Miracle-dz/UniFAR-FTUs huggingface dataset:
Social inequalities in treatment receipt for childhood cancers in Ireland: A population‐based analysis

Treatment advances over the past five decades have resulted in significant improvements in survival from childhood cancer. Although survival rates are relatively high, social disparities in outcomes have been sometimes observed. In a population‐based study, we investigated social inequalities by sex and deprivation in treatment receipt in childhood cancer in Ireland. Cancers incident in people aged 0 to 19 during 1994 to 2012 and treatments received were abstracted from the National Cancer Registry Ireland. Multivariable modified Poisson regression with robust error variance (adjusting for age, and year) was used to assess associations between sex and deprivation category of area of residence at diagnosis and receipt of cancer‐directed surgery, chemotherapy or radiotherapy. Three thousand seven hundred and four childhood cancers were included. Girls were significantly less likely than boys to receive radiotherapy for leukemia overall (relative risk [RR] = 0.70; 95% confidence interval [CI] = 0.50‐0.98), and acute lymphoblastic leukemia specifically (RR = 0.54; 95% CI = 0.36‐0.79), and surgery for central nervous system (CNS) overall (RR = 0.83; 95% CI = 0.74‐0.93) and other CNS (RR = 0.76; 95% CI = 0.60‐0.96). Girls were slightly less likely to receive chemotherapy for non‐Hodgkin lymphoma and surgery for Hodgkin lymphoma (HL), but these results were not statistically significant. Children residing in more deprived areas were significantly less likely to receive chemotherapy for acute myeloid leukemia or surgery for lymphoma overall and HL, but more likely to receive chemotherapy for medulloblastoma. These results may suggest social inequalities in treatment receipt for childhood cancers. Further research is warranted to explore whether similar patterns are evident in other childhood cancer populations and to better understand the reasons for the findings.
Quantifying the Distribution of Electrolyte Decomposition Products in Silicon-Graphite Electrodes by Neutron Depth Profiling

Silicon-based anodes for lithium-ion batteries exhibit severe volumetric changes of the active material particles during (de-)lithiation, resulting in continuously occurring side reactions at the silicon/electrolyte interface over extended charge/discharge cycling. The thus formed and accumulating electrolyte decomposition products lead to a growth of the solid-electrolyte-interphase (SEI) on the silicon particles. This results not only in an ongoing loss of electrolyte but also in a signiﬁcant swelling and impedance increase of silicon-based anodes which signiﬁcantly compromises their cycle-life. In the present study, neutron depth proﬁling (NDP) is used post mortem as a non-destructive, highly lithium-sensitive technique to (i) quantify the amount of lithium-containing electrolyte decomposition productsin silicon-graphite (SiG) electrodes(35wt% silicon, areal capacity ∼ 1.7mAh cm − 2 ),(ii) monitor their distribution across the SiG electrode thickness, and (iii) determine the active material utilization across the electrode over 140 cycles. Hence, SiG negative electrodes are aged and characterized by means of galvanostatic cycling in SiG//LiFePO 4 pseudo- full cells, using a capacitively oversized positive electrode and an electrolyte mixture consisting of 1 M LiPF 6 in EC:EMC with 5 wt% FEC. High-resolution cross-sectional SEM images and post-mortem characterization of the SiG electrodes with respect to changes in electrode mass thickness complement the analysis.
Identification of novel proteins and mRNAs differentially bound to the Leishmania Poly(A) Binding Proteins reveals a direct association between PABP1, the RNA-binding protein RBP23 and mRNAs encoding ribosomal proteins

Poly(A) Binding Proteins (PABPs) are major eukaryotic RNA-binding proteins (RBPs) with multiple roles associated with mRNA stability and translation and characterized mainly from multicellular organisms and yeasts. A variable number of PABP homologues are seen in different organisms however the biological reasons for multiple PABPs are generally not well understood. In the unicellular Leishmania, dependent on post-transcriptional mechanisms for the control of its gene expression, three distinct PABPs are found, with yet undefined functional distinctions. Here, using RNA-immunoprecipitation sequencing analysis we show that the Leishmania PABP1 preferentially associates with mRNAs encoding ribosomal proteins, while PABP2 and PABP3 bind to an overlapping set of mRNAs distinct to those enriched in PABP1. Immunoprecipitation studies combined to mass-spectrometry analysis identified RBPs differentially associated with PABP1 or PABP2, including RBP23 and DRBD2, respectively, that were investigated further. Both RBP23 and DRBD2 bind directly to the three PABPs in vitro, but reciprocal experiments confirmed preferential co-immunoprecipitation of PABP1, as well as the EIF4E4/EIF4G3 based translation initiation complex, with RBP23. Other RBP23 binding partners also imply a direct role in translation. DRBD2, in contrast, co-immunoprecipitated with PABP2, PABP3 and with RBPs unrelated to translation. Over 90% of the RBP23-bound mRNAs code for ribosomal proteins, mainly absent from the transcripts co-precipitated with DRBD2. These experiments suggest a novel and specific route for translation of the ribosomal protein mRNAs, mediated by RBP23, PABP1 and the associated EIF4E4/EIF4G3 complex. They also highlight the unique roles that different PABP homologues may have in eukaryotic cells associated with mRNA translation.
Deep learning based automatic detection of offshore oil slicks using SAR data and contextual information

Ocean surface monitoring, especially oil slick detection, has become mandatory due to its importance for oil exploration and risk prevention on ecosystems. For years, the detection task has been performed manually by photo-interpreters using Synthetic Aperture Radar (SAR) images with the help of contextual data such as wind. This tedious manual work cannot handle the increasing amount of data collected by the available sensors and thus requires automation. Literature reports conventional and semi-automated detection methods that generally focus either on oil slicks originating from anthropogenic (spills) or natural (seeps) sources on limited data collections. As an extension, this paper presents the automation of offshore oil slicks on an extensive database with both kinds of slicks. It builds upon the slick annotations of specialized photo-interpreters on Sentinel-1 SAR data for 6 years over 3 exploration and monitoring areas worldwide. All the considered SAR images and related annotation relate to real oil slick monitoring scenarios. Further, wind estimation is systematically computed to enrich the data collection. Paper contributions are the following : (i) a performance comparison of two deep learning approaches: semantic segmentation using FC-DenseNet and instance segmentation using Mask-RCNN. (ii) the introduction of meteorological information (wind speed) is deemed valuable for oil slick detection in the performance evaluation. The main results of this study show the effectiveness of slick detection by deep learning approaches, in particular FC-DenseNet, which captures more than 92% of oil instances in our test set. Furthermore, a strong correlation between model performances and contextual information such as slick size and wind speed is demonstrated in the performance evaluation. This work opens perspectives to design models that can fuse SAR and wind information to reduce the false alarm rate.
MODIS thermal emissive bands calibration improvements for Collection 7

The MODIS thermal emissive bands (TEB) radiometric calibration uses a quadratic function for the instrument response, and the calibration coefficients look-up tables (LUTs) are updated using the response of an on-board blackbody (BB). After more than 21 and 19 years on-orbit, the TEB performances for Terra and Aqua MODIS have been generally stable. However, contamination from electronic crosstalk, a known issue since prelaunch, has affected the L1B image quality and measurement accuracy. In addition to the photovoltaic (PV) longwave infrared (LWIR) bands crosstalk correction included in Terra MODIS Collection 6.1 (C6.1), a crosstalk correction for select detectors in the Terra and Aqua mid-wave infrared (MWIR) and Aqua PV LWIR bands are applied in C7. The mission-long crosstalk coefficients for the selected detectors are derived and populated in the form of LUTs. The crosstalk correction is applied to both on-orbit calibration and the algorithm used to generate Earth-view L1B products. Among these detectors, the Aqua MODIS band 24 detector 1 crosstalk has the largest impact on image quality, with striping observed over cold scenes for both Terra and Aqua MODIS. The images of C7 L1B and C6.1 are compared to assess the impact of the correction. Additional assessments using Earthview measurements and inter-comparison also revealed the need for improvement of calibration stability and consistence for select bands. Additional improvements for long-term stability and mirror side consistence were developed using quasideep convective clouds (qDCC), Dome-C, ocean, desert, and inter-comparison with other instruments.
Modeling Entry and Barriers to Entry: A Test of Alternative Specifications

Entry and the threat of entry are crucial both in shaping industry structure and determining long-run industry performance. Barriers to entry, which impede the market's normal corrective response to the appearance of economic profits, are perhaps the single most important determinant of an incumbent firm's ability to exercise market power on a sustained basis. ' Recognition of the significant role that entry barriers play in market processes has led econo-
Assessing Adolescent Critical Thinking

Critical thinking skills play an important role in the moral development of adolescents. They must analyze, evaluate, and synthesize information gathered from their interactions with others. They must effectively argue, justify, and reflect on their own personal opinions and motivations as they make decisions, impacting on their sense of decision-making responsibilities. Although there is recognition of the importance of critical thinking skills in moral development and learning (Paul, 1993), investigation into the language skills required to competently express one’s thinking and reasoning has received limited attention. The few studies that have focused on language and critical thinking have reported specifically on syntactic aspects (Nippold et al., 2014, 2015). To address this gap between language, thinking, and knowledge, the authors of this article investigate the language used in critical thinking through adolescents’ responses on a task of moral reasoning. Syntactic development in adolescence is characterized by longer and more grammatically complex sentences with greater use of dependent clauses (Heilmann & Malone, 2014; Nippold, 2016). Through the increased use of subordinate clauses, adolescents are able to express more complex ideas. There is an interplay between cognitive and language processes in the adolescent years that supports growth in critical thinking. Semantically, adolescent language growth involves literate language, including the use and understanding of mental state verbs, abstract nouns, and adverbial conjunctions (Nippold et al., 2017). The increasing command of mental state verbs or metacognitive verbs (such as believe, realize, and remember) allows the adolescent to describe and reflect on another’s thoughts and viewpoint. This category of words represents a salient feature of A newsletter dedicated to speech & language in school-age children
A scalable NoC topology targeting network performance

Scalability, path diversity, and bisection-width are the primary design concerns that significantly affect the NoC performance. The present work proposes an NoC topology taking into account the above design considerations. Rigorous experimentation is done with prevalent counterparts like mesh, torus, flattened-butterfly, butterfly-fat-tree, etc. Low network diameter and sufficient path diversity come up with a minimum of 38% latency improvement among all the cases. A minimum of 5% throughput yield is also found because of relatively high bisection width. Optimized interconnect length makes the design flexible to scale with the increasing network size. Relatively lower router radix brings down the average power consumption of the routers significantly than the existing topologies. A minimum of 27% gain on average router energy is found.
Surface Defect Detection of Aircraft Flared Duct Based on Improved YOLOv4 Algorithm

In order to solve the problems of low detection accuracy, slow detection speed and low detection efficiency existing in traditional methods, an improved YOLOv4 algorithm for the surface defect detection of aircraft flared duct is proposed. Firstly, convolutional layers are added to improve the ability of network to extract defect features, and the layers change in the backbone feature extraction network output and the spatial pyramid pooling structure in the YOLOv4 network. Secondly, the aircraft flared ducts with surface defects datasets is made, and the K-means++ clustering algorithm is put forward to cluster the defect samples to obtain the priors anchor's parameters of different sizes. Finally, due to the uneven distribution of defects, we add rotation detection and improved loss function to improve defect detection capabilities. Experiments show that the mAP value of the improved YOLOv4 algorithm on the test set reaches 91.11%, which is 7.50% higher than original YOLOv4 model. The average detection time for each image is 0.1573s, and the detection performance is optimized.
Iterative Design of Sonification Techniques to Support People with Visual Impairments in Obstacle Avoidance

Obstacle avoidance is a major challenge during independent mobility for blind or visually impaired (BVI) people. Typically, BVI people can only perceive obstacles at a short distance (about 1 m, in case they are using the white cane), and some obstacles are hard to detect (e.g., those elevated from the ground), or should not be hit by the white cane (e.g., a standing person). A solution to these problems can be found in recent computer-vision techniques that can run on mobile and wearable devices to detect obstacles at a distance. However, in addition to detecting obstacles, it is also necessary to convey information about them in real time. This contribution presents WatchOut, a sonification technique for conveying real-time information about the main properties of an obstacle to a BVI person, who can then use this additional feedback to safely navigate in the environment. WatchOut was designed with a user-centered approach, involving four iterations of online listening tests with BVI participants in order to define, improve and evaluate the sonification technique, eventually obtaining an almost perfect recognition accuracy. WatchOut was also implemented and tested as a module of a mobile app that detects obstacles using state-of-the-art computer vision technology. Results show that the system is considered usable and can guide the users to avoid more than 85% of the obstacles.
Machine Learning Framework to Predict Patient Non-Adherence to Medication using Non-Clinical Data: A Prognosis Approach

The need for patient-centric medication adherence intervention system tailored towards addressing individual patient challenges of non-adherence has been well established by several studies. In recent times, many studies have applied machine learning predictive models in medication adherence system across specific chronic diseases using patient's clinical data. However, only a very few studies had attempted to leverage on patient's non-clinical data such as patient's belief, behavioral pattern, knowledge and others as input parameters in their predictive models. Studies outcomes have also shown that these non-clinical data have high influence on patient's non-adherence to medication thereby making them strong determinants. In this paper, we introduce a prognosis approach that can help to predict patient's non-adherence level to medication at the first clinic visit even before prescription commences. The prognosis approach is described by using Adaptive Neuro Fuzzy (ANF) predictive model that adopt the use of non-clinical data of patient to serve as input variables of the model. This study is a preliminary report of an ongoing innovative and intelligent healthcare project to improve patient adherence to medication. We developed a certified questionnaire based on health belief theory to collect the non-clinical data from out-patients with chronic disease who are receiving treatment so as to test the performance of the proposed framework. The predicted patient level of non-adherence output would be optimized and stratified to determine an appropriate intervention functions and delivery techniques towards the particular needs of the patient. Various components of the framework; features and functions; taxonomy of intervention techniques; design guidelines; and recommendations for efficient integrations and deployment in healthcare centers are presented. This would serve as a veritable prognosis and prediction tool to improve adherence to medication as the level of non-adherence to medication of the patient would be easily determined.
A Deep Learning Approach of Recognizing Natural Disasters on Images using Convolutional Neural Network and Transfer Learning

Natural disasters are uncontrollable phenomena occurring yearly which cause extensive damage to lives, property and cause permanent damage to the environment. However by, using Deep Learning, real-time recognition of these disasters can help the victims and emergency response agencies during the onset of these destructive events. At present, there are still gaps in the literature regarding real-time natural disaster recognition. In this paper, we present a dataset for the joint classification of natural disasters and intensity. We also proposed a lightweight convolutional neural network with two classification heads for the two tasks. This study leveraged on transfer learning in training the network to recognize natural disasters, as well as detecting normal, no-disaster images. At the same time, it is also capable of recognizing disaster intensity. Under controlled conditions, the model showed promising results on the two classification tasks. Thus, the study proved that accurate recognition of natural disasters is possible using a lightweight model and transfer learning. We hope that this study would lead to development of monitoring or surveillance systems that can perform accurate, on-the-ground, and real-time recognition of natural disasters allowing for rapid emergency responses mitigating the loss of lives and damages to properties.
K-means for semantically enriched trajectories

Clustering a set of given objects is a standard component of many data analysis tasks. The well-known k-means algorithm is a centroid-based clustering algorithm that optimizes the sum of distances between data objects and their assigned cluster centers. Each centroid then represents all objects assigned to a given cluster. In this paper, we study the special case of clustering semantically enriched spatio-temporal trajectories, i. e. , trajectories where each trace point can be annotated with arbitrary, possibly categorical semantic data in addition to numerical spatio-temporal data. Such trajectories result from, e. g. , tracking animals, humans, or weather phenomena and capture semantic contexts analysts may want to be aware of when interpreting the resulting clusters. Most current clustering algorithms for spatio-temporal categories take into account the numerical spatio-temporal coordinates only; thus, the resulting clusters do not necessarily reflect the characteristics of the additional semantic data. Building upon our earlier work on computing a representative trajectory for a given set of semantically enriched spatio-temporal trajectories, we describe how to implement the k-means algorithm to work with such data. In particular, we define a similarity measure called EFSMSim between a trajectory and a graph-based representation of a cluster centroid and show how to use this in the context of the k-means algorithm. We evaluate our EFSMClust approach by comparing it with state-of-the-art clustering algorithms taking into account either spatio-temporal information only or semantic attributes as well. Our experiments show that our algorithm is competitive even with respect to purely geometric performance measure and at the same time returns a representation of the centroids that can be used by domain experts to interpret both spatio-temporal and semantic information as well as to explore their possible relationships.
An Integrated Climate and Spatio-temporal Determinant for Influenza Forecasting based on Convolution Neural Network

We have proposed convolution neural network (CNN) for influenza forecasting. Our experiment model provided time series datasets consisted of climate variables and the spatio-temporal forms. The climatic variables have included precipitation, snowfall, maximum temperature, and minimum temperature. The spatio-temporal procedure has had two features, a flu feature is the influenza patient count in different time of focus region node. The second feature is the influenza patient count from influenza carrier in adjacent region. We assumed that asymptomatic patients which is a carrier of influenza. They will be able to travel anywhere whenever needed on pedestrians, vehicles or planes in their positions. We have provided two effect flu factors in climatic and human into deep machine learning for accurate predictions of influenza outbreaks results. The integrated variables influenced effectively influenza node predictions. The research compared models on recurrent neural network (RNN), long short-term memory neural network (LSTM) and convolution neural network (CNN). The term of Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Root Mean Square Percentage Error (RMSPE) captured for evaluate model. The performance denoted following resulted the convolution neural network (CNN) combined with Integrated climate and spatio-temporal determinant. CNN approved significant influenza forecasting more effectively than recurrent neural network (RNN) and long short-term memory neural network (LSTM).
Multi-scale framework for attraction place identification across human and animal mobility

A large volume of research has been devoted to the concept of mobility, a theme that is transversal to multiple fields of study and applications, from biology and ecology, to computer science and economy. Despite the considerable efforts made by a few scientific communities and the relevant results obtained so far, only very recently the issue of adopting a unifying and comprehensive approach across has been faced. In this paper, we present an overview of the quantitative framework we have recently developed to analyse and model human mobility, based on symbolic trajectories built on CDR data (Call Detail Record) provided by a telco operator [5]. Driven by a location-centric perspective, the framework includes a novel trajectory summarization technique for the extraction of the locations of interest from symbolic trajectories, a relevance analysis providing a novel location taxonomy and, inspired by ecological studies, a diversity analysis to characterize the movement through a location diversity profile. The ultimate goal of this contribution is to stress the need of a methodological integration between human and animal mobility analytical methods.