Miracle-dz/UniFAR-FTUs huggingface dataset:
A Real-World Prognosis in Idiopathic Pulmonary Fibrosis: A Special Reference to the Role of Antifibrotic Agents for the Elderly

Background: Idiopathic pulmonary fibrosis (IPF) is the most common and severe form of idiopathic interstitial pneumonia, and its prevalence increases with age. In the era of pre-antifibrotic agents, the median survival time of Japanese patients with IPF is 35 months, with a 5-year survival rate in western countries ranging from 20% to 40%. The prevalence of IPF is highest in elderly patients aged ≥75 years; however, the efficacy and safety of long-term use of pirfenidone and/or nintedanib are not fully understood. Objective: This study aimed to determine the efficacy and safety of the sole use of antifibrotic agents (pirfenidone or nintendanib) for IPF in the elderly. Method: We retrospectively reviewed patients with IPF who were diagnosed and treated with either pirfenidone or nintedanib in our hospital between 2008 and 2019. We excluded patients with the subsequent use of both antifibrotic agents. We examined the survival probability and frequency of acute exacerbation, with focus on long-term use (≥1 year), elderly patients (≥75 years of age), and disease severity. Results: We identified 91 patients with IPF (male to female ratio: 63 to 28, age 42 to 90 years). The numbers of patients with disease severity classified by JRS (I/II/III/IV) and GAP stage (I/II/III) were (38/6/17/20) and (39/36/6), respectively. The survival probabilities were comparable between the elderly (n = 46) and non-elderly groups (n = 45, p = 0.877). After the initiation of antifibrotic agents, the cumulative incidence ratio of acute exacerbation of IPF was significantly lower in the early stage (GAP stage I, n = 20) than in the progressive stage of disease (GAP stages II and III, n = 20, p = 0.028). A similar trend was noted in the JRS disease severity classification (I, II vs. III, IV) (n = 27 vs. n = 13, p = 0.072). In the long-term treatment (≥1 year) group (n = 40), the survival probabilities at 2 and 5 years after treatment initiation were 89.0% and 52.4%, respectively, which did not reach the median survival rate. Conclusions: Even in elderly patients (≥75 years of age), antifibrotic agents demonstrated positive effects on survival probability and the frequency of acute exacerbation. These positive effects would be improved for earlier JRS/GAP stages or long-term use.
Multi-Satellite Detection of Long-Range Transport and Transformation of Atmospheric Emissions from the Hunga Tonga-Hunga Ha'apai Volcano

Large volumes of atmospheric pollutants injected into the troposphere and stratosphere from volcanic eruptions can exert significant influence on global climate. Through utilizing multi-satellite observations, we present a large-scale insight into the long-range transport and transformation of sulfur dioxide (SO2) emissions from the Hunga Tonga-Hunga Haʻapai eruption on 15 January 2022. We found that the transport of volcanic emissions, along with the transformation from SO2 to sulfate aerosols, lasted for two months after the Tongan eruption. The emitted volume of SO2 from the volcano eruption was approximately 183 kilotons (kt). Both satellite observation and numerical simulation results show that the SO2 and volcanic ash plumes moved westward at a rate of one thousand kilometers per day across the Pacific and Atlantic Ocean regions and that SO2 transformation in the atmosphere lasted for half a month. The transport and enhancement of aerosols is related to the conversion of SO2 to sulfate. CALIPSO lidar observations show that SO2 reached an altitude of 25–30 km and transformed into sulfate in the stratosphere after 29 January. Sulfate aerosols in the stratosphere deceased gradually with transport and fell back to the background level after two months. Our study shows that satellite observations give a good characterization of volcanic emissions, transport, and SO2-sulfate conversion, which can provide an essential constraint for climate modeling.
Mid‐summer fish behavior in a high‐latitude twilight zone

The behavior of the mesopelagic fish Benthosema glaciale was studied at 60°N in mid‐summer. We hypothesized that diel vertical migration (DVM) is constrained by short and dusk nights (surface illumination > 10−2 μmol m−2 s−1) and that individuals are active at depth during the long summer days. Submerged echosounders provided high‐resolution data throughout the water column. During the day, a part of the population ascended toward the increasing daylight. Short vertical relocations were followed by minutes of vertical inactivity. Swimming included horizontal turns and loops associated with the vertical steps. Normal DVM was initiated ~ 4 h before sunset and reflected independent individual decisions. The fish initially ascended stepwise but switched to mostly straight upwards swimming attaining 3–4 cm s−1. Their vertical speed was faster than the slow ascent of isolumes and even the deepest living fish potentially could reach upper layers shortly after sunset. However, many individuals aborted their ascent and returned to depth before the darkest time of the night, while others returned downward closer to sunrise. The daytime swimming and individual variability in diel migration behavior have implications for encounters with prey and predators in the twilight zone and the biological carbon pump. A principal conclusion is that mesopelagic fishes can modify their behavior and migration patterns to suit a wide range of changing conditions.
Domain decomposition method for crack problems with nonpenetration condition

The work deals with an iteration method for numerical solving the equilibrium problem of two-dimensional elastic body with a crack under the nonpenetration condition. The method is based on the domain decomposition and Uzawa’s algorithm. To construct an algorithm, the domain is partitioned into two subdomains whose common boundary contains the crack. In each subdomain the linear problems are solved. We use Lagrangian multipliers to couple the solutions and provide the nonpenetration condition on the crack.
Calculating the required capital of an insurance company by means of elliptical copulas

A couple of topics on calculating the required capital of an insurance company are covered in this thesis. We derive a new method in order to compare correlation matrices of aggregation methods consisting of one- or two-levels. Different from earlier time series analysis, we show that a prediction for the one-year correlation can be made with the use of frequently available data and employing time series analysis. Furthermore, a robustness analysis of the Standard Formula is performed and an estimation for the degrees of freedom parameter of a t-copula is obtained from market data.
Classical and quantum computation with small space bounds (PhD thesis)

In this thesis, we introduce a new quantum Turing machine (QTM) model that supports general quantum operators, together with its pushdown, counter, and finite automaton variants, and examine the computational power of classical and quantum machines using small space bounds in many different cases. The main contributions are summarized below. Firstly, we consider QTMs in the unbounded error setting: (i) in some cases of sublogarithmic space bounds, the class of languages recognized by QTMs is shown to be strictly larger than that of classical ones; (ii) in constant space bounds, the same result can still be obtained for restricted QTMs; (iii) the complete characterization of the class of languages recognized by realtime constant space nondeterministic QTMs is given. Secondly, we consider constant space-bounded QTMs in the bounded error setting: (i) we introduce a new type of quantum and probabilistic finite automata (QFAs and PFAs, respectively,) with a special two-way input head which is not allowed to be stationary or move to the left but has the capability to reset itself to its starting position; (ii) the computational power of this type of quantum machine is shown to be superior to that of the probabilistic machine; (iii) based on these models, two-way PFAs and two-way classical-head QFAs are shown to be more succinct than two-way nondeterministic finite automata and their one-way variants; (iv) we also introduce PFAs and QFAs with postselection with their bounded error language classes, and give many characterizations of them. Thirdly, the computational power of realtime QFAs augmented with a write-only memory is investigated by showing many simulation results for different kinds of counter automata. Finally, some lower bounds of realtime classical Turing machines in order to recognize a nonregular language are shown to be tight.
Authentication of Satellite-Based Augmentation Systems with Over-the-Air Rekeying Schemes

Here we delineate a complete satellite-based augmentation system (SBAS) authentication scheme, including over-the-air rekeying (OTAR), that uses the elliptic curve digital signature algorithm (ECDSA) and timed efficient stream loss-tolerant authentication (TESLA) without the quadrature (Q) channel. This scheme appends two new message types to the SBAS scheduler without over-burdening the message schedule. We have taken special care to ensure that our scheme (1) meets the appropriate security requirements needed to prevent and deter spoofing; (2) is compatible with existing cryptographic standards; (3) is flexible, expandable, and future-proof to different cryptographic and implementation schemes; and (4) is backward compatible with legacy receivers. The scheme accommodates a diverse set of features, including authenticating core-constellation ephemerides. We discuss the SBAS provider and receiver machine state and its startup, including its use by aircraft that traverse differing SBAS coverage areas. We tested our scheme with existing SBAS simulation and analysis tools and found that it had negligible effects on current SBAS availability and continuity requirements.
Enhanced Mechanical and Hydrophobic Antireflective Nanocoatings Fabricated on Polycarbonate Substrates by Combined Treatment of Water and HMDS Vapor

Polycarbonate (PC) with high transmittance, stable mechanical performance and environmental resistance is crucial for practical applications. In this work, we report a method for the preparation of a robust antireflective (AR) coating by a simple dip-coating process of a mixed ethanol suspension consisting of tetraethoxysilane (TEOS) base-catalyzed silica nanoparticles (SNs) and acid-catalyzed silica sol (ACSS). ACSS greatly improved the adhesion and durability of the coating, and the AR coating exhibited high transmittance and mechanical stability. Water and hexamethyldisilazane (HMDS) vapor treatment were further employed to improve the hydrophobicity of the AR coating. The as-prepared coating exhibited excellent antireflective properties, with an average transmittance of 96.06% in the wavelength range of 400 to 1000 nm, which is 7.55% higher than the bare PC substrate. After sand and water droplet impact tests, the AR coating still maintained enhanced transmittance and hydrophobicity. Our method shows a potential application for the preparation of hydrophobic AR coatings on a PC substrate.
Theoretical Foundations and Algorithms for Learning with Multiple Kernels

Kernel-based algorithms have been used with great success in a variety of machine learning applications. These include algorithms such as support vector machines for classification, kernel ridge regression, ranking algorithms, clustering algorithms, and virtually all popular dimensionality reduction algorithms. But, the choice of the kernel, which is crucial to the success of these algorithms, has been traditionally left entirely to the user. Rather than requesting the user to commit to a specific kernel, multiple kernel algorithms require the user only to specify a family of kernels. This family of kernels can be used by a learning algorithm to form a combined kernel and derive an accurate predictor. This is a problem that has attracted a lot of attention recently, both from the theoretical point of view and from the algorithmic, optimization, and application point of view. This thesis presents a number of novel theoretical and algorithmic results for learning with multiple kernels. It gives the first tight margin-based generalization bounds for learning kernels with Lp regularization. In particular, our margin bounds for L1 regularization are shown to have only a logarithmic dependency on the number of kernels, which is a significant improvement over all previous analyses. Our results also include stability-based guarantees for a class of regression algorithms. In all cases, these guarantees indicate the benefits of learning with a large number of kernels. We also present a family of new two-stage algorithms for learning kernels based on a notion of alignment and give an extensive analysis of the properties of these algorithms. We show the existence of good predictors for the notion of alignment we define and give efficient algorithms for learning a maximum alignment kernel by showing that the problem can be reduced to a simple quadratic program. Finally, we report the results of extensive experiments with our two-stage algorithms, which show an improvement both over the uniform combination of kernels and over other state-of-the-art learning kernel methods for L1 and L2 regularization. These might constitute the first series of results for learning with multiple kernels that demonstrate a consistent improvement over a uniform combination of kernels.
The Rate of Convergence of Binomial Lattice Models for Pricing Vanilla Options

Variations of the binomial tree model are reviewed and extensions to the two most efficient trees studied in a recent literature are proposed. Tian’s modified tree is extended to a more general class of tree, and the third order tree is extended to the seventh order tree. Analysis of the error of American put pricing in the binomial tree model is presented and new trees that have superior performance in pricing in-the-money American puts are developed. To further improve numerical results, a scheme that incorporates different trees is suggested.
Confronting Uncertainties in Pastoral Areas

Abstract: Pastoralists must continuously confront uncertainties, responding to high levels of variability and volatility where the future is unknown. Yet mainstream modernising development in pastoral areas aims to create stability through control, enacted through restrictive plans and policies. Through a series of case studies, this article explores pastoralists’ own sensitive, flexible and caring responses, attuned to the instabilities of pastoral settings. The cases show how uncertainties can be seen as intersecting constructions of knowledge, materiality, experience, embodiment and practice, where flexible, often collective, caring approaches are central to pastoralists’ lives. These insights have wider implications for other contexts where people inhabit uncertain worlds, and so suggest a fundamental challenge to the controlling approaches of conventional development. Résumé : Les éleveurs doivent constamment faire face aux incertitudes de l’à-venir et répondre à de haut niveaux de variabilité et de volatilité. Cependant, la tendance au développement moderne dans les zones pastorales vise à créer une stabilité par la mise en œuvre de stratégies de contrôle et de politiques restrictives. En s’appuyant sur une série de cas, cet article analyse les réponses plus sensibles, flexible et attentionnées des pasteurs, en phase avec l’instabilité de la condition pastorale. Les cas montrent comment les incertitudes peuvent être vues comme des constructions de savoir entrecroisant expérience matérielle, incorporation de compétences et de pratiques. Ces approches bienveillantes, flexibles et souvent collectives sont centrales dans la vie des pasteurs. Ces exemples ont des implications plus larges dans d’autres contextes où des populations vivent dans des environnements incertains. Les approches conventionnelles du développement fondées sur le contrôle sont fondamentalement remises en question.
A two-temperature accretion disk model for Cygnus X-1: structure and spectrum.

We present a model for Cygnus X-1, involving an accretion disk around a black hole, which can explain the observed X-ray spectrum from 8 to 500 keV. In particular we construct a detailed model of the structure of an accretion disk whose inner region is considerably hotter and geometrically thicker than previous disk models. The inner region of the disk is optically thin to absorption, is gas-pressure dominated, and yields, from first principles, electron temperatures of 10/sup 9/ K and ion temperatures 3--300 times hotter. The spectrum above 8 keV is produced by inverse Compton scattering of soft X-ray photons in the two-temperature inner region of the disk. This spectrum is computed by numerical integration of the Kompane'ets equation, modified to account for escape of photons from a region of finite (order unity) electron scattering optical depth. (AIP)
Computational and Experimental Evaluation of the Immune Response of Neoantigens for Personalized Vaccine Design

In the last few years, the importance of neoantigens in the development of personalized antitumor vaccines has increased remarkably. In order to study whether bioinformatic tools are effective in detecting neoantigens that generate an immune response, DNA samples from patients with cutaneous melanoma in different stages were obtained, resulting in a total of 6048 potential neoantigens gathered. Thereafter, the immunological responses generated by some of those neoantigens ex vivo were tested, using a vaccine designed by a new optimization approach and encapsulated in nanoparticles. Our bioinformatic analysis indicated that no differences were found between the number of neoantigens and that of non-mutated sequences detected as potential binders by IEDB tools. However, those tools were able to highlight neoantigens over non-mutated peptides in HLA-II recognition (p-value 0.03). However, neither HLA-I binding affinity (p-value 0.08) nor Class I immunogenicity values (p-value 0.96) indicated significant differences for the latter parameters. Subsequently, the new vaccine, using aggregative functions and combinatorial optimization, was designed. The six best neoantigens were selected and formulated into two nanoparticles, with which the immune response ex vivo was evaluated, demonstrating a specific activation of the immune response. This study reinforces the use of bioinformatic tools in vaccine development, as their usefulness is proven both in silico and ex vivo.
Epizootic Hemorrhagic Disease Virus: Current Knowledge and Emerging Perspectives

Epizootic Hemorrhagic Disease (EHD) of ruminants is a viral pathology that has significant welfare, social, and economic implications. The causative agent, epizootic hemorrhagic disease virus (EHDV), belongs to the Orbivirus genus and leads to significant regional disease outbreaks among livestock and wildlife in North America, Asia, Africa, and Oceania, causing significant morbidity and mortality. During the past decade, this viral disease has become a real threat for countries of the Mediterranean basin, with the recent occurrence of several important outbreaks in livestock. Moreover, the European Union registered the first cases of EHDV ever detected within its territory. Competent vectors involved in viral transmission, Culicoides midges, are expanding its distribution, conceivably due to global climate change. Therefore, livestock and wild ruminants around the globe are at risk for this serious disease. This review provides an overview of current knowledge about EHDV, including changes of distribution and virulence, an examination of different animal models of disease, and a discussion about potential treatments to control the disease.
A Physical Model-Based Self-Supervised Learning Method for Signal Enhancement Under Reverberant Environment

In a reverberant environment, interferences such as reflections and background noise can degrade the perception of the sound source signal. Although the DNN-based methods have made a tremendous breakthrough in addressing this issue, the performance of these models is highly dependent on the completeness of the training dataset, which will limit its generalization under unknown environments. In this article, we propose a physical model-based self-supervised learning (PMSSL) method to realize the DNN model optimization under unknown scenarios. This method incorporates a room reverberation physical model into the sound source enhancement model optimization process, realizing the self-learning of the DNN model under physical constraints. In this process, the time-frequency characteristics of the input signal and the spatial feature of the reverberation environment are utilized for parameter optimization, improving the adaptability of the DNN model under unknown scenarios. Experimental results based on simulated and measured data prove that the proposed method can obtain much more accurate source signal enhancement results compared with the pre-trained models, verifying its effectiveness and adaptability in new environments.
Marker-Assisted Selection in Breeding for Fruit Trait Improvement: A Review

Breeding fruit species is time-consuming and expensive. With few exceptions, trees are likely the worst species to work with in terms of genetics and breeding. Most are characterized by large trees, long juvenile periods, and intensive agricultural practice, and environmental variability plays an important role in the heritability evaluations of every single important trait. Although vegetative propagation allows for the production of a significant number of clonal replicates for the evaluation of environmental effects and genotype × environment interactions, the spaces required for plant cultivation and the intensity of work necessary for phenotypic surveys slow down the work of researchers. Fruit breeders are very often interested in fruit traits: size, weight, sugar and acid content, ripening time, fruit storability, and post-harvest practices, among other traits relevant to each individual species. The translation of trait loci and whole-genome sequences into diagnostic genetic markers that are effective and affordable for use by breeders, who must choose genetically superior parents and subsequently choose genetically superior individuals among their progeny, is one of the most difficult tasks still facing tree fruit geneticists. The availability of updated sequencing techniques and powerful software tools offered the opportunity to mine tens of fruit genomes to find out sequence variants potentially useful as molecular markers. This review is devoted to analysing what has been the role of molecular markers in assisting breeders in selection processes, with an emphasis on the fruit traits of the most important fruit crops for which examples of trustworthy molecular markers have been developed, such as the MDo.chr9. 4 marker for red skin colour in apples, the CCD4-based marker CPRFC1, and LG3_13. 146 marker for flesh colour in peaches, papayas, and cherries, respectively.