Miracle-dz/UniFAR-FTUs huggingface dataset:
Abundances of interstellar atoms from ultaviolet absorption lines

The equivalent widths of interstellar absorption lines of Mg II, P II, Cl I and II, Mn II, Fe II, Cu II, and Ni II, obtained in a Copernicus survey of Bohlin et al (1983) have been analyzed to yield column densities along the lines of sight. The measured depletions are clearly correlated with n sub H, the mean hydrogen column density along the line of sight. Depletions also seem to be weakly correlated with various ratios of hydrogen to extinction by dust grains and also variations of extinction with wavelength, although part of these effects are a secondary result of the correlation with n sub H. The apparent coupling of depletion to the mean density are interpreted in terms of an idealized model due to Spitzer (1985), where each element has one value of depletion in the low-density, warm, neutral gas and an enhanced, different value in cold clouds. The difference of apparent depletions for Mg, P, Cl, Mn, and Fe between warm and cold clouds averages 0.44 + or - 0.12 (rms) dex, and, after an allowance for observational errors is made, the scatter of individual depletions from the overall trend predicted by method is only about 0.10 dex. The identification of the observed apparent depletions with actual ones, while very likely, is not entirely secure, because of the possible presence of highly saturated compoents with very narrow profiles and the possible contamination of the results by H II regions.
Drug treatment strategies for paget’s disease: relieving pain and preventing progression

ABSTRACT Introduction Paget’s disease of bone (PDB) is a focal bone disorder caused by a marked dysregulation of osteoblasts and osteoclasts in basic multicellular units, leading to abnormal and disorganized deposition of collagen fibers (the so-called ‘woven bone’). Therefore, pagetic bones are increased in size, and at increased risk for bone pain, deformities, fractures, osteoarthritis, and, more rarely, neoplastic degeneration. Areas covered In this review, we revise the available information concerning the pharmacological treatment of PDB. Expert opinion PDB progresses slowly within the affected skeletal sites and, if untreated, often leads to bone overgrowth, with bone pain, deformity, and a likely increased risk of complications. Thus, the primary goal of treatment is the restoration of a normal bone turnover, in order to relieve bone pain or other symptoms and possibly prevent the complications. PDB long remained a poorly treatable disorder until the discovery of antiresorptive agents such as calcitonin first and bisphosphonates (BPs) later. With the recent development of potent intravenous BPs like zoledronate, allowing a better control of disease activity over the long term with a single infusion, has contributed to a marked improvement of the clinical management of this invalidating disorder.
3D coordinate transform model of optical images fusing vector distance information

For reducing the error of affine transform while matching the three-dimensional targets in optical images, the model of optical images matching was extended to three dimension using distance information of high characteristics in optical images (vector distance information), the three-dimensional (3D) coordinate transform was proposed. Theoretical analysis shows that when the optical imaging model was simplified to pinhole imaging model, the error of 3D coordinate transform didn’t exist, while avoiding the nonlinear problem. The amount of calculation of 3D coordinate transform was analyzed using least squares estimation and RANSAC estimation as examples; the amount of calculation of 3D coordinate transform is only four times of affine transform, twice when using RANSAC estimates. The simulation analysis of matching tracking algorithm based on SIFT feature points using 3D coordinate transform was taken by the visual simulation software VegaPrime and MATLAB, and the advantages of 3D coordinate transform has been verified.
Image size reduction and enlargement based on circular apertures

In this paper we describe a new method for image size reduction and magnification. The method is based on a concept of circular apertures. In image reduction, a circular region in the original image is mapped to a single pixel in the reduced image. The average intensity of the pixels in the circle is assigned to be the intensity of the resulting pixel. In image magnification, a single pixel in the original image is projected to a circular region in the enlarged image. Weighted averaging is used to determine the intensity values for pixels in the resulting image at places where circles overlap. We have addressed 4 basic reduction/magnification scales in the paper. For each scale we have studied two aperture sizes. Higher scales can be obtained by repeatedly applying the basic ones. We have compared the results produced by the proposed method with the results produced by the commonly used resampling/zero-order hold method and found the proposed method gives results that are superior in both visual comparison and quantitative analysis.
Wishart Random Matrices in Probabilistic Structural Mechanics

Uncertainties need to be taken into account for credible predictions of the dynamic response of complex structural systems in the high and medium frequency ranges of vibration. Such uncertainties should include uncertainties in the system parameters and those arising due to the modeling of a complex system. For most practical systems, the detailed and complete information regarding these two types of uncertainties is not available. In this paper, the Wishart random matrix model is proposed to quantify the total uncertainty in the mass, stiffness, and damping matrices when such detailed information regarding uncertainty is unavailable. Using two approaches, namely, (a) the maximum entropy approach; and (b) a matrix factorization approach, it is shown that the Wishart random matrix model is the simplest possible random matrix model for uncertainty quantification in discrete linear dynamical systems. Four possible approaches for identifying the parameters of the Wishart distribution are proposed and compared. It is shown that out of the four parameter choices, the best approach is when the mean of the inverse of the random matrices is same as the inverse of the mean of the corresponding matrix. A simple simulation algorithm is developed to implement the Wishart random matrix model in conjunction with the conventional finite-element method. The method is applied vibration of a cantilever plate with two different types of uncertainties across the frequency range. Statistics of dynamic responses obtained using the suggested Wishart random matrix model agree well with the results obtained from the direct Monte Carlo simulation.
Evolutionary Insights Into Felidae Iris Color Through Ancestral State Reconstruction

There have been almost no studies with an evolutionary perspective on eye (iris) color, outside of humans and domesticated animals. Extant members of the family Felidae have a great interspecific and intraspecific diversity of eye colors, in stark contrast to their closest relatives, all of which have only brown eyes. This makes the felids a great model to investigate the evolution of eye color in natural populations. Through machine learning cluster image analysis of publicly available photographs of all felid species, as well as a number of subspecies, five felid eye colors were identified: brown, hazel/green, yellow/beige, gray, and blue. Using phylogenetic comparative methods, the presence or absence of these colors was reconstructed on a phylogeny. Additionally, through a new color analysis method, the specific shades of the ancestors’ eyes were quantitatively reconstructed. The ancestral felid population was predicted to have brown eyed individuals, as well as a novel evolution of gray eyed individuals, the latter being a key innovation that allowed the rapid diversification of eye color seen in modern felids, including numerous gains and losses of different eye colors. It was also found that the loss of brown eyes and the gain of yellow/beige eyes is associated with an increase in the likelihood of evolving round pupils, which in turn influence the shades present in the eyes. Along with these important insights, the unique methods presented in this work are widely applicable and will facilitate future research into phylogenetic reconstruction of color beyond irises.
An Ensemble Learning Approach to perform Link Prediction on Large Scale Biomedical Knowledge Graphs for Drug Repurposing and Discovery

Generating knowledge graph embeddings (KGEs) to represent entities (nodes) and relations (edges) in large scale knowledge graph datasets has been a challenging problem in representation learning. This is primarily because the embeddings / vector representations that are required to encode the full scope of data in a large heterogeneous graph needs to have a high dimensionality. The orientation of a large number of vectors requires a lot of space which is achieved by projecting the embeddings to higher dimensions. This is not a scalable solution especially when we expect the knowledge graph to grow in size in order to incorporate more data. Any efforts to constrain the embeddings to lower number of dimensions could be problematic as insufficient space to spatially orient the large number of embeddings / vector representations within limited number of dimensions could lead to poor inferencing on downstream tasks such as link prediction which leverage these embeddings to predict the likelihood of existence of a link between two or more entities in a knowledge graph. This is especially the case with large biomedical knowledge graphs which relate several diverse entities such as genes, diseases, signaling pathways, biological functions etc. that are clinically relevant for the application of KGs to drug discovery. The size of the biomedical knowledge graphs are therefore much larger compared to typical benchmark knowledge graph datasets. This poses a huge challenge in generating embeddings / vector representations of good quality to represent the latent semantic structure of the graph. Attempts to circumvent this challenge by increasing the dimensionality of the embeddings often render hardware limitations as generating high dimensional embeddings is computationally expensive and often times infeasible. To practically deal with representing the latent structure of such large scale knowledge graphs (KGs), our work proposes an ensemble learning model in which the full knowledge graph is sampled into several smaller subgraphs and KGE models generate embeddings for each individual subgraph. The results of link prediction from the KGE models trained on each subgraph are then aggregated to generate a consolidated set of link predictions across the full knowledge graph. The experimental results demonstrated significant improvement in rank-based evaluation metrics on task specific link predictions as well as general link predictions on four open-sourced biomedical knowledge graph datasets.
Compromised beta-cell identity in type 2 diabetes

Compromised beta-cell identity is emerging as an important contributor of beta-cell dysfunction in type 2 diabetes (T2D). Several studies suggest that hyperglycemia induces the inactivation of transcription factors involved in mature beta-cell identity. More specifically, chronic hyperglycemia leads to the downregulation of PDX1 and MAFA, two important beta-cell identity transcription factors and regulators of insulin promoter activity. Regulation of these transcription factors depends on interactions between multiple signaling cascades and processes driven by complex non-linear dynamics and taking place in different cellular compartments. To better understand these non-linear dynamics, we developed an integrated mathematical model of the underlying mechanisms regulating these key transcription factors. Our model was able to reproduce experimentally measured variations in the levels of PDX1, MAFA and insulin mRNA under different glucose concentrations. We used this model to simulate scenarios that could allow to restore PDX1 and MAFA levels and therefore insulin gene expression. From these simulations, FOXO1 emerged as an important target for the restoration of beta-cell identity. Author summary Glucose regulation depends on the secretion of insulin by beta-cells and uptake of glucose by the peripheral cells mediated by the action of insulin. In type 2 diabetes both aspects can be compromised. Defective insulin secretion results from compromised beta-cell function. One of the reasons behind compromised beta-cell function is that beta-cells fail to express one or more of the genes involved in insulin production and secretion and thus maintenance of beta-cell identity. The processes involved in the regulation of insulin production and secretion are complex. In this work, we are particularly interested in the role and downregulation of transcription factors, PDX1 and MAFA, which are critical regulators of insulin production, in relation with compromised beta-cell identity and function in the presence of chronic hyperglycemia. To understand better these complex processes, we use mathematical modelling which enables to generate hypotheses and simulate scenarios to extend our understanding of the mechanisms leading to compromised beta-cell function in the presence of chronic hyperglycemia. Our model and similar models can serve to identify therapeutical targets in beta-cells in order to restore their function.
Balloon observations of galactic high-energy X-ray sources

A number of X-ray sources were observed with the Naval Research Laboratory hard X-ray observatory during balloon flights on 1976 May 11 and 1977 November 24. The primary objective of the observations was to characterize the temporal and spectral behavior of the sources in the energy range from 20 to 250 keV. Special emphasis was placed on the determination of spectrum versus phase for periodic sources and on the search for spectral features such as the reported 60 keV line in the X-ray spectrum of Her X-1. Results of the observations include: evidence for possible phase-dependent spectral variations in the emission from GX 1+4; flaring in the emission from GX 301-2; detection of unmodulated X-ray emission up to approximately 35 keV from GX 304-1; and an unexpectedly hard spectrum above 20 keV from Cyg X-2.
Finite‐time fault detection for nonlinear delayed system with dynamic quantization and multiple packet dropouts

The attention of this article is mainly paid to the finite‐time fault detection (FD) problem for nonlinear delayed system with dynamic quantization and multiple packet dropouts. The probabilistic interval time‐varying delay is addressed, which might fall into two intervals with known probability. The measurement signals are quantized by a dynamic quantizer and then transmitted over communication network, where the multiple packet dropouts might occur. The main objective of the considered problem is to design a finite‐time FD filter such that the FD system is stochastically finite‐time stable (SFTS) with guaranteed H∞$$ {H}_{\infty } $$ performance in the presence of probabilistic interval time‐varying delay, dynamic quantization and multiple packet dropouts. Based on Lyapunov stability theory, sufficient criteria for the presence of the desired finite‐time FD filter are presented. Afterwards, the desired filter gain matrices are given via solving certain linear matrix inequalities (LMIs). Finally, a simulation example is used to show the applicability of the obtained finite‐time FD algorithm.
Wearable and Stretchable SEBS/CB Polymer Conductive Strand as a Piezoresistive Strain Sensor

A wearable and stretchable strain sensor with a gauge factor above 23 was prepared using a simple and effective technique. Conducting nanocomposite strands were prepared from styrene-b-(ethylene-co-butylene)-b-styrene triblock copolymer (SEBS) and carbon black (CB) through a solvent-processing method that uses a syringe pump. This novel nanocomposite preparation technique is a straightforward and cost-effective process and is reported in the literature for the first time. The work included two stages: the flexible nanocomposite preparation stage and the piezoresistive sensor stage. Depending on its molecular structure, the thermoelastic polymer SEBS is highly resilient to stress and strain. The main aim of this work is to fabricate a highly flexible and piezoresistive nanocomposite fibre/strand. Among the prepared composites, a composite corresponding to a composition just above the percolation threshold was selected to prepare the strain sensor, which exhibited good flexibility and conductivity and a large piezoresistive effect that was linearly dependent on the applied strain. The prepared nanocomposite sensor was stitched onto a sports T-shirt. Commercially available knee and elbow sleeves were also purchased, and the nanocomposite SEBS/CB strands were sewn separately on the two sleeves. The results showed a high sensitivity of the sensing element in the case of breathing activity (normal breathing, a 35% change, and deep breathing at 135%, respectively). In the case of knee and elbow movements, simultaneous measurements were performed and found that the sensor was able to detect movement cycles during walking.
Label-free detection of glycated haemoglobin in human blood using silicon-based photonic crystal nanocavity biosensor

Abstract In this paper, we describe a two-dimensional photonic crystal-based biosensor that consists of a waveguide and a nanocavity with high sensitivity. A new method is employed for increasing sensitivity of the biosensor. The simulation results show that biosensor is highly sensitive to the refractive index (RI) variations due to injected biomaterials, like glycated haemoglobin, into the sensing surface. The proposed biosensor is designed for the wavelength range of 1514.4–1896.3 nm. The sensitivity and the quality factor are calculated to be 3000 and 272.43 nm/RIU, respectively. The designed structure can detect a 0.002 change in the RI via resonant wavelength shift of 0.9 nm. The band diagram and transmission spectra are computed using plane wave expansion and finite difference time domain methods.
Substances of Natural Origin in Medicine: Plants vs. Cancer

Continuous monitoring of the population’s health is the main method of learning about disease prevalence. National and international data draw attention to the persistently high rates of cancer incidence. This necessitates the intensification of efforts aimed at developing new, more effective chemotherapeutic and chemopreventive drugs. Plants represent an invaluable source of natural substances with versatile medicinal properties. Multidirectional activities exhibited by natural substances and their ability to modulate key signaling pathways, mainly related to cancer cell death, make these substances an important research direction. This review summarizes the information regarding plant-derived chemotherapeutic drugs, including their mechanisms of action, with a special focus on selected anti-cancer drugs (paclitaxel, irinotecan) approved in clinical practice. It also presents promising plant-based drug candidates currently being tested in clinical and preclinical trials (betulinic acid, resveratrol, and roburic acid).
Design of a novel CAR T system targeting glioblastoma

Glioblastoma is considered one of the most severe malignancies in human adults. Research focusing on glioblastoma has continued for decades, with a limited number of treatments undergoing clinical trials. Recently, T cells bearing chimeric antigen receptors (CARs) have been researched intensely as a promising immunotherapeutic approach, yet dilemmas such as antigen escape, unspecific activation and immune inhibition from tumour cells are limiting their feasibility. In this work, based on the latest reports of advanced CAR T system, we designed a novel CAR T system to treat glioblastoma integrated synthetic Notch receptor, anti-p32 CAR, nuclear receptor subfamily 4A inhibition element and oxygen-dependent degradation domain. It will be activated with a gating strategy and stepwise method to promote its specificity, security and cytotoxicity, which makes it a potentially improved treatment of glioblastoma in the future.
The quasi-steady regime in critically initiated detonation waves

Experimental work is described on the initiation of spherical detonation waves, in oxyacetylene with various diluents, using an exploding wire and a lead azide pellet as sources. The transition of the spherical blast wave to a detonation, when the source energy is either above or at the critical value, is studied by three methods. Changes in wave structure are inferred from the cellular patterns obtained on smoked foils orientated radially to the flow: the wavefront velocity is derived from the Doppler frequencies and the pilot pressure variation is measured. For supercritical source energies the results obtained provide a detailed pressure history of the transition process. At critical source energy it is shown that the transverse wave structure, which is present in the initially overdriven wave, disappears when the low-velocity regime is established. The duration of the quasi-steady regime is approximately 10 mu s (or distance approximately 1 cm) before the establishment of a normal CJ wave.
Randomized Response, Statistical Disclosure Control and Misclassificatio: a Review

This paper discusses analysis of categorical data which have been misclassified and where misclassification probabilities are known. Fields where this kind of misclassification occurs are randomized response, statistical disclosure control, and classification with known sensitivity and specificity. Estimates of true frequencies are given, and adjustments to the odds ratio are discussed. Moment estimates and maximum Likelihood estimates are compared and it is proved that they are the same in the interior of the parameter space. Since moment estimators are regularly outside the parameter space, special attention is paid to the possibility of boundary solutions. An example is given.
Population-based spatial level analysis on Alzheimer’s disease mortality and potential risk factors

By conducting multivariable linear regression and geographically weighted regression (GWR) on Alzheimer's Disease mortality with possible risk factors (cholesterol, hypertension, diabetes, obesity, excessive alcohol usage, and demography), the possible trajectory, distribution, and relationships between AD mortality and risk factors along with demographics (sex, race, age, and median household income) across 50 states in the U.S. can be demonstrated. In this study, we found that median household income (β = 0, P < 0.001), age group above 65 (β = -127, P = 0.001), cholesterol check rate (β = 62, P < 0.001) to be highly correlated with AD mortality on a national level; in addition, we coupled multivariable linear regression with GWR to demonstrate potential linkages and the pattern revealed on a regional level.