nils-herrmann/clean_scirepeval huggingface dataset:
Acute respiratory distress syndrome due to severe pulmonary tuberculosis treated with extracorporeal membrane oxygenation: A case report and review of the literature

Mortality in patients with pulmonary tuberculosis remains high, especially in those who develop acute respiratory distress syndrome (ARDS). We report on a-48-year-old man with ARDS due to severe pulmonary tuberculosis who was rescued by extracorporeal membrane oxygenation (ECMO). He was initially hospitalized in the intensive care unit and noninvasive positive-pressure ventilation started. He was also administered anti-tuberculosis drugs and received systemic corticosteroid therapy. Six days later, further deterioration of gas exchange prompted the decision to intubate. However, he experienced progressive deterioration of arterial oxygenation despite conventional ventilatory support. We therefore decided to administer ECMO on day 9. After initiation of these treatments and ECMO support, pulmonary infiltrate and oxygenation status gradually improved and ECMO was discontinued on day 52. The patient was finally discharged from our hospital without severe disability. ECMO should be considered one of the treatment options for the management of ARDS due to severe pulmonary tuberculosis.
Super resolution techniques for medical image processing

Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Furthermore, a better resolution may substantially improve automatic detection and image segmentation results. The arrival of digital medical imaging technologies such as Computerized Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI) etc. has revolutionized modern medicine. Despite the advances in acquisition technology and the performance of optimized reconstruction algorithms over the two last decades, it is not easy to obtain an image at a desired resolution due to imaging environments, the limitations of physical imaging systems as well as quality-limiting factors such as Noise and Blur. A solution to this problem is the use of Super Resolution (SR) techniques which can be used for processing of such images. Various methods have been described over the years to generate and form algorithms which can be used for building on this concept of Super resolution. This paper details few of the types of medical imaginary, various techniques used to perform super resolution and the current trends which are being followed for the implementation of this concept.
Digit ratio (2D:4D) and sprinting speed in boys.

Digit ratio (2D:4D), a putative correlate of prenatal testosterone, has been found to relate to performance in sport and athletics such that low 2D:4D (high prenatal testosterone) correlates with high performance. Speed in endurance races is strongly related to 2D:4D, and may be one factor that underlies the link between sport and 2D:4D, but nothing is known of the relationship between 2D:4D and sprinting speed. Here we show that running times over 50 m were positively correlated with 2D:4D in a sample of 241 boys (i.e. runners with low 2D:4D ran faster than runners with high 2D:4D). The relationship was also found for 50 m split times (at 20, 30, and 40 m) and was independent of age, BMI, and an index of maturity. However, associations between 2D:4D and sprinting speed were much weaker than those reported for endurance running. This suggests that 2D:4D is a relatively weak predictor of strength and a stronger predictor of efficiency in aerobic exercise. We discuss the effect sizes for relationships between 2D:4D and sport and target traits in general, and identify areas of strength and weakness in digit ratio research.
Identification of rice diseases using deep convolutional neural networks

The automatic identification and diagnosis of rice diseases are highly desired in the field of agricultural information. Deep learning is a hot research topic in pattern recognition and machine learning at present, it can effectively solve these problems in vegetable pathology. In this study, we propose a novel rice diseases identification method based on deep convolutional neural networks (CNNs) techniques. Using a dataset of 500 natural images of diseased and healthy rice leaves and stems captured from rice experimental field, CNNs are trained to identify 10 common rice diseases. Under the 10-fold cross-validation strategy, the proposed CNNs-based model achieves an accuracy of 95.48%. This accuracy is much higher than conventional machine learning model. The simulation results for the identification of rice diseases show the feasibility and effectiveness of the proposed method.
Benefits of an item-centric enterprise-data model in logistics services: A case study

The paper uses a case example to present a novel way of building enterprise information systems. The objective is to bring forth the benefits of an item centric systems design in environments that require real-time material visibility, such as in logistics service provision. The methodology employed is case study and metadata modeling. Managers of SE Mäkinen, a Finnish car distribution company were interviewed on the implementation and operation of their award winning enterprise system. The case example was then analyzed using a generic metadata model of item-centric systems. The main finding of the paper is that introducing an item-centric enterprise-data model facilitated responsive service in the distribution of automobiles. The practical implications are that when starting to develop a new enterprise system, managers in logistics services should consider an item-centric design solution as an option to the conventional account-based design for enterprise-data models.
Real-Time PID Control Strategy for Maglev Transportation System via Particle Swarm Optimization

This paper focuses on the design of a real-time particle-swarm-optimization-based proportional-integral-differential (PSO-PID) control scheme for the levitated balancing and propulsive positioning of a magnetic-levitation (maglev) transportation system. The dynamic model of a maglev transportation system, including levitated electromagnets and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics, is first constructed. The control objective is to design a real-time PID control methodology via PSO gain selections and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. The effectiveness of the proposed PSO-PID control scheme for the maglev transportation system is verified by numerical simulations and experimental results, and its superiority is indicated in comparison with PSO-PID in previous literature and conventional sliding-mode (SM) control strategies. With the proposed PSO-PID control scheme, the controlled maglev transportation system possesses the advantages of favorable control performance without chattering phenomena in SM control and robustness to uncertainties superior to fixed-gain PSO-PID control.
3D surface reconstruction of entomological specimens from uniform multi-view image datasets

Modeling of 3D objects and scenes have become a common tool in different applied fields from simulation-based design in high-end engineering applications (aviation, civil structures, engine components, etc.) to entertainment (computer-based animation, video-game development, etc.). In Biology and related fields, 3D object modeling and reconstruction provide valuable tools to support the visualization, comparison and even morphometric analysis in both academical and applied tasks. Such computational tools, usually implemented as web-based virtual reality applications, significantly reduce the manipulation of fragile samples, preventing their damage and, even, their complete loss. On the other hand, they allow to take the morphological properties of physical specimens to the digital domain, giving support to common entomology tasks such as characterization, morphological taxonomy and teaching. This paper addresses the problem of producing reliable 3D point clouds from the surface of entomological specimens, based on a proved approach for multi view 3D reconstruction from high resolution pictures. Given the traditional issues of macro-photography for small sized objects (i.e. short depth of field, presence of subtle and complex structures, etc.), a pre-processing protocol, based on focus stacking, supported the generation of enhanced views obtained by an acquisition device specifically designed for this work. The proposed approach has been tested on a sample of six representative subjects from the Entomological Collection of the Centro de Biosistemas, Universidad Jorge Tadeo Lozano (Colombia). The resulting point clouds exhibit an overall good visual quality for the body structure the selected specimens, while file sizes are portable enough to support web based visualization.
Soft-NMS — Improving Object Detection with One Line of Code

Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box M with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold) with M are suppressed. This process is recursively applied on the remaining boxes. As per the design of the algorithm, if an object lies within the predefined overlap threshold, it leads to a miss. To this end, we propose Soft-NMS, an algorithm which decays the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process. Soft-NMS obtains consistent improvements for the coco-style mAP metric on standard datasets like PASCAL VOC2007 (1.7% for both R-FCN and Faster-RCNN) and MS-COCO (1.3% for R-FCN and 1.1% for Faster-RCNN) by just changing the NMS algorithm without any additional hyper-parameters. Using Deformable-RFCN, Soft-NMS improves state-of-the-art in object detection from 39.8% to 40.9% with a single model. Further, the computational complexity of Soft-NMS is the same as traditional NMS and hence it can be efficiently implemented. Since Soft-NMS does not require any extra training and is simple to implement, it can be easily integrated into any object detection pipeline. Code for Soft-NMS is publicly available on GitHub http://bit.ly/2nJLNMu.
Quality Evaluation of Apocyni Veneti Folium from Different Habitats and Commercial Herbs Based on Simultaneous Determination of Multiple Bioactive Constituents Combined with Multivariate Statistical Analysis

Apocyni Veneti Folium (AVF) is a kind of staple traditional Chinese medicine with vast clinical consumption because of its positive effects. However, due to the habitats and adulterants, its quality is uneven. To control the quality of this medicinal herb, in this study, the quality of AVF was evaluated based on simultaneous determination of multiple bioactive constituents combined with multivariate statistical analysis. A reliable method based on ultra-fast liquid chromatography tandem triple quadrupole mass spectrometry (UFLC-QTRAP-MS/MS) was developed for the simultaneous determination of a total of 43 constituents, including 15 flavonoids, 6 organic acids, 13 amino acids, and 9 nucleosides in 41 Luobumaye samples from different habitats and commercial herbs. Furthermore, according to the contents of these 43 constituents, principal component analysis (PCA) was employed to classify and distinguish between AVF and its adulterants, leaves of Poacynum hendersonii (PHF), and gray relational analysis (GRA) was performed to evaluate the quality of the samples. The proposed method was successfully applied to the comprehensive quality evaluation of AVF, and all results demonstrated that the quality of AVF was higher than the PHF. This study will provide comprehensive information necessary for the quality control of AVF.
The global burden of oral diseases and risks to oral health.

This paper outlines the burden of oral diseases worldwide and describes the influence of major sociobehavioural risk factors in oral health. Despite great improvements in the oral health of populations in several countries, global problems still persist. The burden of oral disease is particularly high for the disadvantaged and poor population groups in both developing and developed countries. Oral diseases such as dental caries, periodontal disease, tooth loss, oral mucosal lesions and oropharyngeal cancers, human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS)-related oral disease and orodental trauma are major public health problems worldwide and poor oral health has a profound effect on general health and quality of life. The diversity in oral disease patterns and development trends across countries and regions reflects distinct risk profiles and the establishment of preventive oral health care programmes. The important role of sociobehavioural and environmental factors in oral health and disease has been shown in a large number of socioepidemiological surveys. In addition to poor living conditions, the major risk factors relate to unhealthy lifestyles (i.e. poor diet, nutrition and oral hygiene and use of tobacco and alcohol), and limited availability and accessibility of oral health services. Several oral diseases are linked to noncommunicable chronic diseases primarily because of common risk factors. Moreover, general diseases often have oral manifestations (e.g. diabetes or HIV/AIDS). Worldwide strengthening of public health programmes through the implementation of effective measures for the prevention of oral disease and promotion of oral health is urgently needed. The challenges of improving oral health are particularly great in developing countries.
Application of self-tuning fuzzy PID controller on the AVR system

Optimal tuning of proportional-integral-derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a tuning fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.
Germline cysts and the formation of the germinal epithelium during the female gonadal morphogenesis in Cyprinus carpio (Teleostei: Ostariophysi: Cypriniformes).

The formation of both germline cysts and the germinal epithelium is described during the ovary development in Cyprinus carpio. As in the undifferentiated gonad of mammals, cords of PGCs become oogonia when they are surrounded by somatic cells. Ovarian differentiation is triggered when oogonia proliferate and enter meiosis, becoming oocytes. Proliferation of single oogonium results in clusters of interconnected oocytes, the germline cysts, that are encompassed by somatic prefollicle cells and form cell nests. Both PGCs and cell nests are delimited by a basement membrane. Ovarian follicles originate from the germline cysts, about the time of meiotic arrest, as prefollicle cells surround oocytes, individualizing them. They synthesize a basement membrane and an oocyte forms a follicle. With the formation of the stroma, unspecialized mesenchymal cells differentiate, and encompass each follicle, forming the theca. The follicle, basement membrane, and theca constitute the follicle complex. Along the ventral region of the differentiating ovary, the epithelium invaginates to form the ovigerous lamellae whose developing surface epithelium, the germinal epithelium, is composed of epithelial cells, germline cysts with oogonia, oocytes, and developing follicles. The germinal epithelium rests upon a basement membrane. The follicles complexes are connected to the germinal epithelium by a shared portion of basement membrane. In the differentiated ovary, germ cell proliferation in the epithelium forms nests in which there are the germline cysts. Germline cysts, groups of cells that form from a single founder cell and are joined by intercellular bridges, are conserved throughout the vertebrates, as is the germinal epithelium.
Robust architectural support for transactional memory in the power architecture

On the twentieth anniversary of the original publication , following ten years of intense activity in the research literature, hardware support for transactional memory (TM) has finally become a commercial reality, with HTM-enabled chips currently or soon-to-be available from many hardware vendors. In this paper we describe architectural support for TM added to a future version of the Power ISA#8482;. Two imperatives drove the development: the desire to complement our weakly-consistent memory model with a more friendly interface to simplify the development and porting of multithreaded applications, and the need for robustness beyond that of some early implementations. In the process of commercializing the feature, we had to resolve some previously unexplored interactions between TM and existing features of the ISA, for example translation shootdown, interrupt handling, atomic read-modify-write primitives, and our weakly consistent memory model. We describe these interactions, the overall architecture, and discuss the motivation and rationale for our choices of architectural semantics, beyond what is typically found in reference manuals.
Low-Power Pulse-Triggered Flip-Flop Design Based on a Signal Feed-Through

In this brief, a low-power flip-flop (FF) design featuring an explicit type pulse-triggered structure and a modified true single phase clock latch based on a signal feed-through scheme is presented. The proposed design successfully solves the long discharging path problem in conventional explicit type pulse-triggered FF (P-FF) designs and achieves better speed and power performance. Based on post-layout simulation results using TSMC CMOS 90-nm technology, the proposed design outperforms the conventional P-FF design data-close-to-output (ep-DCO) by 8.2% in data-to-Q delay. In the mean time, the performance edges on power and power- delay-product metrics are 22.7% and 29.7%, respectively.
Use of negative pressure wound therapy after infection and flap dehiscence in radical vulvectomy: A case report

Vulvar cancer has a lower incidence in high income countries, but is rising, in part, due to the high life expectancy in these societies. Radical vulvectomy is still the standard treatment in initial stages. Wound dehiscence contitututes one of the most common postoperative complications.



A 76year old patient with a squamous cell carcinoma of the vulva, FIGO staged, IIIb is presented. Radical vulvectomy and bilateral inguinal lymph node dissection with lotus petal flaps reconstruction are performed as the first treatment. Wound infection and dehiscence of lotus petal flaps was seen postoperatively. Initial management consisted in antibiotics administration and removing necrotic tissue from surgical wound. After this initial treatment, negative wound pressure therapy was applied for 37days with good results.



Wound dehiscence in radical vulvectomy remains the most frequent complication in the treatment of vulvar cancer. The treatment of this complications is still challenging for most gynecologic oncologist surgeons.



The utilization of the negative wound pressure therapy could contribute to reduce hospitalization and the direct and indirect costs of these complications.
Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance

Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this paper, we present a fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks that is trained end-to-end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data. Furthermore, we design a novel loss function based on Jaccard distance to eliminate the need of sample re-weighting, a typical procedure when using cross entropy as the loss function for image segmentation due to the strong imbalance between the number of foreground and background pixels. We evaluated the effectiveness, efficiency, as well as the generalization capability of the proposed framework on two publicly available databases. One is from ISBI 2016 skin lesion analysis towards melanoma detection challenge, and the other is the PH2 database. Experimental results showed that the proposed method outperformed other state-of-the-art algorithms on these two databases. Our method is general enough and only needs minimum pre- and post-processing, which allows its adoption in a variety of medical image segmentation tasks.