Miracle-dz/UniFAR-FTUs huggingface dataset:
Review Article Review of Serum Biomarkers and Models Derived from Them in HBV-Related Liver Diseases

A series of predictive scoring systems is available for stratifying the severity of conditions and assessing the prognosis in patients with HBV-related liver diseases. We show nine of the most popular serum biomarkers and their models (i.e., serum cystatin C, homocysteine, C-reactive protein, C-reactive protein to albumin ratio, aspartate aminotransferase to platelet ratio index, fibrosis index based on four factors, gamma-glutamyl transpeptidase to platelet ratio, albumin-bilirubin score, and gamma-glutamyl transpeptidase to albumin ratio) that have gained great interest from clinicians. Compared with traditional scoring systems, these serum biomarkers and their models are easily acquired, simple, and relatively inexpensive. In the present review, we summarize the latest studies focused on these serum biomarkers and their models as diagnostic and prognostic indexes in HBVrelated liver diseases.
CLEP_A_243546 203..213

1Unit of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark; 2Department of Gynecology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark; 3Unit of Statistics and Pharmacoepidemiology, Danish Cancer Society Research Center, Copenhagen, Denmark; 4Department of Pathology, Vejle Hospital, Lillebaelt Hospital, Region of Southern Denmark, Vejle, Denmark; 5Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark Background: Human papillomavirus (HPV) testing is increasingly used as the primary cervical cancer screening test. In a large pilot implementation, we compared participation, referrals and detection of high-grade cervical intraepithelial neoplasia (CIN) in HPVversus cytology-based cervical cancer screening. Methods: The implementation was embedded into the routine screening program at Lillebaelt Hospital, Department of Pathology, Vejle, Denmark. Based on the area of residence, women aged 30–59 years were screened by either HPV testing (with HPV16/18 genotyping and cytology triage) or cytology (with HPV triage for minor abnormalities). Our analysis includes women invited or screened during May 2017–May 2018 (invited: n=35,081; screened: n=28,352) with 6 months of follow-up. Information on screening results and sociodemographic characteristics were obtained from registers. Using logistic regression, we estimated odds ratios (ORs) with 95% confidence intervals (CIs) of participation, referral and CIN3+-detection in HPVversus cytology-based screening, adjusting for sociodemographic characteristics. Results: Participation was virtually identical in the HPVand cytology group (58.4% vs 58.8%; ORadjusted=0.97, 95% CI, 0.93–1.01). Referral to colposcopy was more common in the HPVthan cytology group (3.8% vs 2.1%; ORadjusted=1.88, 95% CI, 1.63–2.17). More cases of CIN3+ were detected in the HPVthan cytology group (1.0% vs 0.7%, ORadjusted=1.47; 95% CI, 1.13–1.91). Conclusion: Participation did not differ between HPVand cytology-based screening. HPVbased screening detected more cases of CIN3+, but in this initial screening round also led to more colposcopies than cytology-based screening.
Gut-Liver Axis Modulation: A Potential Role of Probiotics and Omega-3 Fatty Acids in Management of NAFLD

Gut-Liver Axis Modulation: A Potential Role of Probiotics and Omega-3 Fatty Acids in Management of NAFLD Faruqui AA Department of Pharmacology, Clinical Pharmacologist, Maharashtra, India Received: 05 Sep 2020 Accepted: 21 Sep 2020 Published: 22 Sep 2020 Corresponding author: Arif A. Faruqui, Department of Pharmacology, Clinical Pharmacologist, 504-A, Rizvi Mahal CHS, Maharashtra, India, E-mail: drfaruqui@gmail.com 1. Abstract
A Survey on the Effects of Cutting Parameters on Surface Roughness When Milling 40 Cr Steel Using End Mill Cutter Nguyen

DOI: http://dx.doi. org/10.24018/ejers.2020. 5.1. 1704 33  Abstract—In this paper, the empirical research on the effects of cutting parameters on surface roughness when milling 40Cr steel with face mill is conducted. The cutting parameters in this study include the cutting speed, feed rate and depth of cut. The tests are conducted in the form of Central Composite Design. The analysis shows that, for both Ra and Rz: (1) the feed rate has the greatest effect on surface roughness, followed by the degree of influence of the depth of cut, cutting speed with insignificant effects on surface roughness. (2) only the interaction between the feed rate and depth of cut have a significant effect on both Ra and Rz; the interaction between the cutting speed and feed rate, the interaction between the cutting speed and depth of cut have negligible effects on the surface roughness. In addition, the regression equations showing the relationship between Ra, Rz and cutting parameters is developed in this study.
Degradation of C.I. Acid Blue 25 in water using UV/K2S2O8 process: effect of salts and environmental matrix

The objective of this work was to evaluate the degradation of an anthraquinone dye, C.I. Acid Blue 25 (AB25), in aqueous phase using UV/K2S2O8 (PPS) process. Experiments were conducted in batch mode using a low-pressure mercury lamp emitting mainly at 253.7 nm. The effect of operational conditions such as initial PPS and AB25 concentrations and initial solution pH on the degradation of AB25 was studied. Additionally, the influence of salts addition on the degradation of the dye was examined. Besides, degradation experiments were conducted using a natural mineral water instead of deionized water to check the applicability of UV/PPS oxidation system in a real matrix. It was found that the degradation rate of AB25 was improved significantly by the UV/PPS process compared with UV irradiation alone. The photolytic decay of PPS in water was evidenced spectrophotometrically, showing that SO4 is the primarily radical involved in the degradation of AB25. The degradation rate increased considerably with increasing PPS concentration up to 400 mg L–1, above which further increase enhanced insignificantly the abatement of the dye. Correspondingly, the degradation rate increased significantly with the increase of the initial dye concentration. Acidic condition (pH 2) offered the best conversion rate and the degradation decreased with pH increase in the interval 2–10. Addition of NaCl and Na2SO4 had practically no significant effect on the elimination of the pollutant. The degradation of AB25 was more effective in natural mineral water in the absence and presence of PPS, making UV/PPS an efficient treatment technique for the elimination of organic pollutants from natural waters.
Cardiovascular Disorders in Autoimmune Disease Mini Review

Received date : May 13, 2019; Accepted date : June 24, 2019; Published date : June 27, 2019. Citation : Delcio G Silva Junior. (2019) Cardiovascular Disorders in Autoimmune Disease. J.Clinical Cardiology and Cardiovascular Interventions 2(1): DOI: 10.31579/26410419/015 Copyright : © 2019 Delcio G Silva Junior. This is an open-access article distributed under the terms of The Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Haematology and Serum Chemistry of Local Grower Turkeys Fed Diets Containing Samsorg17 and ICSV400 Varieties of Sorghum

Turkeys are of considerable economic and social significance to the traditional life of Nigerians but its production has been hampered by high cost of feedstuff. This study was carried out to determine the haematological parameters and serum chemistry of local grower turkeys fed Samsorg 17 and ICSV 400 sorghum varieties as replacement for higher cost maize in their diet. One hundred six weeks old grower turkeys were divided into nine groups of three replicates each on sex and weight equalization basis. The groups were randomly assigned to nine experimental turkey grower diets containing 0, 25, 50, 75 and 100% replacement level of maize with each of samsorg17 and ICSV400 varieties of sorghum. The feeding trial lasted 42 days. Six turkeys were randomly selected on trial day 38, blood samples were collected for haematological and serum biochemical analysis. RBC, HB, WBC and PCV were determined while MCHC, MCH AND MCV were calculated using appropriate formulae. Serum protein (albumin and globulin), sugar, cholesterol, urea, minerals and enzymes were also determined. The result indicated that only WBC count was higher and the serum sugar and creatinine was lower than normal range. The ALP level declined while SGPT increased with increasing dietary sorghum but SGOT followed no pattern. There was no observed adverse effect on the blood parameters of experimental grower turkeys fed these sorghum varieties and is therefore recommended as replacement for maize in their diets.
Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching

Federated learning (FL) is a popular paradigm for private and collaborative model training on the edge. In centralized FL, the parameters of a global architecture (such as a deep neural network) are maintained and distributed by a central server/controller to clients who transmit model updates (gradients) back to the server based on local optimization. While many efforts have focused on reducing the communication complexity of gradient transmission, the vast majority of compression-based algorithms assume that each participating client is able to download and train the current and full set of parameters, which may not be a practical assumption depending on the resource constraints of smaller clients such as mobile devices. In this work, we propose a simple yet effective novel algorithm, Comfetch, which allows clients to train large networks using reduced representations of the global architecture via the count sketch, which reduces local computational and memory costs along with bi-directional communication complexity. We provide a nonconvex convergence guarantee and experimentally demonstrate that it is possible to learn large models, such as a deep convolutional network, through federated training on their sketched counterparts. The resulting global models exhibit competitive test accuracy over CIFAR10/100 classification when compared against un-compressed model training.
Void Replenishment: How Voids Accrete Matter Over Cosmic History

Cosmic voids are underdense regions filling up most of the volume in the universe. They are expected to emerge in regions comprising negative initial density fluctuations, and subsequently expand as the matter around them collapses and forms walls, filaments, and clusters. We report results from the analysis of a cosmological simulation specially designed to accurately describe low-density regions, such as cosmic voids. Contrary to the common expectation, we find that voids also experience significant mass inflows over cosmic history. On average, 10% of the mass of voids in the sample at z ∼ 0 is accreted from overdense regions, reaching values beyond 35% for a significant fraction of voids. More than half of the mass entering the voids lingers on periods of time ∼10 Gyr well inside them, reaching inner radii. This would imply that part of the gas lying inside voids at a given time proceeds from overdense regions (e.g., clusters or filaments), where it could have been preprocessed, thus challenging the scenario of galaxy formation in voids, and dissenting from the idea of them being pristine environments.
Adaptive Ridge-Penalized Functional Local Linear Regression

We introduce an original method of multidimensional ridge penalization in functional local linear regressions. The nonparametric regression of functional data is extended from its multivariate counterpart, and is known to be sensitive to the choice of J , where J is the dimension of the projection subspace of the data. Under multivariate setting, a roughness penalty is helpful for variance reduction. However, among the limited works covering roughness penalty under the functional setting, most only use a single scalar for tuning. Our new approach proposes a class of data-adaptive ridge penalties, meaning that the model automatically adjusts the structure of the penalty according to the data sets. This structure has J free parameters and enables a quadratic programming search for optimal tuning parameters that minimize the estimated mean squared error (MSE) of prediction, and is capable of applying different roughness penalty levels to each of the J basis. The strength of the method in prediction accuracy and variance reduction with finite data is demonstrated through multiple simulation scenarios and two real-data examples. Its asymptotic performance is proved and compared to the unpenalized functional local linear regressions.
A Multiparameter Degeneracy in Microlensing Events with Extreme Finite Source Effects

For microlenses with sufficiently low mass, the angular radius of the source star can be much larger than the angular Einstein ring radius of the lens. For such extreme finite source effect (EFSE) events, finite source effects dominate throughout the duration of the event. Here, we demonstrate and explore a continuous degeneracy between multiple parameters of such EFSE events. The first component in the degeneracy arises from the fact that the directly observable peak change of the flux depends on both the ratio of the angular source radius to the angular Einstein ring radius and the fraction of the baseline flux that is attributable to the lensed source star. The second component arises because the directly observable duration of the event depends on both the impact parameter of the event and the relative lens-source proper motion. These two pairwise degeneracies become coupled when the detailed morphology of the light curve is considered, especially when including a limb-darkening profile of the source star. We derive these degeneracies mathematically through analytic approximations and investigate them further numerically with no approximations. We explore the likely physical situations in which these mathematical degeneracies may be realized and potentially broken. As more and more low-mass lensing events (with ever decreasing Einstein ring radii) are detected with improving precision and increasing cadence from microlensing surveys, one can expect that more of these EFSE events will be discovered. In particular, the detection of EFSE microlensing events could increase dramatically with the Roman Space Telescope Galactic Bulge Time Domain Survey.
Unified Fair Allocation of Goods and Chores via Copies

We consider fair allocation of indivisible items in a model with goods, chores, and copies, as a unified framework for studying: (1) the existence of EFX and other solution concepts for goods with copies; (2) the existence of EFX and other solution concepts for chores. We establish a tight relation between these issues via two conceptual contributions: First, a refinement of envy-based fairness notions that we term envy without commons (denoted EFXWC when applied to EFX). Second, a formal duality theorem relating the existence of a host of (refined) fair allocation concepts for copies to their existence for chores. We demonstrate the usefulness of our duality result by using it to characterize the existence of EFX for chores through the dual environment, as well as to prove EFX existence in the special case of leveled preferences over the chores. We further study the hierarchy among envy-freeness notions without commons and their α-MMS guarantees, showing, for example, that any EFXWC allocation guarantees at least \(\frac{4}{11}\) -MMS for goods with copies.
Focus on Impact: Indoor Exploration with Intrinsic Motivation

Exploration of indoor environments has recently experienced a significant interest, also thanks to the introduction of deep neural agents built in a hierarchical fashion and trained with Deep Reinforcement Learning (DRL) on simulated environments. Current state-of-the-art methods employ a dense extrinsic reward that requires the complete a priori knowledge of the layout of the training environment to learn an effective exploration policy. However, such information is expensive to gather in terms of time and resources. In this work, we propose to train the model with a purely intrinsic reward signal to guide exploration, which is based on the impact of the robot's actions on its internal representation of the environment. So far, impact-based rewards have been employed for simple tasks and in procedurally generated synthetic environments with countable states. Since the number of states observable by the agent in realistic indoor environments is non-countable, we include a neural-based density model and replace the traditional count-based regularization with an estimated pseudo-count of previously visited states. The proposed exploration approach outperforms DRL-based competitors relying on intrinsic rewards and surpasses the agents trained with a dense extrinsic reward computed with the environment layouts. We also show that a robot equipped with the proposed approach seamlessly adapts to point-goal navigation and real-world deployment.
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service

Off-policy evaluation (OPE) is the method that attempts to estimate the performance of decision making policies using historical data generated by different policies without conducting costly online A/B tests [1, 2]. Accurate OPE is essential in domains such as healthcare, marketing or recommender systems to avoid deploying poor performing policies, as such policies may hart human lives or destroy the user experience. Thus, many OPE methods with theoretical backgrounds have been proposed, including Direct Method (DM), Inverse Probability Weighting (IPW), and Doubly Robust (DR). One emerging challenge with this trend is that a suitable estimator can be different for each application setting. For example, DM has low variance but has a large bias, and thus, performs better in small sample settings. On the other hand, IPW has a low bias but has a large variance, and thus reveals better performance in large sample settings. It is often unknown for practitioners which estimator to use for their specific applications and purposes. To find out a suitable estimator among many candidates, we use a data-driven estimator selection procedure for off-policy policy performance estimators as a practical solution. As proof of concept, we use our procedure to select the best estimator to evaluate coupon treatment policies on a real-world online content delivery service. In the experiment, we first observe that a suitable estimator might change with different definitions of the outcome variable, and thus the accurate estimator selection is critical in real-world applications of OPE. Then, we demonstrate that, by utilizing our estimator selection procedure, we can easily find out suitable estimators for each purpose.We believe that our estimator selection procedure and case study help practitioners identify the best OPE method for their environments.
Mass Segmentation in Automated 3-D Breast Ultrasound Using Dual-Path U-net

Automated 3-D breast ultrasound (ABUS) is a newfound system for breast screening that has been proposed as a supplementary modality to mammography for breast cancer detection. While ABUS has better performance in dense breasts, reading ABUS images is exhausting and time-consuming. So, a computer-aided detection system is necessary for interpretation of these images. Mass segmentation plays a vital role in the computer-aided detection systems and it affects the overall performance. Mass segmentation is a challenging task because of the large variety in size, shape, and texture of masses. Moreover, an imbalanced dataset makes segmentation harder. A novel mass segmentation approach based on deep learning is introduced in this paper. The deep network that is used in this study for image segmentation is inspired by U-net, which has been used broadly for dense segmentation in recent years. The system's performance was determined using a dataset of 50 masses including 38 malign and 12 benign lesions. The proposed segmentation method attained a mean Dice of 0.82 which outperformed a two-stage supervised edge-based method with a mean Dice of 0.74 and an adaptive region growing method with a mean Dice of 0.65.
Semantic Snapping for Guided Multi-View Visualization Design

Visual information displays are typically composed of multiple visualizations that are used to facilitate an understanding of the underlying data. A common example are dashboards, which are frequently used in domains such as finance, process monitoring and business intelligence. However, users may not be aware of existing guidelines and lack expert design knowledge when composing such multi-view visualizations. In this paper, we present semantic snapping, an approach to help non-expert users design effective multi-view visualizations from sets of pre-existing views. When a particular view is placed on a canvas, it is “aligned” with the remaining views-not with respect to its geometric layout, but based on aspects of the visual encoding itself, such as how data dimensions are mapped to channels. Our method uses an on-the-fly procedure to detect and suggest resolutions for conflicting, misleading, or ambiguous designs, as well as to provide suggestions for alternative presentations. With this approach, users can be guided to avoid common pitfalls encountered when composing visualizations. Our provided examples and case studies demonstrate the usefulness and validity of our approach.
Compilation of Aggregates in ASP

Answer Set Programming (ASP) is a well-known problem-solving formalism in computational logic. Nowadays, ASP is used in many real world scenarios thanks to ASP solvers. Standard evaluation of ASP programs suffers from an intrinsic limitation, knows as Grounding Bottleneck, due to the grounding of some rules that could fit all the available memory. As a result, there exist instances of real world problems that are untractable using the standard Ground and Solve approach. In order to tackle this problem, different strategies have been proposed. Among them we focus on a recent approach based on compilation of problematic constraints as propagators, which revealed to be very promising but is currently limited to constraints without aggregates. Since aggregates are widely used in ASP, in this paper we extend such an approach also to constraints containing aggregates. Good results, that proof the effectiveness of the proposed approach, have been reached.