Miracle-dz/UniFAR-FTUs huggingface dataset:
Liver Enzymes in Early to Mid-pregnancy, Insulin Resistance, and Gestational Diabetes Risk: A Longitudinal Analysis

Background: Liver enzymes may be implicated in glucose homeostasis; liver enzymes progressively change during pregnancy but longitudinal data during pregnancy in relation to insulin resistance and gestational diabetes (GDM) risk are lacking. We investigated longitudinal associations of γ-glutamyl transferase (GGT) and alanine aminotransferase (ALT) with insulin secretion and resistance markers across early to mid-pregnancy and subsequent GDM risk. Methods: Within the prospective Pregnancy Environment and Lifestyle Study cohort, 117 GDM cases were ascertained and matched to 232 non-GDM controls in a nested case-control study. Fasting blood samples were collected at two clinic visits (CV1, gestational weeks 10–13; CV2, gestational weeks 16–19). Linear mixed model and conditional logistic regression were used, adjusting for major risk factors for GDM. Results: In repeated measure analysis, after adjusting for confounders including body mass index and waist-to-hip ratio, GGT per standard deviation increment was associated with elevated fasting glucose and HOMA-IR (% change = 1.51%, 95% CI 0.56–2.46% and 7.43%, 95% CI 1.76–13.11%, respectively) and decreased adiponectin (% change = −2.86%, 95% CI−5.53 to −0.20%) from CV1 to CV2. At CV1 and CV2, GGT levels comparing the highest versus lowest quartile were associated with 3.01-fold (95% CI 1.32–6.85) and 3.51-fold (95% CI 1.37–8.97) increased risk of GDM, respectively. Progressively increased (<median at CV1, ≥median at CV2) and stably high (≥median at both CV1 and CV2) GGT levels were associated with 3.89- and 2.39-fold increased risk of GDM, compared to stably low levels (<median at both CV1 and CV2), respectively (both P < 0.05). Similar but non-significant trends were observed for ALT. Conclusion: Elevated levels of GGT in early and mid-pregnancy, even within the conventional normal range, and its progressive increase from early to mid-pregnancy may be implicated in the pathogenesis of GDM, highlighting its potential to inform early screening or preventive strategies to mitigate subsequent risk of GDM.
The effects of whole body vibration exercise intervention on electroencephalogram activation and cognitive function in women with senile dementia

This study conducted the Korean version of Mini-Mental State Examination (MMSE-K) to women aged 65 or older residing in Gangwon-do Province and screened those who were suspicious to have mild dementia for receiving 23 points or lower in it. For eight weeks, this author tried to verify the effects of whole body vibration exercise intervention on electroencephalogram (EEG) activation and cognitive function in women with senile dementia. According to the results, both EEG activation and cognitive function indicated statistically significant difference in terms of the interactive effect between the measuring times and groups, and there was statistically significant improvement found after the whole body vibration exercise intervention. The results of this study are meaningful because they present the possibility of whole body vibration exercise intervention to be integrated into the plan to improve life quality in patients with senile dementia by stimulating their muscle spindles and sensory organs only with the amplitude and the number of vibrations with no burden of physical activity and enhancing their EEG activation and cognitive function through the responses of the neuromuscular system.
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection

Grammatical error correction, like other machine learning tasks, greatly benefits from large quantities of high quality training data, which is typically expensive to produce. While writing a program to automatically generate realistic grammatical errors would be difficult, one could learn the distribution of naturally-occurring errors and attempt to introduce them into other datasets. Initial work on inducing errors in this way using statistical machine translation has shown promise; we investigate cheaply constructing synthetic samples, given a small corpus of human-annotated data, using an off-the-rack attentive sequence-to-sequence model and a straight-forward post-processing procedure. Our approach yields error-filled artificial data that helps a vanilla bi-directional LSTM to outperform the previous state of the art at grammatical error detection, and a previously introduced model to gain further improvements of over 5% F0.5 score. When attempting to determine if a given sentence is synthetic, a human annotator at best achieves 39.39 F1 score, indicating that our model generates mostly human-like instances.
The Effect of Preoperative Anemia on Patients Undergoing Cardiac Surgery: A Propensity-Matched Analysis.

It is unknown if anemia in the absence of transfusions is an independent risk factor for adverse outcomes in cardiac surgery, and if correction to higher hemoglobin targets impacts these outcomes. This is a retrospective review of 3848 cardiac surgery patients. Propensity matching was completed using 41 covariates. Intraoperative Anemia Analysis matched patients with or without anemia who did not receive intraoperative transfusions (n = 392/group), while Intraoperative Transfusion Analysis matched anemic patients treated conventionally with intraoperative transfusions to end cardiopulmonary bypass hemoglobin greater or less than 95 g/L (n = 261/group). Outcomes of death, renal failure, and 2 composite outcomes were assessed using paired analysis techniques. Study composite 1 consisted of prolonged ventilation, renal failure, myocardial infarction, stroke, or deep sternal wound infection, while composite 2 was the TRICS-III composite. In the Intraoperative Anemia Analysis, anemia was associated with mortality (P = 0.034), stroke (P = 0.021), renal failure (P = 0.015), and a significant increase in the composite measure (control 8.7% vs anemia 16.1%, P = 0.002). These findings were unchanged in patients who did not receive any postoperative transfusions. The Intraoperative Transfusion Analysis showed no difference in mortality or the composite outcome between groups. There was a significant increase in low cardiac output in the lower threshold group (P = 0.001). There were no differences in outcomes between those who did and did not receive postoperative transfusions (P > 0.05). Preoperative anemia in the absence of transfusions is a risk factor for morbidity and mortality after cardiac surgery, and there is no evidence that transfusion to higher end cardiopulmonary bypass hemoglobin levels impacted this risk.
Quantifier Elimination With Structural Learning

We consider the Quantifier Elimination (QE) problem for propositional CNF formulas with existential quantifiers. QE plays a key role in formal verification. Earlier, we presented an approach based on the following observation. To perform QE, one just needs to add a set of clauses depending on free variables that makes the quantified clauses (i.e. clauses with quantified variables) redundant. To implement this approach, we introduced a branching algorithm making quantified clauses redundant in subspaces and merging the results of branches. To implement this algorithm we developed the machinery of D-sequents. A D-sequent is a record stating that a quantified clause is redundant in a specified subspace. Redundancy of a clause is a structural property (i.e. it holds only for a subset of logically equivalent formulas as opposed to a semantic property). So, re-using D-sequents is not as easy as re-using conflict clauses in SAT-solving. In this paper, we address this problem. We introduce a new definition of D-sequents that enables their re-usability. We develop a theory showing under what conditions a D-sequent can be safely re-used.
Privacy and Well-Being in Aged Care Facilities with a Crowded Living Environment: Case Study of Hong Kong Care and Attention Homes

This study aims to understand the relationship between bedroom privacy and well-being of the elderly in aged care facilities with a compact living situation. A majority of studies on this topic were carried out in a low-density population context. The crowded living situation might compromise the well-being of residents. This study proposed five architectural parameters to measure bedroom privacy in aged care facilities: total open surface per unit, openness/solid ratio per bed, height of partition wall, number of people per unit, and personal control over bedroom privacy. SF-12 v.2 Health Survey was used to collect information on physical and mental health status. The study surveyed nine Care & Attention homes and their 213 residents in Hong Kong. The total open surface per unit and the openness/solid ratio per bed were positively associated with the physical health of residents. The height of partition walls was associated negatively with their physical and mental health conditions, and the number of people per unit was negatively associated with their physical health. More than half of respondents preferred a single unit with high partition walls; however, 40% of respondents preferred low partition walls. The provision of privacy for the elderly should be balanced with their needs for social interactions; total open surface per unit, openness/solid ratio per bed and height of partition wall should be taken into consideration. The study provides evidence and design guidelines for improving privacy in aged care facilities with a compact living environment.
Real-Time Android Application for Traffic Density Estimation

This paper deals with automatic traffic density estimation using new technologies that are cost-effective, quick to deploy, and easy to adapt. Therefore, a real-time video processing-based android application (<inline-formula> <tex-math notation="LaTeX">$app$ </tex-math></inline-formula>) is developed. It can be used online by exploiting the smartphone’s camera placed at the roadside or offline and by using pre-recorded vehicles flow videos and an Android emulator. The system autonomously computes the vehicle flow density in real time and saves all the relevant information in the smartphone memory. The developed <italic>app</italic> is validated under real conditions in Sherbrooke and Montreal cities, by considering different meteorological conditions and varying intrinsic and extrinsic camera parameters. Obtained results allow being optimistic about the effectiveness and the applicability of the proposed <italic>app</italic>.
Multichannel electromyography for the assessment of pelvic floor muscle function

M. Chen and colleagues recently published a paper entitled ‘Effects of oral contraceptives on ovulation induction in in vitro fertilization patients with premature ovarian insufficiency’. They claim that oral contraceptives, by reducing follicle stimulating hormone (FSH) levels, may be an effective method for inducing ovulation in such patients. I have a different interpretation of their results. In my own experience, it is 17b-estradiol that may be responsible for such facilitation. The lack of follicular response to stimulation in these cases may be due to a lesser bioactive FSH, to fewer granulosa cell receptors for FSH, and to fewer granulosa cells. Estradiol, the physiological trigger for ovulation, has several important actions: (1) it may affect the synthesis of pituitary FSH isoforms into more bioactive ones; (2) it may induce granulosa cell mitosis; (3) it may increase FSH receptors in granulosa cells; and (4) it may have a positive feedback on luteinizing hormone release, which is the ovulatory trigger. We have succeeded in treating three such patients, who were resistant to FSH stimulation. All three women ovulated and one of them carried a full-term pregnancy to a normal delivery of a healthy baby. In conclusion, I think that this favorable effect of oral contraceptives was due to the effect of ethinylestradiol on the ovary and not to the progestational steroid.
Scientific Temper of Rural and Urban Senior Secondary School Students

The focus of the present research was to study the scientific temper of rural and urban senior secondary school students of district Baramulla of Jammu and Kashmir. The sample consists of 300 (150 rural and 150 urban) senior secondary school students, selected randomly. The data was collected by using N.A. Nadeem and Showkat Rashid Wani’s Scale of scientific temper. The investigator used various statistical techniques viz, mean, S.D. , t-test to analyze the data. The statistical data revealed that there is a significant difference between rural and urban senior secondary school students on various dimensions of scientific temper. Citation: Jan Jahanger, Gh Nabi Dar (2019). Scientific Temper of Rural and Urban Senior Secondary School Students. International Journal of Advanced Multidisciplinary Scientific Research (IJAMSR ) ISSN:2581-4281, 2 (1), January, 2019, # Art.1113, pp 15-20 International Journal of Advanced Multidisciplinary Scientific Research (IJAMSR) ISSN:2581-4281 Volume 2, Issue 1, January, 2019 IJAMSR 2 (1) www.ijamsr. com CrossRef: https://doi.org/10.31426/ijamsr.2019. 2.1. 1113 IJAMSR 2(1) www.ijamsr. com January 2019 16 International Journal of Advanced Multidisciplinary Scientific Research (IJAMSR) ISSN:2581-4281
Current State of the Art to Detect Fake News in Social Media: Global Trendings and Next Challenges

: Nowadays, false news can be created and disseminated easily through the many social media platforms, resulting in a widespread real-world impact. Modeling and characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efﬁcient algorithms and tools for their early detection. A recent surge of researching in this area has aimed to address the key issues using methods based on machine learning, deep learning, feature engineering, graph mining, image and video analysis, together with newly created data sets and web services to identify deceiving content. Majority of the research has been targeting fake reviews, biased messages, and against-facts information (false news and hoaxes). In this work, we present a survey on the state of the art concerning types of fake news and the solutions that are being proposed. We focus our survey on content analysis, network propagation, fact-checking and fake news analysis and emerging detection systems. We also discuss the rationale behind successfully deceiving readers. Finally, we highlight important challenges that these solutions bring.
Detecting Southern California’s White Sharks With Environmental DNA

To improve ability to detect white sharks without the need for tags, or visual census, we developed a species-specific environmental DNA (eDNA) assay that targets a 163 bp fragment of the white shark (Carcharodon carcharias) mitochondrial cytochrome B gene on a digital droplet PCR (ddPCR) platform. We used this marker to detect white shark DNA in 250 ml water samples taken from across two sites in Santa Barbara, California (USA) frequented by juvenile white sharks. We did not detect white shark DNA in samples from two neighboring sites where sharks are presumably absent, suggesting that eDNA can indicate nearby white sharks. This marker development, testing, and opportunistic application in a region with known distributions of white sharks indicates that environmental DNA could be developed further to monitor white sharks, thereby informing conservation planning and public safety. With the potential increase in white shark populations due to decades of protection, there is a need for fishery independent methods for assessing white shark distributions, and eDNA may provide be an ideal, non-intrusive tool for coastal assessments.
Procedural-Memory, Working-Memory, and Declarative-Memory Skills Are Each Associated With Dimensional Integration in Sound-Category Learning

This paper investigates relationships between procedural-memory, declarative-memory, and working-memory skills and adult native English speakers’ novel sound-category learning. Participants completed a sound-categorization task that required integrating two dimensions: one native (vowel quality), one non-native (pitch). Similar information-integration category structures in the visual and auditory domains have been shown to be best learned implicitly (e.g., Maddox et al., 2006). Thus, we predicted that individuals with greater procedural-memory capacity would better learn sound categories, because procedural memory appears to support implicit learning of new information and integration of dimensions. Seventy undergraduates were tested across two experiments. Procedural memory was assessed using a linguistic adaptation of the serial-reaction-time task (Misyak et al., 2010a,b). Declarative memory was assessed using the logical-memory subtest of the Wechsler Memory Scale-4th edition (WMS-IV; Wechsler, 2009). Working memory was assessed using an auditory version of the reading-span task (Kane et al., 2004). Experiment 1 revealed contributions of only declarative memory to dimensional integration, which might indicate not enough time or motivation to shift over to a procedural/integrative strategy. Experiment 2 gave twice the speech-sound training, distributed over 2 days, and also attempted to train at the category boundary. As predicted, effects of declarative memory were removed and effects of procedural memory emerged, but, unexpectedly, new effects of working memory surfaced. The results may be compatible with a multiple-systems account in which declarative and working memory facilitate transfer of control to the procedural system.
Review of Vertebral Augmentation: An Updated Meta-analysis of the Effectiveness

ABSTRACT Background: To update vertebral augmentation literature by comparing outcomes between vertebroplasty (VP), balloon kyphoplasty (BKP), vertebral augmentation with implant (VAI), and nonsurgical management (NSM) for treating vertebral compression fractures (VCFs). Methods: A PubMed literature search was conducted with keywords kyphoplasty, vertebroplasty, vertebral body stent, and vertebral augmentation AND implant for English-language articles from February 1, 2011, to November 22, 2016. Among the results, 25 met the inclusion criteria for the meta-analysis. Inclusion criteria were prospective comparative studies for mid-/lower-thoracic and lumbar VCFs enrolling at least 20 patients. Exclusion criteria included studies that were single arm, systematic reviews and meta-analyses, traumatic nonosteoporotic or cancer-related fractures, lack of clinical outcomes, or non–Level I and non–Level II studies. Standardized mean difference between baseline and end point for each outcome was calculated, and treatment groups were pooled using random effects meta-analysis. Results: Visual analog scale pain reduction for BKP and VP was −4.05 and −3.88, respectively. VP was better than but not significantly different from NSM (−2.66), yet BKP showed significant improvement from both NSM and VAI (−2.77). The Oswestry Disability Index reduction for BKP showed a significant improvement over VAI (P < .001). There was no significant difference in changes between BKP and VP for anterior (P = .226) and posterior (P = .293) vertebral height restoration. There was no significant difference in subsequent fractures following BKP (32.7%; 95% confidence interval [CI]: 8.8%–56.6%) or VP (28.3%; 95% CI: 7.0%–49.7%) compared with NSM (15.9%; 95% CI: 5.2%–26.6%). Conclusions/Level of Evidence: Based on Level I and II studies, BKP had significantly better and VP tended to have better pain reduction compared with NSM. BKP tended to have better height restoration than VP. Additionally, BKP had significant improvements in pain reduction and disability score as compared with VAI. Clinical Relevance: This meta-analysis serves to further define and support the safety and efficacy of vertebral augmentation.
Recsys challenge 2018: automatic music playlist continuation

The ACM Recommender Systems Challenge 2018 focused on automatic music playlist continuation, which is a form of the more general task of sequential recommendation. Given a playlist of arbitrary length, the challenge was to recommend up to 500 tracks that fit the target characteristics of the original playlist. For the Challenge, Spotify released a dataset of one million user-created playlists, along with associated metadata. Participants could submit their approaches in two tracks, i.e., main and creative tracks, where the former allowed teams to use solely the provided dataset and the latter allowed them to exploit publicly available external data too. In total, 113 teams submitted 1,228 runs in the main track; 33 teams submitted 239 runs in the creative track. The highest performing team in the main track achieved an R-precision of 0.2241, an NDCG of 0.3946, and an average number of recommended songs clicks of 1.784. In the creative track, an R-precision of 0.2233, an NDCG of 0.3939, and a click rate of 1.785 was realized by the best team.
Taming VAEs

In spite of remarkable progress in deep latent variable generative modeling, training still remains a challenge due to a combination of optimization and generalization issues. In practice, a combination of heuristic algorithms (such as hand-crafted annealing of KL-terms) is often used in order to achieve the desired results, but such solutions are not robust to changes in model architecture or dataset. The best settings can often vary dramatically from one problem to another, which requires doing expensive parameter sweeps for each new case. Here we develop on the idea of training VAEs with additional constraints as a way to control their behaviour. We first present a detailed theoretical analysis of constrained VAEs, expanding our understanding of how these models work. We then introduce and analyze a practical algorithm termed Generalized ELBO with Constrained Optimization, GECO. The main advantage of GECO for the machine learning practitioner is a more intuitive, yet principled, process of tuning the loss. This involves defining of a set of constraints, which typically have an explicit relation to the desired model performance, in contrast to tweaking abstract hyper-parameters which implicitly affect the model behavior. Encouraging experimental results in several standard datasets indicate that GECO is a very robust and effective tool to balance reconstruction and compression constraints.
Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design

Numerous existing works have shown that, key to the efficiency of distributed machine learning (ML) is proper system and algorithm co-design: system design should be tailored to the unique mathematical properties of ML algorithms, and algorithms can be re-designed to better exploit the system architecture. While existing research has made attempts along this direction, many algorithmic and system properties that are characteristic of ML problems remain to be explored. Through an exploration of system-algorithm co-design, we build a new decentralized system Orpheus to support distributed training of a general class of ML models whose parameters are represented with large matrices. Training such models at scale is challenging: transmitting and checkpointing large matrices incur substantial network traffic and disk IO, which aggravates the inconsistency among parameter replicas. To cope with these challenges, Orpheus jointly exploits system and algorithm designs which (1) reduce the size and number of network messages for efficient communication, 2) incrementally checkpoint vectors for light-weight and fine-grained fault tolerance without blocking computation, 3) improve the consistency among parameter copies via periodic centralized synchronization and parameter-replicas rotation. As a result of these co-designs, communication and fault tolerance costs are linear to both matrix dimension and number of machines in the network, as opposed to being quadratic in existing systems. And the improved parameter consistency accelerates algorithmic convergence. Empirically, we show our system outperforms several existing baseline systems on training several representative large-scale ML models.