nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of aqueous humor cytokine and chemokine levels in diabetic patients with and without retinopathy

To compare the aqueous humor levels of proinflammatory and angiogenic factors of diabetic patients with and without retinopathy.



Aqueous humor was collected at the start of cataract surgery from diabetic subjects and non-diabetic controls. The presence and severity of diabetic retinopathy were graded with fundus examination. Levels of 22 different inflammatory and angiogenic cytokines and chemokines were compared.



Aqueous humor samples from 47 diabetic patients (20 without retinopathy, 27 with retinopathy) and 24 non-diabetic controls were included. Interleukin (IL)-2, IL-10, IL-12, interferon-alpha (IFN-α), and tumor necrosis factor (TNF)-α were measurable in significantly fewer diabetic samples, and where measurable, were at lower levels than in non-diabetic controls. IL-6 was measurable in significantly more diabetic samples, and the median levels were significantly higher in the eyes with retinopathy than the eyes without retinopathy and the non-diabetic eyes. The vascular endothelial growth factor (VEGF) level was significantly higher in the diabetic eyes with and without retinopathy compared to the non-diabetic controls. The IL-6 level positively correlated with the monocyte chemotactic protein-1 (CCL2) and interleukin-8 (CXCL8) levels and negatively with the TNF-α level. The VEGF level negatively correlated with the IL-12 and TNF-α levels.



The aqueous humor cytokine profile of diabetic patients without retinopathy was similar to that of patients with diabetic retinopathy. These cytokines may be useful biomarkers for early detection and prognosis of diabetic retinopathy. Compared to diabetic patients without retinopathy, only the IL-6 and VEGF levels were significantly higher in diabetic patients with retinopathy.
Highly Automated Driving on Highways Based on Legal Safety

This paper discusses driving system design based on traffic rules. This allows fully automated driving in an environment with human drivers, without necessarily changing equipment on other vehicles or infrastructure. It also facilitates cooperation between the driving system and the host driver during highly automated driving. The concept, referred to as legal safety, is illustrated for highly automated driving on highways with distance keeping, intelligent speed adaptation, and lane-changing functionalities. Requirements by legal safety on perception and control components are discussed. This paper presents the actual design of a legal safety decision component, which predicts object trajectories and calculates optimal subject trajectories. System implementation on automotive electronic control units and results on vehicle and simulator are discussed.
Inducing drought tolerance in plants: recent advances.

Undoubtedly, drought is one of the prime abiotic stresses in the world. Crop yield losses due to drought stress are considerable. Although a variety of approaches have been used to alleviate the problem of drought, plant breeding, either conventional breeding or genetic engineering, seems to be an efficient and economic means of tailoring crops to enable them to grow successfully in drought-prone environments. During the last century, although plant breeders have made ample progress through conventional breeding in developing drought tolerant lines/cultivars of some selected crops, the approach is, in fact, highly time-consuming and labor- and cost-intensive. Alternatively, marker-assisted breeding (MAB) is a more efficient approach, which identifies the usefulness of thousands of genomic regions of a crop under stress conditions, which was, in reality, previously not possible. Quantitative trait loci (QTL) for drought tolerance have been identified for a variety of traits in different crops. With the development of comprehensive molecular linkage maps, marker-assisted selection procedures have led to pyramiding desirable traits to achieve improvements in crop drought tolerance. However, the accuracy and preciseness in QTL identification are problematic. Furthermore, significant genetic x environment interaction, large number of genes encoding yield, and use of wrong mapping populations, have all harmed programs involved in mapping of QTL for high growth and yield under water limited conditions. Under such circumstances, a transgenic approach to the problem seems more convincing and practicable, and it is being pursued vigorously to improve qualitative and quantitative traits including tolerance to biotic and abiotic stresses in different crops. Rapid advance in knowledge on genomics and proteomics will certainly be beneficial to fine-tune the molecular breeding and transformation approaches so as to achieve a significant progress in crop improvement in future. Knowledge of gene regulation and signal transduction to generate drought tolerant crop cultivars/lines has been discussed in the present review. In addition, the advantages and disadvantages as well as future prospects of each breeding approach have also been discussed.
A distributed beaconless routing protocol for real-time video dissemination in multimedia VANETs

Vehicular Ad-Hoc Networks (VANETs) will play an important role in Smart Cities and will support the development of not only safety applications, but also car smart video surveillance services. Recent improvements in multimedia over VANETs allow drivers, passengers, and rescue teams to capture, share, and access on-road multimedia services. Vehicles can cooperate with each other to transmit live flows of traffic accidents or disasters and provide drivers, passengers, and rescue teams rich visual information about a monitored area. Since humans will watch the videos, their distribution must be done by considering the provided Quality of Experience (QoE) even in multi-hop, multi-path, and dynamic environments. This article introduces an application framework to handle this kind of services and a routing protocol, the DBD (Distributed Beaconless Dissemination), that enhances the dissemination of live video flows on multimedia highway VANETs. DBD uses a backbone-based approach to create and maintain persistent and high quality routes during the video delivery in opportunistic Vehicle to Vehicle (V2V) scenarios. It also improves the performance of the IEEE 802.11p MAC layer, by solving the Spurious Forwarding (SF) problem, while increasing the packet delivery ratio and reducing the forwarding delay. Performance evaluation results show the benefits of DBD compared to existing works in forwarding videos over VANETs, where main objective and subjective QoE results are measured. Safety and video surveillance car applications are key Information and Communication Technologies (ICT) services for smart city scenarios and have been attracting an important attention from governments, car manufacturers, academia, and society . Nowadays, the distribution of real-time multimedia content over Vehicular Ad-Hoc Networks (VANETs) is becoming a reality and allowing drivers/passengers to have new experiences with on-road videos in a smart city [2,3]. According to Cisco, video traffic will represent over 90% of the global IP data in a few years, where thousands of users will produce, share, and consume multimedia services ubiquitously, including in their vehicles. Multimedia VANETs are well-suited for capturing and sharing environmental monitoring, surveillance, traffic accidents, and disaster-based video smart city applications. Live streaming video flows provide users and authorities (e.g., first responder teams and paramedics) with more precise information than simple text messages and allow them to determine a suitable action, while reducing human reaction times . Vehicles can cooperate with each other to disseminate short videos of dangerous situations to visually inform drivers and rescue teams about them both in the city and on a highway. …
Minimization of ℓ1-2 for Compressed Sensing

We study minimization of the difference of l1 and l2 norms as a non-convex and Lipschitz continuous metric for solving constrained and unconstrained compressed sensing problems. We establish exact (stable) sparse recovery results under a restricted isometry property (RIP) condition for the constrained problem, and a full-rank theorem of the sensing matrix restricted to the support of the sparse solution. We present an iterative method for l1−2 minimization based on the difference of convex functions algorithm (DCA), and prove that it converges to a stationary point satisfying first order optimality condition. We propose a sparsity oriented simulated annealing (SA) procedure with non-Gaussian random perturbation and prove the almost sure convergence of the combined algorithm (DCASA) to a global minimum. Computation examples on success rates of sparse solution recovery show that if the sensing matrix is ill-conditioned (non RIP satisfying), then our method is better than existing non-convex compressed sensing solvers in the literature. Likewise in the magnetic resonance imaging (MRI) phantom image recovery problem, l1−2 succeeds with 8 projections. Irrespective of the conditioning of the sensing matrix, l1−2 is better than l1 in both the sparse signal and the MRI phantom image recovery problems.
Augmented State Kalman Filtering for AUV Navigation

This paper addresses the problem of estimating the motion of an Autonomous Underwater Vehicle (AUV), while it constructs a visual map (“mosaic” image) of the ocean jlool: The vehicle is equipped with a down-looking camera which is used to compute its motion with respect to the seaflool: As the mosaic increases in sue, a systematic bias is introduced in the alignment of the images which form the mosaic. Therefore, this accumulative error produces a dr@ in the estimation of the position of the vehicle. When the arbitrary trajectory of the AUV crosses over itself; it is possible to reduce this propagation of image alignment errors within the mosaic. A Kalman filter with augmented state is proposed to optimally estimate both the visual map and the vehicle position,
Effects of multidisciplinary team working on breast cancer survival: retrospective, comparative, interventional cohort study of 13 722 women

To describe the effect of multidisciplinary care on survival in women treated for breast cancer.



Retrospective, comparative, non-randomised, interventional cohort study.



NHS hospitals, health boards in the west of Scotland, UK.



14,358 patients diagnosed with symptomatic invasive breast cancer between 1990 and 2000, residing in health board areas in the west of Scotland. 13,722 (95.6%) patients were eligible (excluding 16 diagnoses of inflammatory cancers and 620 diagnoses of breast cancer at death).



In 1995, multidisciplinary team working was introduced in hospitals throughout one health board area (Greater Glasgow; intervention area), but not in other health board areas in the west of Scotland (non-intervention area).



Breast cancer specific mortality and all cause mortality.



Before the introduction of multidisciplinary care (analysed time period January 1990 to September 1995), breast cancer mortality was 11% higher in the intervention area than in the non-intervention area (hazard ratio adjusted for year of incidence, age at diagnosis, and deprivation, 1.11; 95% confidence interval 1.00 to 1.20). After multidisciplinary care was introduced (time period October 1995 to December 2000), breast cancer mortality was 18% lower in the intervention area than in the non-intervention area (0.82, 0.74 to 0.91). All cause mortality did not differ significantly between populations in the earlier period, but was 11% lower in the intervention area than in the non-interventional area in the later period (0.89, 0.82 to 0.97). Interrupted time series analyses showed a significant improvement in breast cancer survival in the intervention area in 1996, compared with the expected survival in the same year had the pre-intervention trend continued (P=0.004). This improvement was maintained after the intervention was introduced.



Introduction of multidisciplinary care was associated with improved survival and reduced variation in survival among hospitals. Further analysis of clinical audit data for multidisciplinary care could identify which aspects of care are most associated with survival benefits.
A Low Cost Sensors Approach for Accurate Vehicle Localization and Autonomous Driving Application

Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Laser range finder and stereo vision have been successfully used for obstacle detection, mapping and localization to solve the autonomous driving problem. Unfortunately, Light Detection and Ranging (LIDARs) are very expensive sensors and stereo vision requires powerful dedicated hardware to process the cameras information. In this context, this article presents a low-cost architecture of sensors and data fusion algorithm capable of autonomous driving in narrow two-way roads. Our approach exploits a combination of a short-range visual lane marking detector and a dead reckoning system to build a long and precise perception of the lane markings in the vehicle's backwards. This information is used to localize the vehicle in a map, that also contains the reference trajectory for autonomous driving. Experimental results show the successful application of the proposed system on a real autonomous driving situation.
Nonlinear mapping of massive data sets by fuzzy clustering and neural networks

Producing good low-dimensional representations of high-dimensional data is a common and important task in many data mining applications. Two methods that have been particularly useful in this regard are multidimensional scaling and nonlinear mapping. These methods attempt to visualize a set of objects described by means of a dissimilarity or distance matrix on a low-dimensional display plane in a way that preserves the proximities of the objects to whatever extent is possible. Unfortunately, most known algorithms are of quadratic order, and their use has been limited to relatively small data sets. We recently demonstrated that nonlinear maps derived from a small random sample of a large data set exhibit the same structure and characteristics as that of the entire collection, and that this structure can be easily extracted by a neural network, making possible the scaling of data set orders of magnitude larger than those accessible with conventional methodologies. Here, we present a variant of this algorithm based on local learning. The method employs a fuzzy clustering methodology to partition the data space into a set of Voronoi polyhedra, and uses a separate neural network to perform the nonlinear mapping within each cell. We find that this local approach offers a number of advantages, and produces maps that are virtually indistinguishable from those derived with conventional algorithms. These advantages are discussed using examples from the fields of combinatorial chemistry and optical character recognition.
Performance analysis of PMSM drive based on FOC technique with and without MRAS method

The objective of this paper is to control the speed of Permanent Magnet Synchronous Motor (PMSM) over wide range of speed by consuming minimum time and low cost. Therefore, comparative performance analysis of PMSM on basis of speed regulation has been done in this study. Comparison of two control strategies i.e. Field oriented control (FOC) without sensor less Model Reference Adaptive System (MRAS) and FOC with sensor less MRAS has been carried out. Sensor less speed control of PMSM is achieved by using estimated speed deviation as feedback signal for the PI controller. Performance of the both control strategies has been evaluated in in MATLAB Simulink software. Simulation studies show the response of PMSM speed during various conditions of load and speed variations. Obtained results reveal that the proposed MRAS technique can effectively estimate the speed of rotor with high exactness and torque response is significantly quick as compared to the system without MRAS control system.
Neural Pattern Recognition with Self-organizing Maps for Efficient Processing of Forex Market Data Streams

The paper addresses the problem of using Japanese candlestick methodology to analyze stock or forex market data by neural nets. Self organizing maps are presented as tools for providing maps of known candlestick formations. They may be used to visualize these patterns, and as inputs for more complex trading decision systems. In that case their role is preprocessing, coding and pre-classification of price data. An example of a profitable system based on this method is presented. Simplicity and efficiency of training and network simulating algorithms is emphasized in the context of processing streams of market data.
A TEST OF THE CRITICAL PERIOD HYPOTHESIS FOR LANGUAGE LEARNING * By

A critical period for language learning is often defined as a sharp decline in learning outcomes with age. This study examines the relevance of the critical period for English speaking proficiency among immigrants in the US. It uses microdata from the 2000 US Census, a model of language acquisition, and a flexible specification of an estimating equation based on 64 age-at-migration dichotomous variables. Self-reported English speaking proficiency among immigrants declines more-or-less monotonically with age at migration, and this relationship is not characterized by any sharp decline or discontinuity that might be considered consistent with a “critical” period. The findings are robust across the various immigrant samples, and between the genders.
An optimistic approach in distributed database concurrency control

In this paper, we present an optimistic concurrency control solution. The proposed solution represents an excellent blossom in the concurrency control field. It deals with the concurrency control anomalies, and, simultaneously, assures the reliability of the data before read-write transactions and after successfully committed. It can be used within the distributed database to track data logs and roll back processes to overcome distributed database anomalies. The method is based on commit timestamps for validation and an integer flag that is incremented each time a successful update on the record is committed.
Identifying interesting Twitter contents using topical analysis

Social media platforms such as Twitter are becoming increasingly mainstream which provides valuable user-generated information by publishing and sharing contents. Identifying interesting and useful contents from large text-streams is a crucial issue in social media because many users struggle with information overload. Retweeting as a forwarding function plays an important role in information propagation where the retweet counts simply reflect a tweet's popularity. However, the main reason for retweets may be limited to personal interests and satisfactions. In this paper, we use a topic identification as a proxy to understand a large number of tweets and to score the interestingness of an individual tweet based on its latent topics. Our assumption is that fascinating topics generate contents that may be of potential interest to a wide audience. We propose a novel topic model called Trend Sensitive-Latent Dirichlet Allocation (TS-LDA) that can efficiently extract latent topics from contents by modeling temporal trends on Twitter over time. The experimental results on real world data from Twitter demonstrate that our proposed method outperforms several other baseline methods. With the rise of the Internet, blogs, and mobile devices, social media has also evolved into an information provider by publishing and sharing user-generated contents. By analyzing textual data which represents the thoughts and communication between users, it is possible to understand the public needs and concerns about what constitutes valuable information from an academic, marketing , and policy-making perspective. Twitter (http://twitter.com) is one of the social media platforms that enables its users to generate and consume useful information about issues and trends from text streams in real-time. Twitter and its 500 million registered users produce over 340 million tweets, which are text-based messages of up to 140 characters, per day 1. Also, users subscribe to other users in order to view their followers' relationships and timelines which show tweets in reverse chronological order. Although tweets may contain valuable information, many do not and are not interesting to users. A large number of tweets can overwhelm users when they check their Twitter timeline. Thus, finding and recommending tweets that are of potential interest to users from a large volume of tweets that is accumulated in real-time is a crucial but challenging task. A simple but effective way to solve these problems is to use the number of retweets. A retweet is a function that allows a user to re-post another user's tweet and other information such …
Challenges in Big Data Application: A Review

New invention of advanced technology, enhanced capacity of storage media, maturity of information technology and popularity of social media, business intelligence and Scientific invention, produces huge amount of data which made ample set of information that is responsible for birth of new concept well known as big data. Big data analytics is the process of examining large amounts of data. The analysis is done on huge amount of data which is structure, semi structure and unstructured. In big data, data is generated at exponentially for reason of increase use of social media, email, document and sensor data. The growth of data has affected all fields, whether it is business sector or the world of science. In this paper, the process of system is reviewed for managing &quot;Big Data&quot; and today&apos;s activities on big data tools and techniques.
Fermentation, fermented foods and lactose intolerance

Lactose (milk sugar) is a fermentable substrate. It can be fermented outside of the body to produce cheeses, yoghurts and acidified milks. It can be fermented within the large intestine in those people who have insufficient expression of lactase enzyme on the intestinal mucosa to ferment this disaccharide to its absorbable, simple hexose sugars: glucose and galactose. In this way, the issues of lactose intolerance and of fermented foods are joined. It is only at the extremes of life, in infancy and old age, in which severe and life-threatening consequences from lactose maldigestion may occur. Fermentation as part of food processing can be used for preservation, for liberation of pre-digested nutrients, or to create ethanolic beverages. Almost all cultures and ethnic groups have developed some typical forms of fermented foods. Lessons from fermentation of non-dairy items may be applicable to fermentation of milk, and vice versa.
Modeling and performance analysis of sequential zone picking systems

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