Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database

Objective Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes. Materials and Methods Across 130 sites in the Department of Veterans Affairs Corporate Data Warehouse, we selected the 150 most commonly used laboratory tests with numeric results per site from 2000 through 2016. Using source data text and numeric fields, we developed a machine learning model and manually validated random samples from both labeled and unlabeled datasets. Results The raw laboratory data consisted of >6.5 billion test results, with 2215 distinct LOINC codes. The model predicted the correct LOINC code in 85% of the unlabeled data and 96% of the labeled data by test frequency. In the subset of labeled data where the original and model-predicted LOINC codes disagreed, the model-predicted LOINC code was correct in 83% of the data by test frequency. Conclusion Using a completely automated process, we are able to assign LOINC codes to unlabeled data with high accuracy. When the model-predicted LOINC code differed from the original LOINC code, the model prediction was correct in the vast majority of cases. This scalable, automated algorithm may improve data quality and interoperability, while substantially reducing the manual effort currently needed to accurately map laboratory data.
Protective Effect of Cyclically Pressurized Solid–Liquid Extraction Polyphenols from Cagnulari Grape Pomace on Oxidative Endothelial Cell Death

The aim of this work is the evaluation of a green extraction technology to exploit winery waste byproducts. Specifically, a solid–liquid extraction technology (Naviglio Extractor®) was used to obtain polyphenolic antioxidants from the Cagnulari grape marc. The extract was then chemically characterized by spectrophotometric analysis, high-performance liquid chromatography, and mass spectrometry, revealing a total polyphenol content of 4.00 g/L ± 0.05, and the presence of anthocyanins, one of the most representative groups among the total polyphenols in grapes. To investigate potential biological activities of the extract, its ability to counteract hydrogen peroxide-induced oxidative stress and cell death was assessed in primary human endothelial cells. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test, used to assess potential extract cytotoxicity, failed to show any deleterious effect on cultured cells. Fluorescence measurements, attained with the intracellular reactive oxygen species (ROS) probe 2′,7′-dichlorodihydrofluorescein diacetate (H2DCF-DA), revealed a strong antioxidant potential of the marc extract on the used cells, as indicated by the inhibition of the hydrogen peroxide-induced ROS generation and the counteraction of the oxidative-induced cell death. Our results indicate the Naviglio extraction, as a green technology process, can be used to exploit wine waste to obtain antioxidants which can be used to produce enriched foods and nutraceuticals high in antioxidants.
Self-assembling structural colour in nature

The diversity and vividness of structural colour in the natural world have been recognised as far back as William Hooke in the 17th century. Whilst it is only recently that advances in the field have revealed the elegance and finesse of the physics used to create these effects. In this topical review we will highlight some of the structures and effects responsible for colour in butterfly scales, bird feathers, plants, insects and beetle elytra that have been studied to date. We will discuss the structures responsible and look at similarities and differences in these structures between species. This will be alongside our current understanding of how these are created biologically, how they develop structurally and what control mechanisms nature has at its disposal to control structure formation.
Telemedicine in Neurological Disorders: Opportunities and Challenges.

INTRODUCTION Telemedicine represents an emerging model for the assessment and management of various neurological disorders. METHODS We sought to discuss opportunities and challenges for the integration of telemedicine in the management of common and uncommon neurological disorders by reviewing and appraising studies that evaluate telemedicine as a means to facilitate the access to care, deliver highly specialized visits, diagnostic consultations, rehabilitation, and remote monitoring of neurological disorders. RESULTS Opportunities for telemedicine in neurological disorders include the replacement of or complement to in-office evaluations, decreased time between follow-up visits, reduction in disparities in access to healthcare, and promotion of education and training through interactions between primary care physicians and tertiary referral centers. Critical challenges include the integration of the systems for data monitoring with an easy-to-use, secure, and cost-effective platform that is both widely adopted by patients and healthcare systems and embraced by international scientific societies. CONCLUSIONS Multiple applications may spawn from a model based on digitalized healthcare services. Integrated efforts from multiple stakeholders will be required to develop an interoperable software platform capable of providing not only a holistic approach to care but also one that reduces disparities in the access to care.
Fisher Information and Natural Gradient Learning of Random Deep Networks

The parameter space of a deep neural network is a Riemannian manifold, where the metric is defined by the Fisher information matrix. The natural gradient method uses the steepest descent direction in a Riemannian manifold, but it requires inversion of the Fisher matrix, however, which is practically difficult. The present paper uses statistical neurodynamical method to reveal the properties of the Fisher information matrix in a net of random connections. We prove that the Fisher information matrix is unit-wise block diagonal supplemented by small order terms of off-block-diagonal elements. We further prove that the Fisher information matrix of a single unit has a simple reduced form, a sum of a diagonal matrix and a rank 2 matrix of weight-bias correlations. We obtain the inverse of Fisher information explicitly. We then have an explicit form of the approximate natural gradient, without relying on the matrix inversion.
Daily relationship between positive affect and drinking to cope: the moderating role of difficulties regulating positive emotions

ABSTRACT Background: Alcohol use is highly prevalent and linked to a wide range of negative outcomes among college students. Although emotion dysregulation has been theoretically and empirically linked to alcohol use, few studies have examined emotion dysregulation stemming from positive emotions. Objective: The goal of the current study was to extend extant research by using daily diary methods to examine the potentially moderating role of difficulties regulating positive emotions in the daily relation between positive affect and alcohol use to cope with social and non-social stressors. Methods: Participants were 165 college students (M age = 20.04; 55.2% male) who completed a baseline questionnaire assessing difficulties regulating positive emotions. Participants then responded to questions regarding state positive emotions and alcohol use once a day for 14 days. Results: Difficulties regulating positive emotions moderated the daily relation between positive affect stemming from social stressors and alcohol use to cope with social stressors. Positive affect stemming from social stressors predicted alcohol use to cope with social stressors with high (but not low) levels of difficulties regulating positive emotions. Conclusions: Findings underscore the potential utility of targeting difficulties regulating positive emotions in treatments aimed at reducing alcohol use to cope with social stressors among college students.
Towards energy efficiency in virtual topology design of elastic optical networks

Elastic optical networks offer a reliable platform for achieving energy efficiency by supporting extensive optical grooming of variable‐rate data traffic along with the traditional electrical data aggregation methods. The procedure of routing and spectrum allocation in a topology of nodes has explicit effect to the amount of consumed power. A new energy‐efficient method for designing the virtual topology in IP‐over‐elastic networks is introduced and evaluated. It is concluded that it consumes less power under different elastic transponder types, it designs the virtual topology using fewer transponders, and finally, there is a slight increase in the number of end‐to‐end lightpath hops. The proposed method is characterized by high performance and low lightpath establishment complexity. Therefore, it is suitable for a broad range of network configurations and transponder types.
Leveraging Probabilistic Existential Rules for Adversarial Deduplication

The entity resolution problem in traditional databases, also known as deduplication, seeks to map multiple virtual objects to its corresponding set of real-world entities. Though the problem is challenging, it can be tackled in a variety of ways by means of leveraging several simplifying assumptions, such as the fact that the multiple virtual objects appear as the result of name or attribute ambiguity, clerical errors in data entry or formatting, missing or changing values, or abbreviations. However, in cyber security domains the entity resolution problem takes on a whole different form, since malicious actors that operate in certain environments like hacker forums and markets are highly motivated to remain semi-anonymous—this is because, though they wish to keep their true identities secret from law enforcement, they also have a reputation with their customers. The above simplifying assumptions cannot be made in this setting, and we therefore coin the term “adversarial deduplication”. In this paper, we propose the use of probabilistic existential rules (also known as Datalog+/–) to model knowledge engineering solutions to this problem; we show that tuple-generating dependencies can be used to generate probabilistic deduplication hypotheses, and equality-generating dependencies can later be applied to leverage existing data towards grounding such hypotheses. The main advantage with respect to existing deduplication tools is that our model operates under the open-world assumption, and thus is capable of modeling hypotheses over unknown objects, which can later become known if new data becomes available.
Stepwise wiring of the Drosophila olfactory map requires specific Plexin B levels

The precise assembly of a neural circuit involves many consecutive steps. The conflict between a limited number of wiring molecules and the complexity of the neural network impels each molecule to execute multiple functions at different steps. Here, we examined the cell-type specific distribution of endogenous levels of axon guidance receptor Plexin B (PlexB) in the developing antennal lobe, the first olfactory processing center in Drosophila. We found that different classes of olfactory receptor neurons (ORNs) express PlexB at different levels in two wiring steps – axonal trajectory choice and subsequent target selection. In line with its temporally distinct patterns, the proper levels of PlexB control both steps in succession. Genetic interactions further revealed that the effect of high-level PlexB is antagonized by its canonical partner Sema2b. Thus, PlexB plays a multifaceted role in instructing the assembly of the Drosophila olfactory circuit through temporally-regulated expression patterns and expression level-dependent effects.
k-HEFT: A static task scheduling algorithm in clouds

In the research of task scheduling in the cloud environment, in order to minimize the task scheduling length, an efficient scheduling strategy is critical. A scheduling algorithm based on Top-k strategy is proposed to solve the problem of workflow scheduling with minimizing the completion time in cloud computing. In each assignment, choose the most k schedulable tasks in priority sequence in turn to try to allocate, and selecte the task that with the earliest completion time. The experimental results show that with HEFT and IHEFT algorithm, k-HEFT algorithm can reduce the scheduling length effectively.
API library-based identification and documentation of usage patterns

Application programming interfaces (APIs) are important sources for supporting source code reuse as each API provides a large set of pre-implemented functionalities that support programmers to achieve their daily work in different contexts. However, APIs provide huge number of classes and methods that hinder programmers to understand and use APIs. Numerous client-based approaches have been proposed for facilitating APIs usage through identifying frequent usage pattern. Although they represent significant efforts for helping APIs understanding, the client applications are not available for either newly released APIs libraries or APIs that are not widely used. In this paper, a non-client-based approach for frequent usage patterns identification and documentation is proposed. The approach incorporates hierarchical clustering algorithm and API's source code information. An experimental evaluation is conducted using four widely used APIs. For all studied APIs, the results show that the proposed approach is comparable with client-based approaches in terms of usage patterns cohesion.
Semidefinite Relaxation Based Blind Equalization using Constant Modulus Criterion

Blind equalization is a classic yet open problem. Statistic-based algorithms, such as constant modulus (CM), were widely investigated. One inherent issue with blind algorithms is the phase ambiguity of equalized signals. In this letter, we propose a novel scheme based on CM criterion and take advantage of the asymmetric property in a class of LDPC codes to resolve the phase ambiguity. Specifically, a new formulation with modified CM objective function and relaxed code constraints is presented.
Specific expression pattern of IMP metabolism related-genes in chicken muscle between cage and free range conditions

Inosine monophosphate (IMP) is a key factor affecting the fleshy flavor of meat; meanwhile, the free-range mode is an efficient strategy to improve muscular IMP content. To assess expression differences in IMP metabolism-related genes under different feeding patterns, Illumina Nextseq 500 sequencing was used to catalog the global gene expression profiles of muscle samples from Lueyang black-bone chicken under free-range and caging conditions. A total of 15510 unigenes were assembled, with 13423 (86.54%) and 6088 (39.25%) unigenes correctly annotated in the GO and KOG databases, respectively. Next, the "purine metabolism" pathway in the "nucleotide metabolism group" was assessed in depth. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we retrieved 172 nucleotide- and 5 purine- metabolism related genes that were differentially expressed in muscle samples from free-range and caged chickens. At 60-day-old, AMPD1, NT5C1A and ENTPD8 showed higher levels in the free-range group, while only ENTPD8 was upregulated in 120-day-old chickens. In addition, GART, GARS and ADSL in free-range chickens showed higher levels compared with caged animals. Furthermore, IMPDH levels in free-range chicken were lower than those of caged chicken. Real-time quantitative polymerase chain reaction (qPCR) was used to validate the above findings. These results revealed a set of differentially expressed genes potentially related to IMP metabolism in chicken under different breeding modes, providing novel insights into controlling IMP levels in chicken meat.
Doccurate: A Curation-Based Approach for Clinical Text Visualization

Before seeing a patient, physicians seek to obtain an overview of the patient's medical history. Text plays a major role in this activity since it represents the bulk of the clinical documentation, but reviewing it quickly becomes onerous when patient charts grow too large. Text visualization methods have been widely explored to manage this large scale through visual summaries that rely on information retrieval algorithms to structure text and make it amenable to visualization. However, the integration with such automated approaches comes with a number of limitations, including significant error rates and the need for healthcare providers to fine-tune algorithms without expert knowledge of their inner mechanics. In addition, several of these approaches obscure or substitute the original clinical text and therefore fail to leverage qualitative and rhetorical flavours of the clinical notes. These drawbacks have limited the adoption of text visualization and other summarization technologies in clinical practice. In this work we present Doccurate, a novel system embodying a curation-based approach for the visualization of large clinical text datasets. Our approach offers automation auditing and customizability to physicians while also preserving and extensively linking to the original text. We discuss findings of a formal qualitative evaluation conducted with 6 domain experts, shedding light onto physicians' information needs, perceived strengths and limitations of automated tools, and the importance of customization while balancing efficiency. We also present use case scenarios to showcase Doccurate's envisioned usage in practice.
Preparation and Culture of Human Liver Resident Immune Cells

Co‐cultivation of tumor cells and liver resident immune cells or other non‐parenchymal cells (NPCs) from the same donor is important for the study of cancer metastasis. So far, little is known about the mechanism of tumor cell or pathogen clearance, leukocyte infiltration, and immune cell recruitment in the human liver. To investigate these processes in vitro, the use of primary human hepatocytes and non‐parenchymal cell, especially immune cell, co‐culture systems play essential roles in the establishment of cell–cell and cell–extracellular matrix communications similar to native liver tissues. Hepatic non‐parenchymal cells mainly comprise liver sinusoid endothelial cells (LSECs), microvascular endothelial cells, hepatic stellate cells, Kupffer cells (KCs), natural killer T (iNKT) cells, and dendritic cells (DCs). Here we describe procedures for preparation, isolation, and culture of human liver resident immune cells and other non‐parenchymal cells. © 2018 by John Wiley & Sons, Inc.
MotionRugs: Visualizing Collective Trends in Space and Time

Understanding the movement patterns of collectives, such as flocks of birds or fish swarms, is an interesting open research question. The collectives are driven by mutual objectives or react to individual direction changes and external influence factors and stimuli. The challenge in visualizing collective movement data is to show space and time of hundreds of movements at the same time to enable the detection of spatiotemporal patterns. In this paper, we propose MotionRugs, a novel space efficient technique for visualizing moving groups of entities. Building upon established space-partitioning strategies, our approach reduces the spatial dimensions in each time step to a one-dimensional ordered representation of the individual entities. By design, MotionRugs provides an overlap-free, compact overview of the development of group movements over time and thus, enables analysts to visually identify and explore group-specific temporal patterns. We demonstrate the usefulness of our approach in the field of fish swarm analysis and report on initial feedback of domain experts from the field of collective behavior.
Narvis: Authoring Narrative Slideshows for Introducing Data Visualization Designs

Visual designs can be complex in modern data visualization systems, which poses special challenges for explaining them to the non-experts. However, few if any presentation tools are tailored for this purpose. In this study, we present Narvis, a slideshow authoring tool designed for introducing data visualizations to non-experts. Narvis targets two types of end users: teachers, experts in data visualization who produce tutorials for explaining a data visualization, and students, non-experts who try to understand visualization designs through tutorials. We present an analysis of requirements through close discussions with the two types of end users. The resulting considerations guide the design and implementation of Narvis. Additionally, to help teachers better organize their introduction slideshows, we specify a data visualization as a hierarchical combination of components, which are automatically detected and extracted by Narvis. The teachers craft an introduction slideshow through first organizing these components, and then explaining them sequentially. A series of templates are provided for adding annotations and animations to improve efficiency during the authoring process. We evaluate Narvis through a qualitative analysis of the authoring experience, and a preliminary evaluation of the generated slideshows.
γ2 GABAAR Trafficking and the Consequences of Human Genetic Variation

GABA type A receptors (GABAARs) mediate the majority of fast inhibitory neurotransmission in the central nervous system (CNS). Most prevalent as heteropentamers composed of two α, two β, and a γ2 subunit, these ligand-gated ionotropic chloride channels are capable of extensive genetic diversity (α1-6, β1-3, γ1-3, δ, 𝜀, 𝜃, π, ρ1-3). Part of this selective GABAAR assembly arises from the critical role for γ2 in maintaining synaptic receptor localization and function. Accordingly, mutations in this subunit account for over half of the known epilepsy-associated genetic anomalies identified in GABAARs. Fundamental structure–function studies and cellular pathology investigations have revealed dynamic GABAAR trafficking and synaptic scaffolding as critical regulators of GABAergic inhibition. Here, we introduce in vitro and in vivo findings regarding the specific role of the γ2 subunit in receptor trafficking. We then examine γ2 subunit human genetic variation and assess disease related phenotypes and the potential role of altered GABAAR trafficking. Finally, we discuss new-age imaging techniques and their potential to provide novel insight into critical regulatory mechanisms of GABAAR function.
Optimizing the MIMO Cellular Downlink: Multiplexing, Diversity, or Interference Nulling?

A base-station (BS) equipped with multiple antennas can use its spatial dimensions in three different ways: 1) to serve multiple users, thereby achieving a multiplexing gain; 2) to provide spatial diversity in order to improve user rates; and 3) to null interference in neighboring cells. This paper answers the following question: What is the optimal balance between these three competing benefits? We answer this question in the context of the downlink of a cellular network, where multi-antenna BSs serve multiple single-antenna users using zero-forcing beamforming with equal power assignment, while nulling interference at a subset of out-of-cell users. Any remaining spatial dimensions provide transmit diversity for the scheduled users. Utilizing tools from stochastic geometry, we show that, surprisingly, to maximize the per-BS ergodic sum rate, with an optimal allocation of spatial resources, interference nulling does not provide a tangible benefit. The strategy of avoiding inter-cell interference nulling, reserving some fraction of spatial resources for multiplexing, and using the rest to provide diversity, is already close-to-optimal in terms of the sum-rate. However, interference nulling does bring significant benefit to cell-edge users, particularly when adopting a range-adaptive nulling strategy where the size of the cooperating BS cluster is increased for cell-edge users.