
Venue: 2020 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)
Year: 2020

Years of experience within semiconductor manufacturing facilities have led to optimize processes to serve both quality and cost. The solution to achieve next generational levels requires a new approach: this one is fitting with implementation of advanced analytics and machine learning algorithms. Applied to manufacturing data which corresponds with a real big data context, these algorithms can provide insights and automate responses to detect, prevent and ultimately eliminate the most severe failure modes. The project described in this paper targets a wafer sawing process. Various challenges that are raised in such a project are of different natures. A first one is the need for a high level of technical expertise in the manufacturing process of focus: this is essential to define the meaningful dataset that represents comprehensively the desired output of the process. Another component is the data collection aspect: many data have to be collected, stored and parsed, and some small signals found will become the leading indicator to an upcoming process degradation and capability of capturing them is essential. Another key data is traceability of the processed material. Additionally, ensuring an informatic technology architecture to support collection, storage, parsing and computation of the datasets is a significant challenge. Lastly, project success is related to the data scientist expertise to build adequate machine learning algorithms. Optimization of the models can take several iterations with back and forth communication between data scientists and process technical experts. This paper describes issues revealed, some solutions found, and future expectations.
A Meta Analysis of Cytokines in Major Depression
Venue: Biological Psychiatry
Year: 2010

Major depression occurs in 4.4% to 20% of the general population. Studies suggest that major depression is accompanied by immune dysregulation and activation of the inflammatory response system (IRS) Our objective was to quantitatively summarize the data on concentrations of specific cytokines in patients diagnosed with a major depressive episode and controls.  We performed a meta analysis of studies measuring cytokine concentration in patients with major depression, with a database search of the English literature (to August 2009) and a manual search of references.  Twenty four studies involving unstimulated measurements of cytokines in patients meeting DSM criteria for major depression were included in the meta analysis; 13 for tumor necrosis factor (TNF) alpha, 9 for interleukin (IL) 1beta, 16 for IL 6, 5 for IL 4, 5 for IL 2, 4 for IL 8, 6 for IL 10, and 4 for interferon (IFN) gamma. There were significantly higher concentrations of TNF alpha (p .00001) weighted mean difference (WMD) (95% confidence interval) 3.97 pg/mL (2.24 to 5.71) in depressed subjects compared with control subjects (438 depressed/350 nondepressed) Also, IL 6 concentrations were significantly higher (p .00001) in depressed subjects compared with control subjects (492 depressed/400 nondepressed) with an overall WMD of 1.78 pg/mL (1.23 to 2.33) There were no significant differences among depressed and nondepressed subjects for the other cytokines studied.  This meta analysis reports significantly higher concentrations of the proinflammatory cytokines TNF alpha and IL 6 in depressed subjects compared with control subjects. While both positive and negative results have been reported in individual studies, this meta analytic result strengthens evidence that depression is accompanied by activation of the IRS.
From W Net to CDGAN: Bitemporal Change Detection via Deep Learning Techniques
Venue: IEEE Transactions on Geoscience and Remote Sensing
Year: 2020

Traditional change detection methods usually follow the image differencing, change feature extraction, and classification framework, and their performance is limited by such simple image domain differencing and also the hand crafted features. Recently, the success of deep convolutional neural networks (CNNs) has widely spread across the whole field of computer vision for their powerful representation abilities. Therefore, in this article, we address the remote sensing image change detection problem with deep learning techniques. We first propose an end to end dual branch architecture, termed the W Net, with each branch taking as input one of the two bitemporal images as in the traditional change detection models. In this way, CNN features with more powerful representative abilities can be obtained to boost the final detection performance. In addition, W Net performs differencing in the feature domain rather than in the traditional image domain, which greatly alleviates loss of useful information for determining the changes. Furthermore, by reformulating change detection as an image translation problem, we apply the recently popular generative adversarial network (GAN) in which our W Net serves as the generator, leading to a new GAN architecture for change detection which we call CDGAN. To train our networks and also facilitate future research, we construct a large scale data set by collecting images from Google Earth and provide carefully manually annotated ground truths. Experiments show that our proposed methods can provide fine grained change detection results superior to the existing state of the art baselines.
Tramadol, naloxone, and seizures
Venue: Clinical toxicology
Year: 2019

We read with great interest the article by Murray, et al. titled "Seizures in Tramadol Overdoses Reported in the ToxIC Registry: Predisposing Factors and the Role of Naloxone" and would like to congratulate them for their work in identifying patients at increased risk for seizure activity in a cohort of patients reported to the ToxIC registry  We would like to provide further data from an alternative database, a Regional Poison Center (RPC) which is part of the National Poison Data System (NPDS) to highlight the variability in reported complication rates by database and the inconsistent association of naloxone administration, response to naloxone, and the development of tramadolrelated seizures. We retrospectively reviewed all RPC cases involving tramadol exposure from January 1, 2014 through December 31, 2015. Of the 742 cases reported during this period, 50 (6.7% were associated with seizures, all of which were self limited. This is in stark contrast to the 52.5% seizure rate reported in the present study and more consistent with previous studies utilizing inpatient admission units [2] and the NPDS [3] Naloxone was administered in 93 (12.5% RPC cases with 9 (9.7% involving seizures after administration. One case involved a seizure 5 hours post naloxone administration and another involved a seizure 1 day after discontinuation of a naloxone infusion. Removal of these cases without temporal feasibility revealed a 7.5% seizure rate which was consistent with the baseline rate of 6.7% Of patients receiving naloxone, 23 (24.7% had a documented beneficial response but it is unclear how many, if any, intubations were prevented by its administration. Our data highlight the variability when utilizing different registries such as the ToxIC database and a RPC in evaluating for rates of complications following substance exposures. Because the ToxIC registry involves reporting of cases evaluated at bedside by Medical Toxicologists, these cases often involve more severe illness, as the authors state, and a likely higher rate of complications than those reported to RPCs. Despite the difference in seizure rates, our data corroborate the authors' conclusions regarding the use of naloxone and the development of seizures. Our results indicate no association between the use of naloxone and the development of seizures. In fact, 84 (90.3% patients in our study were administered naloxone and had no subsequent seizure activity. Patients overdosing on tramadol may have seizures regardless of naloxone administration. Finally, we noted an inconsistent beneficial response to naloxone in our data. Given that these data do not support an increased rate of seizures when naloxone is utilized in the treatment tramadol exposures, we advocate for a toxidrome based approach when deciding whether to administer naloxone in suspected tramadol poisoning with a goal to restore respiratory function.
Ebusiness among ethnic minority businesses ethnic entrepreneurs' ICT adoption and readiness.
Year: 2012

A small but growing body of evidence (SBS, 2004; Beckinsale Ram, 2006) has indicated that Ethnic Minority Businesses (EMBs) have not adopted Information Communication Technology (ICT) at comparable rates to their non EMB counterparts, predominantly Small and Medium Sized Enterprises (SMEs) With EMBs accounting for almost 10% of businesses in the UK, the economic impact as ICT adoption continues to further develop across mainstream markets could be highly significant. Existing UK ICT policies also failed to engage with EMBs until the NW ICT Adoption Pilot in 2004. The current, limited body of research is fragmented, provides limited understanding and coherence on reasons of low ICT adoption, and lacks exemplars upon which policy considerations may be made. Firstly, the chapter will examine and review the existing body of literature. Secondly, EMB cases that have developed ICT to a degree where they are engaging in e business activity are statically and dynamically analysed and discussed. The findings provide a number of options and guidance for EMB owners. Finally, the recommendations point to the need for improved ICT awareness, better business support provision nationally, and the importance of generation and education as key drivers.  10.4018/978 1 60960 597 1.ch009
Gas solid two phase flow and erosion calculation of gate valve based on the CFD DEM model
Year: 2020

Gate valves are important pipeline components in pneumatic conveying systems, and they were eroded severely by the impact of solid particles. In this study, the CFD DEM simulation method was adopted to study the gas solid two phase flow characteristics and erosion characteristics of the gate valve. The accuracy of the simulation was validated by comparing the simulation and experimental data. Results show that a high speed jet flow appears under the flashboard and induces a free shear layer, and the free shear layer attaches at the rear wall of the cavity. Particle distribution shows that the particle number at the upstream of the flashboard increases with decreasing valve opening. The particle number inside the cavity has a similar trend. The erosion results indicate that the number of particles plays an important role in erosion. The range of the erosion gradient region and the maximum erosion rate decrease with increasing particle size. Erosion of the cavity is more serious than that of the flashboard.
Color emotion associations in interiors
Venue: Color Research Application
Year: 2019

Correspondence Elif Gunes, Department of Interior Architecture and Environmental Design, Faculty of Art, Design and Architecture, Atilim University, 06836 _ Incek, Golbasi, Ankara, Turkey.  Participants first watched a short video of a 3D model of a living room. Next, they were asked to match the living rooms with facial expressions of six basic emotions. The most stated emotions associated for the red room were disgust and happiness, while the least stated emotions were sadness, fear, anger, and surprise; for the green room, neutral and happiness were the most stated emotions, and anger, surprise, fear, and sadness were the least stated ones; for the blue room, neutral was the most stated emotion, while the least stated emotions were anger and surprise. Neutral, disgust, and sadness were the most stated emotions for the gray room. Gender differences were not found in human emotional reactions to living rooms with different wall colors.
Repurpose Open Data to Discover Therapeutics for COVID 19 Using Deep Learning
Venue: Journal of proteome research
Year: 2020

There have been more than 2.2 million confirmed cases and over 120 000 deaths from the human coronavirus disease 2019 (COVID 19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS CoV 2) in the United States alone. However, there is currently a lack of proven effective medications against COVID 19. Drug repurposing offers a promising route for the development of prevention and treatment strategies for COVID 19. This study reports an integrative, network based deep learning methodology to identify repurposable drugs for COVID 19 (termed CoV KGE) Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed publications. Using Amazon's AWS computing resources and a network based, deep learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID 19 were validated by transcriptomic and proteomics data in SARS CoV 2 infected human cells and data from ongoing clinical trials. Whereas this study by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential to accelerate therapeutic development for COVID 19.
bei jing hai dian qu wu dao kou pian qu yong di bu ju yu jiao tong xie diao fa zhan de tan tao
Year: 2016

Ben Wen Tong Guo Dui Bei Jing Shi Hai Dian Qu Wu Dao Kou Pian Qu De Li Lun Yan Jiu ,Yong Shi Di Diao Cha Shu Ju Fen Xi Deng Fang Shi ,Tan Tao Liao Yong Di Bu Ju He Jiao Tong Xie Diao Fa Zhan De Fang Fa .Tong Shi Yi Qing Hua Dong Lu Xi Kou Wei Li ,Fen Xi Liao Yong Di Bu Ju He Jiao Tong De Bu Xie Diao Fa Zhan ,De Chu Liao Xu Jun Heng Yong Di Jie Gou He Hun He Xing Bu Ju De Jie Lun
Cell free DNA (cfDNA) Clinical Significance and Utility in Cancer Shaped By Emerging Technologies
Venue: Molecular Cancer Research
Year: 2016

Precision oncology is predicated upon the ability to detect specific actionable genomic alterations and to monitor their adaptive evolution during treatment to counter resistance. Because of spatial and temporal heterogeneity and comorbidities associated with obtaining tumor tissues, especially in the case of metastatic disease, traditional methods for tumor sampling are impractical for this application. Known to be present in the blood of cancer patients for decades, cell free DNA (cfDNA) is beginning to inform on tumor genetics, tumor burden, and mechanisms of progression and drug resistance. This substrate is amenable for inexpensive noninvasive testing and thus presents a viable approach to serial sampling for screening and monitoring tumor progression. The fragmentation, low yield, and variable admixture of normal DNA present formidable technical challenges for realization of this potential. This review summarizes the history of cfDNA discovery, its biological properties, and explores emerging technologies for clinically relevant sequence based analysis of cfDNA in cancer patients. Molecular barcoding (or Unique Molecular Identifier, UMI) based methods currently appear to offer an optimal balance between sensitivity, flexibility, and cost and constitute a promising approach for clinically relevant assays for near real time monitoring of treatment induced mutational adaptations to guide evidence based precision oncology.
Representative Bureaucracy in India: An empirical study of gender representation by K 12 teachers
Year: 2019

A bureaucracy representative of historically oppressed groups in society has been linked to better outcomes for those groups, especially in key policy areas such as education, child welfare, and law enforcement. Most of the empirical work in education has used the case of the United States, finding that representation leads to academic improvements for the represented group. Would the same effects be seen in national contexts beyond the Western World? This paper examines how and when a female bureaucrat in the Indian education system leads to enhanced outcomes for female students, using data gathered by the Government of India on nearly 1.5 million schools, from 2014 15 to 2017 18. We find a modest representative effect of female teachers on the academic achievement of female students which is enhanced with more class days, longer teacher hours, and a smaller student teacher ratio. Additionally, in situations where active representation would be extremely difficult due to a severe lack of resources and opportunities (schools located in rural areas and with poor infrastructure) we also see a positive effect of female teachers on female student academic achievement, which possibly indicates the presence of symbolic representation via the role model effect, in these environmental conditions. 1 Draft Paper: to be presented at the 2019 Public Management Research Conference at the University of North Carolina, Chapel