LLukas22/scidocs huggingface dataset:
Closure of an unusually large palatal fistula in a cleft patient by bony transport and corticotomy-assisted expansion.

The management of cleft lip and palate can vary among patients because the size of a cleft defect and its anatomy vary. Although treatments involving orthodontics and surgery have been developed that can help a large number of children with cleft lip and palate, there are always unusual clefts that defy conventional treatments. In such cases, with each gain made during treatment, there also is the possibility of introducing an additional complication. The secondary bone graft performed during the mixed dentition stage of dental development is an example of treatment gains that are balanced against additional complications. Ordinarily, the maxillary segments are expanded to attain a normal archform before the alveolar bone graft. If there is an anterior palatal fistula present before orthodontic expansion, the size of the fistula concomitantly increases as the expanders widen the anterior maxillary segments. There are, however, extreme cases that present with a combination of collapsed maxillary segment, large anterior palatal fistula, and large alveolar clefts. In such cases, orthodontic expansion could make both the alveolar clefts and anterior palatal fistula unmanageable. In the expanded position, there would be insufficient soft tissue to close the fistula or cover the alveolar bone graft. Consequently, dental prosthetics is often needed to cover the palatal fistula to support speech and eating. In this report, we present the case of an 11-year-old girl who had a 20-mm anterior palatal fistula before orthodontic expansion. In the opinion of our surgeons, the fistula was too large to close before the segments were expanded and would prove more difficult to treat after expansion. To make the palatal fistula and cleft sites manageable, the treatment sequence and procedures were modified. Rather than expand the maxillary segments, the segments were compressed to make the cleft space narrower so that the surgeons could graft the alveolar cleft. To provide more bone support and donor soft tissue for a palatal flap, the palatal tooth and surrounding bone were distracted across the anterior palatal opening. A sequence of minor procedures allowed the cleft sites to be grafted, the unusually large palatal fistula to be closed, the lateral segments to be expanded, and the dental alignment to be improved.
Evaluating Entity Linking: An Analysis of Current Benchmark Datasets and a Roadmap for Doing a Better Job

Entity linking has become a popular task in both natural language processing and semantic web communities. However, we find that the benchmark datasets for entity linking tasks do not accurately evaluate entity linking systems. In this paper, we aim to chart strengths and weaknesses of current benchmark datasets and sketch a roadmap for the community to devise better benchmark datasets. An extended abstract that followed from this discussion was submitted to LREC 2016.
Modeling Social Action for AI Agents

AI is a science, not merely technology, engineering. It cannot find an identity (ubi consistam) in a technology, or set of technologies, and we know that such an identification is quite dangerous. AI is the science of possible forms of intelligence, both individual and collective. To rephrase Doyle's claim, AI is the discipline aimed at understanding intelligent beings by constructing intelligent systems. Since intelligence is mainly a social phenomenon and is due to the necessity of social life, we have to construct socially intelligent systems to understand it, and we have to build social entities to have intelligent systems. If we want that the computer is not "just a glorified pencil" [Popper, BBC interview), that it is not a simple tool but a collaborator [Grosz, 1995], an assistant, we need to model social intelligence in the computer. If we want to embed intelligent functions in both the virtual and physical environment (ubiquitous computing) in order to support human action, these distributed intelligences must be social to understand and help the users, and to coordinate, compete and collaborate with each other. In fact Social Intelligence is one of the ways AI responded to and went out of its crisis. It is one of the way it is "back to the future", trying to recover all the original challenges of the discipline, its strong scientific identity, its cultural role and influence, that in the '60s and 70s gave rise to the Cognitive Science, and now wil l strongly impact on the social sciences. This stream is part of the new AI of the '90s where systems and models are conceived for reasoning and acting in open unpredictable worlds, with limited and uncertain knowledge, in real time, with bounded (both cognitive and material) resources, with hybrid architectures, interfering -either cooperatively or competitivelywith other systems. The new password is interaction [Bobrow, 1991): interaction with an evolving environment; among several, distributed and heterogeneous artificial systems in a network; with human users; among humans through computers. Important work has been done in AI (in several domains from D A I to HCI , from Agents to logic for action, knowledge, and speech acts) for modeling social intelligence and behavior. In my talk I wi l l just attempt a principled systematization. On the one side, 1 wil l illustrate what I believe to be the basic ontological categories for social action, structure, and mind; letting, first, sociality (social action, social structure) emerge bottom-up from the action and intelligence of individual agents in a common world, and, second, examine some aspects of the way-down: how emergent collective phenomena shape the individual mind. In this paper I wil l focus on the bottom-up perspective. On the other side, I wi l l propose some critical reflections on current approaches and future directions. Doing this I wi l l in particular stress five points.
Anthropometric and performance differences among high-school football players.

The purpose of this study was to examine physical and performance differences between grade levels and playing positions within High-School football players. Two thousand three hundred and twenty-seven athletes were tested for height, weight, 40-yd sprint time, proagility time, and vertical jump height. Mean scores across age groups and playing positions were compared using repeated-measures analysis of variance (ANOVA) and 1-way ANOVAs. The results indicate that defensive players in the 11th and 12th grades were significantly faster in the 40-yd sprint, quicker in the proagility, and generated more power than 9th and 10th grade defensive players across all positions (p < 0.05). Similarly, offensive players in the 11th and 12th grades were significantly faster, quicker, and jumped higher than did football players in lower grades (p < 0.05). Overall, these data suggest that there are distinct differences in the physical and performance characteristics of high-school football players. The greatest difference is observed between the sophomore and junior years. Older, more mature athletes are faster, quicker, and capable of generating more power than younger athletes. Practically, these data lend support to the common 3-tiered approach (i.e., Freshman, Junior Varsity, and Varsity) most high schools use for their football programs. This approach is likely indicated to allow for physical maturation of young players and to allow time for the development of strength, power, speed, and agility necessary to compete with older players.
Imagination of children

Group exhibition exploring the imagination of children within the collection at the V and A's Museum of Childhood, London
Profiling Java applications using code hotswapping and dynamic call graph revelation

Instrumentation-based profiling has many advantages and one serious disadvantage: usually high performance overhead. This overhead can be substantially reduced if only a small part of the target application (for example, one that has previously been identified as a performance bottleneck) is instrumented, while the rest of the application code continues to run at full speed. The value of such a profiling technology would increase further if the code could be instrumented and de-instrumented as many times as needed at run time.In this paper we present an experimental profiling system called JFluid, which includes a modified Java#8482; VM and a GUI tool, and addresses both of the above issues. Our tool supports instrumentation of a group of methods defined as an arbitrary "root" method plus all methods that it calls (a call subgraph). It appears that static determination of all methods in a call subgraph is difficult in presence of virtual methods, bug fortunately, with dynamic code hotswapping available, two schemes of dynamic call subgraph revelation and instrumentation can be suggested. Measurements that we obtained when performing full and partial program profiling using both schemes show that the overhead can be reduced substantially using this technique, and that one of the schemes generally results in a smaller number of instrumented methods and better performance. Furthermore, we observe that our approach generally works much better for large (for example, J2EE and Web) applications, than for small benchmarks.
Sympathetic activity and early mobilization in patients in intensive and intermediate care with severe brain injuries: a preliminary prospective randomized study

BACKGROUND
Patients who experience severe brain injuries are at risk of secondary brain damage, because of delayed vasospasm and edema. Traditionally, many of these patients are kept on prolonged bed rest in order to maintain adequate cerebral blood flow, especially in the case of subarachnoid hemorrhage. On the other hand, prolonged bed rest carries important morbidity. There may be a clinical benefit in early mobilization and our hypothesis is that early gradual mobilization is safe in these patients. The aim of this study was to observe and quantify the changes in sympathetic activity, mainly related to stress, and blood pressure in gradual postural changes by the verticalization robot (Erigo®) and after training by a lower body ergometer (MOTOmed-letto®), after prolonged bed rest of minimum 7 days.


METHODS
Thirty patients with severe neurological injuries were randomized into 3 groups with different protocols of mobilization: Standard, MOTOmed-letto® or Erigo® protocol. We measured plasma catecholamines, metanephrines and blood pressure before, during and after mobilization.


RESULTS
Blood pressure does not show any significant difference between the 3 groups. The analysis of the catecholamines suggests a significant increase in catecholamine production during Standard mobilization with physiotherapists and with MOTOmed-letto® and no changes with Erigo®.


CONCLUSIONS
This preliminary prospective randomized study shows that the mobilization of patients with severe brain injuries by means of Erigo® does not increase the production of catecholamines. It means that Erigo® is a well-tolerated method of mobilization and can be considered a safe system of early mobilization of these patients. Further studies are required to validate our conclusions.


TRIAL REGISTRATION
The study was registered in the ISRCTN registry with the trial registration number ISRCTN56402432 . Date of registration: 08.03.2016. Retrospectively registered.
Hierarchical Temporal Memory Method for Time-Series-Based Anomaly Detection

Time-series-based anomaly detection is a quite important field that has been researched over years. Many techniques have been developed and applied successfully for certain application domains. However, there are still some challenges, such as continuously learning, tolerance to noise and generalization. This paper present Hierarchical Temporal Memory, a novel biological neural network, to time-series-based anomaly detection. HTM is able to learn the changing pattern of the data and incorporate contextual information from the past to make accurate prediction. We have evaluated HTM on real and artificial datasets. The experiment results show that HTM can successfully discover anomalies in time-series data.
How Does Mode of Input Affect Incidental Vocabulary Learning

............................................................................................................................... i Acknowledgments............................................................................................................... ii Table of
Electro Discharge Machining of Nickel-Titanium Shape Memory Alloys on Surface Roughness and MRR

Shape memory alloys are kind of smart materials, which have distinctive properties and are superior in compare with other alloys. Severe reactions to thermodynamic and mechanical parameters and ability to return to their original shape make these alloys distinctive from other alloy regarding machining ability. Since these alloys have a very useful application in various fields such as aerospace, automobile, medicine, dentistry, the present paper surveys the effect of wire electrical discharge machining on them. Surface roughness and material removal rate are the most important parameters of machining which influence product quality and machining time. Reducing surface roughness improves fatigue resistance, corrosion and wear resistance of work piece. Increasing the removal rate and reducing machining time decline the production costs and increase production. The present research seeks to investigate the effect of wire electrical discharge machining parameters on surface roughness and removal rate of NiTi60. Results reveal that increasing current pulse, pulse on-time and wire speed increase surface roughness and material removal rate. [Daneshmand S, Hessami R, Esfandiar H. Investigation of Wire Electro Discharge Machining of NickelTitanium Shape Memory Alloys on Surface Roughness and MRR. Life Sci J 2012; 9(4):2904-2909] (ISSN: 1097-8135). http://www.lifesciencesite.com. 426
The impact of residential neighborhood type on travel behavior: A structural equations modeling approach

Using a system of structural equations, this paper empirically examines the relationship of residential neighborhood type to travel behavior, incorporating attitudinal, lifestyle, and demographic variables. Data on these variables were collected from residents of five neighborhoods in the San Francisco Bay Area in 1993 (final N 1⁄4 515), including ‘‘traditional’’ and ‘‘suburban’’ as well as mixtures of those two extremes. A conceptual model of the interrelationships among the key variables of interest was operationalized with a nineequation structural model system. The nine endogenous variables included two measures of residential location type, three measures of travel demand, three attitudinal measures, and one measure of job location. In terms of both direct and total e¤ects, attitudinal and lifestyle variables had the greatest impact on travel demand among all the explanatory variables. By contrast, residential location type had little impact on travel behavior. This is perhaps the strongest evidence to date supporting the speculation that the association commonly observed between land use configuration and travel patterns is not one of direct causality, but due primarily to correlations of each of those variables with others. In particular, the results suggest that when attitudinal, lifestyle, and sociodemographic variables are accounted for, neighborhood type has little influence on travel behavior. JEL classification: C31, D12, R14
Electrical Aging Phenomena of XLPE and EPR Cable Insulation Energized by Switching Impulses

During the operation of power system, the insulation of high voltage extruded cables will be placed under high electrical stress, including overvoltages caused by switching surges. In the study, 15 kV XLPE and EPR power cable samples are energized by switching impulses. The aging phenomena of cable insulation are observed by the measurement of partial discharge level and AC breakdown voltage during the aging process. The study helps to determine the reliability of power cables, as well as a better understanding of the aging mechanism. Many factors other than aging by switching surges have to be considered as an aging force for the insulation degradation.
Prevalence of neuropathic pain in emergency patients: an observational study.

OBJECTIVE
Neuropathic pain (NP) in acute conditions has been poorly investigated. A diagnostic score, the DN4 scale (DN4), has been developed to help physicians to detect and treat NP appropriately. DN4 is a 10-item questionnaire. If you have 4 or more positive responses out of 10 items, the answer to the questionnaire is positive and you have a neuropathic pain. We aimed to determine the prevalence of NP in emergency department (ED) patients and to describe this population.


METHODS
We used the DN4 in the patients with NP visiting the adult ED of a university hospital. Patients were asked about the characteristics of their pain using a face-to-face questionnaire.


RESULTS
Among 533 patients with a DN4 score, 114 (21.4%) had NP. Neuropathic pain was independently negatively associated with age of 65 years of older (odds ratio [OR], 0.2, 95% confidence interval [CI], 0.05-0.8) and positively associated with intense pain (OR, 5.2; 95% CI, 1.5-18.2), located to the limbs (OR, 2.3; 95% CI, 1.2-4.0).


CONCLUSION
Neuropathic pain was common in ED patients and associated to a higher level of pain.
An empirical framework for defect prediction using machine learning techniques with Android software

Context: Software defect prediction is important for identification of defect-prone parts of a software. Defect prediction models can be developed using software metrics in combination with defect data for predicting defective classes. Various studies have been conducted to find the relationship between software metrics and defect proneness, but there are few studies that statistically determine the effectiveness of the results. Objective: The main objectives of the study are (i) comparison of the machine-learning techniques using data sets obtained from popular open source software (ii) use of appropriate performance measures for measuring the performance of defect prediction models (iii) use of statistical tests for effective comparison of machine-learning techniques and (iv) validation of models over different releases of data sets. Method: In this study we use object-oriented metrics for predicting defective classes using 18 machinelearning techniques. The proposed framework has been applied to seven application packages of well known, widely used Android operating system viz. Contact, MMS, Bluetooth, Email, Calendar, Gallery2 and Telephony. The results are validated using 10-fold and inter-release validation methods. The reliability and significance of the results are evaluated using statistical test and post-hoc analysis. Results: The results show that the area under the curve measure for Naïve Bayes, LogitBoost and Multilayer Perceptron is above 0.7 in most of the cases. The results also depict that the difference between the ML techniques is statistically significant. However, it is also proved that the Support Vector Machines based techniques such as Support Vector Machines and voted perceptron do not possess the predictive capability for predicting defects. Conclusion: The results confirm the predictive capability of various ML techniques for developing defect prediction models. The results also confirm the superiority of one ML technique over the other ML techniques. Thus, the software engineers can use the results obtained from this study in the early phases of the software development for identifying defect-prone classes of given software. © 2016 Elsevier B.V. All rights reserved.
Antecedents of chronic lung disease following three patterns of early respiratory disease in preterm infants.

INTRODUCTION
The incidence of chronic lung disease (CLD) varies among groups defined by their early pattern of respiratory disease.


METHODS
The study examined data collected prospectively on 1204 of the 1506 infants born in 2002-2004 at 23-27 weeks gestation who survived to 36 weeks post-menstrual age. Based on their initial respiratory presentation and need for supplemental oxygen during the first 2 weeks, infants were classified as having early and persistent pulmonary dysfunction (EPPD), early recovery of pulmonary function followed by deterioration (PD) or consistently good pulmonary function characterised by low FiO(2) (Low FiO(2)).


RESULTS
CLD was diagnosed in 69% of infants with EPPD, 52% with PD, and 17% in the Low FiO(2) group. Birth weight z score <-1 conveyed information about CLD risk in all three groups and was the major risk factor for infants in the Low FiO(2) group (OR 27; 95% CI 7 to 95). Mechanical ventilation at 7 days was associated with increased risk in the PD (OR 4.2, 95% CI 2.5 to 6.9) and EPPD groups (OR 2.7, 95% CI 1.5 to 4.7), but not the Low FiO(2) group (OR 1.5, 95% CI 0.5 to 3.9).


CONCLUSION
The likelihood of a very preterm infant developing CLD and the profile of risk factors linked with CLD are related to the infant's pattern of respiratory disease during the first 2 postnatal weeks. Among infants with little exposure to oxygen during this period, fetal growth restriction, not mechanical ventilation, is the factor with the strongest association with CLD.
Word-Context Character Embeddings for Chinese Word Segmentation

Neural parsers have benefited from automatically labeled data via dependencycontext word embeddings. We investigate training character embeddings on a word-based context in a similar way, showing that the simple method significantly improves state-of-the-art neural word segmentation models, beating tritraining baselines for leveraging autosegmented data.
Circulating Ki67 positive lymphocytes in multiple myeloma and benign monoclonal gammopathy.

AIMS
To estimate the proportion and nature of the proliferating (Ki67+) circulating lymphocytes in a series of patients with multiple myeloma and monoclonal gammopathy of unknown significance (MGUS) and to correlate this with other clinical and laboratory parameters, using blood from healthy adults as a control. To investigate the extent to which the B and T lymphoid components are involved in progression and/or control of disease.


METHODS
Blood lymphocytes from 15 patients with multiple myeloma, 10 patients with MGUS and 10 healthy adults were analysed using a sequential double immunoenzymatic staining technique. Antibodies directed against Ki67 were used to detect cells in cycle, CD3, CD4, and CD8 to identify T cells, HLA-Dr as a marker for B cells and activated T cells, and CD11b as a marker for natural killer cells. Polyclonal antibodies directed against the kappa and lambda immunoglobulin light chains were also used to detect B cells.


RESULTS
The proportion of proliferating (Ki67+) lymphocytes was significantly higher in patients with multiple myeloma (6.8 +/- 2.6) and MGUS (3.5 +/- 1.1) compared with the normal controls (1.69 +/- 0.3); this was also true when multiple myeloma and MGUS cases were compared. In multiple myeloma and MGUS over 50% of the Ki67+ cells were activated T lymphocytes (CD3+/HLA-Dr+); a minority (11%) were non-clonal B lymphocytes. In contrast to controls (6.7 +/- 1.9), in patients with multiple myeloma and MGUS the proportion of proliferating T cells expressing CD8 (23.6 +/- 12.5 and 15.3 +/- 7.7, respectively) and CD11b (13 +/- 8.7 and 11.6 +/- 3.9, respectively) was higher. In multiple myeloma there was a positive correlation between the proportion of Ki67+ lymphocytes, beta-2-microglobulin concentrations and disease stage.


CONCLUSIONS
Although the number of patients investigated is small, this study suggests that Ki67 expression in blood lymphocytes from patients with multiple myeloma may be a good prognostic indicator for aggressive disease and may help to distinguish multiple myeloma from MGUS. The activated proliferating T cells in these diseases may represent an immunological reaction against the tumour.