Miracle-dz/UniFAR-FTUs huggingface dataset:
Isotope Labeling Methods for Protein Dynamics Studies

Protein structure determination by solution NMR spectroscopy has long relied on the uniform stable isotopic enrichment with 13C and 15N to alleviate resonance overlap and to allow multiple distance and angular restraints, at as many atomic sites as possible, to facilitate computing the optimal three-dimensional structural model. (1) Recently, the optimization of these labeling techniques has increased the range of protein sizes amendable to study, enhanced the quality of three-dimensional structures, and simplified the analysis of experimental data. (2) Similarly, the field of protein dynamics has benefited from advances in isotopic labeling techniques that have allowed researchers to study the motional properties of ever larger proteins over a broad range of timescales while more accurately describing the protein motions. In many ways, improvements in isotopic labeling for dynamics have mirrored those used in structural studies. However, spin-relaxation experiments designed for studying protein dynamics have their own unique requirements for residue labeling, requiring special developments in isotope enrichment techniques better suited for these demanding studies.
Tracking Editing Processes in Volunteered Geographic Information: The Case of OpenStreetMap

With an increasing number of applications building on OpenStreetMap, data quality is becoming a pressing issue. Data provenance gives useful hints that facilitate data quality assessments based on the features’ persistence. However, this requires a detailed analysis of the editing history and the corresponding contributors. In order to make this provenance information explicit, we introduce a provenance vocabulary for OpenStreetMap and show how to annotate OpenStreetMap data using this vocabulary. We specify the different types of patterns that can be found in the provenance of features. This paper therefore lays the foundation for approaches to assess data quality that build solely on the intrinsic information collected in the OpenStreetMap database, using the trustworthiness of features as a proxy function for data quality.
Self-Cannulation for Haemodialysis: Patient Attributes, Clinical Correlates and Self-Cannulation Predilection Models

Background and Objectives With emerging evidence in support of home haemodialysis (HHD), patient factors which determine uptake of the modality need to be better understood. Self-cannulation (SC) is a major step towards enabling self-care ‘in-centre’ and at home and remains the foremost barrier to its uptake. Human factors governing this aspect of HD practice are poorly understood. The aim of this study is to better understand self-cannulation preferences and factors which define them in end stage renal disease (ESRD). Design In this multicentre study, 508 of 535 patients from predialysis (Group A: n = 222), in-centre (Group B: n = 213), and home HD (Group C: n = 100) responded to a questionnaire with 3 self-cannulation questions. Simultaneously, data on clinical, cognitive and psychosocial variables were ascertained. The primary outcome measure was ‘perceived ability to self-cannulate AV access’. Predictive models were developed using logistic regression analysis. Results 36.6% of predialysis patients (A) and 29.1% of the ‘in-centre’ haemodialysis patients (B) felt able to consider SC for HD. Technical-skills related apprehension was highest in Group B (14.4%) patients. Response to routine venepuncture and the types of SC concerns were significant predictors of perceived ability to self-cannulate. There was no significant difference in concern for pain across the groups. In multivariable regression analysis, age, education level, 3MS score, hypoalbuminemia in Groups B & C and additionally, attitude to routine phlebotomy and the nature of specific concern for self-cannulation in Groups A, B and C, are significant predictors of SC preference. The unadjusted c-statistics of models 1 (derived from Group A and validated on A) and 2 (derived from B+C and validated on B), are 0.76(95% CI 0.69, 0.83) and 0.80 (95% CI 0.74, 0.87) respectively. Conclusions There is high prevalence of perceived ability to self-cannulate. Modifiable SC concerns exist in ESRD. The use of predictive models to objectively define and target education and training strategies could potentially impact on HD self-management and future uptake of home HD.
Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps

The rapid adoption of mobile messaging Apps has enabled us to collect massive amount of encrypted Internet traffic of mobile messaging. The classification of this traffic into different types of in-App service usages can help for intelligent network management, such as managing network bandwidth budget and providing quality of services. Traditional approaches for classification of Internet traffic rely on packet inspection, such as parsing HTTP headers. However, messaging Apps are increasingly using secure protocols, such as HTTPS and SSL, to transmit data. This imposes significant challenges on the performances of service usage classification by packet inspection. To this end, in this paper, we investigate how to exploit encrypted Internet traffic for classifying in-App usages. Specifically, we develop a system, named CUMMA, for classifying service usages of mobile messaging Apps by jointly modeling user behavioral patterns, network traffic characteristics, and temporal dependencies. Along this line, we first segment Internet traffic from traffic-flows into sessions with a number of dialogs in a hierarchical way. Also, we extract the discriminative features of traffic data from two perspectives: (i) packet length and (ii) time delay. Next, we learn a service usage predictor to classify these segmented dialogs into single-type usages or outliers. In addition, we design a clustering Hidden Markov Model (HMM) based method to detect mixed dialogs from outliers and decompose mixed dialogs into sub-dialogs of single-type usage. Indeed, CUMMA enables mobile analysts to identify service usages and analyze end-user in-App behaviors even for encrypted Internet traffic. Finally, the extensive experiments on real-world messaging data demonstrate the effectiveness and efficiency of the proposed method for service usage classification.
Ambient Ionization and Miniature Mass Spectrometry Systems for Disease Diagnosis and Therapeutic Monitoring

Mass spectrometry has become a powerful tool in the field of biomedicine. The combination of ambient ionization and miniature mass spectrometry systems could most likely fulfill a significant need in medical diagnostics, providing highly specific molecular information in real time for clinical and even point-of-care analysis. In this review, we discuss the recent development of ambient ionization and miniature mass spectrometers as well as their potential in disease diagnosis and therapeutic monitoring, with an emphasis on their capability in analysis of biofluids and tissues. We also speculate the future development of the integrated, miniature MS systems and provide our perspectives on the challenges in technical development as well as possible solutions for path forward.
A Meta-analysis of the Effects of Presenting Treatment Benefits in Different Formats

Purpose. The purpose of this article is to examine the effects of presenting treatment benefits in different formats on the decisions of both patients and health professionals. Three formats were investigated: relative risk reductions, absolute risk reductions, and number needed to treat or screen. Methods. A systematic review of the published literature was conducted. Articles were retrieved by searching a variety of databases and screened for inclusion by 2 reviewers. Data were extracted on characteristics of the subjects and methodologies used. Log-odds ratios were calculated to estimate effect sizes. Results. A total of 24 articles were retrieved that reported on 31 unique experiments. The meta-analysis showed that treatments were evaluated more favorably when the relative risk format was used rather than the absolute risk or number needed to treat format. However, a significant amount of heterogeneity was found between studies, the sources of which were explored using subgroup analyses and metaregression. Although the subgroup analyses revealed smaller effect sizes in the studies conducted on physicians, the metaregression showed that these differences were largely accounted for by other features of the study design. Most notably, variations in effect sizes were explained by the particular wordings that the studies had chosen to use for the relative risk and absolute risk reductions. Conclusions. The published literature has consistently demonstrated that relative risk formats produce more favorable evaluations of treatments than absolute risk or number needed to treat formats. However, the effects are heterogeneous and seem to be moderated by key differences between the methodologies used.
Developing implementation strategies for firearm safety promotion in paediatric primary care for suicide prevention in two large US health systems: a study protocol for a mixed-methods implementation study

Introduction The promotion of safe firearm practices, or firearms means restriction, is a promising but infrequently used suicide prevention strategy in the USA. Safety Check is an evidence-based practice for improving parental firearm safety behaviour in paediatric primary care. However, providers rarely discuss firearm safety during visits, suggesting the need to better understand barriers and facilitators to promoting this approach. This study, Adolescent Suicide Prevention In Routine clinical Encounters, aims to engender a better understanding of how to implement the three firearm components of Safety Check as a suicide prevention strategy in paediatric primary care. Methods and analysis The National Institute of Mental Health-funded Mental Health Research Network (MHRN), a consortium of 13 healthcare systems across the USA, affords a unique opportunity to better understand how to implement a firearm safety intervention in paediatric primary care from a system-level perspective. We will collaboratively develop implementation strategies in partnership with MHRN stakeholders. First, we will survey leadership of 82 primary care practices (ie, practices serving children, adolescents and young adults) within two MHRN systems to understand acceptability and use of the three firearm components of Safety Check (ie, screening, brief counselling around firearm safety and provision of firearm locks). Then, in collaboration with MHRN stakeholders, we will use intervention mapping and the Consolidated Framework for Implementation Research to systematically develop and evaluate a multilevel menu of implementation strategies for promoting firearm safety as a suicide prevention strategy in paediatric primary care. Ethics and dissemination Study procedures have been approved by the University of Pennsylvania. Henry Ford Health System and Baylor Scott & White institutional review boards (IRBs) have ceded IRB review to the University of Pennsylvania IRB. Results will be submitted for publication in peer-reviewed journals.
Survey relationship between the emotions of consumption and costumer satisfaction of food products in Kurdistan province

One of the main topics in modern marketing is investigation emotions of consumption and pressure of costumers to purchase of produce goods and have satisfaction for customers. Based of this study investigates relationship between the emotions of consumption and costumer satisfaction of food products in Kurdistan province. To access this aims 384 person are selected as sample. In this study dimension the emotions of consumption are logical emotion, sustainable emotion and cognitive emotion and costumer satisfaction are measured based of for dimension such as quality, access, applied and sustainability. This study based of goal is applied and base of gathering data is deceptive study. The results of this study are indicated the emotions of consumption for products are good. Sensation dimension have the most average and among dimension of satisfaction access dimension has a good position. Also there is positive and meaningful correlation between emotion of consumption and costumer satisfaction.
Musculoskeletal Disorders Among Full-Time Homemakers in Poor Communities

ABSTRACT Objective: To examine factors associated with self-reported musculoskeletal disorders (MSD) among full-time female home-makers. Methods: Data on socio-demographic characteristics, lifestyle and health were collected on 1266 married women aged 15–59 years living in poor suburbs in the outskirts of Beirut, Lebanon. Independent associations with MSD of factors and characteristics were examined using odds ratios (ORs) derived from multiple logistic regression. Results: Women's age, weight, and number of children were significantly, positively, independently associated with self-reported MSD, while women's conduct of specific household tasks were not. Women with MSD were more likely to be stressed than women without MSD (OR = 1.5). Conclusion: A major finding of this study was the positive association between distress and musculoskeletal disorders. The measures used to assess women's involvement in housework did not account for the duration of time spent performing each household task. Better measures of domestic labor, including housework and childcare, are required to understand better their impact on the health of full-time homemakers and on MSD in particular. Intervention programs to alleviate MSD in full-time homemakers should address psychosocial factors.
Use of a Green Familiar Faces Paradigm Improves P300-Speller Brain-Computer Interface Performance

Background A recent study showed improved performance of the P300-speller when the flashing row or column was overlaid with translucent pictures of familiar faces (FF spelling paradigm). However, the performance of the P300-speller is not yet satisfactory due to its low classification accuracy and information transfer rate. Objective To investigate whether P300-speller performance is further improved when the chromatic property and the FF spelling paradigm are combined. Methods We proposed a new spelling paradigm in which the flashing row or column is overlaid with translucent green pictures of familiar faces (GFF spelling paradigm). We analyzed the ERP waveforms elicited by the FF and proposed GFF spelling paradigms and compared P300-speller performance between the two paradigms. Results Significant differences in the amplitudes of four ERP components (N170, VPP, P300, and P600f) were observed between both spelling paradigms. Compared to the FF spelling paradigm, the GFF spelling paradigm elicited ERP waveforms of higher amplitudes and resulted in improved P300-speller performance. Conclusions Combining the chromatic property (green color) and the FF spelling paradigm led to better classification accuracy and an increased information transfer rate. These findings demonstrate a promising new approach for improving the performance of the P300-speller.
Learning interactions through hierarchical group-lasso regularization

We introduce a method for learning pairwise interactions in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be nonzero, both its associated main effects are also included in the model. We motivate our approach by modeling pairwise interactions for categorical variables with arbitrary numbers of levels, and then show how we can accommodate continuous variables and mixtures thereof. Our approach allows us to dispense with explicitly applying constraints on the main effects and interactions for identifiability, which results in interpretable interaction models. We compare our method with existing approaches on both simulated and real data, including a genome wide association study, all using our R package glinternet.
Distinguishing between True and False Stories using various Linguistic Features

This paper analyzes what linguistic features differentiate true and false stories written in Hebrew. To do so, we have defined four feature sets containing 145 features: POStags, quantitative, repetition, and special expressions. The examined corpus contains stories that were composed by 48 native Hebrew speakers who were asked to tell both false and true stories. Classification experiments on all possible combinations of these four feature sets using five supervised machine learning methods have been applied. The Part of Speech (POS) set was superior to all others and has been found as a key component. The best accuracy result (89.6%) has been achieved by a combination of sixteen POS-tags and one quantitative feature.
Combined bias suppression in single-arm therapy studies

Rationale, aims and objectives For therapy evaluation studies, control groups are sometimes not feasible. In single-arm studies, various bias factors apart from the test therapy can affect clinical outcomes. The objective of this analysis was to improve the methods to minimize bias in single-arm studies. Method We present a procedure for combined suppression of several bias factors, using two methods: sample restriction to patients unaffected by bias, and score adjustment. The procedure was used for a secondary analysis of disease score (doctors’ global rating, 0–10) in a cohort of patients receiving anthroposophic therapies for chronic diseases. Four bias factors were suppressed stepwise: attrition bias (by replacing missing values with the baseline value carried forward), bias from natural recovery (by sample restriction to patients with disease duration of ≥12 months), regression to the mean due to symptom-driven self-selection (by replacing baseline scores with scores three months before enrolment) and bias from adjunctive therapies (by sample restriction to patients not using adjunctive therapies). Results In the cohort analysed, these four bias factors could together explain a maximum of 37% of the 0- to 6-month improvement of disease score. Conclusion Combined bias suppression, using sample restriction and score adjustment, is a transparent procedure to minimize bias in single-arm therapy studies. Further applicability of the procedure should be tested in future studies.
First-order Cascade ARTMAP

First-order theory refinement using neural networks is still an open problem. Towards a solution to this problem, we define a First-Order extension of the Cascade ARTMAP (FOCA) system, using Inductive Logic Programming techniques. To present such a first-order extension of Cascade ARTMAP, we: a) modify the network structure to handle first-order objects; b) define firstorder versions of the main functions that guide all Cascade ARTMAP dynamics, the choice and match functions; c) define a first-order version of the propositional learning algorithm, that approximates Plotkin’s least general generalization (lgg). Results show that our initial goal, learning logic programs using neural networks, has been achieved.
Face Gender Influences the Looking Preference for Smiling Expressions in 3.5-Month-Old Human Infants

Young infants are typically thought to prefer looking at smiling expressions. Although some accounts suggest that the preference is automatic and universal, we hypothesized that it is not rigid and may be influenced by other face dimensions, most notably the face’s gender. Infants are sensitive to the gender of faces; for example, 3-month-olds raised by female caregivers typically prefer female over male faces. We presented neutral versus smiling pairs of faces from the same female or male individuals to 3.5-month-old infants (n = 25), controlling for low-level cues. Infants looked longer to the smiling face when faces were female but longer to the neutral face when faces were male, i.e., there was an effect of face gender on the looking preference for smiling. The results indicate that a preference for smiling in 3.5-month-olds is limited to female faces, possibly reflective of differential experience with male and female faces.
Fast 2D-3D Registration for Navigation System of Surgical Robot

Abstract —This paper presents a new registration algorithm of2D color images and 3D geometric models for navigation systemof surgical robot. A 2D-3D registration procedure is used tosuperimpose a tumor model on an endoscopic image precisely,and is therefore indispensable for the surgical navigation system.And thus, the performance of the 2D-3D registration procedureinﬂuences directly the usability of the surgical robot operatingsystem. One of the typical techniques developed so far istheuse ofexternal markers, butthe accuracy of this method is gettingworsedue to the breath, the heart beat, or other unknown factors. Forprecise registration of 3D models and 2D images without externalmarkers or special measurement devices, the new registrationmethod is proposed which utilizes the 2D images and theirdistance maps created by the Fast Marching Method. We showsome results of fundamental experiments using simulated modelsand actual images of the endoscopic operation. I. I NTRODUCTION Development work on a practical surgical robot system isproceeding aiming at a minimally invasive surgical operationand a favourable postoperative process. Fig.1 shows the sur-gical robot system named Intuitive Surgical’s “da Vinci”.
A 20 mA quiescent current CV/CC parallel operation HBT power amplifier for W-CDMA terminals

A novel constant voltage/constant current (CV/CC) parallel operation HBT power amplifier (PA) configuration is proposed. By combining two HBT's having different base bias circuits for each (one is CV and the other is CC), non-linearity at large back-off region can be canceled out. Therefore, both low quiescent current and enough ACPR performance can be achieved. The fabricated CV/CC parallel operation HBT PA demonstrates extremely low quiescent current of 20 mA and ACPR of less than -38 dBc at the output power range up to 27.5 dBm.
Incidental Learning of Real-World Regularities

The large literature on incidental learning relies almost exclusively on laboratory experiments. Whenever researchers have attempted to demonstrate incidental learning of real-world regularities, they have typically failed to show learning. For example, it is well established that people do not learn regularities in everyday objects, such as the left-right orientation of faces on coins, despite a very large exposure to them. In this report, we examine this apparent contradiction. We argue that most studies exploring real-life incidental learning use tests that are not as sensitive to low-confidence information as those traditionally used in laboratory tasks. Using more sensitive measures, we show that it is possible to learn regularities from British and Japanese cultural life as a direct result of exposure to these regularities. Further, confidence measures suggest that although the information may be acquired incidentally, it can be expressed with and without concomitant awareness of that knowledge.