Stand-alone Program Research Articles

Findings from the Mighty Girls Efficacy Trial: Changes in Acceptance of Dating Violence

Background/Objectives: Test efficacy of the social emotional learning (SEL)-based Mighty Girls program, a program culturally tailored for English-speaking Hispanic/Latino girls in seventh grade comprised of classroom sessions and a virtual reality computer game. We hypothesized that the curriculum would decrease risky sexual behaviors in a program that can be used as part of a comprehensive sex education curriculum or as a stand-alone program. Methods: A randomized group trial was conducted in which 22 low-income, predominately Hispanic schools within the Miami-Dade County Public School System were randomly assigned to intervention (consented n = 335) and control (consented n = 217) conditions. All study activities occurred after school. Primary outcome measures were resistance self-efficacy, acceptance of dating violence, sexual intentions, and sexual behavior. Assessments occurred at baseline, immediately post-intervention, 3-, 12-, and 24-months post-intervention. Changes in outcomes from baseline to 24 months were modeled using multi-level models to account for nesting of students within schools with full information maximum likelihood to account for missing data and baseline school attendance and enrollment in free and reduced lunch as covariates. Analyses are also controlled for multiple testing. Results: The program had a significant effect on reducing acceptance of dating violence at 24 months post-intervention (estimate = −0.083, p ≤ 0.05), but no effect on resistance self-efficacy, sexual intentions, or sexual behavior (p ≥ 0.58). Conclusion: Study findings demonstrate that a social emotional learning (SEL) curriculum can impact sexual behaviors such as susceptibility to dating violence. Low baseline levels for sexual intentions and behaviors as well as a high baseline of efficacy may have impacted findings for the other outcomes.

Building bridges - Fostering collaborative education in training dental informaticians.

Dental informatics is an emerging discipline. Although the accreditation agency governing dental education programs asserts the importance of informatics as foundational knowledge, no well-defined dental informatics courses currently exist within the standard predoctoral dental curriculum. There is a nationwide lack of dental informatics academic programs. This training gap is due to a lack of qualified dental informaticians to impart knowledge on dental informatics. This paper aims to introduce a novel conceptual framework for an interdisciplinary dental informatics program in preparing students to become dental informaticians. In 2023, we developed a standalone graduate certificate program in dental informatics at Indiana University (IU) School of Dentistry in collaboration with IU Luddy School of Informatics, Computing, and Engineering and IU Fairbanks School of Public Health. Feedback was collected through online surveys to assess course quality from students who took Introduction to Health Information in Dentistry. Feedback was analyzed qualitatively, utilizing a thematic analysis approach. Common responses relevant to dental informatics education were grouped into themes. Five major themes emerged during our analysis of the students' feedback: foundational knowledge and skills; experiential learning: learning by doing; access to resources and working on clinical information systems; health promotion through team-based learning; and retention of knowledge assessment and application. A conceptual framework was formulated through these themes as a guideline for future program improvement. This interdisciplinary educational program framework showed how students and faculty from various disciplines could collaborate, learn from each other, and bring in expertise from different domains. The collaboration happens in clinical, laboratory, and virtual settings to acquire hands on learning through practice and research projects. The developed conceptual framework aligned with the interdisciplinary nature of dental informatics. It can potentially be adopted by other interdisciplinary informatics programs in health and non-health care disciplines.

A scoping review of universal school-based resilience programs for adolescents.

Resilience, broadly defined as effective adaptation to stress, adversity, or change, is an important capacity to foster in adolescence. To date, there has been little review of the literature on universal school-based resilience programs for teens. The current scoping review had three aims. The first aim was to report on the scope of literature on universal school-based resilience programs for adolescents. The second aim was to code and summarize the features and outcomes of these programs (stand-alone programs and whole-school programs). The third was to identify key themes emanating from nonempirical articles (i.e., theoretical articles, review articles, published guidelines, and reports) about what makes an effective school-based resilience program for adolescents. A total of 34 articles met the criteria for the review (47% empirical and 53% nonempirical). The collated data present a summary of who (e.g., sample demographics), how (e.g., research designs, the duration and number of posttest evaluations), and what has been studied (e.g., the types of programs and the types of outcomes). Randomized control trial evaluations (53%) and quasi-experimental designs (47%) were the common designs. The studies displayed large variation when it came to program delivery aspects such as number of lessons, length of the lessons, duration of the program, type of teaching, and program facilitators. Six themes were identified for creating effective universal school-based resilience programs: dual focus (ill-being and well-being), ethos and embedding, nurturing environment, adopting a systems approach, building teacher resilience, and fostering real-time resilience through implicit and explicit teaching. Suggestions for future research are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Internet-Based Prevention of Re-Victimization for Youth with Care Experience (EMPOWER-YOUTH): Results of a Randomized Controlled Trial.

Traumatic experiences and maltreatment are highly prevalent among adolescents in foster or institutional care and have severe long-term effects on mental health. Childhood maltreatment increases the risk of revictimization. This study aimed to evaluate the efficacy of the internet-based prevention program EMPOWER YOUTH in reducing victimization experiences among youth with care experience. A total of 163 youth, that is, adolescents in foster or institutional care, adopted adolescents, or young care leavers aged 14 to 21 years (Mage = 17.68 years; SD = 2.11) were randomized to the six-module guided program or a wait-list control group. The primary endpoint was the decrease in victimization experiences at an 18-week follow-up. Secondary endpoints included risk perception, aggressive tendencies, empathy, prosocial behavior, depressiveness, post-traumatic stress symptoms, and loneliness. About half of the youth exhibited increased psychopathology. No significant interaction between time-point and group was found for victimization measures, though both groups saw a significant reduction over time (p = 0.012, ηp2 = 0.06; p = 0.017, ηp2 = 0.06). The intervention group showed a significant increase in perception of thrill-seeking and rebellious risks (p = 0.036, ηp2 = 0.04; p = 0.026, ηp2 = 0.05). While EMPOWER YOUTH effectively increased risk perception of thrill-seeking and rebellious behaviors, it should be considered an add-on to more intensive interventions for common mental health disorders in this vulnerable group, rather than a stand-alone online program.

Exploring protein functions from structural flexibility using CABS-flex modeling.

Understanding protein function often necessitates characterizing the flexibility of protein structures. However, simulating protein flexibility poses significant challenges due to the complex dynamics of protein systems, requiring extensive computational resources and accurate modeling techniques. In response to these challenges, the CABS-flex method has been developed as an efficient modeling tool that combines coarse-grained simulations with all-atom detail. Available both as a web server and a standalone package, CABS-flex is dedicated to a wide range of users. The web server version offers an accessible interface for straightforward tasks, while the standalone command-line program is designed for advanced users, providing additional features, analytical tools, and support for handling large systems. This paper examines the application of CABS-flex across various structure-function studies, facilitating investigations into the interplay among protein structure, dynamics, and function in diverse research fields. We present an overview of the current status of the CABS-flex methodology, highlighting its recent advancements, practical applications, and forthcoming challenges.

Changing Nepali children's attitudes toward conservation through a participatory education program.

People's attitude toward wildlife is known to affect the success of conservation programs. Public education can increase support for conservation measures by providing the knowledge necessary to make informed decisions, improve pro-environmental behaviour and potentially enlist participation. To that end, we developed a participatory conservation education program in Nepal with a target audience of secondary school students. Our lessons emphasized ecological balance by highlighting the roles of individual species, including the rhesus macaque (Macaca mulatta). This species presents a challenge for educating the public about maintaining balance in nature, because although an important seed disperser and species of religious significance, macaques are frequently involved in human-wildlife conflict and considered a pest species. We evaluated the impact of our program by surveying students from six socioeconomically similar schools, four that participated in our program and two that had not. Our questionnaire was comprised of knowledge questions about local wildlife and conservation issues, an open-ended listing exercise to determine species preferences, and statements that elicited students' opinions regarding empathy toward animals. Knowledge about and attitude toward wildlife, including M. mulatta, were greater in students that participated in the program. More importantly, these students were more likely to understand the role individual species play in a balanced ecosystem, how human behaviour can negatively impact wildlife, and how they could participate in conservation efforts. Gender was not a factor in any response. We discuss the merits of using a participatory learning platform in a standalone education program that has long-term purpose, funding, and buy-in.

MICRO-СREDETIALS IN SOCIAL WORK: INTERNATIONAL EXPERIENCE

Micro-credentials, as a contemporary form of short-term learning, have become a vital tool for enhancing professionals' qualifications across various fields. This research aims to analyze the international experience of implementing micro-credentials in social work, determining their content and advantages, and forming recommendations for their use in the national system. The experiences of the USA and Ireland demonstrate how micro-credentials can contribute to the development of professional skills and adaptation to modern challenges. In the USA, micro-credentials are offered to both university students and practicing social workers. They can be standalone programs or part of broader educational initiatives, allowing for flexible learning approaches. Ireland focuses on continuous professional development for social sector workers, implementing micro-credentials through online courses with fixed dates. Comparing these countries' approaches highlights several key points. In the USA, the emphasis is on additional specialization and license renewal for social workers, while in Ireland, micro-credentials target innovations in social work and policy, along with supporting professional development. Both countries use program accreditation to ensure quality and national recognition. For Ukraine, implementing micro-credentials in social work can be a significant step in enhancing professionals' qualifications, especially given rapid social and technological changes. Essential tasks include creating flexible educational programs, actively involving practicing professionals, fostering partnerships between universities and organizations, and ensuring transparency in micro-credential information. Additionally, there is a pressing need for implementing a system of continuous professional development, regulating requirements for social service providers, and creating career opportunities for social workers, incentivizing micro-credential attainment.

PLM_Sol: predicting protein solubility by benchmarking multiple protein language models with the updated Escherichia coli protein solubility dataset.

Protein solubility plays a crucial role in various biotechnological, industrial, and biomedical applications. With the reduction in sequencing and gene synthesis costs, the adoption of high-throughput experimental screening coupled with tailored bioinformatic prediction has witnessed a rapidly growing trend for the development of novel functional enzymes of interest (EOI). High protein solubility rates are essential in this process and accurate prediction of solubility is a challenging task. As deep learning technology continues to evolve, attention-based protein language models (PLMs) can extract intrinsic information from protein sequences to a greater extent. Leveraging these models along with the increasing availability of protein solubility data inferred from structural database like the Protein Data Bank holds great potential to enhance the prediction of protein solubility. In this study, we curated an Updated Escherichia coli protein Solubility DataSet (UESolDS) and employed a combination of multiple PLMs and classification layers to predict protein solubility. The resulting best-performing model, named Protein Language Model-based protein Solubility prediction model (PLM_Sol), demonstrated significant improvements over previous reported models, achieving a notable 6.4% increase in accuracy, 9.0% increase in F1_score, and 11.1% increase in Matthews correlation coefficient score on the independent test set. Moreover, additional evaluation utilizing our in-house synthesized protein resource as test data, encompassing diverse types of enzymes, also showcased the good performance of PLM_Sol. Overall, PLM_Sol exhibited consistent and promising performance across both independent test set and experimental set, thereby making it well suited for facilitating large-scale EOI studies. PLM_Sol is available as a standalone program and as an easy-to-use model at https://zenodo.org/doi/10.5281/zenodo.10675340.

Deep Learning-Based Detection of Android Malware using Graph Convolutional Networks (GCN)

The study is centered around identifying Android malware using deep learning methods through Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs). With Android being widely used worldwide ensuring the security of released applications poses a challenge. Conventional malware detection techniques, like dynamic analysis have limitations in recognizing new malware types leading to a shift towards machine learning and deep learning solutions. The research introduces a malware detection system that employs GNNs particularly focusing on GCNs to analyze the relationships within an applications code by transforming APK files into graph formats. The system follows stages including data gathering, feature extraction, graph construction, model training and implementation. By concentrating on function call graphs the system proves effective in identifying software surpassing traditional machine learning methods in terms of accuracy, precision, recall and F1 score. The GCN based model shows enhancements over approaches with an accuracy rate of 95% compared to 89%, for traditional machine learning models. This progress highlights the potential of learning techniques in bolstering Android security. The system excels not in identifying software but also proves versatile, for different uses like screening apps, in stores and functioning as a standalone antivirus program.

Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2'-O-methylation sites in human RNA.

2'-O-methylation (2-OM or Nm) is a widespread RNA modification observed in various RNA types like tRNA, mRNA, rRNA, miRNA, piRNA, and snRNA, which plays a crucial role in several biological functional mechanisms and innate immunity. To comprehend its modification mechanisms and potential epigenetic regulation, it is necessary to accurately identify 2-OM sites. However, biological experiments can be tedious, time-consuming, and expensive. Furthermore, currently available computational methods face challenges due to inadequate datasets and limited classification capabilities. To address these challenges, we proposed Meta-2OM, a cutting-edge predictor that can accurately identify 2-OM sites in human RNA. In brief, we applied a meta-learning approach that considered eight conventional machine learning algorithms, including tree-based classifiers and decision boundary-based classifiers, and eighteen different feature encoding algorithms that cover physicochemical, compositional, position-specific and natural language processing information. The predicted probabilities of 2-OM sites from the baseline models are then combined and trained using logistic regression to generate the final prediction. Consequently, Meta-2OM achieved excellent performance in both 5-fold cross-validation training and independent testing, outperforming all existing state-of-the-art methods. Specifically, on the independent test set, Meta-2OM achieved an overall accuracy of 0.870, sensitivity of 0.836, specificity of 0.904, and Matthew's correlation coefficient of 0.743. To facilitate its use, a user-friendly web server and standalone program have been developed and freely available at http://kurata35.bio.kyutech.ac.jp/Meta-2OM and https://github.com/kuratahiroyuki/Meta-2OM.

An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)

AbstractDetailed, accurate information on flow patterns in river channels can improve understanding of habitat conditions, geomorphic processes, and potential hazards to help inform water management. Data describing flow patterns in river channels can be obtained efficiently via image‐based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image‐based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)‐based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image‐derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications.

OrthoRefine: automated enhancement of prior ortholog identification via synteny

BackgroundIdentifying orthologs continues to be an early and imperative step in genome analysis but remains a challenging problem. While synteny (conservation of gene order) has previously been used independently and in combination with other methods to identify orthologs, applying synteny in ortholog identification has yet to be automated in a user-friendly manner. This desire for automation and ease-of-use led us to develop OrthoRefine, a standalone program that uses synteny to refine ortholog identification.ResultsWe developed OrthoRefine to improve the detection of orthologous genes by implementing a look-around window approach to detect synteny. We tested OrthoRefine in tandem with OrthoFinder, one of the most used software for identification of orthologs in recent years. We evaluated improvements provided by OrthoRefine in several bacterial and a eukaryotic dataset. OrthoRefine efficiently eliminates paralogs from orthologous groups detected by OrthoFinder. Using synteny increased specificity and functional ortholog identification; additionally, analysis of BLAST e-value, phylogenetics, and operon occurrence further supported using synteny for ortholog identification. A comparison of several window sizes suggested that smaller window sizes (eight genes) were generally the most suitable for identifying orthologs via synteny. However, larger windows (30 genes) performed better in datasets containing less closely related genomes. A typical run of OrthoRefine with ~ 10 bacterial genomes can be completed in a few minutes on a regular desktop PC.ConclusionOrthoRefine is a simple-to-use, standalone tool that automates the application of synteny to improve ortholog detection. OrthoRefine is particularly efficient in eliminating paralogs from orthologous groups delineated by standard methods.

JAVA Based Quiz Application

The Java-based Quiz Application is a versatile, standalone program designed to operate independently of an internet connection, making it ideal for offline use in various settings such as training sessions, schools, and areas with limited internet access. It boasts a comprehensive set of features for quiz creation, administration, and participation. Key among its features is the offline user authentication system, enabling users to securely create accounts and log in without internet connectivity. This ensures accessibility and autonomy for users regardless of their location or internet availability. The application offers a user-friendly interface for adding various question types, including multiple choice, to offline quizzes, allowing users to customize quizzes to suit their needs and cover diverse topics. Furthermore, users can manage quizzes offline, facilitating real-time adjustments by educators, trainers, or organizers without the need for an internet connection. The application provides players with a seamless offline quiz experience, offering swift performance feedback and navigation to enhance engagement. Users can select quizzes from offline categories, attempt them offline, and receive immediate scoring and feedback. Emphasizing security, the Java Quiz Application implements robust encryption techniques to safeguard user data and ensure confidentiality offline. This commitment to security ensures the protection of sensitive information even in offline environments, maintaining user trust and confidence in the application's integrity. Key Words: Java, No Internet Connection, Offline Quiz, user Friendly

The Use of Roughness Meter of Geophone Basis for Evaluating Functional Performance of Highways

The performance of a road link is globally divided into two categories: structural and functional. The structural performance is a condition shown by the condition of its remaining life, while the functional one relates to the comfort of road users passing on the pavement link. The roughness meter used is based on the censor of geophones and GPS, while the general roughness meters are based on accelerometer sensors such as ARRB, IRI meter, etc. The main reason to use the geophone is because the accelerometer and geophone are the sensors used to measure displacement. The theories behind this roughness meter are based on Watanabe et al. The retrieved data uses a stand-alone program based on Visual Basic 6. An Arduino Mega 2560 is used for the microcontroller, and the post-processing operation is programmed with MATLAB. Another advantage of this roughness meter is that the results of IRI values can be presented on a map, making it much easier for the road authority to determine the condition of the road links tested. This type of roughness meter has been tested for two road links in the Central Java province. Both of the two links have an average value of IRI of 3.7, which means that they are still in routine maintenance, but soon, they will get to the stage of minor rehabilitation.

Addressing gender-based violence through social protection: a scoping review

ABSTRACT Development practitioners are searching for novel ways to address gender-based violence (GBV) in the wake of what the UN Secretary General termed a “shadow pandemic” of violence against women (VAW). Social protection systems, which are oriented towards preventing poverty and improving quality of life, contain a wide range of policy tools with potential for addressing GBV, yet their application has been largely underexplored. This paper brings the fields of social protection and GBV together through a comprehensive scoping review and presentation of the “state of the evidence”. The paper moves beyond a focus on standalone programs to synthesise findings on the cross-cutting mechanisms by which the policies, programs, and administrative features of social protection systems can be leveraged to address violence against women in particular. The paper contributes to scholarship and practice by identifying promising entry points and design factors, as well as future directions for an actionable research agenda at the nexus of social protection and GBV prevention and response.

Online Exercise Programs for Chronic Nonspecific Neck Pain: A Randomized Controlled Trial Reveals Comparable Effects of Global, Local, and Combined Approaches.

Specific neck exercises are recommended in the rehabilitation of chronic nonspecific neck pain (CNNP). They are unfortunately often accompanied by acute pain flare-ups. Global exercises might be a beneficial addition, as they activate endogenous analgesia without overloading painful structures. However, it is still unclear which type of exercise is most effective. This randomized controlled trial was done to evaluate the effect of an online blended program of global and specific neck exercises, compared to programs including only 1 of both types of exercise. Forty-eight patients with CNNP were randomized into 3 groups. Online questionnaires were collected at baseline, at midtreatment, immediately after treatment, and at the 3-month follow-up. Quantitative sensory testing and actigraphy were assessed at baseline and after treatment. Linear mixed-model analyses were performed to evaluate treatment effects within and between groups. Neck pain-related disability after treatment was considered the primary outcome. No time × treatment interaction effects were found. All groups improved in neck pain-related disability, pain intensity, self-reported symptoms of central sensitization, local pain sensitivity, physical activity, and pain medication use. No effects were found on quality of life, sleep quality, depression, anxiety, stress, widespread pain sensitivity, health economics, or actigraphy measurements. A higher global perceived effect was reported after performing the blended program, compared to the other groups. A blended exercise program was not superior to the stand-alone programs in reducing disability. Nevertheless, the global perceived effect of this type of exercise was higher. Future research necessitates larger sample sizes to adequately explore the optimal type of exercise for patients with CNNP. Exercise therapy should be an important part of the rehabilitation of patients with CNNP, regardless of the type of exercise.

PSF-Radon transform algorithm: Measurement of the point-spread function from the Radon transform of the line-spread function.

In this article, we present a new method called point spread function (PSF)-Radon transform algorithm. This algorithm consists on recovering the instrument PSF from the Radon transform (in the line direction axis) of the line spread function (i.e., the image of a line). We present the method and tested with synthetic images, and real images from macro lens camera and microscopy. A stand-alone program along with a tutorial is available, for any interested user, in Martinez (PSF-Radon transform algorithm, standalone program). RESEARCH HIGHLIGHTS: Determining the instrument PSF is a key issue. Precise PSF determinations are mandatory if image improvement is performed numerically by deconvolution. Much less exposure time to achieve the same performance than a measurement of the PSF from a very small bead. Does not require having to adjust the PSF by an analytical function to overcome the noise uncertainties.

A resource database for protein kinase substrate sequence-preference motifs based on large-scale mass spectrometry data

BackgroundProtein phosphorylation is one of the most prevalent posttranslational modifications involved in molecular control of cellular processes, and is mediated by over 520 protein kinases in humans and other mammals. Identification of the protein kinases responsible for phosphorylation events is key to understanding signaling pathways. Unbiased phosphoproteomics experiments have generated a wealth of data that can be used to identify protein kinase targets and their preferred substrate sequences.MethodsThis study utilized prior data from mass spectrometry-based studies identifying sites of protein phosphorylation after in vitro incubation of protein mixtures with recombinant protein kinases. PTM-Logo software was used with these data to generate position-dependent Shannon information matrices and sequence motif ‘logos’. Webpages were constructed for facile access to logos for each kinase and a new stand-alone application was written in Python that uses the position-dependent Shannon information matrices to identify kinases most likely to phosphorylate a particular phosphorylation site.ResultsA database of kinase substrate target preference logos allows browsing, searching, or downloading target motif data for each protein kinase (https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/). These logos were combined with phylogenetic analysis of protein kinase catalytic sequences to reveal substrate preference patterns specific to particular groups of kinases (https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinaseTree.html). A stand-alone program, KinasePredictor, is provided (https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinasePredictor.html). It takes as input, amino-acid sequences surrounding a given phosphorylation site and generates a ranked list of protein kinases most likely to phosphorylate that site.ConclusionsThis study provides three new resources for protein kinase characterization. It provides a tool for prediction of kinase-substrate interactions, which in combination with other types of data (co-localization, etc.), can predict which kinases are likely responsible for a given phosphorylation event in a given tissue.-q3N7pjxaL8wVBVRsJ9wBTVideo

At What Cost? Is Technical Education Worth the Investment?

Abstract Career and technical education (CTE) has existed in the United States for over a century, and only in recent years have there been opportunities to assess the causal impact of participating in these programs while in high school. To date, no work has assessed whether the relative costs of these programs meet or exceed the benefits as described in recent evaluations. In this paper, we use available cost data to compare average costs per pupil in standalone high school CTE programs in Connecticut and Massachusetts to the most likely counterfactual schools. Under a variety of conservative assumptions about the monetary value of known educational and social benefits, we find that programs in Massachusetts offer clear positive returns on investment, whereas programs in Connecticut offer smaller, though mostly non-negative expected returns. We also consider the potential cost effectiveness of CTE programs offered in other contexts to address questions of generalizability.

Calculation of electric field gradients with the exact two-component (X2C) quasi-relativistic method and its local approximations.

When calculating electric field gradients (EFGs), relativistic and electron correlation effects are crucial for obtaining accurate results, and the commonly used density functional methods produce unsatisfactory results, especially for heavy elements and/or strongly correlated systems. In this work, a stand-alone program is presented, which enables calculation of EFGs from the molecular orbitals supplied by an external high accuracy quantum chemical calculation and includes relativistic effects through the exact two-component (X2C) formalism and efficient local approximations to it. Application to BiN and BiP molecules shows that a high precision can be achieved in the calculation of nuclear quadrupole coupling constants of 209Bi by combining advanced ab initio methods with the X2C approach. For seventeen iron compounds, the Mössbauer nuclear quadrupole splittings (NQS) of 57Fe calculated using a double-hybrid functional method are in very good agreement with the experimental values. It is shown that, for strongly correlated molecules, the double-hybrid functionals are much more accurate than the commonly used hybrid functionals. The computer program developed in this study furnishes a useful utility for obtaining EFGs and related nuclear properties with high accuracy.