Miracle-dz/UniFAR-FTUs huggingface dataset:
Title A Mutation in the Gene Encoding Mitochondrial Mg 2 + Channel MRS 2 Results in Demyelination in the Rat

The rat demyelination (dmy) mutation serves as a unique model system to investigate the maintenance of myelin, because it provokes severe myelin breakdown in the central nervous system (CNS) after normal postnatal completion of myelination. Here, we report the molecular characterization of this mutation and discuss the possible pathomechanisms underlying demyelination. By positional cloning, we found that a G-to-A transition, 177 bp downstream of exon 3 of the Mrs2 (MRS2 magnesium homeostasis factor (Saccharomyces cerevisiae)) gene, generated a novel splice acceptor site which resulted in functional inactivation of the mutant allele. Transgenic rescue with wild-type Mrs2-cDNA validated our findings. Mrs2 encodes an essential component of the major Mg influx system in mitochondria of yeast as well as human cells. We showed that the dmy/dmy rats have major mitochondrial deficits with a markedly elevated lactic acid concentration in the cerebrospinal fluid, a 60% reduction in ATP, and increased numbers of mitochondria in the swollen cytoplasm of oligodendrocytes. MRS2-GFP recombinant BAC transgenic rats showed that MRS2 was dominantly expressed in neurons rather than oligodendrocytes and was ultrastructurally observed in the inner membrane of mitochondria. Our observations led to the conclusion that dmy/dmy rats suffer from a mitochondrial disease and that the maintenance of myelin has a different mechanism from its initial production. They also established that Mg homeostasis in CNS mitochondria is essential for the maintenance of myelin. Citation: Kuramoto T, Kuwamura M, Tokuda S, Izawa T, Nakane Y, et al. (2011) A Mutation in the Gene Encoding Mitochondrial Mg Channel MRS2 Results in Demyelination in the Rat. PLoS Genet 7(1): e1001262. doi:10.1371/journal.pgen. 1001262 Editor: Gregory S. Barsh, Stanford University, United States of America Received June 5, 2010; Accepted November 29, 2010; Published January 6, 2011 Copyright: 2011 Kuramoto et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by grants-in-aid for Scientific Research from the Japan Society for the Promotion of Science [21300153 to TK] and a grant-inaid for Cancer Research from the Ministry of Health, Labour, and Welfare. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: tkuramot@anim.med.kyoto-u.ac. jp
Transition from thin-film to bulk properties of metamaterials

Effective material parameters for metamaterials are usually extracted for a single thin layer only. We show that, in general, these parameters cannot directly be assigned to a multilayered (bulk) metamaterial. The principal issue is the presence of higher-order Bloch modes. These modes are usually negligible for a single layer but can have considerable influence on the results for a periodically stacked structure. In particular, we show examples where a stack of single-layer negative refractive index metamaterials exhibits a positive index. Furthermore, on the basis of the dispersion relation of the respective Bloch modes, we derive criteria for the validity of the effective parameter approach.
Inelastic electron tunneling spectra of MgO-based magnetic tunnel junctions with different electrode designs

MgO-based magnetic tunnel junctions with up to 230% tunnel magnetoresistance ratio at room temperature and up to 345% at 13 K are prepared. The lower electrode is either exchange-biased or free, while the top electrode is free or an exchanged-biased artificial ferrimagnet, respectively. Additionally, a pseudo-spin-valve (hard-soft switching) design with two unpinned electrodes is used. Inelastic electron-tunneling spectra for each of these systems show a strong variation in the zero-bias anomaly with a reduced peak for some of the junctions. At voltages around 200 mV additional structures are found, which are not known from junctions with lower magneto resistance, such as alumina-based junctions. We discuss the spectra for the different electrode types and compare our findings with respect to barrier material and magnetoresistance ratio.
Cox models with time‐varying covariates and partly‐interval censoring–A maximum penalised likelihood approach

Time‐varying covariates can be important predictors when model based predictions are considered. A Cox model that includes time‐varying covariates is usually referred to as an extended Cox model. When only right censoring is presented in the observed survival times, the conventional partial likelihood method is still applicable to estimate the regression coefficients of an extended Cox model. However, if there are interval‐censored survival times, then the partial likelihood method is not directly available unless an imputation, such as the middle point imputation, is used to replaced the left‐ and interval‐censored data. However, such imputation methods are well known for causing biases. This paper considers fitting of the extended Cox models using the maximum penalised likelihood method allowing observed survival times to be partly interval censored, where a penalty function is used to regularise the baseline hazard estimate. We present simulation studies to demonstrate the performance of our proposed method, and illustrate our method with applications to two real datasets from medical research.
Lie theory and the wave equation in space-time. 4. The Klein-Gordon equation and the Poincaré group

A detailed classification is made of all orthogonal coordinate systems for which the Klein–Gordon equation in space–time, ψtt−Δ3ψ=λψ, admits a separation of variables. We show that the Klein–Gordon equation is separable in 261 orthogonal coordinate systems. In each case the coordinate systems presented are characterized in terms of three symmetric second order commuting operators in the enveloping algebra of the Poincare group. This paper also consitutes an important step in the study of separation of variables for the wave equation in space–time ψtt−Δ3ψ=0, and its relation to the underlying conformal symmetry group O(4,2) of this equation.
Thermodynamics and nonlinear mechanics of materials with photoresponsive microstructure

The ability to directly convert visible light radiation into useful mechanical work provides many opportunities in the field of smart materials and adaptive structures ranging from biomedical applications to control of heliostat mirrors for solar harvesting. The complexities associated with coupling time-dependent Maxwell’s equations with linear momentum and mechanics is discussed by introducing a set of electronic order parameters that govern the coupling between electromagnetic radiation and mechanics of a deformable solid. Numerical examples are given illustrating how this methodology is applied to a special class of liquid crystal polymer networks containing azobenzene. The dynamics associated with light absorption and its effect on deformation of the polymer are solved in three dimensions using finite difference methods and compared to experimental results. Particular emphasis is placed on the effect of polarized light on microstructure evolution and stresses that occur during photoisomerization of the optically active microstructure.
Tracking The Sensitivity of The Learning Models Toward Exact and Near Duplicates

— Most real-world datasets contaminated by quality issues have a severe effect on the analysis results. Duplication is one of the main quality issues that hinder these results. Different studies have tackled the duplication issue from different perspectives. However, revealing the sensitivity of supervised and unsupervised learning models under the existence of different types of duplicates, deterministic and probabilistic, is not broadly addressed. Furthermore, a simple metric is used to estimate the ratio of both types of duplicates regardless of the probability by which the record is considered duplicate. In this paper, the sensitivity of five classifiers and four clustering algorithms toward deterministic and probabilistic duplicates with different ratios (0% - 15%) is tracked. Five evaluation metrics are used to accurately track the changes in the sensitivity of each learning model, MCC, F1-Score, Accuracy, Average Silhouette Coefficient, and DUNN Index. Also, a metric to measure the ratio of probabilistic duplicates within a dataset is introduced. The results revealed the effectiveness of the proposed metric that reflects the ratio of probabilistic duplicates within the dataset. All learning models, classification, and clustering models are differently sensitive to the existence of duplicates. RF and Kmeans are positively affected by the existence of duplicates which means that their performce increase as the percentage of duplicates increases. Furthermore, the rest of classifiers and clustering algorithms are sensitive toward duplicates existence, especially within high percentage that negatively affect their performance.
Random Fields Generated by Dilution in Zero External Field

It is shown that arbitrarily small amounts of bond or site dilution generate (in zero magnetic field) effects in some Ising antiferromagnets much like random fields and are, therefore, not covered by the criterion of Harris. All antiferromagnets on f.c. c. lattices as well as antiferromagnets of type-II in b.c.c. lattice suffer this effect. We argue that the cross-over exponent () into the impurity-dominated regime fulfills γ in any dimensionality, and that an Ising model with J2 < 0 and |J1| < 2|J2| is unstable against any small site or bond dilution. Numerical results for strips, obtained by the transfer matrix method confirm this picture.
Role of the Rho GTPase in Bradykinin-Stimulated Nuclear Factor-κB Activation and IL-1β Gene Expression in Cultured Human Epithelial Cells

Recent evidence suggests a novel role of bradykinin (BK) in stimulating gene transcription. This study examined the effect of BK on nuclear factor κB (NF-κB) activation and IL-1β synthesis in human epithelial cells. Stimulation of A549 cells and primary bronchial epithelial cells with BK rapidly activated NF-κB. BK also increased the level of secreted immunoreactive IL-1β in A549 culture supernatants, an effect that was blocked by actinomycin D and the B2 BK receptor antagonist HOE-140. The role of NF-κB activation in BK-induced IL-1β synthesis was demonstrated by the ability of BK to stimulate increased chloramphenicol acetyltransferase (CAT) activity in A549 cells transfected with a reporter plasmid containing three κB enhancers from the IL-1β gene, while deletion of the κB enhancer sequences eliminated BK-stimulated CAT activity. C3 transferase exoenzyme, an inhibitor of Rho, abolished BK-induced NF-κB activation at 10 μg/ml and significantly inhibited BK-stimulated IL-1β synthesis at 5 μg/ml. A dominant-negative form of RhoA (T19N) inhibited BK-stimulated reporter gene expression in a dose-dependent and κB-dependent manner. Cotransfection of A549 cells with an expression vector encoding a constitutively active form of RhoA (Q63L) along with the IL-1β promoter-CAT reporter plasmid resulted in a marked increase in NF-κB activity compared with transfection with the IL-1β promoter-CAT reporter plasmid alone. These results demonstrate that BK stimulates NF-κB activation and IL-1β synthesis in A549 cells, and that RhoA is both necessary and sufficient to mediate this effect.
The Problem of Choosing the Language of Communication: Ukrainian Realities

The Problem of Choosing the Language of Communication: Ukrainian RealitiesThe article analyses the situational change in the language behaviour (code switching) of representatives from different regions of Ukraine based on the data of a statistically significant mass survey of the adult population of different regions in 2017. The dependence of language behaviour on the language situation in a region is confirmed and certain patterns inherent in each of the regions are revealed. The use of the Ukrainian language has a similar dependence on the language situation in all regions, but at different levels: it is used more often when communicating with Ukrainian-speakers, officials, in education, etc. It is rarely used in the information sphere. The connection between the degree of Russification of a certain territory and the laws of language choice in a particular situation is revealed. The articles proposes using the results of mass surveys to study language behaviour through the prism of territorial features, which will help to pursue a balanced state language policy, adjusting tactics in accordance with regional language features. Problem wyboru języka komunikacji: realia ukraińskieW artykule dokonano analizy zmiany sytuacyjnej zachowań językowych (przełączanie kodu językowego) przedstawicieli różnych regionów Ukrainy na materiale danych pochodzących z wiarygodnego pod względem statystycznym ankietowania dorosłych mieszkańców różnych regionów Ukrainy w 2017 roku. Potwierdzono zależność tej zmiany od sytuacji językowej w regionie. Wykazano pewne prawidłowości właściwe dla każdego z nich. We wszystkich regionach posługiwanie się językiem ukraińskim wykazuje podobną zależność od sytuacji komunikacyjnej, realizowane jest jednak w różnym stopniu: najczęściej język ukraiński jest używany w rozmowie z urzędnikami ukraińskojęzycznymi, w oświacie, najrzadziej – w sferze informacyjnej. Ustalono związek między stopniem rusyfikacji na określonym terenie a prawidłowościami wyboru języka komunikowania się w danej sytuacji. W artykule zaproponowano szersze wykorzystanie rezultatów masowego ankietowania do badań nad zachowaniem językowym przez pryzmat cech regionalnych, co pomoże w realizacji zrównoważonej polityki językowej państwa, dostosowującej taktykę odpowiednio do specyfiki regionów.
Exploiting Korean Language Model to Improve Korean Voice Phishing Detection

Text classification task from Natural Language Processing (NLP) combined with state-of-the-art (SOTA) Machine Learning (ML) and Deep Learning (DL) algorithms as the core engine is widely used to detect and classify voice phishing call transcripts. While numerous studies on the classification of voice phishing call transcripts are being conducted and demonstrated good performances, with the increase of non-face-to-face financial transactions, there is still the need for improvement using the latest NLP technologies. This paper conducts a benchmarking of Korean voice phishing detection performances of the pre-trained Korean language model KoBERT, against multiple other SOTA algorithms based on the classification of related transcripts from the labeled Korean voice phishing dataset called KorCCVi. The results of the experiments reveal that the classification accuracy on a test set of the KoBERT model outperforms the performances of all other models with an accuracy score of 99.60%
Multitemporal climate variability over the Atlantic Ocean and Eurasia: linkages with Mediterranean and West African climate

More linkages between the Atlantic multidecadal oscillation (AMO, and thermohaline circulation), the Polar vortex oscillation (PVO, and vorticity intrusion), the North Africa–West Asia index (NAWA, and NA–WA poles) and Sahelian rainfall, are investigated. Significant positive correlations are found between winter/spring AMO and spring NA pole. Significant positive (negative) correlations are found between winter PVO and NAWA (WA pole). NAWA is shown being modulated by winter AMO to the west, and winter PVO over Eurasia to the east. Winter AMO and PVO can thus be used operationally by decision makers as early predictors of Mediterranean climate and Sahelian rainfall during summer. Copyright © 2008 Royal Meteorological Society
A hybrid finite element-boundary integral formulation of the eddy current problem

A method is presented for the solution of the two dimensional eddy current problem in terms of the magnetic vector potential. In the formulation presented, any region in the problem may be represented by either finite elements or a boundary integral. This information is specified through use of an interactive graphics pre-processor which automatically generates the boundary elements and the finite elements. Examples are given.
TCP-QNCC: congestion control algorithm based on deep Q-network

When the demand for network resources exceeds the available capacity of the network, network congestion occurs on the Internet. Network congestion can cause transmission delays, packet loss, and even congestion collapse. When this happens, all communication across the network comes to a halt. Therefore, congestion has long been a concern. Although traditional congestion control algorithms have performed well in early simple networks, their limitations prevent them from being effective before network congestion occurs, making them difficult to cope with the challenges of network complexity and service diversification. In this paper, we model network congestion control as a Markov decision process and optimize congestion control strategies using a deep Q network in deep reinforcement learning, proposing a congestion control algorithm, QNCC, that is purely data-driven and does not rely on any assumptions. QNCC uses a fully connected neural network to approximate the value function, enabling it to automatically learn features and have good generalization ability. Experiments show that QNCC performs better overall than traditional congestion control algorithms such as TCP-Cubic and TCP-Vegas in multiple network scenarios.
Analysis of Content Based Image Retrieval using Deep Feature Extraction and Similarity Matching

—Image retrieval using a textual query becomes a major challenge mainly due to human perception subjectivity and the impreciseness of image annotations. These drawbacks can be overcome by focusing on the content of images rather than on the textual descriptions of images. Traditional feature extraction techniques demand for expert knowledge to select the limited feature types and are also sensitive to changing imaging conditions. Deep feature extraction using Convolutional Neural Network (CNN) are a solution to these drawbacks as they can learn the feature representations automatically. This work carries out a detailed performance comparison of various pre-trained models of CNN in feature extraction. Features are extracted from men footwear and women clothing datasets using the VGG16, VGG19, InceptionV3, Xception and ResNet50 models. Further, these extracted features are used for classification using SVM, Random Forest and K-Nearest Neighbors classifiers. Results of feature extraction and image retrieval show that VGG19, Inception and Xception features perform well with feature extraction, achieving a good image classification accuracy of 97.5%. These results are further justified by performing a comparison of image retrieval efficiency, with the extracted features and similarity metrics. This work also compares the accuracy obtained by features extracted by the selected pre-trained CNN models with the results obtained using conventional classification techniques on CIFAR 10 dataset. The features extracted using CNN can be used in image-based systems like recommender systems, where images have to be analyzed to generate item profiles.
High reflectivity airgap distributed Bragg reflectors realized by wet etching of AlInN sacrificial layers

The authors report on the achievement of a vertically oriented three pair airgap/GaN distributed Bragg reflector realized by controlled oxidation and wet-chemical etching of AlInN sacrificial layers. Microreflectivity measurements exhibit high peak reflectivity values of 87% around 500 nm after the oxidation process and 90% around 600 nm after the etching process in overall good agreement with simulations. The broad stopband of airgap/GaN mirrors, about 250 nm wide, results from the strong refractive index contrast between air and GaN layers.
Effectiveness of low-level laser therapy and myofascial release technique on health-related outcomes of pregnant women with temporomandibular myofascial pain: A Randomized Controlled Trial

: Objective: This research aimed to investigate the usefulness of low-level laser therapy (LLLT) combined with myofascial release (MFR) technique for Temporomandibular myofascial pain (TMP) during pregnancy. Methods: A randomized, controlled experiment with a prospective, double-blind design was conducted on 30 pregnant women, with a mean age of 28.2±3.4 years, at 24 weeks of pregnancy, seeking treatment for chronic TMP (lasting more than 3 months). The patients were randomly assigned into either control group (CG) who received standard treatment alone in the form of therapeutic ultrasound, hot pack and exercise program or experimental group (EG) who received the standard treatment combined with LLLT and MFR. Pain intensity and temporomandibular joint (TMJ) function as a primary outcome and pain pressure threshold (PPT) as a secondary outcome were assessed pre and post the treatment (four weeks program). Results: Participants from both groups improved all outcomes after the treatment (p < .001). The EG showed better improvement in the primary outcome function compared to the CG with (MD) = -2.07 (95%CI = -3.66 to -1.59). In addition, both groups significantly increased PPT at the TMJ, Masseter and Temporal anterior muscles in both sides (p < .001). Although the combined treatment resulted in clinically important changes in pain intensity, this change was more prominent in participants from the control group (MD = 2.6, 95%CI = 0.3 to 1.91). Conclusion: LLLT combined with MFR, and standard treatment is superior to standard treatment alone to reduce PPT and increase TMJ function in pregnant women with TMP
Learning in the transformation of organizations with the implementation of agile methods

Objective: This article aims to understand the influence of organizational learning on adaptation during the implementation of agile methods in the project management environment in companies. The study focuses on changing project management processes from the traditional “waterfall” to agile management models to meet the needs of organizations. It aims to identify how the organization based on traditional project management migrates to a new process, adapting the new agile methodology to its day-to-day activities. Design/methodology/approach: We carried out a systematic literature review, answering the question: How does organizational learning influence the adaptation of agile methods? Results: We grouped the articles into five fields of research: Agile Learning; Organizational Learning in the implementation of Lean and Scrum methodologies: Implementation of Lean and Scrum methodologies; Learning of the individual in the organization and Organizational Learning. Originality/Value: This article seeks interests in the potential of applying agile projects and the organizational learning acquired from this new model.