Miracle-dz/UniFAR-FTUs huggingface dataset:
Alagille syndrome: an orphan disease in Colombia and summary of recent advances in treatment and survival – a case report

Introduction: Alagille syndrome has been described as a multisystemic clinical spectrum caused by an autosomal dominant genetic disorder. Although it is estimated that there is 1 case per 100 000 live births, the prognosis for survival and quality of life for these patients is varied but tends to be negative. In Colombia, this condition is considered an orphan disease with difficult management due to the lack of specialized centers that have all the medical specialties and subspecialties. Some reports state that no more than 30 cases have been published in this country. Materials and methods: The authors report a case of a male baby who, at 8 days old, he was taken to the general practitioner’s outpatient clinic for persistent jaundice. At 3 months of age, he was reviewed by the pediatric gastroenterology department, which requested liver and biliary tract scintigraphy, showing atresia of the biliary tract, hepatomegaly, and the absence of a gallbladder. Results: Liver transplantation is the definitive solution. However, in low- and middle-income countries, where there are no well-established organ transplantation programs, the prognosis for these patients is presumed to be worse. Conclusion: Alagille syndrome is a rare disease that requires an accurate and early diagnosis and timely multidisciplinary management to reduce the impact of multisystemic complications. It is necessary to advance in transplant programs in low- and middle-income countries, to provide a solution to cases where there are no other therapeutic alternatives, and to contribute to the quality of life of the affected patient.
A Comprehensive Review on Study Methods of Aerosol Optical Properties in Different Dimensions

A comprehensive understanding of the optical properties of atmospheric aerosol is essential for a variety of applications, such as optical imaging, optical communication, and remote sensing. In recent years, many theories and numerical simulation methods have been developed to connect aerosol physical-chemical properties to their intrinsic and integral optical properties. Usually, simulations and measurements are intertwined to synergistically attain the retrieval of aerosol optical properties and mitigate or even eliminate the adverse impacts of aerosol during imaging, sensing, or communication. This review covers the fundamental theories of aerosol optical properties, the development of numerical simulations, the instrument-based sampling measurements, and the cutting-edge techniques of remote sensing. Numerical simulations have been progressing from symmetric particles to asymmetric particles over the past two decades, although any simulation method is limited by specific shape and a restricted size parameter range. Thus, this review also examines the most typical advances in aerosol instrumentation that are frequently used to measure the intrinsic optical properties of unknown aerosols. Such obtained properties validate simulations and constitute the basis of integral optical properties. In terms of practical applications, integral optical properties are the most critical knowledge about atmospheric aerosol. Remote sensing measurements, be it ground-based, airborne, or satellite-based, all retrieve integral optical properties of atmospheric aerosol from various perspectives, which are elaborated upon in this review. In conclusion, this review provides an all-encompassing comprehension of aerosol optical properties.
miR-31-5p as a Potential Circulating Biomarker and Tracer of Clinical Improvement for Chronic Inflammatory Demyelinating Polyneuropathy.

Background MicroRNAs are endogenous, small noncoding RNA molecules that play a pivotal role in the regulation of gene expression. MicroRNAs are involved in many biological processes such as proliferation, cell differentiation, neovascularization, and apoptosis. Studies on microRNA expression may contribute to a better understanding of the pathomechanism of chronic inflammatory demyelinating polyneuropathy (CIDP) and consequently enable the development of new therapeutic measures using antisense miRNAs (antagomirs). In this study, we evaluated the level of miR-31-5p in the serum of patients with CIDP and its correlation with the miR-31-5p level and clinical presentation and electrophysiological and biochemical parameters. Methods The study group consisted of 48 patients, mean age 61.60 ± 11.76, who fulfilled the diagnostic criteria of a typical variant of CIDP. The expression of miR-31-5p in patient serum probes was investigated by droplet digital PCR. The results were correlated with neurophysiological findings and the patient's clinical and biochemical parameters. Results The mean copy number of miRNA-31 in 100 μl serum was 1288.64 ± 2001.02 in the CIDP group of patients, while in the control group, it was 3743.09 ± 4026.90. There was a significant positive correlation (0.426) between IgIV treatment duration and miR-31-5p expression. Patients without IgIV treatment showed significantly lower levels of miR-31 compared to the treated group (259.44 ± 304.02 vs. 1559.48 ± 2168.45; p = 0.002). The group of patients with body weight > 80 kg showed statistically significantly lower levels of miRNA-31-5p than the patients with lower body weight (934.37 ± 1739.66 vs. 1784.62 ± 2271.62, respectively; p = 0.014). Similarly, the patients with elevated cerebrospinal fluid (CSF) protein levels had significantly higher miRNA-31-5p expression than those with normal protein levels (1393.93 ± 1932.27 vs. 987.38 ± 2364.10, respectively; p = 0.044). Conclusion The results may support the hypothesis that miR-31-5p is strongly involved in the autoimmune process in CIDP. The positive correlation between higher miR-31-5p levels and duration of IVIg treatment may be an additional factor explaining the efficacy of prolonged IVIg therapy in CIDP.
On the Laziness of Monte-Carlo Game Tree Search in Non-tight Situations

In many different games, including the heavy-weight classic Go, Monte-Carlo game tree search performs surprisingly well ([Bru 1993], [Cou 2006], [Gel 2007]). Already in several situations it was observed that Monte-Carlo (and variants and extensions of it) seems to play really well when the situation in the game is tight. In non-tight situations, however, Monte-Carlo (called MC for short in the sequel) often shows some laziness.
Full waveform inversion as training a neural network

Full waveform inversion (FWI) is usually solved as a nonlinear least-squares problem to minimize the discrepancy between the recorded signal and the synthetic data by some gradient-based optimization methods. In this paper, we investigate acoustic FWI as training a neural network. We recast the time-domain difference scheme into a forward propagation process of vanilla recurrent neural network (RNN) and then find that the parameters of RNN coincide with the physical parameters in the wave equation. As a result, the FWI problem is resolved as training such a kind of neural network. Some stochastic optimization methods such as the Adam optimizer are applied to update the model parameters. We demonstrate our method numerically with three typical models including the benchmark Marmousi model. The numerical results show that the fast convergence and good velocity inversion result can be achieved.
‘All possible sounds’: speech, music, and the emergence of machine listening

ABSTRACT “Machine listening” is one common term for a fast-growing interdisciplinary field of science and engineering that “uses signal processing and machine learning to extract useful information from sound”. This article contributes to the critical literature on machine listening by presenting some of its history as a field. From the 1940s to the 1990s, work on artificial intelligence and audio developed along two streams. There was work on speech recognition/understanding, and work in computer music. In the early 1990s, another stream began to emerge. At institutions such as MIT Media Lab and Stanford’s CCRMA, researchers started turning towards “more fundamental problems of audition”. Propelled by work being done by and alongside musicians, speech and music would increasingly be understood by computer scientists as particular sounds within a broader “auditory scene”. Researchers began to develop machine listening systems for a more diverse range of sounds and classification tasks: often in the service of speech recognition, but also increasingly for their own sake. The soundscape itself was becoming an object of computational concern. Today, the ambition is “to cover all possible sounds”. That is the aspiration with which we must now contend politically, and which this article sets out to historicise and understand.
Large spread in the representation of compound long-duration dry and hot spells over Europe in CMIP5

Abstract. Long-duration, sub-seasonal dry spells in combination with high temperature extremes during summer have led to extreme impacts on society and ecosystems in the past. Such events are expected to become more frequent due to increasing temperatures as a result of anthropogenic climate change. However, there is little information on how long-duration dry and hot spells are represented in global climate models (GCMs). In this study, we evaluate 33 CMIP5 (coupled model intercomparison project 5) GCMs in their representation of long-duration dry spells and temperatures during dry spells. We define a dry spell as a consecutive number of days with a daily precipitation of less than 1 mm. CMIP5 models tend to underestimate the persistence of dry spells in northern Europe, while a large variability exists between model estimates in central and southern Europe, where models have contrasting biases. Throughout Europe, we also find a large spread between models in their representation of temperature extremes during dry spells. In central and southern Europe this spread in temperature extremes between models is related to the representation of dry spells, where models that produce longer dry spells also produce higher temperatures, and vice versa. Our results indicate that this variability in model estimates is due to model differences and not internal variability. At latitudes between 50–60∘ N, the differences in the representation of persistent dry spells are strongly related to the representation of persistent anticyclonic systems, such as atmospheric blocking and subtropical ridges. Furthermore, models simulating a higher frequency of anticyclonic systems than ERA5 also simulate temperatures in dry spells that are between 1.4, and 2.8 K warmer than models with a lower frequency in these areas. Overall, there is a large spread between CMIP5 models in their representation of long-duration dry and hot events that is due to errors in the representation of large-scale anticyclonic systems in certain parts of Europe. This information is important to consider when interpreting the plausibility of future projections from climate models and highlights the potential value that improvements in the representation of anticyclonic systems may have for the simulation of impactful hazards.
RIFA: A Differential Gene Connectivity Algorithm

With the invention of microarray technology, scientists finally had a means to measure global changes in gene expression between two biological states [1]. This has led to thousands of scientific publications describing long lists of differentially expressed genes in each scientist’s favorite experimental system. What has gradually become apparent to biologists is that having a list of differentially expressed genes, while an important first step in understanding the differences between two phenotypes (where phenotype represents the physical manifestation of one or more traits), is often not enough to identify the genes most directly responsible for driving changes in phenotype. While it is true that genes that are differentially expressed between two biological states may be important in explaining those differences, it is also possible that genes whose expression is not changed can also be pivotal in driving phenotypic differences.
Does Internet Banking Option Substitute Traditional Banking System? Empirical Study Of State Bank of India’s Customers at Vadodara City

Internet banking can be defined as a facility provided by banking and financial institutions that enable the user to execute bank related transactions through the Internet. Internet banking has become a necessary survival weapon and is fundamentally changing the banking industry worldwide. Today, the click of the mouse offers customers banking services at a much lower cost and also empowers them with unprecedented freedom in choosing vendors for their financial service needs. Internet banking allows customers to monitor accounts, download transactions, transfer funds between accounts
Potential for Renewable Energy Generation from Water Sources in the Batang River Area

The ever-increasing use of fossil fuels causes the climate to become unbalanced due to the resulting pollution. Renewable energy is gaining widespread adoption because it is more cost-effective and eco-friendly. Using water as micro-hydro power plants or PLTMH can be a viable alternative to non-renewable fuel sources. Indonesia is one of the countries with the greatest application potential for micro-hydro power plants or PLTMH, particularly in Batang Regency, Central Java Province. This study focused on reducing the use of fossil fuels as a source of electricity in Batang Regency by utilizing water resources. Environmental approaches and hydrological analysis were employed in this study to achieve precise and optimal PLTMH planning outcomes. The results of the analysis indicated that the total discharge required to operate the PLTMH turbine is 2.22 m3/s with an 80% mainstay discharge, while the design flood discharge is 113,679 m3/s (Q100year). Implementing micro-hydro power plants or PLTMH in Batang Regency could be the first step in Indonesia’s transition from fossil fuels to renewable energy sources.
The mediation role of self-esteem and hope on the relationship of quality of life and unmet needs of elderly with psychiatric disorders

BACKGROUND: Self-esteem is one of the factors affecting the quality of life. On the other hand, quality of life decreases in people with psychiatric disorders. The aim of the present study was to evaluate the mediating role of self-esteem and hope on the relationship between unmet needs and quality of life of the elderly with psychiatric disorders. MATERIALS AND METHODS: This descriptive–analytical study was performed on 112 chronic psychiatric patients hospitalized in the geriatric ward of (blinded) 2020. Based on the inclusion criteria, a total of 100 samples were entered into the study by census. The World Health Organization (WHO) quality of life questionnaire, Rosenberg Self-Esteem Scale, Snyder Hope Scale, and Camberwell Assessment of Need Short Appraisal Schedule (CANSAS) were used to collect data. The research model was tested using the path analysis technique. Data was analyzed using Statistical Package for the Social Sciences (SPSS) Ver. 26 and LISREL Ver. 2/9. RESULTS: Unmet needs were negatively related to the other three variables of the study, namely, self-esteem, hope, and quality of life. There was a significant relationship between unmet needs and quality of life with a mediating role of self-esteem and hope (P < 0.05). Quality of life was also negatively related to unmet needs and directly related to self-esteem and hope. CONCLUSION: Based on the findings of this study, it is essential that health-care providers consider planning to provide programs to improve self-esteem and hope in order to reduce unmet needs and increase the quality of life.
Planetary nebula abundance determinations: A view from 1D-RHD simulations

Abstract In the last years (metallicity-dependent) radiation-hydrodynamics simulations have become a powerful tool to understand the formation and evolution of PNe in terms of simple morphologies and kinematics. Contrary to photoionization models, with their ad-hoc assumptions on structure and physics, the RHD models are self-consistent with respect to their density distribution, velocity field, chemical composition, and stellar evolution. We use our models as simple proxies for real PNe and investigate the reliability of common abundance determination methods, which are based on either plasma diagnostics or static photoionization (PI) models.
Quadratic B-Spline Interpolation Curves with Tangent Constraints on Data Points

In this paper a new interpolation method for quadratic B-spline curves is proposed so as to fully utilize the degrees of freedom provided by parameterization and knot vector to control the shapes of the interpolation curves intuitively by the tangent constraints on data points. Without solving any equation systems, the interpolation procedure of the method is a recursive one in which the parameter values at data points, the knots and the control points are determined recursively according to the data points and the tangent constraints on data points. With the method the connection points of adjacent curve segments are not necessarily coincident with data points, so that the shapes of quadratic B-spline interpolation curves are more natural. Furthermore, under the restriction of the tangent constraints, there are still some degrees of freedom in constructing interpolation curves by the method: the shapes of the interpolation curves can be further adjusted by the selection of knots. Besides, the quadratic B-spline interpolation curves constructed by the method possess rather good local properties for the relieving disturbances on data points. Some examples are given to compare the method proposed in the paper with several other interpolation methods. The experimental results show that this method is effective.
Decentralized Scheduling for Concurrent Tasks in Mobile Edge Computing via Deep Reinforcement Learning

Mobile Edge Computing (MEC) is a promising solution to enhance the computing capability of resource-limited networks. A fundamental problem in MEC is efficiently offloading tasks from user devices to edge servers. However, there still exists a gap to deploy in real-world environments: 1) traditional centralized approaches needs complete information of edge network, ignoring the communication costs generated by synchronization, 2) previous works do not consider concurrent computation on edge servers, which may cause dynamic changes in the environment, and 3) the scheduling algorithm should deliver individualized decisions for different users independently and with high efficiency To solve this mismatch, we studied a multi-user task offloading problem where user devices make offloading decisions independently. We consider the concurrent execution of tasks and formulate a non-divisible and delay-aware task offloading problem to jointly minimize the dropped task ratio and long-term latency. We propose a decentralized task scheduling algorithm based on DRL that makes offloading decisions without knowing the information of other user devices. We employ Double-DQN, Dueling-DQN, Prioritized Replay Memory, and Recurrent Neural Network (RNN) techniques to improve the algorithm's performance. The results of simulation experiments show that our method can significantly reduce the long-term latency and dropped task ratio compared to the baseline algorithms.
Partial Discharge Activity in Rotating Machine Type II Insulation Systems as a Function of Temperature and AC Supply Frequency

The main purpose of this study was to investigate if the application of very low frequencies (VLFs) for partial discharge (PD) measurements in type II generator bar insulation is relevant when compared to applying power line frequency (PLF) at different test temperatures. Laboratory experiments were performed on two stator bar types rated 6.4 and 7.4 kV, labeled as Groups A and B, respectively. The stator bars were taken from service and spare part storage (pristine). The samples were preconditioned at the maximum test voltage of $1.5{U}_{{0}}$ before sweeping the ac high-voltage supply from 0.1 and 50 Hz. The test temperature was varied between 20 °C and 155 °C. Investigation of cross sections of the generator bars was done to qualitatively describe the voids. The main results showed that the PD inception voltage (PDIV) for Group A was temperature-independent at 50 Hz and decreased by 40% at 0.1 Hz with increasing temperatures, whereas the PDIV for Group B was frequency-independent and increased by 70% with temperature. Similarly, the PD magnitude and total apparent charge decreased by 90% for Group B with increasing temperature, whereas it more than doubled for Group A with temperature. The tested bars from Group A showed significantly larger voids, located close to the copper conductors, compared to Group B, where voids were mainly located within the mainwall insulation. Results indicated that tests performed at 0.1 Hz cannot directly represent results obtained at 50 Hz. Using a wide range of frequencies and test temperatures broadens the understanding of the behavior of the voids inside the mainwall insulation.
On the recurrence of simple random walks on some fractals

In this paper, the recurrence or transience of simple random walks on some lattice fractals is investigated. As results, we obtain that the simple random walk on the pre-Sierpinski gasket in d dimensions is recurrent for all d ≧ 2, and on the pre-Sierpinski carpet in d dimensions it is recurrent for d = 2 and transient for all d ≧ 3.
The Logic of Experimental Questions

The pair (A, Δ ), where A is a physical quantity (an observable) and Δ a subset of the reals, may be called an 'experimental question'. The set Q of experimental questions is, in classical mechanics, a Boolean algebra, and in quantum mechanics an orthomodular lattice (and also a transitive partial Boolean algebra). The question is raised: can we specify a priori what algebraic structure Q must have in any theory whatsoever? Several proposals suggesting that Q must be a lattice are discussed, and rejected in favor of the weak claim that Q must be a Boolean atlas.
A conceptual framework for developing dashboards for big mobility data

ABSTRACT Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets. Key policy highlights Defines essential components of big mobility dashboards for stakeholders to understand key considerations and data management/pre-processing needs Clarifies the differences between dashboards and visual analytics applications Provides guidance for gathering information from end-users to ensure displays are fit-for-purpose Illustrates the application of the conceptual framework for dashboard design by discussing several examples of mobility data dashboards