Miracle-dz/UniFAR-FTUs huggingface dataset:
The reduced lncRNA NKILA inhibited proliferation and promoted apoptosis of chondrocytes via miR-145/SP1/NF-κB signaling in human osteoarthritis.

OBJECTIVE Growing evidence has shown that long non-coding RNAs (lncRNAs) play some roles in the progression of osteoarthritis. In this study, we investigated the functions and mechanisms of lncRNA NKILA (NKILA) of chondrocytes in human osteoarthritis (OA). PATIENTS AND METHODS RT-PCR was used to detect the expressions of NKILA and miR-145 in OA tissues. After transfection of NKILA overexpression lentivirus (LV-NKILA) and NKILA downregulation lentivirus (LV-shNKILA) into primary chondrocytes, MTT assay was carried out to measure the cell proliferation of chondrocytes. The expressions of SP1, Bcl-2, Bax, cleaved caspase-3 and NF-κB signaling factors were detected by Western blot. Moreover, luciferase assay was performed to explore the binding site of NKILA and miR-145, miR-145 and SP1. Finally, JSH, a NF-κB signaling inhibitor, was added into chondrocytes transfected with LV-shNKILA or miR-145 mimic to detect that NKILA functions via miR-145/SP1/NF-κB signaling pathway. RESULTS We found that NKILA and SP1 were significantly reduced, miR-145 was increased in cartilage tissues of OA patients. After LV-NKILA transfection, the proliferation ability of chondrocytes was improved and cell apoptosis was inhibited; however, the proliferation ability of chondrocytes was repressed, and cell apoptosis was increased in LV-sh NKILA group. MiR-145 was predicted to be a potential target of NKILA and luciferase gene reporter assay confirmed that NKILA could directly bind with miR-145. Furthermore, SP1 was predicted to be a target gene of miR-145 and luciferase gene reporter assay proved that miR-145 could directly bind with SP1. Finally, we added JSH, a NF-κB signaling inhibitor, into chondrocytes with LV-shNKILA or miR-145 mimic. Results showed that the repressed SP1 was reversed after the addition of JSH in both LV-shNKILA and miR-145 mimic group. Further, the repressed proliferation capacities and promoted cell apoptosis were also reversed after the addition of JSH. CONCLUSIONS According to the results, this study uncovers NKILA is reduced in human osteoarthritic cartilage tissues. Furthermore, we firstly uncover that the reduced NKILA could function as a ceRNA to improve miR-145, which inhibited SP1 expression and regulated NF-κB signaling pathway, thereby promoting tissue inflammation, and inhibiting proliferation and promoting apoptosis of chondrocytes. Thus, it may be used as a promising prognostic marker and a potential target for osteoarthritis.
H2O2 induces oxidative stress damage through the BMP‐6/SMAD/hepcidin axis

Age‐related macular degeneration (AMD) is one of the leading causes of blindness in elderly individuals worldwide. Oxidative stress injury to retinal pigment epithelial (RPE) cells plays a major role in the pathogenesis of AMD. The purpose of this study was to observe the correlation between Hepcidin and neovascular age‐related macular degeneration (nAMD) and to further observe whether oxidative stress can inhibit Hepcidin expression through relevant signaling pathways to produce oxidative damage. We compared the concentrations of Hepcidin in the aqueous humor of nAMD patients and a control group and found that the concentration of Hepcidin was lower in nAMD patients. Through PCR and western blotting, we observed that H2O2 can significantly inhibit the expression of Bone morphogenetic protein‐6 (BMP‐6) and Hepcidin and increase the intracellular iron concentration in RPE cells, while BMP‐6 can reverse the inhibition of Hepcidin and the increase in iron concentration caused by H2O2. In addition, alterations in smad1 and smad5 expression were examined, and pretreatment with BMP‐6 was demonstrated to reduce H2O2‐induced activation of smad1 and smad5. The effects of BMP‐6 were attenuated by smad1 and smad5 siRNA, further verifying that oxidative stress inhibits the expression of Hepcidin by inhibiting activation of the BMP/SMAD signaling pathway. To some extent, this study verified that oxidative stress injury plays a role in nAMD by affecting the level of hepcidin, which lays a foundation for exploring new methods to treat nAMD.
Blood Aspergillus PCR: The Good, the Bad, and the Ugly

Invasive Aspergillosis (IA) is one of the most common invasive fungal diseases and is accompanied by high morbidity and mortality. In order to maximize patient outcomes and survival, early and rapid diagnosis has been shown to be pivotal. Hence, diagnostic tools aiding and improving the diagnostic process are ambitiously searched for. In this context, polymerase chain reaction (PCR) may represent a potential candidate. Its additional value and benefits in diagnosis have been demonstrated and are scientifically established. Nevertheless, standardized and widespread usage is sparse because several factors influence diagnostic quality and need to be considered in order to optimize diagnostic performance and outcome. In the following review, the current role of PCR in the diagnosis of IA is explored, with special focus on the strengths and limitations of PCR in different settings.
Molecular and functional characterization of the SBP-box transcription factor SPL-CNR in tomato fruit ripening and cell death

Abstract SlSPL-CNR, an SBP-box transcription factor (TF) gene residing at the epimutant Colourless non-ripening (Cnr) locus, is involved in tomato ripening. This epimutant provides a unique model to investigate the (epi)genetic basis of fruit ripening. Here we report that SlSPL-CNR is a nucleus-localized protein with a distinct monopartite nuclear localization signal (NLS). It consists of four consecutive residues ‘ 30KRKR33’ at the N-terminus of the protein. Mutation of the NLS abolishes SlSPL-CNR’s ability to localize in the nucleus. SlSPL-CNR comprises two zinc-finger motifs (ZFMs) within the C-terminal SBP-box domain. Both ZFMs contribute to zinc-binding activity. SlSPL-CNR can induce cell death in tomato and tobacco, dependent on its nuclear localization. However, the two ZFMs have differential impacts on SlSPL-CNR’s induction of severe necrosis or mild necrotic ringspot. NLS and ZFM mutants cannot complement Cnr fruits to ripen. SlSPL-CNR interacts with SlSnRK1. Virus-induced SlSnRK1 silencing leads to reduction in expression of ripening-related genes and inhibits ripening in tomato. We conclude that SlSPL-CNR is a multifunctional protein that consists of a distinct monopartite NLS, binds to zinc, and interacts with SlSnRK1 to affect cell death and tomato fruit ripening.
Framework for Safety in Autonomous Vehicles

The integration of the Safety dimension has been a critical requirement when developing and deploying Autonomous Vehicles (AV). While much progress has been achieved within the past years, most work has centred on providing vehicles with the ability to navigate autonomously. Safety has emerged as the major challenge. This paper proposes a reference architecture that incorporates the notion of self-safety into existing AV architectures. This architecture consists in a multi-layered control loop aimed at managing self-adaptation processes in order to ensure safety at run-time
Digitization of Higher Education Institutions

The fourth industrial revolution, the digitization of industry, is driving business landscape and associated skills development, including tertiary education. Universities and institutions of higher learning have evolved into technological hubs, developing and delivering skills for the future. The operations and systems together with workflows of delivery at a tertiary institution should be modified to deliver services and a product that is 4IR savvy, more importantly, the systems and processes must be 4IR enabled so as to deliver a seamless, efficient, smart digital experience. This paper reviews tertiary institutional operations and provides an architecture to deliver digitization at institutional level. This research adopts a functional and architectural view of the system and systems of systems. A Digital Education Evaluation Model (DEEM) is proposed for evaluation of traditional and digitized practices, for identification of digitized technologies for adoption. The DEEM is demonstrated by comparatively analyzing traditional and virtual classrooms.
ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction

The ICDAR 2019 Challenge on "Scanned receipts OCR and key information extraction" (SROIE) covers important aspects related to the automated analysis of scanned receipts. The SROIE tasks play a key role in many document analysis systems and hold significant commercial potential. Although a lot of work has been published over the years on administrative document analysis, the community has advanced relatively slowly, as most datasets have been kept private. One of the key contributions of SROIE to the document analysis community is to offer a first, standardized dataset of 1000 whole scanned receipt images and annotations, as well as an evaluation procedure for such tasks. The Challenge is structured around three tasks, namely Scanned Receipt Text Localization (Task 1), Scanned Receipt OCR (Task 2) and Key Information Extraction from Scanned Receipts (Task 3). The competition opened on 10th February, 2019 and closed on 5th May, 2019. We received 29, 24 and 18 valid submissions received for the three competition tasks, respectively. This report presents the competition datasets, define the tasks and the evaluation protocols, offer detailed submission statistics, as well as an analysis of the submitted performance. While the tasks of text localization and recognition seem to be relatively easy to tackle, it is interesting to observe the variety of ideas and approaches proposed for the information extraction task. According to the submissions' performance we believe there is still margin for improving information extraction performance, although the current dataset would have to grow substantially in following editions. Given the success of the SROIE competition evidenced by the wide interest generated and the healthy number of submissions from academic, research institutes and industry over different countries, we consider that the SROIE competition can evolve into a useful resource for the community, drawing further attention and promoting research and development efforts in this field.
Data-Driven “Market Basket”-Pricing and Personalized Health Information Services Using Salesforce's Model-Driven Systems Service Design

Risk management and customer loyalty are increasingly critical. Being able to reduce risks through data-driven packaging and pricing and sustaining customer loyalty are therefore important. This study presents a case study on data-driven packaging (simulating market-basket analysis) and pricing to enable patients, and caregivers to obtain personalized health information services and for hospitals, knowledge sharing on marketing opportunities (who and what to target), which services should be paired with which, corresponding pricing and to predict based on past trends, resource allocation. Our personalized health information system, E-healthzone, builds on Salesforce's model-driven systems service design. It is currently a management information system but we hope to include intelligence eventually. Technology acceptance by sample users indicate positive acceptance. E-healthzone is perceived as useful especially if hospital fees are expensive, and quality and reasonably-priced services are preferred. Most users intend to use the system when it is released. Expert evaluations view the pricing and booking system and gaining insights through the analytics and dashboards as E-healthzone‘s greatest strengths.
Music Symbol Sequence Indexing in Medieval Plainchant Manuscripts

Huge amounts of musical manuscripts are preserved in cathedrals, abbeys, and archives. However, without reliable transcripts, their contents are inaccessible. Manual transcription is unaffordable for large collections, and current automatic technologies—such as Optical Music Recognition or Handwritten Music Recognition—do not provide sufficient accuracy for a fully-automatic scenario. In many cases, perfect transcripts are not really needed, given that content-based search with some degree of reliability would already be extremely useful. Spotting just single music symbols is rather useless (most of the symbols generally appear in all pages); instead, helpful search targets are melodic patterns, which typically correspond to music symbol sequences. We explore approaches for accurate retrieval of melodic patterns, represented by music symbol sequences, from collections of Medieval plainchant manuscripts. Our statistical framework, based on the use of convolutional recurrent neural networks and probabilistic indices, is shown to be useful for retrieving music patterns which appear frequently in this untranscribed images, yielding an Average Precision of 86 %.
Study of lipid profile and pulmonary functions in subjects participated in Sudarshan Kriya Yoga.

We intended to study the effect of Sudarshan Kriya Yoga, a novel breathing technique conceived by the world renowned spiritual leader and founder of The Art of Living Foundation Sri Sri Ravishankar. Millions of followers all over the world are practicing and reporting positive well being and better health. To see the effect of Sudarshan Kriya Yoga on Lipid Profile, Pulmonary Function and Hemoglobin concentration, we conducted a workshop of 8 days consisting of 150 participants. Out of which 55 were included in the study group. Our results show that after practicing Sudarshan Kriya, there is decrease in Total Cholesterol, LDL-Cholesterol along with significant increase in HDL-Cholesterol. There are significant changes in Pulmonary Function, but statistically non-significant changes in Hematological parameters. From the observation Sudarshan Kriya Yoga may play vital role in reducing Total Cholesterol (P<0.05), LDL-Cholesterol (P<0.001) and significantly increasing HDLCholesterol (P<0.001). Spirometric Pulmonary Function Tests studied were Forced Vital Capacity, Forced Expiratory Volume in first second, Peak Expiratory Flow Rate and Maximum Voluntary Ventilation. The results showed improvement in all Pulmonary Function parameters in all subjects as compared to before practicing Sudarshan Kriya Yoga. Thus Sudarshan Kriya Yoga may have therapeutic implication in the adjunctive (non pharmacological) management of cardiovascular diseases and respiratory diseases. The present study confirmed the positive effect of Sudarshan Kriya Yoga on Lipid Profile and Pulmonary Function over period of 8 days.
Productization and Product Structure as the Backbone for Product Data and Fact-based Analysis of Company Products

Data are a strategic asset for companies but are not fully capitalized to serve the business. Company data assets that reside in the enterprise applications, the corporate business IT, have the potential to provide additional value aside serving the typical functional role of these applications. One possible application is the fact-based profitability analysis. Understanding company level or business line profitability is not enough for sustainable long-term business success, but product level analysis is needed. Currently product level analysis is often either seen not possible or entails laborious manual work. This study analyses the roles of productization and product structure as preconditions for fact-based analysis of company products. The study is realized as a combination of a literature review and analysis of eight companies. Those responsible for products and financial results may benefit of the understanding of the significance of productization and product structure for fact-based analysis and reporting on company products. The findings support data-driven approach and fact-based analysis.
Training Full-Page Handwritten Text Recognition Models without Annotated Line Breaks

Training Handwritten Text Recognition (HTR) models typically requires large amounts of labeled data which often are line or page images with corresponding line-level ground truth (GT) transcriptions. Many digital collections have page-level transcriptions for each image, but the transcription is unformatted, i.e., line breaks are not annotated. Can we train lined-based HTR models using such data? In this work, we present a novel alignment technique for segmenting page-level GT text into text lines during HTR model training. This text segmentation problem is formulated as an optimization problem to minimize the cost of aligning predicted lines with the GT text. Using both simulated and HTR model predictions, we show that the alignment method identifies line breaks accurately, even when the predicted lines have high character error rates (CER). We removed the GT line breaks from the ICDAR-2017 READ dataset and trained a HTR model using the proposed alignment method to predict line breaks on-the-fly. This model achieves comparable CER w.r. t. to the same model trained with the GT line breaks. Additionally, we downloaded an online digital collection of 50K English journal pages (not curated for HTR research) whose transcriptions do not contain line breaks, and achieve 11% CER.
The Interplay Between Knowledge Creation Strategies: The Case of European Information-and-Communications-Technology Firms

This paper explores the interplay between two dimensions of organizational learning - internal knowledge creation and external knowledge sourcing. Based on a sample of European information-and-communications-technology firms, this research demonstrates that there are differences in behavior as well as co-movement between the dimensions. The companies engaged in external learning tend to possess more internal knowledge stock as well as that higher internal knowledge stock triggers more active external learning strategies through business combinations, especially divestments.
DeepTabStR: Deep Learning based Table Structure Recognition

This paper presents a novel method for the analysis of tabular structures in document images using the potential of deformable convolutional networks. In order to assess the suitability of the model to the task of table structure recognition, most of the prior methods have been tested on the smaller ICDAR-13 table structure recognition dataset comprising of just 156 tables. We curated a new image-based table structure recognition dataset, TabStructDB2, comprising of 1081 tables densely labeled with row and column information. Instead of collecting new images for this purpose, we leveraged the famous Page-Object Detection dataset from ICDAR-17, and added structural information for all the tabular regions present in the dataset. This new publicly available dataset will enable the development of more sophisticated table structure recognition techniques in the future. We performed extensive evaluation on the two datasets (ICDAR-13 and TabStructDB) including crossdataset testing in order to evaluate the efficacy of the proposed approach. We achieved state-of-the-art results with deformable models on ICDAR-13 with an average F-Measure of 92.98% (89.42% for rows and 96.55% for columns) and report baseline results on TabStructDB for guiding future research efforts with an F-Measure of 93.72% (91.26% for rows and 95.59% for columns). Despite promising results, structural analysis of tables with arbitrary layouts is still far from achievable at this point.
On the Use of Attention Mechanism in a Seq2Seq Based Approach for Off-Line Handwritten Digit String Recognition

In this work, we investigate the use of the attention mechanism in deep learning for a better reading of handwritten digit strings in digitized images. The proposed recognition system built upon a CNN (Convolutional Neural Network) and two RNNs (Recurrent Neural Networks), acting as Encoder and Decoder and using the attention mechanism. We used a 1D mechanism for attention location with a "soft" alignment attention which has the peculiarity of having an easily calculable gradient and thus to integrate well with the network. Experimental results on data from ORAND-CAR A, ORAND-CAR B and CVL HDS databases compare favorably to other published methods.
Mapping the Drivers in Implementing Halal Logistic

The purpose of this study is to identify, rank and classify the important drivers for implementing Halal Logistic in Indonesian Food and Beverages Industries. This study adopts interpretive structural modelling (ISM) method to present a multilevel hierarchical model of identified drivers and a digraph, which illustrations their driving power and dependence. The result of data processing with ISM method indicated that two drivers are having strong driving power, namely ‘increasing of halal market demand’ and ‘the easier for business enterprise to distribute the product to the other Muslim countries because of halal guarantee’. Those two drivers will further support other drivers for implementing Halal Logistic in Food and Beverages Industries.
Pricing Decisions with Product Return and Consumer Fit Uncertainty

When firms introduce new products to the market, consumers usually have product fit uncertainty towards these new products and may not fully understand their characteristics before purchasing them. It is thus prevalent that firms offer product returns in order to mitigate consumers' valuation risk. The objective of this study is to develop a two-period model with product return and investigate how consumers' fit uncertainty and product knowledge affect the firm's decisions and profit. The firm sells new products in the first period and refurbishes the returned products and sells them, along with the new products, in the second period. We find that when more consumers find product fit or when more consumers understand product characteristics, the quantity of returned products will be reduced, the selling prices of the products will be lowered, and the firm's profit will be increased. On the other hand, when consumers have higher product valuation in the second period, the quantity of returned products will be reduced, the first-period selling price of the product will be lowered and the firm's profit will be lowered.
Exercise-Induced Adaptations to the Mouse Striatal Adenosine System

Adenosine acts as a key regulator of striatum activity, in part, through the antagonistic modulation of dopamine activity. Exercise can increase adenosine activity in the brain, which may impair dopaminergic functions in the striatum. Therefore, long-term repeated bouts of exercise may subsequently generate plasticity in striatal adenosine systems in a manner that promotes dopaminergic activity. This study investigated the effects of long-term voluntary wheel running on adenosine 1 (A1R), adenosine 2A (A2AR), dopamine 1 (D1R), and dopamine 2 (D2R) receptor protein expression in adult mouse dorsal and ventral striatum structures using immunohistochemistry. In addition, equilibrative nucleoside transporter 1 (ENT1) protein expression was examined after wheel running, as ENT1 regulates the bidirectional flux of adenosine between intra- and extracellular space. The results suggest that eight weeks of running wheel access spared age-related increases of A1R and A2AR protein concentrations across the dorsal and ventral striatal structures. Wheel running mildly reduced ENT1 protein levels in ventral striatum subregions. Moreover, wheel running mildly increased D2R protein density within striatal subregions in the dorsal medial striatum, nucleus accumbens core, and the nucleus accumbens shell. However, D1R protein expression in the striatum was unchanged by wheel running. These data suggest that exercise promotes adaptations to striatal adenosine systems. Exercise-reduced A1R and A2AR and exercise-increased D2R protein levels may contribute to improved dopaminergic signaling in the striatum. These findings may have implications for cognitive and behavioral processes, as well as motor and psychiatric diseases that involve the striatum.