Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the effectiveness of a custom‐made toothbrush in maintaining oral hygiene and gingival health in cerebral palsy patients

AIM To evaluate and compare the efficacy of customized toothbrushes in maintenance of oral hygiene and gingival health with that of conventional toothbrushes in children with cerebral palsy. METHODS Thirty patients with cerebral palsy in the age group of 6 to 18 years were randomly divided into two groups-group I (normal toothbrush) and group II (customized toothbrush). Common oral hygiene instructions were given to both the groups. Before beginning the study, plaque index (PI) and modified gingival index (MGI) were recorded, following which patients were made to practice their routine twice a day brushing with either normal toothbrush or custom-made toothbrush for 3 weeks. At the end of 3 weeks, PI and MGI were rerecorded. The subjects belonging to group II were also made to perform muscle exercises using the modified brush head. RESULTS Significantly high percentage drop between the pre- and post-PI as well as MGI (31.55% and 30.23%, respectively) was observed in the custom-made toothbrush group, while the percentage drop of only 8.34% (PI) and 14.51% (MGI) was seen in the normal toothbrush group. CONCLUSIONS Custom-made tooth brushes increased the efficiency of maintaining oral hygiene and gingival health of individuals with cerebral palsy.
Packed red cells versus whole blood transfusion for severe paediatric anaemia, pregnancy‐related anaemia and obstetric bleeding: an analysis of clinical practice guidelines from sub‐Saharan Africa and evidence underpinning recommendations

Blood component transfusion is increasingly promoted in sub‐Saharan Africa (SSA), but is resource‐intensive so whole blood is often used. We examined SSA recommendations about whole blood and packed red cell transfusions for pregnancy‐related bleeding or anaemia, and paediatric anaemia, and evaluated the evidence underpinning these recommendations.
Spark-parSketch: A Massively Distributed Indexing of Time Series Datasets

A growing number of domains (finance, seismology, internet-of-things, etc.) collect massive time series. When the number of series grow to the hundreds of millions or even billions, similarity queries become intractable on a single machine. Further, naive (quadratic) parallelization won't work well. So, we need both efficient indexing and parallelization. We propose a demonstration of Spark-parSketch, a complete solution based on sketches / random projections to efficiently perform both the parallel indexing of large sets of time series and a similarity search on them. Because our method is approximate, we explore the tradeoff between time and precision. A video showing the dynamics of the demonstration can be found by the link http://parsketch.gforge. inria.fr/video/parSketchdemo_720p. mov.
The Prognostic Impact of Synchronous Ipsilateral Multiple Breast Cancer: Survival Outcomes according to the Eighth American Joint Committee on Cancer Staging and Molecular Subtype

Background In the current American Joint Committee on Cancer staging system of breast cancer, only tumor size determines T-category regardless of whether the tumor is single or multiple. This study evaluated if tumor multiplicity has prognostic value and can be used to subclassify breast cancer. Methods We included 5,758 patients with invasive breast cancer who underwent surgery at Samsung Medical Center, Seoul, Korea, from 1995 to 2012. Results Patients were divided into two groups according to multiplicity (single, n = 4,744; multiple, n = 1,014). Statistically significant differences in lymph node involvement and lymphatic invasion were found between the two groups (p < .001). Patients with multiple masses tended to have luminal A molecular subtype (p < .001). On Kaplan-Meier survival analysis, patients with multiple masses had significantly poorer disease-free survival (DFS) (p = .016). The prognostic significance of multiplicity was seen in patients with anatomic staging group I and prognostic staging group IA (p = .019 and p = .032, respectively). When targeting patients with T1-2 N0 M0, hormone receptor–positive, and human epidermal growth factor receptor 2 (HER2)–negative cancer, Kaplan-Meier survival analysis also revealed significantly reduced DFS with multiple cancer (p = .031). The multivariate analysis indicated that multiplicity was independently correlated with worse DFS (hazard ratio, 1.23; 95% confidence interval, 1.03 to 1.47; p = .025). The results of this study indicate that tumor multiplicity is frequently found in luminal A subtype, is associated with frequent lymph node metastasis, and is correlated with worse DFS. Conclusions Tumor multiplicity has prognostic value and could be used to subclassify invasive breast cancer at early stages. Adjuvant chemotherapy would be necessary for multiple masses of T1–2 N0 M0, hormone-receptor-positive, and HER2-negative cancer.
Pulmonary Toxicity Induced by N-Hexane in Wistar Male Rats After Oral Subchronic Exposure

Studies of pulmonary toxicity induced by oral exposure to n-hexane are very few, in contrast to those studying the exposure by inhalation. This research tackles the oral toxic effect of n-hexane solvent on the lungs after subchronic exposure of Wistar male rats at 300, 600, and 1200 mg/kg, respectively, each day for 8 weeks. The pneumotoxicity observed in this study was marked by an immune toxicity in the form of a significant increase in the levels of white blood cells, lymphocytes, granulocytes, and eosinophils, as well as a significant increase in relative and absolute lung weight in both groups treated at the doses of 600 and 1200 mg/kg. n-Hexane also resulted in a significant increase in serum total proteins and acid phosphatase in the 3 doses tested daily for 8 weeks. In addition, we found a significant increase in total protein and a decrease in glutathione at 600 and 1200 mg/kg, in the pulmonary homogenate. Furthermore, the rate of lipid peroxidation increased in the 3 doses tested. Histological findings revealed a pneumonia characterized by bronchopneumonia, fibronecrotic lesions, congestion, hemorrhage, type II pneumocyte hyperplasia, alveolar lesions, bronchial epithelium degradation, and inflammation.
PRRE: Personalized Relation Ranking Embedding for Attributed Networks

Attributed network embedding focuses on learning low-dimensional latent representations of nodes which can well preserve the original topological and node attributed proximity at the same time. Existing works usually assume that nodes with similar topology or similar attributes should also be close in the embedding space. This assumption ignores the phenomenon of partial correlation between network topological and node attributed similarities i.e. nodes with similar topology may be dissimilar in their attributes and vice versa. Partial correlation between the two information sources should be considered especially when there exist fraudulent edges (i.e., information from one source is vague) or unbalanced data distributions (i.e, topology structure similarity and node attribute similarity have different distributions). However, it is very challenging to consider the partial correlation between topology and attributes due to the heterogeneity of these two information sources. In this paper, we take partial correlation between topology and attributes into account and propose the Personalized Relation Ranking Embedding (PRRE) method for attributed networks which is capable of exploiting the partial correlation between node topology and attributes. The proposed PRRE model utilizes two thresholds to define different node relations and employs the Expectation-Maximization (EM) algorithm to learn these thresholds as well as other embedding parameters. Extensive experiments results on multiple real-world datasets show that the proposed PRRE model significantly outperforms the state-of-the-art methods in terms of various evaluation metrics.
Role of resting state MRI temporal latency in refractory pediatric extratemporal epilepsy lateralization

Pediatric epilepsy affects 0.5–1% of children, with 10–30% of these children refractory to medical anticonvulsant therapy and potentially requiring surgical intervention. Analysis of resting state functional MRI (rsMRI) signal temporal differences (latency) has been proposed to study the pathological cognitive processes.
Reduction of stratum corneum ceramides in Neu-Laxova syndrome caused by phosphoglycerate dehydrogenase deficiency[S]

Neu-Laxova syndrome (NLS) is a very rare autosomal recessive congenital disorder characterized by disturbed development of the central nervous system and the skin and caused by mutations in any of the three genes involved in de novo l-serine biosynthesis: PHGDH, PSAT1, and PSPH. l-Serine is essential for the biosynthesis of phosphatidylserine and sphingolipids. The extracellular lipid of the stratum corneum, of which sphingolipid constitutes a significant part, plays a primary role in skin barrier function. Here, we describe a Japanese NLS pedigree with a previously unreported nonsense mutation in PHGDH and a unique inversion of chromosome 1. In addition, the levels of 11 major ceramide classes in the tape-stripped stratum corneum of the NLS patient’s skin were assessed by LC/MS. Notably, lower amounts of ceramides of all classes were found in the patient’s stratum corneum than in those of controls. This is the first report to demonstrate the reduction of ceramides in the stratum corneum of an NLS patient due to PHGDH mutations. The clinical findings and a detailed analysis of ceramides from the stratum corneum in the family extend the spectrum of clinical anomalies and give us a clue to the pathomechanisms of ichthyosis in NLS patients with phosphoglycerate dehydrogenase deficiency.
Propensity‐score matching with competing risks in survival analysis

Propensity‐score matching is a popular analytic method to remove the effects of confounding due to measured baseline covariates when using observational data to estimate the effects of treatment. Time‐to‐event outcomes are common in medical research. Competing risks are outcomes whose occurrence precludes the occurrence of the primary time‐to‐event outcome of interest. All non‐fatal outcomes and all cause‐specific mortality outcomes are potentially subject to competing risks. There is a paucity of guidance on the conduct of propensity‐score matching in the presence of competing risks. We describe how both relative and absolute measures of treatment effect can be obtained when using propensity‐score matching with competing risks data. Estimates of the relative effect of treatment can be obtained by using cause‐specific hazard models in the matched sample. Estimates of absolute treatment effects can be obtained by comparing cumulative incidence functions (CIFs) between matched treated and matched control subjects. We conducted a series of Monte Carlo simulations to compare the empirical type I error rate of different statistical methods for testing the equality of CIFs estimated in the matched sample. We also examined the performance of different methods to estimate the marginal subdistribution hazard ratio. We recommend that a marginal subdistribution hazard model that accounts for the within‐pair clustering of outcomes be used to test the equality of CIFs and to estimate subdistribution hazard ratios. We illustrate the described methods by using data on patients discharged from hospital with acute myocardial infarction to estimate the effect of discharge prescribing of statins on cardiovascular death.
Tasty Art: 'The Scream' as a Burger...

This paper introduces the methodological exploration of a design led approach towards utilizing human-food-interactions in teaching art appreciation. In workshops participants express emotional and cognitive responses towards works of art, then interpret and re-imagine those responses into the creation of representational-food dishes. From this we deconstruct and analyze the multi-sensory and multi-modal experience of food in its complex network of taste, colour, texture, and emotions.
CloudVR: Cloud Accelerated Interactive Mobile Virtual Reality

High quality immersive Virtual Reality experience currently requires a PC setup with cable connected head mounted display, which is expensive and restricts user mobility. This paper presents CloudVR which is a system for cloud accelerated interactive mobile VR. It is designed to provide short rotation and interaction latencies through panoramic rendering and dynamic object placement. CloudVR also includes rendering optimizations to reduce server-side computational load and bandwidth requirements between the server and client. Performance measurements with a CloudVR prototype suggest that the optimizations make it possible to double the server's framerate and halve the amount of bandwidth required and that small objects can be quickly moved at run time to client device for rendering to provide shorter interaction latency. A small-scale user study indicates that CloudVR users do not notice small network latencies (20ms) and even much longer ones (100-200ms) become non-trivial to detect when they do not affect the interaction with objects. Finally, we present a design of CloudVR extension to multi-user scenarios.
Personalization Myopia: A Viewpoint to True Personalization of Information Systems

In this commentary paper, we seek to argue that there exists a relatively common false idea about the level and type of personalization in the offerings of contemporary systems and services. In a nutshell, there is a tendency for researchers to think that there are many more personalized services than there really is, the general audience to think that they are offered personalized services when they really aren't, and practitioners to have a mistaken idea of what really makes a service personalized. We call this Personalization Myopia. And yet in an era, which mashes up big data, data analytics, deep learning and persuasive systems, true personalization is a most promising approach for innovating and developing new types of systems and services, especially for supporting behavior change. Because of this prevalence of the Personalization Myopia, one should not judge the potential of personalization by considering only the current implementations or dominant way of thinking about these systems and services. This paper will help carry out research and design into personalized information systems.
Praising others differently: neuroanatomical correlates to individual differences in trait gratitude and elevation

Abstract Differing from basic emotions such as happiness, gratitude and elevation are important other-praising emotions. Previous behavioral studies have established that these complex emotions differ from each other; however, it remains under-investigated whether proneness to these emotions have common or distinct neuroanatomical correlates. Here we used voxel-based morphometry to identify the common and distinct neuroanatomical correlates of trait (i.e. proneness to) gratitude and elevation. We used the Gratitude Questionnaire-6 and the trait elevation scale to measure these affective traits. We demonstrated that trait gratitude was positively correlated with gray matter volume (GMV) in the left cerebellum extending to fusiform gyrus, and also the right middle occipital gyrus (MOG) extending to posterior superior temporal sulcus (pSTS) and temporoparietal junction (TPJ), while trait elevation was negatively correlated with GMV in the left dorsolateral prefrontal cortex. While controlling each other, all the regions still held significant, except the right MOG and pSTS/TPJ. The results indicate that there are distinct neuroanatomical correlates for proneness to gratitude and elevation, while the evidence is mixed that pSTS/TPJ may be the common correlates for them. The implications of these findings are discussed.
Cumulative Nets for Edge Detection

Lots of recent progress have been made by using Convolutional Neural Networks (CNN) for edge detection. Due to the nature of hierarchical representations learned in CNN, it is intuitive to design side networks utilizing the richer convolutional features to improve the edge detection. However, different side networks are isolated, and the final results are usually weighted sum of the side outputs with uneven qualities. To tackle these issues, we propose a Cumulative Network (C-Net), which learns the side network cumulatively based on current visual features and low-level side outputs, to gradually remove detailed or sharp boundaries to enable high-resolution and accurate edge detection. Therefore, the lower-level edge information is cumulatively inherited while the superfluous details are progressively abandoned. In fact, recursively Learningwhere to remove superfluous details from the current edge map with the supervision of a higher-level visual feature is challenging. Furthermore, we employ atrous convolution (AC) and atrous convolution pyramid pooling (ASPP) to robustly detect object boundaries at multiple scales and aspect ratios. Also, cumulatively refining edges using high-level visual information and lower-lever edge maps is achieved by our designed cumulative residual attention (CRA) block. Experimental results show that our C-Net sets new records for edge detection on both two benchmark datasets: BSDS500 (i.e., .819 ODS, .835 OIS and .862 AP) and NYUDV2 (i.e., .762 ODS, .781 OIS, .797 AP). C-Net has great potential to be applied to other deep learning based applications, e.g., image classification and segmentation.
Inhibition of miR-194 suppresses the Wnt/β-catenin signalling pathway in gastric cancer

A mounting body of evidence has revealed that microRNAs (miRs) serve pivotal roles in various developmental processes, and in tumourigenesis, by binding to target genes and subsequently regulating gene expression. Continued activation of the Wnt/β-catenin signalling is positively associated with human malignancy. In addition, miR-194 dysregulation has been implicated in gastric cancer (GC); however, the molecular mechanisms underlying the effects of miR-194 on GC carcinogenesis remain to be elucidated. The present study demonstrated that miR-194 was upregulated in GC tissues and SUFU negative regulator of Ηedgehog signaling (SUFU) was downregulated in GC cell lines. Subsequently, inhibition of miR-194 attenuated nuclear accumulation of β-catenin, which consequently blocked Wnt/β-catenin signalling. In addition, the cytoplasmic translocation of β-catenin induced by miR-194 inhibition was mediated by SUFU. Furthermore, genes associated with the Wnt/β-catenin signalling pathway were revealed to be downregulated following inhibition of the Wnt signalling pathway by miR-194 suppression. Finally, the results indicated that cell apoptosis was markedly increased in response to miR-194 inhibition, strongly suggesting the carcinogenic effects of miR-194 in GC. Taken together, these findings demonstrated that miR-194 may promote gastric carcinogenesis through activation of the Wnt/β-catenin signalling pathway, making it a potential therapeutic target for GC.
A New Approach to Face Detection Based on Binary Texture Extraction Algorithm

In1 this paper we proposed a new face detection process and a new texture-based detection algorithm----binary texture extraction algorithm for image preprocessing. This algorithm is applicable to different lighting, different skin colors and complex background. Furthermore, we invented a rough face inspection method called "reduction" according to the principles of the image morphology. Finally, by integrating the algorithm with the mainstream face detection methods, such as BP (Back Propagation) neural network, Gabor and Adaboost, we demonstrated the applicability and robustness of the binary texture extraction algorithm through a set of Matlab experiments containing a variety of lighting environments and different skin colors test library.
User Group Analytics: Discovery, Exploration and Visualization

User data is becoming increasingly available in various domains from the social Web to patient health records. User data is characterized by a combination of demographics (e.g., age, gender, occupation) and user actions (e.g., rating a movie, following a diet). User data analytics is usually based on identifying group-level behaviors such as "countryside teachers who watch Woody Allen movies. " User Group Analytics (UGA) addresses peculiarities of user data such as noise and sparsity. This tutorial reviews research on UGA and discusses different approaches and open challenges for group discovery, exploration, and visualization.
Where is this? Video geolocation based on neural network features

In this work we propose a method that geolocates videos within a delimited widespread area based solely on the frames visual content. Our proposed method tackles video-geolocation through traditional image retrieval techniques considering Google Street View as the reference point. To achieve this goal we use the deep learning features obtained from NetVLAD to represent images, since through this feature vectors the similarity is their L2 norm. In this paper, we propose a family of voting-based methods to aggregate frame-wise geolocation results which boost the video geolocation result. The best aggregation found through our experiments considers both NetVLAD and SIFT similarity, as well as the geolocation density of the most similar results. To test our proposed method, we gathered a new video dataset from Pittsburgh Downtown area to benefit and stimulate more work in this area. Our system achieved a precision of 90% while geolocating videos within a range of 150 meters or two blocks away from the original position.
Global Prevalence of Protein-Energy Wasting in Kidney Disease: A Meta-analysis of Contemporary Observational Studies From the International Society of Renal Nutrition and Metabolism.

OBJECTIVE To better define the prevalence of protein-energy wasting (PEW) in kidney disease is poorly defined. METHODS We performed a meta-analysis of PEW prevalence from contemporary studies including more than 50 subjects with kidney disease, published during 2000-2014 and reporting on PEW prevalence by subjective global assessment or malnutrition-inflammation score. Data were reviewed throughout different strata: (1) acute kidney injury (AKI), (2) pediatric chronic kidney disease (CKD), (3) nondialyzed CKD 3-5, (4) maintenance dialysis, and (5) subjects undergoing kidney transplantation (Tx). Sample size, period of publication, reporting quality, methods, dialysis technique, country, geographical region, and gross national income were a priori considered factors influencing between-study variability. RESULTS Two studies including 189 AKI patients reported a PEW prevalence of 60% and 82%. Five studies including 1776 patients with CKD stages 3-5 reported PEW prevalence ranging from 11% to 54%. Finally, 90 studies from 34 countries including 16,434 patients on maintenance dialysis were identified. The 25th-75th percentiles range in PEW prevalence among dialysis studies was 28-54%. Large variation in PEW prevalence across studies remained even when accounting for moderators. Mixed-effects meta-regression identified geographical region as the only significant moderator explaining 23% of the observed data heterogeneity. Finally, two studies including 1067 Tx patients reported a PEW prevalence of 28% and 52%, and no studies recruiting pediatric CKD patients were identified. CONCLUSION By providing evidence-based ranges of PEW prevalence, we conclude that PEW is a common phenomenon across the spectrum of AKI and CKD. This, together with the well-documented impact of PEW on patient outcomes, justifies the need for increased medical attention.