nils-herrmann/clean_scirepeval huggingface dataset:
Cell viability assessment using the Alamar blue assay: a comparison of 2D and 3D cell culture models.

Comparisons of 2D and 3D cell culture models in literature have indicated differences in cellular morphology and metabolism, commonly attributed the better representation of in vivo conditions of the latter cell culture environment. Thus, interest in the use of 3D collagen gels for in vitro analysis has been growing. Although comparative studies to date have indicated an enhanced resistance of cells on collagen matrices against different toxicants, in the present study it is demonstrated that non-adapted protocols can lead to misinterpretation of results obtained from classical colorimetric dye-based cytotoxic assays. Using the well established Alamar blue assay, the study demonstrates how the transfer from 2D substrates to 3D collagen matrices can affect the uptake of the resazurin itself, affecting the outcome of the assay. Using flow cytometry, it is demonstrated that the cell viability is unaffected when cells are grown on collagen matrices, thus the difference seen in the fluorescence is a result of a dilution of the resazurin dye in the collagen matrix, and an increased uptake rate due to the larger cell surface exposed to the surrounding environment, facilitating more effective diffusion through the cellular membrane. The results are supported by a rate equation based simulation, verifying that differing uptake kinetics can result in apparently different cell viability. Finally, this work highlights the feasibility to apply classical dye-based assays on collagen based 3D cell culture models. However, the diffusion and bioavailability of test substances in 3D matrices used in in vitro toxicological assays must be considered and adaption of the protocols is necessary for direct comparison with the traditional 2D models. Moreover, the observations made based on the resazurin dye can be applied to drugs or nanoparticles which freely diffuse through the collagen matrices, thus affecting the effective concentration exposed to the cells.
Identifying the determinants of childhood immunization in the Philippines.

A key method of reducing morbidity and mortality is childhood immunization, yet in 2003 only 69% of Filipino children received all suggested vaccinations.  Results of the multinomial logistic regression analyses indicate that mothers who have less education, and who have not attended the minimally-recommended four antenatal visits are less likely to have fully immunized children. To increase immunization coverage in the Philippines, knowledge transfer to mothers must improve.
The role of home literacy practices in preschool children's language and emergent literacy skills.

This study examined how 4 specific measures of home literacy practices (i.e., shared book reading frequency, maternal book reading strategies, child's enjoyment of reading, and maternal sensitivity) and a global measure of the quality and responsiveness of the home environment during the preschool years predicted children's language and emergent literacy skills between the ages of 3 and 5 years. Study participants were 72 African American children and their mothers or primary guardians primarily from low-income families whose home literacy environment and development have been followed since infancy. Annually, between 18 months and 5 years of age, the children's mothers were interviewed about the frequency they read to their child and how much their child enjoyed being read to, and the overall quality and responsiveness of the home environment were observed. Mothers also were observed reading to their child once a year at 2, 3, and 4 years of age, and maternal sensitivity and types of maternal book reading strategies were coded. Children's receptive and expressive language and vocabulary were assessed annually between 3 years of age and kindergarten entry, and emergent literacy skills were assessed at 4 years and kindergarten entry. The specific home literacy practices showed moderate to large correlations with each other, and only a few significant associations with the language and literacy outcomes, after controlling for maternal education, maternal reading skills, and the child's gender. The global measure of overall responsiveness and support of the home environment was the strongest predictor of children's language and early literacy skills and contributed over and above the specific literacy practice measures in predicting children's early language and literacy development.
Social learning platforms and the flipped classroom

This paper examines the use of social learning platforms in conjunction with the emergent pedagogy of the `flipped classroom'. In particular the attributes of the social learning platform “Edmodo” is considered alongside the changes in the way in which online learning environments are being implemented, especially within British education. Some observations are made regarding the use and usefulness of these platforms along with a consideration of the increasingly decentralized nature of education in the United Kingdom.
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization

Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object's location, one can take a sliding window approach, but this strongly increases the computational cost because the classifier or similarity function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages. It converges to a globally optimal solution typically in linear or even sublinear time, in contrast to the quadratic scaling of exhaustive or sliding window search. We show how our method is applicable to different object detection and image retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest-neighbor classifiers based on the lambda2 distance. We demonstrate state-of-the-art localization performance of the resulting systems on the UIUC Cars data set, the PASCAL VOC 2006 data set, and in the PASCAL VOC 2007 competition.
Design, development and evaluation of a highly versatile robot platform for minimally invasive single-port surgery

This work presents a new robot platform developed for laparoscopic single-port surgery. As a new approach, this platform enables the introduction of flexible endoscopic instruments through the designed hollow manipulators with Ø12mm and 6 DOFs. Two such highly versatile manipulators and a 5 DOF telescope are combined in a unit that can be inserted through a 30mm incision into the abdominal cavity. The entire platform is actuated by bowden wires in 2m distance at the periphery. This design overcomes the problem of having a bulky and heavy platform near the patient. The design of the platform and the individual components are presented in this paper. The implemented control and simulation environment are also illustrated. The working range of one manipulator and the applicable forces were measured. Furthermore, the results of the accomplished in-vivo studies are presented.
Design Of Fuzzy Logic Traffic Controller For Isolated Intersections With Emergency Vehicle Priority System Using MATLAB Simulation

Traffic is the chief puzzle problem which every country faces because of the enhancement in number of vehicles throughout the world, especially in large urban towns. Hence the need arises for simulating and optimizing traffic control algorithms to better accommodate this increasing demand. Fuzzy optimization deals with finding the values of input parameters of a complex simulated system which result in desired output. This paper presents a MATLAB simulation of fuzzy logic traffic controller for controlling flow of traffic in isolated intersections. This controller is based on the waiting time and queue length of vehicles at present green phase and vehicles queue lengths at the other phases. The controller controls the traffic light timings and phase difference to ascertain sebaceous flow of traffic with least waiting time and queue length. In this paper, the isolated intersection model used consists of two alleyways in each approach. Every outlook has different value of queue length and waiting time, systematically, at the intersection. The maximum value of waiting time and vehicle queue length has to be selected by using proximity sensors as inputs to controller for the ameliorate control traffic flow at the intersection. An intelligent traffic model and fuzzy logic traffic controller are developed to evaluate the performance of traffic controller under different pre-defined conditions for oleaginous flow of traffic. Additionally, this fuzzy logic traffic controller has emergency vehicle siren sensors which detect emergency vehicle movement like ambulance, fire brigade, Police Van etc. and gives maximum priority to him and pass preferred signal to it. Fuzzy Traffic Controller; Isolated Intersection; Vehicle Actuated Controller; Emergency Vehicle Selector.
DropLasso: A robust variant of Lasso for single cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure the genome-wide transcriptome of many individual cells in parallel, but results in noisy data with many dropout events. Existing methods to learn molecular signatures from bulk transcriptomic data may therefore not be adapted to scRNA-seq data, in order to automatically classify individual cells into predefined classes. We propose a new method called DropLasso to learn a molecular signature from scRNA-seq data. DropLasso extends the dropout regularisation technique, popular in neural network training, to estimate sparse linear models. It is well adapted to data corrupted by dropout noise, such as scRNA-seq data, and we clarify how it relates to elastic net regularisation. We provide promising results on simulated and real scRNA-seq data, suggesting that DropLasso may be better adapted than standard regularisations to infer molecular signatures from scRNA-seq data. DropLasso is freely available as an R package at https://github.com/jpvert/droplasso
Multi-cue onboard pedestrian detection

Various powerful people detection methods exist. Surprisingly, most approaches rely on static image features only despite the obvious potential of motion information for people detection. This paper systematically evaluates different features and classifiers in a sliding-window framework. First, our experiments indicate that incorporating motion information improves detection performance significantly. Second, the combination of multiple and complementary feature types can also help improve performance. And third, the choice of the classifier-feature combination and several implementation details are crucial to reach best performance. In contrast to many recent papers experimental results are reported for four different datasets rather than using a single one. Three of them are taken from the literature allowing for direct comparison. The fourth dataset is newly recorded using an onboard camera driving through urban environment. Consequently this dataset is more realistic and more challenging than any currently available dataset.
Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization

Endmember extraction is a process to identify the hidden pure source signals from the mixture. In the past decade, numerous algorithms have been proposed to perform this estimation. One commonly used assumption is the presence of pure pixels in the given image scene, which are detected to serve as endmembers. When such pixels are absent, the image is referred to as the highly mixed data, for which these algorithms at best can only return certain data points that are close to the real endmembers. To overcome this problem, we present a novel method without the pure-pixel assumption, referred to as the minimum volume constrained nonnegative matrix factorization (MVC-NMF), for unsupervised endmember extraction from highly mixed image data. Two important facts are exploited: First, the spectral data are nonnegative; second, the simplex volume determined by the endmembers is the minimum among all possible simplexes that circumscribe the data scatter space. The proposed method takes advantage of the fast convergence of NMF schemes, and at the same time eliminates the pure-pixel assumption. The experimental results based on a set of synthetic mixtures and a real image scene demonstrate that the proposed method outperforms several other advanced endmember detection approaches
Baker’s Cyst

Popliteal synovial cysts, also known as Baker's cysts, are commonly found in association with intra-articular knee disorders, such as osteoarthritis and meniscus tears. Histologically, the cyst walls resemble synovial tissue with fibrosis evident, and there may be chronic nonspecific inflammation present. Osteocartilaginous loose bodies may also be found within the cyst, even if they are not seen in the knee joint. Baker's cysts can be a source of posterior knee pain that persists despite surgical treatment of the intra-articular lesion, and they are routinely discovered on magnetic resonance imaging scans of the symptomatic knee. Symptoms related to a popliteal cyst origin are infrequent and may be related to size.



A PubMed search was conducted with keywords related to the history, diagnosis, and treatment of Baker's cysts-namely, Baker's cyst, popliteal cyst, diagnosis, treatment, formation of popliteal cyst, surgical indications, and complications. Bibliographies from these references were also reviewed to identify related and pertinent literature.



Clinical review.



Baker's cysts are commonly found associated with intra-articular knee disorders. Proper diagnosis, examination, and treatment are paramount in alleviating the pain and discomfort associated with Baker's cysts.



A capsular opening to the semimembranosus-medial head gastrocnemius bursa is a commonly found normal anatomic variant. It is thought that this can lead to the formation of a popliteal cyst in the presence of chronic knee effusions as a result of intra-articular pathology. Management of symptomatic popliteal cysts is conservative. The intra-articular pathology should be first addressed by arthroscopy. If surgical excision later becomes necessary, a limited posteromedial approach is often employed. Other treatments, such as arthroscopic debridement and closure of the valvular mechanism, are not well studied and cannot yet be recommended.
Coverage Based Test-Case Generation Using Model Checkers

This paper presetits a method for automatically generating test cases to structural coverage criteria. We show how a niodel checker can be used to autoniutically generate complete test sequetices that will provide a predefined coverage of uti? soffivare developnietit artifact that can be represented as a ffiriitr state niodel. Our goal is to help reduce the high cost of developitig test cases f o r safep-critical sojfivare applications that require a certain level of coveruge for certijicatioti, f o r example, safep-critical avionics sxstenis that need to denlotistrate MC/DC (modijied cotidition arid decision) coverage of the code. We deftie aJmiial franiework suitable for modeling soft\care artifacts, like, reqitirenients models, software spec$cations, or inipletnetitatiotis. We then show how various structural coverage criteria can be formalized and used to make a triode1 checker provide test sequences to achieve this coverqe. To illustrate our approach, we demonstrate, for the first titiie, how a niodel checker can be used to generate test sequerice.sfor MUDC coverage of a m a l l case example.
Data Gathering for Gesture Recognition Systems Based on Mono Color-, Stereo Color- and Thermal Cameras

In this paper, we present our results of automatic gesture recognition systems using different types of cameras in order to compare them in reference to their performances in segmentation. The acquired image segments provide the data for further analysis. The images of a single camera system are mostly used as input data in the research area of gesture recognition. In comparison to that, the analysis results of a stereo color camera and a thermal camera system are used to determine the advantages and disadvantages of these camera systems. On this basis, a real-time gesture recognition system is proposed to classify alphabets (A-Z) and numbers (0-9) with an average recognition rate of 98% using Hidden Markov Models (HMM).
Congenital malformations of the female genital tract: the need for a new classification system.

Current proposals for classifying female genital anomalies seem to be associated with limitations in effective categorization, creating the need for a new classification system that is as simple as possible, clear and accurate in its definitions, comprehensive, and correlated with patients' clinical presentation, prognosis, and treatment on an evidence-based foundation. Although creating a new classification system is not an easy task, it is feasible when taking into account the experience gained from applying the existing classification systems, mainly that of the American Fertility Society.
A CMOS-compatible boosted transistor having >2× drive current and low leakage current

A novel boosted MOS structure with buried n-well current booster providing >2× higher drive current and low off current is experimentally demonstrated on 28 nm bulk silicon technology. TCAD analysis is performed to investigate the boosting mechanism as well as to demonstrate scalability to 7 nm FinFET technology. Constant bias applied to the booster terminal results in a gate voltage controlled body current source intrinsic vertical BJT that only turns on at high gate voltage. The body current then amplifies lateral BJT current. The inherent vertical and lateral BJTs are automatically turned off at low gate voltage, maintaining low off-state current.
Multi-label Zero-Shot Learning with Structured Knowledge Graphs

In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework that incorporates knowledge graphs for describing the relationships between multiple labels. Our model learns an information propagation mechanism from the semantic label space, which can be applied to model the interdependencies between seen and unseen class labels. With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks. Compared to state-of-the-art approaches, comparable or improved performances can be achieved by our method.
On a Linear Cryptanalysis of a Family of Modified DES Ciphers with Even Weight S-boxes

We investigate the effect of inserting extra linearity in the Data Encryption Standard (DES) through appropriate nonsingular linear encodings of the output of the individual S-boxes. More specifically, we examine the general situation when the output of each S-box of the DES is precoded separately into a properly constructed copy of the inherent even-weight code of length 4. The study is focused on finding multi-round linear characteristics for thus modified DES ciphers having maximal effectiveness. Depending on the particular encodings, it turns out that the effectiveness of interest may be larger but in most cases is smaller than that one for the original DES with the same number of rounds. The latter means that the complexity of successful linear cryptanalysis against these ciphers will mainly increase comparing to the DES itself. The present research extends in a natural way our previous work [Linear Cryptanalysis and Modified DES with Parity Check in the S-boxes, LNCS 9540 (2016), pp. 60-78].
International Olympic Committee (IOC) Consensus Statement on Relative Energy Deficiency in Sport (RED-S): 2018 Update.

Note. This article has been co-published in the British Journal of Sports Medicine (). Mountjoy is with the Department of Family Medicine, Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Canada. Sundgot-Borgen is with the Department of Sports Medicine, The Norwegian School of Sport Sciences, Oslo, Norway. Burke is with Sports Nutrition, Australian Institute of Sport, Belconnen, Australia, and Centre for Exercise and Nutrition, Mary MacKillop Institute for Health Research, Melbourne, Australia. Ackerman is with the Divisions of Sports Medicine and Endocrinology, Boston Children’s Hospital and the Neuroendocrine Unit, Massachusetts General Hospital; Harvard Medical School, Boston, Massachusetts. Blauwet is with the Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital/Brigham and Women’s Hospital, Boston, Massachusetts. Constantini is with the Heidi Rothberg Sport Medicine Center, Shaare Zedek Medical Center, Hebrew University, Jerusalem, Israel. Lebrun is with the Department of Family Medicine, Faculty of Medicine & Dentistry, and Glen Sather Sports Medicine Clinic, University of Alberta, Edmonton, Alberta, Canada. Melin is with the Department of Nutrition, Exercise and Sport, University of Copenhagen, Frederiksberg, Denmark. Meyer is with the Health Sciences Department, University of Colorado, Colorado Springs, Colorado. Sherman is a counselor in Bloomington, Indiana. Tenforde is with the Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, Massachusetts. Klungland Torstveit is with the Faculty of Health and Sport Sciences, University of Agder, Kristiansand, Norway. Budgett is with the IOC Medical and Scientific Department, Lausanne, Switzerland. Address author correspondence to Margo Mountjoy at mmsportdoc@mcmaster.ca. Margo Mountjoy McMaster University
PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection

Fully convolutional neural networks (FCNs) have shown outstanding performance in many computer vision tasks including salient object detection. However, there still remains two issues needed to be addressed in deep learning based saliency detection. One is the lack of tremendous amount of annotated data to train a network. The other is the lack of robustness for extracting salient objects in images containing complex scenes. In this paper, we present a new architecture−PDNet, a robust prior-model guided depth-enhanced network for RGB-D salient object detection. In contrast to existing works, in which RGBD values of image pixels are fed directly to a network, the proposed architecture is composed of a master network for processing RGB values, and a sub-network making full use of depth cues and incorporate depth-based features into the master network. To overcome the limited size of the labeled RGB-D dataset for training, we employ a large conventional RGB dataset to pre-train the master network, which proves to contribute largely to the final accuracy. Extensive evaluations over five benchmark datasets demonstrate that our proposed method performs favorably against the state-of-the-art approaches.