Miracle-dz/UniFAR-FTUs huggingface dataset:
Anticoagulation Monitoring Under ECMO Support: A Comparative Study Between the Activated Coagulation Time and the Anti-Xa Activity Assay

Purpose: Extra Corporeal Membrane Oxygenation (ECMO) is used in cases of severe respiratory and/or circulatory failure over periods of several days to several weeks. Its circuitry requires a closely monitored anticoagulation therapy that is empirically supported by activated clotting time (ACT)—a method often associated with large inter- and intraindividual variability. We aimed to compare the measurement of heparin activity with ACT and the direct measurement of the heparin activity (anti-Xa) in a large ECMO population. Methods: All patients treated by venoarterial or venovenous ECMO in our intensive care unit between January 2014 and December 2015 were prospectively included. A concomitant measurement of the anti-Xa activity and ACT was performed on the same sample collected twice a day (morning–evening) for unfractionated heparin adaptation with an ACT target range of 180 to 220 seconds. Results: One hundred and nine patients (men 69.7%, median age 54 years) treated with ECMO (70.6% venoarterial) were included. Spearman analysis found no correlation between anti-Xa and ACT (ρ < 0.4) from day 1 and worsened over time. Kappa analysis showed no agreement between the respective target ranges of ACT and anti-Xa. Conclusions: We demonstrate that concomitant measurement of ACT and anti-Xa activity is irrelevant in ECMO patients. Since ACT is poorly correlated with heparin dosage, anti-Xa activity appears to be a more suitable assay for anticoagulation monitoring.
An Analysis of Guaranteed Lifetime Withdrawal Benefits Linked to Target Volatility Portfolio

GLWB is currently the most popular type of a variable annuity. GWLB promises the policyholder to annually withdraw a fixed amount from his investment account for the rest of his life, even if the value of the investment account drops to zero. However, from the insurer's point of view, GLWB contains several types of risk such as mortality risk, interest rate risk and financial risk. Target volatility strategy is used to create a dynamic re-balancing portfolio such that the overall volatility of portfolio maintains a stable level at all time. The strategy shifts the allocation of equity asset to non-risky asset in order to protect the portfolio from equity market crashes. Many insurance companies offer or consider GLWB linked to target volatility portfolios. This paper provides an extensive analysis of the GLWB linked to target volatility portfolio. In particular, we investigate the impact of stochastic volatility and mortality intensity on the pricing of the GLWB linked to target volatility portfolio. Moreover, we examine the lifetime probability of ruin for the target volatility portfolios.
Effects of Gender Stereotypes on Trust and Likability in Spoken Human-Robot Interaction

As robots enter more and more areas of everyday life, it becomes necessary for them to interact in an understandable and trustworthy way. In many regards this requires a human-like interaction pattern. This research investigates the inﬂuence of gender stereotypes on trust and likability of humanoid robots. In this endeavor, explicit (name and voice) and implicit gender (personality) of robots have been manipulated along with the stereotypicality of a task. 40 participants interacted with a NAO robot to gain feedback on a task they were working on and rated the perception of the robot cooperation partner. While no gender stereotypes were found for the explicit gender, implicit gender showed a strong effect on trust and likability in the stereotypical male task. Participants trusted the male robot more and rated it as more reliable and competent than the female personality robot, while the female robot was perceived as more likable. These ﬁndings indicate that for gender stereotypes in robot interaction a differentiation between explicit and implicit stereotypical features have to be drawn and that the task context needs consideration. Future research may look into situational variables that drive stereotypiﬁcation in human-robot interaction.
Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography

Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for studying the native structure and organization of macromolecules inside single cells. For systematic recognition and recovery of macromolecular structures captured by CECT, methods for several important tasks such as subtomogram classification and semantic segmentation have been developed. However, the recognition and recovery of macromolecular structures are still very difficult due to high molecular structural diversity, crowding molecular environment, and the imaging limitations of CECT. In this paper, we propose a novel multi-task 3D convolutional neural network model for simultaneous classification, segmentation, and coarse structural recovery of macromolecules of interest in subtomograms. In our model, the learned image features of one task are shared and thereby mutually reinforce the learning of other tasks. Evaluated on realistically simulated and experimental CECT data, our multi-task learning model outperformed all single-task learning methods for classification and segmentation. In addition, we demonstrate that our model can generalize to discover, segment and recover novel structures that do not exist in the training data.
A method for system calls sandboxing based on atomic trusted code region

This paper presents a new algorithm for the sandboxing system calls based on the atomic trusted code region. The algorithm successfully protects against any kind of code-injection attacks as well as any kind of mimicry attack including known-address attacks and scanning attacks. The algorithm is lightweight and simple. The implementation of algorithm does not need any change on an untrusted machine code and does not need extensive changes on system source code. Whole security policy could be enforced in user space as a plug-in, which gives great flexibility.
Preparation and characterisation of CNF/MWCNT carbon aerogel as efficient adsorbents.

In recent years, carbon aerogels have attracted much attention in basic research and as potential applications in many fields. Herein, the authors report a novel approach using bamboo powder as raw material to fabricate cellulose nanofibers (CNFs)/multi-walled carbon nanotubes (MWCNTs) carbon aerogels by a simple dipping and carbonisation process. The developed material exhibits many exciting properties including low density (0.056 g cm-3), high porosity (95%), efficient capability for separation of oily droplets from water, and high adsorption capacity for a variety of oils and organic solvents by up to 110 times its own weight. Furthermore, the CNF/MWCNT carbon aerogels (CMCA) can be recycled many times by distillation and combustion, satisfying the requirements of practical oil-water separation. Taken together with its economical, environmentally benign manufacturing process, sustainability of the precursor and versatility of material, the CMCA developed in this study will be a promising candidate for addressing the problems arising from the spills of oily compounds.
Seasonal differences in landbird migration strategies

ABSTRACT Migrating birds make strategic decisions at multiple temporal and spatial scales. They must select flight altitudes, speeds, and orientations in order to maintain preferred directions of movement and to minimize energy expenditure and risk. Spring flights follow a rapid phenology, but how this rapid transit translates to in-flight decisions is not clear. We described flight strategies of nocturnally migrating landbirds using 6 weather surveillance radars during spring (2013–2015) and fall (2013–2014) migratory periods in the eastern United States to investigate seasonal decision-making patterns and how climate change may influence these trends. During spring, we found groundspeed and airspeed of migrants to be significantly higher than those of fall migrants; compensation for wind drift was also significantly greater during spring. Our results indicate that birds make more rapid and precise flights in spring that are only partially explained by meteorological phenomena. Future applications at greater spatial scales will allow direct comparisons of in-flight behaviors with predictions from migration theory.
Proteomic approaches to study osteoclast biology

Bone is a dynamic tissue whose remodeling throughout life is orchestrated by repeated cycles of destruction mediated by osteoclasts and rebuilding by osteoblasts. Current understanding of osteoclast biology has largely relied on the generation of knockout mice exhibiting an abnormal bone phenotype. This has provided a better understanding of osteoclast biology and the key proteins that support osteoclast function. However, mouse models alone do not provide an integrated view on protein networks and post‐translational modifications that might be important for osteoclast function. During the past years, a number of MS‐based quantitative methods have been developed to investigate the complexity of biological systems. This review will summarize how such approaches have contributed to the understanding of osteoclast differentiation and function.
The First 100 Days: A Corpus Of Political Agendas on Twitter

The first 100 days corpus is a curated corpus of the first 100 days of the United States of America’s President and the Senate. During the first 100 days, the political parties in the USA try to push their agendas for the upcoming year under the new President. As communication has changed this is primarily being done on Twitter so that the President and Senators can communicate directly with their constituents. We analyzed the current President along with 100 Senators ranging the political spectrum to see the differences in their language usage. The creation of this corpus is intended to help Natural Language Processing (NLP) and Political Science research studying the changing political climate during a shift in power through language. To help accomplish this, the corpus is harvested and normalized in multiple formats. As well, we include gold standard part-of-speech tags for selected individuals including the President. Through analysis of the text, a clear distinction between political parties can be found. This analysis shows the important item of their political agendas during the first 100 days of a new party in power.
Automatic segmentation of pulmonary nodules on CT images by use of NCI lung image database consortium

Accurate segmentation of small pulmonary nodules (SPNs) on thoracic CT images is an important technique for volumetric doubling time estimation and feature characterization for the diagnosis of SPNs. Most of the nodule segmentation algorithms that have been previously presented were designed to handle solid pulmonary nodules. However, SPNs with ground-glass opacity (GGO) also affects a diagnosis. Therefore, we have developed an automated volumetric segmentation algorithm of SPNs with GGO on thoracic CT images. This paper presents our segmentation algorithm with multiple fixed-thresholds, template-matching method, a distance-transformation method, and a watershed method. For quantitative evaluation of the performance of our algorithm, we used the first dataset provided by NCI Lung Image Database Consortium (LIDC). In the evaluation, we employed the coincident rate which was calculated with both the computerized segmented region of a SPN and the matching probability map (pmap) images provided by LIDC. As the result of 23 cases, the mean of the total coincident rate was 0.507 +/- 0.219. From these results, we concluded that our algorithm is useful for extracting SPNs with GGO and solid pattern as well as wide variety of SPNs in size.
Analysis of Obstacles to Uptake of Internet Banking Services in Nigeria

There is ample evidence that the external environment has always thrown challenges even to the best business organisation. And the various stories from around the world of business give credence to these challenges. Today, as always, the increasing speed and scale of changes, which has become part of every business cycle, means threats and opportunities arise faster and more often. As rising competitive intensity forces even the best businesses to return to basics, the old adage that past successes has never guaranteed future success is never truer than now. This study investigated and analysed the factors affecting the adoption of internet banking services in Nigeria. The objectives, among others, were to explore the reasons for the usage ad non-usage of internet banking services in the Nigerian. The theoretical model utilised was the Technological Acceptance Model (TAM), a model which have been used to explain consumers’ intention to use a technology. Subjects for the study were bank managers, bank customers and students. The study found all the beliefs especially security, privacy and infrastructures to be significant factors affecting the adoption of internet banking services in Nigeria. The deduction from the findings was that for internet banking to assume a developmental dimension in Nigeria and for the country to be fully integrated and respected in the global financial environment, the prevalent level of frauds in Nigeria (and among Nigerians) must be addressed.
Reusable workflows for gender prediction

This paper presents a system for author proﬁling (AP) modeling that reduces the complexity and time of building a sophisticated model for a number of different AP tasks. The system is implemented in a cloud-based visual programming platform ClowdFlows and is publicly available to a wider audience. In the platform, we also implemented our already existing state of the art gender prediction model and tested it on a number of cross-genre tasks. The results show that the implemented model, which was trained on tweets, achieves results comparable to state of the art models for cross-genre gender prediction. There is however a noticeable decrease in accuracy when the genre of a test set is different from the genre of the train set.
OGT (O-GlcNAc Transferase) Selectively Modifies Multiple Residues Unique to Lamin A

The LMNA gene encodes lamins A and C with key roles in nuclear structure, signaling, chromatin organization, and genome integrity. Mutations in LMNA cause >12 diseases, termed laminopathies. Lamins A and C are identical for their first 566 residues. However, they form distinct filaments in vivo with apparently distinct roles. We report that lamin A is O-GlcNAc modified in human hepatoma (Huh7) cells and in mouse liver. In vitro assays with purified OGT enzyme showed robust O-GlcNAcylation of recombinant mature lamin A tails (residues 385-646), with no detectable modification of lamin B1, lamin C, or ‘progerin’ (Δ50) tails. Using mass spectrometry, we identified 11 O-GlcNAc sites in a ‘sweet spot’ unique to lamin A, with up to seven sugars per peptide. Most sites were unpredicted by current algorithms. Double-mutant (S612A/T643A) lamin A tails were still robustly O-GlcNAc-modified at seven sites. By contrast, O-GlcNAcylation was undetectable on tails bearing deletion Δ50, which causes Hutchinson-Gilford progeria syndrome, and greatly reduced by deletion Δ35, suggesting this region is required for substrate recognition or modification by OGT in vitro. These results suggest OGT, an essential protein and master regulator, regulates partners or function(s) unique to lamin A that are lost in progeria.
MiR-33 promotes myocardial fibrosis by inhibiting MMP16 and stimulating p38 MAPK signaling

Myocardial fibrosis occurs in the late stages of many cardiovascular diseases, and appears to be stimulated by various microRNAs (miRNAs). We previously found that miR-33 may stimulate cardiac remodeling. Here, we examined the involvement of miR-33 in myocardial fibrosis. Proximal left coronary descending artery occlusion was performed in rat, and antagomiR-33a was injected. Primary cardiac fibroblasts were cultured and transfected with miR-33a mimics and inhibitors. miR-33a levels were increased in the rat after surgery, and collagen deposition and heart fibrosis were observed in vivo. Inhibition of miR-33a suppressed fibroblast proliferation, reduced the mRNA and protein levels of collagen-related markers in vitro and in vivo, and rescued the histological damage in vivo. A dual-luciferase reporter system showed that matrix metalloproteinase 16 (MMP16) gene was the direct target of MiR-33a. These results suggest that miR-33 promoted myocardial fibrosis by inhibiting MMP16 and stimulating p38 mitogen-activated protein kinase (p38 MAPK) signaling pathway. MiR-33 may act as a novel therapeutic target for treating myocardial fibrosis.
Parallel Programming Models: A Survey

Parallel programming and the design of efficient parallel programs is a development area of growing importance. Parallel programming models are almost used to integrate parallel software concepts into a sequential code. These models represent an abstraction of the hardware capabilities to the programmer. In fact, a model is a bridge between the application to be parallelized and the machine organization. Up to now, a variety of programming models have been developed, each having its own approach. This paper enumerates various existing parallel programming models in the literature. The purpose is to perform a comparative evaluation of the mostly used ones, namely MapReduce, Cilk, Cilk++, OpenMP and MPI, within some extracted features. Keywords—Parallel programming models; parallel computing; MapReduce; Cilk/Cilk++; OpenMP; MPI.
Survival outcomes of radical prostatectomy and external beam radiotherapy in clinically localized high-risk prostate cancer: a population-based, propensity score matched study

Objective This study was aimed to compare survival outcomes in high-risk prostate cancer (PCa) patients receiving external beam radiotherapy (EBRT) or radical prostatectomy (RP). Materials and methods The Surveillance, Epidemiology, and End Results (SEER) database was used to identify PCa patients with high-risk features who received RP alone or EBRT alone from 2004 to 2008. Propensity-score matching (PSM) was performed. Kaplan–Meier survival analysis was used to compare cancer-specific survival (CSS) and overall survival (OS). Multivariate Cox regression analysis was used to identify independent prognostic factors. Results A total of 24,293 patients were identified, 14,460 patients receiving RP and 9833 patients receiving EBRT. Through PSM, 3828 patients were identified in each group. The mean CSS was 128.6 and 126.7 months for RP and EBRT groups, respectively (P<0.001). The subgroup analyses showed that CSS of the RP group was better than that of the EBRT group for patients aged <65 years (P<0.001), White race (P<0.001), and married status (P<0.001). However, there was no significant difference in CSS for patients aged ≥65 years, Black race, other race, and unmarried status. Similar trends were observed for OS. Multivariate analysis showed that EBRT treatment modality, T3–T4 stage, Gleason score 8–10, and prostate-specific antigen >20 ng/mL were significant risk factors for both CSS and OS. Conclusion This study suggested that survival outcomes might be better with RP than EBRT in high-risk PCa patients aged <65 years; however, RP and EBRT provided equivalent survival outcomes in older patients, which argues for primary radiotherapy in this older cohort.
The Development of Blended-Learning Teaching Portfolio Course Using TBL Approach

This﻿article﻿was﻿written﻿to﻿develop﻿a﻿teaching﻿portfolio﻿that﻿helps﻿lecturers﻿maximize﻿the﻿benefits﻿of﻿blended﻿learning,﻿a﻿combination﻿of﻿in-person﻿and﻿online﻿learning,﻿through﻿the﻿use﻿of﻿Team-Based﻿ Learning﻿(TBL)﻿teaching﻿and﻿learning﻿approach. ﻿Studies﻿show﻿that﻿TBL﻿can﻿provide﻿opportunities﻿in﻿ developing﻿teamwork﻿capabilities﻿and﻿enhancing﻿active﻿learning,﻿which﻿are﻿effects﻿that﻿could﻿remedy﻿ the﻿weaknesses﻿of﻿implementing﻿blended﻿learning. ﻿A﻿blended﻿teaching﻿portfolio﻿was﻿developed﻿for﻿ an﻿International﻿Human﻿Resource﻿Management﻿course﻿that﻿consisted﻿of﻿a﻿course﻿overview,﻿graduate﻿ competency,﻿a﻿syllabus,﻿course﻿material﻿resources,﻿a﻿teaching﻿scenario,﻿a﻿reading﻿assurance﻿test,﻿ midterm/final﻿exams,﻿student﻿assignments,﻿assessment﻿of﻿learning﻿outcomes,﻿and﻿a﻿course﻿quality﻿ improvement﻿sheet. ﻿Each﻿item﻿was﻿constructed﻿based﻿on﻿the﻿characteristics﻿of﻿the﻿course. ﻿The﻿portfolio﻿ was﻿considered﻿a﻿valuable﻿tool﻿for﻿lecturers﻿to﻿manage﻿a﻿blended-learning﻿course﻿that﻿can﻿help﻿students﻿ in﻿achieving﻿higher﻿scores﻿and﻿in﻿motivating﻿them﻿to﻿read﻿course﻿materials﻿prior﻿to﻿class﻿sessions.
Instant SLAM Initialization for Outdoor Omnidirectional Augmented Reality

The initialization and absolute scale are two critical issues for an Augmented Reality (AR) system. Most existing methods have to resort to some external sensors or some special steps, to initialize an AR system and to obtain a correct scale. In this paper, we introduce an omnidirectional AR system, which can be instantly initialized, recover the absolute scale without other sensor data, and provide a full 360-degrees field-of-view (Fov) to give users the best immersive feelings. Our system shows how to firstly estimate the absolute orientation using the meaningful line and point cues from a single panorama, and how to estimate the camera global position by aligning a panorama after semantic segmentation with a widely available 2.5D map. Based on resulting absolute pose from a single frame, we subsequently render a depth map to initialize a SLAM system. We can then fuse the virtual elements with a real scene according to the continuous camera motion from the SLAM system. We evaluate the SLAM initialization approach on a challenging dataset. The experiments indicate that localization precision from our method is obviously superior to that from consumer GPS devices and we remain unbeatable in time performance compared to previous methods.
A systematic review of the diagnostic accuracy of natriuretic peptides for heart failure.

BACKGROUND The diagnosis of heart failure is difficult, with both overdiagnosis and underdiagnosis occurring commonly in practice. Natriuretic peptides have been proposed as a possible test for assisting diagnosis. We assessed the diagnostic accuracy of brain natriuretic peptide (BNP), including a comparison with atrial natriuretic peptide (ANP). METHODS Electronic searches were conducted of MEDLINE and EMBASE from January 1994 to December 2002 and handsearches of reference lists of included studies. We included studies that assessed the diagnostic accuracy of BNP against echocardiographic or clinical criteria or that compared the diagnostic accuracy of BNP with ANP. Two reviewers assessed studies for inclusion and quality and extracted the relevant data. A meta-analysis was performed by pooling the diagnostic odds ratios for studies that used a common reference standard. RESULTS Twenty studies were included. For the 8 studies (n = 4086) that measured BNP against the criterion of left ventricular ejection fraction of 40% or less (or equivalent), the pooled diagnostic odds ratio was 11.6 (95% confidence interval, 8.4-16.1). The pooled diagnostic odds ratio was greater, 30.9 (95% confidence interval, 27.0-35.4), in the 7 studies (n = 2374) that measured BNP against clinical criteria (generally a consensus view using all other clinical information). The diagnostic odds ratio was similar in studies conducted in general practice and in hospital settings. Three studies compared BNP with N-terminal-ANP, a precursor form of ANP, and pooling of the results of these studies showed BNP to be a more accurate marker of heart failure than NT-ANP. CONCLUSIONS Brain natriuretic peptide is an accurate marker of heart failure. Use of a cutoff value of 15 pmol/L achieves high sensitivity, and BNP values below this exclude heart failure in patients in whom disease is suspected. As the diagnostic odds ratio for BNP is greater when assessed against clinical criteria than against left ejection fraction alone, BNP may also be detecting patients with "diastolic" heart failure.