Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of blood biomarkers to screen for obstructive sleep apnea

Purpose Obstructive sleep apnea (OSA) is a highly prevalent disorder associated with increased risk for cardiovascular disease, diabetes, and other chronic conditions. Unfortunately, up to 90% of individuals with OSA remain without a diagnosis or therapy. We assess the relationship between OSA and blood biomarkers, and test the hypothesis that combinations of markers provide a characteristic OSA signature with diagnostic screening value. This validation study was conducted in an independent cohort in order to replicate findings from a prior feasibility study. Patients and methods This multicenter prospective study consecutively enrolled adult male subjects with clinically suspected OSA. All subjects underwent overnight sleep studies. An asymptomatic control group was also obtained. Five biomarkers were tested: glycated hemoglobin (HbA1c), C-reactive protein (CRP), uric acid, erythropoietin (EPO), and interleukin-6 (IL-6). Results The study enrolled 264 subjects. The combination of HbA1c+CRP+EPO (area under the curve 0.78) was superior to the Epworth Sleepiness Scale (ESS; 0.53) and STOP-Bang (0.70) questionnaires. In non-obese subjects, the combination of biomarkers (0.75) was superior to body mass index (BMI; 0.61). Sensitivity and specificity results, respectively, were: HbA1c+CRP+EPO (81% and 60%), ESS (78% and 19%), STOP-Bang (75% and 52%), BMI (81% and 56%), and BMI in non-obese patients (81% and 38%). Conclusion We verify our hypothesis and replicate our prior feasibility findings that OSA is associated with a characteristic signature cluster of biomarker changes in men. Concurrent elevations of HbA1c, CRP, and EPO levels should generate a high suspicion of OSA and may have utility as an OSA screening tool. Biomarker combinations correlate with OSA severity and, therefore, may assist sleep centers in identifying and triaging higher risk patients for sleep study diagnosis and treatment.
Cranial morphology of Sinovenator changii (Theropoda: Troodontidae) on the new material from the Yixian Formation of western Liaoning, China

A new three-dimensionally preserved troodontid specimen consisting of most of the skull, partial mandibles and six articulated cervical vertebrae (PMOL-AD00102) from the Early Cretaceous Yixian Formation of Beipiao, western Liaoning, China is identified as Sinovenator changii on the basis of a surangular with a “T”-shaped cross-section. High-resolution computed tomographic data for the skull of this new specimen facilitated a detailed description of the cranial anatomy of S. changii. New diagnostic features of S. changii include a well-developed medial shelf on the jugal, a slender bar in the parasphenoid recess, a lateral groove on the pterygoid flange of the ectopterygoid, and the lateral surface of the anterior cervical vertebrae bearing two pneumatic foramina. Our new observation confirms that the braincase of Sinovenator is not as primitive as previously suggested, although it still shows an intermediate state between derived troodontids and non-troodontid paravians in having an initial stage of the subotic recess and the otosphenoidal crest. Additionally, this new specimen reveals some novel and valuable anatomical information of troodontids regarding the quadrate-quadratojugal articulation, the stapes, the epipterygoid and the atlantal ribs.
Sentence Selection and Weighting for Neural Machine Translation Domain Adaptation

Neural machine translation (NMT) has been prominent in many machine translation tasks. However, in some domain-specific tasks, only the corpora from similar domains can improve translation performance. If out-of-domain corpora are directly added into the in-domain corpus, the translation performance may even degrade. Therefore, domain adaptation techniques are essential to solve the NMT domain problem. Most existing methods for domain adaptation are designed for the conventional phrase-based machine translation. For NMT domain adaptation, there have been only a few studies on topics such as fine tuning, domain tags, and domain features. In this paper, we have four goals for sentence level NMT domain adaptation. First, the NMT's internal sentence embedding is exploited and the sentence embedding similarity is used to select out-of-domain sentences that are close to the in-domain corpus. Second, we propose three sentence weighting methods, i.e., sentence weighting, domain weighting, and batch weighting, to balance the data distribution during NMT training. Third, in addition, we propose dynamic training methods to adjust the sentence selection and weighting during NMT training. Fourth, to solve the multidomain problem in a real-world NMT scenario where the domain distributions of training and testing data often mismatch, we proposed a multidomain sentence weighting method to balance the domain distributions of training data and match the domain distributions of training and testing data. The proposed methods are evaluated in international workshop on spoken language translation (IWSLT) English-to-French/German tasks and a multidomain English-to-French task. Empirical results show that the sentence selection and weighting methods can significantly improve the NMT performance, outperforming the existing baselines.
A Generic Method to Guide HTN Progression Search with Classical Heuristics

HTN planning combines actions that cause state transition with grammar-like decomposition of compound tasks that additionally restricts the structure of solutions. There are mainly two strategies to solve such planning problems: decomposition-based search in a plan space and progression-based search in a state space. Existing progression-based systems do either not rely on heuristics (e.g. SHOP2) or calculate their heuristics based on extended or modified models (e.g. GoDeL). Current heuristic planners for standard HTN models (e.g. PANDA) use decomposition-based search. Such systems represent search nodes more compactly due to maintaining a partial order between tasks, but they have no current state at hand during search. This makes the design of heuristics difficult. In this paper we present a progression-based heuristic HTN planning system: We (1) provide an improved progression algorithm, prove its correctness, and empirically show its efficiency gain; and (2) present an approach that allows to use arbitrary classical (non-hierarchical) heuristics in HTN planning. Our empirical evaluation shows that the resulting system outperforms the state-of-the-art in HTN planning.
End-To-End Machine Learning Data Feature Representation Trained / Tuned Model Predictions Model Evaluation / Inference

The use of machine learning techniques has expanded in education research, driven by the rich data from digital learning environments and institutional data warehouses. However, replication of machine learned models in the domain of the learning sciences is particularly challenging due to a confluence of experimental, methodological, and data barriers. We discuss the challenges of end-to-end machine learning replication in this context, and present an open-source software toolkit, the MOOC Replication Framework (MORF), to address them. We demonstrate the use of MORF by conducting a replication at scale, and provide a complete executable container, with unique DOIs documenting the configurations of each individual trial, for replication or future extension at https://github.com/ educational-technology-collective/fy2015-replication. This work demonstrates an approach to end-to-end machine learning replication which is relevant to any domain with large, complex or multi-format, privacy-protected data with a consistent schema.
Subword-augmented Embedding for Cloze Reading Comprehension

Representation learning is the foundation of machine reading comprehension. In state-of-the-art models, deep learning methods broadly use word and character level representations. However, character is not naturally the minimal linguistic unit. In addition, with a simple concatenation of character and word embedding, previous models actually give suboptimal solution. In this paper, we propose to use subword rather than character for word embedding enhancement. We also empirically explore different augmentation strategies on subword-augmented embedding to enhance the cloze-style reading comprehension model (reader). In detail, we present a reader that uses subword-level representation to augment word embedding with a short list to handle rare words effectively. A thorough examination is conducted to evaluate the comprehensive performance and generalization ability of the proposed reader. Experimental results show that the proposed approach helps the reader significantly outperform the state-of-the-art baselines on various public datasets.
Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach

Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.
Prediction of Slope Stability Using Four Supervised Learning Methods

The analysis and prediction of slope stability are very important, because slope failure can lead to large disasters. This paper focused on a performance comparison of four supervised learning methods for slope stability prediction. Based on characteristics of slope instability and analysis of data availability, six typical slope parameters—the unit weight, cohesion, internal friction angle, slope inclination, slope height, and pore water ratio—were chosen to establish the evaluation index system. The gravitational search algorithm (GSA), random forest (RF), support vector machine, and naive Bayesian (Bayes) were proposed to establish classifiers. A data set from more than 10 domestic and abroad slope projects was established to train and test the four classifiers, and then, key parameters of the four models were optimized by using the method of 10-fold cross validation. The prediction performances of the four supervised learning methods were compared and analyzed. The results of accuracy, Kappa, and receiver operating characteristic curves reveal that both GSA and RF models can achieve satisfactory results, and the GSA model can obtain the best results when compared with the other three learning methods. Finally, seven models with varying indicators are investigated to obtain the parameter sensitivity based on RF and GSA models.
Edinburgh Research Explorer Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence

highly heritable 1 and a major determinant of human health and well-being 2 . Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence 3-7 , but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=269,867), identifying 205 associated genomic loci (190 novel) and 1,016 genes (939 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest protective effects of intelligence for Alzheimer’s disease and ADHD, and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders. bxy =−0.685, P =2.02×10 −0.214, P =4.19×10 -52 additional of previous both a causal link and genetic overlap between these phenotypes 32,33 Our results that a protective effect on ADHD (OR=0.48, bxy =−0.734, P =2.57×10 -46 ) disease bxy =−0.435, P =3.59×10 -14 but was associated with higher risk of bxy =0.321, P =1.12×10 (RS), Generation R (GENR), Twin Registry (STR), Spit for (S4S), High-IQ/Health and (HiQ/HRS), Twins Early Development (TEDS), (DTR), Longitudinal Twin Study (BLTS), (NESCOG), for (GfG),
Is publication bias present in gastroenterological research? An analysis of abstracts presented at an annual congress

Background Publication bias is the tendency of investigators, reviewers, and editors to submit or accept manuscripts for publication based on their direction or strength of findings. In this study, we investigated if publication bias was present in gastroenterological research by evaluating abstracts at Americas Hepato-Pancreato-Biliary Congresses from 2011 to 2013. Methods We searched Google, Google Scholar, and PubMed to locate the published reports of research described in these abstracts. If a publication was not found, a second investigator searched to verify nonpublication. If abstract publication status remained undetermined, authors were contacted regarding reasons for nonpublication. For articles reaching publication, the P value, study design, time to publication, citation count, and journals in which the published report appeared were recorded. Results Our study found that of 569 abstracts presented, 297 (52.2%) reported a P value. Of these, 254 (85.5%) contained P values supporting statistical significance. The abstracts reporting a statistically significant outcome were twice as likely to reach publication than abstracts with no significant findings (OR 2.10, 95% CI [1.06–4.14]). Overall, 243 (42.7%) abstracts reached publication. The mean time to publication was 14 months and a median time of nine months. Conclusion In conclusion, we found evidence for publication bias in gastroenterological research. Abstracts with significant P values had a higher probability of reaching publication. More than half of abstracts presented from 2011 to 2013 failed to reach publication. Readers should take these findings into consideration when reviewing medical literature.
Sum-Rate Performance of Millimeter Wave MIMO Shared Spectrum Systems

This paper study the effect of interference management arising due to millimeter wave (mmWave) communication on the performance of multiple input multiple output (MIMO) based spectrum sharing cognitive systems. The highly directed mmWave signals have short wavelength property which enables to fabricate an increased number of antennas in a small confined space of the communication system. The large antenna elements are utilized to harvest high beamforming gains and to improve the signal to noise ratio (SNR) by analog processing at the cognitive base station (CBS) and secondary users. Analog processing at the CBS and at secondary user have hardware and computation restrictions. These restrictions are solved by designing radio frequency (RF) precoding and RF combining. In the digital domain, it is shown that interference between secondary users can be canceled by baseband block diagonalization technique. Towards this end, the underlay mode of operation is considered to control interference on the primary user and to derive the expression for optimal power allocation. The derived power allocation is a solution to the rate maximization problem of mmWave MIMO cognitive radio (CR) systems. Finally, numerical simulations are performed to yield various significant insights into for different mmWave CR system conditions and also validate the improvement in performance in comparison to the only digital communication systems.
Contextualizing a professional social network for health care: Experiences from an action design research study

Professional social networks (PSN) are online environments where practitioners can present themselves, get in contact and socialize with coworkers, share and discuss ideas, or exchange business‐related knowledge. Despite the fact that collaboration and information sharing are becoming more relevant for delivering high‐quality services, PSN are not yet widely adopted in complex domains such as health care. While most of the literature is still focusing on the exposition of the unbound potential of PSN, this paper seeks to clarify the question of how to capture and manage the professional identity of an industry such that PSN can be purposefully anchored in the working context. Following an Action Design Research approach, we describe practical design propositions and possible tensions along the contextualization of a PSN, which was specifically catered for improving interprofessional and interorganizational collaboration in and between hospitals. We identify several implications for future research. In particular, we explain intended and unintended uses of PSN in hospitals and provide metaphors for explaining possible alternative understandings of domain engineering.
Temporal processing and context dependency in Caenorhabditis elegans response to mechanosensation

A quantitative understanding of how sensory signals are transformed into motor outputs places useful constraints on brain function and helps to reveal the brain’s underlying computations. We investigate how the nematode Caenorhabditis elegans responds to time-varying mechanosensory signals using a high-throughput optogenetic assay and automated behavior quantification. We find that the behavioral response is tuned to temporal properties of mechanosensory signals, such as their integral and derivative, that extend over many seconds. Mechanosensory signals, even in the same neurons, can be tailored to elicit different behavioral responses. Moreover, we find that the animal’s response also depends on its behavioral context. Most dramatically, the animal ignores all tested mechanosensory stimuli during turns. Finally, we present a linear-nonlinear model that predicts the animal’s behavioral response to stimulus.
“Just how risky is it anyway?” The role of risk perception and trust on click-through intention

ABSTRACT The prevalence of cybercrimes threatens internet enabled services such as e-commerce, search engines, and social media. This results in individuals having to evaluate the risk of malware before making decisions to click-through internet links. Drawing on two theoretical frameworks in trust in online transactions and risky decision-making, we propose a research model that incorporates both trust and risk frameworks to explain how individuals cultivate click-through intention. We offer theoretical and practical contributions.
Local Interpolated Compressive Sampling for Internet Traffic Reconstruction

In this paper, we propose the integration of local interpolation on compressive sampling (CS) for application on internet traffic reconstruction. CS managed to solve the problem of missing Internet traffic but not in the case of extreme missing like Missing Elements at Random (MER) and Combine Missing Patterns (CMP). To overcome the problem is done by merging between local interpolations with CS. In this study conducted a comparison of local interpolation using correlation method and Euclidean norm and tested the effect of similarity parameters between rows and similarity between columns. The simulation results show that the incorporation of local interpolation can improve accuracy with insignificant time increments, mainly on the addition of Euclidean norm technique.
Answer to July 2018 Photo Quiz

The brain abscess grew Nocardia farcinica. The Gram stain of the organism showed long, thin, beaded, branching Gram-positive bacilli. The bacterium was partially acid fast with modified Kinyoun staining. The colonies appeared smooth and moist on sheep blood agar, in contrast to the typical dry, chalky, wrinkled colony morphology of other Nocardia species. The organism was identified as N. farcinica by matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS), with a log score of 2.173 (Biotyper RTC version 4.0.11) (Bruker Daltonics, Billerica, MA), in our laboratory, and this identification was later confirmed with secA1 gene sequencing in a reference laboratory, with a 100% match to the type strain. N. farcinica is a common cause of nocardial brain abscesses, 45% of which present as primary abscesses (1). This organism is associated with high mortality rates, as high as 55% in immunocompromised patients despite treatment. Long-term therapy with high-dose prednisone has been shown as a risk factor for Nocardia infection (2, 3). Definitive identification of Nocardia is clinically important because antibiotic susceptibilities differ greatly within the genus, with N. farcinica being resistant to multiple antibiotics, including thirdand fourth-generation cephalosporins, doxycycline, tobramycin, and clarithromycin (4). In most clinical laboratories, Nocardia isolates are sent to a referral laboratory with molecular capabilities for 16S rRNA or secA1 gene sequencing or, more recently, multilocus sequencing analysis (MLSA) (5, 6). Although these techniques provide accurate species identification, the long turnaround time can delay treatment, resulting in poor patient outcomes (7). Recent studies have evaluated the use of MALDI-TOF MS to identify Nocardia. Currently, Nocardia species are absent in the FDA-cleared databases of Bruker and bioMérieux. It has been shown that database enhancement through library construction can overcome this limitation, especially for the lowprevalence species (6, 8, 9). Modified extraction procedures using heat or silica beads improve sensitivity (8). The use of young colonies after 18 to 48 h of growth yields better results than the use of colonies from older culture (9). Based on the identification of N. farcinica by MALDI-TOF MS, the antibiotic regimen of our patient was changed to linezolid and trimethoprim-sulfamethoxazole. The patient remained stable until an episode of heart failure exacerbation a month later, and he ultimately passed away. The N. farcinica isolate was confirmed to be susceptible to amikacin, amoxicillin-clavulanic acid, moxifloxacin, linezolid, and trimethoprimsulfamethoxazole, intermediate to ceftriaxone, imipenem, doxycycline, and minocycline, and resistant to ciprofloxacin, tobramycin, and clarithromycin by broth microdilution (10).