Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Recognized and Unrecognized Myocardial Infarction With Depressive and Anxiety Disorders in 125,988 Individuals: A Report of the Lifelines Cohort Study

Supplemental digital content is available in the text. ABSTRACT Objective No previous study has focused on recognition of myocardial infarction (MI) and the presence of both depressive and anxiety disorders in a large population-based sample. The aim of this study was to investigate the association of recognized MI (RMI) and unrecognized MI (UMI) with depressive and anxiety disorders. Methods Analyses included 125,988 individuals enrolled in the Lifelines study. Current mental disorders according to the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) were assessed with the Mini-International Neuropsychiatric Interview. UMI was detected using electrocardiogram in participants who did not report a history of MI. The classification of RMI was based on self-reported MI history together with the use of either antithrombotic medications or electrocardiogram signs of MI. Analyses were adjusted for age, sex, smoking, somatic comorbidities, and physical health–related quality of life as measured by the RAND 36-Item Health Survey in different models. Results Participants with RMI had significantly higher odds of having any depressive and any anxiety disorder as compared with participants without MI (depressive disorder: odds ratio [OR] = 1.86, 95% confidence interval [CI] = 1.38–2.52; anxiety disorder: OR = 1.60, 95% CI = 1.32–1.94) after adjustment for age and sex. Participants with UMI did not differ from participants without MI (depressive disorder: OR = 1.60, 95% CI = 0.96–2.64; anxiety disorder: OR = 0.73, 95% CI = 0.48–1.11). After additional adjustment for somatic comorbidities and low physical health–related quality of life, the association between RMI with any depressive disorder was no longer statistically significant (OR = 1.18; 95% CI =0.84–1.65), but the association with any anxiety disorder remained (OR = 1.27, 95% CI = 1.03–1.57). Conclusions Recognition of MI seems to play a major role in the occurrence of anxiety, but not depressive, disorders.
Clinical impact of PET imaging in prostate cancer management.

PURPOSE OF REVIEW Imaging of prostate cancer has been a rapidly evolving field in recent years with the introduction of multiple new PET tracer agents. Introduction of novel imaging techniques into clinical practice requires careful evaluation, with the ultimate aims of improved patient outcomes, better sequencing of treatments, and cost effectiveness. The increased sensitivity and specificity of these new PET agents present both challenges and opportunities. We know they frequently change management, but are these effective management changes, and is it always in the best interests of the patients? RECENT FINDINGS This review will focus on recent publications that provide high-level evidence for the use of PET in prostate cancer. It will discuss studies that have evaluated the clinical impact of PET imaging in prostate cancer and will review a number of trials that demonstrate the potential of PET to change current standard of care, from diagnosis, to prognostic capabilities in men with metastatic prostate cancer. SUMMARY Evidence for the use of PET in prostate cancer is building with studies evaluating diagnostic accuracy of PET at all stages of prostate cancer. We review the evidence available, focusing on prospective trials that are measuring the impact of new technology on patient outcomes.
Research on Image Reconstruction of Compressed Sensing Based on a Multi-Feature Residual Network

In order to solve the problem of how to quickly and accurately obtain crop images during crop growth monitoring, this paper proposes a deep compressed sensing image reconstruction method based on a multi-feature residual network. In this method, the initial reconstructed image obtained by linear mapping is input to a multi-feature residual reconstruction network, and multi-scale convolution is used to autonomously learn different features of the crop image to realize deep reconstruction of the image, and complete the inverse solution of compressed sensing. Compared with traditional image reconstruction methods, the deep learning-based method relaxes the assumptions about the sparsity of the original crop image and converts multiple iterations into deep neural network calculations to obtain higher accuracy. The experimental results show that the compressed sensing image reconstruction method based on the multi-feature residual network proposed in this paper can improve the quality of crop image reconstruction.
Zooplankton population and community responses to the pesticide azinphos‐methyl in freshwater littoral enclosures

We tested the effects of four concentrations (0.2, 1.0, 4.0, and 20.0 μg/L) of the organophosphorus insecticide azinphosmethyl on natural zooplankton communities using littoral ecosystem enclosures. Individual taxa and major zooplankton groups (cladocerans, copepods, and rotifers) exhibited concentration‐dependent responses, with most of the significant effects at the two highest concentrations. Analyses of population abundance were more sensitive to the effects of azinphos‐methyl than were analyses of major zooplankton groups. Principal component analysis of communities differentiated the contributions of cladocerans, copepods, and rotifers to total community variance. The highest principal components were associated with cladocerans, the group also identified by univariate statistics to be most sensitive to azinphos‐methyl. Ordination using the first two principal components depicted the multivariate status of the zooplankton communities through time and showed concentration‐dependent effects and recovery from the pesticide at the community level. Principal component ordination was a more sensitive metric of community‐level effects than was taxon richness. Communities in the highest treatment concentration had not fully recovered by the last sampling date, 78 d after treatment.
Managing Organizatinal Resources as Platform Boundary Resources

Approaching digital innovation via digital platforms shifts firms’ locus of attention to the different actors in their ecosystems. Firms tend to empower the platform’s ecosystem through expanding developer contribution via introducing boundary resources such as application programming interfaces (APIs). This study addresses the challenge of platform owners managing their internal assets as platform boundary resources. We seek to answer how platform owners can identify and visualize values of potential boundary resources by conducting a single case study at a large international company active in embeded software development area. This study suggests e3 value modelling as a tool to assist platform owners in understanding the platform ecosystem actors, the values of assets for the ecosystem and how these values can be interchanged among the actors.
Using High Definition Maps to Estimate GNSS Positioning Uncertainty

Map-matching can be used to estimate the Horizontal Uncertainty Level (HUL) of GNSS position fixes. Integrity monitoring is indeed an important issue for autonomous vehicles navigation. The method is based on the use of a high definition map that stores accurate information about the road network. This additional source of information is crucial for autonomous navigation. The matched position is computed using proprioceptive sensors from the car and GNSS fixes that are handled using a precautionary principle with Horizontal Protection Levels (HPL). A Particle Filter is used for its ability to manage multiple hypotheses if needed. Estimating different likely map-matched hypotheses allows to determine the level of uncertainty of the GNSS which is defined as the maximum distance between a map-matched hypothesis and a given GNSS position. This distance can be seen as a Map-Aided Horizontal Uncertainty Level (MA-HUL), providing a confidence indicator to the vehicle for integrity monitoring. This paper presents the multi-hypotheses map-matching algorithm and a method to compute the MA-HUL values in real-time. Experimental results carried out in open road conditions support the evaluation and show that this metric provides reliable confidence information.
Implementasi Metode Backpropogation dengan Inisialisasi Bobot Nguyen Widrow untuk Peramalan Harga Saham

Jaringan saraf tiruan merupakan suatu ilmu yang terus berkembang pesat hingga saat ini. Jaringan saraf tiruan merupakan suatu ilmu komputasi yang didasarkan dan terinspirasi dari cara kerja sistem saraf manusia. Sama halnya dengan sistem saraf manusia, jaringan saraf tiruan bekerja melalui proses pembelajaran terhadap data-data yang sudah ada untuk memformulakan keluaran dari data-data baru. Jaringan saraf tiruan dengan metode backpropagation mampu melakukan peramalan untuk data nonlinear seperti bentuk data harian harga saham. Salah satu algoritma inisialisasi bobot yang dapat meningkatkan waktu eksekusi adalah nguyen-widrow. Pada penelitian ini akan dilakukan implementasi metode backpropagation dengan inisialisasi bobot nguyen widrow untuk meramalkan harga saham. Proses implementasi melalui 3 tahapan, yaitu preprosesing data, pelatihan jaringan, dan pengujian jaringan. Hasil dari penelitian ini menunjukkan bahwa pelatihan jaringan saraf tiruan dengan jumlah dataset yang banyak membutuhkan perhitungan yang kompleks, sehingga jaringan saraf tiruan dengan arsitektur jaringan yang sederhana kurang efektif dan dapat terjebak pada titik lokal minimum. Hasil peramalan untuk harga close saham BBCA.JK memiliki nilai MAPE 0,85% dan untuk harga close saham AALI.JK memiliki nilai MAPE sebesar 1,84%. AbstractArtificial neural network is a hot topic and invite a lot of admiration in the last decade. Artificial Neural Network is one of the artificial representations of the humans brain who always try to simulate the learning process of the humans brain. Artificial neural network with backpropagation method is able to forecast nonlinear data such as daily data form stock price. One of the weight initialization algorithms that can be increase the execution time is nguyen-widrow. In this research will be implemented backpropagation method with nguyen widrow weight initialization to forecast stock prices. The process of implementation through 3 stages, that is preprosesing data, training, and testing or simulate. The results of this research indicate that the training of artificial neural networks with many datasets required a complex calculations, so the artificial neural network with simple architectures is less effective and can get stuck at minimum local points. The results forecasting for the close price of BBCA.JK have a MAPE value 0.85% and for the close price of AALI.JK have 1.84% of MAPE value
Impact of Functional Integration and Spatial Proximity on the Post-entry Performance of Knowledge Intensive Business Service Firms

Knowledge intensive business service firms (KIBS) are an important element of modern economies, and are thus attracting increasing interest in scientific research. In the existing literature, it is argued that due to the important role of knowledge, innovation and user-producer interaction in the KIBS sector, functional integration and spatial proximity are particularly decisive for a firm's development. Nevertheless, due to the absence of suitable micro data there is a lack of studies empirically analysing the impact of functional integration and spatial proximity using micro firm data. Based upon new firm micro data from three German metropolitan regions, this article supplements the literature by giving a multivariate empirical analysis of the determinants of post-entry performance of KIBS. The results of the estimates of fi rm growth equations show that functional linkages to knowledge providers, customers and cooperation partners matter indeed for the performance of young KIBS. Regarding spatial proximity, however, a high diversifi cation of geographical reach is proven to be especially crucial.
POTENCIAIS ALTERNATIVAS TERAPÊUTICAS EM ESTUDO PARA A TOXOPLASMOSE CONGÊNITA: UMA REVISÃO BIBLIOGRÁFICA

Toxoplasmosis is an infection caused by the protozoan Toxoplasma gondii and is a serious publichealth problem that can be fatal in immunocompromised individuals or congenital infections.Congenital toxoplasmosis disease results from transplacental parasite transfer to the fetus duringchildbirth or primary infection of the mother or acute relapse. The clinical manifestations aredifferent for neonates, with potentially fatal or debilitating consequences that primarily affect thecentral nervous system and/or generate ocular involvement. Depending on the gestational stage,the current treatment of congenital toxoplasmosis is limited to the use of spiramycin or, in fetalinfection, supported by the combination of pyrimethamine and sulfadiazine associated with folinicacid. However, these regimens may be toxic to the embryo/fetus. Therefore, research has examinedalternative therapies for this infection and thus the aim of this manuscript was to review the mainstudies involving different compounds as potential treatments for congenital toxoplasmosis. Amongthe compounds most frequently cited in the literature as an alternative therapy for congenitaltoxoplasmosis, were artemisinin, atovaquone, azithromycin and diclazuril. It was observed thatartemisinin and atovaquone may have teratogenic dose-dependent effects. Already azithromycinand diclazuril have shown promising results when used in murine models or cell culture, but theireffectiveness has not been fully confirmed in humans. The compounds mentioned here, despite stillbeing in experimental and clinical trials, add new data and perspectives for the treatment of infection.
BPDIMS: A Blockchain-based Personal Data and Identity Management System

Recent scandals on the abuse of personal information from social media platforms and numerous user identity data breaches raise concerns about technical, commercial, and ethical aspects of privacy and security of user data. European Union’s new General Data Protection Regulation (GDPR) is one of the largest changes in data privacy regulation and entails several key regulatory measures for both data controllers and data processors to empower and protect EU citizens’ privacy. In this research work, we propose a conceptual design and high-level architecture for a Blockchain-based Personal Data and Identity Management System (BPDIMS), a human-centric and GDPR-compliant personal data and identity management system based on the blockchain technology. We describe how BPDIMS’s architecture utilizes blockchain technology to provide a high-level of security, trust and transparency. We discuss how BPDIM’s human-centric approach with GDPR compliance shifts the control over personal data to the end users and empowers them better.
Transient Expression of Exogenous DNA in Intact, Viable Wheat Embryos Following Particle Bombardment

Expression of foreign DNA has been detected in intact, germinating wheat embryos (Triticum aestivum L.) following bombardment with tungsten particles complexed with a reporter gene encoding the bacterial enzyme β-glucuronidase. Expression was detected in situ in individual cells and groups of cells, by supplying the germinating embryos with the chromogenic substrate of the GUS enzyme, «X-gluc»
Genebank data-management software incorporating seed-viability test results

The National Institute of Agrobiological Sciences' (NIAS) Genebank is the coordinating institute in Japan for the conservation of plant, microorganism and animal genetic resources related to food and agriculture. The NIAS Genebank manages this activity in collaboration with sub-banks located across Japan and acts as the central bank. It is important for genebanks to maintain the viability of genetic resources during conservation. Generally, seeds are stored at low temperature and low humidity. Germination percentage is used to monitor seed viability. We have developed data-management software to manage germination testing data in the NIAS Genebank. The software can monitor the pattern of the loss of seed viability of each accession and also support the selection of accessions that need regeneration of seeds. Web-based plant accessions search software displays the latest data on the availability and germination percentages of accessions.
Transcriptional regulation by non-coding RNAs in Saccharomyces cerevisiae

Genome-wide studies in Saccharomyces cerevisiae have revealed that the majority of the genome is transcribed on both strands, producing both coding and non-coding RNAs (ncRNAs). Initially, these ncRNAs were regarded as spurious transcripts but some have since been shown to have important roles as transcriptional regulators. Very little is understood about how ncRNAs are initiated, terminated and processed or how this influences their function. To address these questions, the expression, stability, and subcellular localization of the ncRNAs at the endogenous GAL locus was analysed. This revealed a complex interleaved transcript map, challenging the conventional view of a transcription unit (TU) flanked by 5’ sequences or promoters (P) that initiate transcription and 3’ regions, known as terminators (T), which control events such as transcript cleavage, polyadenylation, export and transcription termination. By creating conventional (PGAL-T) or unconventional (PGAL-P) hybrid TUs at the GAL locus, in which a promoter or terminator is positioned downstream of a galactose-inducible promoter, this work shows that both promoters and terminators are able to initiate antisense transcription to yield stable antisense transcripts. The data suggest that terminators contribute to efficient but variable expression from the promoter. An unconventional P-P TU, lacking a terminator, is transcribed on both strands but the sense transcript remains at low levels, through the repressive action of antisense transcription, and is retained in the nucleus. In contrast, the conventional P-T bi-directional TUs are plastic, with the Rrp6 component of the nuclear exosome and TATA-like sequences in the 3’ UTR determining whether the predominant transcript is antisense or sense. By relieving the repressive action of antisense transcription, this allows high levels of sense transcript to accumulate in the cytoplasm, contributing to gene expression, supporting a novel mode of gene regulation involving components of RNA quality control pathways acting through the 3’ region of genes.
Multiple modernities and multiple proximities: McDonald’s and Kentucky Fried Chicken in Chinese television commercials

Homogenization and diversification are major themes in studies of globalization and culture. McDonald’s and Kentucky Fried Chicken (KFC) are often used as evidence to describe these two themes. The aim of this study is to examine the extent to which homogenization and diversification occur in the television commercials for McDonald’s and KFC in China as well as the strategic models they adopt to manipulate the tension between homogenization and diversification. La Pastina and Straubhaar’s theory is used to map out two different strategic models: genre proximity and cultural proximity. The study finds that homogenization exceeds diversification in the commercials of McDonald’s and KFC as a whole. Comparatively, McDonald’s adopts genre proximity and KFC adopts cultural proximity. The adoption of different models offers a cultural explanation for KFC’s triumph in the competition with McDonald’s in the Chinese fast food market.
Anonymous Reputation System for IIoT-Enabled Retail Marketing Atop PoS Blockchain

Industrial Internet of Things (IIoT) is revolutionizing the retail industry for manufacturers, suppliers, and retailers to improve operational efficiency and consumer experience. In IIoT-enabled retail marketing, reputation systems play a critical role to boost mutual trust among industrial entities and build consumer confidence. In this paper, we focus on reputation management in the consumer–retailer channel, where retailers can accumulate reputations from consumer feedbacks. To encourage consumers to post feedbacks without worrying about being tracked or retaliated, we propose an anonymous reputation system that preserves consumer identities and individual review confidentialities. To increase system transparency and reliability, we further exploit the tamper-proof nature and the distributed consensus mechanism of the blockchain technology. With system designs based on various cryptographic primitives and a Proof-of-Stake consensus protocol, our blockchain-based reputation system is more efficient to offer high levels of privacy guarantees compared with existing ones. Finally, we explore the implementation challenges of the blockchain-based architecture and present a proof-of-concept prototype system by Parity Ethereum. We measure the on/off-chain performance with the scalability discussion to demonstrate the feasibility of the proposed system.
A Decision Support System (DSS) for Breast Cancer Detection Based on Invariant Feature Extraction, Classification, and Retrieval of Masses of Mammographic Images

This paper presents an integrated system for the breast cancer detection from mammo- grams based on automated mass detection, classification, and retrieval with a goal to support decision-making by retrieving and displaying the relevant past cases as well as predicting the images as benign or malignant. It is hypothesized that the proposed diagnostic aid would refresh the radiologist’s mental memory to guide them to a precise diagnosis with concrete visualizations instead of only suggesting a second diagnosis like many other CAD systems. Towards achieving this goal, a Graph-Based Visual Saliency (GBVS) method is used for automatic mass detection, invariant features are extracted based on using Non-Subsampled Contourlet transform (NSCT) and eigenvalues of the Hessian matrix in a histogram of oriented gradients (HOG), and finally classification and retrieval are performed based on using Support Vector Machines (SVM) and Extreme Learning Machines (ELM), and a linear combination-based similarity fusion approach. The image retrieval and classification performances are evaluated and compared in the benchmark Digital Database for Screening Mammography (DDSM) of 2604 cases by using both the precision-recall and classification accuracies. Experimental results dem - onstrate the effectiveness of the proposed system and show the viability of a real-time clinical application.
Seasonal changes in diets of coastal and riverine mink: the role of spawning Pacific salmon

Feeding niches of riverine and coastal mink (Mustela vison) in southeast Alaska differ in prey composition and abundance and diving medium during spring and summer. In autumn, however, the upstream migration of spawning Pacific salmon (Oncorhynchus sp.) creates a pulse of food for mink. We hypothesized that diets of coastal and riverine mink, and therefore their stable isotope ratios (δ13C, δ15N), would differ significantly during periods when salmon were absent, but that salmon carcasses would constitute a large portion of the diet of both groups during the salmon spawning season. Stable isotope analyses of clotted blood cells from 24 live-captured mink and muscle tissue from 25 mink carcasses were used to indicate the composition of diets of individual mink in 1992 and 1993. These isotope values were then compared with stable isotope ratios of prey, using a multiple-source mixing model. Our results indicate that riverine mink depended on salmon (carcasses and fry), with little seasonal or individual var.