Miracle-dz/UniFAR-FTUs huggingface dataset:
Mirikizumab Improves Quality of Life in Patients With Moderately-to-Severely Active Ulcerative Colitis: Results From the Phase 3 LUCENT-1 Induction and LUCENT-2 Maintenance Studies

Abstract Background Mirikizumab, an anti-IL-23p19 antibody, demonstrated efficacy in phase 3, randomized, double-blind, placebo-controlled LUCENT-1 (induction/NCT03518086) and LUCENT-2 (maintenance/NCT03524092) ulcerative colitis (UC) studies. We evaluated the effect of mirikizumab on quality-of-life (QoL) outcomes in these studies. Methods In LUCENT-1, 1162 patients with moderately-to-severely active UC were randomized 3:1 to receive mirikizumab 300 mg intravenous or placebo every 4 weeks (Q4W) for 12 weeks. In LUCENT-2, mirikizumab induction responders (N = 544) were re-randomized 2:1 to receive mirikizumab 200 mg subcutaneous or placebo Q4W through week (W) 40 (W52 of treatment). QoL was assessed at W12 and W52 using patient-reported outcomes. Treatments were statistically compared using analysis of covariance model (continuous outcomes) and Cochran–Mantel–Haenszel test (binary outcomes). Results At W12 and W52, mirikizumab showed significant improvement in Inflammatory Bowel Disease Questionnaire (IBDQ) total and domain scores (P < .001); 36-Item Short Form Health Survey (SF-36) Physical Component Summary (PCS), Mental Component Summary (MCS), and domain scores (P < .05); EQ-5D-5L scores (P < .001); Work Productivity and Activity Impairment Questionnaire (UC) scores (P < .05); Patient Global Rating of Severity (P < .001); and Patient Global Rating of Change (P < .01) scores. A significantly higher proportion of mirikizumab-treated patients achieved IBDQ response (W12: 72.7% vs 55.8%; W52: 79.2% vs 49.2%; P < .001), IBDQ remission (W12: 57.5% vs 39.8%; W52: 72.3% vs 43.0%; P < .001), and clinically important improvements in PCS (W12: 50.6% vs 41.5%; W52: 61.9% vs 36.9%; P < .01) and MCS (W12: 44.2% vs 37.8%; W52: 51.2% vs 34.6%; P < .05) scores. Conclusions Mirikizumab improved QoL in patients with moderately-to-severely active UC in phase 3 LUCENT-1 and LUCENT-2 studies. Clinical trials registration number LUCENT-1: NCT03518086; LUCENT-2: NCT03524092
Fault Diagnosis of Hierarchical Discrete-Event Systems Based on State-Tree Structures

In this article, fault diagnosis of hierarchical discrete event systems (HDES) is investigated using state-tree structures (STS). As a structured formalism, an STS provides a compact representation for an HDES model and utilizes binary decision diagrams (BDDs) for efficient symbolic computation. Building on the above advantages of STS, with “on-the-fly” analysis technique addressed, the issue of offline diagnosability verification can be efficiently tackled. A symbolic approach of diagnoser construction is presented based on predicates and predicate transformers. With the BDD representation of predicates, the state space of the diagnoser is compressed and managed such that the occupied computer memory space is greatly reduced. Besides, instead of flattening an STS model to its equivalent monolithic model at first and then constructing the entire diagnoser for such a model, a heuristic on-the-fly algorithm based on the depth-first search, which unfolds the STS model gradually, is proposed for the level-by-level diagnosability analysis. Finally, several case studies are provided for evaluating the effectiveness and the scalability of the proposed method.
Scalable Computation of Fuzzy Joins Over Large Collections of JSON Data

Fuzzy joins are widely used in a variety of data analysis applications such as data integration, data mining, and master data management. In the context of Big Data, computing fuzzy joins is challenging due to the high computational cost required and the communication cost. While on one hand big fuzzy joins on relational data and on the other hand joins on tree-structured data have been investigated in the literature, to the best of our knowledge, combining the two is still an open problem. In this context, we study methods for leveraging distributed environments in order to compute fuzzy joins over large collections of JSON documents. Our algorithms take into account both the text-similarity of the joining data, as well as its structural similarity.
Melanoma Skin Cancer Detection using SVM and CNN

In the field of cancer detection and prevention, doctors and patients are facing numerous challenges when it comes to cancer prediction. Melanoma skin cancer is a deadly type of skin cancer with a multitude of variants spread across the world. Traditional methods involved manual inspection followed by various tests of samples. This time-consuming work and inaccurate predictions sometimes risk the overall health of the patient. The two aspects of solving skin cancer detection problems utilising both conventional image-processing techniques and methods based on machine learning and deep learning are elaborated in this article. It gives a review of current skin cancer detection techniques, weighs the benefits and drawbacks of those techniques, and introduces some relevant cancer datasets. The proposed method focuses mainly on Melanoma skin cancer detection and its previous stages (Common Nevus and Atypical Nevus). The methods being proposed employ a blend of colour, texture, and shape characteristics to derive distinguishing attributes from the images. Using CNN (convolutional neural networks) and SVM (support vector machine) algorithms to identify the type of skin cancer the patient is affected with and achieved an accuracy of 92% and 95% respectively.
Application of Natural (Plant) Fibers Particularly Hemp Fiber as Reinforcement in Hybrid Polymer Composites - Part II. Volume of Hemp Cultivation, Its Application and Sales Market

ABSTRACT The paper is a second part of a multi-aspect analysis of whether it is feasible and practical to use (plant) natural fibers processed into fabrics and mats as homogeneous or hybrid reinforcement in polymer composites used in various industrial sectors. The paper presents the volume of hemp cultivation worldwide, in Europe and in Poland 2016–2022. It also sets out the percentage share of hemp application in nine industrial sectors. The presented statistics includes total volume of industrial hemp stalks market, which is a by-product of hemp farming in the main geographical regions of the world and in Poland. The paper gives examples of current sales of hemp at the USA market, with sales until 2022. The paper discusses the possibility of using industrial hemp as “noble reinforcement that is fully recyclable” of a new generation of polymer structural composites which can be applied in modern sectors of bulky structures, including sea-going vessels.
TGFBR1 Variants Can Associate with Non-Syndromic Congenital Heart Disease without Aortopathy

Background: Congenital heart diseases (CHD) are the most common congenital malformations in newborns and remain the leading cause of mortality among infants under one year old. Molecular diagnosis is crucial to evaluate the recurrence risk and to address future prenatal diagnosis. Here, we describe two families with various forms of inherited non-syndromic CHD and the genetic work-up and resultant findings. Methods: Next-generation sequencing (NGS) was employed in both families to uncover the genetic cause. In addition, we performed functional analysis to investigate the consequences of the identified variants in vitro. Results: NGS identified possible causative variants in both families in the protein kinase domain of the TGFBR1 gene. These variants occurred on the same amino acid, but resulted in differently substituted amino acids (p.R398C/p.R398H). Both variants co-segregate with the disease, are extremely rare or unique, and occur in an evolutionary highly conserved domain of the protein. Furthermore, both variants demonstrated a significantly altered TGFBR1-smad signaling activity. Clinical investigation revealed that none of the carriers had (signs of) aortopathy. Conclusion: In conclusion, we describe two families, with various forms of inherited non-syndromic CHD without aortopathies, associated with unique/rare variants in TGFBR1 that display altered TGF-beta signaling. These findings highlight involvement of TGFBR1 in CHD, and warrant consideration of potential causative TGFBR1 variants also in CHD patients without aortopathies.
? Late Pliocene-Early Pleistocene Microvertebrates from the Upper Siwalik Subgroup of Jammu, Jammu and Kashmir, India

Microvertebrate remains comprising isolated fish teeth and spines, lacertilian dentaries, unidentified mammalian claws and phalanges and a couple of astragali referable to Rattus (Rodentia) are being described for the first time from the mudstone horizon immediately u nderlying the geochronologically dated (2.48 m.y., Late Pliocene-Early Pleistocene) bentonitized tuff band of the Nagrota Formation (=Pinjore Formation, Upper Siwalik Subgroup) at Barakhetar, Khanpur, Anandpur and Uttarbehani localities, Jammu province, India. The age and palaeoecology of the recoverd fauna has also been discussed in the present paper.
Prediction of Burr Formation in End Micro Milling Poly methyl Methacrylate (PMMA) and Polycarbonate (PC) Substrates Using Convolutional Neural Network (CNN)

Polymer microfluidic device is growing in the fields of disease detection, drug synthesis, and environmental monitoring because of the benefits of the miniaturized platforms that provides rapid high-throughput analysis at small sample volumes. A machining technique called micro milling is employed in the manufacture of micro components (micro fluidic devices) such as poly methyl methacrylate (PMMA) or polycarbonate (PC). Micro milling has the advantage of being a quicker, more affordable, and more effective method for fabricating more complex structures. PMMA has been used as the substrate in this study for micro milling followed by factor analysis. This aim of this study is to understand the influence of each micro milling parameter to the surface quality. This paper includes 450 microscopic images of the micro-milling substrate by different parameters like spindle speed, depth of cut and Surface quality. The microscopic images are divided to test, train and Val dataset, using three datasets and a Convolutional Neural Network (CNN) is designed.
Compressional and shear wave velocities of ringwoodite γ-Mg2SiO4 to 12 GPa

Abstract Compressional and shear wave velocities of ringwoodite (γ-Mg2SiO4) were measured on a polycrystalline specimen to 12 GPa at room temperature using ultrasonic interferometry techniques. Velocity measurements at ambient conditions yielded Vp = 9.86(3) and Vs = 5.78(2) km/s. Finite strain analysis of the high pressure velocity data yields K = 185(2) GPa, G = 120(1) GPa, K' = 4.5(2), and G' = 1.5(1) for the elastic moduli and their pressure derivatives, respectively. The velocities and elastic moduli at ambient conditions are indistinguishable from aggregate properties of polycrystalline ringwoodite calculated using single crystal elastic constants (Cij). Comparison of the current results on pressure derivatives of bulk and shear moduli with previous acoustic data on iron free ringwoodite to 3 GPa and iron bearing sample to 16 GPa indicated that the discrepancy can be explained by the pressure range of the experiments rather than iron content. Current results suggest that ringwoodite and wadsleyite posses very similar pressure dependence in elastic properties to the transition zone depth. In a pyrolite mantle with 1400°C adiabatic foot temperature, the wadsleyite to ringwoodite phase transition is characterized as a seismic reflector spreading about 20 km in width with a density jump of 2.1% and impedance jumps of 2.4% and 3.1% for P and S waves near 520 km depth, which is consistent with seismic observations in long period data
Effect of Calculating Pointwise Mutual Information using a Fuzzy Sliding Window in Topic Modeling

Topic modeling is a popular method for analysing large amounts of unstructured text data and extracting meaningful insights. The coherence of the generated topics is a critical metric for determining the model quality and measuring the semantic relatedness of the words in a topic. The distributional hypothesis, a fundamental theory in linguistics, states that words occurring in the same contexts tend to have similar meanings. Based on this theory, word co-occurrence in a given context is often used to reflect word association in coherence scores. To this end, many coherence scores use Normalised Pointwise Mutual Information (NPMI), which uses a sliding window to describe the neighbourhood that defines the context. It is assumed that there is no other structure in the neighbourhood except for the presence of words. Inspired by the distributional hypothesis, we hypothesise the word distance to be relevant for determining the word association. Hence, we propose using a fuzzy sliding window to define a neighbourhood in which the association between words depends on the membership of the words in the fuzzy sliding window. To this end, we propose Fuzzy Normalized Pointwise Mutual Information (FNPMI) to calculate fuzzy coherence scores. We implement two different neighbourhood structures by the definition of the membership function of the sliding window. In the first implementation, the association between two words correlates positively with the distance, whereas the correlation is negative in the second. We compare the correlation of our proposed new coherence metrics with human judgment. We find that the use of a fuzzy sliding window correlates less with human judgment than a crisp sliding window. This finding indicates that word distance within a window is less important than defining the window size itself.
Enhancing Consistency in Posterior Malleolus Fracture Classification: A Comprehensive Interobserver Reliability Study With 20 Raters Using the Mason & Molloy Classification

Introduction: Over the past decade, there has been a growing interest in the identification and treatment of posterior malleolus fragments, driven by a better understanding of their significance. The Mason & Molloy (M&M) classification system has emerged as a valuable tool for systematically categorizing these fractures and assisting clinicians in formulating treatment. We aim to assess the interobserver reliability of the M&M classification for posterior malleolus fracture by using 20 raters. Methodology: The study was conducted at a major foot and ankle referral center in Wythenshawe, Manchester, UK. Thirty-eight Computed Tomography (CT) scans were evaluated by 20 independent raters: 15 general orthopedic and trauma surgeons plus five foot and ankle surgeons. Each rater classified the posterior malleolus fracture according to M&M classification into type 1, 2A, 2B, 3, or not classifiable. Statistical analysis was done with the R software package and SPSS (v26; IBM Corp., Armonk, NY). Fleiss kappa (κ) coefficient with a 95% confidence interval (CI) was applied. Results: The interobserver agreement was moderate with a global κ value of 0.531 (95% CI: 0.518, 0.544). There were good agreements for identifying type 3 M&M (κ=0.785) and those that are not applicable for M&M classification (κ=0.785). There was a strong correlation between all raters in using M&M classification (Tb=0.53-0.59) except for Rater 12. Conclusion: M&M classification remains a valuable tool to guide the management of patients with these subsets of ankle fractures.
Performance evaluation of the sterilization process with Bowie & Dick test and biological indicator in the quality control of a blood bank in Peru

Managing contaminated waste in blood banks is a crucial process that must end with the safe disposal of blood products. In this sense, practical methods and indicators must be used to guarantee the operability of autoclave equipment and the sterility of treated waste to mitigate the risk of biological exposure and hospital biosafety. We designed a cross-sectional study to evaluate the performance the autoclaving process with Bowie & Dick test and biological indicator at the Hemotherapy and Blood Bank Unit of the Cayetano Heredia Hospital in Peru. Fifty autoclaving processes were carried out independently for the Bowie & Dick tests and biological indicators based on Geobacillus stearothermophilus spores. Autoclaving was programmed at 134°C for 3.5 minutes for the Bowie & Dick test, while, for the biological indicator, it was programmed at 121°C for 5 minutes. Both in the presence of contaminated waste. The autoclaving process evaluated by the Bowie & Dick test had a compliance rate of 80% (CI95: 66.3%–90.0%), while, by the biological indicator, 90% (CI95: 78.2%–96.7%). We did not find significant differences between the results of both tests (P = .689; Fisher exact test). The compliance rate in the autoclaving process within the blood bank of a Peruvian Hospital is acceptable; however, there are actions for continuous improvement, especially at the vacuum level in the autoclaving process.
A Comparative Study on Sulfonated PEEK and PVDF Blend Membranes for Direct Methanol Fuel Cells

Various polymeric blend membranes based on sulfonated poly(ether ether ketone) (sPEEK) and poly(vinylidene fluoride) (PVDF) are prepared by solution casting method for direct methanol fuel cells (DMFCs). Physicochemical properties of the membranes are studied after blending sPEEK with PVDF in different ratios. It is found that the thermal stabilities of all blend membranes are above 200oC which is sufficiently high for use in DMFC. Water uptake values of the blend membranes vary between 9-20% as a function of the blend composition. Similar to the water uptake property, proton conductivity values and surface hydrophilicity of the samples increase with sPEEK portion in the blend. Water uptake of sPEEK30/PVDF70 blend membrane is about 9.2%, the proton conductivity of this membrane is 0.8 mS.cm-1, and the methanol permeability of sPEEK30/PVDF70 blend membrane is about 1/10 that of untreated NafionÂ®117 reported in the literature. Based on the results, it is concluded that the sPEEK/PVDF polymer blends can be considered as a candidate membrane for DMFC applications considering their controllable properties, cheapness and easy preparation.
Effect of Microplastic Particles on the Rheological Properties of Human Saliva and Mucus

Pollution by plastic microparticles is rising rapidly. One avenue of human exposure to nanoparticles is through inhalation. The main source of microplastics in indoor environments, leading to unintended inhalation, is synthetic fabric used in clothing. Other sources include curtains, carpets, furniture, wall paints, and floor finishes. Occupational exposure is particularly significant in waste management and recycling operations, during exposure to high heat, during high-energy treatment of polymer composites, and during 3D printing. In outdoor environments, exposure can happen through breathing in contaminated aerosols from ocean waves or airborne particles from dried wastewater treatments. Airborne particles affect human health in various ways, including via direct interactions with the epithelium and its mucus layer after deposition in the mouth and respiratory system. Exposure due to the ingestion of microplastics present in various environmental compartments may occur either directly or indirectly via the food chain or drinking water. This study aimed to determine the effects of plastic microparticles on the rheology of mucus and saliva, and, thus, their functioning. The experiments used artificial mucus, saliva, and plastic nanoparticles (namely, PS—polystyrene and PE—polyethylene). The rheological properties of saliva and mucus were determined via the use of an oscillatory rheometer at various temperatures (namely, 36.6 °C and 40 °C, which correspond to healthy and ill humans). The results were compared with those obtained for pure saliva and mucus. An increase in apparent viscosity was observed for saliva, which is behavior typical of for solid particle suspensions in liquids. In contrast, for mucus, the effect was the opposite. The influence of the presence of the particles on the parameters of the constitutive viscosity equations was studied. Plastic micro- and nanoparticles in the saliva and mucus may interfere with their physiological functions.
Bilevel optimal parameter learning for a high-order nonlocal multiframe super-resolution problem

This work elaborated an improved method to multiframe super-resolution (SR), which involves a nonlocal first-order regularization combined with a nonlocal p-Laplacian term. The nonlocal TV term excels at edge preserving, whilst the nonlocal p-Laplacian is commonly used to perfectly reconstruct image textures. Firstly, we discuss the existence and uniqueness of a solution to our new model in a well posed framework. Then, we derive a modified Primal-dual iteration to compute the super-resolved solution. Furthermore, we introduce a new bilevel optimization approach to learn two regularization parameters. The included tests validate that the introduced optimization procedure performs favorably compared to numerous SR approaches in terms of efficiency and accuracy.
Gesneriatuberifera (Gesneriaceae), a new lithophytic species from the Sierra de Bahoruco, Barahona Peninsula of southern Hispaniola (Dominican Republic)

﻿Abstract A narrowly endemic new species of Gesneria is described from the Sierra de Bahoruco in the Dominican Republic’s Barahona Peninsula of southern Hispaniola. Gesneriatuberifera J.L. Clark & T.Clase, sp. nov. differs from all other congeners by the presence of a tuber and tubular red corollas with yellow lobes. Images and a discussion are provided to summarize the presence of tubers in other Gesneriaceae and differentiate Gesneriatuberifera from congeners that share a lithophytic habit. Based on IUCN guidelines, a preliminary conservation status of Endangered (EN) is assessed for G.tuberifera.
Application of CE-QUAL-W2 in the Shanzai Reservoir, Fujian Province

A two-dimensional, laterally averaged Model CE-QUAL-W2 was applied to the Shanzai Reservoir to simulate the hydrodynamics and water quality in the water column. The modeling effort was supported with data collected by fieldwork and experiments within half a year. Furthermore, the study made a water quality assessment of the eutrophication in the outlet of reservoir from Apr. 7th, 2006 to Jun 7th, 2006, using Trophic State Indexes (TSI) method and analytical hierarchy process(AHP). The result showed that, On May 12th, TSI was higher than the other days, almost reached 80; and the first big water blooms lasted from Apr. 29th to the beginning of June, 2006. The modeling result of three scenarios (keeping original flux, decreasing water level and increasing flux) indicated that decreasing water level and increasing flux could greatly improve the water quality of outlet.
HUBUNGAN CAREER SELF-EFFICACY DAN KESIAPAN KERJA PADA MAHASISWA TINGKAT AKHIR DI MASA PANDEMI COVID-19

Tujuan penelitian ini untuk mengetahui hubungan antara career self-efficacy dengan kesiapan kerja pada mahasiswa tingkat akhir di masa pandemi COVID-19). Dengan menggunakan metode penelitian kuantitatif dan desain penelitian korelasional, penelitian ini melibatkan mahasiswa tingkat akhir yang sedang menyusun skripsi dengan rentang usia dari 20 sampai 25 tahun sebanyak 365 partisipan (170 mahasiswa laki-laki dan 195 mahasiswa perempuan). Alat ukur yang digunakan untuk mengukur career self-efficacy adalah Career Decision-Making Self-efficacy Scale Short Form (CDMSE-SF; Betz, Klein, & Taylor, 1996; Ramadini, 2021) dan alat ukur kesiapan Kerja (Caballero et al., 2011; Permata, 2022). Berdasarkan analisis dengan teknik korelasi Spearman Rho diperoleh hasil bahwa terdapat hubungan antara career self-efficacy dengan kesiapan kerja pada mahasiswa tingkat akhir di masa Pandemi COVID-19), dengan nilai r (365) = 0.53, p = . 00 < 0.05. Hal ini menunjukkan bahwa mahasiswa memiliki career self-efficacy yang tinggi maka mahasiswa memiliki kesiapan untuk bekerja yang tinggi. Adanya kesiapan kerja yang tinggi dapat menekan angka pengangguran