Miracle-dz/UniFAR-FTUs huggingface dataset:
Thrombolysis for acute upper extremity deep vein thrombosis.

BACKGROUND About 5% to 10% of all deep vein thromboses occur in the upper extremities. Serious complications of upper extremity deep vein thrombosis, such as post-thrombotic syndrome and pulmonary embolism, may in theory be avoided using thrombolysis. No systematic review has assessed the effects of thrombolysis for the treatment of individuals with acute upper extremity deep vein thrombosis. OBJECTIVES To assess the beneficial and harmful effects of thrombolysis for the treatment of individuals with acute upper extremity deep vein thrombosis. SEARCH METHODS The Cochrane Vascular Information Specialist (CIS) searched the Specialised Register (29 March 2017), the Cochrane Central Register of Controlled Trials (CENTRAL; 2017, Issue 2), and three trial registries (World Health Organization International Clinical Trials Registry, ClinicalTrials.gov, and ISRCTN registry) for ongoing and unpublished studies. We additionally searched the registries of the European Medical Agency and the US Food and Drug Administration (December 2016). SELECTION CRITERIA We planned to include randomised clinical trials irrespective of publication type, publication date and language that investigated the effects of thrombolytics added to anticoagulation, thrombolysis versus anticoagulation, or thrombolysis versus any other type of medical intervention for the treatment of acute upper extremity deep vein thrombosis. DATA COLLECTION AND ANALYSIS Two review authors independently screened all records to identify those that met inclusion criteria. We planned to use the standard methodological procedures expected by Cochrane. We planned to use trial domains to assess the risks of systematic error (bias) in the trials. We planned to conduct trial sequential analyses to control for the risk of random errors and to assess the robustness of our conclusions. We planned to consider a P value of 0.025 or less as statistically significant. We planned to assess the quality of the evidence using the GRADE approach. Our primary outcomes were severe bleeding, pulmonary embolism, and all-cause mortality. MAIN RESULTS We found no trials eligible for inclusion. We also identified no ongoing trials. AUTHORS' CONCLUSIONS There is currently insufficient evidence from which to draw conclusion on the benefits or harms of thrombolysis for the treatment of individuals with acute upper extremity deep vein thrombosis as an add-on therapy to anticoagulation, alone compared with anticoagulation, or alone compared with any other type of medical intervention. Large randomised clinical trials with a low risk of bias are warranted. They should focus on clinical outcomes and not solely on surrogate measures.
A Versatile Implementation of the TraderBots Approach for Multirobot Coordination

This paper reports details of a versatile implementation of the TraderBots approach: A market-based approach to multirobot coordination. The architectural layout, implementation details, and variety of features are described. Experimental results are presented using a team of Pioneer II DX robots engaged in exploration and distributed sensing tasks. Different features and strengths of the approach and the implementation are highlighted in the experimental results.
The Resurgence of Élite Research: Promise and Prospects. A Comment on the Symposium

Research on business élites has experienced a resurgence in recent years. Although the methods used by contemporary scholars are more sophisticated than those of their predecessors, the current research shares many of the strengths, but also some of the pitfalls, of the earlier work. I discuss four papers from this volume, highlighting their contributions to our understanding of both national and international élite networks. I conclude with a plea that the authors use their subsequent work to more fully address the theoretical implications of their findings.
Modulating mechanical cues and oxygen availability to regulate chondrogenesis within MSC laden hydrogels

TISSUE engineering is a potential alternative to conventional surgical techniques for regenerating damaged tissues that lack the ability to spontaneously repair. MSCs are a promising cell source for tissue engineering strategies intended to treat a range of injuries and diseases. Within the synovial joint environment, a complex array of biochemical, environmental and biomechanical stimuli arise. If MSCs are to be successfully used in vivo, understanding the effects on their differentiation and function in response to these stimuli is vital. A central hypothesis of this thesis is that oxygen availability and mechanical stimulation can direct chondrogenic differentiation and hypertrophy of MSCs. This thesis examines the response of MSC-laden hydrogels to oxygen availability, substrate stiffness and dynamic compressive strain. The overall objective is to develop the precursor to an osteochondral graft by mechanically modulating chondrogenesis and hypertrophy of MSCs within a 3-dimensional hydrogel environment. To enable this, this thesis first systemically explored the response of MSCs undergoing chondrogenesis to altered levels of substrate stiffness, dynamic compression and oxygen availability. The application of dynamic compression after 21 days at different magnitudes in 20 % O2 to dynamic compression to MSC-laden agarose hydrogels was found to alter calcification in a magnitude dependent manner. High magnitudes (20 % strain) tended to reduce the increases in calcification produced at moderate strains. The thesis next explored the role of substrate stiffness in regulating chondrogenesis in altered oxygen environments. In MSC-laden, RGD-modified alginate hydrogels, it was found that soft hydrogels enhance early MSC chondrogenesis. Altering oxygen levels from from 20 % to 5 % altered the spatial distribution of cartilage-specific proteins producing a more homogenous tissue. In a subsequent study, dynamic compression applied after 5 days in 5 %
Space lower bounds for distance approximation in the data stream model

(MATH) We consider the problem of approximating the distance of two <i>d</i>-dimensional vectors <b>x</b> and <b>y</b> in the data stream model. In this model, the 2<i>d</i> coordinates are presented as a "stream" of data in some arbitrary order, where each data item includes the index and value of some coordinate and a bit that identifies the vector (<b>x</b> or <b>y</b>) to which it belongs. The goal is to minimize the amount of memory needed to approximate the distance. For the case of <i>L<sup>p</sup></i>-distance with <i>p</i> ε [1,2], there are good approximation algorithms that run in polylogarithmic space in <i>d</i> (here we assume that each coordinate is an integer with <i>O</i>(log <i>d</i>) bits). Here we prove that they do not exist for <i>p</i>ρ2. In particular, we prove an optimal approximation-space tradeoff of approximating <i>L</i><sup>&infty;</sup> distance of two vectors. We show that any randomized algorithm that approximates <i>L</i><sup>&infty;</sup> distance of two length <i>d</i> vectors within factor of <i>d</i><sup>δ</sup> requires ω(<i>d</i><sup>1—4δ</sup>) space. As a consequence we show that for <i>p</i>ρ2/(1—4δ), any randomized algorithm that approximate <i>L</i><sup><i>p</i></sup> distance of two length <i>d</i> vectors within a factor <i>d</i><sup>δ</sup> requires ω(<i>d</i> <sup>1— <sup>2</sup>< \over <sub>p</sub>—4δ</sup>) space.The lower bound follows from a lower bound on the two-party one-round communication complexity of this problem. This lower bound is proved using a combination of information theory and Fourier analysis.
Alexithymia, social support, psycho-social stress and mental health in a female population

Background: We wanted to look into what impact the level of alexithymia, as assessed by the Toronto Alexithymia Scale with 20 items (TAS-20), has on presence of social support and vulnerability for psycho-social stress assessed as occurrence of serious life-events, in an all-female population. Methods: A total of 1032 of females employed in a child-care programme in Sweden participated. They were asked to fill in a questionnaire and 864 (83.6%) delivered complete tests. Results: The prevalence of alexithymia was 7.9% and there were significant associations between high TAS-20 scores with low level of education. In addition, this assessment showed significant relationships between global TAS-20, as well as the sub-factors of TAS-20 and lower social support. Having a low level of social support was found to be 3.5 times more common in the part of the population who was alexithymic. It was also 2.6 times more common for the alexithymic part of the population not to have someone to turn to. Alexithymics with low support and no life events had overall mean scores on all the three variables presenting them as worse off from a mental health point of view. When life events were present alexithymics remained worse off than non-alexithymics even when a higher level of support was in place. There was a significant direct protective (salutary) effect of social support found for social disability in the alexithymic population. Conclusion: Alexithymics in this study showed a lack of social support and a proneness to high levels of negative emotion and to social distress.
Goal-Directed Inductive Matrix Completion

Matrix completion (MC) with additional information has found wide applicability in several machine learning applications. Among algorithms for solving such problems, Inductive Matrix Completion(IMC) has drawn a considerable amount of attention, not only for its well established theoretical guarantees but also for its superior performance in various real-world applications. However, IMC based methods usually place very strong constraints on the quality of the features(side information) to ensure accurate recovery, which might not be met in practice. In this paper, we propose Goal-directed Inductive Matrix Completion(GIMC) to learn a nonlinear mapping of the features so that they satisfy the required properties. A key distinction between GIMC and IMC is that the feature mapping is learnt in a supervised manner, deviating from the traditional approach of unsupervised feature learning followed by model training. We establish the superiority of our method on several popular machine learning applications including multi-label learning, multi-class classification, and semi-supervised clustering.
A Model-based Approach to Second-Language Learning of Grammatical Constructions

The goal of this work is to examine how second-language learners acquire grammatical knowledge. To measure this knowledge, our research examines how native speakers of English choose between constructions that have similar meanings, but occur in different discourse contexts. An example is the dative alternation (give someone the book vs. give the book to someone). The distribution of these two constructions is explained by a four-factor model that describes the patterns or rules that underlie native-speaker usage. We hypothesized that training examples could be selected to instantiate these four factors in a way that would lead to improved learning of the dative alternation. In addition, we predicted that explicit instruction of the four grammar principles would further enhance learning. Two studies were conducted. Results showed rapid learning and good retention across a week-long delay. Examples that were labeled as “easy” or prototypical by the grammar model were learned faster than “hard” examples. In addition, explicit instruction gave an additional boost in performance. We conclude with a description of ongoing work in computational modeling and new studies that test transfer of learning based on the four-factor model. The long-term goal is to use this approach to design learning interventions that are cognitively and linguistically based and that lead to robust learning of grammar in a second language.
An FPGA-accelerated testbed for hardware component development in MIMO wireless communication systems

FPGA-based prototyping is nowadays common practice in the functional verification of hardware components since it allows to cover a large number of test cases in a shorter time compared to HDL simulation. In addition, an FPGA-based emulator significantly accelerates the simulation with respect to bit-true software models. This speed-up is crucial when the statistical properties of a system have to be analyzed by Monte Carlo techniques. In this paper we consider a multiple-input multiple-output (MIMO) wireless communication system and show how integrating an FPGA accelerator in the software simulation framework is key to enable the development of complex hardware components in the receiver, from algorithm all the way to chip testing. In particular, we focus on a MIMO detector implementation based on the depth-first sphere decoding algorithm. The speed-up of up to 3 orders of magnitude achieved by hardware-accelerated simulation compared to a pure software testbed enables an extensive fixed-point exploration. Furthermore, it allows a unique characterization of the system communication performance and the MIMO detector run-time characteristics, which vary for different configuration parameters and operating scenarios and hence require a thorough investigation.
Attempted suicide in the elderly: characteristics of suicide attempters 70 years and older and a general population comparison group.

OBJECTIVE To identify factors associated with attempted suicide in the elderly. DESIGN Social, psychological, and psychiatric characteristics were compared in suicide attempters (70 years and older) and a representative population sample. SETTINGS Emergency departments at five hospitals in western Sweden and a representative sample of the elderly population. PARTICIPANTS Persons with Mini Mental State Examination (MMSE) score <15 were excluded. One hundred forty persons who sought hospital treatment after a suicide attempt were eligible and 103 participated (57 women, 46 men, and mean age 80 years). Comparison subjects matched for gender and age group (N = 408) were randomly selected among participants in our general population studies. MEASUREMENTS Symptoms were rated with identical instruments in cases and comparison subjects. The examination included the MMSE and tests of short- and long-term memory, abstract thinking, aphasia, apraxia, and agnosia. Depressive symptomatology was measured using the Montgomery-Asberg Depression Rating Scale, and major and minor depressions were diagnosed according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, using symptom algorithms. RESULTS Factors associated with attempted suicide included being unmarried, living alone, low education level, history of psychiatric treatment, and previous suicide attempt. There was no association with dementia. Odds ratios were increased for both major (odds ratio [OR]: 47.4, 95% confidence interval [CI]: 19.1-117.7) and minor (OR: 2.6, 95% CI: 1.5-4.7) depressions. An association was observed between perceived loneliness and attempted suicide; this relationship was independent of depression (OR: 2.8, 95% CI: 1.3-6.1). CONCLUSIONS Observed associations mirrored those previously shown for completed suicide. Results may help to inform clinical decisions regarding suicide risk evaluation in this vulnerable and growing age group.
A brain-derived metric for preferred kinetic stimuli

We here address the question of whether there is any correlation between subjective preference for simple configurations within a specific visual domain such as motion and strength of activity in visual areas in which that domain is emphasized. We prepared several distinctive patterns of dots in motion with various characteristics and asked humans to rate them according to their preference, before and while scanning the activity in their brains with functional magnetic resonance imaging. For simplicity, we restricted ourselves to motion in the fronto-parallel plane. Moving patterns produced activity in areas V1, V2, the V3 complex (V3, V3A, V3B) and V5, but only in areas V5, V3A/B and parietal cortex did the preferred kinetic patterns produce stronger activity when compared with the non-preferred ones. In addition, preferred patterns produced activity within field A1 of medial orbito-frontal cortex (mOFC), which is not otherwise activated by kinetic stimuli. Hence, for these areas, stronger neural activity correlated with subjective preference. We conclude that configurations of kinetic stimuli that are subjectively preferred correlate with stronger activity within early visual areas and within mOFC. This opens up the possibility of more detailed studies to relate subjective preferences to strength of activity in early visual areas and to relate activity in them to areas whose activity correlates with the subjective experience of beauty.
ReCrashJ: a tool for capturing and reproducing program crashes in deployed applications

Many programs have latent bugs that cause the program to fail. In order to fix a failing program, is it crucial to be able to reproduce the failure consistently. However, reproducing a failure can be difficult and time-consuming, especially when the failure is discovered by a user in a deployed application. We present ReCrash, an approach to reproduce failures efficiently, both locally and in deployed applications, without any changes to the host's environment, and with low execution overhead. During execution, ReCrash efficiently stores part of the state of method arguments. If the program fails, ReCrash uses the stored information to create unit tests that reproduce the failure. This is effective because programs written in object-oriented style rely mostly on near-by state. This demo presents ReCrashJ, an implementation of ReCrash for Java. We show the ReCrashJ Eclipse plug-in (for developers) and the ReCrashJ command-line modules (for deployed software).
The differences in requirement elicitation between community-and firm-driven open source software projects on Github

Knowledge about different development methods when starting up a new software development project is crucial for the developers, the governing bodies and the end product. Therefore new and unfamiliar options are taken out of the equation to make sure that the work gets done and that the solution will be delivered on time and with high quality. This behaviour in the long term does, however, exclude new and better ways of executing the work in the process. To shine light upon new development methods and enlighten those who are in need of insight into a new viable option we chose to investigate the differences in requirement elicitation within the area of Open Source Software development. By examining how and by who requirements are elicited in a firm-driven project compared to a community driven project, we framed a total of three research questions to base our case study on. The case study showed that in community driven Open Source Software projects external users have low participation, in other words contributions to project artefacts, compared to firm-driven projects where the participation of external users is high. Finally, we discuss the potential implications of the findings for both communityand firm-driven OSS projects. We could conclude for both types that it’s possible to increase both development speed and customer product value. Keywords—open source software, development process, requirement elicitation, git, Github, Atom, Neovim
Towards a self-organizing network: An algorithm

In this work we deal with the Achilles' heel of wireless sensor networks - energy efficient operation. By a combined MAC/physical layer attack on the issues contributing to energy overhead, we demonstrate the possibility of reducing the overall power consumption in a relatively straight-forward way without severe alteration of common communication protocols. Results of simulations of networks using the proposed scheme are shown, partially validating the crucial ideas behind it.
CNF1 Improves Astrocytic Ability to Support Neuronal Growth and Differentiation In vitro

Modulation of cerebral Rho GTPases activity in mice brain by intracerebral administration of Cytotoxic Necrotizing Factor 1 (CNF1) leads to enhanced neurotransmission and synaptic plasticity and improves learning and memory. To gain more insight into the interactions between CNF1 and neuronal cells, we used primary neuronal and astrocytic cultures from rat embryonic brain to study CNF1 effects on neuronal differentiation, focusing on dendritic tree growth and synapse formation, which are strictly modulated by Rho GTPases. CNF1 profoundly remodeled the cytoskeleton of hippocampal and cortical neurons, which showed philopodia-like, actin-positive projections, thickened and poorly branched dendrites, and a decrease in synapse number. CNF1 removal, however, restored dendritic tree development and synapse formation, suggesting that the toxin can reversibly block neuronal differentiation. On differentiated neurons, CNF1 had a similar effacing effect on synapses. Therefore, a direct interaction with CNF1 is apparently deleterious for neurons. Since astrocytes play a pivotal role in neuronal differentiation and synaptic regulation, we wondered if the beneficial in vivo effect could be mediated by astrocytes. Primary astrocytes from embryonic cortex were treated with CNF1 for 48 hours and used as a substrate for growing hippocampal neurons. Such neurons showed an increased development of neurites, in respect to age-matched controls, with a wider dendritic tree and a richer content in synapses. In CNF1-exposed astrocytes, the production of interleukin 1β, known to reduce dendrite development and complexity in neuronal cultures, was decreased. These results demonstrate that astrocytes, under the influence of CNF1, increase their supporting activity on neuronal growth and differentiation, possibly related to the diminished levels of interleukin 1β. These observations suggest that the enhanced synaptic plasticity and improved learning and memory described in CNF1-injected mice are probably mediated by astrocytes.
IPSS: A Hybrid Reasoner for Planning and Scheduling

In this paper we describe IPSS (Integrated Planning and Scheduling System), a domain independent solver that integrates an AI heuristic planner, that synthesizes courses of actions, with a constraint-based scheduler for reasoning about time and resources. IPSS is able to solve planning problems with time (precedence constraints, deadline, time windows, etc) and binary resource usage/consumption. Experimental results show that the contextual reasoning of the planner with the constraint-based solver allows to improve the total makespan on a set of problems characterized by multiple agents.