Miracle-dz/UniFAR-FTUs huggingface dataset:
Altered functional connectivity within the default mode network in two animal models with opposing episodic memories

Memory enhancement and memory decline are two opposing cognitive performances commonly observed in clinical practice, yet the neural mechanisms underlying these two different phenomena remain poorly understood. Accumulating evidence has demonstrated that the default-mode network (DMN) is implicated in diverse cognitive, social, and affective processes. In the present study, we used the retrosplenial cortex as a seed region to study the functional connectivity within the DMN in two animal models with opposing episodic memories, of which memory enhancement was induced by footshocks to mimic posttraumatic stress disorder (PTSD) and memory decline was induced by lipopolysaccharide (LPS) challenge to mimic sepsis-associated encephalopathy (SAE). Our results showed that LPS challenge and footshocks induced opposing episodic memories. With regard to the imaging data, there were significant differences in the functional connectivity between the retrosplenial cortex and the medial prefrontal cortex (mPFC), insular lobe, left piriform cortex, left sensory cortex, and right visual cortex among the three groups. Post-hoc comparisons showed the LPS group had a significantly increased functional connectivity between the retrosplenial cortex and mPFC as compared with the control group. Compared with the LPS group, the PTSD group displayed significantly decreased functional connectivity between the retrosplenial cortex and the right visual cortex, retrosplenial cortex, insular lobe, left piriform cortex, and left sensory cortex. In summary, our study suggests that there is a significant difference in the functional connectivity within the DMN between SAE and PTSD rats.
Identifying stops from mobile phone location data by introducing uncertain segments

Identifying stops is a primary step in acquiring activity‐related information from mobile phone location data to understand the activity patterns of individuals. However, signal jumps in mobile phone location data may create “fake moves,” which will generate fake activity patterns of “stops‐and‐moves. ” These “fake moves” share similar spatiotemporal features with real short‐distance moves, and the stops and moves of trajectories (SMoT), which is the most extensively used stop identification model, often fails to distinguish them when the dataset has coarse temporal resolution. This study proposes the stops, moves, and uncertainties of trajectories (SMUoT) model to address this issue by introducing uncertain segment analysis to distinguish “fake moves” and real short‐distance moves. A real mobile phone location dataset collected in Shenzhen, China is used to evaluate the performance of SMUoT. We find that SMUoT improves the performance (i.e., 15 and 19% increase in accuracy and recall rate for a one‐hour temporal resolution dataset, respectively) of stop identification and exhibits high robustness to parameter settings. With a better reliability of “stops‐and‐moves” pattern identification, the proposed SMUoT can benefit various individual activity‐related research based on mobile phone location data for many fields, such as urban planning, traffic analysis, and emergency management.
Temporal Video Scene Segmentation By Fused Bags-of-Features

Temporal segmentation of video into semantically coherent scenes is a fundamental step to enhance video operations like browsing, retrieval and recommendation. Available automatic scene segmentation methods in the literature are still far, in terms of efficacy, from reasonable practical application requirements. Towards to lowering this gap, this paper presents a new multimodal early fusion based scene segmentation method, which extends the classical and powerful singlemodal bags-of-features latent semantics discriminative capability to a multimodal paradigm. This approach was designed to refine the latent semantics from singlemodal data by identifying and representing audiovisual patterns while still preserving singlemodal visual/aural words patterns. Experiments have been performed over a publicly available dataset where the proposed method achieved higher average values for the FCO metric than previous state-of-the-art approaches.
Labyrinth: Compiling Imperative Control Flow to Parallel Dataflows

Parallel dataflow systems have become a standard technology for large-scale data analytics. Complex data analysis programs in areas such as machine learning and graph analytics often involve control flow, i.e., iterations and branching. Therefore, systems for advanced analytics should include control flow constructs that are efficient and easy to use. A natural approach is to provide imperative control flow constructs similar to those of mainstream programming languages: while-loops, if-statements, and mutable variables, whose values can change between iteration steps. However, current parallel dataflow systems execute programs written using imperative control flow constructs by launching a separate dataflow job after every control flow decision (e.g., for every step of a loop). The performance of this approach is suboptimal, because (a) launching a dataflow job incurs scheduling overhead; and (b) it prevents certain optimizations across iteration steps. In this paper, we introduce Labyrinth, a method to compile programs written using imperative control flow constructs to a single dataflow job, which executes the whole program, including all iteration steps. This way, we achieve both efficiency and ease of use. We also conduct an experimental evaluation, which shows that Labyrinth has orders of magnitude smaller per-iteration-step overhead than launching new dataflow jobs, and also allows for significant optimizations across iteration steps.
Remission of proteinuria in multidrug‐resistant idiopathic nephrotic syndrome following immunoglobulin immunoadsorption

Complete or partial resistance to prednisone and calcineurin inhibitors in children with idiopathic nephrotic syndrome often leads to end‐stage renal disease. The aim of the study was to report the outcome of patients with multidrug‐resistant nephrotic syndrome treated with an association of immunoglobulin immunoadsorption, intravenous immunoglobulins and B‐cell depletion.
Feasibility of DAIly NURSE: A nursing intervention to change nursing staff behaviour towards encouraging residents’ daily activities and independence in the nursing home

Abstract Aims and objectives To examine the feasibility of DAIly NURSE and a nursing intervention to encourage nursing home residents’ daily activities and independence. Background Nursing home residents are mainly inactive during the day. DAIly NURSE was developed to change nursing behaviour towards encouraging nursing home residents’ activities and independence by creating awareness. It consists of three components: education, coaching‐on‐the‐job and policy. Design A mixed‐method study. Methods The feasibility of DAIly NURSE in practice was tested in six psychogeriatric nursing home wards, using attendance lists (reach), evaluation questionnaires (fidelity, dose received and barriers), notes made by the researcher (dose delivered and fidelity) and a focus group interview (dose received and barriers) with nursing home staff (n = 8) at the end of the study. Results The feasibility study showed that all three components (education, coaching‐on‐the‐job and policy) were implemented in practice. The attendance rate in the workshops was high (average: 82%). Nursing home staff were satisfied with the workshops (mean score 9 out of 10 points) and agreed that DAIly NURSE was feasible in daily nursing care practice. Recommendations to optimise the feasibility of DAIly NURSE included the following: Add video observations of a specific moment of the day to create awareness of nursing behaviour; educate all nursing staff of the ward during the workshops; and organise information meetings for family members before the start of the intervention. Nursing staff were satisfied with the intervention and provided recommendations for adjustments to the content of the three components. The most important adjustment is the use of video observations to create awareness of nursing staff behaviour. Conclusions DAIly NURSE, consisting of education, coaching‐on‐the‐job and policy, is feasible in nursing home practice. Relevance to clinical practice DAIly NURSE might help to change nursing behaviour towards encouraging residents’ daily activities and independence.
Extraction and Classification of Semantic Data from Twitter

Twitter is a social network and microblogging service in which registered users read and post messages called Tweets. Tweets have a maximum of 280 characters and cover every conceivable subject, from simple activity updates and news coverage to opinions about arbitrary topics. In this way, Twitter emerges as a valuable data source to get information about what people think and feel about the most different subjects. In this context, this work presents different approaches for extracting and processing information from Twitter using Natural Language Processing (NLP) and Machine Learning techniques, examining tools and methods to collect and analyze semantic information from Tweets.
Feasibility of clinical hypnosis for transesophageal echocardiography in children and adolescents

Background: The use of hypnosis in children has been described more than two centuries ago, with a more recent research and clinical application. There is currently a good level of evidence for the efficacy of clinical hypnosis in children for minor surgery, medical procedures or pain management. The use of clinical hypnosis, in paediatric cardiology, for invasive procedures such as transesophageal echocardiography, has not been reported. Aims: This study evaluated the feasibility of clinical hypnosis in children undergoing transesophageal echocardiography. Method: This prospective, non-randomised, cross-sectional study was carried out over 24 months in a paediatric cardiology referral centre. All children aged 10–18 years requiring a transesophageal echocardiography examination, outside the operating room and the catheterisation laboratory, were eligible for the study. Children and families could choose between transesophageal echocardiography under clinical hypnosis or under general anaesthesia (<15 years) or sedation (⩾15 years). Results: We included 16 children aged 11–18 years (seven girls, mean age 14.1±2.5 years). The hypnotic state was achieved for 15 out of the 16 participating children (94%). The transesophageal echocardiography examination could be completely achieved with a full diagnosis for 15 out of 16 children (94%). In all cases, a transesophageal echocardiography examination under clinical hypnosis provided a complete diagnosis. Conclusion: This study demonstrated that hypnosis was feasible and effective for transesophageal echocardiography in adolescents and might be a good alternative to general anaesthesia. Further study with larger numbers of subjects and more diverse congenital cardiac conditions are needed to confirm the results in a more diverse sample.
Genotoxicity and mutagenicity assessment of food contaminant mixtures present in the French diet

Through diet, people are exposed simultaneously to a variety of contaminants (e.g. heavy metals, mycotoxins, pesticides) that could have combined adverse effects on human health. A previous study identified six main mixtures of food contaminants to which French adult consumers are exposed. These complex mixtures are comprised of 11 to 19 chemicals that have numerous toxic properties. In the present study, we investigated the genotoxic effects of these food contaminants, as single molecules and in mixtures that reflect their occurrence in the French diet, using the γH2AX assay in two human cell lines (HepG2, LS‐174 T). Results of detailed analysis of the 49 individual contaminants (including 21 tested in this study) demonstrated a positive genotoxic response to 14 contaminants in HepG2 and 12 in LS‐174 T cells. Next, our results indicated that two mixtures out of six triggered significant γH2AX induction after 24 hr of treatment, at concentrations for which individual compounds did not induce any DNA damage, suggesting more than additive interactions between chemicals. γH2AX positive mixtures were then tested for mutagenicity with the innovative in vitro PIG‐A assay in HepG2 cells coupled with the soft agar colony formation assay. The two γH2AX positive mixtures led to a significant increase in the frequency of PIG‐A GPI‐deficient cells and in the number of colonies formed in soft agar. In conclusion, our study demonstrates that two mixtures of contaminants present in the French diet induce genotoxicity and mutagenicity, and that the combined effects of single molecules present in these mixtures are likely not additive, highlighting potential problems for hazard assessment of mixtures. Environ. Mol. Mutagen. 59:742–754, 2018. © 2018 Wiley Periodicals, Inc.
Is rotation forest the best classifier for problems with continuous features?

In short, our experiments suggest that yes, on average, rotation forest is better than the most common alternatives when all the attributes are real-valued. Rotation forest is a tree based ensemble that performs transforms on subsets of attributes prior to constructing each tree. We present an empirical comparison of classifiers for problems with only real-valued features. We evaluate classifiers from three families of algorithms: support vector machines; tree-based ensembles; and neural networks tuned with a large grid search. We compare classifiers on unseen data based on the quality of the decision rule (using classification error) the ability to rank cases (area under the receiver operating characteristic) and the probability estimates (using negative log likelihood). We conclude that, in answer to the question posed in the title, yes, rotation forest is significantly more accurate on average than competing techniques when compared on three distinct sets of datasets. Further, we assess the impact of the design features of rotation forest through an ablative study that transforms random forest into rotation forest. We identify the major limitation of rotation forest as its scalability, particularly in number of attributes. To overcome this problem we develop a model to predict the train time of the algorithm and hence propose a contract version of rotation forest where a run time cap is imposed {\em a priori}. We demonstrate that on large problems rotation forest can be made an order of magnitude faster without significant loss of accuracy. We also show that there is no real benefit (on average) from tuning rotation forest. We maintain that without any domain knowledge to indicate an algorithm preference, rotation forest should be the default algorithm of choice for problems with continuous attributes.
Physical and sociocultural facilitators and barriers to access and utilization of a nutrition program in rural Malawi: a qualitative study

ABSTRACT Childhood stunting holds consequences for child development. A nutrition program delivering small-quantity lipid based nutrient supplements (SQ-LNS) to children 6-23 months and child feeding messages was implemented in Malawi to reduce stunting. This study sought to understand the facilitators and barriers to program participation using in-depth interviews, pile sorts, direct observations, and focus group discussions with caretakers, village leaders and program volunteers. Perceptions of the LNS were positive, and visible changes in child health contributed to program participation. Conflicting priorities that prevented monthly collection of SQ-LNS and limited knowledge of child feeding messages constituted barriers to program participation.
Verb Tense Classification And Automatic Exercise Generation

This paper describes a system developed for verb tenses classification in the English language and automatic generation of verb-tenses-oriented exercises, based on texts for study chosen by the user. The classification rules were analyzed iteratively. This research brings a method for generation of exercises of transposition of verb tenses. By this method, it is possible to transpose each verb tense on a sentence to another verb tense. The verb tenses transposition method can also be applied in other contexts, making it easy to choose an action (verb) and applying it to verb tense.
Your Apps Give You Away

Understanding mobile app usage has become instrumental to service providers to optimize their online services. Meanwhile, there is a growing privacy concern that users' app usage may uniquely reveal who they are. In this paper, we seek to understand how likely a user can be uniquely re-identified in the crowd by the apps she uses. We systematically quantify the uniqueness of app usage via large-scale empirical measurements. By collaborating with a major cellular network provider, we obtained a city-scale anonymized dataset on mobile app traffic (1.37 million users, 2000 apps, 9.4 billion network connection records). Through extensive analysis, we show that the set of apps that a user has installed is already highly unique. For users with more than 10 apps, 88% of them can be uniquely re-identified by 4 random apps. The uniqueness level is even higher if we consider when and where the apps are used. We also observe that user attributes (e.g., gender, social activity, and mobility patterns) all have an impact on the uniqueness of app usage. Our work takes the first step towards understanding the unique app usage patterns for a large user population, paving the way for further research to develop privacy-protection techniques and building personalized online services.
B-type natriuretic peptide, disease progression and clinical outcomes in atrial fibrillation

Objective The association with B-type natriuretic peptide (BNP), disease progression and outcomes in patients with atrial fibrillation (AF) has not been thoroughly investigated. Methods We evaluated the association between BNP levels and outcomes, including AF progression, composite outcome of major adverse cardiovascular or neurological events (MACNE) and major bleeding, via pooled logistic regression and Cox frailty models in Outcomes Registry for Better Informed Treatment of Atrial Fibrillation II registry. AF progression was defined as either paroxysmal becoming persistent or permanent, or persistent becoming permanent at any follow-up. Results Among 13 375 patients with AF, 2797 with BNP values at baseline (median age (IQR), 72.0 (63.0–80.0) years; 43.0% women; median BNP, 238 (102–502) ng/L; 42.3% prior heart failure) were included in the models evaluating the association between BNP levels and MACNE or major bleeding. Of these, 1282 patients with paroxysmal or persistent AF at baseline were analysed in AF progression model. The likelihood of AF progression (adjusted OR, 1.11 for every 100 ng/mL; 95% CI 1.03 to 1.19) and MACNE (adjusted HR, 1.11 for every doubling in BNP values; 95% CI 1.01 to 1.22) increased with BNP concentration, while the elevated BNP values were not associated with increased risks of major bleeding. BNP values improved the risk prediction of AF progression and MACNE when added to conventional risk estimates. Conclusions BNP levels are associated with increased risk of AF progression and cardiovascular outcomes in patients with AF. Further studies are required to assess whether biomarker-based risk stratification improves patient outcomes. Clinical trial registration NCT01701817.
Real-Time Joint Axes Estimation of the Hip and Knee Joint during Gait using Inertial Sensors

Inertial Measurement Units (IMUs) have proven to be a promising candidate for joint kinematics assessment during human locomotion. The benefits associated with IMU-based joint angle measurements are ease of handling, flexibility and low cost. However, a known limitation is that the joint axes in terms of the attached IMUs need to be identified in order to decompose IMU measurements into joint angles. Conventionally, careful alignment of the IMUs with respect to the body segments and/or calibration motions are required. In this paper, a novel approach is proposed to estimate the joint axes of the hip and knee joint during gait. Our method is easy to use, self-calibrating and real-time capable using the obtained IMU data during gait. In addition to prior methods, the algorithm profits from the periodicity during gait in order to deal with three (rotational) degrees of freedom (3-DoF) motions. Experiments with 8 healthy subjects walking on a motor-driven treadmill have been conducted. The joint axes converged onto the expected axes in all trials and the convergence times averaged less than 15 seconds.
Full-Duplex Inter-Group All-to-All Broadcast Algorithms with Optimal Bandwidth

MPI inter-group collective communication patterns can be viewed as bipartite graphs that divide processes into two disjoint groups in which messages are transferred between but not within the groups. Such communication patterns can serve as basic operations for scientific application workflows. In this paper, we present parallel algorithms for inter-group all-to-all broadcast (Allgather) communication with optimal bandwidth for any message size and process number under single-port communication constraints. We implement the algorithms using MPI point-to-point and intra-group collective communication functions and evaluate their performance on the Cori supercomputer at NERSC. Using message sizes ranging from 256B to 64MB, the experiments show a significant performance improvement achieved by our algorithm, which is up to 9.27 times faster than production MPI libraries that adopt the so called root-gathering algorithm.
Internet-delivered aftercare following multimodal rehabilitation program for chronic pain: a qualitative feasibility study

Purpose Methods for delivering aftercare to help chronic pain patients to continue practice self-management skills after rehabilitation are needed. Internet-delivered cognitive behavioral therapy (ICBT) has the potential to partly fill this gap given its accessibility and emphasis on self-care. Methods for engaging and motivating patients to persist throughout the full length of treatment are needed. The aim of this study was to describe how chronic pain patients work in an ICBT program, through their descriptions of what is important when they initiate behavior change in aftercare and their descriptions of what is important for ongoing practice of self-management skills in aftercare. Patients and methods Following a multimodal rehabilitation program, 29 chronic pain patients participated in a 20-week-long Internet-delivered aftercare program (ACP) based on acceptance-based cognitive behavioral therapy. Latent content analysis was made on 138 chapters of diary-like texts written by participants in aftercare. Results Attitudes regarding pain and body changed during ACP, as did attitudes toward self and the future for some participants. How participants practiced self-management skills was influenced by how they expressed motivation behind treatment goals. Whether they practiced acceptance strategies influenced their continuous self-management practice. Defusion techniques seemed to be helpful in the process of goal setting. Mindfulness strategies seemed to be helpful when setbacks occurred. Conclusion Self-motivating goals are described as important both to initiate and in the ongoing practice of self-management skills. Experiencing a helpful effect of acceptance strategies seems to encourage participants to handle obstacles in new ways and to persist throughout treatment. Research on whether tailored therapist guidance might be helpful in stating self-motivating goals and contribute to ongoing practice of self-management skills is needed.