Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Action between Schisandrin A and Schisandrin B in Eliciting an Anti-Inflammatory Action: The Depletion of Reduced Glutathione and the Induction of an Antioxidant Response

Schisandrin A (Sch A) and schisandrin B (Sch B) are active components of Schisandrae Fructus. We compared the biochemical mechanism underlying the anti-inflammatory action of Sch A and Sch B, using cultured lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages and concanavalin (ConA)-stimulated mouse splenocytes. Pre-incubation with Sch A or Sch B produced an anti-inflammatory action in LPS-stimulated RAW264.7 cells, as evidenced by the inhibition of the pro-inflammatory c-Jun N-terminal kinases/p38 kinase/nuclear factor-κB signaling pathway as well as the suppression of various pro-inflammatory cytokines and effectors, with the extent of inhibition by Sch A being more pronounced. The greater activity of Sch A in anti-inflammatory response was associated with a greater decrease in cellular reduced glutathione (GSH) level and a greater increase in glutathione S-transferase activity than corresponding changes produced by Sch B. However, upon incubation, only Sch B resulted in the activation of the nuclear factor (erythroid-derived 2)-like factor 2 and the induction of a significant increase in the expression of thioredoxin (TRX) in RAW264.7 cells. The Sch B-induced increase in TRX expression was associated with the suppression of pro-inflammatory cytokines and effectors in LPS-stimulated macrophages. Studies in a mouse model of inflammation (carrageenan-induced paw edema) indicated that while long-term treatment with either Sch A or Sch B suppressed the extent of paw edema, only acute treatment with Sch A produced a significant degree of inhibition on the inflammatory response. Although only Sch A decreased the cellular GSH level and suppressed the release of pro-inflammatory cytokines and cell proliferation in ConA-simulated splenocytes in vitro, both Sch A and Sch B treatments, while not altering cellular GSH levels, suppressed ConA-stimulated splenocyte proliferation ex vivo. These results suggest that Sch A and Sch B may act differentially on activating GST/ depleting cellular GSH and inducing an antioxidant response involved in their anti-inflammatory actions.
Improved prediction of myelotoxicity using a patient-specific imaging dose estimate for non-marrow-targeting (90)Y-antibody therapy.

UNLABELLED For calculation of radiation dose to the marrow, standard dosimetry for radiopharmaceuticals that do not bind to the marrow includes dose contributions from radioactivity in blood and the remainder of the body. For a pure beta -emitter such as (90)Y, marrow dose is usually determined by the blood contribution. However, myelotoxicity from (90)Y-antibody therapy often correlates poorly with marrow dose estimated using the blood method. This study proposes a method to address 2 possible factors affecting marrow dose estimates. These include (a) recycled (90)Y in bone/marrow space after (90)Y-antibody has been processed in the liver and (b) use of the marrow mass of Reference Man for individual patients. METHODS Thirty-three patients with advanced non-small cell lung cancer were treated with (90)Y-anti-TAG-72 murine antibody (CC49). TAG-72 is often expressed in epithelial-derived tumors but not in normal marrow. (111)In-CC49 was used as a tracer. The marrow doses from blood were calculated on the basis of radioactivity concentrations in blood. Marrow dose in the lumbar vertebrae was estimated from images for (111)In-CC49 uptake in L2-L4. In 20 patients who had CT images, trabecular bone volumes of L2-L4 were measured from CT images to estimate patient-specific marrow mass in L2-L4. The fraction of baseline platelet counts at nadir was used as an indicator of myelotoxicity. RESULTS Marrow dose per unit injected radioactivity estimated from blood was lower than that from L2-L4 uptake values. Prediction of myelotoxicity using marrow dose estimated from blood was poorer than that using injected dose per body surface area (GBq/m(2)) (r = 0.31 vs. 0.51). Prediction was improved using marrow dose estimated from L2-L4 uptake, assuming the marrow mass of Reference Man (r = 0.67 for n = 33; r = 0.70 for n = 20). Prediction was worse if reference marrow mass was adjusted by body weight (r = 0.56 for n = 33; r = 0.63 for n = 20). Prediction was not improved if adjusted by body surface area or lean body mass but was improved if adjusted by height (r = 0.72 for n = 33; r = 0.78 for n = 20). The best prediction was obtained (r = 0.85 for n = 20) using patient-specific L2-L4 marrow mass estimated from CT. CONCLUSION Marrow dose estimated from the blood radioactivity method was not a good predictor of myelotoxicity for non-marrow-targeting (90)Y-antibody therapy. Thrombocytopenia in this group of patients correlated much better with dose estimated from lumbar vertebrae imaging and patient-specific marrow mass than with that estimated from GBq/m(2) or standard marrow dose based on blood.
Spreading of Liquids in Textile Assemblies

Scptembcr 30, 1987 experiments. The fabric was mounted on 8 15.0 x 15.0 cm2 wooden frame and placed at the top stage along with a timct and 'J, standard a m (5.0 crn diametct). The cell was covered and the fabric W;LS lell overnight. A measured amount (0.05 tO0.20 ml) o f n d m n e ws introduccd onto the fabric through a sealable orifice by a micropipclt. The arm ofthe SPMding liquid, the timct, and the stmhrd t w e recorded simultaDcar Sit:
Negative autoregulation matches production and demand in synthetic transcriptional networks

We propose a negative feedback architecture that regulates activity of artificial genes, or “genelets”, to meet their output downstream demand, achieving robustness with respect to uncertain open-loop output production rates. In particular, we consider the case where the outputs of two genelets interact to form a single assembled product. We show with analysis and experiments that negative autoregulation matches the production and demand of the outputs: the magnitude of the regulatory signal is proportional to the “error” between the circuit output concentration and its actual demand. This two-device system is experimentally implemented using in vitro transcriptional networks, where reactions are systematically designed by optimizing nucleic acid sequences with publicly available software packages. We build a predictive ordinary differential equation (ODE) model that captures the dynamics of the system, and can be used to numerically assess the scalability of this architecture to larger sets of interconnected genes. Finally, with numerical simulations we contrast our negative autoregulation scheme with a cross-activation architecture, which is less scalable and results in slower response times.
Primate origin of the cell-derived sequences of simian sarcoma virus

We sought to identify the species of origin of the cell-derived (sis) sequences of simian sarcoma virus. A molecular clone comprised of sis DNA detected related nucleotide sequences at low copy numbers in normal cellular DNAs of species as diverse as humans and quail. The extent of hybridization and degree of base-pair matching with sis DNA were greatest with New World primate DNAs. The thermal denaturation curve midpoints of hybrids formed between sis and woolly monkey DNAs were indistinguishable from homologous sis DNA hybrids, establishing the woolly monkey (Lagothrix spp.) as the source of sis sequences. In comparative studies, sis was shown to be more conserved among mammalian species than unique-sequence woolly monkey cellular DNA. There was no detectable homology between sis and the cell-derived sequences of other fibroblast-transforming retroviruses. These findings indicate that sis is likely to be a unique onc gene among transforming retroviruses.
Data mining for household water consumption analysis using self-organizing maps

: Household water consumption is a part of the human related water cycle that can get into the core of water resources management. Analysis of water consumption data can reveal great potentials of individualized water services planning. Data mining is the process of identifying and extracting potentially useful information from data sets. Self-Organizing Maps (SOMs) is a data mining technique that involves an unsupervised learning method to analyze, cluster, and model various types of large data sets. In this paper, it is presented how the daily water consumption of a household in Sosnowiec, Poland, can be clustered into days of the week, through some features. The features used to discretize the days of water consumption are statistic metrics and time zone consumption metrics. The time zoning is realized in two ways, the first being the typical morning, noon, afternoon, evening and night and the second considering the local working hour time zones of three main working sectors, banks, offices and shops. We use the SOM algorithm in three approaches. In each approach, we use some of the selected features. We have managed to get some clusters with specific features that divide the days of this household in weekdays and weekends.
REPRODUCTIVE BEHAVIOR IN THE CONTEMPORARY UNITED STATES ?

Can evolutionary psychology explain human behavior in postindustrial societies? Critics point out that, contrary to its prediction, wealthy men in contemporary societies do not have more children than poor men. I replicate Pérusse’s (1993) finding with a large, representative sample and demonstrate that, while they do not have more children, wealthy men nonetheless have more sex partners and copulate more frequently than poor men. They would therefore have achieved greater reproductive success in the ancestral environment without effective means of contraception.
Non-averaging Subsets and Non-vanishing Transversals

It is shown that every set ofnintegers contains a subset of size? (n1/6) in which no element is the average of two or more others. This improves a result of Abbott. It is also proved that for every? >0 and everym>m(? ) the following holds. IfA1,? ,Amaremsubsets of cardinality at leastm1+? each, then there area1? A1,? ,am? Amso that the sum of every nonempty subset of the set {a1,? ,am} is nonzero. This is nearly tight. The proofs of both theorems are similar and combine simple probabilistic methods with combinatorial and number theoretic tools.
Genetic Structure and Diversity of the Endangered Fir Tree of Lebanon (Abies cilicica Carr.): Implications for Conservation

The threatened conifer Abies cilicica currently persists in Lebanon in geographically isolated forest patches. The impact of demographic and evolutionary processes on population genetic diversity and structure were assessed using 10 nuclear microsatellite loci. All remnant 15 local populations revealed a low genetic variation but a high recent effective population size. FST-based measures of population genetic differentiation revealed a low spatial genetic structure, but Bayesian analysis of population structure identified a significant Northeast-Southwest population structure. Populations showed significant but weak isolation-by-distance, indicating non-equilibrium conditions between dispersal and genetic drift. Bayesian assignment tests detected an asymmetric Northeast-Southwest migration involving some long-distance dispersal events. We suggest that the persistence and Northeast-Southwest geographic structure of Abies cilicica in Lebanon is the result of at least two demographic processes during its recent evolutionary history: (1) recent migration to currently marginal populations and (2) local persistence through altitudinal shifts along a mountainous topography. These results might help us better understand the mechanisms involved in the species response to expected climate change.
Robot juggling: implementation of memory-based learning

Issues involved in implementing robot learning for a challenging dynamic task are explored in this article, using a case study from robot juggling. We use a memory-based local modeling approach (locally weighted regression) to represent a learned model of the task to be performed. Statistical tests are given to examine the uncertainty of a model, to optimize its prediction quality, and to deal with noisy and corrupted data. We develop an exploration algorithm that explicitly deals with prediction accuracy requirements during exploration. Using all these ingredients in combination with methods from optimal control, our robot achieves fast real-time learning of the task within 40 to 100 trials. <<ETX>>
Comparison of different color spaces for image segmentation using graph-cut

Graph-cut optimization has been successfully applied in many image segmentation tasks. Within this framework color information has been extensively used as a perceptual property of objects to segment the foreground object from background. There are different representations of color in digital images, each with special characteristics. Previous work on segmentation lacks a systematic study of which color space is better suited for image segmentation. This work applies the Graph Cut algorithm for image segmentation based on five different, widespread color spaces and evaluates their performance on public benchmark datasets. Most of the tested color spaces lead to similar results. Segmentations based on L*a*b* color space are of slightly higher or similar quality as all the other methods. In contrast, RGB-based segmentations are mostly worse than a segmentation based on any other tested color space.
LSH-Preserving Functions and Their Applications

Locality sensitive hashing (LSH) is a key algorithmic tool that is widely used both in theory and practice. An important goal in the study of LSH is to understand which similarity functions admit an LSH, that is, are LSHable. In this article, we focus on the class of transformations such that given any similarity that is LSHable, the transformed similarity will continue to be LSHable. We show a tight characterization of all such LSH-preserving transformations: they are precisely the probability generating functions, up to scaling. As a concrete application of this result, we study which set similarity measures are LSHable. We obtain a complete characterization of similarity measures between two sets A and B that are ratios of two linear functions of ∣A∩ B∣, ∣A▵B∣, ∣A∪B∣: such a measure is LSHable if and only if its corresponding distance is a metric. This result generalizes the well-known LSH for the Jaccard set similarity, namely, the minwise-independent permutations, and obtains LSHs for many set similarity measures that are used in practice. Using our main result, we obtain a similar characterization for set similarities involving radicals.
Social background and breastfeeding among American mothers.

Using 1965 National Fertility Survey data, the national trends and patterns of breastfeeding among American mothers are examined. A review of the literature finds most results of studies to be inconsistent. In this study the total sample was 4918 mothers with first births. The general conclusion is that breastfeeding has been declining since the turn of the century. 50% breastfed their first child. Black and Latin American mothers are about 7 points above the average. Northwest European (English and German) mothers had higher rates than mothers from Irish, Slavic, French or Italian backgrounds. Being a Catholic makes a mother less likely to breastfeed her first baby for most all nationality groups except Latin American. Farm mothers were much more likely to have breastfed their first baby than nonfarm mothers, 64% to 49%. The South and West geographical districts were 6-8 points above the grand mean while the Midwest and Northeast are below it. Only 40% of mothers in the Northeast breastfeed. By education, the lowest educational category (6 years or less) have the highest breastfeeding rates, 74%, which declines to high school graduates who have a low of 44%, then rises with a college education. The college educated group is the next highest category after the lowest educated group. White collar workers and service employed were the highest groups among occupations of those breastfeeding.
Shoshin Beriberi Induced by Long-Term Administration of Diuretics: A Case Report

Previous studies have suggested that diuretic therapy for heart failure may lead to thiamine deficiency due to the increased urinary thiamine excretion. Herein, we present the case of a 61-year-old man with shoshin beriberi, a fulminant form of wet beriberi, induced by long-term diuretic therapy. The patient had a history of heart failure with preserved ejection fraction and was receiving furosemide and trichlormethiazide therapy. He presented with worsening exertional dyspnea and was admitted for heart failure exacerbation. His condition failed to improve even after intensive treatment. A hemodynamic evaluation with the Swan-Ganz catheter revealed high-output heart failure with low peripheral vascular resistance. Thiamine was administered for suspected shoshin beriberi; his hemodynamic status improved dramatically within the next six hours. The serum thiamine level was below the normal range; the patient was therefore diagnosed with shoshin beriberi. The common causes of thiamine deficiency were not identified. Long-term diuretic therapy with furosemide and thiazide was thought to have played a major role in the development of thiamine deficiency. This case illustrates the importance of considering wet beriberi as a possible cause of heart failure exacerbation in patients taking diuretics, even when the common thiamine deficiency causes are not identified with history-taking.
Decoupling Temporal Dynamics for Naturalistic Affect Recognition in a Two-Stage Regression Framework

Automatic continuous affect recognition from multiple modalities is one of the most active research areas in affective computing. In addressing this regression problem, the advantages of a model, such as Support Vector Regression (SVR), or a model that can capture temporal dependencies within a predefined time window, such as Time Delay Neural Network (TDNN), Long Short-Term Memory (LSTM) or Kalman Filter (KF), have been frequently explored, but in an isolated way. The motivation is towards decoupling temporal information from its features at the semantic level, in order to exploit the slow-changing emotional property at decision level. This paper explores and proposes 2-stage regression framework where SVR, that has been regarded as the baseline approach on affective recognition task, is concatenated together with subsequent models. Extensive experiments have been carried out on a naturalistic emotion dataset, using eight modalities present in RECOLA database. The results shows the proposed framework can capture temporal information at the prediction level, and outperform state-of-theart approaches in continuous affective recognition.
Time-Tradeoff Sequences for Analyzing Discounting and Time Inconsistency

textabstractABSTRACT. This paper introduces time-tradeoff (TTO) sequences as a new tool to analyze time inconsistency and intertemporal choice. TTO sequences simplify the measurement of discount functions, requiring no assumption about utility. They also simplify the qualitative testing of time inconsistencies, and allow for quantitative measurements thereof. TTO sequences can easily be administered using only pencil and paper. They readily show which subjects are most prone to time inconsistencies. We further use them to axiomatically analyze and empirically test (quasi-)hyperbolic discount functions. An experiment demonstrates the feasibility of measuring TTO sequences. Our data falsify (quasi-)hyperbolic discount functions and call for the development of models that can accommodate increasing impatience.
Constructing Precisely Computing Networks with Biophysical Spiking Neurons

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons “recorded” from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. SIGNIFICANCE STATEMENT We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks including irregular, Poisson-like spike times, and a tight balance between excitation and inhibition. These results significantly increase the biological plausibility of the spike-based approach to network computation, and uncover how several components of biological networks may work together to efficiently carry out computation.