Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrinsic clearance rate of O-desmethyltramadol (M1) by glucuronide conjugation and phase I metabolism by feline, canine and common brush-tailed possum microsomes

Abstract Quantitative aspects of in vitro phase II glucuronidative metabolism of O-desmethyltramadol (O-DSMT or M1), the active metabolite of the analgesic drug tramadol, by feline, canine and common brush-tailed possum hepatic microsomes are described. Whilst previous studies have focused on the phase I conversion of tramadol to M1, this is the first report in which the phase II glucuronidative metabolic pathway of M1 has been isolated by an in vitro comparative species study. Using the substrate depletion method, microsomal phase II glucuronidative in vitro intrinsic clearance (Clint) of M1 was determined. The in vitro Clint (mean ± SD) by pooled common brush-tailed possum microsomes was 9.9 ± 1.7 μL/min/mg microsomal protein whereas the in vitro Clint by pooled canine microsomes was 1.9 ± 0.07 μL/min/mg microsomal protein. The rate of M1 depletion by feline microsomes, as measured solely by high pressure liquid chromatography, was too slow to determine. Liquid chromatography-mass spectrometry identified O-DSMT glucuronide in samples generated from all three species' microsomes, although the amount detected under the feline condition was minimal. This study indicates that M1 likely undergoes in vitro phase II glucuronidation by canine and common brush-tailed possum microsomes and, to a minor extent, by feline microsomes. The rate of depletion of M1 by phase I metabolism was also undertaken. When incubated with phase I co-factors and common brush-tailed possum microsomes or canine microsomes, M1 had an in vitro Clint of 47.6 and 22.8 μL/min/mg microsomal protein, respectively. However, due to a lack of CYP2B-like activity in the feline liver, unsurprisingly, M1 did not deplete when incubated with feline microsomes. Consequently, major M1 elimination pathways, using feline microsomes, were not determined. ”
Mitochondrial Mass Assessment in a Selected Cell Line under Different Metabolic Conditions

Changes of quantity and/or morphology of cell mitochondria are often associated with metabolic modulation, pathology, and apoptosis. Exogenous fluorescent probes used to investigate changes in mitochondrial content and dynamics are strongly dependent, for their internalization, on the mitochondrial membrane potential and composition, thus limiting the reliability of measurements. To overcome this limitation, genetically encoded recombinant fluorescent proteins, targeted to different cellular districts, were used as reporters. Here, we explored the potential use of mitochondrially targeted red fluorescent probe (mtRFP) to quantify, by flow cytometry, mitochondrial mass changes in cells exposed to different experimental conditions. We first demonstrated that the mtRFP fluorescence intensity is stable during cell culture and it is related with the citrate synthase activity, an established marker of the mitochondrial mass. Incidentally, the expression of mtRFP inside mitochondria did not alter the oxygen consumption rate under both state 3 and 4 respiration conditions. In addition, using this method, we showed for the first time that different inducers of mitochondrial mass change, such as hypoxia exposure or resveratrol treatment of cells, could be consistently detected. We suggest that transfection and selection of stable clones expressing mtRFP is a reliable method to monitor mitochondrial mass changes, particularly when pathophysiological or experimental conditions change ΔΨm, as it occurs during mitochondrial uncoupling or hypoxia/anoxia conditions.
Associations between Source-Specific Particulate Matter and Respiratory Infections in New York State Adults

The response of respiratory infections to source-specific particulate matter (PM) is an area of active research. Using source-specific PM2.5 concentrations at six urban sites in New York State, a case-crossover design, and conditional logistic regression, we examined the association between source-specific PM and the rate of hospitalizations and emergency department (ED) visits for influenza or culture-negative pneumonia from 2005 to 2016. There were at most N = 14 764 influenza hospitalizations, N = 57 522 influenza ED visits, N = 274 226 culture-negative pneumonia hospitalizations, and N = 113 997 culture-negative pneumonia ED visits included in our analyses. We separately estimated the rate of respiratory infection associated with increased concentrations of source-specific PM2.5, including secondary sulfate (SS), secondary nitrate (SN), biomass burning (BB), pyrolyzed organic carbon (OP), road dust (RD), residual oil (RO), diesel (DIE), and spark ignition vehicle emissions (GAS). Increased rates of ED visits for influenza were associated with interquartile range increases in concentrations of GAS (excess rate [ER] = 9.2%; 95% CI: 4.3%, 14.3%) and DIE (ER = 3.9%; 95% CI: 1.1%, 6.8%) for lag days 0–3. There were similar associations between BB, SS, OP, and RO, and ED visits or hospitalizations for influenza, but not culture-negative pneumonia hospitalizations or ED visits. Short-term increases in PM2.5 from traffic and other combustion sources appear to be a potential risk factor for increased rates of influenza hospitalizations and ED visits.
The effect of pelvic tilt on three‐dimensional coverage of the femoral head: A computational simulation study using patient‐specific anatomy

Improved understanding of how three‐dimensional (3D) femoral head coverage changes as the pelvic sagittal inclination (PSI) is altered would advance clinical diagnosis of hip pathoanatomy. Herein, we applied computer modeling of 3D computed tomography reconstructions of the pelvis and proximal femur to quantify relationships between the PSI and regional 3D femoral head coverage. Eleven healthy, young adult participants with typically developed hip anatomy were analyzed. The orientation of the pelvis was altered to define a PSI of −30° to 30° at 1° increments. Hip adduction and rotation were fixed in a standing position, which was measured by direct in vivo imaging of the pelvis and femur bones using dual fluoroscopy. Femoral head coverage was quantified in the anterior, superior, posterior, and inferior regions for each PSI position. Change in coverage was largest in the anterior region (29.8%) and smallest in the superior region (6.5%). Coverage increased linearly in the anterior region as the PSI increased, while a linear decrease was found in the posterior region and the inferior region (all p < .001). The slopes of the regression line for these regions were 0.513, −0.316, and −0.255, respectively. For the superior region, coverage increased when the PSI was altered from −30° to 5° and decreased when the PSI was larger than 5°. Overall, a 1° increase in PSI resulted in an increase of 0.5% in anterior coverage and a decrease of 0.3% in posterior coverage. Our findings provide baseline data that improve understanding of the effect of PSI on femoral coverage.
Sensitivity of transcription factors to DNA methylation

Abstract Dynamic binding of transcription factors (TFs) to regulatory elements controls transcriptional states throughout organism development. Epigenetics modifications, such as DNA methylation mostly within cytosine-guanine dinucleotides (CpGs), have the potential to modulate TF binding to DNA. Although DNA methylation has long been thought to repress TF binding, a more recent model proposes that TF binding can also inhibit DNA methylation. Here, we review the possible scenarios by which DNA methylation and TF binding affect each other. Further in vivo experiments will be required to generalize these models.
A Haptic Model of Entanglement, Gauge Symmetries and Minimal Interaction Based on Knot Theory

The Heegaard splitting of S U ( 2 ) is a particularly useful representation for quantum phases of spin j-representation arising in the mapping S 1 → S 3, which can be related to ( 2 j , 2 ) torus knots in Hilbert space. We show that transitions to homotopically equivalent knots can be associated with gauge invariance, and that the same mechanism is at the heart of quantum entanglement. In other words, (minimal) interaction causes entanglement. Particle creation is related to cuts in the knot structure. We show that inner twists can be associated with operations with the quaternions ( I , J , K ), which are crucial to understand the Hopf mapping S 3 → S 2. We discuss the relationship between observables on the Bloch sphere S 2, and knots with inner twists in Hilbert space. As applications, we discuss selection rules in atomic physics, and the status of virtual particles arising in Feynman diagrams. Using a simple paper strip model revealing the knot structure of quantum phases in Hilbert space including inner twists, a h a p t i c model of entanglement and gauge symmetries is proposed, which may also be valid for physics education.
Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements

<p><span>In the coming years, Artificial Intelligence (AI), for which Deep Learning (DL) is an essential component, is expected to transform society in a way that is compared to the introduction of electricity or the introduction of the internet. The high expectations are founded on the many impressive results of recent DL studies for AI tasks (e.g. computer vision, text translation, image or text generation.). Also for weather and climate observations, a large potential for </span><span>AI</span><span> application exists. </span></p><p><span>We present the results of the recent paper [Moraux et al, 2019], which is one of the first demonstrations of the application </span><span>of </span><span>cutting edge deep learning technique</span><span>s</span><span> to a practical weather observation problem. We developed a multiscale encoder-decoder convolutional neural network using the three most relevant SEVIRI/MSG spectral images at 8.7, 10.8 and 12.0 micron and in situ rain gauge measurements as input. The network is trained to reproduce precipitation measured by rain gauges in Belgium, the Netherlands and Germany. Precipitating pixels are detected with a POD of 0.75 and a FAR of 0.3. Instantaneous precipitation rate is estimated with a RMSE of 1.6 mm/h. </span></p><p>&#160;</p><p><span>Reference:</span></p><p><span>[Moraux et al, 2019] Moraux, A. ; Dewitte, S. ; Cornelis, B. ; Munteanu, A. Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements. </span><em><span>Remote Sens. </span></em> <span><strong>2019</strong></span><span>, </span><em><span>11</span></em><span>, 2463. </span></p>
Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. Road extraction base on high-resolution remote sensing images has become a hot topic. Presently, most of the researches are based on traditional machine learning algorithms, which are complex and computational because of impervious surfaces such as roads and buildings that are discernible in the images. Given the above problems, we propose a new method to extract the road network from remote sensing images using a DenseUNet model with few parameters and robust characteristics. DenseUNet consists of dense connection units and skips connections, which strengthens the fusion of different scales by connections at various network layers. The performance of the advanced method is validated on two datasets of high-resolution images by comparison with three classical semantic segmentation methods. The experimental results show that the method can be used for road extraction in complex scenes.
Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients

The image is the best information carrier in the current digital era and the easiest to manipulate. Image manipulation causes the integrity of this information carrier to be ambiguous. The image splicing technique is commonly used to manipulate images by fusing different regions in one image. Over the last decade, it has been confirmed that various structures in science and engineering can be demonstrated more precisely by fractional calculus using integrals or derivative operators. Many fractional-order-based techniques have been used in the image-processing field. Recently, a new specific fractional calculus, called conformable calculus, was delivered. Herein, we employ the combination of conformable focus measures (CFMs), and focus measure operators (FMOs) in obtaining redundant discrete wavelet transform (RDWT) coefficients for improving the image splicing forgery detection. The process of image splicing disorders the content of tampered image and causes abnormality in the image features. The spliced region’s boundaries are usually blurring to avoid detection. To make use of the blurred information, both CFMs and FMOs are used to calculate the degree of blurring of the tampered region’s boundaries for image splicing detection. The two public image datasets IFS-TC and CASIA TIDE V2 are used for evaluation of the proposed method. The obtained results of the proposed method achieved accuracy rate 98.30% for Cb channel on IFS-TC image dataset and 98.60% of the Cb channel on CASIA TIDE V2 with 24-D feature vector. The proposed method exhibited superior results compared with other image splicing detection methods.
Meaning and Well-Being

The topic of meaning in life is coming to the fore in well-being research. From an arcane existential idea to a cornerstone of wellbeing, the path from obscurity to recognition was established through empirical research. This chapter summarizes the development of theory and of measurement in meaning scholarship over the past 70 years, and highlights replicated research results. Among the relationships that have been found repeatedly in the literature are positive correlations between meaning and happiness, life satisfaction, positive emotions, hope, self-esteem, autonomy, positive relationships with others, competence, extraversion, conscientiousness, health, and longevity. Negative correlations have been replicated between meaning and negative emotions, depression, anxiety, stress, hopelessness, neuroticism, substance use problems, and suicidality. The body of evidence regarding meaning is large and growing quickly, and appears to reliably demonstrate the importance of meaning to human wellbeing and flourishing
Differentially Private Data Sharing: Sharing Models versus Sharing Data

In this paper, we study two different approaches to enable data sharing for learning tasks while preserving data privacy. The first approach is to share representation learning models with multiple parties, for whichwe choose to use adifferentiallyprivate autoencoderbased generative model (DP-AuGM). The second approach is to share generated data with multiple parties through generative models, for which we choose to use a differentially private variational autoencoder-based generative model (DP-VaeGM). To achieve differential privacy, we train both models by adding differential privacy noise to the gradient. We evaluate the performance of our two proposed approaches across various differential privacy budgets. We also present the robustness of our two proposed approaches against model inversion attacks [15], membership inference attacks [32], and generative adversarial network (GAN) based attacks against collaborative deep learning [20] only in the extended version of this paper available at https://arxiv.org/pdf/1812.02274. pdf.
The Saving Glut of the Rich and the Rise in Household Debt

Rising income inequality since the 1980s in the United States has generated a substantial increase in saving by the top of the income distribution, which we call the saving glut of the rich. The saving glut of the rich has been as large as the global saving glut, and it has not been associated with an increase in investment. Instead, the saving glut of the rich has been linked to the substantial dissaving and large accumulation of debt by the non-rich. Analysis using variation across states shows that the rise in top income shares can explain almost all of the accumulation of household debt held as a financial asset by the household sector. Since the Great Recession, the saving glut of the rich has been financing government deficits to a greater degree.
Dialect Text Normalization to Normative Standard Finnish

We compare different LSTMs and transformer models in terms of their effectiveness in normalizing dialectal Finnish into the normative standard Finnish. As dialect is the common way of communication for people online in Finnish, such a normalization is a necessary step to improve the accuracy of the existing Finnish NLP tools that are tailored for normative Finnish text. We work on a corpus consisting of dialectal data of 23 distinct Finnish dialects. The best functioning BRNN approach lowers the initial word error rate of the corpus from 52.89 to 5.73.
DNS Security: Now and The Future

Domain Name System (DNS) is an essential Internet subsystem for binding domain names and various resources including IP addresses. The specification of DNS, however, is not formally defined on documentations, and the operators have to learn the system mostly from the behavior of running implementations. The operators also have to have a high level of expertise to understand the implementation-dependent issues in details for maintaining the interoperability of DNS programs. This expertise-dependent operation makes DNS fragile and prone to security attacks. In this paper, we analyze the emerging DNS security issues from the operational points of view, and show possible countermeasures to solve the issues.
Impact of Government Bailout on Banks’ Cost of Equity: Evidence from the Financial Bailout of 2008-2009 (March 23, 2015)

We evaluate the effect of the government bailout during the 2008-2009 financial crisis on the cost of equity of 227 publicly-listed banks that received the funding. We find that the increased liquidity injected by the government bailout to the banking industry has reduced the cost of equity in the industry. We also document the moderating effect of institutional ownership on the impact of government bailout on banks’ cost of equity; higher institutional investor shareholding contributes to reduced cost of equity, especially for the firms dominated by domestic and grey institutional investors. Our findings have important implications for the assessment of government bailout programs and investment management practices such as portfolio allocation and performance evaluation.
The liquid nucleome – phase transitions in the nucleus at a glance

ABSTRACT Cells organize membrane-less internal compartments through a process called liquid–liquid phase separation (LLPS) to create chemically distinct compartments, referred to as condensates, which emerge from interactions among biological macromolecules. These condensates include various cytoplasmic structures such as P-granules and stress granules. However, an even wider array of condensates subcompartmentalize the cell nucleus, forming liquid-like structures that range from nucleoli and Cajal bodies to nuclear speckles and gems. Phase separation provides a biophysical assembly mechanism underlying this non-covalent form of fluid compartmentalization and functionalization. In this Cell Science at a Glance article and the accompanying poster, we term these phase-separated liquids that organize the nucleus the liquid nucleome; we discuss examples of biological phase transitions in the nucleus, how the cell utilizes biophysical aspects of phase separation to form and regulate condensates, and suggest interpretations for the role of phase separation in nuclear organization and function. Summary: This Cell Science at a Glance article discusses how membrane-less compartments in the nucleus are formed by phase separation and help to organize and regulate nuclear functions.
Estimating load in running: proposal of a novel simulation method

Running is a popular activity, but also an activity associated with a high incidence of injuries. In order to properly understand the aetiology of running, methods that can estimate the load of specific bodily structures, such as ligaments and muscles, in large, outdoor studies are needed. A novel simulation method based on a parametric statistical model of running and optimization of a few easily obtainable kinematic and anthropometric measures, is proposed. A candidate optimization objective was found using one set of data and the validity of the candidate objective in terms of sagital plane kinematics and kinetics was assessed in another set of data. In both data sets, there were poor agreement between the measured and the simulated kinematic and kinetic parameters. The most likely explanation for the lack of agreement was an insufficient setup of the optimization procedure, such as not using a full kinematic model in the optimization.
Milyen a jó Humor?

Kivonat. Magyar nyelvű szövegek morfológiai elemzésére elterjedten alkalmazzák a MorphoLogic Kft. által kifejlesztett Humor programot. Bár maga a program hatékony eszköznek bizonyult, a Humor adatbázisának formátumával problémák voltak a karbantarthatóság, az olvashatóság, a javíthatóság és a bővíthetőség szempontjából. Ez az előadás azt mutatja be, hogyan sikerült ezt a problémát az elemzőprogram módosítása nélkül a nyelvi adatbázis többszintűvé tételével orvosolni. Kulcsszavak: automatikus morfológiai elemzés, nyelvi adatbázis.
Uncertainty and confidence scores for sequence data

In and of themselves, confidence scores are a useful indication of the robustness and reliability of the transcriptions produced by Automatic Speech Recognition (ASR) systems. The value of these confidence scores extends beyond the transcription to various downstream and upstream applications. The work presented in this thesis begins with an overview of the ASR fundamentals and a focus on the different structures which can be obtained from the decoding process. The structures considered in this investigation are lattices, confusion networks, and one-best sequences. This leads to a discussion on traditional confidence estimation techniques such as word posterior probabilities. Following this, a discussion on deep learning techniques for sequence data, such as recurrent neural networks and attention mechanisms, is presented. Thereafter, end-to-end deep learning models, such as Bi-directional Long Short-Term Memory (BiLSTM) and LatticeRNN, are described for confidence score estimation. An extension of the LatticeRNN model to incorporate sub-word level features, such as graphemes, is proposed. This is accompanied by an algorithm for matching arcs between corresponding graph-like structures in order to obtain additional feature estimates. Empirical experimentation quantitatively demonstrates that the introduction of grapheme features can lead to improved confidence scores. Finally, a discussion of the results and suggestions for future work are presented.