Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of C-reactive protein and low-density lipoprotein cholesterol levels in the prediction of first cardiovascular events

A number of prospective case-control studies have found that elevated C-reactive protein (CRP), a marker of systemic inflammation, correlates with an increased risk of myocardial infarction (MI), stroke, and sudden cardiac death. The fact that atherothrombosis frequently is not accompanied by hyperlipidemia prompted a comparison of CRP and low-density lipoprotein (LDL) cholesterol as possible predictors of an initial cardiovascular event. The study population included 27,939 generally healthy American women aged 45 and older who were taking part in the Women's Health Study, an ongoing assessment of aspirin and vitamin E for primary prevention of cardiovascular events. The women, whose mean age at baseline was 55 years, were followed up for a mean period of 8 years, noting the occurrence of MI, ischemic stroke, coronary revascularization, and cardiovascular death. One fourth of women were hypertensive, 12% were smokers, and 44% were currently using hormone replacement therapy (HRT). The relative risk of a first cardiovascular event, adjusted for age and other risk factors, correlated strongly with both CRP and LDL cholesterol. Analyzing individual components of the combined end point, which included stroke, coronary heart disease, and cardiovascular death, indicated that CRP was the better predictor of the two. The two parameters themselves were only minimally correlated with one another. Similar results were found in women currently using HRT and nonusers. More than three fourths (77%) of all events occurred in women whose LDL cholesterol was below 160 mg/dl, and close to half (46%) when the level was less than 130 mg/dl. Screening for both markers was prognostically superior to screening for either one by itself, suggesting that they identify different high-risk groups. CRP remained a strong independent predictor of cardiovascular risk after adjusting for all components of the Framingham risk score. The investigators conclude that CRP predicts future cardiovascular events in women aged 45 and above more reliably than does elevated LDL cholesterol but that screening for both of these markers may be the best policy.
Chitons’ apparent camouflage does not reduce predation by green crabs Carcinus maenas

ABSTRACT Chitons are very common molluscs on European rocky shores. They are common prey of fish and crabs and often display several colour morphs within a given habitat. Predation is one of the potential mechanisms accounting for chiton colour polymorphism. The colour variation is considered to provide a camouflage protection through a match with the substratum surface typology. However, the effectiveness of chiton polymorphism as a predation defence requires further investigation. Previously we found a relationship between chiton colour morphs and substrate characteristics, with chitons most commonly found on substrates that were of similar colour to their shells. Here, we examined whether each morph displayed an active choice for matching the substratum. Next, we assessed if the predation success of the intertidal common crab Carcinus maenas varied significantly with the absence/presence of an apparent camouflage effect created between the chiton colour morph and the substratum type. The present study indicates that chiton colour morphs probably actively choose substratum types where they blend in. Carcinus maenas was able to prey on all Lepidochitona cinereus colour morphs, regardless of the substrate camouflage effect. Surprisingly, the predation frequency was higher on camouflaged chitons than on contrasting chitons. It was concluded that chiton camouflage is probably not a defence mechanism against predation by the crab C. maenas, and that chiton colour polymorphism is probably promoted by other, more visual predators.
Genetic Diversity of Primula ovalifolia from Central and Southwest China Based on ISSR Markers

Primula ovalifolia Franchet (Primulaceae) is mainly distributed in a band through central China to southwest China and grows in moist shady places with the altitude between 1200 - 2500 m. Eighty-five individuals from five natural populations in Hubei (central) and Sichuan (southwest) China were sampled. The inter-simple sequence repeats (ISSR) produced 207 polymorphic band loci and identified 85 ISSR genotypes. The results obtained from calculation of Shannon’s index and Nei’s gene diversity shows that there is no difference of genetic diversity between the three populations in southwest China and two populations in central China. The UPGMA clustering analysis also shows that there is no distinct genetic differentiation between the natural populations in central and southwest China.
EFFECTS OF THE ENDOCANNABINOID ANANDAMIDE ON EXCITABILITY AND CALCIUM SIGNALING IN RAT VENTRICULAR MYOCYTES

Endogenous carmabinoids (endocannabinoids) exert a wide range of biological effect . In addition to having their well-known neurobehavioral effects , a role for the major endocannabinoid anandamide (N-arachidonoyl ethanolamide; AEA), in the cardiovascular system in various pathological conditions has been reported. The aim of this thesis is to explore the effects of AEA on contractility, Ca2+ signaling, and action potential (AP) characteristics in rat ventricular myocytes. A video edge detection system was used to measure myocyte shortening. Intracellu lar Ca2 was measured in cells loaded with the fluorescent indicator fura-2 AM. AEA (l �M) caused a significant decrease in the amplitude of electrical ly-e oked myocyte shortening. The effect of AEA was not altered in the presence of pertussis toxin ( PTX), AM251 and S R141716 (CB, arltagonists) or AM630 and S R 144528 (CB2 antagonists). AEA also caused a significant decrease in the amplitudes of electrically-evoked Ca2+ transients. However, the amplitudes of caffeine-evoked Ca2+ transients and the rate of recovery of electrical ly-e oked Ca2+ trarlsients fol lowing caffeine application were not altered. In the whole-cel l mode of patch-clamp technique, AEA (1 �M) significant ly decreased the duration of APs. The inhibition was not altered in the presence of PTX, AM251 and AM630. Furthermore, AEA inhibited voltage-activated inward Na+ (INa) and Ca2+ (h,ca) currents ; major ionic currents shaping the APs in ventricular myocytes, in a voltage and PTX-independent manner. Cardiac Na+/Ca2+ exchanger (NCX1)­ mediated currents were also suppressed by AEA. The effect of AEA was not influenced by the inhibition of fatty acid amide hydrolase ( F AAH) or in the presence of PTX, AM251 and AM630 or fol lowing the inclusion of GDP-�-S in pipette solution. The results of this study indicate for the first time that impaired VI cl+ signaling underlies the negati e inotropic actions of AEA in rat entricular myocyte and that the direct interaction of AEA with ion channel(s) shaping APs mediate, at least in part, the effects of AEA on myocyte contractility. In addition, the results indicate for the first time that, under normal conditions, AEA can direct ly inrubit the activity of CX l in ventricular myocytes. In view of the massive relea e of various -acylethanolamines (NAEs), including AEA, during cardiac ischemia and hypoxic conditions, further understanding of their mechanism(s) of action and target proteins is essential in the development of better treatment modalities under pathological conditions.
What is the impact of tax and welfare reforms on fiscal stabilisers? A simple model and an application to EMU

Reforms aiming at lowering the tax burden and cutting social benefits may boost efficiency and output, and improve market adjustment to shocks, but, by reducing the size of automatic stabilisers, may also imply less cyclical smoothing. This would be problematic in EMU given the loss of national monetary autonomy. This paper argues that the alleged trade-off between efficiency/flexibility and stabilisation depends on the typology of shocks affecting the economy.
YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles

This paper summarizes our studies on propaganda detection techniques for news articles in the SemEval-2020 task 11. This task is divided into the SI and TC subtasks. We implemented the GloVe word representation, the BERT pretraining model, and the LSTM model architecture to accomplish this task. Our approach achieved good results for both the SI and TC subtasks. The macro- F 1 - score for the SI subtask is 0.406, and the micro- F 1 - score for the TC subtask is 0.505. Our method significantly outperforms the officially released baseline method, and the SI and TC subtasks rank 17th and 22nd, respectively, for the test set. This paper also compares the performances of different deep learning model architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the detection of news promotion techniques.
Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles

This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles. We participate in both the span identification and technique classification subtasks and report on experiments using different BERT-based models along with handcrafted features. Our models perform well above the baselines for both tasks, and we contribute ablation studies and discussion of our results to dissect the effectiveness of different features and techniques with the goal of aiding future studies in propaganda detection.
Detection perceptual underwater image enhancement with deep learning and physical priors

Underwater image enhancement, as a pre-processing step to improve the accuracy of the following object detection task, has drawn considerable attention in the field of underwater navigation and ocean exploration. However, most of the existing underwater image enhancement strategies tend to consider enhancement and detection as two independent modules with no interaction, and the practice of separate optimization does not always help the underwater object detection task. In this paper, we propose two perceptual enhancement models, each of which uses a deep enhancement model with a detection perceptor. The detection perceptor provides coherent information in the form of gradients to the enhancement model, guiding the enhancement model to generate patch level visually pleasing images or detection favourable images. In addition, due to the lack of training data, a hybrid underwater image synthesis model, which fuses physical priors and data-driven cues, is proposed to synthesize training data and generalise our enhancement model for real-world underwater images. Experimental results show the superiority of our proposed method over several state-of-the-art methods on both real-world and synthetic underwater datasets.
Indium-Tin-Oxide Transistors with One Nanometer Thick Channel and Ferroelectric Gating.

In this work, we demonstrate high performance indium-tin-oxide (ITO) transistors with the channel thickness down to 1 nm and ferroelectric Hf0.5Zr0. 5O2 as gate dielectric. On-current of 0.243 A/mm is achieved on sub-micron gate-length ITO transistors with a channel thickness of 1 nm, while it increases to as high as 1.06 A/mm when the channel thickness increases to 2 nm. A raised source/drain structure with a thickness of 10 nm is employed, contributing to a low contact resistance of 0.15 Ω⋅mm and a low contact resistivity of 1.1×10-7 Ω⋅cm2. The ITO transistor with a recessed channel and ferroelectric gating demonstrates several unique advantages over 2D semiconductor transistors and other thin film transistors, including large-area wafer-size nanometer thin film formation, low contact resistance and contact resistivity, atomic thin channel being immunity to short channel effects, large gate modulation of high carrier density by ferroelectric gating, high-quality gate dielectric and passivation formation, and a large bandgap for the low-power back-end-of-line (BEOL) CMOS application.
Predicting Helpfulness of Online Reviews

E-commerce dominates a large part of the world's economy with many websites dedicated to online selling products. The vast majority of e-commerce websites provide their customers with the ability to express their opinions about the products/services they purchase. These feedback in the form of reviews represent a rich source of information about the users' experiences and level of satisfaction, which is of great benefit to both the producer and the consumer. However, not all of these reviews are helpful/useful. The traditional way of determining the helpfulness of a review is through the feedback from human users. However, such a method does not necessarily cover all reviews. Moreover, it has many issues like bias, high cost, etc. Thus, there is a need to automate this process. This paper presents a set of machine learning (ML) models to predict the helpfulness online reviews. Mainly, three approaches are used: a supervised learning approach (using ML as well as deep learning (DL) models), a semi-supervised approach (that combines DL models with word embeddings), and pre-trained word embedding models that uses transfer learning (TL). The latter two approaches are among the unique aspects of this paper as they follow the recent trend of utilizing unlabeled text. The results show that the proposed DL approaches have superiority over the traditional existing ones. Moreover, the semi-supervised has a remarkable performance compared with the other ones.
Data Programming using Semi-Supervision and Subset Selection

The paradigm of data programming~\cite{bach2019snorkel} has shown a lot of promise in using weak supervision in the form of rules and labelling functions to learn in scenarios where labelled data is not available. Another approach which has shown a lot of promise is that of semi-supervised learning where we augment small amounts of labelled data with a large unlabelled dataset. In this work, we argue that by not using any labelled data, data programming based approaches can yield sub-optimal performance, particularly, in cases when the labelling functions are noisy. The first contribution of this work is to study a framework of joint learning which combines un-supervised consensus from labelling functions with semi-supervised learning and \emph{jointly learns a model} to efficiently use the rules/labelling functions along with semi-supervised loss functions on the feature space. Next, we also study a subset selection approach to \emph{select} the set of examples which can be used as the labelled set. We evaluate our techniques on synthetic data as well as four publicly available datasets and show improvement over state-of-the-art techniques\footnote{Source code of the paper at \url{this https URL}}.
UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL FACULDADE DE FARMÁCIA PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIAS FARMACÊUTICAS

The aim of this study was the development and validation of analytical methods to the determination of cefpirome in powder for injectable preparation and the stability study of the drug after reconstitution of the pharmaceutical dosage form with injectable water. Cefpirome is a fourth-generation cephalosporin active against a broad spectrum of gram-negative and gram-positive bacterial infections and its principal indication is in the treatment of patients’ septic shock or several sepsis. The substance used as reference standard in the analysis was characterized by infrared spectroscopy and nuclear magnetic resonance. The qualitative analysis was performed by thin layer chromatography, melting range determination, infrared and ultraviolet spectroscopy and high performance liquid chromatography, allowing the identification of samples. The quantitative determination was performed by ultraviolet spectrophotometry, high performance liquid chromatography and cylinder-plate microbiological assay (3 x 3). The proposed methods were validated following official guides and all of them demonstrated to be specific, linear, precise and accurate. The obtained results were analyzed by the ANOVA method and they are not statistically different. The degradation factors evaluated were the temperature (40 oC) and light (UVC – 254 nm) and the accelerated stability demonstrated that the degradation kinetics from thermal and photo degradation were first-order reactions. A thermal degradation product was isolated and identified, by nuclear magnetic resonance, infrared spectroscopy and mass spectrometry, as 6-7-dihidro-5Hcyclopenta[b]pyridinium sulfate.
Outliers in the LIGO black hole mass function from coagulation in dense clusters

The advanced LIGO O3a run catalogue has been recently published, and it includes several events with unexpected mass properties, including mergers with individual masses in the lower and upper mass gaps, as well as mergers with unusually small mass ratios between the binary components. Here, we entertain the possibility that these outliers are the outcome of hierarchical mergers of black holes or neutron stars in the dense environments of globular clusters. We use the coagulation equation to study the evolution of the black hole mass function within a typical cluster. Our prescription allows us to monitor how various global quantities change with time, such as the total mass and number of compact objects in the cluster, its overall merger rate, and the probability to form intermediate-mass black holes via a runaway process. By accounting for the LIGO observational bias, we predict the merger event distributions with respect to various variables such as the individual masses M1 and M2, their ratio q, and redshift z, and we compare our predictions with the published O3a data. We study how these distributions depend on the merger-rate and ejections parameters and produce forecasts for the (tight) constraints that can be placed on our model parameters using the future data set of the O5 run. Finally, we also consider the presence of a static channel with no coagulation producing merger events alongside the dynamic channel, finding that the two can be distinguished based solely on the merger mass distribution with future O5 data.
Can feeding the broiler breeder improve chick quality and offspring performance

The impact of nutrition on the performance of broiler breeders in terms of bodyweight profile, egg production, egg size and shell quality, mortality, fertility and hatchability has been well documented. However, studies on the direct effects of breeder nutrition on subsequent chick quality and offspring performance are limited. The present paper reviews the nutritional factors in broiler breeders that may improve chick quality and progeny performance. Recent research has suggested that the feeding of low crude protein and medium to high energy breeder diets in both rearing and laying period will not only have a positive impact on egg production, egg size, breeder fertility, hatchability and embryonic livability, but also on offspring performance. Improvements include better chick quality, higher chick weight, lower mortality, increased immune responses, enhanced litter quality and broiler growth. Breast-meat yield in the progeny can be improved as the energy to protein ratio increases from the rearing phase to the laying phase, indicating that nutrition of pullets can also affect growth and yields. The effects of maternal diet on offspring performance may depend on the sex of the progeny, with male broilers responding more than female broilers to breeders’ dietary protein and energy concentrations. However, little work has been conducted on the effects of individual amino acids in breeder diets on the subsequent generation. There seems to be some benefits with the use of long-chain poly-unsaturated fats in breeder diets on carcass quality of offspring, but these effects appear inconsistent at this stage. There is substantial evidence that confirms the direct effects of vitamin D and E, and trace minerals, especially in organic form, such as selenium, zinc and manganese, on chick quality and progeny performance. In fact, the impact of breeder nutrition on progeny performance in the field is more likely to be greater under conditions of poor breeder-flock uniformity, low vitamin and trace mineral contents or certain types of stress in the broiler house.
A Computational Analysis of Real-World DJ Mixes using Mix-To-Track Subsequence Alignment

A DJ mix is a sequence of music tracks concatenated seamlessly, typically rendered for audiences in a live setting by a DJ on stage. As a DJ mix is produced in a studio or the live version is recorded for music streaming services, computational methods to analyze DJ mixes, for example, extracting track information or understanding DJ techniques, have drawn research interests. Many of previous works are, however, limited to identifying individual tracks in a mix or segmenting it, and the sizes of the datasets are usually small. In this paper, we provide an in-depth analysis of DJ music by aligning a mix to its original music tracks. We set up the subsequence alignment such that the audio features are less sensitive to the tempo or key change of the original track in a mix. This approach provides temporally tight mix-to-track matching from which we can obtain cue-points, transition length, mix segmentation, and musical changes in DJ performance. Using 1,557 mixes from 1001Tracklists including 13,728 tracks and 20,765 transitions, we conduct the proposed analysis and show a wide range of statistics, which may elucidate the creative process of DJ music making.
Canonical Polymorphisms of Ramsey Structures and the Unique Interpolation Property

Constraint satisfaction problems for first-order reducts of finitely bounded homogeneous structures form a large class of computational problems that might exhibit a complexity dichotomy, P versus NP-complete. A powerful method to obtain polynomial-time tractability results for such CSPs is a certain reduction to polynomial-time tractable finite-domain CSPs de-fined over k-types, for a sufficiently large k. We give sufficient conditions when this method can be applied and illustrate how to use the general results to prove a new complexity dichotomy for first-order expansions of the basic relations of the spatial reasoning formalism RCC5.
Quasi-Bernoulli Stick-breaking: Infinite Mixture with Cluster Consistency.

In mixture modeling and clustering application, often, the number of components is not known. The stick-breaking model is an appealing construction that assumes infinitely many components, while shrinking most of the redundant weights to near zero. However, it has been discovered that such shrinkage is unsatisfactory: even when the component distribution is correctly specified, small and spurious weights would appear and give an inconsistent estimate on the cluster number. In this article, we propose a simple solution that directly controls the redundant weights: when breaking each stick, the remaining proportion is further multiplied by a quasi-Bernoulli random variable, supported at one and a positive constant close to zero. This effectively shrinks the redundant weights much closer to zero, leading to a consistent estimate on the cluster number in theory; at the same time, it avoids the singularity at zero weight, maintaining support in the infinite-dimensional space and enjoying efficient posterior computation. Compared to existing infinite mixture models, our model demonstrates superior performances in the simulations and data application, showing a substantial reduction in the number of clusters.