Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Network Pharmacology and Bioinformatics to Explore the Effects of Dangshen (Codonopsis pilosula) Against Hepatocellular Carcinoma: Validation Based on the Active Compound Luteolin

Purpose This study aimed to explore the pharmacological mechanism of Dangshen (Codonopsis pilosula) against hepatocellular carcinoma (HCC) based on network pharmacology and bioinformatics, and to verify the anticancer effect of luteolin, the active ingredient of Codonopsis pilosula, on HCC cells. Methods The effective compounds and potential targets of Codonopsis pilosula were established using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database. The genes related to HCC were obtained through the GeneCards database. The interactive genes were imported into the Visualization and Integrated Discovery database for Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal enrichment, and the hub genes were screened out. The Cancer Genome Atlas database was used to construct a prognosis model, and the prognosis and clinicopathological correlation were analyzed. In in vitro experiments, we verified the effects of luteolin, an active compound of Codonopsis pilosula, on the proliferation, cell cycle, apoptosis and migration of HCC cells. Results A total of 21 effective compounds of Codonopsis pilosula and 98 potential downstream target genes were screened through the TCMSP database, and 1406 HCC target genes were obtained through the GeneCards database. Finally, 53 interacting genes between the two databases were obtained, among which, the 10 key node genes were CASP3, TP53, MDM2, AKT1, ESR1, BCL2L1, MCL1, HSP90AA1, CASP9, and CCND1, involving 77 typical GO terms and 72 KEGG signals. The Kaplan–Meier survival curve of the model group showed that the overall survival of the low-risk group was significantly higher than that of the high-risk group. Luteolin significantly inhibited the proliferation and migration of HCC cells, induced apoptosis, and increased the G2/M phase ratio. Mechanistically, luteolin significantly inhibited the phosphorylation of MAPK-JNK and Akt (Thr308) and subsequently led to upregulation of ESR1. Pharmacological inhibition of ESR1 with fulvestrant enhanced cell viability and migration and attenuated apoptosis. Conclusion Codonopsis pilosula has potential for clinical development due to its anti-HCC properties. Luteolin, the effective component of Codonopsis pilosula, plays anti-HCC role through AKT- or MAPK-JNK signaling mediated ESR1.
Correlations in primary spectral standards

Correlations in spectral standards between values at different wavelengths should be considered when estimating uncertainties of spectral integrals. Simple correlations are introduced through fixing an absolute value to a relative distribution over wavelengths, or through systematic errors. Such correlations are easily treated when calculating effects that depend only on the relative distribution. A much more complex situation arises when a fitting process is used as part of the primary derivation. Two examples are given: that of spectral responsivity when the quantum defect of silicon photodiodes is fitted to measured data, and that of spectral irradiance where filter radiometers are used to calibrate a lamp directly and a fitted function is used for interpolation. In both cases, correlations between spectral points are significant and varied, and while these are diluted in the transfer from the primary scales, the effects may be significant in calculating correction factors in subsequent measurement where values at different wavelengths are combined.
Influence of Microstructure on Fracture Mechanisms of the Heat-Treated AlSi10Mg Alloy Produced by Laser-Based Powder Bed Fusion

Few systematic studies on the correlation between alloy microstructure and mechanical failure of the AlSi10Mg alloy produced by laser-based powder bed fusion (L-PBF) are available in the literature. This work investigates the fracture mechanisms of the L-PBF AlSi10Mg alloy in as-built (AB) condition and after three different heat treatments (T5 (4 h at 160 °C), standard T6 (T6B) (1 h at 540 °C followed by 4 h at 160 °C), and rapid T6 (T6R) (10 min at 510 °C followed by 6 h at 160 °C)). In-situ tensile tests were conducted with scanning electron microscopy combined with electron backscattering diffraction. In all samples the crack nucleation was at defects. In AB and T5, the interconnected Si network fostered damage at low strain due to the formation of voids and the fragmentation of the Si phase. T6 heat treatment (T6B and T6R) formed a discrete globular Si morphology with less stress concentration, which delayed the void nucleation and growth in the Al matrix. The analysis empirically confirmed the higher ductility of the T6 microstructure than that of the AB and T5, highlighting the positive effects on the mechanical performance of the more homogeneous distribution of finer Si particles in T6R.
Parametric robust H/sub /spl infin// controller synthesis: comparison and convergence analysis

Our previous papers (1996, 1997) have demonstrated the effectiveness of our iterative algorithm using linear matrix inequalities (LMIs) on several parametric robust H/sub /spl infin// control designs. This paper presents two additional important components to the discussion on the behavior of the new LMI-based iterative algorithm: a comparison study between the LMI synthesis technique and the existing iterative approaches of the complex and mixed /spl mu//K/sub m/ synthesis; and a convergence analysis of this new algorithm. The results indicate that the Popov H/sub /spl infin// controller synthesis provides a viable alternative for designing real parametric robust controllers and exhibits properties similar to the D-K and D,G-K iteration of the complex and mixed /spl mu//K/sub m/ synthesis. The key potential advantage of using the LMI approach is the elimination of the curve-fitting for the D and G scaling functions.
Dynamic Effective Connectivity using Physiologically informed Dynamic Causal Model with Recurrent Units: A functional Magnetic Resonance Imaging simulation study

Functional MRI (fMRI) is an indirect reflection of neuronal activity. Using generative biophysical model of fMRI data such as Dynamic Causal Model (DCM), the underlying neuronal activities of different brain areas and their causal interactions (i.e., effective connectivity) can be calculated. Most DCM studies typically consider the effective connectivity to be static for a cognitive task within an experimental run. However, changes in experimental conditions during complex tasks such as movie-watching might result in temporal variations in the connectivity strengths. In this fMRI simulation study, we leverage state-of-the-art Physiologically informed DCM (P-DCM) along with a recurrent window approach and discretization of the equations to infer the underlying neuronal dynamics and concurrently the dynamic (time-varying) effective connectivities between various brain regions for task-based fMRI. Results from simulation studies on 3- and 10-region models showed that functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) responses and effective connectivity time-courses can be accurately predicted and distinguished from faulty graphical connectivity models representing cognitive hypotheses. In summary, we propose and validate a novel approach to determine dynamic effective connectivity between brain areas during complex cognitive tasks by combining P-DCM with recurrent units.
Signal crossing in perpendicular nanomagnetic logic

In this paper, for the first time, a magnetic signal crossing device in field-coupled logic with perpendicular magnetic anisotropy is demonstrated experimentally. A 3D arrangement is used to cross magnetic signals. One of the signals to be crossed is routed through a second functional layer and lead back to its original layer by field-interaction of magnets lying above each other. As input signals, the magnetization states “up” or “down” corresponding to the logic states “1” or “0” are injected into the crossing device and unimpeded, directed signal flow is proven by magnetic force microscopy. The field-coupling between overlying magnets is measured by magneto-optical microscopy, and the results are compared to numerical simulations being in very good agreement. The presented crossing structure will enable a flexible design of compact logic blocks and a high integration density in perpendicular nanomagnetic logic.
Bootstrap: a fast blind adaptive signal separator

A fast multidimensional adaptive algorithm, Bootstrap, is proposed for multiple signal separation. It separates multiple uncorrelated signals imposed on each other. The bootstrap adaptive algorithm, which does not require training sequences, uses an optimization criteria that is based on minimization of output signal correlations. The learning process of this algorithm is compared with that of the least mean square (LMS) algorithm for different eigenvalue spreads. It has been found from computer simulations that the Bootstrap algorithm converges much faster than the LMS algorithm. The learning process of the Bootstrap algorithm is almost independent of eigenvalue spread. <<ETX>>
A low-resolution real-time face recognition using extreme learning machine and its variants

ABSTRACT Extreme Learning Machine (ELM) is an excellent candidate for its exemplary performance as a generalized Single Layer Feed-forward Network (SLFN). In this regard, ELM has attracted widespread attention for tackling multiclass classification and regression problems with relative ease. Due to the random allocation of weights to the input and hidden layer biases, the running time spans of ELM are observed to be in seconds and agree with real-time events whilst giving a competitive performance. In this work, the suitability of ELM and its Online Sequential variant for recognition of unknown face samples is investigated under uncontrolled environments, including varying pose, variance, and illumination. The presented face recognition approach is evaluated sing four datasets from YALE, CMU, BIOID, and LFW. To track the face features of a sample face in its spatial domain with varying windows corresponding to different datasets, the Viola-Jones object detection framework is used. The face features are then extracted and evaluated using a Histogram of Oriented Gradients, which forms the dataset to be fed to ELM and OS-ELM classifiers. The running time for training the proposed face recognition approach is found to span within milliseconds to seconds with the computed time complexity of , justifying the suitability of ELM and OS-ELM classifiers for real-time face recognition applications with a high recognition rate outcome.
Bistatic bottom scattering: Model, experiments, and model/ data comparison

A model is presented for bistatic scattering from ocean sediments. It treats scattering due to both roughness of the seabed and volume inhomogeneities within the sediment. Accordingly, the scattered intensity is assumed to be a sum of two terms, one proportional to the roughness-scattering cross section and the other proportional to the volume-scattering cross section. The model is tested against data acquired as part of the Coastal Benthic Boundary Layer (CBBL) research program. As part of that program, an autonomous, circularly scanning sonar system was deployed in well-characterized regions. This sonar operated at 40 kHz, had a 5° horizontal beam, and acquired backscattering data over a 50-m radius. During part of the deployment, it operated in conjunction with a mobile receiving array so as to acquire bistatic data. The experimental apparatus and procedures are presented, and results are compared with model predictions.
Effect of Nutrient Intake, Nutritional Status, Physical Activity and Sunlight Exposure on Bone Mineral Density in Women: A Systematic Review

Osteoporosis is a condition or disease that can cause bones to become brittle and break easily. The risk of osteoporosis in women is greater than in men, which is four times greater. Bone mineral density (BMD) or bone density is the total mineral present in bone. Measurement of BMD is important to show the degree of bone mineralization associated with osteoporosis. There are factors associated with reduced bone density. Thus, this systematic review aimed to analyze several factors related to BMD in women including nutrient intake, nutritional status, physical activity, and sunlight exposure. Studies in this systematic review were gathered by the (PRISMA) guidelines and identified through PubMed, Google Scholar, and ScienceDirect databases from 2017 until 2022. A total of 17 articles passed the eligibility and were analyzed in this systematic review. Studies found the effect of nutrient intake, BMI, physical activity, and sunlight exposure on BMD in women from different age groups. So it is necessary to make eating habits with good nutritional intake and have a lifestyle that routinely carries out sufficient physical activity and gets sufficient sunlight to improve and maintain bone health until old age.
57-Year Ice Velocity Dynamics in Byrd Glacier Based on Multisource Remote Sensing Data

Long time-series glacier ice velocity reflects the local climate changes and can be used to estimate mass balance (MB) changes, which is a critical parameter for understanding of glacier–climate interactions and prediction of sea level rise. However, due to the difficulty in image matching caused by the poor quality of historical satellite images, there was insufficient glacier ice velocity data before 1999. Here, we proposed a multiple-constraint dense image matching approach for mapping historical ice velocity based on the early poor-quality images from ARGON, Landsat-1, and Landsat-4/5. We successfully applied this method to Byrd Glacier to generate its historical ice velocity maps from 1963 to 1999. Additionally, ice velocity maps of Byrd Glacier from 2000 to 2014 were generated by IMCORR software using Landsat-7 and Landsat-8 images. Combining with the ice velocity maps from the Global Land Ice Velocity Extraction from Landsat-8 dataset since 2014, we obtained the ice velocity of Byrd Glacier for 57 years. Our results showed that the glacier experienced slight fluctuations in ice velocity, which may not be due to the calving events in the studied portion of the Ross Ice Shelf or the air temperature changes, but by the activity of subglacial drainage systems. Furthermore, Byrd Glacier showed a positive MB (average rate of 2.6 ± 2.0 Gt/year) from 1963 to 2020, indicating that global climate change may have a limited impact on it.
Some results on minimum magnitude regulated response

Analytical results are obtained for the minimum peak tracking error magnitude achievable by some finite settling time control systems in response to a step reference input. The limits of these results as the settling time approaches infinity are also obtained. These represent performance bounds which apply for any finite order linear time-invariant (LTI) controller of a given configuration (i.e., one-parameter or two-parameter). The systems considered are one-parameter discrete-time single-input, single-output (SISO) where the plant has one unstable pole and one non-minimum-phase zero. The result for a two-parameter compensator for plants with one non-minimum-phase zero is presented. >
Fabrication and characterization of evanescent wave coupled PbS quantum dots fiber amplifier based on sol-gel method

In this paper, size-tunable PbS semiconductor quantum dots were synthesized by sol-gel route and a novel optical fiber amplifier was proposed. Based on fused tapered fiber coupler, the PbS quantum dots film was deposited in the couple region and evanescent wave coupled semiconductor quantum dots fiber amplifier was fabricated. Due to the thinned fiber shape, evanescent wave penetrates into outer surface. When the pumped light are propagated in the evanescent field area, the evanescent wave of pumped light is absorbed by PbS quantum dots, so the PbS quantum dots are excited from ground state to excited state. And when the signal light are propagated in the evanescent field area, the evanescent wave of signal light stimulate PbS quantum dots, the quantum dots return to ground state and release photons which have the same frequency and phase with signal light. With interaction between the evanescent wave power and the coated PbS quantum dots film, the light can be amplified. The optical amplification was obtained at 1310nm wavelength with 980nm wavelength LD as pump.
A Model for Positively Correlated Count Variables

An α‐permanental random field is briefly speaking a model for a collection of non‐negative integer valued random variables with positive associations. Though such models possess many appealing probabilistic properties, many statisticians seem unaware of α‐permanental random fields and their potential applications. The purpose of this paper is to summarize useful probabilistic results, study stochastic constructions and simulation techniques, and discuss some examples of α‐permanental random fields. This should provide a useful basis for discussing the statistical aspects in future work.
Raman spectroscopy detection of platelet for Alzheimer’s disease with predictive probabilities

Alzheimer’s disease (AD) is a common form of dementia. Early and differential diagnosis of AD has always been an arduous task for the medical expert due to the unapparent early symptoms and the currently imperfect imaging examination methods. Therefore, obtaining reliable markers with clinical diagnostic value in easily assembled samples is worthy and significant. Our previous work with laser Raman spectroscopy (LRS), in which we detected platelet samples of different ages of AD transgenic mice and non-transgenic controls, showed great effect in the diagnosis of AD. In addition, a multilayer perception network (MLP) classification method was adopted to discriminate the spectral data. However, there were disturbances, which were induced by noise from the machines and so on, in the data set; thus the MLP method had to be trained with large-scale data. In this paper, we aim to re-establish the classification models of early and advanced AD and the control group with fewer features, and apply some mechanism of noise reduction to improve the accuracy of models. An adaptive classification method based on the Gaussian process (GP) featured, with predictive probabilities, is proposed, which could tell when a data set is related to some kind of disease. Compared with MLP on the same feature set, GP showed much better performance in the experimental results. What is more, since the spectra of platelets are isolated from AD, GP has good expansibility and can be applied in diagnosis of many other similar diseases, such as Parkinson’s disease (PD). Spectral data of 4 month and 12 month AD platelets, as well as control data, were collected. With predictive probabilities, the proposed GP classification method improved the diagnostic sensitivity to nearly 100%. Samples were also collected from PD platelets as classification and comparison to the 12 month AD. The presented approach and our experiments indicate that utilization of GP with predictive probabilities in platelet LRS detection analysis turns out to be more accurate for early and differential diagnosis of AD and has a wide application prospect.
A simplified procedure for the economic design of control charts : a unified approach

A simple procedure for selecting the optimal economic design of a control chart for a single assignable cause system is presented. The procedure consists only of solving an explicit equation for h (sampling interval) in terms of n (sample size) and L (control limit factor). We conclude that our model is not only more accurate, more applicable, and more flexible, but also simpler to solve than Lorenzen and Vance's Model3.
Self-calibration of a rig with varying internal parameters and rotations

The paper describes a new method of self-calibration of a moving vision platform (a rig) when the cameras' centers relative to each other during the motion have been determined. Given the projective structure from the corresponding points across the set of images acquired by the cameras, the varying internal and external parameters can be computed using only the positions of the cameras ' centers. The method in this paper shows that the maximal generality is reached when the rig consists of 5 cameras whose centers of projection are given. Furthermore, if the only goal is to construct the Euclidean structure from a projective structure, the internal and external parameters need not be computed, and the process is linear and hence it is more convenient and stable.
U.S. public transportation ridership analysis and prediction based on COVID-19

In this paper, a research was conducted to analyse and predict the impacts of COVID-19 on public transportation ridership in the U.S. and 5 most populous cities of the U.S. (New York City, Los Angeles, Chicago, Houston, Philadelphia). The paper aims to exploit the correlation between COVID-19 and public transportation ridership in the U.S. and make the reasonable prediction by machine learning models, including ARIMA and Prophet, to help the local governments improve the rationality of their policy implementation. After correlation analyses, high level of significant and negative correlations between monthly growth rate of COVID-19 infections and monthly growth rate of public transportation ridership are decidedly validated in the total U.S., and New York City, Los Angeles, Chicago, Philadelphia, except Houston. To analyse the errors of Houston, we consult the literature and made a discussion of Influencing factors. We find that the level of public transportation in quantity and utilization is terribly low in Houston. In addition, the factors, such as the lack of planning law and estimation of urban expressways, the high level of citizens’ dependence on private cars and pride of owning cars play a considerable roll in the errors. And the impacts can be predicted to a certain extent through two forecasting models (ARIMA and Prophet), although the precision of our models is not enough to make a precise forecast due to the limitations of model tuning and model design. According to the comparison of the two models, ARIMA models' forecasting accuracy is between 6% and 10%, and Prophet's forecasting accuracy is between 8%-12%, depending on the city. Since the insufficient stationarity, periodicity, seasonality of time series, the Prophet models are hard be more refined.