Miracle-dz/UniFAR-FTUs huggingface dataset:
On the road to precision cosmology with high redshift HII galaxies

We report the first results of a programme aimed at studying the properties of high redshift galaxies with on-going massive and dominant episodes of star formation (HII galaxies). We use the $L(\mathrm{H}\beta) - \sigma$ distance estimator based on the correlation between the ionized gas velocity dispersions and Balmer emission line luminosities of HII galaxies and Giant HII regions to trace the expansion of the Universe up to $z \sim 2.33$. This approach provides an independent constraint on the equation of state of dark energy and its possible evolution with look-back time. Here we present high-dispersion (8,000 to 10,000 resolution) spectroscopy of HII galaxies at redshifts between 0.6 and 2.33, obtained at the VLT using XShooter. Using six of these HII galaxies we obtain broad constraints on the plane $\Omega_m - w_0$. The addition of 19 high-z HII galaxies from the literature improves the constraints and highlights the need for high quality emission line profiles, fluxes and reddening corrections. The 25 high-z HII galaxies plus our local compilation of 107 HII galaxies up to $z=0.16$ were used to impose further constraints. Our results are consistent with recent studies, although weaker due to the as yet small sample and low quality of the literature data of high-z HII galaxies. We show that much better and competitive constraints can be obtained using a larger sample of high redshift HII galaxies with high quality data that can be easily obtained with present facilities like KMOS at the VLT.
Heterogeneous network propagation with forward similarity integration to enhance drug–target association prediction

Identification of drug–target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug–drug and nine target–target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug–disease associations, and the cosine scores of drug–drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.
Global Detection and Analysis of Coastline-Associated Rainfall Using an Objective Pattern Recognition Technique

AbstractCoastally associated rainfall is a common feature, especially in tropical and subtropical regions. However, it has been difficult to quantify the contribution of coastal rainfall features to the overall local rainfall. The authors develop a novel technique to objectively identify precipitation associated with land–sea interaction and apply it to satellite-based rainfall estimates. The Maritime Continent, the Bight of Panama, Madagascar, and the Mediterranean are found to be regions where land–sea interactions play a crucial role in the formation of precipitation. In these regions ~40%–60% of the total rainfall can be related to coastline effects. Because of its importance for the climate system, the Maritime Continent is a region of particular interest, with high overall amounts of rainfall and large fractions resulting from land–sea interactions throughout the year. To demonstrate the utility of this study’s identification method, the authors investigate the influence of several modes of variabil.
Influence of the Local Void on measurements of the clustering dipole

In measurements of the clustering dipole from all-sky surveys, an important problem is the lack of information about galaxy distribution in the so-called zone of avoidance (ZoA). The existence of the Local Void (LV) has a systematic effect on these measurements. If the ZoA is randomly filled with mock galaxies, then the calculated acceleration of the Local Group (LG) of galaxies has a spurious component, resulting from the lack of real galaxies in the intersection of the LV with the ZoA. This component affects both the misalignment angle between the clustering dipole and the CMB dipole, and the inferred value of mean matter density Ω m . We calculate the amplitude of the spurious acceleration acting on the LG due to the LV. Its value depends on the geometry and size of the LV, as well as on its density contrast. However, under the simplest assumption of the LV being spherical and completely empty, within the linear theory this amplitude amounts only to about 45 km s -1 in units of velocity. The resulting change in the misalignment angle is smaller than 1°, and the fractional change in the deduced value of Ω m is about 5 per cent. Accounting for observationally indicated elongation of the LV and maintaining the maximizing assumption of a complete lack of galaxies inside increases these numbers only moderately. Specifically, the amplitude of the spurious acceleration rises to about 60 km s -1 , the misalignment angle remains still smaller than 1° and the fractional change in the deduced value of Ω m is enhanced to about 7 per cent. Thus, despite the overall importance of the LV for the motion of the LG, the influence of the intersection of the LV with the ZoA on measurements of the clustering dipole is found to be only a minor systematic effect.
Simulation of axisymmetric stepped surfaces with a facet

A crystal lattice with a small miscut from the plane of symmetry has a surface which consists of a series of atomic height steps separated by terraces. If the surface of this crystal is not in equilibrium with the surrounding medium, then its evolution is strongly mediated by the presence of these steps, which act as sites for attachment and detachment of diffusing adsorbed atoms ('adatoms'). In the absence of material deposition and evaporation, steps move in response to two main physical effects: line tension, which is caused by curvature of the step edge, and step-step interactions which can arise because of thermal step fluctuations, or elastic effects. This thesis focuses on axisymmetric crystals, with the result that the position of a step is uniquely described by a single scalar variable, and the step positions obey a coupled system of "step-flow" Ordinary Differential Equations (step flow ODEs). Chapter 2 of this thesis concentrates on the derivation and numerical solution of these equations, and their properties in the limits of slow adatom terrace diffusion and slow adatom attachmentdetachment. Chapter 3 focuses on the analysis carried out by Margetis, Aziz and Stone ('MAS') [78] on a Partial Differential Equation (PDE) description of surface evolution. Here, the crystal is also axisymmetric and has a single macroscopically flat region, a facet. It is discovered that the boundary condition of Step Chemical Potential Continuity, first suggested by Spohn [109] yields results that are inconsistent with the scalings predicted by the MAS analysis and with results from the step flow ODEs. The 'step drop' condition suggested by Israeli and Kandel [50] is implemented instead, and is shown to give good agreement with the results from the step flow ODEs. Chapters 4 and 5 explore the evolution of algebraic profiles: instead of starting with steps that are equally spaced, the step radii are initialized as a more general algebraic function of the height. In these two chapters, results are presented which involve approximate self-similarity of the profiles, a stability analysis of small perturbations, and quantification of decay rates. Chapter 6 of this thesis details the numerical procedure used to integrate the step flow equations. A 'multi-adaptive' time integrator is used where different time steps are taken for different components of the solution. This procedure has benefits over
Integral Points on Elliptic Curves and the Bombieri-Pila Bounds

Let C be an affine, plane, algebraic curve of degree d with integer coefficients. In 1989, Bombieri and Pila showed that if one takes a box with sides of length N then C can obtain no more than O_{d,\epsilon}(N^{1/d+\epsilon}) integer points within the box. Importantly, the implied constant makes no reference to the coefficients of the curve. Examples of certain rational curves show that this bound is tight but it has long been thought that when restricted to non-rational curves an improvement should be possible whilst maintaining the uniformity of the bound. In this paper we consider this problem restricted to elliptic curves and show that for a large family of these curves the Bombieri-Pila bounds can be improved. The techniques involved include repulsion of integer points, the theory of heights and the large sieve. As an application we prove a uniform bound for the number of rational points of bounded height on a general del Pezzo surface of degree 1.
Assumptions in quantum cryptography

Quantum cryptography uses techniques and ideas from physics and computer science. The combination of these ideas makes the security proofs of quantum cryptography a complicated task. To prove that a quantum-cryptography protocol is secure, assumptions are made about the protocol and its devices. If these assumptions are not justified in an implementation then an eavesdropper may break the security of the protocol. Therefore, security is crucially dependent on which assumptions are made and how justified the assumptions are in an implementation of the protocol. This thesis is primarily a review that analyzes and clarifies the connection between the security proofs of quantum-cryptography protocols and their experimental implementations. In particular, we focus on quantum key distribution: the task of distributing a secret random key between two parties. We provide a comprehensive introduction to several concepts: quantum mechanics using the density operator formalism, quantum cryptography, and quantum key distribution. We define security for quantum key distribution and outline several mathematical techniques that can either be used to prove security or simplify security proofs. In addition, we analyze the assumptions made in quantum cryptography and how they may or may not be justified in implementations. Along with the review, we propose a framework that decomposes quantum-key-distribution protocols and their assumptions into several classes. Protocol classes can be used to clarify which proof techniques apply to which kinds of protocols. Assumption classes can be used to specify which assumptions are justified in implementations and which could be exploited by an eavesdropper. Two contributions of the author are discussed: the security proofs of two two-way quantum-key-distribution protocols and an intuitive proof of the data-processing inequality.
Maximum Correntropy Criterion With Variable Center

Correntropy is a local similarity measure defined in kernel space, and the maximum correntropy criterion (mcc) has been successfully applied in many areas of signal processing and machine learning in recent years. The kernel function in correntropy is usually restricted to the Gaussian function with the center located at zero. However, the zero-mean Gaussian function may not be a good choice for many practical applications. In this letter, we propose an extended version of correntropy, whose center can be located at any position. Accordingly, we propose a new optimization criterion called maximum correntropy criterion with variable center (MCC-VC). We also propose an efficient approach to optimize the kernel width and center location in the MCC-VC. Simulation results of regression with linear-in-parameter (LIP) models confirm the desirable performance of the new method.
Evaluation of the Sensititre YeastOne and Etest in Comparison with CLSI M38-A2 for Antifungal Susceptibility Testing of Three Azoles, Amphotericin B, Caspofungin, and Anidulafungin, against Aspergillus fumigatus and Other Species, Using New Clinical Breakpoints and Epidemiological Cutoff Values

Aspergillosis is an invasive fungal disease associated with high mortality. Antifungal susceptibility testing (AFST) is receiving increasing consideration for managing patients, as well as for surveilling emerging drug resistance, despite having time-consuming and technically complex reference methodologies. The Sensititre YeastOne (SYO) and Etest methods are widely utilized for yeasts but have not been extensively evaluated for Aspergillus isolates. We obtained Posaconazole (POS), Voriconazole (VCZ), Itraconazole (ITC), Amphotericin B (AMB), Caspofungin (CAS), and Anidulafungin (AND) minimum inhibitory concentrations (MICs) for both the Etest (n = 330) and SYO (n = 339) methods for 106 sequenced clinical strains. For 84 A. fumigatus, we analyzed the performance of both commercial methods in comparison with the CLSI-AFST, using available cutoff values. An excellent correlation could be demonstrated for Etest-AMB and Etest-VCZ (p < 0.01). SYO-MICs of AMB, VCZ, and POS resulted in excellent essential agreement (>93%), and >80% for AMB, VCZ, and ITC Etest-MICs. High categoric agreement was found for AMB, ITC, and CAS Etest-MICs (>85%) and AMB SYO-MICs (>90%). The considerable number of major/very major errors found using Etest and SYO, possibly related to the proposed cutoffs and associated with the less time-consuming processes, support the need for the improvement of commercial methods for Aspergillus strains.
Machine Learning Quantification of Tumor-Stroma Ratio in Early Muscle Invasive Urothelial Carcinomas

Tumor-Stroma Ratio, a marker of tumor microenvironment, proved to be a reliable independent prognostic predictor in many solid tumors but it’s value in transitional carcinoma is still under research. Visual quantification of tumoral and stromal areas is possible but is time consuming and subjective. Machine learning image segmentation can improve diagnostic precision. Our research interest is to evaluate how precision pathology tools (machine learning segmentation of whole slide images) may improve quantification of the tumor-stroma ratio in early muscle invasive bladder tumors and increase histologic diagnostic prognostic value. 10 cases of pathology stage T2A bladder cancers whole slide images were carefully matched (sex, age and smoking status) with 10 cases of pT2B form the same open database (Cancer Genome Atlas Urothelial Bladder Carcinoma dataset, TCGA-BLCA). The machine learning segmentation used a trained approach and was performed under 3 labels (tumor, stroma, other). The mean tumor to stroma ratio was significant higher (tumor>stroma) in pT2A cohort (p<0.0001). Vital prognostic was different between groups: 90% of subjects were alive at 3 years after diagnostic in pT2A cohort and only 40% in pT2B cohort. Our proof-of-concept study suggest the utility of the tumor-stroma ratio in differentiating challenging diagnostics of early muscle invasive urothelial carcinoma. A larger, real world data study will have to confirm the benefits of this marker in everyday clinical settings.
F-string Solution in AdS4 X CP3 PP-wave Background

We present supergravity solution for F-string in pp wave background obtained from AdS4 X CP3 with zero flat directions.The classical solution is shown to break all space-time supersymmetries. We explicitly write down the standard as well as supernumerary Killing spinors both for the background and F-string solution.
AddaVax-Adjuvanted H5N8 Inactivated Vaccine Induces Robust Humoral Immune Response against Different Clades of H5 Viruses

Since some cases of human infections with H5N8 avian influenza virus have been reported and caused great concern in recent years, it is important to develop an effective vaccine for human use to prevent a potential H5N8 pandemic. In the present study, a vaccine candidate virus based on newly human-infected A/Astrakhan/3212/2020 H5N8 virus was constructed by reverse genetics (RG) technology. The immunogenicity of H5N8 whole virion inactivated vaccine was evaluated by various doses of vaccine antigen formulated with squalene-based adjuvant (AddaVax), aluminum hydroxide (Al(OH)3) or without adjuvant in mice. The results showed AddaVax-adjuvanted H5N8 inactivated vaccine could stimulate the mice to produce a stronger protective immune response with higher titers of IgG antibodies, hemagglutination inhibition (HI), neuraminidase inhibition (NI) and microneutralization (MN) antibodies than vaccine formulations with Al(OH)3 adjuvant or without adjuvant, and achieve a dose-sparing effect. Moreover, the AddaVax-adjuvanted formulation also exhibited potent cross-reactive response in HI antibodies against different clades of H5 viruses. A significant correlation and a curve fitting among HI, NI and MN were found by the correlation analysis to predict the protective effect of the vaccine. With these findings, our study demonstrates that AddaVax adjuvant can enhance the immunogenicity of H5N8 inactivated vaccine remarkably, and proposes an effective strategy for dealing with a potential H5N8 virus pandemic.
Deciphering the Impact of HER2 Alterations on Non-Small-Cell Lung Cancer: From Biological Mechanisms to Therapeutic Approaches

Despite the recent increase in the number of types of treatments, non-small-cell lung cancer (NSCLC) remains the major cause of death from cancer worldwide. So, there is an urgent need to develop new therapeutic strategies. The HER2 gene codes for tyrosine kinase receptor whose alterations are known to drive carcinogenesis. HER2 alterations, including amplification, mutations, and overexpression, have been mainly described in breast and gastric cancers, but up to 4% of NSCLC harbor actionable HER2 mutations. HER2-targeted therapy for NSCLC with trastuzumab, pertuzumab, and trastuzumab emtansine has failed to demonstrate an improvement in survival. Nevertheless, recent data from phase II trials have shed light on promising specific therapies for HER2-mutant NSCLC such as trastuzumab deruxtecan. Herein, we aimed to provide an updated review on the biology, epidemiology, molecular testing, and therapeutic strategies for NSCLC with HER2 molecular alterations.
Predicting Soil Nitrogen Availability for Maize Production in Brazil

Maize (Zea mays L.) is a crop widely cultivated in the state of São Paulo, and the sustainable management of nitrogen (N) nutrition is crucial to improving productivity and the environment, which calls for a reliable means of predicting potentially available soil N. A study was undertaken to evaluate and compare biological and chemical indices of potential N availability for a diverse set of 17 soils collected in the northwest region of São Paulo state. For this purpose, mineralization assays were performed at three distinct temperatures, and chemical assessments were carried out using the Illinois Soil Nitrogen Test (ISNT) and by fractionation of hydrolysable soil N. In addition, a greenhouse experiment was conducted to determine dry matter and N accumulation in the aboveground parts of maize plants. Potentially available N estimated by the incubation methods increased with increasing temperature and was strongly correlated with N uptake (r = 0.90). Hydrolysable N fractions varied widely among the soils studied and were more variable for amino sugar N than for other fractions. Potentially available N estimated by the ISNT was highly correlated with hydrolysable amino acid N and amino sugar N (r = 0.95–0.96) and also with plant dry matter accumulation (r = 0.82) and N uptake (r = 0.93). The ISNT has potential to improve fertilizer N recommendations for maize production in Brazil, provided that the test values are interpreted relative to an appropriate calibration database, planting density, and other factors affecting crop N requirement.
Green Route to Produce Silver Nanoparticles Using the Bioactive Flavonoid Quercetin as a Reducing Agent and Food Anti-Caking Agents as Stabilizers

In previous work, the isolated polyphenolic compound (PPC) quercetin was used as a reducing agent in the formation of silver nanoparticles (AgNPs), testing two types of quercetin. This PPC is a bioactive molecule that provides the electrons for the reduction of silver ions to zerovalent silver. The results demonstrated that quercetin in dietary supplement presentation was better than reagent grade quercetin for the synthesis of AgNPs, and the difference between them was that the dietary supplement had microcrystalline cellulose (CM) in its formulation. Therefore, this dietary anti-caking agent was added to the reagent-grade quercetin to validate this previously found improvement. AgNPs were obtained at neutral pH by a green route using quercetin as a reducing agent and microcrystalline cellulose and maltodextrin as stabilizing agents. In addition, different ratios were evaluated to find the optimum ratio. Ultraviolet-Visible spectroscopy (UV-VIS), Atomic Force Microscope (AFM), Z-potential, Dynamic Light Scattering (DLS) and X-ray Powder Diffraction (XRD) were used for characterization. The antibacterial activity of the S. aureus and E. coli agent was tested by the disk diffusion and microdilution method. According to the results, this green synthesis needs the use of food stabilizer when working at pH 7 to maintain AgNPs in the long term. The ideal ratio of reducing the agent:stabilizing agent was 1:2, since with this system stable AgNPs are obtained for 2 months and with improved antimicrobial activity, validating this method was ecologically and economically viable.
Slow-roll trajectories in Chromo-Natural and Gauge-flation Models, an exhaustive analysis

Inﬂation paradigm is the main framework for the earlyuniverse cosmology today which solves the old cosmo-logical puzzles such as horizon and ﬂatness problems aswell as providing a natural mechanism for generating cos-mic perturbations. Despite of its many phenomenologicalsuccesses, however, inﬂation faces some theoretical chal-lenges. Most of the successful inﬂationary models in theliterature are based on using one or more scalar ﬁeldswith a suﬃciently ﬂat potential to guarantee the slow-roll condition and leads to enough number of e-foldings.On the other hand, without providing a consistent highenergy theory setting, such extremely ﬂat potentials gen-erally is not protected against the quantum correctionsunless we highly ﬁne-tuned the system. This theoreticalconcern, the so-called η-problem, has driven many eﬀortsto avoid ﬁne-tuning in the potential. First used in Nat-ural Inﬂation [1, 2], one possible solution is consideringaxion ﬁelds as inﬂaton. In fact, since the axion ﬁeld has aperturbatively exact cosine-type potential, it would notreceived any perturbative quantum corrections. How-ever, in order to have a successful slow-roll inﬂation, theaxion decay constant f should be very large in this model(f∼ M