Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomarkers of erythropoiesis response to intravenous iron in a crossover pilot study in unexplained anemia of the elderly

ABSTRACT Anemia is common in older adults, but often unexplained. Previously, we conducted a randomized, controlled trial of intravenous (IV) iron sucrose to study its impact on the 6-minute walk test and hemoglobin in older adults with unexplained anemia and ferritin levels of 20–200 ng/mL. In this report, we present for the first time the response of hemoglobin, as well as the dynamic response of biomarkers of erythropoiesis and iron indices, in a pooled analysis of the initially IV iron-treated group of 9 subjects and the subsequently IV iron treated 10 subjects from the delayed treatment group. We hypothesized that there would be a reproducible hemoglobin response from IV iron, and that iron indices and erythropoietic markers would reflect appropriate iron loading and reduced erythropoietic stress. To investigate the biochemical response of anemia to IV iron, we studied the dynamics of soluble transferrin receptor (STfR), hepcidin, erythropoietin (EPO), and iron indices over 12 weeks after treatment. In total, all 19 treated subjects were evaluable: 9 from initial treatment and 10 after cross-over. Hemoglobin rose from 11.0 to 11.7 g/dL, 12 weeks after initiating IV iron treatment of 1000 mg divided weekly over 5 weeks. We found early changes of iron loading after 1–2 IV iron dose: serum iron increased by 184 mcg/dL from a baseline of 66 mcg/dL, ferritin by 184 ng/mL from 68 ng/mL, and hepcidin by 7.49 ng/mL from 19.2 ng/mL, while STfR and serum EPO declined by 0.55 mg/L and 3.5 mU/mL from 19.2 ng/mL and 14 mU/mL, respectively. The erythroid response and evidence of enhanced iron trafficking are consistent with the hypothesis that IV iron overcomes iron deficient or iron-restricted erythropoiesis. These data provide new insight that iron-restricted erythropoiesis is a potential and targetable mechanism for patients diagnosed with unexplained anemia of the elderly and offers support for larger prospective trials of IV iron among anemic older adults of low to normal ferritin.
Survival outcomes used to generate version 9 American Joint Committee on Cancer staging system for anal cancer

The American Joint Committee on Cancer (AJCC) staging system for all cancer sites, including anal cancer, is the standard for cancer staging in the United States. The AJCC staging criteria are dynamic, and periodic updates are conducted to optimize AJCC staging definitions through a panel of experts charged with evaluating new evidence to implement changes. With greater availability of large data sets, the AJCC has since restructured and updated its processes, incorporating prospectively collected data to validate stage group revisions in the version 9 AJCC staging system, including anal cancer. Survival analysis using AJCC eighth edition staging guidelines revealed a lack of hierarchical order in which stage IIIA anal cancer was associated with a better prognosis than stage IIB disease, suggesting that, for anal cancer, tumor (T) category has a greater effect on survival than lymph node (N) category. Accordingly, version 9 stage groups have been appropriately adjusted to reflect contemporary long‐term outcomes. This article highlights the changes to the now published AJCC staging system for anal cancer, which: (1) redefined stage IIB as T1–T2N1M0 disease, (2) redefined stage IIIA as T3N0–N1M0 disease, and (3) eliminated stage 0 disease from its guidelines altogether.
Reporting of Adverse Events in Muscle Strengthening Interventions in Youth: A Systematic Review.

Clear definition, identification, and reporting of adverse event (AE) monitoring during training interventions are essential for decision making regarding the safety of training and testing in youths. PURPOSE To document the extent to which AEs, resulting from intervention studies targeting muscle strengthening training (MST) in youth, are reported by researchers. METHODS Electronic databases (CINAHL, PubMed, SPORTDiscus, and Web of Science) were searched for English peer-reviewed articles published before April 2018. Inclusion criteria were: (1) average age <16 years, (2) use of MST, (3) statement(s) linked to the presence/absence of AEs, and (4) randomized controlled trials or quasi-experimental designs. Risk of reporting bias for AEs followed recommendations by the Cochrane Collaboration group. RESULTS One hundred and ninety-one full-text articles were screened. One hundred and thirty met all MST criteria, out of which only 44 (33.8%; n = 1278, age = 12.1 [1.1] y) included a statement as to the presence/absence of adverse events. The 86 other studies (66.2%) included no such statement. Of the reporting 44 studies, 18 (40.1%) indicated one or more adverse events. Of the 93 reported adverse events, 55 (59.1%) were linked to training or testing. CONCLUSIONS Most MST studies in youth do not report presence/absence of adverse events, and when reported, adverse events are not well defined.
Elastokinematics of a rectilinear rear independent suspension

The rectilinear rear independent suspension investigated in this paper benefits from its excellent kinematic characteristics. Because of the over-constraints of the rectilinear rear independent suspension as a rigid-flexible coupled multi-body system, its elastokinematics are heavily dependent on the compliance characteristics. This paper proposes an efficient approach to establish its elastokinematic model based on the transfer matrix method. First, the overall system transfer equation of the rectilinear rear independent suspension is established. Then, the statics equation is obtained by introducing external loads and expanding the overall system transfer equation. Different configurations of rubber bushings are discussed with respectg to the natural frequency and the static deflections. Comparisons between numerical simulations and kinematic and compliance tests not only verified the approach but also demonstrated the excellent wheel alignment capacity of the rectilinear rear independent suspension.
A Fast Inverse Hough Transform Based on Curve Feature

A new inverse Hough transform based on curve feature(CF-IHT) algorithm was presented.Based on the feature of the curve to be detected,the new algorithm selects some feature points in the edge image.According to the coordinates of the feature points and the CF,it finds some grew points.The grew points will be the new feature points in the next process of finding the other new grew points.This process will be end until the detecting result meets the established criteria.Experimental results show that the CF-IHT is less blind and reduces the computation complexity and storage burden of standard IHT(SIHT).
Re: One-day voiding diary in the evaluation of Lower Urinary Tract Symptoms in children

We read the recent article by Franck et al published in the International Brazilian Journal of Urology with great interest (1). In their single‑center cross‑sectional observational study, the authors analyzed ninety‑eight children, of which 59 had primary monosymptomatic enuresis (PMNE) and 30 had overactive bladder (OAB) respectively. The authors concluded that a one day voiding diary (1dVD) is sufficient to assess these children. The authors further stated that the 1dVD has high sensitivity, and a good correlation to the three‑day voiding diary (3dVD) when evaluating these children. The study used the maximum voided volume (MVV) as a surrogate to evaluate the bladder capacity in these chil‑ dren and noted that it was as close as 68% of that obtained by the expected bladder capacity (EBC). PMNE and OAB are extremely troublesome issues in the paediatric populations, and accurate evaluation of these symptoms is crucial for effective management. The author’s revealed that those with high post‑voided residual on ultrasound or an interrupted or staccato curve on uroflowmetry were excluded in the study. A note on the reason for exclusion may add strength to the study as a child with a neurogenic bladder may also present with such symptoms and patterns, and the bladder diary is the first step in assessing these children.
The Effect of Tonsillectomy and Adenoidectomy on Upper Airway Obstruction Patterns in Children with Obstructive Sleep Apnea

Abstract Introduction Alterations in upper airway flow dynamics and sites of airway obstruction immediately after tonsillectomy and adenoidectomy (TA) have not been assessed. Identification of the changes in airway obstruction patterns after TA potentially improves the surgical management of children with obstructive sleep apnea (OSA). Objective To evaluate the effect of TA on upper airway obstruction patterns detected with drug-induced sleep endoscopy (DISE). Methods The medical records of patients who underwent pre-TA DISE during the induction of anesthesia and post-TA DISE at the end of TA were reviewed. Data pertaining to polysomnography and DISE findings were analyzed. Results Twenty-seven patients (15 male and 12 females aged between 2 and 18 years old) were identified. All patients had obstruction at multiple sites of the upper airway. Prior to TA, airway obstruction was at the level of the velum in 27 patients, of the oropharynx/lateral walls in 27, of the tongue in 7, and of the epiglottis in 4. After TA, airway obstruction was at the level of the velum in 24 patients, of the oropharynx/lateral walls in 16, of the tongue in 6, and of the epiglottis in 4. The degree of obstruction at the levels of the velum and oropharynx/lateral walls after TA was significantly decreased. Conclusions Drug-induced sleep endoscopy performed prior to TA revealed that most of the sites of airway obstruction persisted after TA in OSA children with multiple sites of airway obstruction. Further studies in larger group of children with OSA are needed to establish the value of DISE findings in predicting residual OSA after TA, surgical planning, determining the need for post TA sleep study, and counseling caregivers.
Performance Monitoring and Cognitive Inhibition during a Speech-in-Noise Task in Older Listeners

Abstract The goal of this study was to examine the effect of hearing loss on theta and alpha electroencephalography (EEG) frequency power measures of performance monitoring and cognitive inhibition, respectively, during a speech-in-noise task. It was hypothesized that hearing loss would be associated with an increase in the peak power of theta and alpha frequencies toward easier conditions compared to normal hearing adults. The shift would reflect how hearing loss modulates the recruitment of listening effort to easier listening conditions. Nine older adults with normal hearing (ONH) and 10 older adults with hearing loss (OHL) participated in this study. EEG data were collected from all participants while they completed the words-in-noise task. It hypothesized that hearing loss would also have an effect on theta and alpha power. The ONH group showed an inverted U -shape effect of signal-to-noise ratio (SNR), but there were limited effects of SNR on theta or alpha power in the OHL group. The results of the ONH group support the growing body of literature showing effects of listening conditions on alpha and theta power. The null results of listening condition in the OHL group add to a smaller body of literature, suggesting that listening effort research conditions should have near ceiling performance.
A Chinese Fine-grained Financial Event Extraction Dataset

The existing datasets are mostly composed of official documents, statements, news articles, and so forth. So far, only a little attention has been paid to the numerals in financial social comments. Therefore, this paper presents CFinNumAttr, a financial numeral attribute dataset in Chinese via annotating the stock reviews and comments collected from social networking platform. We also conduct several experiments on the CFinNumAttr dataset with state-of-the-art methods to discover the importance of the financial numeral attributes. The experimental results on the CFinNumAttr dataset show that the numeral attributes in social reviews or comments contain rich semantic information, and the numeral clue extraction and attribute classification tasks can make a great improvement in financial text understanding.
An Exploration on Cryptocurrency Corporations’ Fiscal Opportunities

As the decentralized finance industry gains traction, governments worldwide are creating or modifying legislations to regulate such financial activities. To avoid these new legislations, decentralized finance enterprises may shop for fiscally advantageous jurisdictions. This study explores global tax evasion opportunities for decentralized finance enterprises. Opportunities are identified by considering various jurisdictions’ tax laws on cryptocurrencies along with their corporate income tax rates, corporate capital gains tax rates, level of financial development and level of cryptocurrency adoption. They are visualized with the manifold approximation and projection for dimension reduction (UMAP) technique. The study results show that there exist a substantial number of tax evasion opportunities for decentralized finance enterprises through both traditional offshore jurisdictions and crypto-advantageous jurisdictions. The latter jurisdictions are usually considered high-tax fiscal regimes; but, given that they do not apply tax laws, tax evasion opportunities arise, especially in jurisdictions that have high financial development and high cryptocurrency adoption. Further research should investigate these new opportunities and how they are evolving. Understanding the global landscape surrounding tax evasion opportunities in decentralized finance represents a first step at preventing corporate capital flight of cryptocurrencies.
Scientific Data Extraction from Oceanographic Papers

Scientific data collected in the oceanographic domain is invaluable to researchers when performing meta-analyses and examining changes over time in oceanic environments. However, many of the data samples and subsequent analyses published by researchers are not uploaded to a repository leaving the scientific paper as the only available source. Automated extraction of scientific data is, therefore, a valuable tool for such researchers. Specifically, much of the most valuable data in scientific papers are structured as tables, making these a prime target for information extraction research. Using the data relies on an additional step where the concepts mentioned in the tables, such as names of measures, units, and biological species, are identified within a domain ontology. Unfortunately, state-of-the-art table extraction leaves much to be desired and has not been attempted on a large scale on oceanographic papers. Furthermore, while entity linking in the context of a full paragraph of text has been heavily researched, it is still lacking in this harder task of linking single concepts. In this work, we present an annotated benchmark dataset of data tables from oceanographic papers. We further present the result of an evaluation on the extraction of these tables and the linking of the contained entities to the domain and general-purpose knowledge bases using the current state of the art. We highlight the challenges and quantify the performance of current tools for table extraction and table-concept linking.
Child Sexual Abuse Awareness and Support Seeking on Reddit: A thematic Analysis

Child sexual abuse (CSA) is a pervasive issue in both online and physical contexts. Social media has grown in popularity as a platform for offering awareness, support, and community for those seeking help or advice regarding CSA. One popular social media platform in which such communities has formed is Reddit. In this study, we use both LDA and a reflexive thematic analysis to understand the types of engagements users have with subreddits aimed at CSA awareness. Through the reflexive thematic analysis, we identified six themes including strong negative emotions and phrasing, seeking help, personal experiences and their impact, measurement strategies to prevent abuse, provisioning of support, and the problematic nature of the Omegle platform. This research has implications for those creating awareness materials around CSA safety as well as for child advocacy groups.
Graph-based Approach for Studying Spread of Radical Online Sentiment

The spread of radicalization through the Internet is a growing problem. We are witnessing a rise in online hate groups, inspiring the impressionable and vulnerable population towards extreme actions in the real world. In this paper, we study the spread of hate sentiments in online forums by collecting 1,973 long comment threads (30+ comments per thread) posted on dark-web forums and containing a combination of benign posts and radical comments on the Islamic religion. This framework allows us to leverage network analysis tools to investigate sentiment propagation through a social network. By combining sentiment analysis, social network analysis, and graph theory, we aim to shed light on the propagation of hate speech in online forums and the extent to which such speech can influence individuals. The results of the intra-thread analysis suggests that sentiment tends to cluster within comment threads, with around 75% of connected members sharing similar sentiments. They also indicate that online forums can act as echo chambers where people with similar views reinforce each other’s beliefs and opinions. On the other hand, the inter-thread shows that 64% of connected threads share similar sentiments, suggesting similarities between the ideologies present in different threads and that there likely is a wider network of individuals spreading hate speech across different forums. Finally, we plan to study this work with a larger dataset, which could provide further insights into the spread of hate speech in online forums and how to mitigate it.
Identifying Stable States of Large Signed Graphs

Signed network graphs provide a way to model complex relationships and interdependencies between entities: negative edges allow for a deeper study of social dynamics. One approach to achieving balance in a network is to model the sources of conflict through structural balance. Current methods focus on computing the frustration index or finding the largest balanced clique, but these do not account for multiple ways to reach a consensus or scale well for large, sparse networks. In this paper, we propose an expansion of the frustration cloud computation and compare various tree-sampling algorithms that can discover a high number of diverse balanced states. Then, we compute and compare the frequencies of balanced states produced by each. Finally, we investigate these techniques’ impact on the consensus feature space.
Integrating Stand-Alone New Media Technologies Such as Games and Virtual and Augmented Reality Software into Learning Management Systems: Integrating Stand-Alone Media Software into LMSs

Both learning management systems (LMSs) and new media technologies (NMTs, e.g., Augmented/Virtual Reality software or Serious Games) can enhance teaching and learning, however, the integration of both technologies within an E-Learning system is challenging. NMTs are often developed as bulky stand-alone applications, for example, using game engines, which make them difficult to integrate into the LMS courses. Thus, connecting middleware technologies are needed, however, various technologies exist. In this paper, we introduce three techniques (SCORM-Data, xAPI-Injection, and JSON-REST) that integrate stand-alone NMT learning software into LMSs based on existing technologies and standards from the E-Learning domain. We discuss the techniques and their underlying processes based on a prototype implementation and the results of an expert interview and point out the advantages and disadvantages of each technique to give practitioners differentiated advice on which technique to use. Although SCORM-Data has fewer technical requirements, we conclude that our JSON-REST technique was favored through applicability, simplicity, ease of integration, and efficiency.
Lower Risks, Better Choices: Stock Correlation Based Portfolio Selection in Stock Markets

Over the past few years, we’ve seen a huge interest in applying AI techniques to develop investment strategies both in academia and the finance industry. However, we note that generating returns is not always the sole investment objective. Take large pension funds for example, they are considerably more risk-averse as opposed to profit-seeking. With this observation, we propose a Risk-balanced Deep Portfolio Constructor (RDPC) that takes risk into explicit consideration. RDPC is an end-to-end reinforcement learning-based transformer trained to optimize both returns and risk, with a hard attention mechanism that learns the relationship between asset pairs, imitating the powerful pairs trading strategy widely adopted by many investors. Experiments on real-world data show that RDPC achieves state-of-the-art performance not just on risk metrics such as maximum drawdown, but also on risk-adjusted returns metrics including Sharpe ratio and Calmar ratio.
Provenance Tracking for End-to-End Machine Learning Pipelines

Software systems that learn from data are being deployed in increasing numbers in real-world application scenarios. It is a difcult and tedious task to ensure at development time that the end-to-end ML pipelines for such applications adhere to sound experimentation practices, such as the strict isolation of train and test data. Furthermore, there is a need to enforce legal and ethical compliance in automated decision-making with ML. For example, to determine whether a model works equally well for diferent groups. To enforce privacy rights (such as the ‘right to be forgotten’), we must identify which models consumed the user’s data for model training to retrain them without this data. Moreover, model predictions can be corrupted due to undetected data distribution shift, e.g., when the train/test data was incorrectly sampled or changed over time (covariate shift) or when the distribution of the target label changed (label shift). Data scientists also require support for pipeline debugging and for uncovering erroneous data, e.g., to identify samples that are not helpful for the classifer and potentially dirty or mislabeled or to identify subsets of data for which a model does not work well. Towards automated low-efort tooling for ML pipelines. Most of the listed issues are typically addressed manually in an ad-hoc way and require signifcant expertise and extra code. In many cases, there is no system support for detecting particular issues. Typically, data has to be integrated frst, as common libraries assume the input to be in a single table. Furthermore, specialised solutions are often incompatible with popular libraries. Even simple tasks like computing fairness metrics can be challenging. This situation is in stark contrast to the software engineering world, with established best practices and infrastructure for testing and CI. Provenance is all you need. At the core of these issues is missing provenance tracking in current tooling. For example, we fnd that can automate the detection of many common correctness issues in ML pipelines with access to (�) the materialised artifacts of a pipeline (its input relations, and its outputs, e.g., the feature matrices, labels, and predictions of a classifer) as well as (��) their whyprovenance [4] (e.g., which input records were used to compute a particular output). This allows us to design screening techniques with low invasiveness for declaratively written ML pipelines. Modeling ML pipelines as datafow computations. We base our approach on a recently introduced model of treating ML pipelines