Miracle-dz/UniFAR-FTUs huggingface dataset:
The Current State of Charcot–Marie–Tooth Disease Treatment

Charcot–Marie–Tooth disease (CMT) and associated neuropathies are the most predominant genetically transmitted neuromuscular conditions; however, effective pharmacological treatments have not established. The extensive genetic heterogeneity of CMT, which impacts the peripheral nerves and causes lifelong disability, presents a significant barrier to the development of comprehensive treatments. An estimated 100 loci within the human genome are linked to various forms of CMT and its related inherited neuropathies. This review delves into prospective therapeutic strategies used for the most frequently encountered CMT variants, namely CMT1A, CMT1B, CMTX1, and CMT2A. Compounds such as PXT3003, which are being clinically and preclinically investigated, and a broad array of therapeutic agents and their corresponding mechanisms are discussed. Furthermore, the progress in established gene therapy techniques, including gene replacement via viral vectors, exon skipping using antisense oligonucleotides, splicing modification, and gene knockdown, are appraised. Each of these gene therapies has the potential for substantial advancements in future research.
Effect of Blade Electrode Shape on Electrohydrodynamic Jets with Different Dielectric Properties in a Blade-Plane Configuration

The actuator structure of an electroconvective flow is one of the key factors affecting the efficiency of an EHD pump. This paper presents a computational simulation considering the effect of injection and field-enhanced dissociation in a blade-plane geometry simultaneously. Calculations based on the whole set of EHD equations are performed with the help of a finite element method-based software package. The coupling characteristics of flow and charge behavior are investigated in a system of semicircular-tip and needle-tip blade electrodes, and the effect of adjusting the mobility and inter-electrode gap length on the flow rate is demonstrated. The results show that an impinging jet generated from needle-tip blade electrode is superior to that from semicircular-tip blade electrode, and the maximum velocity can reach 0.97 m/s.
Could Gravitons from a Prior Universe Survive a (LQG Inspired) “Quantum Bounce” to Re-Appear in Our Present Universe?

We ask the question if a formula for entropy, as given by with a usual value ascribed of initial entropy of the onset of inflation can allow an order of magnitude resolution of the question of if there could be a survival of a graviton from a prior to the present universe, using typical Planckian peak temperature values of . We obtain values consistent with up to 1038 gravitons contributing to an energy value of if we assume a relic energy contribution based upon each graviton initially exhibiting a frequency spike of 1010 Hz. The value of is picked from looking at the aftermath of what happens if there exists a quantum bounce with a peak density value of [1] in a regime of LQG bounce regime radii of the order of magnitude of meters. The author, in making estimates specifically avoids using , by setting the chemical potential for ultra high temperatures for reasons which will be brought up in the conclusion.
Set estimation: Another bridge between statistics and geometry.

Set estimation has to do with the statistical reconstruction of sets from random set of points. This theory is closely related with nonparametric functional estimation as well as with stochastic geometry. A nonexhaustive expository overview of set estimation theory is given. The aim is to present the basic ideas, some typical tools involved in the theory and a few applications. Most technicalities are omitted or summarized.
Hiatal hernia: diagnostic and therapeutic approach

Hiatal hernia is a common condition of the digestive system characterized by displacement of the stomach into the chest through the esophageal hiatus of the diaphragm. As research progresses and more scientific evidence accumulates, continued advances in the surgical approach to hiatal hernia are expected to occur. This will allow for a better understanding of the disease’s pathophysiology and the development of new surgical techniques and approaches that further improve the long-term outcomes and quality of life of patients with hiatal hernia.
Information sharing in a hybrid workplace: understanding the role of ease-of-use perceptions of communication technologies in advice-seeking relationship maintenance

Shifts to hybrid work prompted by the COVID-19 pandemic have the potential to substantially impact social relationships at work. Hybrid employees rely heavily on digital collaboration technologies to communicate and share information. Therefore, employees’ perceptions of the technologies are critical in shaping organizational networks. However, the dyadic-level misalignment in these perceptions may lead to relationship dissolution. To explore the social network consequences of hybrid work, we conducted a two-wave survey in a department of an industrial manufacturing firm (N = 169). Our results show that advice seekers were less likely to maintain their advice-seeking ties when they had a mismatch in ease-of-use perceptions of technology with their advisors. The effect was more substantial when advice seekers spent more time working remotely. The study provides empirical insights into how congruence in employees’ perceptions of organizational communication technologies affects how they maintain advice networks during hybrid work.
Chemical Recycling of Fully Recyclable Bio-Epoxy Matrices and Reuse Strategies: A Cradle-to-Cradle Approach

Currently, the epoxy resin market is expressing concerns about epoxy resins’ non-recyclability, which can hinder their widespread use. Moreover, epoxy monomers are synthesized via petroleum-based raw materials, which also limits their use. So, it is crucial to find more environmentally friendly alternative solution for their formulation. Within this context, the aim of this paper is to exploit a Cradle-to-Cradle approach, which consists of remodeling and reshaping the productive cycle of consumer products to make sure that they can be infinitely reused rather than just being recycled with a downgrading of their properties or uses, according to the principle of the complete circular economy. Indeed, after starting with a fully-recyclable bio-based epoxy formulation and assessing its recyclability as having a process yield of 99%, we obtained a recycled polymer that could be reused, mixing with the same bio-based epoxy formulation with percentages varying from 15 wt% to 27 wt%. The formulation obtained was thoroughly characterized by a dynamic-mechanical analysis, differential scanning calorimetry, and flexural tests. This approach had two advantages: (1) it represented a sustainable disposal route for the epoxy resin, with nearly all the epoxy resin recovered, and (2) the obtained recycled polymer could be used as a green component of the primary bio-based epoxy matrix. In the end, by using replicated general factorial designs (as statistical tools) combined with a proper optimization process, after carrying out a complete thermo-mechanical characterization of the developed epoxy formulations, the right percentage of recycled polymer content was selected with the aim of identifying the most performing epoxy matrix formulation in terms of its thermo-mechanical properties.
Identification, Biological Function Profiling and Biosynthesis of Secondary Metabolites in Medicinal Orchids

The secondary metabolites present in medicinal orchids are diverse and possess a vast array of biological activities. They represent valuable raw materials for modern pharmaceuticals and clinical medicine and have tremendous potential for future development. A systematic collation of secondary metabolites’ composition and a summary of the biological activities of medicinal orchids represent a crucial step in unlocking the potential of these valuable resources in drug development. Furthermore, such information can provide essential guidance for comprehensively analyzing the pharmacological and therapeutic mechanisms of these valuable herbs in traditional Chinese herbal medicine. This review article presents an overview of the types and main biological functions of the secondary metabolites found in medicinal orchids, as well as the conventional synthesis methods for these compounds. Our aim is to provide a useful reference for future research and the drug development of secondary metabolic products of medicinal orchids.
Direct and inverse problems for a Schrödinger-Steklov eigenproblem on different domains and spectral geometry for the first normalized Steklov eigenvalue on domains with one hole

We present some results related with the asymptotic expansion of the eigenvalues for the Schrödinger-Steklov eigenvalue problem. We find explicit expressions for this asymptotic behavior in the cases when the domain is the unit disk in R2 and the unit ball in R3. We also show a uniqueness result for the potential function q based on the knowledge of the Steklov spectrum. Several numerical examples are included to illustrate different strategies in the recovery of the potential function q from the knowledge of some of the eigenvalues. Finally, we describe a shape derivative approach to provide a candidate for an optimal domain in the class of bounded planar domains with one hole for the first nontrivial Steklov eigenvalue. This approach is an adaptation of the work on the extremal eigenvalue problem for the Wentzell-Laplace operator developed by Dambrine, Kateb and Lamboley.
Prevalence and molecular characterization of circoviruses, astroviruses and hepadnaviruses in the avifauna of Kopački rit Nature Park, Croatia

Wild birds harbour a plethora of viruses but the research into them has mainly focused on those possessing zoonotic potential or causing significant economic losses in poultry, while others, such as astroviruses (AstVs), circoviruses (CV) and hepadnaviruses (HBV), remain neglected. In this study, 35 dead wild birds belonging to at least 21 species from five orders, collected in Kopački rit Nature Park were tested for the presence of AstVs, CV and HBV using PCR with subsequent nucleotide sequencing. A positive result was found in 18 birds. AstVs were found in three passerine birds, CV was detected in 11 birds belonging to Accipitriformes, Anseriformes, Charadriiformes, Passeriformes or Piciformes orders while HBV was found in four birds belonging to either Anseriformes or Passeriformes. More than one virus was found in a single species (AstV and CV) although in two separate birds. Phylogenetic analysis revealed divergent AstVs somewhat similar to different unclassified AstVs detected in undetermined birds. One AstV shared the highest homology with a bat AstV (98.77%). All CV found in the study resemble avian CV, but none of them clusters in the phylogenetic tree with any avian or avian-like CV sequence. All four HBV sequences detected in this study cluster within the duck HBV group, indicating unusual interspecies transmission to cohabitating passerines. The results of this study represent an important contribution to knowledge about the prevalence, ecology and epidemiology of AstVs, CV and HBV in different wild bird species, indicating the need for further studies, including whole genome sequencing of the detected viruses.
The Potential of Nanocellulose Acetate as Surfactant for Water-Vegetable Oil Systems

Indonesia, as an agricultural country, has a variety of abundant plants. Cellulose is a component in plants that can be modified to increase its economic value. Resizing cellulose to nanocellulose and modification of nanocellulose to nanocellulose acetate can increase its potential as a surfactant. Resizing cellulose can be done using the strong acid hydrolysis method. An acetic anhydride reagent was utilized to convert the surface hydroxyl functional group into acetyl. The successful production and modification of nanocellulose were confirmed using fourier transform infrared and particle size analysis characterization. The infrared absorption spectrum of cellulose and nanocellulose showed no difference in peaks. Particle size distribution showed that nanocellulose I (CNC I) and nanocellulose II (CNC II) has sizes of 142 nm and 319 nm, respectively. The property of nanocellulose molecules in an oil-water system was simulated using molecular dynamics with GROMACS 2020.6 software. Appropriate trends can be seen in the interfacial tension of water-vegetable oil systems. The value of interfacial tension decreases with the addition of nanocellulose acetate compared to the addition of nanocellulose. With the agreement between the experimental and computational results, nanocellulose acetate can act as a surfactant.
Vitamin D3 and chronic rhinosinusitis, Is there a relationship?

Background : One of the most prevalent conditions without a proven long-term treatment is chronic rhinosinusitis (CRS). There are two phenotypes of CRS: CRS without nasal polyps (CRSsNP) and CRS with nasal polyps (CRSwNP). Immunomodulation is thought to be a function of vitamin D (VD), particularly in allergic disorders. Objective : This investigation was done to assess the link between blood VD3 levels and CRS, whether it had polyps or not. Patients and methods : 90 people from both sexes participated in this study. Adults were divided into three categories and given the diagnosis of sinusitis by the American Academy of Otolaryngology Head and Neck Surgery: CRSsNP (30 subjects), CRSwNP (30 subjects), and control group (30 subjects). Using an ELISA (enzyme-linked immunosorbent assay), VD levels were measured (ELISA). Results : The serum 25 (OH) D level varied between the CRSsNP Group (23.10 ± 7.40), CRSwNP Group (22.20 ± 8.12), and Control Group (26.22 ± 8.25). Regarding VD3, there was no discernible difference between the studied groups. Age-related differences across the groups were statistically signi ﬁ cant, with CRSwNP being more prevalent in the older age group (55.07 ± 5.78). There was no discernible difference in symptomatology or sex. Conclusions : CRS, whether it had polyps or not, did not correlate with serum vitamin D3 levels. This indicates that low levels of serum 25 (OH) D are not a potential risk factor for CRS in adult patients.
Gait-based Biometric System Using Pressure Sensing Mats and Machine Learning Algorithms

The COVID-19 pandemic has emphasized the awareness of avoiding contact with shared or public devices, such as those used in traditional biometric systems. Common biometric systems, including fingerprint, palmprint, and iris recognition, require physical contact with the device, which increases the risk of contracting infectious diseases. As a result, non-contact biometric systems, such as gait recognition, may be increasingly important in the future. In this paper, we present an accurate gait recognition system that uses pressure sensing mats. Our proposed system employs high-density pressure sensing mats that significantly reduce computational complexity when compared to traditional gait recognition methods that use cameras. We acquired pressure distribution data from 30 subjects, including 19 males and 11 females, and developed an algorithmic framework that involves data preprocessing and classification to identify different subjects. We implemented five supervised machine learning models as classifiers, and our results indicate that the Convolutional Neural Networks (CNN) model performed the best, with a classification accuracy of 92.08%. Our study shows that the proposed gait recognition system is an effective non-contact biometric system that can distinguish different individuals with high accuracy. The use of pressure sensing mats reduces the risks associated with physical contact, making it a promising solution for biometric recognition in public spaces during the ongoing COVID-19 pandemic. In conclusion, our research contributes to the development of non-contact biometric systems and presents a viable solution for future applications.
Learning to Fuse Multiple Brain Functional Networks for Automated Autism Identification

Simple Summary This study aims to provide computer-aided diagnosis and valuable biomarkers for autism spectrum disorders by leveraging functional connectivity networks (FCNs) from resting-state functional magnetic resonance imaging data. We propose a novel framework for multi-FCN fusion to adaptively learn the fusion weights of component FCNs during the classifer’s learning process, guided by label information. It is simple and has better discriminability for autism spectrum disorder identification. Abstract Functional connectivity network (FCN) has become a popular tool to identify potential biomarkers for brain dysfunction, such as autism spectrum disorder (ASD). Due to its importance, researchers have proposed many methods to estimate FCNs from resting-state functional MRI (rs-fMRI) data. However, the existing FCN estimation methods usually only capture a single relationship between brain regions of interest (ROIs), e.g., linear correlation, nonlinear correlation, or higher-order correlation, thus failing to model the complex interaction among ROIs in the brain. Additionally, such traditional methods estimate FCNs in an unsupervised way, and the estimation process is independent of the downstream tasks, which makes it difficult to guarantee the optimal performance for ASD identification. To address these issues, in this paper, we propose a multi-FCN fusion framework for rs-fMRI-based ASD classification. Specifically, for each subject, we first estimate multiple FCNs using different methods to encode rich interactions among ROIs from different perspectives. Then, we use the label information (ASD vs. healthy control (HC)) to learn a set of fusion weights for measuring the importance/discrimination of those estimated FCNs. Finally, we apply the adaptively weighted fused FCN on the ABIDE dataset to identify subjects with ASD from HCs. The proposed FCN fusion framework is straightforward to implement and can significantly improve diagnostic accuracy compared to traditional and state-of-the-art methods.
Hyperedge2vec: Distributed Representations for Hyperedges

Data structured in form of overlapping or non-overlapping sets is found in a variety of domains, sometimes explicitly but often subtly. For example, teams, which are of prime importance in social science studies are \enquote{sets of individuals}; \enquote{item sets} in pattern mining are sets; and for various types of analysis in language studies a sentence can be considered as a \enquote{set or bag of words}. Although building models and inference algorithms for structured data has been an important task in the fields of machine learning and statistics, research on \enquote{set-like} data still remains less explored. Relationships between pairs of elements can be modeled as edges in a graph. However, modeling relationships that involve all members of a set, a hyperedge is a more natural representation for the set. In this work, we focus on the problem of embedding hyperedges in a hypergraph (a network of overlapping sets) to a low dimensional vector space. We propose a probabilistic deep-learning based method as well as a tensor-based algebraic model, both of which capture the hypergraph structure in a principled manner without loosing set-level information. Our central focus is to highlight the connection between hypergraphs (topology), tensors (algebra) and probabilistic models. We present a number of interesting baselines, some of which adapt existing node-level embedding models to the hyperedge-level, as well as sequence based language techniques which are adapted for set structured hypergraph topology. The performance is evaluated with a network of social groups and a network of word phrases. Our experiments show that accuracy wise our methods perform similar to those of baselines which are not designed for hypergraphs. Moreover, our tensor based method is quiet efficient as compared to deep-learning based auto-encoder method. We therefore, argue that we have proposed more general methods which are suited for hypergraphs (and therefore also for graphs) while maintaining accuracy and efficiency.
Detecting brain dynamics during resting state: a tensor based evolutionary clustering approach

Human brain is a complex network with connections across different regions. Understanding the functional connectivity (FC) of the brain is important both during resting state and task; as disruptions in connectivity patterns are indicators of different psychopathological and neurological diseases. In this work, we study the resting state functional connectivity networks (FCNs) of the brain from fMRI BOLD signals. Recent studies have shown that FCNs are dynamic even during resting state and understanding the temporal dynamics of FCNs is important for differentiating between different conditions. Therefore, it is important to develop algorithms to track the dynamic formation and dissociation of FCNs of the brain during resting state. In this paper, we propose a two step tensor based community detection algorithm to identify and track the brain network community structure across time. First, we introduce an information-theoretic function to reduce the dynamic FCN and identify the time points that are similar topologically to combine them into a tensor. These time points will be used to identify the different FC states. Second, a tensor based spectral clustering approach is developed to identify the community structure of the constructed tensors. The proposed algorithm applies Tucker decomposition to the constructed tensors and extract the orthogonal factor matrices along the connectivity mode to determine the common subspace within each FC state. The detected community structure is summarized and described as FC states. The results illustrate the dynamic structure of resting state networks (RSNs), including the default mode network, somatomotor network, subcortical network and visual network.
Nonparametric Instrumental Variable Estimation in Practice

Abstract This paper investigates recent developments in the literature on nonparametric instrumental variables estimation and considers the practical importance of the features of these estimators in the context of typically applied econometric models. Our primary focus is on the estimation of econometric models with endogenous regressors, and their marginal effects, without a known functional form. We develop an estimator for the marginal effects and investigate its finite sample performance. We show that when instruments are weak, in the classic sense, the nonparametric estimates of the marginal effect outperforms the classic two-stage least squares estimator, even when the model is correctly specified. When the instruments are strong, we show that the nonparametric estimator for the partial effects is still effective compared to the two-stage least squares estimator even as the number of IVs increases. We also investigate bandwidth choice and find that a rule-of-thumb bandwidth performs relatively well. Whereas cross-validation leads to a better fit when the number of instruments is small, as the number of instruments increases the rule-of-thumb standard actually results in better model fit. In an empirical application we estimate the work-horse aggregate logit demand model, discuss the required nonparametric identification properties, and document the differences between nonparametric and parametric specifications on the estimation of demand elasticities.