Additional Features Research Articles

EXPLORING ASPECTS OF LEXICAL CONVERGENCE AND DIVERGENCE IN SOME TRANSLATIONS OF THE HOLLY QUR’AN

It is commonly observed that languages all over the world may converge or diverse in their linguistic features such as lexical and semantic perspectives. These are linguistic phenomena that happen in all languages as a resulting consequence of the evolution and development of all human languages on earth. This present study is primarily designed to tackle these aspects of convergence and divergence in some selected translations of the Holly Qur’an. Special consideration will be given to the impact of these aspects of lexical convergence and divergence on the selected translations of the verses of the Holly Qur’an that is to say whether they add or deduce to the semantic value of the selected lexical items. Our initial anticipation is that no selected items of these translations of the meanings of the Holly Qur’an will have the same value of the original Arabic term that is used in the verses of the Holly Qur’an; however, some English Lexical items will have distinct semantic value in explaining the meaning under investigation or interpretation as they have more or less the same semantic import of the Arabic items, i.e. if we take just a simple example of such lexical items such as the word child in English that may either be used to mean a young or a small - aged person, or to mean someone's son or daughter. The latter meaning may give the exact meaning of sons and daughters in some of the verses that will be selected in this study. In such a case we feel that the lexical choice helps in selecting an item of a positive convergence between Arabic and English. The divergence in the lexical items may cause some problems for the translators of the meanings of the Holly Qur’an; therefore, they may depend on additional linguistic features to bridge the gap between the Arabic items and the English ones such as literal translations, the denotative dictionary definitions, or the use of bracketing or foot notes. This study will be a qualitative survey and a content analysis approach whereby some previously related studies together with some selected verses of the Holly Qur’an that will be chosen from “The Noble Qur’an" and "The Glorious Qur'an”. All the important elements of a scientific research such as the Statement of the study, its importance, methodology, discussion, findings and suggestions for further future studies will be tackled in this present study.

Efficient plant leaf detection through machine learning approach based on corn leaf image classification

Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the improved k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.

Factors Influencing Adoption of E-Banking Services in Selected Areas of Gujarat

In the contemporary era, the rapid evolution of technology has ushered in a paradigm shift in the way individuals manage their financial transactions. Among the myriad advancements, the adoption of mobile banking applications stands out as a transformative force, redefining the landscape of banking services. The present study tried to study the awareness of mobile banking services of banking applications and to ascertain the factors influencing consumer behavior adoption of Mobile. Further study carried out to study the association among demographic variables regarding factors studied for consumer behaviour adoption of mobile. In conclusion, the analysis of customers' preferences underscores a moderate response to the mobile banking application. While the current features of the application are satisfactory, customers express a desire for additional features akin to those found in alternative mobile banking applications such as SBI and HDFC. The prevailing perception among customers is that the application offered by Kotak Mahindra Bank is characterized by basic and uncomplicated features. Despite this, customer satisfaction with the Mobile Banking Application remains notably high, attributed to an extensive array of features and services. This observation accentuates the importance of ongoing efforts to enhance the mobile banking application in alignment with evolving customer expectations and industry standards.

ACCOUNTING OF CAPITAL RESERVES AS A RISK MANAGEMENT TOOL FOR PROTECTING THE CAPITAL OF THE ENTERPRISE

The activities of business entities are conducted in conditions of uncertainty, which leads to the emergence of various types of business risks. The accounting system plays an important role in the information provision of risk management.Reliable accounting data can be a reliable source of information when making decisions on economic risk management both by the managers of the enterprise and by other parties interested in its activities.The article is devoted to the study of the accounting provision of capital reserves as a risk management tool for the protection of the capital of the enterprise.In the classification of capital reserves, an additional feature is distinguished by the identification of risks for the management of which the reserves are formed. It has been established that the risks of enterprises are the subject of the formation of reserves, and therefore it is advisable for the enterprise to create specific types of reserves that are necessary to cover the types of risks that are inherent in its activities.

Patient Sex and Origin Influence Distribution of Driver Genes and Clinical Presentation of Paraganglioma.

Sexual and ancestral differences in driver gene prevalence have been described in many cancers but have not yet been investigated in pheochromocytoma and paraganglioma (PPGL). This study aims to assess whether sex and ancestry influence prevalence of PPGL driver genes and clinical presentation. We conducted a retrospective analysis of patients with PPGL considering studies from 2010 onwards that included minimal data of type of disease, sex, mutated gene, and country of origin. Additional features were recorded when available (age, tumor location, bilateral or multifocal, somatic or germline, and metastatic disease). We included 2162 patients: 877 in Europe and 757 in Asia. Males presented more often with germline pathogenic variants (PVs) in genes activating hypoxia pathways (P = .0006) and had more often sympathetic paragangliomas (P = .0005) and metastasis (P = .0039). On the other hand, females with PPGLs due to MAX PVs were diagnosed later than males (P = .0378) and more often developed metastasis (P = .0497). European but not Asian females presented more often with PPGLs due to PVs in genes related to kinase signaling (P = .0052), particularly RET and TMEM127. Contrary to experiences from Europe, Asian patients with PPGL due to PVs in kinase signaling genes NF1, HRAS, and FGFR1 showed a high proportion of sympathetic tumors, while European patients almost exclusively had adrenal tumors (P < .005). Personalized management of patients with PPGL might benefit from considering sexual and ancestral differences. Further studies with better clinically aligned cohorts from various origins are required to better dissect ancestral influences on PPGL development.

12-Lead ECG Reconstruction Based on Data From the First Limb Lead.

Electrocardiogram (ECG) data obtained from 12 leads are the most common and informative source for analyzing the cardiovascular system's (CVS) condition in medical practice. However, the large number of electrodes, specific placements on the body, and the need for specialized equipment make the ECG acquisition procedure complex and cumbersome. This raises the challenge of reducing the number of ECG leads by reconstructing missing leads based on available data. Most existing methods for reconstructing missing ECG leads rely on utilizing signals simultaneously from multiple known leads. This study proposes a method for reconstructing ECG data in 12 leads using signal data from the first lead, lead I. Such an approach can significantly simplify the ECG registration procedure. The study demonstrates the effectiveness of using unique models with a developed architecture of artificial neural networks (ANNs) to generate the reconstructed ECG signals. Fragments of ECG from lead I, with a duration of 128 samples and a sampling frequency of 100 Hz, are input to the models. ECG fragments can be extracted from the original signal at arbitrary time points. Each model generates an ECG signal of the same length at its output for the corresponding lead. Despite existing limitations, the proposed method surpasses known solutions regarding ECG generation quality when using a single lead. The study shows that introducing an additional feature of the direction of the electrical axis of the heart (EAH) as input to the ANN models enhances the generation quality. The quality of ECG generation by the proposed ANN models is found to be dependent on the presence of CVS diseases. The developed ECG reconstruction method holds significant potential for use in portable registration devices, screening procedures, and providing support for medical decision-making by healthcare specialists.

Voice-based age, gender, and language recognition based on ResNet deep model and transfer learning in spectro-temporal domain

In personal identity recognition systems, detecting a person's age, gender, and language using voice signal characteristics is a crucial issue, especially because of the importance of security considerations. Age, gender, and language classification problems are important in signal processing because they are used to analyze and understand human behavior, interactions, and preferences. This can be especially useful in the fields of human-computer interaction, psychology, and social science research. In this paper, a new system for detecting a speaker's age, gender, and language based on deep learning models is presented. Deep learning models have shown great efficacy in various fields of signal processing. For this paper, a range of deep models were tested, including convolutional neural networks (CNNs), recurrent neural network (RNN), and a fine-tuning ResNet34 architecture. Additionally, techniques such as transfer learning were applied to improve the efficiency of the proposed system. The input voice signals are preprocessed by applying the spectro-temporal transform to obtain additional features that can be fed to the ResNet34 model, which is designed specifically for the task of voice signal processing. The dataset used in this paper was sourced from the Mozilla common voice initiative, which is dedicated to advancing speech recognition and language identification technologies. The performance of the proposed algorithm was evaluated in the presence of Gaussian noise to determine its robustness. The experimental results demonstrated that the proposed algorithm significantly outperformed basic algorithms and other deep neural networks in terms of age and gender recognition from voice signals.

UPF1 ATPase autoinhibition and activation modulate RNA binding kinetics and NMD efficiency.

The RNA helicase UPF1 interacts with mRNAs, mRNA decay machinery, and the terminating ribosome to promote nonsense-mediated mRNA decay (NMD). Structural and biochemical data have revealed that UPF1 exists in an enzymatically autoinhibited 'closed' state. Upon binding the NMD protein UPF2, UPF1 undergoes an extensive conformational change into a more enzymatically active 'open' state, which exhibits enhanced ATPase and helicase activity. However, mechanically deficient UPF1 mutants (i.e. poorly processive, slow, and mechanochemically uncoupled) can support efficient NMD, bringing into question the roles of UPF1 enzymatic autoinhibition and activation in NMD. Here, we identify two additional important features of the activated open state: slower RNA binding kinetics and enhanced ATP-stimulated RNA dissociation kinetics. Computational modeling based on empirical measurements of UPF1, UPF2 and RNA interaction kinetics predicts that the majority of UPF1-RNA binding and dissociation events in cells occur independently of UPF2 binding. We find that UPF1 mutants with either reduced or accelerated dissociation from RNA have NMD defects, whereas UPF1 mutants that are more dependent on UPF2 for catalytic activity remain active on well-established NMD targets. These findings support a model in which the kinetics of UPF1-mRNA interactions are important determinants of cellular NMD efficiency.

Multivariate GRU and LSTM models for wave forecasting and hindcasting in the southern Caspian Sea

Reliable wave forecasts and hindcasts are pivotal elements for a wide range of marine activities, coastal engineering applications, renewable energy and wave climate studies. This study explores capability of recurrent neural networks for wave hindcasting and multi-step wave forecasting in the southern Caspian Sea, a region with a limited observational data. Gated recurrent unit (GRU) and long short-term memory (LSTM) models are fed by buoy records for wave forecasting up to 6 h ahead and ERA5 reanalysis data as the model inputs for wave hindcasting. Special efforts are devoted to improve the accuracy of the models by including some additional features (e.g., wave age and swell) in the input layer. Different error measures are employed to evaluate performance of the models at two stations, Amirabad and Anzali. Using multivariate GRU and LSTM models, suitable wave forecasts and hindcasts are achieved for the locations. Although the model performance deteriorates with extending the forecasting time horizon, the forecasts of significant wave height (Hs) up to 6 h ahead are still satisfactory. The best hindcasting model yields a high linear correlation between hindcasted and measured Hs with R2 = 0.75 for Anzali and R2 = 0.83 for Amirabad. Finally, wave age and significant height of total swell (Hs-swell) can be considered as tentative input variables to improve efficiency of the models.

Improving the second-tier classification of methylmalonic acidemia patients using a machinelearning ensemble method.

Methylmalonic acidemia (MMA) is a disorder of autosomal recessive inheritance, with an estimated prevalence of 1:50,000. First-tier clinical diagnostic tests often return many false positives [five false positive (FP): one true positive (TP)]. In this work, our goal was to refine a classification model that can minimize the number of false positives, currently an unmet need in the upstream diagnostics of MMA. We developed machinelearning multivariable screening models for MMA with utility as a secondary-tier tool for false positives reduction. We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients, followed by additional ratio feature construction. Feature selection strategies (selection by filter, recursive feature elimination, and learned vector quantization) were used to determine the input set for evaluating the performance of 14 classification models to identify a candidate model set for an ensemble model development. Our work identified computational models that explore metabolic analytes to reduce the number of false positives without compromising sensitivity. The best results [area under the receiver operating characteristic curve (AUROC) of 97%, sensitivity of 92%, and specificity of 95%] were obtained utilizing an ensemble of the algorithms random forest, C5.0, sparse linear discriminant analysis, and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the supervisor. The model achieved a good performance trade-off for a screening application with 6% false-positive rate (FPR) at 95% sensitivity, 35% FPR at 99% sensitivity, and 39% FPR at 100% sensitivity. The classification results and approach of this research can be utilized by clinicians globally, to improve the overall discovery of MMA in pediatric patients. The improved method, when adjusted to 100% precision, can be used to further inform the diagnostic process journey of MMA and help reduce the burden for patients and their families.

Catalytic effects on peristaltic flow of Jeffrey fluid through a flexible porous duct under oblique magnetic field: Application in biomimetic pumps for hazardous materials

Homogenous and heterogeneous reactions play an essential role in polymer production, combustion, ceramic production, catalysis, distillation, and biochemical procedures. As a result of this advancement, a mathematical model is developed to examine the role of catalytic chemical reaction effects on the magneto-hydrodynamic peristaltic transport in a flexible divergent duct containing a non-deformable porous medium as a model for complex hazardous waste bio-inspired pumping mechanisms. The Jeffrey model is utilized to simulate strong non-Newtonian characteristics. Additional multi-physical features included in the model are heat generation, thermal radiation, Hall current, and complaint wall properties along with an inclined (oblique) magnetic field. The long wavelength and low Reynolds number approximations are deployed to transform the primitive conservation equations from a moving boundary problem to a fixed frame. Appropriate transformations are then utilized to render the model dimensionless. The exact solutions are obtained for stream function and velocity. Further, the R-K-Fehlberg integration scheme is applied to solve the energy and concentration equations. Velocity, temperature, concentration, skin friction, and Nusselt number profiles are visualized graphically. Streamline patterns are also included to capture bolus dynamics. Validation with previous simpler studies has been included. A detailed appraisal of key control parameters on transport characteristics is conducted. It is found that velocity is suppressed with elevation in the damping force parameter while it is enhanced with an increment in regime permeability i. e. Darcy number. Both homogeneous and heterogeneous reactions are observed to exert a significant impact on the species concentration. The volume of the trapped fluid bolus size is shown to be an increasing function of the peristaltic wave amplitude ratio. The present work generalizes existing studies to consider catalytic effects on dissipative peristaltic pumping of the Jeffrey fluid with heat generation and radiative flux, aspects which have been neglected in a single model previously. The simulations are relevant to better characterize the complex transport phenomena in nuclear reactor waste transport via biologically inspired pumping designs.

DSTEA: Improving Dialogue State Tracking via Entity Adaptive pre-training

Dialogue State Tracking (DST) is critical for comprehensively interpreting user and system utterances, thereby forming the cornerstone of efficient dialogue systems. Despite past research efforts focused on enhancing DST performance through alterations to the model structure or integrating additional features like graph relations, they often require additional pre-training with external dialogue corpora. In this study, we propose DSTEA, improving Dialogue State Tracking via Entity Adaptive pre-training, which can enhance the encoder through by intensively training key entities in dialogue utterances. DSTEA identifies these pivotal entities from input dialogues utilizing four different methods: ontology information, named-entity recognition, the spaCy toolkit, and the flair library. Subsequently, it employs selective knowledge masking to train the model effectively. Remarkably, DSTEA only requires pre-training without the direct infusion of extra knowledge into the DST model. This approach results in substantial performance improvements of four robust DST models on MultiWOZ 2.0, 2.1, and 2.2, with joint goal accuracy witnessing an increase of up to 2.69% (from 52.41% to 55.10%). Comparative experiments considering various entity types and different entity adaptive pre-training configurations, such as masking strategy and masking rate, further validated the efficacy of DSTEA.

Infodemic – “Epidemic of Rumours”

The purpose of the analysis described in the text was to identify the characteristic features of the infodemic as a phenomenon on the example of the infodemic accompanying the COVID-19 epidemic in 2020. The author began with the definition developed by the World Health Organization in 2018, and traced the earliest scientific studies describing the phenomenon. Then, based on results of the examination of the studies and experts’ comments relating to the COVID-19 infodemic, the author described the main features of infodemic and their indicators. As a result of the investigation, the author decided to add four additional features to the initial infodemic characteristics. This is a new approach to the topic: the scientific literature on the does not contain such detailed characteristics of infodemic and focuses rather on selected phenomena associated with it.

Development and initial testing of mindful journey: a digital mindfulness-based intervention for promoting recovery from Substance use disorder

Background/Objectives There is a great unmet need for accessible adjunctive interventions to promote long-term recovery from substance use disorder (SUD). This study aimed to iteratively develop and test the initial feasibility and acceptability of Mindful Journey, a novel digital mindfulness-based intervention for promoting recovery among individuals with SUD. Patients/Materials Ten adults receiving outpatient treatment for SUD. Methods Phase 1 (n = 5) involved developing and testing a single introductory digital lesson. Phase 2 included a separate sample (n = 5) and involved testing all 15 digital lessons (each 30- to 45-minutes) over a 6-week period, while also receiving weekly brief phone coaching for motivational/technical support. Results Across both phases, quantitative ratings (rated on a 5-point scale) were all at or above a 4 (corresponding with ‘agree’) for key acceptability dimensions, such as usability, understandability, appeal of visual content, how engaging the content was, and helpfulness for recovery. Additionally, in both phases, qualitative feedback indicated that participants particularly appreciated the BOAT (Breath, Observe, Accept, Take a Moment) tool for breaking down mindfulness into steps. Qualitative feedback was used to iteratively refine the intervention. For example, based on feedback, we added a second core mindfulness tool, the SOAK (Stop, Observe, Appreciate, Keep Curious), and we added more example clients and group therapy videos. In Phase 2, 4 out of 5 participants completed all 15 lessons, providing initial evidence of feasibility. Participants reported that the phone coaching motivated them to use the app. The final version of Mindful Journey was a smartphone app with additional features, including brief on-the-go audio exercises and a library of mindfulness practices. Although, participants used these additional features infrequently. Conclusions Based on promising initial findings, future acceptability and feasibility testing in a larger sample is warranted. Future versions might include push notifications to facilitate engagement in the additional app features.

Super SDKs: Tracking personal data and platform monopolies in the mobile

In this article we address the question ‘what is tracking in the mobile ecosystem’ through a comprehensive overview of the Software Development Kit (SDK). Our research reveals a complex infrastructural role for these technical objects connecting end-user data with app developers, third parties and dominant advertising platforms like Google and Facebook. We present an innovative theoretical framework which we call a data monadology to foreground this interrelationship, predicated on an economic model that exchanges personal data for the infrastructural services used to build applications. Our main contribution is an SDK taxonomy, which renders them more transparent and observable. We categorise SDK services into three main categories: (i) Programmatic AdTech for monetisation; (ii) App Development, for building, maintaining and offering additional artificial intelligence features and (iii) App Extensions which more visibly embed third parties into apps like maps, wallets or other payment services. A major finding of our analysis is the special category of the Super SDK, reserved for platforms like Google and Facebook. Not only do they offer a vast array of services across all three categories, making them indispensable to developers, they are super conduits for personal data and the primary technical means for the expansion of platform monopolisation across the mobile ecosystem.

Laying the experimental foundation for corrosion inhibitor discovery through machine learning

Creating durable, eco-friendly coatings for long-term corrosion protection requires innovative strategies to streamline design and development processes, conserve resources, and decrease maintenance costs. In this pursuit, machine learning emerges as a promising catalyst, despite the challenges presented by the scarcity of high-quality datasets in the field of corrosion inhibition research. To address this obstacle, we have created an extensive electrochemical library of around 80 inhibitor candidates. The electrochemical behaviour of inhibitor-exposed AA2024-T3 substrates was captured using linear polarisation resistance, electrochemical impedance spectroscopy, and potentiodynamic polarisation techniques at different exposure times to obtain the most comprehensive electrochemical picture of the corrosion inhibition over a 24-h period. The experimental results yield target parameters and additional input features that can be combined with computational descriptors to develop quantitative structure–property relationship (QSPR) models augmented by mechanistic input features.

Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining.

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system's efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods.

Wood of Qualea from the Piedra Chamana in-situ fossil forest (late Middle Eocene, Peru) and the comparative wood anatomy of Vochysiaceae and Myrtaceae

Summary A wood from a late Middle Eocene in-situ fossil forest in northern Peru shows affinity with Qualea (Vochysiaceae), a genus of South American lowland tropical trees, based on features including vestured pits, aliform-confluent to banded paratracheal parenchyma, and homocellular rays. Additional features suggest adaptation to dry conditions. The fossil is named as a new species within Qualeoxylon (Q. lafila). Vochysiaceae and sister-group Myrtaceae share features unique to Myrtales (vestured pits, intraxylary phloem) or common in the order (vessels generally solitary and in &gt;1 diameter class, heterocellular rays) but in other ways are clearly differentiated. Vochysiaceae has narrow to very wide vessels with elaborated axial parenchyma and lacks most characters considered less specialized such as scalariform perforations. Myrtaceae generally has vessels with vasicentric tracheids, usually exclusively solitary, very narrow to wide, and often diagonally arranged, and includes taxa with less to more specialized features. Woods with elaborated paratracheal parenchyma and vessels wide to very wide occur in wet tropical forest environments in both families Erisma, Vochysia and Qualea/Ruizterania (Vochysiaceae) in the Neotropics and Syzygium (Myrtaceae) in the Asian tropics. The Eucalyptus spp. dominating open forests in Australia have typical myrtaceous features (including vasicentric tracheids) plus homocellular rays, whereas the eucalypt clade Angophora + Corymbia, occurring mainly in the northern, monsoonal regions of Australia, has both vasicentric tracheids and aliform to banded axial parenchyma. The fossil is an early record for Vochysiaceae and adds to indications that Vochysiaceae and sister-group Myrtaceae showed significant diversification by the late Paleogene.

EPDFpy: A Python-based interactive GUI tool for electron pair distribution function analysis of amorphous materials

ePDFpy is an interactive analysis program with a graphical user interface (GUI), designed to process the electron Pair Distribution Function (PDF) analysis of diffraction patterns from Transmission Electron Microscope (TEM), to identify the local atomic structure of amorphous materials. The program offers a user-friendly Python-based interface, providing a straightforward and adaptable workflow for PDF analysis. Various optimization and fitting processes were implemented to accurately reduce the electron diffraction data, including center-fitting and elliptical correction of diffraction data. An improved parameter-estimation feature is available to enhance the efficiency of the fitting process, along with an interactive GUI. ePDFpy will be freely distributed for academic purposes, with additional features, including a beam mask drawing module. Program summaryProgram Title: ePDFpyCPC Library link to program files:https://doi.org/10.17632/sym3sfnh7w.1Developer's repository link:https://github.com/GWlab-SKKU/ePDFpyLicensing provisions: GNU GPLv3Programming language: PythonNature of problem: The general process of pair distribution function analysis consists of two major steps: image process on diffraction pattern and fitting appropriate parameters. Both of the procedures are affected by the user's proficiency, which can be responsible for producing inconsistent results and inefficiency. Thus, accurate calculation methods along with fully automated feature are required to enhance the quality of the analysis result.Solution method: ePDFpy offers an unbiased automated image process based on a computer vision algorithm to produce the consistent output of intensity profiles from diffraction patterns. In addition, converting the data structures into a multi-dimensional array enables efficient multi-parameter fitting features by performing parallel computation. All of these features are accomplished using various open-source libraries in the Python community, along with an interactive GUI.

Emerging imaging markers in radiologically isolated syndrome: implications for earlier treatment initiation.

The presence of central nervous system lesions fulfilling the criteria of dissemination in space and time on MRI leads to the diagnosis of a radiologically isolated syndrome (RIS), which may be an early sign of multiple sclerosis (MS). However, some patients who do not fulfill the necessary criteria for RIS still evolve to MS, and some T2 hyperintensities that resemble demyelinating lesions may originate from mimics. In light of the recent recognition of the efficacy of disease-modifying therapy (DMT) in RIS, it is relevant to consider additional imaging features that are more specific of MS. We performed a narrative review on cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim lesions (PRL) in patients with RIS. In previous RIS studies, the reported prevalence of CLs ranges between 20.0 and 40.0%, CVS + white matter lesions (WMLs) between 87.0 and 93.0% and PRLs between 26.7 and 63.0%. Overall, these imaging findings appear to be frequent in RIS cohorts, although not consistently taken into account in previous studies. The search for CLs, CVS + WML and PRLs in RIS patients could lead to earlier identification of patients who will evolve to MS and benefit from DMTs.