Miracle-dz/UniFAR-FTUs huggingface dataset:
Is the use of Stereotactic Electroencephalography Safe and Effective in Children? A Meta-Analysis of the use of Stereotactic Electroencephalography in Comparison to Subdural Grids for Invasive Epilepsy Monitoring in Pediatric Subjects.

BACKGROUND Stereoelectroencephalography (SEEG) is an alternative addition to subdural grids (SDG) in invasive extra-operative monitoring for medically refractory epilepsy. Few studies exist on the clinical efficacy and safety of these techniques in pediatric populations. OBJECTIVE To provide a comparative quantitative summary of surgical complications and postoperative seizure freedom associated with invasive extra-operative presurgical techniques in pediatric patients. METHODS The systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A literature search was conducted utilizing Ovid Medline, Embase, Pubmed, and the Cochrane database. RESULTS Fourteen papers with a total of 697 pediatric patients undergoing invasive SDG monitoring and 9 papers with a total of 277 pediatric patients undergoing SEEG monitoring were utilized in the systemic review. Cerebral spinal fluid (CSF) leaks were the most common adverse event in the SDG studies (pooled prevalence 11.9% 95% confidence interval [CI] 5.7-23.3). There was one case of CSF leak in the SEEG studies. Intracranial hemorrhages (SDG: 10.7%, 95% CI 5.3-20.3; SEEG: 2.9%, 95% CI -0.7 to 10.8) and infection (SDG: 10.8%, 95% CI 6.7-17) were more common in the SDG studies reviewed. At the time of the last postoperative visit, a greater percentage of pediatric patients achieved seizure freedom in the SEEG studies (SEEG: 66.5%, 95% CI 58.8-73.4; SDG: 52.1%, 95% CI 43.0-61.1). CONCLUSION SEEG is a safe alternative to SDG and should be considered on an individual basis for selected pediatric patients.
Mixed signals from the cell's antennae: primary cilia in cancer

Primary cilia (PC) are antenna‐like organelles that protrude from most mammalian cells. They are essential for the regulation of several signaling pathways such as Hedgehog and WNT. It is therefore not surprising that a dysfunction of PC is frequently associated with pathologies. Originally, PC were found to be involved in a variety of diseases commonly referred to as ciliopathies including cystic kidney diseases. Evidence is accumulating that PC play also an important role in cancer formation and regulation, which is the focus of this review.
A comparative study of fruit detection and counting methods for yield mapping in apple orchards

We present a modular end‐to‐end system for yield estimation in apple orchards. Our goal is to identify fruit detection and counting methods with the best performance for this task. We propose a novel semantic segmentation‐based approach for fruit detection and counting and perform extensive comparative analysis against other state‐of‐the‐art techniques. This is the first work comparing multiple fruit detection and counting methods head‐to‐head on the same data sets. Fruit detection results indicate that the semisupervised method, based on Gaussian Mixture Models, outperforms the deep learning‐based methods in the majority of the data sets. For fruit counting though, the deep learning‐based approach performs better for all of the data sets. Combining these two methods, we achieve yield estimation accuracies ranging from 95.56% to 97.83%.
"Bridge": Enhanced Signed Directed Network Embedding

Signed directed networks with positive or negative links convey rich information such as like or dislike, trust or distrust. Existing work of sign prediction mainly focuses on triangles (triadic nodes) motivated by balance theory to predict positive and negative links. However, real-world signed directed networks can contain a good number of "bridge'' edges which, by definition, are not included in any triangles. Such edges are ignored in previous work, but may play an important role in signed directed network analysis. %Such edges serve as fundamental building blocks and may play an important role in signed network analysis. In this paper, we investigate the problem of learning representations for signed directed networks. We present a novel deep learning approach to incorporating two social-psychologic theories, balance and status theories, to model both triangles and "bridge'' edges in a complementary manner. The proposed framework learns effective embeddings for nodes and edges which can be applied to diverse tasks such as sign prediction and node ranking. Experimental results on three real-world datasets of signed directed social networks verify the essential role of "bridge" edges in signed directed network analysis by achieving the state-of-the-art performance.
Efficient and Effective Query Expansion for Web Search

Query Expansion (QE) techniques expand the user queries with additional terms, e.g., synonyms and acronyms, to enhance the system recall. State-of-the-art solutions employ machine learning methods to select the most suitable terms. However, most of them neglect the cost of processing the expanded queries, thus selecting effective, yet very expensive, terms. The goal of this paper is to enable QE in scenarios with tight time constraints proposing a QE framework based on structured queries and efficiency-aware term selection strategies. In particular, the proposed expansion selection strategies aim at capturing the efficiency and the effectiveness of the expansion candidates, as well as the dependencies among them. We evaluate our proposals by conducting an extensive experimental assessment on real-world search engine data and public TREC data. Results confirm that our approach leads to a remarkable efficiency improvement w.r. t. the state-of-the-art: a reduction of the retrieval time up to 30 times, with only a small loss of effectiveness.
KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare

The goal of diagnosis prediction task is to predict the future health information of patients from their historical Electronic Healthcare Records (EHR). The most important and challenging problem of diagnosis prediction is to design an accurate, robust and interpretable predictive model. Existing work solves this problem by employing recurrent neural networks (RNNs) with attention mechanisms, but these approaches suffer from the data sufficiency problem. To obtain good performance with insufficient data, graph-based attention models are proposed. However, when the training data are sufficient, they do not offer any improvement in performance compared with ordinary attention-based models. To address these issues, we propose KAME, an end-to-end, accurate and robust model for predicting patients' future health information. KAME not only learns reasonable embeddings for nodes in the knowledge graph, but also exploits general knowledge to improve the prediction accuracy with the proposed knowledge attention mechanism. With the learned attention weights, KAME allows us to interpret the importance of each piece of knowledge in the graph. Experimental results on three real world datasets show that the proposed KAME significantly improves the prediction performance compared with the state-of-the-art approaches, guarantees the robustness with both sufficient and insufficient data, and learns interpretable disease representations.
Inflammation-Induced Citrullinated Glucose-Regulated Protein 78 Elicits Immune Responses in Human Type 1 Diabetes

The β-cell has become recognized as a central player in the pathogenesis of type 1 diabetes with the generation of neoantigens as potential triggers for breaking immune tolerance. We report that posttranslationally modified glucose-regulated protein 78 (GRP78) is a novel autoantigen in human type 1 diabetes. When human islets were exposed to inflammatory stress induced by interleukin-1β, tumor necrosis factor-α, and interferon-γ, arginine residue R510 within GRP78 was converted into citrulline, as evidenced by liquid chromatography-tandem mass spectrometry. This conversion, known as citrullination, led to the generation of neoepitopes, which effectively could be presented by HLA-DRB1*04:01 molecules. With the use of HLA-DRB1*04:01 tetramers and ELISA techniques, we demonstrate enhanced antigenicity of citrullinated GRP78 with significantly increased CD4+ T-cell responses and autoantibody titers in patients with type 1 diabetes compared with healthy control subjects. Of note, patients with type 1 diabetes had a predominantly higher percentage of central memory cells and a lower percentage of effector memory cells directed against citrullinated GRP78 compared with the native epitope. These results strongly suggest that citrullination of β-cell proteins, exemplified here by the citrullination of GRP78, contributes to loss of self-tolerance toward β-cells in human type 1 diabetes, indicating that β-cells actively participate in their own demise.
Pregel: a system for large-scale graph processing

Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. This vertex-centric approach is flexible enough to express a broad set of algorithms. The model has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier. Distribution-related details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.
AVEC 2018 Workshop and Challenge: Bipolar Disorder and Cross-Cultural Affect Recognition

The Audio/Visual Emotion Challenge and Workshop (AVEC 2018) "Bipolar disorder, and cross-cultural affect recognition'' is the eighth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: bipolar disorder classification, cross-cultural dimensional emotion recognition, and emotional label generation from individual ratings, respectively.
Neural Transition-based Syntactic Linearization

The task of linearization is to find a grammatical order given a set of words. Traditional models use statistical methods. Syntactic linearization systems, which generate a sentence along with its syntactic tree, have shown state-of-the-art performance. Recent work shows that a multilayer LSTM language model outperforms competitive statistical syntactic linearization systems without using syntax. In this paper, we study neural syntactic linearization, building a transition-based syntactic linearizer leveraging a feed forward neural network, observing significantly better results compared to LSTM language models on this task.
ChipGAN: A Generative Adversarial Network for Chinese Ink Wash Painting Style Transfer

Style transfer has been successfully applied on photos to generate realistic western paintings. However, because of the inherently different painting techniques adopted by Chinese and western paintings, directly applying existing methods cannot generate satisfactory results for Chinese ink wash painting style transfer. This paper proposes ChipGAN, an end-to-end Generative Adversarial Network based architecture for photo to Chinese ink wash painting style transfer. The core modules of ChipGAN enforce three constraints -- voids, brush strokes, and ink wash tone and diffusion -- to address three key techniques commonly adopted in Chinese ink wash painting. We conduct stylization perceptual study to score the similarity of generated paintings to real paintings by consulting with professional artists based on the newly built Chinese ink wash photo and image dataset. The advantages in visual quality compared with state-of-the-art networks and high stylization perceptual study scores show the effectiveness of the proposed method.
Cationic Cyclopentadienyliron Complex as a Novel and Successful Nucleating Agent on the Crystallization Behavior of the Biodegradable PHB Polymer

Cationic cyclopentadienyliron (CpFe+) is one of the most fruitful organometallic moieties that has been utilized to mediate the facile synthesis of a massive number of macromolecules. However, the ability of this compound to function as a nucleating agent to improve other macromolecule properties has not been explored. This report scrutinizes the influence of the cationic complex as a novel nucleating agent on the spherulitic morphology, crystal structure, and isothermal and non-isothermal crystallization behavior of the Poly(3-hydroxybutyrate) (PHB) bacterial origin. The incorporation of the CpFe+ into the PHB materials caused a significant increase in its spherulitic numbers with a remarkable reduction in the spherulitic sizes. Unlike other nucleating agents, the SEM imageries exhibited a good dispersion without forming agglomerates of the CpFe+ moieties in the PHB matrix. Moreover, according to the FTIR analysis, the cationic organoiron complex has a strong interaction with the PHB polymeric chains via the coordination with its ester carbonyl. Yet, the XRD results revealed that this incorporation had no significant effect on the PHB crystalline structure. Though the CpFe+ had no effect on the polymer’s crystal structure, it accelerated outstandingly the melt crystallization of the PHB. Meanwhile, the crystallization half-times (t0.5) of the PHB decreased dramatically with the addition of the CpFe+. The isothermal and non-isothermal crystallization processes were successfully described using the Avrami model and a modified Avrami model, as well as a combination of the Avrami and Ozawa methods. Finally, the effective activation energy of the PHB/CpFe+ nanocomposites was much lower than those of their pure counterparts, which supported the heterogeneous nucleation mechanism with the organometallic moieties, indicating that the CpFe+ is a superior nucleating agent for this class of polymer.
Learning new words from an interactive electronic storybook intervention

Background Children who enter school with limited vocabulary knowledge are at risk for reading failure. This study investigated the efficacy of an interactive e-book, implemented as a mobile application, to facilitate vocabulary learning in Grade 1 isiXhosa-speaking children (n = 65). Objective The purpose was to measure if an e-book intervention, specifically developed for use in the South African context, could facilitate the acquisition and retention of new words at different levels of lexical representation. Method A randomised pre-test and/or post-test between-subject design was used where an experimental group that received the e-book intervention was compared to a control group before the control group received a delayed intervention. Follow-up testing was performed to measure retention of the new vocabulary after eight weeks. Mixed-model repeated-measure Analysis of Variance (ANOVAs) were used to determine differences between the participants in the experimental and control groups. Results The short-term e-book intervention not only facilitated fast-mapping of new words but enabled participants to develop more robust lexical representations of the newly acquired words. Follow-up assessment showed that they retained their newly acquired word knowledge. Conclusion Multimedia technology can be used to provide explicit and embedded vocabulary training to young children at risk for academic failure. These findings are particularly relevant for South African environments where there is limited parental support and lack of educational resources to promote vocabulary learning in young children.
Dietary non-enzymatic antioxidant capacity and the risk of myocardial infarction: the Swedish National March Cohort.

Background Results from randomized trials of antioxidant supplementation have cast doubt on observational data linking diets high in antioxidants to a reduced risk of cardiovascular diseases. We hypothesized that supplementation of one or a few antioxidants might not simulate the complex actions of all antioxidants in the human diet. We therefore investigated the association between dietary Non Enzymatic Antioxidant Capacity (NEAC), reflecting the antioxidant potential of the whole diet, and the risk of myocardial infarction (MI). Methods In the Swedish National March Cohort, 34 543 men and women free from cardiovascular diseases and cancer were followed through record linkages from 1997 until 2010. NEAC was assessed with a validated food-frequency questionnaire at baseline. The distribution of NEAC was categorized into sex-specific quartiles. We fitted multivariable Cox proportional hazards regression models to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). Results During a mean follow-up time of 12.7 years, we identified 1142 incident cases of MI. Successively higher quartiles (Qs) of dietary NEAC were accompanied by a monotonic trend of decreasing MI incidence, both for overall MI (HR Q4 vs Q1: 0.77; 95% CI: 0.61-0.96; p for trend = 0.008) and non-fatal MI (HR Q4 vs Q1: 0.72; 95% CI: 0.56-0.92; p for trend = 0.004). No such association was found for fatal MI. Conclusions A diet rich in antioxidants might protect from MI.
Every Word has its History: Interactive Exploration and Visualization of Word Sense Evolution

Human language constantly evolves due to the changing world and the need for easier forms of expression and communication. Our knowledge of language evolution is however still fragmentary despite significant interest of both researchers as well as wider public in the evolution of language. In this paper, we present an interactive framework that permits users study the evolution of words and concepts. The system we propose offers a rich online interface allowing arbitrary queries and complex analytics over large scale historical textual data, letting users investigate changes in meaning, context and word relationships across time.
ACTN1 mutations lead to a benign form of platelet macrocytosis not always associated with thrombocytopenia

The inherited thrombocytopenias (IT) are a heterogeneous group of diseases resulting from mutations in more than 30 different genes. Among them, ACTN1‐related thrombocytopenia (ACTN1‐RT; Online Mendelian Inheritance in Man: 615193) is one of the most recently identified forms. It has been described as a mild autosomal dominant macrothrombocytopenia caused by mutations in ACTN1, a gene encoding for one of the two non‐muscle isoforms of α‐actinin. We recently identified seven new unrelated families with ACTN1‐RT caused by different mutations. Two of them are novel missense variants (p.Trp128Cys and p.Pro233Leu), whose pathogenic role has been confirmed by in vitro studies. Together with the 10 families we have previously described, our cohort of ACTN1‐RT now consists of 49 individuals carrying ACTN1 mutations. This is the largest case series ever collected and enabled a critical evaluation of the clinical aspects of the disease. We concluded that ACTN1‐RT is the fourth most frequent form of IT worldwide and it is characterized by platelet macrocytosis in all affected subjects and mild thrombocytopenia in less than 80% of cases. The risk of bleeding, either spontaneous or upon haemostatic challenge, is negligible and there are no other associated defects, either congenital or acquired. Therefore, ACTN1‐RT is a benign form of IT, whose diagnosis provides affected individuals and their families with a good prognosis.
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such an inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.
The Vagus Nerve Can Predict and Possibly Modulate Non-Communicable Chronic Diseases: Introducing a Neuroimmunological Paradigm to Public Health

Global burden of diseases (GBD) includes non-communicable conditions such as cardiovascular diseases, cancer and chronic obstructive pulmonary disease. These share important behavioral risk factors (e.g., smoking, diet) and pathophysiological contributing factors (oxidative stress, inflammation and excessive sympathetic activity). This article wishes to introduce to medicine and public health a new paradigm to predict, understand, prevent and possibly treat such diseases based on the science of neuro-immunology and specifically by focusing on vagal neuro-modulation. Vagal nerve activity is related to frontal brain activity which regulates unhealthy lifestyle behaviors. Epidemiologically, high vagal activity, indexed by greater heart rate variability (HRV), independently predicts reduced risk of GBD and better prognosis in GBD. Biologically, the vagus nerve inhibits oxidative stress, inflammation and sympathetic activity (and associated hypoxia). Finally, current non-invasive methods exist to activate this nerve for neuro-modulation, and have promising clinical effects. Indeed, preliminary evidence exists for the beneficial effects of vagal nerve activation in diabetes, stroke, myocardial infarction and possibly cancer. Thus, we propose to routinely implement measurement of HRV to predict such GBD in populations, and to test in randomized controlled trials effects of non-invasive vagal nerve activation on prevention and treatment of GBD, reflecting possible neuro-modulation of health.
A Predictive Processing Model of Perception and Action for Self-Other Distinction

During interaction with others, we perceive and produce social actions in close temporal distance or even simultaneously. It has been argued that the motor system is involved in perception and action, playing a fundamental role in the handling of actions produced by oneself and by others. But how does it distinguish in this processing between self and other, thus contributing to self-other distinction? In this paper we propose a hierarchical model of sensorimotor coordination based on principles of perception-action coupling and predictive processing in which self-other distinction arises during action and perception. For this we draw on mechanisms assumed for the integration of cues for a sense of agency, i.e., the sense that an action is self-generated. We report results from simulations of different scenarios, showing that the model is not only able to minimize free energy during perception and action, but also showing that the model can correctly attribute sense of agency to own actions.