nils-herrmann/clean_scirepeval huggingface dataset:
3D printing in medicine of congenital heart diseases
Venue: 3D Printing in Medicine

Congenital heart diseases causing significant hemodynamic and functional consequences require surgical repair. Understanding of the precise surgical anatomy is often challenging and can be inadequate or wrong. Modern high resolution imaging techniques and 3D printing technology allow 3D printing of the replicas of the patient's heart for precise understanding of the complex anatomy, hands on simulation of surgical and interventional procedures, and morphology teaching of the medical professionals and patients. CT or MR images obtained with ECG gating and breath holding or respiration navigation are best suited for 3D printing. 3D echocardiograms are not ideal but can be used for printing limited areas of interest such as cardiac valves and ventricular septum. Although the print materials still require optimization for representation of cardiovascular tissues and valves, the surgeons find the models suitable for practicing closure of the septal defects, application of the baffles within the ventricles, reconstructing the aortic arch, and arterial switch procedure. Hands on surgical training (HOST) on models may soon become a mandatory component of congenital heart disease surgery program. 3D printing will expand its utilization with further improvement of the use of echocardiographic data and image fusion algorithm across multiple imaging modalities and development of new printing materials. Bioprinting of implants such as stents, patches and artificial valves and tissue engineering of a part of or whole heart using the patient's own cells will open the door to a new era of personalized medicine.
Prediction of heart diseases using associative classification
Venue: 2016 5th International Conference on Wireless Networks and Embedded Systems (WECON)

Today's health care services have come a long way to provide medical care to the patients and protect them from various diseases. This paper comprises the development of a framework based on associative classification techniques on heart dataset for early diagnosis of heart based diseases. It is hard to diagnose the heart diseases with just observation that arrives suddenly and may prove fatal when it's uncontrolled. The implementation of work is done on Cleveland heart diseases dataset from the University of California Irvine (UCI) machine learning repository to test on different data mining techniques. The various attributes related to cause of heart diseases are viz: gender, age, chest pain type, blood pressure, blood sugar etc that can predict early symptoms heart disease. Various data mining algorithms such as Aprior, FP Growth, Naive bayes, ZeroR, OneR, J48 and k nearest neighbor are applied in this study for prediction of heart diseases. On basis of best results the development of heart disease prediction system is done by using hybrid technique for classification associative rules (CARs) to achieve the prediction accuracy of 99.19%
Assistant Personal Robot (APR) Conception and Application of a Tele Operated Assisted Living Robot
Venue: Sensors

This paper presents the technical description, mechanical design, electronic components, software implementation and possible applications of a tele operated mobile robot designed as an assisted living tool. This robotic concept has been named Assistant Personal Robot (or APR for short) and has been designed as a remotely telecontrolled robotic platform built to provide social and assistive services to elderly people and those with impaired mobility. The APR features a fast high mobility motion system adapted for tele operation in plain indoor areas, which incorporates a high priority collision avoidance procedure. This paper presents the mechanical architecture, electrical fundaments and software implementation required in order to develop the main functionalities of an assistive robot. The APR uses a tablet in order to implement the basic peer to peer videoconference and tele operation control combined with a tactile graphic user interface. The paper also presents the development of some applications proposed in the framework of an assisted living robot.
Fault Localization and Fault Tolerant Control for rudders of AUVs
Venue: 2016 35th Chinese Control Conference (CCC)

This paper addresses the problem of Fault Localization (FL) and Fault Tolerant Control (FTC) for rudders of the torpedo like Autonomous Underwater Vehicle (AUV) The qualitative force analysis is adopted to derive the positive/negative nature of each fault factor while the rudder deformation fault occurs. A positive/negative nature table is generated to localize the deformation fault together with the estimated fault factors generated by the fault detection. By combining the control input formula and the estimated fault factors, the modified control inputs are produced to tolerate the rudder deformation fault. A simulation with applying the methods to an AUV depth control is presented to illustrate the effectiveness of the proposed scheme.
Obstetric Facility Quality and Newborn Mortality in Malawi: A Cross Sectional Study
Venue: PLoS medicine

Ending preventable newborn deaths is a global health priority, but efforts to improve coverage of maternal and newborn care have not yielded expected gains in infant survival in many settings. One possible explanation is poor quality of clinical care. We assess facility quality and estimate the association of facility quality with neonatal mortality in Malawi.  Data on facility infrastructure as well as processes of routine and basic emergency obstetric care for all facilities in the country were obtained from 2013 Malawi Service Provision Assessment. Birth location and mortality for children born in the preceding two years were obtained from the 2013 2014 Millennium Development Goals Endline Survey. Facilities were classified as higher quality if they ranked in the top 25% of delivery facilities based on an index of 25 predefined quality indicators. To address risk selection (sicker mothers choosing or being referred to higher quality facilities) we employed instrumental variable (IV) analysis to estimate the association of facility quality of care with neonatal mortality. We used the difference between distance to the nearest facility and distance to a higher quality delivery facility as the instrument. Four hundred sixty seven of the 540 delivery facilities in Malawi, including 134 rated as higher quality, were linked to births in the population survey. The difference between higher and lower quality facilities was most pronounced in indicators of basic emergency obstetric care procedures. Higher quality facilities were located a median distance of 3.3 km further from women than the nearest delivery facility and were more likely to be in urban areas. Among the 6,686 neonates analyzed, the overall neonatal mortality rate was 17 per 1,000 live births. Delivery in a higher quality facility (top 25% was associated with a 2.3 percentage point lower newborn mortality (95% confidence interval [CI] 0.046, 0.000, p value 0.047) These results imply a newborn mortality rate of 28 per 1,000 births at low quality facilities and of 5 per 1,000 births at the top 25% of facilities, accounting for maternal and newborn characteristics. This estimate applies to newborns whose mothers would switch from a lower quality to a higher quality facility if one were more accessible. Although we did not find an indication of unmeasured associations between the instrument and outcome, this remains a potential limitation of IV analysis.  Poor quality of delivery facilities is associated with higher risk of newborn mortality in Malawi. A shift in focus from increasing utilization of delivery facilities to improving their quality is needed if global targets for further reductions in newborn mortality are to be achieved.
Targeted disruption of sp7 and myostatin with CRISPR Cas9 results in severe bone defects and more muscular cells in common carp
Venue: Scientific reports

The common carp (Cyprinus carpio) as one of the most important aquaculture fishes produces over 3 million metric tones annually, approximately 10% the annual production of the all farmed freshwater fish worldwide. However, the tetraploidy genome and long generation time of the common carp have made its breeding and genetic studies extremely difficult. Here, TALEN and CRISPR Cas9, two versatile genome editing tools, are employed to target common carp bone related genes sp7, runx2, bmp2a, spp1, opg, and muscle suppressor gene mstn. TALEN were shown to induce mutations in the target coding sites of sp7, runx2, spp1 and mstn. With CRISPR Cas9, the two common carp sp7 genes, sp7a and sp7b, were mutated individually, all resulting in severe bone defects; while mstnba mutated fish have grown significantly more muscle cells. We also employed CRISPR Cas9 to generate double mutant fish of sp7a;mstnba with high efficiencies in a single step. These results demonstrate that both TALEN and CRISPR Cas9 are highly efficient tools for modifying the common carp genome, and open avenues for facilitating common carp genetic studies and breeding.
Facilitating and hindering factors in Internet delivered treatment for insomnia and depression
Venue: Internet interventions

Insomnia and depression is a common and debilitating comorbidity, and treatment is usually given mainly for depression. Guided Internet based cognitive behavioral therapy for insomnia (ICBT i) was, in a recent study on which this report is based, found superior to a treatment for depression (ICBT d) for this patient group, but many patients did not reach remission.  To identify facilitating and hindering factors for patients in ICBT i and ICBT d and formulate hypotheses for future research.  Qualitative telephone interviews at the time of the 6 month follow up. Thirty five interviews were done and analyzed with a grounded theory approach. Based on the qualitative results, an iterative method triangulation including quantitative and semi qualitative was performed.  The interviews were coded into 738 sentences, condensed into 47 categories and finally 11 themes. Four areas were investigated further with method triangulation: Opinions about treatment, adherence, hindering symptoms and acceptance. Patients in ICBT i were more positive regarding the treatment than patients in ICBT d. Using treatment components was positively associated with outcome in both groups. Symptoms of insomnia, depression and other comorbidities were perceived as more hindering for ICBT d than for ICBT i. Acceptance of diagnose related problems as well as negative emotions and cognitions was positively associated with outcome for ICBT i. Proposed future research hypotheses 1) A combination of CBT for insomnia and CBT for depression is more effective than only one of the treatments. 2) Additional therapist support increases outcomes for patients with more comorbidities. 3) Acceptance is a mechanism of change in CBT i.
Sparsely Connected and Disjointly Trained Deep Neural Networks for Low Resource Behavioral Annotation: Acoustic Classification in Couples' Therapy
Venue: INTERSPEECH

Observational studies are based on accurate assessment of human state. A behavior recognition system that models interlocutors' state in real time can significantly aid the mental health domain. However, behavior recognition from speech remains a challenging task since it is difficult to find generalizable and representative features because of noisy and high dimensional data, especially when data is limited and annotated coarsely and subjectively. Deep Neural Networks (DNN) have shown promise in a wide range of machine learning tasks, but for Behavioral Signal Processing (BSP) tasks their application has been constrained due to limited quantity of data. We propose a Sparsely Connected and Disjointly Trained DNN (SD DNN) framework to deal with limited data. First, we break the acoustic feature set into subsets and train multiple distinct classifiers. Then, the hidden layers of these classifiers become parts of a deeper network that integrates all feature streams. The overall system allows for full connectivity while limiting the number of parameters trained at any time and allows convergence possible with even limited data. We present results on multiple behavior codes in the couples' therapy domain and demonstrate the benefits in behavior classification accuracy. We also show the viability of this system towards live behavior annotations.
Semi Supervised and Cross Lingual Knowledge Transfer Learnings for DNN Hybrid Acoustic Models Under Low Resource Conditions
Venue: INTERSPEECH

Semi supervised and cross lingual knowledge transfer learnings are two strategies for boosting performance of lowresource speech recognition systems. In this paper, we propose a unified knowledge transfer learning method to deal with these two learning tasks. Such a knowledge transfer learning is realized by fine tuning of Deep Neural Network (DNN) We demonstrate its effectiveness in both monolingual based semisupervised learning task and cross lingual knowledge transfer learning task. We then combine these two learning strategies to obtain further performance improvement.
Mapping orientational and microstructural metrics of neuronal integrity with in vivo diffusion MRI

Diagnosis of brain disorders is hindered by the lack of an imaging technique that reveals the architecture of neuronal tissue at the cellular level, where the associated pathological processes develop. Accessing tissue integrity at the micrometer scale, three orders of magnitude below MRI resolution, would be akin to super resolution in microscopy. For MRI, bridging the resolution gap relies on biophysical modeling of water diffusion hindered by cell walls. Here we develop a general framework for estimating orientational and microstructural parameters of neurites (axons and dendrites) By employing a set of rotational invariants, we analytically reveal the nontrivial topology of parameter estimation landscape, showing that multiple branches of parameters describe the measurement almost equally well, with only one of them corresponding to the biophysical reality. A comprehensive acquisition shows that the branch choice differs for white and for gray matter. Our framework reveals hidden degeneracies in MRI parameter estimation for neuronal tissue, provides microstructural and orientational maps in the whole brain without constraints or priors, and connects modern biophysical modeling with clinical MRI.
A Survey on Recurrent Neural Network Based Modelling of Gene Regulatory Network

The correct inference of gene regulatory networks (GRN) remains as a fascinating task for researchers to understand the detailed process of complex biological regulations and functions. With availability of large dimensional microarray data, relationships among thousands of genes can be extracted simultaneously that is a reverse engineering problem. Among the different popular models to infer GRN, Recurrent Neural Networks (RNN) are considered as most popular and promising mathematical tool to model the dynamics of, as well as to infer the correct dependencies among genes from, biological data like time series microarray. RNN is closed loop Neural Network with a delay feedback. By observing the weights of RNN model, it is possible to extract the regulations among genes. Several metaheuristics or optimization techniques were already proposed to search the optimal value of RNN model parameters. In this review, we illustrate different problems regarding reverse engineering of GRN and how different proposed models can overcome these problems. It is observed that finding out the most suitable and efficient optimization techniques for the accurate inference of small artificial, large artificial, Dream4 Network,and real world GRNs with less computational complexity are still an open research problem to all.
Acoustic properties of binary mixtures of 3 acetyl 4 methyl 6 chloro 8 nitrocoumarin with 1 propanol and 2 propanol at 300, 303, and 307 K
Venue: Journal of Analytical Science and Technology

BackgroundThe present work deals with the study of acoustic parameters i.e. ultrasonic velocity, density, adiabatic compressibility (bs) apparent molar compressibility (phk) and specific acoustic impedance (Z) which reflects structural interaction by interferometer by 3 acetyl 4 methyl 6 chloro 8 nitrocoumarin at different percentage of 1 propanol water and 2 propanol water solvent system at 300K, 303K and 307K are studied.MethodsIn present investigation, ultrasonic velocity and density of 3 acetyl 4 methyl 6 chloro 8 nitrocoumarin have been studied at 0.01 M concentration at different concentrations (70% 75% 80% 85% and 90% in 100% 1 propanol and 2 propanol at 300K, 303K and 307K. From these values, adiabatic compressibility (bs) apparent molar compressibility (phk) and specific acoustic impedance (Z) are calculated.ResultsMeasurement of ultrasonic velocity is the best tool to investigate solute solvent, solute solute and ion solvent interactions. Therefore, in last four decades ultrasonic interferometric study created its own identity for determining solute solvent interactions. By this study, adiabatic compressibility (bs) apparent molar compressibility (phk) and specific acoustic impedance (Z) acoustic properties are determined which explain how these interactions occur and responsible for breaking and making of the structure in the solution. So in the present work these acoustic parameters were studied for synthesized ligands, which are used as solutes.ConclusionTrends of acoustic properties indicate the presence of molecular interaction in the present binary mixture under study. It may be qualitatively inferred that the interaction between unlike molecules is mainly due to hydrogen bonding. The present studies investigated that with increasing the concentration of 1 propanol and 2 propanol bs and phk increases while Z decreases at 300 k, 303K and 307K by taking constant concentration of 3 acetyl 4 methyl 6 chloro 8 nitrocoumarin (0.01M) Better values of parameters are found for the 2 propanol and water system in 90% ratio at 307K. From this study it is clear that properties, which are directly or indirectly responsible for this are protic nature of solvent, dielectric constant, polarity, density, tendency of forming hydrogen bonding, surface tension, viscosity of solvent etc.
A primal dual method for SVM training
Venue: Neurocomputing

Training support vector machines (SVM) consists of solving a convex quadratic problem (QP) with one linear equality and box constraints. In this paper, we solve this QP by a primal dual approach that combines the adaptive method with an interior point method. To initialize the algorithm, a procedure of an interior point method is used to construct an initial support. The proposed approach provides an efficient implementation of a new algorithm that exploits the advantage of the adaptive method for training SVM problems. It is based on the principle of the support and the suboptimality estimate. Experimental results confirm the efficiency of our approach over state of the art SVM algorithms such as SMO, LIBSVM and SVMLight for medium sized problems.
Regularized Primal Dual Subgradient Method for Distributed Constrained Optimization
Venue: IEEE Transactions on Cybernetics

In this paper, we study the distributed constrained optimization problem where the objective function is the sum of local convex cost functions of distributed nodes in a network, subject to a global inequality constraint. To solve this problem, we propose a consensus based distributed regularized primal dual subgradient method. In contrast to the existing methods, most of which require projecting the estimates onto the constraint set at every iteration, only one projection at the last iteration is needed for our proposed method. We establish the convergence of the method by showing that it achieves an O(K 1/4) convergence rate for general distributed constrained optimization, where K is the iteration counter. Finally, a numerical example is provided to validate the convergence of the propose method.
Online learning early skip decision method for the HEVC Inter process using the SVM based Pegasos algorithm

High efficient video coding (HEVC) the latest coding standard, has an encoding complexity much higher compared with H.264/advanced video coding (AVC) The greater efficiency in HEVC is obtained at much greater computational cost compared with AVC. A fast coding unit (CU) splitting algorithm is proposed for the HEVC encoder, which early terminates the CU partitioning process based on an adaptive classification model. This model is generated by an online learning method based on a Pegasos (primal estimated sub gradient solver for SVM) algorithm. The proposed method is implemented over the HEVC reference implementation on its version 16.7. Experimental results show that the proposed method reduces the computational complexity of HEVC encoder to 35% without any loss, resulting in a 1% of Bjontegaard Delta rate gain in the low delay B configuration without any offline training phase.
Open label placebo treatment in chronic low back pain: a randomized controlled trial
Venue: Pain

This randomized controlled trial was performed to investigate whether placebo effects in chronic low back pain could be harnessed ethically by adding open label placebo (OLP) treatment to treatment as usual (TAU) for 3 weeks. Pain severity was assessed on three 0 to 10 point Numeric Rating Scales, scoring maximum pain, minimum pain, and usual pain, and a composite, primary outcome, total pain score. Our other primary outcome was back related dysfunction, assessed on the Roland Morris Disability Questionnaire. In an exploratory follow up, participants on TAU received placebo pills for 3 additional weeks. We randomized 97 adults reporting persistent low back pain for more than 3 months' duration and diagnosed by a board certified pain specialist. Eighty three adults completed the trial. Compared to TAU, OLP elicited greater pain reduction on each of the three 0 to 10 point Numeric Rating Scales and on the 0 to 10 point composite pain scale (P 0.001) with moderate to large effect sizes. Pain reduction on the composite Numeric Rating Scales was 1.5 (95% confidence interval: 1.0 2.0) in the OLP group and 0.2 0.3 to 0.8) in the TAU group. Open label placebo treatment also reduced disability compared to TAU (P 0.001) with a large effect size. Improvement in disability scores was 2.9 (1.7 4.0) in the OLP group and 0.0 1.1 to 1.2) in the TAU group. After being switched to OLP, the TAU group showed significant reductions in both pain (1.5, 0.8 2.3) and disability (3.4, 2.2 4.5) Our findings suggest that OLP pills presented in a positive context may be helpful in chronic low back pain.