nils-herrmann/clean_scirepeval huggingface dataset:
Breast Reinnervation: DIEP Neurotization Using the Third Anterior Intercostal Nerve

The purpose of this article is to evaluate a new method of DIEP flap neurotization using a reliably located recipient nerve. We hypothesize that neurotization by this method (with either nerve conduit or direct nerve coaptation) will have a positive effect on sensory recovery.



Fifty-seven deep inferior epigastric perforator (DIEP) flaps were performed on 35 patients. Neurotizations were performed to the third anterior intercostal nerve by directly coapting the flap donor nerve or coapting with a nerve conduit. Nine nonneurotized DIEP flaps served as controls and received no attempted neurotization. All patients were tested for breast sensibility in 9 areas of the flap skin-island and adjacent postmastectomy skin. Testing occurred at an average of 111 weeks (23-309) postoperatively.



At a mean of 111 weeks after breast reconstruction, neurotization of the DIEP flap resulted in recovery of sensibility that was statistically significantly better (lower threshold) in the flap skin (P < 0.01) and statistically significantly better than in the native mastectomy skin into which the DIEP flap was inserted (P < 0.01). Sensibility recovered in DIEP flaps neurotized using the nerve conduit was significantly better (lower threshold) than that in the corresponding areas of the DIEP flaps neurotized by direct coaptation (P < 0.01).



DIEP flap neurotization using the third anterior intercostal nerve is an effective technique to provide a significant increase in sensory recovery for breast reconstruction patients, while adding minimal surgical time. Additionally, the use of a nerve conduit produces increased sensory recovery when compared direct coaptation.
Evaluating Osseointegration Into a Deeply Porous Titanium Scaffold: A Biomechanical Comparison With PEEK and Allograft.

This was a biomechanical push-out testing study using a porcine model.



The purpose was to evaluate the strength of implant-bone interface of a porous titanium scaffold by comparing it to polyetheretherketone (PEEK) and allograft.



Osseointegration is important for achieving maximal stability of spinal fusion implants and it is desirable to achieve as quickly as possible. Common PEEK interbody fusion implants appear to have limited osseointegration potential because of the formation of fibrous tissue along the implant-bone interface. Porous, three-dimensional titanium materials may be an option to enhance osseointegration.



Using the skulls of two swine, in the region of the os frontale, 16 identical holes (4 mm diameter) were drilled to 10 mm depth in each skull. Porous titanium, PEEK, and allograft pins were press fit into the holes. After 5 weeks, animals were euthanized and the skull sections with the implants were cut into sections with each pin centered within a section. Push-out testing was performed using an MTS machine with a push rate of 6 mm/min. Load-deformation curves were used to compute the extrinsic material properties of the bone samples. Maximum force (N) and shear strength (MPa) were extracted from the output to record the bonding strength between the implant and surrounding bone. When calculating shear strength, maximum force was normalized by the actual implant surface area in contact with surrounding bone.



Mean push-out shear strength was significantly greater in the porous titanium scaffold group than in the PEEK or allograft groups (10.2 vs. 1.5 vs. 3.1 MPa, respectively; P < 0.05).



The push-out strength was significantly greater for the implants with porous titanium coating compared with the PEEK or allograft. These results suggest that the material has promise for facilitating osseointegration for implants, including interbody devices for spinal fusion.
BA-Net: Dense Bundle Adjustment Network

This paper introduces a nueral network to solve the structure-from-motion (SfM) problem via feature bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature reprojection error. The whole pipeline is differentiable, so that the network can learn suitable feature representations that make the BA problem more trackable. Furthermore, this work introduces a novel depth parameterization to recover dense per-pixel depth. The network first generates some bases depth maps according to the input image, and optimizes the final depth as a linear combination of these bases via feature BA. The bases depth map generator is also learned via end-to-end training. The whole system nicely combines domain knowledge (i.e. hard-coded multi-view geometry constraints) and machine learning (i.e. feature learning and basis depth map generator learning) to address the challenging SfM problem. Experiments on large scale real data prove the success of the proposed method.
Lumbar disc herniation mimicking meralgia paresthetica: case report.

Meralgia paresthetica, a syndrome of pain and/or dysesthesia in the anterolateral thigh, is normally caused by an entrapment of the lateral femoral cutaneous nerve (LFCN) at the anterior superior iliac spine. In a few cases compression of the nerve in the retroperitoneum has been reported to mimic meralgia paresthetica.



A 67-year-old woman presented with a 5-year history of permanent paresthesia in the anterolateral thigh. Motor weakness was not detected. Electromyography showed a neurogenic lesion at the level of L3. Lumbar spine MRI detected a foraminal-extraforaminal disc herniation at L2/L3, which was extirpated via a lateral transmuscular approach. The patient was free of symptoms on the first postoperative day.



In patients with meralgia paresthetica we emphasize a complete radiological investigation of the lumbar spine, including MRI, to exclude radicular compression by a disc herniation or a tumour at the level of L2 or L3.
Estimation of Maximum Angular Operation Range for Permanent-Magnet Slotted Limited-Angle Torque Motor

This paper presents an analytical method for estimating the maximum angular operation range (MAOR) in a permanent-magnet slotted limited-angle torque motor (LATM). The operating principle of three topologies is analyzed and discussed, including a toroidal LATM, a slotted LATM with common pole/slot combination, and a slotted LATM with special pole/slot combination. The analysis indicates that no effective methods have been proposed to estimate the MAOR for a slotted LATM with special pole/slot combination. Hence, this paper is focused on solving this problem. The results obtained from the analytical method are validated by a finite-element (FE) method. The average time of the proposed method takes far less than that of an FE method, so that the design cycle of LATMs can be shortened. Finally, two prototype machines are manufactured, and their experimental results are presented and discussed. The FE and experimental results validate the accuracy of an analytical method.
Pure and declarative syntax definition: paradise lost and regained

Syntax definitions are pervasive in modern software systems, and serve as the basis for language processing tools like parsers and compilers. Mainstream parser generators pose restrictions on syntax definitions that follow from their implementation algorithm. They hamper evolution, maintainability, and compositionality of syntax definitions. The pureness and declarativity of syntax definitions is lost. We analyze how these problems arise for different aspects of syntax definitions, discuss their consequences for language engineers, and show how the pure and declarative nature of syntax definitions can be regained.
Modelling Stars Using XML

We suppose collections of XML data described by Document Type Definitions (DTDs). This data has been generated by applications and plays a role of OLTP database(s). A star schema, a well-known technique used in data warehousing, can be applied. Then dimension information is supposed to be contained in XML data. We will use the notions of subDTD and view, and formulate referential integrity constraints in XML environment. We use simple pattern matching capabilities of current XML query languages for XML view specification and tree embedding algorithms for these purposes. A dimension hierarchy is defined as a set of logically connected collections of XML data. Facts may be also conceived as elements of an XML document. Due to the structural complexity of XML data the approach requires subtler formal model than it is done with conventional dimension and fact tables described by classical star schemes. In consequence, our approach captures more from heterogeneity of source databases than it is done in classical relational approaches to data warehousing.
Neurocristic cutaneous hamartoma of the scalp.

Neurocristic cutaneous hamartomas (NCHs) result from aberrant development of the neuromesenchyme. In addition to a dermal melanocytic component, these tumors can contain neuro sustentacular and fibrogenic components. The clinical importance of these lesions includes the potential for misdiagnosis as well as the development of malignant melanomas over a poorly described period of time. We present a rare case of NCH of the scalp in a 1-year-old female.
GreenBag: Energy-Efficient Bandwidth Aggregation for Real-Time Streaming in Heterogeneous Mobile Wireless Networks

Modern mobile devices are equipped with multiple network interfaces, including 3G/LTE and WiFi. Bandwidth aggregation over LTE and WiFi links offers an attractive opportunity of supporting bandwidth-intensive services, such as high-quality video streaming, on mobile devices. However, achieving effective bandwidth aggregation in mobile environments raises several challenges related to deployment, link heterogeneity, network fluctuation, and energy consumption. We present GreenBag, an energy-efficient bandwidth aggregation middleware that supports real-time data-streaming services over asymmetric wireless links, requiring no modifications to the existing Internet infrastructure and servers. GreenBag employs several techniques, including medium load balancing, efficient segment management, and energy-aware mode control, to resolve such challenges. We implement a prototype of GreenBag on Android-based mobile devices which hosts, to the best knowledge of the authors, the first LTE-enabled bandwidth aggregation prototype for energy-efficient real-time video streaming. Our experiment results in both emulated and real-world environments show that GreenBag not only achieves good bandwidth aggregation to provide QoS in bandwidth-scarce environments but also efficiently saves energy on mobile devices. Moreover, energy-aware GreenBag can minimize video interruption while consuming 14-25% less energy than the non-energy-aware counterpart in real-world experiments.
Clinical problem-solving. Beware of first impressions.

n engl j med 359;6 www.nejm.org august 7, 2008 628 From the Department of Medicine (O.O.F., E.S.A.) and the Division of Infectious Diseases (P.A.M.), Johns Hopkins Bayview Medical Center, Johns Hopkins School of Medicine, Baltimore; the Division of Infectious Diseases (D.R.K.) and the Division of General Medicine (S.S.), University of Michigan Medical School,  Arbor; and the Department of Veterans Affairs Health Services Research and Development Center of Excellence, Ann Arbor, MI (S.S.). Address reprint requests to Dr. Antonarakis at the Johns Hopkins Bayview Medical Center, Department of Medicine, B-1 North, 4940 Eastern Ave., Baltimore, MD 21224, or at eantona1@ jhmi.edu.
Aligning IT with Firm Business Strategies Using the Balance Scorecard System

High levels of investment in IT and related products and services over the last two decades have produced only mixed results. Research has shown that one of the most significant determinants of successful IT investment is the alignment between IT and the competitive strategies of a firm. Yet, it is largely unclear to both researchers and practitioners how to achieve such alignment in a complex business environment. In this paper, using the IT alignment model of Reich and Benbasat as the underlying theory, we present the preliminary findings of a case study on how one company used a well established strategic management tool, the Balanced Scorecard, as the framework for aligning its IT initiatives with business strategies, that resulted in financial success for the company.
Automatic Production of an Ontology with NLP: Comparison between a Prolog Based Approach and a Cloud Approach Based on Bluemix Watson Service

Nowadays, most of the information available on the web is in Natural Language. Extracting such knowledge from Natural Language text is an essential work and a very remarkable research topic in the Semantic Web field. The logic programming language Prolog, based on the definite-clause formalism, is a useful tool for implementing a Natural Language Processing (NLP) systems. However, web-based services for NLP have also been developed recently, and they represent an important alternative to be considered. In this paper we present the comparison between two different approaches in NLP, for the automatic creation of an OWL ontology supporting the semantic annotation of text. The first one is a pure Prolog approach, based on grammar and logic analysis rules. The second one is based on Watson Relationship Extraction service of IBM Cloud platform Bluemix. We evaluate the two approaches in terms of performance, the quality of NLP result, OWL completeness and richness.
Techniques of measurement for Parkinson's tremor highlighting advantages of embedded IMU over EMG

For Parkinson's disease (PD) detection at its best, early diagnosis of the disorder is the key criteria to be focused . Tremors are one of the primary characteristics of PD [11][13]. The paper presents some of the effective biomechanical techniques that can aid in measuring PD tremors with a high grade of accuracy and sensitivity. The paper describes the application of salient tracking tools like magnetic trackers and optical markers, electrical diagnostic practices like Electromyogram (EMG) and the use of Microelectromechanical systems (MEMS) based inertial sensor modules(a combination of 3 axes accelerometer + 3 axes gyroscope) to record movement. The IMU and electrodes were suitably mounted on the hands of subjects who were made to perform certain pre-defined actions to record the wrist movement and electrical activity simultaneously. The ability of a subject to precisely follow a pre-defined task's motion trajectory is a crucial indicator of tremor. In this paper we study two techniques of trajectory measurement, in terms of the accuracy of measurement of the slightest deviations from a prescribed trajectory. It is shown that an IMU based motion tracking is more accurate than that tracked by EMG sensors.
An Effective Hierarchical IDS for Wireless Sensor Networks

This paper emphasizes on safeguarding the hierarchical structure in wireless sensor network by presenting an Intrusion Detection Technique, which is very useful, simple and works effectively in improving and enhancing the security in wireless sensor Network. This IDS works on the combination of anomaly detection algorithm and intrusion detection nodes. Here all the features and numerous architectures of popular IDS(s) along with their confines and benefits are also being described.
Rivaroxaban or Aspirin for Extended Treatment of Venous Thromboembolism.

Although many patients with venous thromboembolism require extended treatment, it is uncertain whether it is better to use full- or lower-intensity anticoagulation therapy or aspirin.



In this randomized, double-blind, phase 3 study, we assigned 3396 patients with venous thromboembolism to receive either once-daily rivaroxaban (at doses of 20 mg or 10 mg) or 100 mg of aspirin. All the study patients had completed 6 to 12 months of anticoagulation therapy and were in equipoise regarding the need for continued anticoagulation. Study drugs were administered for up to 12 months. The primary efficacy outcome was symptomatic recurrent fatal or nonfatal venous thromboembolism, and the principal safety outcome was major bleeding.



A total of 3365 patients were included in the intention-to-treat analyses (median treatment duration, 351 days). The primary efficacy outcome occurred in 17 of 1107 patients (1.5%) receiving 20 mg of rivaroxaban and in 13 of 1127 patients (1.2%) receiving 10 mg of rivaroxaban, as compared with 50 of 1131 patients (4.4%) receiving aspirin (hazard ratio for 20 mg of rivaroxaban vs. aspirin, 0.34; 95% confidence interval [CI], 0.20 to 0.59; hazard ratio for 10 mg of rivaroxaban vs. aspirin, 0.26; 95% CI, 0.14 to 0.47; P<0.001 for both comparisons). Rates of major bleeding were 0.5% in the group receiving 20 mg of rivaroxaban, 0.4% in the group receiving 10 mg of rivaroxaban, and 0.3% in the aspirin group; the rates of clinically relevant nonmajor bleeding were 2.7%, 2.0%, and 1.8%, respectively. The incidence of adverse events was similar in all three groups.



Among patients with venous thromboembolism in equipoise for continued anticoagulation, the risk of a recurrent event was significantly lower with rivaroxaban at either a treatment dose (20 mg) or a prophylactic dose (10 mg) than with aspirin, without a significant increase in bleeding rates. (Funded by Bayer Pharmaceuticals; EINSTEIN CHOICE ClinicalTrials.gov number, NCT02064439 .).
Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit

Contextual multi-armed bandit problems have gained increasing popularity and attention in recent years due to their capability of leveraging contextual information to deliver online personalized recommendation services (e.g., online advertising and news article selection). To predict the reward of each arm given a particular context, existing relevant research studies for contextual multi-armed bandit problems often assume the existence of a fixed yet unknown reward mapping function. However, this assumption rarely holds in practice, since real-world problems often involve underlying processes that are dynamically evolving over time.
 In this paper, we study the time varying contextual multi-armed problem where the reward mapping function changes over time. In particular, we propose a dynamical context drift model based on particle learning. In the proposed model, the drift on the reward mapping function is explicitly modeled as a set of random walk particles, where good fitted particles are selected to learn the mapping dynamically. Taking advantage of the fully adaptive inference strategy of particle learning, our model is able to effectively capture the context change and learn the latent parameters. In addition, those learnt parameters can be naturally integrated into existing multi-arm selection strategies such as LinUCB and Thompson sampling. Empirical studies on two real-world applications, including online personalized advertising and news recommendation, demonstrate the effectiveness of our proposed approach. The experimental results also show that our algorithm can dynamically track the changing reward over time and consequently improve the click-through rate.
Separation and some properties of two acid phosphatases from suspension cultures ofIpomoea sp.

Two acid phosphatases isolated from culturedIpomoea (moring glory) cells were separated by column chromatography on DEAE-cellulose. The two acid phosphatases have different pH optima (pH 4.8–5.0 and 6.0) and do not require the presence of divalent ions. The enzymes possess high activity toward pyrophosphate,p-nitrophenylphosphate, nucleoside di- and triphosphates, and much less activity toward nucleoside monophosphates and sugar esters. The two phosphatases differ from each other in Michaelis constants, in the degree of inhibition by arsenate, fluoride and phosphate and have quantitative differences of substrate specificity. In addition, they also differ in their response to various ions.
Fast Online Upper Body Pose Estimation from Video

Estimation of human body poses from video is an important problem in computer vision with many applications. Most existing methods for video pose estimation are offline in nature, where all frames in the video are used in the process to estimate the body pose in each frame. In this work, we describe a fast online video upper body pose estimation method (CDBN-MODEC) that is based on a conditional dynamic Bayesian network model, which predicts upper body pose in a frame without using information from future frames. Our method combines fast single image based pose estimation methods with the temporal correlation of poses between frames. We collect a new high frame rate upper body pose dataset that better reflects practical scenarios calling for fast online video pose estimation. When evaluated on this dataset and the VideoPose2 benchmark dataset, CDBN-MODEC achieves improvements in both performance and running efficiency over several state-of-art online video pose estimation methods.
Analyzing the Blogosphere for Predicting the Success of Music and Movie Products

Over the last decade blogs became an important part of the Web, where people can announce anything that is on their mind. Due to their high popularity blogs have great potential to mine public opinions regarding products. Such knowledge is very valuable as it could be used to adjust marketing campaigns or advertisement of products accordingly. In this paper we investigate how the blogosphere can be used to predict the success of products in the domain of music and movies. We analyze and characterize the blogging behavior in both domains particularly around product releases, propose different methods for extracting characteristic features from the blogosphere, and show that our predictions correspond to the real world measures Sales Rank and box office revenue respectively.
IoT Based Patient Health Monitoring System Using LabVIEW and Wireless Sensor Network

Patient Health Monitoring is the one of the field that is rapidly growing very fast nowadays with the advancement of technologies many researchers have come with differentdesigns for patient health monitoring systems as per the technological development. With the widespread of internet, Internet of things is among of the emerged field recently in which many have been able to incorporate it into different applications. In this paper we introduce the system called Iot based patient health monitoring system using LabVIEW and Wireless Sensor Network (WSN).The system will be able to take patients physiological parameters and transmit it wirelessly via Xbees, displays the sensor data onLabVIEW and publish on webserver to enable other health care givers from far distance to visualize, control and monitor continuously via internet connectivity.
Segmentation of Multiple Sclerosis lesion in brain MR images using Fuzzy C-Means

Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy -c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.